Time synchronization is a critical piece of infrastructure for any distributed system. Wireless sensor networks have emerged as an important and promising research area in the recent years. Time synchronization is important for many sensor network applications that require very precise mapping of gathered sensor data with the time of the events, for example, in tracking and vehicular surveillance. It also plays an important role in energy conservation in MAC layer protocols. The paper studies different existing methods, protocols, significant time parameters (clock drift, clock speed, synchronization errors, and topologies) to achieve accurate synchronization in a sensor network. The studied Synchronization protocols include conventional time sync protocols (RBS, Timing-sync Protocol for Sensor Networks -TPSN, FTSP), and other application specific
approaches such as all node-based approach, a diffusion-based method and group sync approaches aiming at providing network-wide time. The goal for writing this paper is to study most common existing time synchronization approaches and stress the need of a new class of secure-time synchronization protocol that is scalable, topology independent, fast convergent, energy efficient, less latent and less application dependent in a heterogeneous hostile environment. Our survey provides a valuable framework by which protocol designers can compare new and
existing synchronization protocols from various metric discussed in the paper. So, we are hopeful that this paper will serve a complete one-stop investigation to study the characteristics of existing time synchronization protocols and its implementation mechanism in a Sensor network environment.
SPINS: Security Protocols for Sensor NetworksAbhijeet Awade
This document summarizes the SPINS security protocols for sensor networks. It discusses two protocols: SNEP for basic node-to-base station security and μTESLA for authenticated broadcast. SNEP provides data confidentiality through symmetric encryption and data authentication using message authentication codes. μTESLA provides authentication for broadcast messages through disclosure of symmetric keys along a key chain. The document also gives examples of applications these protocols can enable, such as authenticated routing and pairwise key agreement between nodes.
This document discusses wireless sensor networks and middleware approaches for them. It describes wireless sensor networks as consisting of distributed autonomous sensor nodes that monitor physical environments cooperatively. It outlines common sensor node components and network architectures. It then defines middleware as a software layer that manages complexity and heterogeneity. Key middleware design principles for wireless sensor networks include supporting limited resources, scalability, and data aggregation. The document outlines several middleware approaches, including those based on global behavior, local behavior, virtual machines, databases, and modular programming.
This document discusses different types of sensor node hardware: augmented general-purpose computers, dedicated embedded sensor nodes, and system-on-chip devices. It notes that Berkley motes have gained popularity due to their small size, open source software, and commercial availability. The document also outlines programming challenges for sensor networks and different approaches like event-driven execution, node-level software platforms, and state-centric programming.
This document discusses state-centric programming and collaboration groups in wireless sensor networks. It defines a collaboration group as a set of entities that contribute to a state update, with a scope defining its membership and a structure defining roles and data flow. Examples of groups include geographically constrained groups based on region, N-hop neighborhood groups within a number of hops from an anchor, publish/subscribe groups of consumers and producers, and acquaintance groups where members invite others. Mixing and matching different group types can make algorithms more scalable and efficient.
Lecture 19 22. transport protocol for ad-hoc Chandra Meena
This document discusses transport layer protocols for mobile ad hoc networks (MANETs). It begins with an introduction to MANETs and the need for new network architectures and protocols to support new types of networks. It then provides an overview of TCP/IP and how TCP works, including congestion control mechanisms. The document discusses challenges for TCP over wireless networks, where packet losses are often due to errors rather than congestion. It covers different versions of TCP and their approaches to congestion control. The goal is to design transport layer protocols that can address the unreliable links and frequent topology changes in MANETs.
This document discusses power aware routing protocols for wireless sensor networks. It begins by describing wireless sensor networks and how they are used to monitor environmental conditions. It then classifies routing protocols for sensor networks based on their functioning, node participation style, and network structure. Specific examples are provided for different types of routing protocols, including LEACH, TEEN, APTEEN, SPIN, Rumor Routing, and PEGASIS. Chain-based and clustering routing protocols are also summarized.
The document discusses wireless sensor networks and describes their key characteristics. It notes that wireless sensor networks consist of low-power smart sensor nodes distributed over a large field to enable wireless sensing and data networking. The sensor nodes contain sensors, processors, memory, and radios. Wireless sensor networks can be either unstructured with dense node distribution or structured with few scattered nodes.
SPINS: Security Protocols for Sensor NetworksAbhijeet Awade
This document summarizes the SPINS security protocols for sensor networks. It discusses two protocols: SNEP for basic node-to-base station security and μTESLA for authenticated broadcast. SNEP provides data confidentiality through symmetric encryption and data authentication using message authentication codes. μTESLA provides authentication for broadcast messages through disclosure of symmetric keys along a key chain. The document also gives examples of applications these protocols can enable, such as authenticated routing and pairwise key agreement between nodes.
This document discusses wireless sensor networks and middleware approaches for them. It describes wireless sensor networks as consisting of distributed autonomous sensor nodes that monitor physical environments cooperatively. It outlines common sensor node components and network architectures. It then defines middleware as a software layer that manages complexity and heterogeneity. Key middleware design principles for wireless sensor networks include supporting limited resources, scalability, and data aggregation. The document outlines several middleware approaches, including those based on global behavior, local behavior, virtual machines, databases, and modular programming.
This document discusses different types of sensor node hardware: augmented general-purpose computers, dedicated embedded sensor nodes, and system-on-chip devices. It notes that Berkley motes have gained popularity due to their small size, open source software, and commercial availability. The document also outlines programming challenges for sensor networks and different approaches like event-driven execution, node-level software platforms, and state-centric programming.
This document discusses state-centric programming and collaboration groups in wireless sensor networks. It defines a collaboration group as a set of entities that contribute to a state update, with a scope defining its membership and a structure defining roles and data flow. Examples of groups include geographically constrained groups based on region, N-hop neighborhood groups within a number of hops from an anchor, publish/subscribe groups of consumers and producers, and acquaintance groups where members invite others. Mixing and matching different group types can make algorithms more scalable and efficient.
Lecture 19 22. transport protocol for ad-hoc Chandra Meena
This document discusses transport layer protocols for mobile ad hoc networks (MANETs). It begins with an introduction to MANETs and the need for new network architectures and protocols to support new types of networks. It then provides an overview of TCP/IP and how TCP works, including congestion control mechanisms. The document discusses challenges for TCP over wireless networks, where packet losses are often due to errors rather than congestion. It covers different versions of TCP and their approaches to congestion control. The goal is to design transport layer protocols that can address the unreliable links and frequent topology changes in MANETs.
This document discusses power aware routing protocols for wireless sensor networks. It begins by describing wireless sensor networks and how they are used to monitor environmental conditions. It then classifies routing protocols for sensor networks based on their functioning, node participation style, and network structure. Specific examples are provided for different types of routing protocols, including LEACH, TEEN, APTEEN, SPIN, Rumor Routing, and PEGASIS. Chain-based and clustering routing protocols are also summarized.
The document discusses wireless sensor networks and describes their key characteristics. It notes that wireless sensor networks consist of low-power smart sensor nodes distributed over a large field to enable wireless sensing and data networking. The sensor nodes contain sensors, processors, memory, and radios. Wireless sensor networks can be either unstructured with dense node distribution or structured with few scattered nodes.
This document provides an overview of wireless sensor networks. It discusses key definitions, advantages, applications and challenges. Sensor networks can provide energy and detection advantages over traditional systems. They enable applications in various domains including military, environmental monitoring, healthcare and home automation. The document also outlines enabling technologies and discusses important considerations like network architectures, hardware components, energy consumption and optimization goals.
The document provides information on the evolution of wireless networks from 1G to 3G. It discusses the key components and architecture of cellular systems including base stations, mobile switching centers and their connection to the public switched telephone network. It also compares the differences between wireless and wired networks, and describes some of the limitations of early wireless networking. Finally, it covers topics like traffic routing, circuit switching, packet switching and the X.25 protocol.
The document summarizes several routing protocols used in wireless networks. It discusses both table-driven protocols like DSDV and on-demand protocols like AODV. It provides details on how each protocol performs routing and maintains routes. It also outlines some advantages and disadvantages of protocols like DSDV, AODV, DSR, and TORA.
