Int J Wireless Inf Networks
DOI 10.1007/s10776-015-0271-2
Comparative Analysis of Routing Protocols in Wireless
Sensor–Actor Networks: A Review
Jagadeesh Kakarla1
•
Banshidhar Majhi1 • Ramesh Babu Battula2
Received: 6 February 2014 / Accepted: 15 May 2015
Ó Springer Science+Business Media New York 2015
Abstract Wireless sensor–actor networks (WSANs) are
applicable in versatile domains ranging from very common
to those which demand reliable actions in the event area.
The unique characteristics of WSANs and the resourceconstrained nature of the constituent sensor nodes give rise
to design energy and delay efficient routing protocols.
Many researchers have made considerable efforts to meet
these challenges by designing energy and delay efficient
protocols. In this paper, a comprehensive survey is presented on existing routing protocols for WSAN to analyze
and list out their merits and demerits. The study broadly
segregates the existing routing protocols into two categories i.e., cluster based and non-cluster based protocols.
The cluster based protocols structure the physical network
into virtual groups, whereas the non-cluster based protocols
either use flooding or broadcasting mechanisms for communication. All the routing protocols under consideration
have been simulated in a common simulation platform.
Their performances are analyzed with respect to average
end-to-end delay, packet delivery ratio, and average energy
dissipation in the network individually. In addition, the best
protocols among both the categories are compared to
derive an overall conclusion.
& Jagadeesh Kakarla
jagadeesh0826@gmail.com
Banshidhar Majhi
bmajhi@nitrkl.ac.in
Ramesh Babu Battula
ramsbattula@gmail.com
1
Department of CSE, NIT Rourkela, Rourkela, Odisha, India
2
Department of CSE, MNIT Jaipur, Jaipur, Rajasthan, India
Keywords Actor Delay Packet delivery ratio
Energy Sensor
1 Introduction
Wireless Sensor Networks (WSNs) have been considered
as one of the most important technologies in the twenty
first century [1, 2]. These are tiny, cheap, and smart sensors
communicating with wireless technologies deployed in a
physical area. WSN is widely used in various applications
such as environmental monitoring, battlefield surveillance,
industrial process control, and home applications [3–5].
These networks are distinguished from traditional wireless
networks because of its unique characteristics namely, the
dense node deployment, unreliability of sensor nodes,
severe energy consumption, and storage constraints [6–9].
WSN is a passive network, where sensors gather and forward environmental information to the sink. It does not
perform any actions in the environment.
The realization of intelligent interaction in the physical
area has led to the evolution of a new class of wireless
networks called as wireless sensor–actor networks
(WSAN) [10]. They are capable of monitoring the environment and perform necessary actions on it [11, 12].
Application areas of WSANs comprise but are not limited
to habitat monitoring [13, 14], health environment monitoring [15, 16], military applications [17, 18], industrial
and consumer applications [19, 20], and preventing
chemical, biological, or nuclear threats in an area [21].
WSAN consists of both resource rich actors and resourceimpoverished sensors. An actor is a transducer that accepts
a signal and converts it to a physical action. It is also a
network entity that performs network-related functionalities such as receive, transmit, process, and relay data. The
123
Int J Wireless Inf Networks
actors are mobile elements with high processing, communication capabilities, and are less constrained to energy
resources than the static sensors [22–24]. Typical examples
of actors are robots, electrical motors, etc. The resource
constrained sensors gather the environmental information
and forward it to the actors. The actors process the data and
perform application dependent actions on the physical
world. Consider a battlefield application, where image
sensors are used to detect the presence of enemy targets
and tasks. The smart weapons and ambulance can be
considered as actors for destroying the enemy targets and
rescuing the injured soldiers.
WSAN supports three types of data communication
modes, namely event-driven, periodic, and on-demand
[25–27]. In the event-driven mode, sensors report their
sensing data to the sink or actors on occurrence of an event.
The data transmission delay is an important parameter in
event driven mode [28–30]. Target detection and tracking
applications use this mode of communication. In the periodic mode, sensors sense the environment at predefined
intervals and send the data to the sink or actor. Data
gathered in periodic mode does not require quick delivery
to the sink. In the on-demand mode, users gather the event
information based on their interest. They send instructions
to the sink consisting of their interests to know the environmental conditions [31–34].
