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In recent years, wireless sensor networks (WSNs) have been considered one of the important topics for researchers due to their wide applications in our life. Several researches have been conducted to improve WSNs performance and solve their issues. One of these issues is the energy limitation in WSNs since the source of energy in most WSNs is the battery. Accordingly, various protocols and techniques have been proposed with the intention of reducing power consumption of WSNs and lengthen their lifetime. Cluster-oriented routing protocols are one of the most effective categories of these protocols. In this article, we consider a major issue affecting the performance of this category of protocols, which we call the intra/inter-cluster event-reporting problem (IICERP). We demonstrate that IICERP severely reduces the performance of a cluster-oriented routing protocol, so we suggest an effective Solution for IICERP (SIICERP). To assess SIICERP’s performance, comprehensive simulations were performed to demonstrate the performance of several cluster-oriented protocols without and with SIICERP. Simulation results revealed that SIICERP substantially increases the performance of cluster-oriented routing protocols.
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Fog Computing: Issues, Challenges and Future Directions IJECEIAES
In Cloud Computing, all the processing of the data collected by the node is done in the central server. This involves a lot of time as data has to be transferred from the node to central server before the processing of data can be done in the server. Also it is not practical to stream terabytes of data from the node to the cloud and back. To overcome these disadvantages, an extension of cloud computing, known as fog computing, is introduced. In this, the processing of data is done completely in the node if the data does not require higher computing power and is done partially if the data requires high computing power, after which the data is transferred to the central server for the remaining computations. This greatly reduces the time involved in the process and is more efficient as the central server is not overloaded. Fog is quite useful in geographically dispersed areas where connectivity can be irregular. The ideal use case requires intelligence near the edge where ultralow latency is critical, and is promised by fog computing. The concepts of cloud computing and fog computing will be explored and their features will be contrasted to understand which is more efficient and better suited for realtime application.
This document summarizes a research paper that proposes integrating mobile devices with grid computing using an efficient scheduling algorithm. The paper presents a layered architecture with four layers - mobile devices, a mobile device interface, an interface to grid services, and grid services. It focuses on the local resource scheduling problem in the second layer. The paper describes implementing the architecture using Java-enabled mobile devices and Globus Toolkit-based grid services. It also proposes using an Artificial Bee Colony (ABC) algorithm for local resource scheduling to address issues like unreliable connectivity and battery power in mobile environments. The paper evaluates the performance of the traditional and new ABC scheduling algorithms.
AN EFFICIENT INTRUSION DETECTION SYSTEM WITH CUSTOM FEATURES USING FPA-GRADIE...IJCNCJournal
An efficient Intrusion Detection System has to be given high priority while connecting systems with a network to prevent the system before an attack happens. It is a big challenge to the network security group to prevent the system from a variable types of new attacks as technology is growing in parallel. In this paper, an efficient model to detect Intrusion is proposed to predict attacks with high accuracy and less false-negative rate by deriving custom features UNSW-CF by using the benchmark intrusion dataset UNSW-NB15. To reduce the learning complexity, Custom Features are derived and then Significant Features are constructed by applying meta-heuristic FPA (Flower Pollination algorithm) and MRMR (Minimal Redundancy and Maximum Redundancy) which reduces learning time and also increases prediction accuracy. ENC (ElasicNet Classifier), KRRC (Kernel Ridge Regression Classifier), IGBC (Improved Gradient Boosting Classifier) is employed to classify the attacks in the datasets UNSW-CF, UNSW and recorded that UNSW-CF with derived custom features using IGBC integrated with FPA provided high accuracy of 97.38% and a low error rate of 2.16%. Also, the sensitivity and specificity rate for IGB attains a high rate of 97.32% and 97.50% respectively.
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Hybrid medium access control (MAC) scheme is one of the prominent mechanisms to offer energy efficiency in wireless sensor network where the potential features for both contention-based and schedule-based approaches are mechanized. However, the review of existing hybrid MAC scheme shows many loopholes where mainly it is observed that there is too much inclusion of time-slotting or else there is an inclusion of sophisticated mechanism not meant for offering flexibility to sensor node towards extending its services for upcoming applications of it. Therefore, this manuscript introduces a novel hybrid MAC scheme which is meant for offering cost effective and simplified scheduling operation in order to balance the performance of energy efficiency along with data aggregation performance. The simulated outcome of the study shows that proposed system offers better energy consumption, better throughput, reduced memory consumption, and faster processing in contrast to existing hybrid MAC protocols.
Supercapacitors can store electric charge through a process called double layer capacitance. They have a higher power density than batteries but a lower energy density. A supercapacitor increases its capacitance and energy storage capacity by increasing the surface area of its electrodes and decreasing the distance between them. While supercapacitors have limitations like lower energy density and higher cost than batteries, they charge and discharge much faster than batteries and can be cycled millions of times, making them useful for applications that require bursts of energy or regeneration of energy. Recent research is focused on improving supercapacitors' energy density to make them a viable alternative to batteries for more applications.
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The document discusses various techniques for wireless transmission of energy including near field techniques like inductive coupling and resonant inductive coupling, and far field techniques like laser power transmission, microwave power transmission, and solar power satellites. It provides details on how inductive coupling, resonant inductive coupling, microwave power transmission, and solar power satellites work. The document also discusses advantages and disadvantages of near field and far field techniques as well as applications of wireless power transmission including electric vehicle charging, consumer electronics, and industrial uses.