This document discusses medium access control (MAC) protocols, which regulate access to a shared wireless medium between nodes. It covers key requirements for MAC protocols including throughput efficiency, fairness, and low overhead. It also describes challenges like the hidden terminal problem, exposed terminal problem, and sources of overhead from collisions, overhearing, and idle listening. Finally, it categorizes common MAC protocols as fixed assignment, demand assignment, and random access and notes additional energy conservation requirements for wireless sensor networks.
1. Wireless sensor networks consist of distributed sensor nodes that communicate wirelessly to monitor physical or environmental conditions, such as temperature, sound, or pollution levels.
2. The sensor nodes gather and route data back to a central sink/gateway node where the information can be analyzed.
3. Communication protocols and algorithms are required for efficient multi-hop routing of data between sensor nodes and the sink node.
IS-95 CDMA is an air interface standard that uses code division multiple access (CDMA). It employs various techniques to improve system capacity and performance, including bandwidth recycling, power control, soft handoffs, diversity combining, and variable rate vocoding. Key aspects of IS-95 include the use of quadrature phase shift keying modulation at a 1.2288 Mcps chip rate, forward error correction coding, and multiple logical channels (pilot, sync, paging, traffic) defined using orthogonal Walsh codes.
TinyOS is an open source operating system designed for wireless sensor networks. It uses a component-based architecture and event-driven execution model to achieve low power consumption and memory footprint. TinyOS programs are built by wiring together components that provide and use interfaces to communicate through events and commands. It also uses a non-preemptive task scheduler and static memory allocation to improve efficiency for energy constrained sensor nodes. The nesC language extends C to support TinyOS's programming model and execution model.
The document discusses ad hoc networks and wireless sensor networks. It defines an ad hoc network as a temporary network composed of mobile nodes without preexisting infrastructure that is self-organizing. Wireless sensor networks are introduced as a collection of sensor nodes densely deployed to monitor conditions and cooperatively pass data back to central nodes. The document outlines key characteristics of both networks including their temporary and adaptive nature, multi-hop routing, and challenges of mobility, power constraints, and dynamic topology changes.
Wireless sensor network and its applicationRoma Vyas
The document discusses wireless sensor networks (WSN) and their applications. It defines a WSN as a collection of sensor nodes that communicate wirelessly and self-organize after deployment. Sensor nodes collect data at regular intervals, convert it to electrical signals, and send it to a base station. The document outlines the components of sensor nodes and describes how WSNs are used for applications like forest fire detection, air/water pollution monitoring, landslide detection, and military surveillance. It also discusses the TinyOS operating system commonly used for WSNs and its features for efficiently utilizing energy in sensor nodes.
This document discusses wireless sensor network applications and energy consumption. It provides examples of WSN applications including disaster relief, environment monitoring, healthcare, and more. It then discusses various factors that influence energy consumption in sensor nodes, including operation states, microcontroller usage, radio transceivers, memory, and the relationship between computation and communication. Specific power consumption numbers are given for different components like radios, sensors, and microprocessors. The goals of optimization for WSNs are discussed as quality of service, energy efficiency, scalability, and robustness.
Medium Access Control :-
1.Distributed Operation
2.Synchronization
3.Hidden Terminals
4.Exposed terminals
5.Throughput
6.Access delay
7.Fairness
8.Real-time Traffic support
9.Resource reservation
10.Ability to measure resource availability
11.Capability for power control
Adaptive rate control
Use of directional antennas
Minimize energy per packet (or per bit)
Maximize network lifetime
Routing considering available battery energy
Maximum Total Available Battery Capacity
Minimum Battery Cost Routing (MBCR)
Min– Max Battery Cost Routing (MMBCR)
Conditional Max – Min Battery Capacity Routing (CMMBCR)
Minimize variance in power levels
Minimum Total Transmission Power Routing (MTPR)
LTE Basic Parameters, Data Rates, Duplexing & Accessing, Modulation, Coding & MIMO, Explanation of different nodes and Advantage & Disadvantages of different nodes.
Sensor node hardware and network architectureVidhi603146
The document discusses the hardware components and architecture of sensor nodes. It describes the main components as the controller module, memory module, communication module, sensing modules, and power supply module. The controller is the core and processes data from sensors. Memory stores programs and data. The communication device allows nodes to exchange data wirelessly. Sensors interface with the physical environment. Power is stored and replenished through batteries or energy scavenging from the environment. TinyOS was developed as an operating system for sensor networks since traditional OSes were not suitable due to constraints like limited memory and power.
The document introduces TinyOS, nesC, and TOSSIM. TinyOS is an open-source operating system for wireless sensor networks. It is designed for low-power embedded devices and uses nesC as its programming language. TOSSIM simulates TinyOS applications by replacing hardware components with simulation implementations. The document discusses TinyOS and nesC programming, TOSSIM simulation, and troubleshooting TinyOS and TOSSIM.
6LoWPAN allows the use of IPv6 over low-power wireless networks. It compresses IPv6 packet headers to accommodate the small packet sizes of low-power wireless standards like 802.15.4. 6LoWPAN finds applications in home automation, healthcare, industrial automation, and environmental monitoring. It defines adaptations for addressing, forwarding, routing, header compression, and security to enable IPv6 connectivity over low-power wireless networks. Implementations of 6LoWPAN exist in open-source operating systems like Contiki and TinyOS, as well as commercial solutions.
Physical channels carry information over the air interface between the mobile station and base transceiver station. Logical channels map user data and signaling information onto physical channels. There are two main types of logical channels - traffic channels which carry call data, and control channels which communicate service information. Control channels include broadcast channels which transmit cell-wide information, common channels used for paging and access procedures, and dedicated channels for signaling during calls or when not on a call. Logical channels are mapped onto physical channels to effectively transmit information wirelessly between network components in a GSM system.
This document discusses localization techniques in wireless sensor networks. It begins with introducing wireless sensor networks and their components. It then discusses the need for localization to track objects within sensor networks. There are two main types of localization schemes - range-based which uses distance or angle measurements, and range-free which uses approximate distance estimates. Examples of range-based techniques include time of arrival, time difference of arrival, received signal strength indicator, and angle of arrival. Range-free techniques include proximity and distance-based localization using hop counts. The document compares the advantages and disadvantages of different localization methods.
Clock synchronization estimation of non deterministic delays in wireless mess...IJCNCJournal
Clock synchronization is significantly essential as they require universal time on WSN nodes for time measurement, event ordering and coordinated actions, and power management. This paper gives an insight of solving the problem of the non-deterministic delays that exist in the wireless message delivery. Sensor nodes consisting of Arduino Mega and 2.4 GHz nRF24L01+ radio modules are used, and based on the estimation of non-deterministic delays a clock synchronization protocol for WSN is proposed. The results obtained are quiet promising compared to the existing synchronization protocols for WSNs.
This document summarizes research on reference broadcast time synchronization in wireless sensor networks. It discusses how previous protocols like flooding time synchronization and gradient time synchronization have drawbacks like slow propagation speed and inability to maintain synchronization when nodes crash. It then introduces the reference broadcast synchronization protocol which chooses a reference node using an agreement algorithm and broadcasts time information to synchronize the network. It presents the system architecture and algorithm for how reference broadcast synchronization works to flood time information, perform synchronization based on messages from the parent node, and timestamp events in the network. Evaluation results showing the protocol implemented on line and distributed topologies are also included.
This document provides an overview of wireless sensor networks. It discusses key definitions, advantages, applications and challenges. Sensor networks can provide energy and detection advantages over traditional systems. They enable applications in various domains including military, environmental monitoring, healthcare and home automation. The document also outlines enabling technologies and discusses important considerations like network architectures, hardware components, energy consumption and optimization goals.
The document provides information on the evolution of wireless networks from 1G to 3G. It discusses the key components and architecture of cellular systems including base stations, mobile switching centers and their connection to the public switched telephone network. It also compares the differences between wireless and wired networks, and describes some of the limitations of early wireless networking. Finally, it covers topics like traffic routing, circuit switching, packet switching and the X.25 protocol.