WSAN supports three types of architectures namely
automated architecture, semi-automated architecture, and
cooperative architecture [35–39]. In the automated architecture, sensors sense the environment and report directly
to the actor. Based on the received data, actors coordinate
among themselves and perform appropriate actions in an
event area. The automated architecture minimizes the
communication delay and energy consumption as the
sensing data is directly communicated to the nearest actor.
In semi-automated architecture sensors first transfer the
data to the sink node. Then sink process the data and issue
commands to an actor based on the sensors information
[40]. The working principle of semi-automated architecture
is similar to that of traditional WSN [41–43]. In cooperative architecture sensors transfer the sensed information to
an actor. The actor processes the data and consults the sink
before performing any action in the event area.
In WSAN, the sensors are deployed more densely as
compared to resource rich actors [44–47]. The sensorsensor, sensor–actor, and actor–actor coordination is
important in WSAN and as a result routing in WSAN is
more complex as compared to WSN [48–53]. WSAN
includes the combined characteristics of WSN and ad-hoc
networks [54, 55]. Hence existing routing protocols for
WSN and ad-hoc networks cannot be directly applied to
WSAN [56–60]. In this paper, an attempt has been made to
survey the plethora of routing protocols for WSAN by
123
simulating them in a common platform. The objective is to
understand their relative performances and guide the
researchers in WSAN to utilize the best protocols suitable.
The rest of the paper is organized as follows: Section 2
presents the simulation setup used for carrying out the
simulations. Section 3 describes the design goals of routing
protocols for WSAN. Section 4 discusses the performance
of existing cluster based routing protocols with their merits
and demerits. Section 5 provides analysis of cluster based
routing protocols. Section 6 discusses the performance of
existing non-cluster based routing protocols and list out
their relative issues. Section 7 analysis non-cluster based
routing protocols. Section 8 provides comparison between
HEROP and DEARP. Finally, Sect. 9 concludes the paper.
2 Simulation Setup
To understand the routing protocols better we use NS2
discrete simulator which supports wired, wireless, and
sensor networks to study various performance parameters
[61]. For simulation analysis, 100–1000 static sensors and
8–12 mobile actors are deployed in a 1000 m 1000 m
network area. The other network parameters like duration
of simulation, traffic flow, etc. are listed in Table 1.
In the simulation, a radio model is assumed for sensor
node energy dissipation for transmission and receiving as
shown in Fig. 1. The free space ðEfs Þ and multi-path fading
ðEmp Þ channel models are used based on the distance
between the transmitter and receiver [62, 63]. The free
space model is used, if distance between transmitter and
receiver is less than threshold do , otherwise multi-path
Table 1 Simulation parameters
Parameters
Values
Network area
1000 9 1000 m2
Simulation duration
200 s
Traffic flow
MAC layer
CBR
IEEE 802.15.4
Number of sensors
100–1000
Number of actors
8–12
Actor’s mobility speed
0–16 m/s
Mobility pattern
Random waypoint
Sensor’s transmission range
100 m
Actor’s transmission range
300 m
Packet size
512 b
Sensor’s initial energy
2J
Eelec
50 nJ/bit
Efs
10 pJ=bit=m2
Emp
0:0013 pJ=bit=m4
Int J Wireless Inf Networks
protocols uses flooding mechanism to learn about their
neighbors. These protocols do not structure the physical
network into virtual groups. The working principles of
cluster as well as non-cluster based routing protocols are
discussed in the following sections.
Fig. 1 Radio energy dissipation model
4 Cluster Based Routing Protocols
model is used. The energy required to transmit a b bit
message over a distance d ðETX ðbÞÞ and to receive the
message ðERX ðbÞÞ are represented defined as,
ETX ðbÞ ¼ ETXelec ðbÞ þ ETXamp ðb; dÞ
(
bEelec þ bEfs d2 ; d\d0
¼
bEelec þ bEmp d4 ; d d0
ð1Þ
ðbÞ ¼ bEelec
ð2Þ
ERX ðbÞ ¼ ERXelec
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
.
where, d0 ¼ Efs Emp , electrical energy ðEelec Þ depends
on digital coding, modulation, and filtering mechanism of
the signal. The amplifier energy, Efs d 2 or Emp d4 depends on
the distance between transmitter and receiver, and the
acceptable bit-error rate.