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This document describes a novel technique for using supercapacitors in a hybrid electric vehicle to reduce battery stress. It proposes connecting supercapacitors in parallel with the vehicle's batteries. The supercapacitors would supply transient current demands, reducing the battery current drawn by up to 30% and extending the battery lifespan. It provides background on hybrid electric vehicles, supercapacitors, and compares their advantages to batteries. Diagrams show how the proposed energy storage system would operate under different driving conditions.
This document presents an overview of ultracapacitors by Bharat Gupta for Dr. Anwar Sadat. It begins with an introduction to ultracapacitors, their principles, construction, taxonomy, comparisons to batteries and capacitors, advantages and disadvantages, and applications. The body of the document then provides more detailed explanations of these topics, describing the technological aspects of ultracapacitors including their principles of storing charge, construction with electrodes and electrolytes, different types (electrochemical double-layer, pseudocapacitors, and hybrids), performance comparisons in terms of energy and power densities, and various applications from transportation to military uses. The document concludes that ultracapacitors have great potential in applications requiring high power and cycling
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This document discusses supercapacitors, also known as ultracapacitors. It provides a brief history, noting they were first developed in 1957 and licensed for market production in 1978. Supercapacitors store energy electrostatically at the interface between an electrode and electrolyte through a double-layer capacitance effect. They have a higher power density than batteries but lower energy density. The document outlines the key components of a supercapacitor including polarized electrodes made of highly porous activated carbon, electrolytes that allow ion migration during charging and discharging, and separators that provide insulation between electrodes while allowing ion conduction. Applications mentioned include use in diesel engines, trains, power systems, and missiles to recover and deliver braking energy.
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Literature Surveys: A Methodological Approachflmkessels
This document outlines the steps for conducting a literature survey in a methodological way. It discusses establishing a clear problem description by identifying the problem, research questions, and objectives. It also covers developing a search plan with keywords, potential sources, and an execution process. Finally, it addresses reporting the results by transforming summaries from a text plan into a structured written report with critical interpretation. The overall goal is to provide guidance for systematically planning and conducting a literature survey to answer a research question.
ENERGY EFFICIENT COMPUTING FOR SMART PHONES IN CLOUD ASSISTED ENVIRONMENTIJCNCJournal
In recent years, the employment of smart mobile phones has increased enormously and are concerned as an area of human life. Smartphones are capable to support immense range of complicated and intensive applications results shortened power capability and fewer performance. Mobile cloud computing is the newly rising paradigm integrates the features of cloud computing and mobile computing to beat the constraints of mobile devices. Mobile cloud computing employs computational offloading that migrates the computations from mobile devices to remote servers. In this paper, a novel model is proposed for dynamic task offloading to attain the energy optimization and better performance for mobile applications in the cloud environment. The paper proposed an optimum offloading algorithm by introducing new criteria such as benchmarking for offloading decision making. It also supports the concept of partitioning to divide the computing problem into various sub-problems. These sub-problems can be executed parallelly on mobile device and cloud. Performance evaluation results proved that the proposed model can reduce around 20% to 53% energy for low complexity problems and up to 98% for high complexity problems.
A Comparison of Cloud Execution Mechanisms Fog, Edge, and Clone Cloud Computing IJECEIAES
Cloud computing is a technology that was developed a decade ago to provide uninterrupted, scalable services to users and organizations. Cloud computing has also become an attractive feature for mobile users due to the limited features of mobile devices. The combination of cloud technologies with mobile technologies resulted in a new area of computing called mobile cloud computing. This combined technology is used to augment the resources existing in Smart devices. In recent times, Fog computing, Edge computing, and Clone Cloud computing techniques have become the latest trends after mobile cloud computing, which have all been developed to address the limitations in cloud computing. This paper reviews these recent technologies in detail and provides a comparative study of them. It also addresses the differences in these technologies and how each of them is effective for organizations and developers.
Contemporary Energy Optimization for Mobile and Cloud Environmentijceronline
Cloud and mobile computing applications are increasing heavily in terms of usage. These two areas extending usability of systems. This review paper gives information about cloud and mobile applications in terms of resources they consume and the need of choosing variety of features for users from several locations and the evolutionary provisions for service provider and end users. Both the fields are combined to provide good functionality, efficiency and effectiveness with mobile phones. The enhancement by considering power consumption by means of resource constrained nature of devices, communication media and cost effectiveness. This paper discuss about the concepts related to power consumption, underlying protocols and the other performance issues
IRJET- Fog Route:Distribution of Data using Delay Tolerant NetworkIRJET Journal
This document summarizes a research paper that proposes using delay tolerant network (DTN) approaches for data dissemination in fog computing networks. It describes a hybrid data dissemination framework with a two-plane architecture: 1) the cloud serves as a control plane to process content updates and organize data flows, and 2) geometrically distributed fog servers form a data plane to disseminate data among themselves using DTN techniques. This allows non-urgent, high-volume content to be distributed across fog servers in an efficient manner without relying on expensive bandwidth between the fog and cloud layers.