The document summarizes several routing protocols used in wireless networks. It discusses both table-driven protocols like DSDV and on-demand protocols like AODV. It provides details on how each protocol performs routing and maintains routes. It also outlines some advantages and disadvantages of protocols like DSDV, AODV, DSR, and TORA.
This document discusses medium access control (MAC) protocols, which regulate access to a shared wireless medium between nodes. It covers key requirements for MAC protocols including throughput efficiency, fairness, and low overhead. It also describes challenges like the hidden terminal problem, exposed terminal problem, and sources of overhead from collisions, overhearing, and idle listening. Finally, it categorizes common MAC protocols as fixed assignment, demand assignment, and random access and notes additional energy conservation requirements for wireless sensor networks.
1. Wireless sensor networks consist of distributed sensor nodes that communicate wirelessly to monitor physical or environmental conditions, such as temperature, sound, or pollution levels.
2. The sensor nodes gather and route data back to a central sink/gateway node where the information can be analyzed.
3. Communication protocols and algorithms are required for efficient multi-hop routing of data between sensor nodes and the sink node.
IS-95 CDMA is an air interface standard that uses code division multiple access (CDMA). It employs various techniques to improve system capacity and performance, including bandwidth recycling, power control, soft handoffs, diversity combining, and variable rate vocoding. Key aspects of IS-95 include the use of quadrature phase shift keying modulation at a 1.2288 Mcps chip rate, forward error correction coding, and multiple logical channels (pilot, sync, paging, traffic) defined using orthogonal Walsh codes.
TinyOS is an open source operating system designed for wireless sensor networks. It uses a component-based architecture and event-driven execution model to achieve low power consumption and memory footprint. TinyOS programs are built by wiring together components that provide and use interfaces to communicate through events and commands. It also uses a non-preemptive task scheduler and static memory allocation to improve efficiency for energy constrained sensor nodes. The nesC language extends C to support TinyOS's programming model and execution model.
The document discusses ad hoc networks and wireless sensor networks. It defines an ad hoc network as a temporary network composed of mobile nodes without preexisting infrastructure that is self-organizing. Wireless sensor networks are introduced as a collection of sensor nodes densely deployed to monitor conditions and cooperatively pass data back to central nodes. The document outlines key characteristics of both networks including their temporary and adaptive nature, multi-hop routing, and challenges of mobility, power constraints, and dynamic topology changes.
Wireless sensor network and its applicationRoma Vyas
The document discusses wireless sensor networks (WSN) and their applications. It defines a WSN as a collection of sensor nodes that communicate wirelessly and self-organize after deployment. Sensor nodes collect data at regular intervals, convert it to electrical signals, and send it to a base station. The document outlines the components of sensor nodes and describes how WSNs are used for applications like forest fire detection, air/water pollution monitoring, landslide detection, and military surveillance. It also discusses the TinyOS operating system commonly used for WSNs and its features for efficiently utilizing energy in sensor nodes.
This document discusses wireless sensor network applications and energy consumption. It provides examples of WSN applications including disaster relief, environment monitoring, healthcare, and more. It then discusses various factors that influence energy consumption in sensor nodes, including operation states, microcontroller usage, radio transceivers, memory, and the relationship between computation and communication. Specific power consumption numbers are given for different components like radios, sensors, and microprocessors. The goals of optimization for WSNs are discussed as quality of service, energy efficiency, scalability, and robustness.
Medium Access Control :-
1.Distributed Operation
2.Synchronization
3.Hidden Terminals
4.Exposed terminals
5.Throughput
6.Access delay
7.Fairness
8.Real-time Traffic support
9.Resource reservation
10.Ability to measure resource availability
11.Capability for power control
Adaptive rate control
Use of directional antennas
Minimize energy per packet (or per bit)
Maximize network lifetime
Routing considering available battery energy
Maximum Total Available Battery Capacity
Minimum Battery Cost Routing (MBCR)
Min– Max Battery Cost Routing (MMBCR)
Conditional Max – Min Battery Capacity Routing (CMMBCR)
Minimize variance in power levels
Minimum Total Transmission Power Routing (MTPR)
LTE Basic Parameters, Data Rates, Duplexing & Accessing, Modulation, Coding & MIMO, Explanation of different nodes and Advantage & Disadvantages of different nodes.
Sensor node hardware and network architectureVidhi603146
The document discusses the hardware components and architecture of sensor nodes. It describes the main components as the controller module, memory module, communication module, sensing modules, and power supply module. The controller is the core and processes data from sensors. Memory stores programs and data. The communication device allows nodes to exchange data wirelessly. Sensors interface with the physical environment. Power is stored and replenished through batteries or energy scavenging from the environment. TinyOS was developed as an operating system for sensor networks since traditional OSes were not suitable due to constraints like limited memory and power.
The document introduces TinyOS, nesC, and TOSSIM. TinyOS is an open-source operating system for wireless sensor networks. It is designed for low-power embedded devices and uses nesC as its programming language. TOSSIM simulates TinyOS applications by replacing hardware components with simulation implementations. The document discusses TinyOS and nesC programming, TOSSIM simulation, and troubleshooting TinyOS and TOSSIM.
6LoWPAN allows the use of IPv6 over low-power wireless networks. It compresses IPv6 packet headers to accommodate the small packet sizes of low-power wireless standards like 802.15.4. 6LoWPAN finds applications in home automation, healthcare, industrial automation, and environmental monitoring. It defines adaptations for addressing, forwarding, routing, header compression, and security to enable IPv6 connectivity over low-power wireless networks. Implementations of 6LoWPAN exist in open-source operating systems like Contiki and TinyOS, as well as commercial solutions.
Physical channels carry information over the air interface between the mobile station and base transceiver station. Logical channels map user data and signaling information onto physical channels. There are two main types of logical channels - traffic channels which carry call data, and control channels which communicate service information. Control channels include broadcast channels which transmit cell-wide information, common channels used for paging and access procedures, and dedicated channels for signaling during calls or when not on a call. Logical channels are mapped onto physical channels to effectively transmit information wirelessly between network components in a GSM system.
This document discusses localization techniques in wireless sensor networks. It begins with introducing wireless sensor networks and their components. It then discusses the need for localization to track objects within sensor networks. There are two main types of localization schemes - range-based which uses distance or angle measurements, and range-free which uses approximate distance estimates. Examples of range-based techniques include time of arrival, time difference of arrival, received signal strength indicator, and angle of arrival. Range-free techniques include proximity and distance-based localization using hop counts. The document compares the advantages and disadvantages of different localization methods.
Clock synchronization estimation of non deterministic delays in wireless mess...IJCNCJournal
Clock synchronization is significantly essential as they require universal time on WSN nodes for time measurement, event ordering and coordinated actions, and power management. This paper gives an insight of solving the problem of the non-deterministic delays that exist in the wireless message delivery. Sensor nodes consisting of Arduino Mega and 2.4 GHz nRF24L01+ radio modules are used, and based on the estimation of non-deterministic delays a clock synchronization protocol for WSN is proposed. The results obtained are quiet promising compared to the existing synchronization protocols for WSNs.
This document summarizes research on reference broadcast time synchronization in wireless sensor networks. It discusses how previous protocols like flooding time synchronization and gradient time synchronization have drawbacks like slow propagation speed and inability to maintain synchronization when nodes crash. It then introduces the reference broadcast synchronization protocol which chooses a reference node using an agreement algorithm and broadcasts time information to synchronize the network. It presents the system architecture and algorithm for how reference broadcast synchronization works to flood time information, perform synchronization based on messages from the parent node, and timestamp events in the network. Evaluation results showing the protocol implemented on line and distributed topologies are also included.