Parameters such as average end-to-end delay (AEED),
packet delivery ratio (PDR), and average energy dissipation (AED) in the network are used to analyze the performance of the existing routing protocols. Average packet
delay is denoted as average time required for transferring a
data packet from source sensor to the destination actor.
Packet delivery ratio is defined as the number of packets
that are successfully transferred from source sensor to the
destination actor. Average energy dissipation is the amount
of energy consumed for network establishment and to
transfer the event information from source sensor to destination actor.
3 Routing in Sensor–Actor Networks
Routing is an important issue in the network layer. The
essential function of this layer is to forward the data from
source to destination in a multi-hop fashion [64]. In
WSAN, sensors sense the physical area and forward the
event information to an actor. The actor processes the
information and performs reliable actions on the event
area. The design goal of any routing protocol in WSAN
need to be simple, energy efficient, and distributed.
The existing routing protocols of WSAN are broadly
classified into cluster based and non-cluster based protocols. The cluster based protocols virtually divide the nodes
into groups using their physical properties. The key idea of
these protocols is to exploit the capabilities of actors to
reduce the overhead on the sensors. The non-cluster based
Clustering can be defined as the virtual partitioning of the
nodes into various groups based on the distance between
them [65]. In WSAN, cluster head acts as a local coordinator for its cluster member in inter-cluster and intracluster routing for proper utilization of resources. The
gateway node works as the intermediate node for two
cluster heads. The process of clustering is a combination of
two phases namely, cluster formation and maintenance.
The cluster formation phase deals with the logical partition
of the nodes into several groups and selects a suitable node
as a cluster head for every group. The maintenance phase
helps to preserve the existing clustering structure as long as
possible. The existing cluster based routing protocols are
described below with their working principles and relative
merits and demerits.
4.1 Hierarchical, Reliable, and Energy Efficient
Routing Protocol (HEROP)
Eduardo et al. [66] have proposed a hierarchical, reliable,
and energy efficient routing protocol for WSAN. It uses
meta-data to create energy efficient clusters. HEROP
consists of three phases namely discovery phase, joining
phase, and routing phase. In discovery phase, each sensor
discovers its neighbor information using location-based
protocols. The discovery phase is controlled by three
parameters such as discovery protocol frequency, execution time, and inactivity time.
In joining phase, sensors use the routing tables created
in the discovery phase to transmit the event information to
the nearest cluster head. Finally, in routing phase data is
transferred from sensors to a sink via cluster head. HEROP
is a scalable approach, and consider sensors energy while
transmitting data to them. Hence, it is a energy efficient
mechanism. It also provides fault tolerance routing and
reliable data transmission in the network. HEROP does not
consider the node heterogeneity property. The actors
mobility control, coordination among actors and sensors
are also not addressed properly.
Packet delivery ratio (PDR) for the HEROP is shown in
Fig. 2. The number of mobile actors for each evaluation is
considered to be 8, 10, and 12. It is observed that PDR
decreases with increase in the number of sensors for a
particular set of actors. Further with increase in number of
123
Int J Wireless Inf Networks
Fig. 2 Packet delivery ratio versus number of sensors (HEROP)
actors, the PDR also increases for any fixed set of sensors.
Figure 3 shows the average energy dissipation (AED)
decreases with increase in number of sensors. This is due to
the fact that similar functional properties are assigned to
sensors and actors during data transmission which causes
high communication overhead in sensors.
Figure 4 depicts the average end-to-end delay (AEED)
which is directly proportional to number of sensors for any
fixed number of actors, where as it is inversely proportional
to the number of actors with fixed number of sensors. The
HEROP has to address the mobility of actors and node
heterogeneity in order to improve its performance.
4.2 Hierarchical Geographic Clustering Protocol
(HGCP)
Haidong et al. [67] have proposed a three level coordination model using hierarchical geographic clustering
mechanism. In this protocol, network area is divided into
fixed zones called as virtual grids which are used to optimally divide the network area and distribute the work load
among actors.