HYBRID OPTICAL AND ELECTRICAL NETWORK FLOWS SCHEDULING IN CLOUD DATA CENTRESijcsit
This document summarizes a research paper on scheduling flows in hybrid optical and electrical networks for cloud data centers. The paper proposes a strategy for selecting which flows are suitable to switch from the electrical packet network to the optical circuit network. It presents techniques for detecting bottlenecks in the packet network and selecting flows to offload. Simulation results showed improved network performance from this flow selection approach, including higher average throughput, lower configuration delay, and more stable offloaded flows.
ERMO2 algorithm: an energy efficient mobility management in mobile cloud comp...IJECEIAES
Recently, mobile devices are becoming the primary platforms for every user who always roam around and access the cloud computing applications. Mobile cloud computing (MCC) combines the both mobile and cloud computing, which provides optimal services to the mobile users. In next-generation mobile environments, mainly due to the huge number of mobile users in conjunction with the small cell size and their portable information‟s, the influence of mobility on the network performance is strengthened. In this paper, we propose an energy efficient mobility management in mobile cloud computing (E2M2MC2) system for 5G heterogeneous networks. The proposed E2M2MC2 system use elective repeat multi-objective optimization (ERMO2) algorithm to determine the best clouds based on the selection metrics are delay, jitter, bit error rate (BER), packet loss, communication cost, response time, and network load. ERMO2 algorithm provides energy efficient management of user mobility as well as network resources. The simulation results shows that the proposed E2M2MC2 system helps in minimizing delay, packet loss rate and energy consumption in a heterogeneous network.
This document summarizes a study on mobile cloud computing. It discusses mobile cloud computing integrating cloud computing into mobile environments to overcome obstacles related to performance, environment, and security for mobile devices. The study aims to test if cloud applications can improve battery lifetime for mobile devices. It proposes a system with mobile users, cloud service providers, and trusted third parties to provide secure data storage in the cloud while addressing issues like data confidentiality and access control for mobile cloud computing.
Assessment to Delegate the Task to Cloud for Increasing Energy Efficiency of ...IRJET Journal
This document discusses assessing whether tasks on mobile phones should be offloaded to the cloud to improve energy efficiency. It presents a model where mobile devices can offload computationally intensive tasks to the cloud via wireless networks. An experiment is described that compares the energy consumption and time taken of a video conversion task performed locally on a mobile phone versus offloading the task or different parts of the task to a cloud. The results show that offloading the entire task to the cloud reduces energy consumption and processing time compared to performing the task locally on the mobile phone. The document concludes offloading tasks to the cloud can increase a mobile phone's energy efficiency and discusses areas for future work.
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Cloud computing is a network-based environment that focuses on sharing computations, Cloud computing networks access to a shared pool of configurable networks, servers, storage, service, applications & other important Computing resources. In modern era of Information Technology, the accesses to all information about the important activities of the related fields. In this paper discuss the advantages, disadvantages, characteristics, challenge, deployment model, cloud service model, cloud service provider & various applications areas of cloud computing such as small & large scale (manufacturing, automation, television, broadcast, constructions industries), Geographical Information system (GIS), Military intelligence fusion (MIS), business management, banking, Education, healthcare, Agriculture sector, E-Governance, project planning, cloud computing in family etc. Keywords: Cloud computing, community model, hybrid model, Public model, private model
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Method of broadcasting is the well known operation that is used for providing support to different computing protocols in cloud computing. Attaining energy efficiency is one of the prominent challenges, that is quite significant in the scheduling process that is used in cloud computing as, there are fixed limits that have to be met by the system. In this research paper, we are particularly focusing on the cloud server maintenance and scheduling process and to do so, we are using the interactive broadcasting energy efficient computing technique along with the cloud computing server. Additionally, the remote host machines used for cloud services are dissipating more power and with that they are consuming more and more energy. The effect of the power consumption is one of the main factors for determining the cost of the computing resources. With the idea of using the avoidance technology for assigning the data center resources that dynamically depend on the application demands and supports the cloud computing with the optimization of the servers in use.
Opportunistic job sharing for mobile cloud computingijccsa
Cloud Computing is the evolution of new business era which is covered with many of technologies.These
technology are taking advantage of economies of scale and multi tenancy which are used to decrees the
cost of information technology resources. Many of the organization are eager to reduce their computing
cost through the means of virtualization. This demand of reducing the computing cost and time has led to
the innovation of Cloud Computing. Itenhanced computing through improved deployment and
infrastructure costs and processing time. Mobile computing & its applications in smart phones enable a
new, rich user experience. Due to extreme usage of limited resources in smart phones it create problems
which are battery problems, memory space and CPU. To solve this problem, we propose a dynamic mobile
cloud computing architecture framework to use global resources instead of local resources. In this
proposed framework the usefulness of job sharing workload at runtime reduces the load at the local client
and the dynamic throughput time of the job through Wi-Fi Connectivity.