A REFERENCE BASED, TREE STRUCTURED TIME SYNCHRONIZATION APPROACH AND ITS ANAL...ijasuc
Time synchronization for wireless sensor networks (WSNs) has been studied in recent years as a
fundamental and significant research issue. Many applications based on these WSNs assume local clocks
at each sensor node that need to be synchronized to a common notion of time. Time synchronization in a
WSN is critical for accurate time stamping of events and fine-tuned coordination among the sensor nodes
to reduce power consumption. This paper proposes a bidirectional, reference based, tree structured time
synchronization service for WSNs along with network evaluation phase. This offers a push mechanism for
(i) accurate and (ii) low overhead for global time synchronization. Analysis study of proposed approach
shows that it is lightweight as the number of required broadcasting messages is constant in one
broadcasting domain.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Distributed Approach for Clock Synchronization in Wireless Sensor NetworkEditor IJMTER
Time synchronization is an important service in WSNs. existing time synchronization algorithms
provide on average good synchronization between arbitrary nodes, however, as we show in this paper, close-by
nodes in a network may be synchronized poorly. We propose the Distributed Time Synchronization Algorithm
(DTSA) which is designed to provide accurately synchronized clocks between nearest-neighbours. DTSA works
in a completely decentralized fashion: Every node periodically broadcasts its time information. Synchronization
messages received from direct neighbours are used to calibrate the logical clock. The algorithm requires neither a
tree topology nor a reference node, which makes it robust against link and node failures.
Reference broadcast synchronization and time division multiple access impleme...TELKOMNIKA JOURNAL
Various kinds of technology have been developed to assist obtain information. One of them is a Wireless Sensor Network (WSN). WSN is a wireless network consisting of multiple nodes connected wirelessly. WSN nodes on a device have small resources in the form of batteries. The main problem which owned by WSN was in the data collection process possible collisions data, there are nodes that transmit data at the same time. Time Division Multiple Access (TDMA) was able to provide data on the delivery schedule of each node. So no nodes that transmit data at the same time. But in order to apply the system each node should have equal time. One method that able to provide equalization time was Reference Broadcast Synchronization (RBS). This method synchronizes multiple nodes that have different local time (on the receiver) with the help of node that provides synchronization marks (beacons). Hence this each node was able to transmit data in accordance with the TDMA method that has been implemented. In addition time synchronization performed using RBS give equal time with accuracy up to microseconds. That case certainly makes the WSN node able to provide accurate information to guarantee the absence of errors due to data collisions. This research succesfully sending data delivery schedule by time slots that provided by RBS and time synchronization by TDMA average time delay 2285.9 microseconds.
REAL-TIME ROUTING PROTOCOLS FOR WIRELESS SENSOR NETWORKS: A SURVEYcscpconf
Wireless sensor networks can be termed as a new generation of distributed embedded systems
that has a capability of meeting broad range of real-time applications. Examples include
radiation monitoring, fire monitoring, border surveillance, and medical care to name but a few.
Wireless sensor networks that are deployed in time/mission-critical applications with highly
dynamic environments have to interact with the physical phenomenon under stringent timing
constraints and severe resource limitations. For such real-time wireless sensor networks,
designing and developing a real-time routing protocol that meets the required real-time
guarantee of data packets communication is a stimulating field of study that raised many
challenges and research issues. In this paper, we present a comprehensive survey of real-time
routing protocols in WSN, by discussing each protocol with its key features. Finally, we concluded this paper with open research issues and challenges of real-time routing in WSN.
Clock Synchronization using Truncated Mean and Whale Optimization for Cluster...IJCNCJournal
Clock synchronization is an important component in many distributed applications of wireless sensor networks (WSNs). The deprived method of clock offset and skew estimation causes inaccuracy, synchronization delay, and communication overhead in the protocols. Hence, this paper exploits two techniques of variation truncated mean (VTM) and whale optimization (WO) to enhance the synchronization metrics. Sensor nodes are grouped into several non-overlapped clusters. The cluster head collects the member nodes’ local time and computes the synchronization time 푆푍푡 using the truncated mean method. Nodes with a high variation in the timings compared to a preset value are truncated. The head node broadcasts the 푆푍푡 in which the whale optimization is aiming at each node to reduce the synchronization error. The intra and inter-cluster synchronizations are accomplished through the multihop message exchange approach. The theoretical analysis is validated, and the simulation outcomes show that the performance metrics in the proposed work are better than the conventional methods by achieving minimum error value.
This document proposes a novel sleep scheduling method for event monitoring in wireless sensor networks to achieve low broadcasting delay. The method uses a level-by-level offset schedule where when a node detects a critical event, it transmits an alarm message along a predetermined path with offsets between nodes to avoid collisions. It then uses a colored connected dominant set to establish paths for the center node to broadcast the alarm to other nodes. The proposed system is intended for applications like military and forest fire monitoring where quick dissemination of alarm messages is important.
Scheduling different types of packets, such as
real-time and non-real-time data packets, at sensor nodes with
resource constraints in Wireless Sensor Networks (WSN) is of
vital importance to reduce sensors’ energy consumptions and endto-end
data transmission delays. Most of the existing packetscheduling
mechanisms of WSN use First Come First Served
(FCFS), non pre-emptive priority and pre-emptive priority
scheduling algorithms. These algorithms incur a high processing
overhead and long end-to-end data transmission delay due to the
FCFS concept, starvation of high priority real-time data packets
due to the transmission of a large data packet in non pre-emptive
priority scheduling, starvation of non-real-time data packets due
to the probable continuous arrival of real-time data in preemptive
priority scheduling, and improper allocation of data
packets to queues in multilevel queue scheduling algorithms.
Moreover, these algorithms are not dynamic to the changing
requirements of WSN applications since their scheduling policies
are predetermined.
In the Advanced Multilevel Priority packet scheduling
scheme, each node except those at the last level has three levels of
priority queues. According to the priority of the packet and
availability of the queue, node will schedule the packet for
transmission. Due to separated queue availability, packet
transmission delay is reduced. Due to reduction in packet
transmission delay, node can goes into sleep mode as soon as
possible. And Expired packets are deleted at the particular node
at itself before reaching the base station, so that processing
burden on the node is reduced. Thus, energy of the node is saved.
Unit 5-Performance and Trafficmanagement.pptxABYTHOMAS46
1) The document discusses performance modeling and analysis of wireless sensor networks. It covers topics like basic models, network models, performance metrics, and a case study on computing system lifespan.
2) A case study demonstrates a simple method to compute the system lifespan by making assumptions about the network topology, energy consumption factors, and data transmission rates.
3) Practical examples are given for evaluating routing protocol performance through simulation. Metrics like packet delivery ratio and energy consumption are measured under different network conditions.
The document summarizes the SPEED routing protocol for wireless sensor networks. SPEED aims to provide soft real-time communication by maintaining a consistent delivery speed across the network. It uses stateless non-deterministic geographic forwarding and neighborhood feedback to route packets while balancing energy consumption and avoiding congestion. Simulation results using MATLAB show that SPEED achieves low miss ratios and end-to-end delays while balancing energy usage across nodes in the network.
Security based Clock Synchronization technique in Wireless Sensor Network for...iosrjce
This document proposes a secure clock synchronization technique for wireless sensor networks used in event-driven measurement applications. The technique aims to 1) provide high synchronization accuracy around detected events, 2) ensure long network lifetime, and 3) provide secure packet transmission. It divides nodes into an improved synchronization subset (ISS) with high accuracy around events, and a default synchronization subset (DSS) with lower accuracy elsewhere. When an event is detected, neighboring nodes in the ISS exchange synchronization packets more frequently for better accuracy. Authentication is used to securely transmit packets and identify intercepted messages. Simulation results show the technique accurately records event occurrence times while maintaining network lifetime through efficient energy usage.
This document proposes a secure clock synchronization technique for wireless sensor networks used in event-driven measurement applications. The technique aims to 1) provide high synchronization accuracy around detected events, 2) ensure long network lifetime, and 3) provide secure packet transmission. It divides nodes into an Improved Synchronization Subset (ISS) around events, where accuracy is high, and a Default Synchronization Subset (DSS) where accuracy is low to preserve energy. An authentication-based security mechanism is used to securely transmit packets. Simulation results show the technique accurately records event occurrence times while maintaining network lifetime through efficient energy usage.