Fig. 3 Average energy dissipation versus number of sensors
(HEROP)
123
Fig. 4 Average end-to-end delay versus number of sensors (HEROP)
The main objective of sensor-sensor coordination is to
gather event information in an energy efficient manner in
the deployed area. The size of the virtual grid depends on
the sensor range. The distance between any two sensors in
adjacent grids should not go beyond the sensor range. The
virtual grid is a square, where the length of each side is
r units defined as,
R
r pffiffiffi
5
ð3Þ
where R is the range of a sensor. In every grid, one sensor
acts as a cluster head based on its residual energy. The
cluster head aggregates the data received from its associated cluster members and forward it to an appropriate
actor.
In sensor–actor coordination, each cluster head maintains a routing table which consists of nearest actor information. The cluster head is responsible for monitoring and
transferring data to its closest actor. The actor–actor
coordination has been divided into two schemes namely,
action-first scheme and decision-first scheme. In the actionfirst scheme, the actor starts performing action and informs
it to other actors. Then the actors take decision independently whether to join in the action or not. On the other
hand, decision-first scheme communicates with its neighbor actors before performing action on the event area. This
scheme distributes the load properly among actors and
maximize the overall task performance. The reduced size
of the grid area causes formation of more number of
clusters that reduces the network lifetime. HGCP does not
address the delay parameter properly which is important in
real time application of WSAN. Finally, it assumes that
both the sensors and actors are static.
It is observed that HGCP does not perform well for
average end-to-end delay with the increase in number of
sensors as shown in Fig. 5. HGCP considers both the
sensors and actors as static, which is a non-realistic
assumption in many WSAN applications. When the actor
Int J Wireless Inf Networks
Fig. 5 Average end-to-end delay versus number of sensors (HGCP)
moves far away from the cluster head, it results in the
increase in packet delay. From Fig. 6, it can be observed
that the value of AED is directly proportional to the
number of sensors. The reduced size of the grid area causes
formation of more number of clusters and also consume
lots of energy of the sensors.
4.3 QoS-Aware Routing Protocol (QARP)
Boukerche et al. [68] proposed a QoS aware routing protocol for WASN. It uses a publish-subscribe approach for
interaction among nodes in the network. QARP comprises
of three phases. In the first phase, sink initiates the hop tree
construction process. The hop tree is used for flooding the
subscription messages and data around the network. Similarly, every actor initiates their hop tree construction to
divide the entire network into acting areas. In the second
phase, sink and actor flood the subscription messages
across the network. The final phase is responsible for
packet delivery from sensor nodes to the sink.
In QARP, whenever a sensor senses an event in its
sensing range, it checks its subscription table to find
whether any interest on the event is registered or not. If any
Fig. 6 Average energy dissipation versus number of sensors (HGCP)
node is registered, then the sensor selects a path to transfer
the packet based on its priority. A queuing model is
designed to transfer low priority packets, which does not
consider latency in a less-expensive path to reduce the
energy loss in the network. A packet scheduling approach
is used to set the weights for flow types. QARP uses direct
diffusion technique to transfer the event to the actors. It
also uses packet prioritization technique to give priority to
the delay sensitive packets. QARP assumes that both the
sensors and actors are static, which is non-realistic nature
for many WSAN applications. It does not properly utilize
resource rich actors, which causes extra communication
burden on sensors and leads to reduction in network
lifetime.
The AEED of QARP for 8, 10, and 12 actors are shown
in Fig. 7 and it is observed that AEED increases with the
increase in number of sensors and decreases with the
number of actors. The packet delay increases because of
mobile actors and frequent path failures. The value of AED
is directly proportional to the number of sensors, because it
uses sensor for complex process such as hop tree construction, maintenance, and flooding mechanism as shown
in Fig. 8.
Figure 9 depicts the PDR of the existing QARP. Due to
the actor mobility a lot of packets are dropped before
reaching the destination. Hence, the PDR of the QARP
does not perform well under mobile actors and reduce with
the increase in number of actors. The simulation results
indicate that QARP does not perform well under node
heterogeneity conditions and mobile actors.