Energy Optimized Link Selection Algorithm for Mobile Cloud ComputingEswar Publications
Mobile cloud computing is the revolutionary distributed computing research area which consists of three different domains: cloud computing, wireless networks and mobile computing targeting to improve the task computational capabilities of the mobile devices in order to minimize the energy consumption. Heavy computations can be offloaded to the cloud to decrease energy consumption for the mobile device. In some mobile cloud applications, it has been more energy inefficient to use the cloud compared to the conventional computing conducted in the local device. Despite mobile cloud computing being a reliable idea, still faces several
problems for mobile phones such as storage, short battery life and so on. One of the most important concerns for mobile devices is low energy consumption. Different network links has different bandwidths to uplink and downlink task as well as data transmission from mobile to cloud or vice-versa. In this paper, a novel optimal link selection algorithm is proposed to minimize the mobile energy. In the first phase, all available networks are
scanned and then signal strength is calculated. All the calculated signals along with network locations are given
input to the optimal link selection algorithm. After the execution of link selection algorithm, an optimal network link is selected.
Cloud Computing for hand-held Devices:Enhancing Smart phones viability with C...IOSR Journals
This document discusses computation offloading from mobile devices to the cloud in order to save energy and extend battery life. It begins by introducing cloud computing and how it can provide shared resources to devices like smartphones. Computation offloading involves moving intensive processes from mobile devices to more powerful servers in the cloud. This reduces the computation done on the mobile device, saving energy. The document analyzes several research papers on computation offloading and mobile cloud computing. It discusses the benefits of offloading like extended battery life and improved reliability. It also examines challenges like low bandwidth, availability issues, and security concerns. Overall, the document argues that computation offloading to the cloud can help minimize mobile energy usage and increase battery life.
Finding your Way in the Fog: Towards a Comprehensive Definition of Fog ComputingHarshitParkar6677
The cloud is migrating to the edge of the network, where
routers themselves may become the virtualisation infrastructure,
in an evolution labelled as “the fog”. However, many
other complementary technologies are reaching a high level
of maturity. Their interplay may dramatically shift the information
and communication technology landscape in the
following years, bringing separate technologies into a common
ground. This paper offers a comprehensive definition
of the fog, comprehending technologies as diverse as cloud,
sensor networks, peer-to-peer networks, network virtualisation
functions or configuration management techniques. We
highlight the main challenges faced by this potentially breakthrough
technology amalgamation.
Implementing K-Out-Of-N Computing For Fault Tolerant Processing In Mobile and...IJERA Editor
Despite the advances in hardware for hand-held mobile devices, resource-intensive applications (e.g., video and imagestorage and processing or map-reduce type) still remain off bounds since they require large computation and storage capabilities.Recent research has attempted to address these issues by employing remote servers, such as clouds and peer mobile devices.For mobile devices deployed in dynamic networks (i.e., with frequent topology changes because of node failure/unavailability andmobility as in a mobile cloud), however, challenges of reliability and energy efficiency remain largely unaddressed. To the best of ourknowledge, we are the first to address these challenges in an integrated manner for both data storage and processing in mobilecloud, an approach we call k-out-of-n computing. In our solution, mobile devices successfully retrieve or process data, in the mostenergy-efficient way, as long as k out of n remote servers are accessible. Through a real system implementation we prove the feasibilityof our approach. Extensive simulations demonstrate the fault tolerance and energy efficiency performance of our framework in largerscale networks.
Secured Way Of Offloading Mobile Cloud Process For Smart PhoneIRJET Journal
This document proposes a secured way of offloading mobile cloud processes to reduce computation power usage on smartphones. It introduces a ternary decision making (TDM) framework that determines whether a task should be performed on the smartphone or offloaded to the cloud based on estimated execution time and energy consumption. The tasks are offloaded to the cloud for remote execution using an encryption technique for security. This results in reduced computation power usage and battery consumption on smartphones by utilizing cloud resources instead.
ABSTRACT
In today’s world, the swift increase of utilizing mobile services and simultaneously discovering of the cloud computing services, made the Mobile Cloud Computing (MCC) selected as a wide spread technology among mobile users. Thus, the MCC incorporates the cloud computing with mobile services for achieving facilities in daily using mobile. The capability of mobile devices is limited of computation context, memory capacity, storage ability, and energy. Thus, relying on cloud computing can handle these troubles in the mobile surroundings. Cloud Computing gives computing easiness and capacity such provides availability of services from anyplace through the Internet without putting resources into new foundation, preparing, or application authorizing. Additionally, Cloud Computing is an approach to expand the limitations or increasing the abilities dynamically. The primary favourable position of Cloud Computing is that clients just use what they require and pay for what they truly utilize. Mobile cloud computing is a form for various services, where a mobile gadget is able to utilize the cloud for data saving, seeking, information mining, and multimedia preparing. Cloud computing innovation is also causes many new complications in side of safety and gets to direct when users store significant information with cloud servers. As the clients never again have physical ownership of the outsourced information, makes the information trustworthiness, security, and authenticity insurance in Cloud Computing is extremely difficult and conceivably troublesome undertaking. In MCC environments, it is hard to find a paper embracing most of the concepts and issues such as: architecture, computational offloading, challenges, security issues, authentications and so on. In this paper we discuss these concepts with presenting a review of the most recent papers in the domain of MCC.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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A New Improved Storage Model of Wireless Devices using the Cloud
1. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.1, January 2013
"A New Improved Storage Model of Wireless
Devices using the Cloud"
Zyad Nossire1 and Khaled Elleithy2 and Boraq Ammourah3
1
Department of Computer Science and Engineering, University of Bridgeport,
Bridgeport, CT 06604
znossire@bridgeport.edu
2
Department of Computer Science and Engineering,
University of Bridgeport, Bridgeport, CT 06604
elleithy@bridgeport.edu
3
Department of Computer Science,
Najran University, Najran, Kingdom of Saudi Arabia
baammourah@nu.edu.sa
Abstract
This paper focuses on the development of new storage model by using cloud computing for
mobile devises. The concept of cloud computing has been applied to mobile devices for improving the
existing model (battery time and data saving) of mobile devices. In the recent eras, different types of
cloud computing techniques have been used for improving the efficiency of mobile devices. The paper
has combined the calibration and current launch amount characteristics with the trial results for drop in
battery voltage. A mathematical equation has been derived for mote operation scenario. Through this
equation, the power provide by the power supply as well as the average time of battery can be measured.