A survey on various time synchronization techniques in underwater sensor netw...IAEME Publication
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Clock synchronization is an issue of vital importance in applications of wireless sensor networks (WSNs). This paper proposes a proportional integral estimator-based protocol (EBP) to achieve clock synchronization for wireless sensor networks. As each local clock skew gradually drifts, synchronization accuracy will decline over time. Compared with existing consensus-based approaches, the proposed synchronization protocol improves synchronization accuracy under time-varying clock skews. Moreover, by restricting synchronization error of clock skew into a relative small quantity, it could reduce periodic re-synchronization frequencies. At last, a pseudo-synchronous implementation for skew compensation is introduced as synchronous protocol is unrealistic in practice. Numerical simulations are shown to illustrate the performance of the proposed protocol.
The document discusses a fault tolerance mechanism for wireless sensor networks (WSNs) using recovery nodes. It proposes detecting faulty sensor nodes by calculating the round trip delay (RTD) time for discrete round trip paths (RTPs) and comparing them to a threshold value. Once a faulty node is identified, neighboring nodes with higher energy ("recovery nodes") are selected to take over transmission duties and maintain quality of service. The method is tested through simulations with different numbers of sensor nodes to evaluate scalability.
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تتميز هذهِ الملزمة بعِدة مُميزات :
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2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
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4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
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TIME SYNCHRONIZATION IN WIRELESS SENSOR NETWORKS: A SURVEY
1. 10.5121/iju.2010.1206 92
TIME SYNCHRONIZATION IN WIRELESS
SENSOR NETWORKS: A SURVEY
Prakash Ranganathan, Kendall Nygard
Department of Computer Science, North Dakota State University, Fargo, ND, USA
prakashranganathan@mail.und.edu
ABSTRACT ___
Time synchronization is a critical piece of infrastructure for any distributed system. Wireless
sensor networks have emerged as an important and promising research area in the recent years. Time
synchronization is important for many sensor network applications that require very precise mapping of gathered
sensor data with the time of the events, for example, in tracking and vehicular surveillance. It also plays an
important role in energy conservation in MAC layer protocols. The paper studies different existing methods,
protocols, significant time parameters (clock drift, clock speed, synchronization errors, and topologies) to achieve
accurate synchronization in a sensor network. The studied Synchronization protocols include conventional time sync
protocols (RBS, Timing-sync Protocol for Sensor Networks -TPSN, FTSP), and other application specific
approaches such as all node-based approach, a diffusion-based method and group sync approaches aiming at
providing network-wide time. The goal for writing this paper is to study most common existing time synchronization
approaches and stress the need of a new class of secure-time synchronization protocol that is scalable, topology
independent, fast convergent, energy efficient, less latent and less application dependent in a heterogeneous hostile
environment. Our survey provides a valuable framework by which protocol designers can compare new and
existing synchronization protocols from various metric discussed in the paper. So, we are hopeful that this paper
will serve a complete one-stop investigation to study the characteristics of existing time synchronization protocols
and its implementation mechanism in a Sensor network environment.
Keywords: Secure-time, Synchronization, MAC Layer
1. INTRODUCTION
The time synchronization problem to synchronize the local clocks of sensor nodes in the wireless network have been
extensively studied in literatures over the last two decades and yet there is no specific time synchronization scheme
available to achieve higher order of accuracy with greater scalability independent of topology and application [1-4].
This reflects the complexity associated with the collaborative nature of sensor nodes, in many sensor networks
causing it to be a trivial problem in itself. One of the basic middleware services of sensor networks is network-wide
time synchronization within the network. Many sensor network applications require time to be synchronized within
the network. Examples of such applications include environmental monitoring, mobile object (target) tracking, data
fusioning, TDMA radio scheduling, message ordering, to name a few. Consider the application of mobile object
tracking, in which a sensor network is deployed in an area of interest to monitor passing objects. When an object
appears, the detecting nodes record the detecting location and the detecting time. Later, these location and time
information are sent to the aggregation (sometime referred as fusion) node which estimates the moving trajectory of
the object. Without an accurate time synchronization scheme, the estimated trajectory of the tracked object could
differ significantly from the actual one. Hence, a precise either global (or localized) timesync service is critical and
must be made available at each of the sensor nodes for any application. In this paper, we study all class of existing
time synchronizing schemes and provide a comprehensive analysis to future researches.
2. TIME SYNCHRONIZATION PROBLEM
The time of a computer clock is measured as a function of the hardware oscillator
2. 93
where
is the angular frequency of the oscillator, is a constant for that oscillator, and t is the time. The change of the
value leads to the events (or interrupts) that can be captured by the sensor. The clocks in a sensor network can
be inconsistent due to several reasons. The clock may drift due to environment changes, such as temperature,
pressure, battery voltage, etc. This has been a research topic in the operating system and Internet communities for
many years. There are three reasons for the nodes to be representing different times in their respective clocks – (1)
The nodes might have been started at different times, (2) The quartz crystals at each of these nodes might be running
at slightly different frequencies, causing the clock values to gradually diverge from each other (termed as the skew
error), (3) The frequency of the clocks can change variably over time because of aging or ambient conditions such as
temperature (termed as the drift error). All the above said errors are three sources that contribute to different time
within a sensor network. The nodes in a sensor network may not be synchronized well initially, when the network is
deployed. The sensors may be turned on at the different times and their clocks may be running according to different
initial values. The results of events on specific sensors may also affect the clock. For example, the Berkeley Mote
sensors may miss clock interrupts and the chance to increase the clock time value when they are busy handling
message transmission or sensing tasks [1]. We will explore the time synchronization problem due to clock offset by
analyzing some related work on existing time synchronization schemes in the next section.
3. RELATED WORK ON TIME- SYNC SCHEMES
The early time sync protocol used in the internet domain is the Network Time Protocol (NTP) devised by Mills [2].
The NTP clients synchronize their clocks to the NTP time servers with accuracy in the order of milliseconds by
statistical analysis of the round-trip time. The time servers are synchronized by external time sources, typically using
GPS. The NTP has been widely deployed and proved to be effective, secure and robust in the internet. In WSN,
however, non-determinism in transmission time caused by the Media Access Channel (MAC) layer of the radio
stack can introduce several hundreds of milliseconds delay at each hop. Therefore, without further adaptation, NTP
is suitable only for WSN applications with low precision demands [2]. There were other schemes such as Reference
Broadcast schemes proposed by Elson et al. [3] which eliminate the uncertainty of the sender by removing the
sender from the critical path. Many of the time synchronization protocols use a sender to receiver synchronization
method where the sender will transmit the timestamp information and the receiver will synchronize. RBS is different
because it uses receiver to receiver synchronization. The idea is that a third party will broadcast a beacon to all the
receivers (A and B) . The beacon does not contain any timing information; instead the receivers will compare their
clocks (ta and tb) to one another to calculate their relative phase offsets. The timing is based on when the node
receives the reference beacon.
Figure 1: RBS Scheme
A
ta
B
tb
Reference Node ack
e
beacon
beacon
3. 94
The simplest form of RBS is one broadcast beacon and two receivers. The timing packet will be broadcasted to the
two receivers. The receivers will record when the packet was received according to their local clocks. Then, the two
receivers will exchange their timing information and be able to calculate the offset [3]. This is enough information to
retain a local timescale. RBS can be expanded from the simplest form of one broadcast and two receivers to
synchronization between n receivers, where n is greater than two. This may require more than one broadcast to be
sent. Increasing the broadcasts will increase the precision of the synchronization. The following figure shows the
exchange of timing information with their neighboring nodes in RBS scheme, as the nodes will then be able to
calculate their offset as seen above in fig 1.