4.4 Event Driven Clustering Protocol (EDCP)
Tommaso et al. [69] have proposed an on-demand routing
protocol for WSAN. In EDCP, clusters are created on-thefly when an event is triggered. It deals with both sensor–
actor and actor–actor coordination. In the sensor–actor
coordination, an aggregation tree is created between each
Fig. 7 Average end-to-end delay versus number of sensors (QARP)
123
Int J Wireless Inf Networks
events occur simultaneously or the occurrence of events are
frequent in nature.
The simulation is performed under concurrent event
scenarios. The event waiting time is defined as the difference between occurrence of an event and the starting time
of an action. Figure 10 shows the waiting time for an event
under multiple events scenario. The event waiting time is
increased with the increase in number of sensors. The
simulation results indicate that EDCP does not perform
well, when multiple events occur concurrently.
Fig. 8 Average energy dissipation versus number of sensors (QARP)
Fig. 9 Packet delivery ratio versus number of sensors (QARP)
actor and its sensors in the transmission range. Each sensor
consists of four states namely idle, start-up, speed-up, and
aggregation. Initially every sensor is in idle state. A sensor
enters into start-up state when it detects an event or an
event information is received from any of its neighbors. If
the sensor does not reach its minimum event reliability
threshold value, then it enters into speed-up state. The
sensor enters into aggregation state, if its event reliability
is more than maximum threshold value. In the speed-up
state, each sensor uses greedy routing scheme to reduce the
packet end-to-end latency. The aim of the aggregation
state is to reduce the energy consumption by performing
data aggregation on each sensor.
For actor–actor coordination a distributed solution has
been proposed based on a real-time auction protocol. A
localized auction takes place in every overlapping area.
The actor which has more residual energy and also takes
less completion time of an action wins the auction. EDCP
utilizes actors properly in data communication to reduce
the burden on sensors. It also uses greedy routing scheme
to improve packet delay in sensor–actor coordination.
EDCP is not well suited for scenarios where multiple
123
4.5 Hierarchical, Energy Efficient Routing Protocol
(HEERP)
Fei et al. [70] have proposed an energy efficient member
recognition protocol. Each actor segregates the network
area into domains consisting a set of sensors. Each domain
is further divided into ripples based on the distance
between sensor and the actor. Few sensors are randomly
selected as masters to aggregate the received data. The
network architecture of HEERP is shown in Fig. 11. Suppose master node D is not able to reach master node B (due
to its battery dissipation or the unreliable wireless link),
then the master node D checks the reachability of B’s
neighbors i.e., C and G. If any one of them is reachable,
then master node D forwards the data to it. If C and G both
fail to find any neighbor, then it goes back one more ripple
to master node F. If F finds D’s neighbors it delivers the
packet, otherwise the packet will be dropped. HEERP
constructs virtual domains and zones around an actor,
which is similar to the hierarchical geographic clustering
protocol (HGCP). HEERP does not perform well in sparse
network and the delay parameter has also not been
addressed properly.
The performance of the HEERP using AEED parameter
is shown in Fig. 12. When a node is not reachable, then it
forwards the packet to its neighbor which causes high
packet delay. The value of AEED is inversely proportional
Fig. 10 Event waiting time for concurrent events scenario in EDCP
Int J Wireless Inf Networks
maximize its coverage area based on the density of the
sensors. It uses energy constrained sensor nodes for data
aggregation process that consume lots of energy. Further,
WBMP does not address the end-to-end delay parameter
properly. The end-to-end delay parameter is used to analyze the simulation results. Figure 13 shows the value of
AEED for the WBMP under heterogeneous node scenario
with 8, 10, and 12 actors respectively. It may be observed
that WBMP does not address end-to-end delay parameter
properly.
4.7 Sensor–Actor Coordination Protocol (SCP)
Fig. 11 Network architecture of HEERP
Fig. 12 Average end-to-end delay versus number of sensors
(HEERP)
to the number of actors. The simulation results also reveal
that HEERP causes a lot of packet delay in the network.