Key word
"Cloud computation, mobile application, topology management (save battery energy lifetime), mobile
data archiving "
1. INTRODUCTION
With the explosion accessibility of internet entry via mobile phone, here with the
growing number of cloud computing service. Smart phones, PDAs and IPods are set to become
a leading access point and interface, and it will be helpful and more efficiently to carry out
anywhere at any time when they want to make shopping via the internet (E-shopping) such as
booking ticket purchase, ever more perceived as the most convenient access point. As a result,
optimizing these devices to improved access to cloud services becomes critical. [1]
However, the cell phone services applications are different from the frequently manner in the
capacity to provide execute rich user applications involving widely use. Foremost, in the mobile
devices you can say these devices involve that they are battery-powered, limiting their power
capabilities. As smart phones are most famous portable technology, the most intensive of
computation capacity point and the main key of restricted access will be energy consumption in
these days. In fact the majorly of memory in usual computer versus the mobility in this time,
with this comparison will find the memory limitation in mobile. [2]
Actually, that will happen within execute the heavy application such as voice recognition,
mobile phone tracker, navigations system via mobile and face recognitions. Mean while, H/W
of mobile device and mobile networks are going on to evolve and to improve, the weakly points
in the mobile devices, the unreachable connection with network, limitation outsourcing within
access the networks, energy power of life time battery, for thus points we purpose to use the
DOI : 10.5121/ijcnc.2013.5105 69
2. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.1, January 2013
cloud computing outsource with access via mobile phone. In the cloud computing have
unlimited outsourcing same as IEEE library, in that provides a public cloud as storage resources
that will discuss later on. According to increase lifetime battery there have algorithm that will
enhance and reduce of consuming energy power of lifetime battery, it’s called topology
algorithm. [3]
2. Problem Background
Cloud computing is a new concept which combines many fields of computing. The
foundation of cloud computing is built on the software and processing capacity, delivery of
efficient services, reducing consumption of battery lifetime, increasing storage, decoupling of
services from technology, computerizing systems and flexibility and mobility of information.
The problem in this model is that achievement of proposed benefits is far behind the achieved
benefits for mobile devices. The above mentioned application models of the mobile computing
are used in different scales. It is very important to propose a model where proposed benefits of
mobile computing can be achieved in reality.
3. Problem Statement
Mobile phones have spread all over the world. The basic aim behind these devices is to
achieve worldwide interface for accessing services and cloud computing applications. Use of
cloud computing techniques allows two types of configurations. First is how to reduce the
consumption of energy power for getting improved battery life time. Second is to establish
special archiving data for saving power and improving battery lifetime. This can happen when
archiving is established and user goes to sleep mode for saving battery time.
4. Cloud Computing
Clouted-intensive applications to the cloud is very important in energy efficient the
response time and battery consumption. Without an accurate Clouded-intensive, the minimum
energy battery lifetime could not find the optimal dynamic mobile outsourcing. In addition, we
will first present some topology algorithm assumptions, which used to saving power of lifetime
battery. To optimization of decreasing the access time of the source code on out- sourcing
framework [17]
4.1 public clouds
When we need to turn on public cloud, a service provider will first need to classify the
services that will be accessible to enterprise that want to place their over load in the cloud.