Reference Broadcast scheme (RBS) eliminate the uncertainty of the sender by removing the sender from the critical
path. By removing the sender, the only uncertainty is the propagation and receives time. The propagation time is
negligible in networks where the range is relatively small. It is claimed that the reference beacon will arrive at all the
receiving nodes instantaneously. By removing the sender and propagation uncertainty the only room for error is the
receiver uncertainty. TPSN is a traditional sender-receiver based synchronization that uses a tree to organize the
network topology [4] [5] [6]. The concept is broken up into two phases, the level discovery phase and the
synchronization phase. The level discovery phase creates the hierarchical topology of the network in which each
node is assigned a level. Only one node resides on level zero, the root node. In the synchronization phase all i level
nodes will synchronize with i-1 level nodes. This will synchronize all nodes with the root node. The level discovery
phase is run on network deployment. First, the root node should be assigned. If one node was equipped with a GPS
receiver, then that could be the root node and all nodes on the network would be synced to the world time. If not,
then any node can be the root node and other nodes can periodically take over the functionality of the root node to
share the responsibility [3][4][5].
Once the root node is determined, it will initiate the level discovery. The root, level zero, node will send out the
level_discovery packet to its neighboring nodes. Included in the level_discovery packet is the identity and level of
the sending node. The neighbors of the root node will then assign themselves as level one. They will in turn send out
the level_discovery packet to their neighboring nodes. This process will continue until all nodes have received the
level_discovery packet and are assign a level. Once again all nodes are assigned a level to create a tree type
topology. The root node is level zero continuing down the tree with level one and so on. All nodes of level i will
broadcast the level_discovery with all nodes of level i-1. This is maintained until all nodes are assigned a level [5].
The basic concept of the synchronization phase is two-way communications between two nodes. As mentioned
before this is a sender to receiver communication. Similar to the level discovery phase, the synchronization phase
begins at the root node and propagates through the network.
Figure 2: Two –way messaging
Figure 2 illustrates the two-way messaging between a pair of nodes. This messaging can synchronize a pair of
nodes by following this method. The times T1, T2, T3, and T4 are all measured times. Node A will send the
synchronization_pulse packet at time T1 to Node B. This packet will contain Node A's level and the time T1 when it
T2 T3
T1 T4
B
LOCAL TIME
LOCAL TIME
A
4. 95
was sent. Node B will receive the packet at time T2. Time T3 is when Node B sends the acknowledgment_packet to
Node A. That packet will contain the level number of Node B as well as times T1, T2, and T3. By knowing the drift,
Node A can correct its clock and successfully synchronize to Node B. This is the basic communication for TPSN.
The synchronization process is again initiated by the root node. It broadcasts a time_sync packet to the level one
nodes. These nodes will wait a random amount of time before initiating the two-way messaging. The root node will
send the acknowledgment and the level one nodes will adjust their clocks to be synchronized with the root nodes.
The level two node will be able to hear the level one nodes communication since at least one level one node is a
neighbor of a level two node. On hearing this communication the level two nodes will wait a random period of time
before initiating the two-way messaging with the level one nodes. This process will continue until all nodes are
synchronized to the root node. Again the synchronization process executes much the same as the level discovery
phase. All communication begins with the root node broadcasting information to the level 1 nodes. This
communication propagates through the tree until all level i-1 nodes are synchronized with the level i nodes. At this
point all nodes will be synchronized with the root node [4] [5].
3.1. UNCERTAINITY IN THE SYNC PACKET
Any synchronization packet has the four delays discussed in [7] send time, access time, propagation time, and
receive time. Eliminating any of these would be a plus. A hardware RF transreceiver implementation for reception
time is discussed in detail in [7]. Although TPSN does not eliminate the uncertainty of the sender it does, however,
minimize it. Also, TPSN is designed to be a multi-hop protocol; so transmission range is not an issue. Unlike RBS,
TPSN has uncertainty in the sender. They attempt to reduce this non-determinism by time stamping packets in the
MAC layer. It is claimed that the sender's uncertainty contributes very little to the total synchronization error. By
reducing the uncertainty with low level time stamping, it is claimed that TPSN has a 2 to 1 better precision than
RBS and that the sender to receiver synchronization is superior to the receiver to receiver synchronization. [2]. RBS
also is limited by the transmission range. It was stated that RBS can ignore the propagation time if the range of
transmission was relatively small. If it is a large multi-hop network, this is not the case. RBS would have to send
more reference beacons for the node to synchronize. TPSN on the other hand was designed for multi-hop networks.
Their protocol uses the tree based scheme so the timing information can accurately propagate through the network.
Lightweight Tree-based Synchronization (LTS), proposed by Greunen and Rabaey [8] is distinguished from other
work in the sense that the aim is not to maximize accuracy, but to minimize the complexity of the synchronization.
Thus the needed synchronization accuracy is assumed to be given as a constraint, and the target is to devise a
synchronization algorithm with minimal complexity to achieve given precision. This approach is supported by the
claim of authors that the maximum time accuracy needed in sensor networks is relatively low (within fractions of a
second), and therefore it is sufficient to use a relaxed, or lightweight, synchronization scheme in sensor networks.
The algorithm is a centralized algorithm, and needs a spanning tree to be constructed first. Then pair wise
synchronization is done along the n - 1 edges of the spanning tree. In the centralized algorithm, the reference node is
the root of the spanning tree and has the responsibility of initiating a resynchronization" as needed. Using the
assumption that the clock drifts are bounded, and given the required precision, the reference node calculates the time
period that a single synchronization step will be valid. Since the depth of the spanning tree affects the time to
synchronize the whole network, and also the precision error at the leaf nodes, the depth of the tree is communicated
back to the root node so that it can use this information in its resynchronization time decision [8].
Another time-sync class of protocol is Flooded Time Sync protocol (FTSP) [9]. This protocol is similar to TPSN,
but it improves on the disadvantages to TPSN. It is similar in the fact that it has a structure with a root node and that
all nodes are synchronized to the root. The root node will transmit the time synchronization information with a
single radio message to all participating receivers. A Radio Channel cannot be accessed simultaneously by two or
more nodes that are in a radio interference range—neighboring nodes may cause “conflict” or signal interference at
some nodes if transmitting at the same time on the same channel. In wireless sensor networks, controlling access to
the channel, generally known as medium access control (MAC), plays a key role in determining channel utilization,
network delays, and, more important, power consumption, also influencing congestion and fairness in channel
usage.
5. 96
The message contains the sender's time stamp of the global time at transmission. The receiver notes its local time
when the message is received. Having both the sender's transmission time and the reception time, the receiver can
estimate the clock offset. The message is MAC layer time stamped, as in TPSN, on both the sending and receiving
side. To keep high precision compensation for clock drift is needed. FTSP uses linear regression for this. FTSP was
designed for large multi-hop networks. The root is elected dynamically and periodically reelected and is responsible
for keeping the global time of the network. The receiving nodes will synchronize themselves to the root node and
will organize in an ad hoc fashion to communicate the timing information amongst all nodes. The network structure
is mesh type topology instead of a tree topology as in TPSN. Typical WSN operate in areas larger than the broadcast
range of a single node; therefore, the FTSP provides multi-hop synchronization. The root of the network—a single,
dynamically (re) elected node—maintains the global time and all other nodes synchronize their clocks to that of the
root. The nodes form an adhoc structure to transfer the global time from the root to all the nodes, as opposed to a
fixed spanning-tree based approach. This saves the initial phase of establishing the tree and is more robust against
node and link failures and dynamic topology changes.
3.2. SECURE TIME SYNCHRONIZATION
The primary functionality of wireless sensor networks is to sense the environment and transmit the acquired
information to base stations for further processing with secure time information. Studies and experiences (e.g., [10-
14, 22-24]) have shown that considering security in the design stage is the best way to provide security for sensor
network routing. Several recent contributions to the literature have addressed security and privacy issues in sensor
networks [9-13,16] for routing, but it is difficult to implement along with existing time synchronization approaches
because they require lots of computations for routing. Thus, the existing time synchronization schemes in sensor
networks were not designed with security in mind, thus leaving them vulnerable to security attacks in addition to
establishing the need of secure time sync for individual sensor nodes. Security in RBS Scheme is vulnerable and it is
prone to many attacks [11]. Taking the RBS scheme as an example, an attacker may launch different kinds of attacks
to break the protocol.