4.6 Weighted Bi-partite Matching Protocol
(WBMP)
WBMP deals with a data forwarding mechanism, where
sensors form clusters using weighted bi-partite matching
method [71]. The distance between sensor and actor is
denoted as the weight of an edge. The cluster head size is
restricted to limit the traffic in a cluster. If any cluster is
full, then the sensor will choose next best cluster using
greedy mechanism. Whenever a new node joins the network, it gets the cluster information from its neighbors. It
sends the cluster join message to its nearest actor.
To improve the network lifetime, WBMP employs
resource rich actors as cluster heads. An actor collects the
event information from its associated cluster members and
perform reliable actions in the event area. To reduce the
latency between sensing and acting tasks, the actor should
Zhicheng et al. [72] have proposed a sensor–actor coordination for WSAN based on Voronoi diagram and the
shortest path tree. It is divided into various rounds and each
round consists of two phases namely cluster set-up phase
and data transmission phase. During cluster set-up phase,
actor acts as a cluster head and sends its residual energy to
the sink. The sink constructs a weighted actor Voronoi
diagram and sends back to an actor. Finally, every actor
informs its Voronoi region information to its cluster
members. In the data transmission phase, the shortest path
tree is calculated from sensor to the actor in a cluster. The
weight of each edge is represented as the energy required to
send a data packet. Sensors transmit their data to the actor
using shortest path tree to reduce the packet delay. It
requires complete topological information and also consumes a lot of energy to calculate the shortest path tree.
SCP does not consider sensor–sensor and actor–actor
coordination. It assumes that both the sensors and actors
are static in nature. SCP requires complete topological
information and consumes lot of energy to calculate the
shortest path tree. The average end-to-end delay, average
energy dissipation, and packet delivery ratio for the existing SCP under actors mobility are shown in Figs. 14, 15,
16 respectively. In case of mobile actors, the lifetime of a
Fig. 13 Average end-to-end delay versus number of sensors
(WBMP)
123
Int J Wireless Inf Networks
path is very less as compared to the static actors scenario.
The sensors around an actor should always update their
neighbor list due to the mobility of actors. This mechanism
consumes lot of energy and also degrades the packet
delivery ratio. Hence, SCP does not perform well under
static sensors and mobile actors, which is a realistic scenario for WSAN.
improve the packet delay. Finding k-hop IDS is a NP hard
problem and requires complete topological information of
the network. Hence, it consumes lot of energy of the sensors and also decreases the network lifetime. From Fig. 17
4.8 Distributed Actor Positioning and Clustering
Protocol (DAPCP)
Akkaya et al. [73] have proposed a distributed actor positioning and clustering protocol. In DAPCP, actors act as
cluster heads and are placed in an appropriate positions to
improve the coverage and packet delay. The k-hop independent dominating set ðk IDSÞ is used to find the actor
position. In k IDS, three different messages are exchanged among sensors namely alive, dominator, and border.
Each sensor sends an alive message to its k-hop neighbors
to announce its existence. If a sensor desires to become a
dominator, then it sends a dominator message to its k-hop
neighbors. Finally, sensor sends a border message that is at
kth hop from a dominator. Each sensor calculates its suitability score to become a cluster head based on the number
of received alive and the closest border messages.
The suitability score of a sensor is calculated as
B þ Mi ACi
si ¼
ð4Þ
2BTTL
where, B indicates sensor probability to become a dominator, Mi is a weight assigned to other nodes with in a khop to the sensor i, ACi is the total number of heard alive
messages by node i, and BTTL is the time-to-live of the
closest border message.
DAPCP uses actor as a cluster head to reduce the
communication burden on sensors. It also uses node degree
parameter while selecting the cluster head, it leads to
Fig. 14 Average end-to-end delay versus number of sensors (SCP)
123
Fig. 15 Average energy dissipation versus number of sensors (SCP)
Fig. 16 Packet delivery ratio versus number of sensors (SCP)
Fig. 17 Average energy dissipation versus number of sensors
(DAPCP)
Int J Wireless Inf Networks
also we can observe that DAPCP protocol consumes lot of
energy from the sensors and reduces the network lifetime.