Works with service providers through some
Programs, most not are ably the Cloud Data Center Services program, to make sure a lowest
level of cloud service capability. [18]
4.2 private clouds
Is an environment which is able of running and implementing characteristics of cloud
computing like virtualization and layered services more than the network.[18]
5. Topology algorithm
The topology management can be considered a primary module in managing network of
wireless sensor network. The major objective of topology management is to minimize the
node’s energy consumption and increase the network period. [19]
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3. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.1, January 2013
Topology discovery request used to define the location and certifying the connectivity of each
node in sensor network. Power control is used to maintain the range of the connection between
the nodes by conducting the power of the transceivers. The key goal for using sleep cycle in
topology management is turning off some nodes for periods of time in order to avoid the
redundancy in the network. The last area is clustering, which is involves into dividing the nodes
into groups to improve the scalability and power efficiencies. [19]
6. Recent Techniques
6.1. Cloudlets
Most of mobile devices use the energy saving techniques, used in wireless networks, to
increase the latency and jitter average; this is to make mobile device’s switch on only for little
time. The Cloudlet is therefore proposed to ease the amount of data sent transversely the
network. Cached version of cloud are Decentralized, Available everywhere cloudlet and high
speed connection. [4]
Internet
LAN
WiFI
Figure 1. Cloudlets
6.2. Virtual cloud Provider
Mobile Clint access the same cloud service on the same time ,the Clint can share the
reply from cloud service, the users get a part of translation reply using Internet, and in this
technique using Per 2 Per (P2P) protocol to share with everyone in Local Wi‐Fi, all users have
the full translation and helps when bandwidth is small. The process for the creation and usage of
a virtual cloud provider is easy ,for example If a user is at a stable place and wants to complete
a task which need more resources than available at the device, the system listens for nodes in
the area. If available or not, the system intercept the application loading and modifies and the
applications in order to use the virtual cloud. This is a main idea for virtual cloud provider [16]
Figure 2. Virtual cloud Provider
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6.3 WINC Sleep
Sometimes we may have to wait completely a long time There perhaps also be
sometime between inputs from User. Wireless Network Interface Card (WNIC) is energy
consuming when switch on before receiving answer from cloud multimedia streams makes
wireless network interface (WNIC) energy consumption an especially acute problem for mobile
clients. [14]
In my work I will to allow the clients to move in the WNIC to a lower power consuming sleep
stat. In the following table, benefits of techniques used in the project are summarized:
Table 1. Comber Between Techniques
Cloudlets Virtual Cloud Provider WINC Sleep
Ease in data transfer Helpful when bandwidth is small Lower power consumption
Available everywhere Easy process of usage Speedy connection
Speedy connection All users have the full translation Connectable between cable and
device
7. Google Gears Geo Location
From here, I will try to describe, based on the above info, how Google might use
this information to give location information. In the case of GPS, data sent to Google contains:
1. GPS data
2. GSM towers
3. Wi-Fi towers
In the case of a mobile that supports a GPS, it is enough to determine the GSM towers to
have accurate info about location. This info is sent to Google as part of the request. But as
we can see, this information already contains the geo location, so Google might do the
following:
1. Calculate you location [latitude and longitude].
2. Use Wi-Fi data to calculate your relative position to each Wi-Fi.
3. Calculate the absolute position of each Wi-Fi tower based on a and b:
a. Mobile absolute location is already known.
b. Wi-Fi relative location from the mobile is known.
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5. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.1, January 2013
Figure 3. Google Gears Geo Location
Now, Google stores the mac_address and the SSID together with the absolute location in a geo
database that is kept updated when possible. In case you open your laptop and connect to
Google Latitude, Google can determine your absolute location using the Wi-Fi towers only
because it already knows, from other queries from you and different persons, the absolute
location of the Wi-Fi towers. As we can see in the following figure, the blue person sends GPS,
GSM towers info, and Wi-Fi towers info. Google calculates the Wi-Fi towers info. Now the
green person can know his location by querying Google with Wi-Fi towers only. This is not that
easy. We need to think of many challenges such as similar Wi-Fi names, changing Mac-
addresses, Wi-Fi turning on/off etc. [15]
7. Cloudlets
7.1. Cloudlets Can Help
Can we obtain the advantages of cloud computing without being WAN-limited fairly
than counting on a handheld distant control “cloud,” the source hardship of a cellular phone can
be addressed by using a close by resource rich cloudlet? The need for real-time entertaining
reaction can be met by low latency, one-hop, high-bandwidth Wi-Fi entry to the cloudlet.[5]
The cellular phone features as a slim customer, with all significant calculations happening in the
encompassing cloudlet. Physical vicinity of the cloudlet is essential: the end-to-end reaction
duration of programs performing in the cloudlet needs to be fast (few milliseconds) and
foreseen. If no cloudlet is available close by, the cellular phone can beautifully lower to a
fallback function that includes a handheld distant control reasoning or, in the toughest,
completely its own sources. Full efficiency and efficiency can come back later, when a close by
cloudlet is discovered
Cloudlets are decentralized and widely-dispersed online facilities whose estimate periods and
storage space sources can be utilized by nearby cellular computer systems. A cloudlet can be
considered as a “data center in a box.” It is self-managing, demanding little more than power,
online connection, and accessibility control for installation. This convenience of management
goes along to an equipment style of processing sources, and makes it simple to set up on a
business property such as a cafe or a physician's office. Inner, a cloudlet may be considered as a
group of multi-core computer systems, with gigabit internal connection and a high-bandwidth
Wi-Fi LAN. For safe implementation in unmonitored areas, the cloudlet may be packed in a
mess proof or tamper-evident housing with third-party distant tracking of components
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6. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.1, January 2013
reliability, a cloudlet only contains smooth state such as storage space cache duplicates of data
or value that is available elsewhere. Loss or destruction of a cloudlet is hence not catastrophic.
[6]
7.1. Temporary Cloudlet Customization
We think about an upcoming in which cloudlet facilities is implemented much like Wi-
Fi accessibility points these days. Indeed, it would be relatively uncomplicated to include
cloudlet and Wi-Fi entry way components into a single easily deployable entity. A key task is to
easily simplify cloudlet control. [7] Extensive implementation of cloudlet facilities will not
happen unless program control of that facility is simple preferably, it should be completely self-
managing. Firmly reducing program on cloudlets to easily simplify control is unappealing
because it constrains program advancement and progress. Instead, a perfect cloudlet would
support the greatest possible range of mobile customers, with little restrictions on their program.