In [11], the authors discuss four different type of attacks. The first attack is called masquerade attack. Suppose a
node A sends out a reference beacon to its two neighbors B and C. An attacker E can pretend to be B and exchange
wrong time information with C, disrupting the time synchronization process between B and C. A second attack is
called replay attack. Using the same scenario in the first attack, the attacker E can replay B’s old timing packets,
misleading C to be synchronized to a wrong time. A third attack is called message manipulation attack. In this
attack, an attacker may drop, modify, or even forge the exchanged timing messages to interrupt the time
synchronization process.
For the message dropping attack, the attacker can selectively drop the packets and thus prolong the converging time
of the synchronization process. This can be done on a random or arbitrary basis, making it more difficult to be
detected. For the message forging attack, the attacker can forge many reference beacon messages and flood the
network. This not only incorrectly synchronizes the neighbors, but also causes those nodes to consume power to
process these unwanted and faked timing messages. If some nodes run out of power, coverage holes or network
partitions may appear. The last type of attack is the Delay attack, where, the attacker deliberately delays some of the
time messages (e.g., the beacon message in the RBS scheme) so as to fail the time synchronization process. It is
noted that this attack cannot be defended against by cryptographic techniques. We can certainly employ some
cryptographic techniques [12][13] to address the aforementioned attacks. For example, providing authentication of
every exchanged message will prevent an outside attacker from impersonating other nodes or altering the content of
an exchanged message. Adding a sequence number to beacon messages or other messages will prevent message
replay attacks.
For RBS, an attack on the synchronization can be executed easily. RBS works by receiver to receiver
synchronization in which both nodes receive the reference beacon and then calculate their offset with one another.
An attack would be as simple as compromising one of the nodes with an incorrect time. The non compromised node
will then calculate an incorrect offset during the exchange period. Remember TPSN is a sender to receiver tree
based protocol with two phases, the level discovery phase and the synchronization phase. Both of the phases are
initiated by the root node. In the synchronization phase the level number and the time are both sent through the tree.
6. 97
An attack would simply be to compromise a non-root node with the incorrect time. This will propagate through the
tree and the closer the compromised node is to the root node, the more incorrect synchronization will occur. Also a
node could lie about its level. That would cause other nodes to request synchronization information in which it could
give inaccurate information. That node also could refuse to participate in the level discovery phase, which could
eliminate its children from the network. The fundamental problem in FTSP [9] is that it allows for any node to elect
itself the root after a period of time of not receiving the synchronization information. A corrupt node could claim
itself to be the root and now the other nodes will respond to its timing information instead of the correct information
from the real root node. The will of course propagate through the network until all nodes have incorrectly calculated
their skew and offset. Since none of the protocols were designed with security in mind, attacks on the
synchronization are easily executed by following the rules of the protocol. In the sender to receiver synchronization,
an attack will institute more damage because it will propagate through the network. Several papers have studies on
security issues [8-13] in sensor network which are attack resilient.
We now discuss another class of sync protocol called the Gradient Time Synchronization Protocol (GTSP) which is
a completely distributed time synchronization protocol [14]. In this sync method, nodes periodically broadcast
synchronization beacons to their neighbors. Using a simple update algorithm, they try to agree on a common logical
clock with their neighbors. It can be shown by theoretical analysis that by employing this algorithm, the logical
clock of nodes converges to a common logical clock. GTSP relies on local information only, making it robust to
node failures and changes in the network topology. Experiments on a test bed setup of 20 Mica2 nodes and
simulations showed that the remaining synchronization error between neighbors is small while still maintaining an
acceptable global skew. Furthermore, the authors have shown that GTSP can improve the synchronization error
between neighboring sensor nodes compared to tree-based time synchronization protocols. The FTSP clock
synchronization algorithm exhibits an error that grows exponentially with the size of the network, for instance [9].
Since the involved parameters are small, the error only becomes visible in midsize networks of about 10-20 nodes.
In contrast, the authors of another class of sync protocol PulseSync, discussed a new clock synchronization
algorithm that is asymptotically optimal. They evaluate PulseSync on a Mica2 testbed, and by simulation on larger
networks. On a 20 node network, the prototype implementation of PulseSync outperforms FTSP by a factor of 5.
Theory and simulation show that for larger networks, PulseSync offers an accuracy which is several orders of
magnitude better than FTSP [9].
Figure 3: Synchronization error in (µS) vs number of Hops for FTSP and PulseSync
FTSP PulseSync
Distance (Hops) Distance (Hops)
Synchronizationerror(µs)
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To round off the presentation, the PulseSync, investigate several optimization issues,e.g. media access and local
skew[15].Furthermore, the authors of PulseSync claim that PulseSync converges rapidly to a common logical time
since the time information is flooded in a single round from the reference node to all other nodes throughout the
network. The authors proved that the PulseSync and FTSP have the same message complexity (one message per
node and synchronization period), the fast flooding approach of Pulse-Sync achieves significantly improved
synchronization accuracy on the same network topology. Furthermore, their experiments confirm with a finding that
the synchronization error to root node must increase exponentially when using FTSP, while PulseSync performs
better as shown in above figure[15].
3.3. ENERGY CONSUMPTION OF SYNCHRONIZATION SCHEMES
Most wireless sensor networks to date employ nodes with limited power, processing, storage, and communication
capabilities. Additionally, they typically include relatively simple sensors (e.g., light, temperature, pressure,
magnetometer, etc.). In these deployments, energy consumed by sensing-related tasks is relatively low, which means
that the communication subsystem (i.e., the radio) dominates energy consumption. RBS and TPSN both achieve
accurate clock synchronization within a few microseconds of uncertainty. However, they are both designed for
networks with a small number of sensors and are not specifically geared towards energy conservation; although
these algorithms will work for larger networks, their energy consumption becomes inefficient and network
connectivity is not maintained once nodes begin losing power. Simulating each of these methods shows that
synchronizing a large sensor network requires an unnecessarily large number of transmissions, which will quickly
deplete sensors and reduce the network’s coverage area.
The hybrid algorithm proposed in [7] chooses RBS over TPSN based on receiver threshold and number of receivers.
The results from Table 3.1. show that RBS’s energy consumption is more dependent on the density of sensors in a
given area. In contrast, TPSN and the hybrid algorithm are less affected by the size of the network. When the
network size increases from 250 sensors to 500 sensors (for the same area of 1 km2
), RBS becomes less energy
efficient than TPSN. The hybrid algorithm outperforms TPSN by 15.7%, while outperforming RBS by 20.8%. Once
the network increases to 750 sensors, RBS clearly becomes less efficient than TPSN. The hybrid algorithm still
outperforms TPSN by 12.7%. Since RBS consumes more energy, the hybrid algorithm now outperforms it by 32%.
Hybrid algorithm, the power reduction is even more drastic in large multi-hop sensor networks.
The method also improves upon these algorithms by maintaining a large area of coverage even when some
sensors lose power [7][17]. Please see Table 3.1 for the savings on energy in milliWatts range with no of sensors
used[]. For the MICA2Dot platform [18], a reception uses approximately 24 mW of power, while a transmission
requires 75 mW at -5 dBm.
# Sensors 250 500 750 1000 1250 1500
RBS 466 1046 1844 2762 3756 5060
TPSN 511 983 1434 1885 2331 2770
Hybrid 404 828 1253 1672 2095 2514
Savings over RBS 9.29% 20.8% 32.0% 39.4% 44.2% 50.3%
Savings over TPSN 20.8% 15.7% 12.7% 11.2% 10.1% 9.2%
TABLE 3.1: AVERAGE ENERGY CONSUMPTION (mW) IN RBS, TPSN & HYBRID SYNC METHODS
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3.4. POST-FACTO SYNCHRONIZATION
To save energy in a sensor network, it is a desirable to keep nodes in a low-power state, if not turned off completely,
for as long as possible. Sensor network hardware is often designed with this goal in mind; processor have various
“sleep” modes or are capable of powering down high-energy peripherals when not in use. In post-facto scheme
proposed by Jeremy Elson and Deborah Estrin [3], nodes' clocks are normally unsynchronized. When a stimulus
(signal) arrives, each node records the time of the stimulus with respect to its own local clock. Immediately
afterwards, a “third party” node acting as a beacon broadcasts a synchronization pulse to all nodes in the area using
its radio. Nodes that receive this pulse use it as an instantaneous time reference and can normalize their stimulus
timestamps with respect to that reference. This kind of synchronization is not applicable in all situations, it is limited
in scope to the transmit range of the beacon and creates only an “instant” of synchronized time as discussed in the
paper [3].