5 Analysis of Cluster Based Routing Protocols
In the previous section, cluster based routing protocols are
analyzed individually to study their performance in isolation. To derive an overall inference about the cluster based
routing protocols, the performance parameters are analyzed
together. The simulation results of the cluster based protocols presented in the previous section. HEROP is a
scalable and fault tolerance routing protocol. It is an energy
efficient approach as it uses meta data to create clusters, but
it does not consider node heterogeneity and actors mobility
control. Similarly, WBMP is a scalable approach. The size
of each cluster is restricted to limit the traffic load in the
cluster. A sensor acts as a cluster head, which causes lot of
communication burden on sensor and results in degrading
the network lifetime. The HEERP, SCP, QARP, and HGCP
have considered both sensors and actors are static. This
assumption is not feasible for many WSAN applications.
EDCP creates cluster on-the-fly, but it does not perform
well when multiple events occur or events are frequent in
nature. The DAPCP finds optimal position of an actor
based on the sensors density. It requires complete topological information and consumes lot of energy. To visualize the merits and demerits of all cluster based routing
protocols, we have listed them together in Table 2. It may
be observed that with respect to AED, AEED, PDR
parameters under consideration, HEROP outperforms other
cluster based routing protocols.
Ta;n
8
< Ta;n1 a if SQ [ 0
¼ Ta;n1
:
if SQ ¼ 0
b
ð5Þ
where, Ta;n1 is the previous wake-up interval, and a and b
are the constants. The a and b values determine the packet
delay and energy consumed by the sensors.
The values of a and b are defined as 2. Whenever a
sensor wakes up, it broadcasts a beacon message consisting
of its own id, the start time of its next sleep period, and its
residual energy. In the geographical routing phase, it uses a
greedy mechanism to transfer data to the forwarding candidate set. Each node forwards the packet to an active
neighbor that is closer to the destination than itself with a
certain threshold distance. This forwarding mechanism
provides loop free routing to the destination for transferring
the data, but it cannot deliver the packet to the destination
in the presence of holes. Since WSAN is a dense network,
there is no scope for existence of holes in the network.
DEARP provides reliable data transmission and also uses
greedy routing scheme to improve the packet delay.
The values of AEED, AED, and PDR for the existing
DEARP is shown in Figs. 18, 19, 20 respectively. DEARP
does not specify how to select an actor to forward the data.
It just forwards the data to a nearest actor. Hence, it creates
a problem for border nodes which are having more than
one closest actor. The path lifetime is less under actors
mobility as compared to static actors scenario [75, 76]. The
sensors around the actor should always update their
neighbor list because of actors mobility. The simulation
results also reveal that DEARP does not perform well
under actors mobility.
6.2 Anycast Tree Based Communication
Mechanism (ATCM)
6 Non-cluster Based Routing Protocols
The non-cluster based routing protocols either use flooding
or broadcast mechanisms for communication. They do not
structure the physical network into virtual groups. Different
non-cluster based routing protocols are described below
with their working principles and relative merits and
demerits.
6.1 Delay Energy Aware Routing Protocol
(DEARP)
DEARP consists of two major components namely, a
random wake-up scheme and geographic routing protocol
[74]. The main idea of the random wake-up scheme is that
every node should wake-up once in every slot for a predetermined time. The sensor active period ðTa;n Þ depends
on its queue size ðSQ Þ which is defined as,
Wen et al. [77] have proposed an anycast communication
mechanism for a hybrid sensor actuator network. ATCM
constructs an anycast tree rooted at the sensor. A sink can
dynamically join in the sink tree or leave. It uses directed
diffusion mechanism, which is a data-centric and reversepath based communication for sensor networks. In ATCM,
every sensor forms an anycast tree. When a sink enters into
the network, a new branch leading to the sink must be
added to each anycast tree. After initial setup, if a data
packet arrives sensor looks up its anycast table to forward
the packet to the nearest sink. Every sink periodically sends
a beacon packet to refresh anycast table entries.