[2]
Figure 4. Cloudlet Customization
Table 2. Cloudlet& Cloud
Cloudlet Cloud
State Only soft state Hand and soft state
Management Self management little to no Professional administered 24x7
professional attention operator
Environment "Datacenter in a box" at Machines room with power
business premises conditioning and cooling
Ownership Decentralized ownership by Centralized ownership by Amazon,
local business Yahoo
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7. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.1, January 2013
Network LAN latency/ bandwidth Internet latency/ bandwidth
Sharing Few User at time 100s-1000 of user at time
7.2. Joining to Cloudlet Infrastructure
The operator of the temporary capturing between cellular phone and cloudlet is a user-
level procedure known as Kimberley Control Administrator (KCM). An example of KCM
operates on it and on the cloudlet, and they together summary service development and system
management from the rest of Kimberley. KCM can handle the surfing around and posting of
solutions using the procedure in A Linux System Unix.[8] The first thing in the capturing series
is the establishment of a protected TCP tube using SSL between KCM circumstances on a
system and a cloudlet. This tube is then used by the relax of the capturing series, which typically
involves individual verification and optionally available charging connections. Kimberley can
handle the Simple Authentication and Protection Part (SASL) structure, which provides an
extensible user interface for developing different verification systems. After effective
verification, the cloudlet KCM completes a management. This brings the VM overlay from the
cellular phone or a Web site, decrypts and decompresses it, and is applicable the overlay to the
platform VM. The revoked VM is then released, and is willing to offer solutions to the cellular
phone. [9]
Figure 5. Cloudlet Infrastructure
8. WINC Sleep
8.1. Power Consumption Modeling
It is feasible to evaluate the power use of the Telos (B) platform primary segments
based on the profiling procedure offered. I will explain how to simplify the program's level of
power consumption using models that do not have a decrease in precision. "According to the
measurements’ results, the difference in power consumption among the analyzed segments is
quite significant. This particular understanding is crucial when assessing methods that
guarantee greater expected gains in power preservation".[10] Moreover, these methods are
essential for determining the most critical operations with regards to power consumption
corresponding to the HW segments used, which can be conducted by a WSN mote, to determine
the battery's functional life-time. Therefore, this section is designed to offer combined details
extracted from my research, regarding important power consumption of primary efficient
segments in useful and realistic ways assisting by my suggested technique. [11]
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8. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.1, January 2013
The most sensible element to start with is the MSP430 micro-processor. By comparing the
results, it is proven that although the primary handling device provides five possible low power
ways, an easy conversion to the LPM1 power condition is enough to offer considerable power
consumption gains in a program stage range. Such a conclusion is further highlighted when
considering concurrent power consumption linked to modules that save more power, which will
be proven later in this area. When the battery is asleep, the outcome to present a decrease in the
cloud may provide only a minor distinction in a WSN common program. The mobile's battery is
saved when considering the transitioning delays back to the mobile's complete effective
condition, as well as various restrictions in the function of side-line elements included in the
microcontroller processor. Furthermore, when the CPU is effective, the kind of function or
control performing does not affect the calculated present sketch. More specifically, depending
on the CPU dimensions, such current stages for concern of battery loss are defined in Desk I,
assuming a CPU regularity of 4MHz. Therefore, the particular power consumption computation
can quickly be of use by the multiplication of certain present stages during which the CPU is
located in a particular efficient condition. [12]
The stations transceiver relevant power consumption is the most crucial element regarding the
program's power level consumption. This is due to the fact that facilitating radio connections are
probably the most elementary function of a common WSN program and, that as an element; it
provides the highest current draw specifications than any other primary HW element. As
experimental dimensions indicate, only two different existing draw levels are actually identified
in Table I, assuming that the nodes are required to transmit/listen/receive at complete power, for
which current demands are roughly the same. Based on these levels, the power consumption
offered by the air programs' operation can be calculated considering the amount of energy and
energy frame percentages regarding the air programs “ON” and “OFF” states. By using a
program that switches the mobile off, more energy is saved and signs do not execute so large a
part in mote lifetime development for common deployments, because the average existing
specifications will continue to be helpful. As far as receptors and existing monitoring are
concerned, the information supply by an AD ripper resources path, measurements have
confirmed that the existing drawn depends greatly on the ADC path consumption, being
approximately 0.5mA. Lastly, LEDs contain the most consistently used indicator concerning all
effective states of a WSN mote and can be considered as a type of actuator for Telos(B) and
other WSN techniques, which generate proportional current specifications to the current use.
Measurements concerning the LEDs’ function show that each one of them constitutes
considerable power source consumption. [13]
With respect to a WSN program, the most straightforward guideline is to avoid the use of LEDs
all together. If that is not possible, then any particular future development must be satisfied by
boasting the battery's consumption function, rather than by a constant “ON” state, considering
the existing draw indicated in Table I. In conclusion, the system-level power consumption of a
WSN node can be related to a limited and definite number of factors. In most WSN techniques,
the identification and accurate figure of these factors, allows the formulation of a particular
genuine and easily applicable equation which styles the mote’s power consumption pattern. [13]
Table 3. HW COPMONETS CURRENT DRAW LEVELS
CPU Mode Measured Current
LPM0 2.35mA
LPM1 1.85µA
Radio Mode
Active 23mA
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9. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.1, January 2013
(transmute/receive/listen)
Idle 2.5mA
Type of Led
Blue 3.4mA
Red 5.5mA
AD converter
Sensors 0.6mA
8.2. Scientific power supply potential and life-time characteristic
The calibration and current launch amount characteristics are combined with the trial
results for the battery voltage drop relatively to time duration for each particular mote function.