This makes it inappropriate for an application that needs to communicate a timestamp over long distances or times.
However, it does provide exactly the service necessary for beam-forming applications, localization systems, and
other situations in which we need to compare the relative arrival times of a signal at a set of spatially local detectors.
Post-facto sync method need to be tested in real WSN.
3.5. GLOBAL CLOCK SYNCHRONIZATION
Many emerging sensor network applications require that the sensors in the network agree on the time. A global
clock in a sensor system will help process and analyze the data correctly and predict future system behavior. For
example, in the vehicle tracking application, each sensor may know the time when a vehicle is approaching. By
matching the sensor location and sensing time, the sensor system may predict the vehicle moving direction and
speed. Without a global agreement on time, the data from different sensors cannot be matched up. There are several
algorithms developed and studies in literatures [19] [20] using global sync messages such as the basic and popular
all-node approach by Qun Li and Rus [19] shown in Figure 4. The main idea of their algorithm is to send a message
along a loop and record the initial time and the end time of the message. Then by using the message traveling time,
we can average the time to different segments of the loop and smooth over the error of the clocks. But the all node
method are not scalable for very large networks. The initiating node may encounter failure and thus the approach is
not fault tolerant. The nodes that participate in the synchronization must execute the related code approximately at
the same time, which may be too hard in a large system.
Figure 4: Qun Li’s - All-node method
There are also approaches which uses diffusion techniques [21] either they diffuse low or high or average clock
readings from one node to next or neighboring node. The average diffusion based algorithm is more robust and
reasonable, Since they use global average value as the ultimate synchronization clock reading. The main idea of the
9. 100
algorithms is to average all the clock time readings and set each clock to the average time. A node with high clock
time reading diffuses that time value to its neighbors and levels down its clock time. A node with low time reading
absorbs some of the values from its neighbors and increases its value. After a certain number of rounds of diffusion
the clock in each sensor will have the same value, but these methods use global time information sent to all the
nodes and thus they are not scalable for very large networks. The initiating node may encounter failure and thus the
approach is not fault tolerant. The nodes that participate in the Synchronization must execute the related code
approximately at the same time, which may be too hard in a large system. Diffusion techniques may avoid this
scalability problem, where synchronization is done locally by spreading the local synchronization information to the
whole system. It can choose various global values to synchronize the network provided that each node in the overall
network agrees to change its clock reading to the consensus value. An easy and possible way is to choose the highest
or lowest reading over the network. Several paper contributions were made on diffusion techniques [21].
A master-slave protocol assigns one node as the master and the other nodes as slaves. The slave nodes consider the
local clock reading of the master as the reference time and attempt to synchronize with the master. In general, the
master node requires CPU resources proportional to the number of slaves, and nodes with powerful processors or
lighter loads are assigned to be the master node. Mock et al. [25] have adopted the IEEE 802.11 clock
synchronization protocol due to its simple, non-redundant, master/slave structure. Ping’s protocol [26] also adheres
to the master-slave mode.
Nazemi et.al [27] used passive clustering and linear regression Scalable Light weight Time Sync protocol (SLTP) to
reduce the energy consumption of network nodes and also decrease the overhead of creating and maintaining the
clusters. Moreover SLTP uses linear regression to compute the time. Therefore, it can calculate the clock skew and
offset between each node and its cluster head in order to estimate the local time of remote nodes in the future or the
past. Simulation results using NS2 Simulator show that the SLTP can gain considerable improvements in power
consumption, accuracy and scalability in comparison to similar algorithms [27][15]
TABLE 3.2: A COMPARISON CHART OF VARIOUS TIME SYNC SCHEMES AND METRICS
Time Sync
methods
Accuracy Energy
Efficiency
Complexity Scalability Fault
Tolerance
Byte
Alignment
1. RBS Low, 29.1µs is the average
error per hop
High High Good No Note Handled
2. TPSN 16.9µs per hop High Low Poor No Not Handled
3. FTSP 1.48 µs per hop High High Average No Handled
4. GTSP Average network wide
error, 14µs,
Avg. neighbor sync error
is 4µs
High Average Good Yes Not Handled
5. PulseSync 4.4µs (compared to
23.96µs for FTSP between
any two nodes)
High High Good No Unknown
6.All-node Low Low Low No No No
7.Diffusion
techniques
Average Average High Good No No
8. Mock et.al High Low Low NA Yes Unknown
9 .Ganeriwal [4, 5] High Average Low Good Yes No
10 .Ping High High Low Good Yes Unknown
11.SLTP High High Average High No NA
10. 101
4. GROUP SYNCHRONIZATION
All current approaches use the technique of pair wise synchronization but this is insufficient in cases where a group
of nodes need to be synchronized for instance in tracking application. In a group sync model, a single beacon
beacons, all receivers respond with their offset to the beacon. The beacon has one measurement to relate each pair of
nodes which either form a sender-receiver (sender-receiver) pair or a receiver-receiver (receiver-receiver) pair. This
approach will have a 4 times extra cost but will result in only one measurement between any pair of sensor nodes
resulting in achieving better accuracy readings. One way to do this through beam-forming arrays, which can perform
“spatial filtering”, receiving only signals arriving from a certain direction. This depends on the relative time offsets
of the array’s sensors. Another problem in Synchronization algorithm is duplicate detection of events. The time of
an event helps nodes in the cluster determine if they are seeing two distinct real-world events, or a single event seen
from two vantage points. If they are indeed seeing the same event, they can further fuse their observations to get
much meaningful information about the event. All these applications will function accurately only if the
synchronization error between nodes in a group is bounded. So far, all the existing work in the research literatures
concentrates on establishing pair wise relationships between a pair of nodes at a given instant of time. However,
there is need of developing a unified framework that uses one optical beacon to synchronize all nodes in group or
network wide at the instant of time[16][15][17].
5. CONCLUSION
A family of existing time synchronization algorithms are studied and a comparative summary has been presented in
the paper. As increasing demand in use and promising application of sensor network are emerging, the need of very
precise secure clock measurement algorithms are vital for error free clock time measurements whether it is loosely
or densely packed or it is deployed in star or mesh or any other framework. The authors strongly believe that using
this comparative survey paper, it will make future researchers to explore existing time sync approaches with much
easier and will give them a choice to implement their application based on our study on various time sync protocols.
6. ACKNOWLEDGEMENTS
The authors are thankful to all synchronization papers that are credited as references below. This is a comprehensive
study paper on synchronization algorithms or approaches collectively in one paper form cited from various
contributions from the authors mentioned below.
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Authors
Prakash Ranganathan is a faculty in Electrical Engineering at University of North Dakota, Grand Forks. He earned his undergraduate degree from
University of Madras, India and graduate degree from North Dakota State University, Fargo. He is currently teaching Electric Circuits, Computer
Aided Measurements and controls. His research interests are in Sensor Networks, Smart Grid technologies and Engineering Education. He is
active member of IEEE and ASEE.
Kendall E. Nygard is Professor of Computer Science and Operations Research at North Dakota State University where he has served on the
faculty since 1977. From 1996 to 2005 he served as Department Chair. In 1986 and 1987 he was on the adjunct faculty in operations research at
the Naval Postgraduate School in Monterey, California. In 1994 and 1995 he was Director of the Scientific Computing Center at the University of
North Dakota in Grand Forks. In 1984 he was a visiting scientist at the Air Force Logistics Command at Wright-Patterson AFB in Ohio. In 2000
he was a research fellow at the Air Vehicle Directorate of the Air Force Research Lab. His research areas involve combinatorial optimization
methods, with applications to management of networks and sensor networks, cooperative mission control for unmanned air vehicles (UAVs),
computer-based transportation analysis, and bioinformatics.