The latter combination can produce a graph relating the normal launch amount with the power
in mAh and power supply life-time, defined by the threshold of 1.5V being the cut-off current
for the Telos(B) platform, where any communicational function can no longer be performed.
The relation between total power offered by power supply and the average launch amount is
described by power supply capacity characteristics, whilst the algorithm between power supply
life-time and average launch amount is described by power supply lifetime characteristic. [14]
Considering the power potential provided, and its identified reliance on the release
amount, an average discharge amount can be produced corresponding to the overall battery life-
time as portrayed in the following formula.
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10. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.1, January 2013
The above formula explains that the Eoffered is the power potential provided by power supply
power, tmax is power supply life-time, Iaverage is the normal launch rate and Ii is the present
attracted for a specific time period ∆ti. Experimental results concerning the current fall of the
batteries for the air stations functions and the removal of the current attracted by the RX
function are shown in Fig. 4. The type of power supply power used is AA alkaline provided by
Energizer Company (E91, AA, 1.5V, LR6, AM3) with 1.5V nominal voltage. The area within
the present bend symbolizes the energy potential provided and is calculated by summing the
multiples of present per time steps as provided in. Using the produced present fall for
every mote operation scenario, the power provided by power supply power as well as the
average present attracted can be measured with. The lifetime tmax symbolizes enough time.
[14]
9. Conclusion
The paper has attempted to present a new model for the mobile devices by using
techniques of mobile computing. The literature has discussed that cloud intensive applications
are very important for achieving energy efficiency in relation to battery time and energy saving.
Without developing an appropriate and accurate cloud computing model, the minimum energy
consumption and battery lifetime cannot be obtained. The paper has attempted to consider the
power potential and its reliance on the release amount. An equation has been derived through
cloud computing techniques through which battery time and average discharge time to
corresponding overall battery life can be determined.
10. Reference
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[11] Annamalai, M., Birrell, A., Fetterly, D., and Wobber, . Implementing Portable Desktops: a New
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Authors
Mr. Zyad Nossire: received the B.Sc. in Management Information System
from Al-Albyat University, Al-Mafrq ON, Jordan in 2006, and the MCA (Master
of science information and Communication Technology ) from Utara University
– Malaysia in 2008. In 2012 Zyad Nossire joined University of Bridgeport as
Ph.D. student in computer science and engineering at the University of
Bridgeport, Connecticut-USA. From 2008-2009, Zyad Nossire was Assistant
Lecturer in science and Technology Community College on Irbid - Al-Balqa
Applied University-Jordan. From 2009 to 2011 Zyad Nossire joined Njran and
Al-Emma Mohamed Ben Saoud University's –Saudi Arabia as assistant lecturer. Zyad Nossire research
interest is in the general area of Cloud computing ,Mobile ,wireless communications and networks. My
email addresses: znossire@bridgeport.edu, ziad.jo2009@yahoo.com
Dr. Khaled Elleithy: is the Associate Dean for Graduate Studies in the School
of Engineering at the University of Bridgeport. His research interests are in the
areas of, network security, mobile wireless communications formal approaches
for design and verification and Mobile collaborative learning. He has published
more than one two hudereds research papers in international journals and
conferences in his areas of expertise.
Dr. Elleithy is the co-chair of International Joint Conferences on Computer,
Information, and Systems Sciences, and Engineering (CISSE).CISSE is the first Engineering/ Computing
and Systems Research E-Conference in the world to be completely conducted online in real-time via the
internet and was successfully running for four years. Dr. Elleithy is the editor or co-editor of 10 books
published by Springer for advances on Innovations and Advanced Techniques in Systems, Computing
Sciences and Software.
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12. International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.1, January 2013
Dr. Elleithy received the B.Sc. degree in computer science and automatic control from Alexandria
University in 1983, the MS Degree in computer networks from the same university in 1986, and the MS
and Ph.D. degrees in computer science from The Center for Advanced Computer Studies in the
University of Louisiana at Lafayette in 1988 and 1990, respectively. He received the award of
"Distinguished Professor of the Year", University of Bridgeport, during the academic year 2006-2007.
I am in progress of my educational life by holding degree in Software
Engineering from Philadelphia University in Jordan, after that holding master
degree from University Utara Malaysia (UUM). Now I am working as a lecturer
in Najran University in KSA till now. After obtaining my Master degree in
Information Technology from UTARA University in Malaysia, I am currently
working to refine my knowledge and skills in my areas of interest. I believe this
will also server to give direction to my goal of career as a research professional
at an academic or commercial, research-oriented organization. I want to delve
deeper into the subject to be able to carry out independent research and analysis. I also interest in
different areas of study, such as Cloud computing, Database management, Object Oriented Software
engineering, Object Oriented Analysis and design. Basically, i deeply interest in Cloud Computing,
according to life demands for new technology that accelerate the transition from small-scale, closed, in-
house computing and data storage to mega-scale, and service-oriented infrastructure, called Cloud
Computing. baammourah@nu.edu.sa , mr.boraq@gmail.com
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