ATCM approach is similar to the direct diffusion routing
protocol. The anycast table size is controlled by storing
only nearest sink information. It performs well when the
updates from the sink are not frequent. ATCM mechanism
has been simulated using IEEE 802.11 MAC protocol,
123
Int J Wireless Inf Networks
Table 2 Comparison of cluster based routing protocols
Year
First author
Approach
Merits
Demerits
2005
Tommaso
Melodia
Event driven clustering protocol
(EDCP)
Clusters are created on-the-fly
It does not perform well when
multiple events occur
It is a state less protocol
Not feasible when events are
frequent in nature
2005
2005
Fei Hu
Yen-Ting Lin
Hierarchical, energy dfficient
routing protocol (HEERP)
Weighted bi-partite matching
protocol (WBMP)
It uses greedy routing scheme to reduce
packet delay
It is an energy efficient member
recognition protocol
Sensors are used for data
aggregation process
It performs well in the dense network
It does not address properly the
real time requirements of WSAN
It is a scalable approach
Sensor is used for data aggregation
process
The packet delay parameter is not
addressed properly
The size of each cluster is restricted to
limit the intra-cluster traffic
Actor act as a cluster head
2006
Haidong Yuan
Hierarchical geographic clustering
protocol (HGCP)
It discuss about three level coordination
mechanism in WSAN
It uses virtual grids
Small grid area
Actor acts as a cluster head
Delay parameter is not addressed
properly
Action-first and decision first action
schemes are discussed in actor–actor
coordination
2006
Boukerche
QoS-aware routing protocol
(QARP)
It assumed that both the sensors
and actors are static
It uses packet prioritization scheme
It assumes that both the sensors
and actors are static
A publish/subscribe approach is used
among nodes
Actor nodes are not used properly
Hop-trees are constructed to flood the
subscription messages and data
2007
Kemal akkaya
Distributed actor positioning and
clustering protocol (DAPCP)
The K-IDS mechanism is used to find the
actor positions
Computing K-IDS is a NP hard
problem
Actor acts as a cluster head
It requires complete topological
information about a network
The cluster head is selected based on its
neighborhood degree
2009
ZhiCheng Dai
Sensor–actor coordination
protocol (SCP)
Actor Constructs its Voronoi diagram
It considers only sensor–actor
coordination
Non-overlapping clusters are created
It assumed that both the sensors
and actors are static
Shortest path tree is calculated from
sensors to actor in a cluster
2012
Eduardo Canete
Hierarchical, reliable, and energy
efficient routing protocol
(HEROP)
It uses meta data to create clusters
Node heterogeneity is not
considered
It is a scalable approach
Lack of mobility control of actor
nodes
Energy efficient mechanism
It does not provide coordination
mechanism among nodes
Fault tolerance routing
Reliable data transmission
which is designed explicitly for WLANs. It is generally not
suitable for energy constrained networks such as WSN and
WSAN [78, 79].
In this paper, the ATCM is simulated and analyzed
using IEEE 802.15.4 MAC protocol. The values of PDR,
123
AEED, and AED of ATCM under IEEE 802.15.4 MAC
protocol are depicted in Figs. 21, 22, 23 respectively.
ATCM mechanism has assumed that always sensors are in
active state. This assumption leads to degrade the ATCM
performance in WSAN.
Int J Wireless Inf Networks
Fig. 18 Average end-to-end delay versus number of sensors
(DEARP)
Fig. 19 Average energy dissipation versus number of sensors
(DEARP)
Fig. 21 Packet delivery ratio versus number of sensors (ATCM)
Fig. 22 Average end-to-end delay versus number of sensors (ATCM)
Fig. 20 Packet delivery ratio versus number of sensors (DEARP)
Fig. 23 Average energy dissipation versus number of sensors
(ATCM)
6.3 Delay Sensitive Routing protocol (DSRP)
Ngai et al. [80] have proposed a reliability-centric framework for event reporting in WSAN. The reliability in
WSAN not only depends on the accuracy, but also depend
on the freshness of data. The network area is segregated
into virtual grids for event monitoring. DSRP consists of
three key phases namely, data aggregation phase, priority
based event reporting, and adaptive actor allocation
mechanism. In the aggregation phase, an aggregating node
123
Int J Wireless Inf Networks
collects the event data and calculates the median m of it.
This node compares each sensor data xi with m and
removes those sensors data that has a difference greater
than a threshold value. Then in a grid, the aggregating node
re-calculates the mean value from the remaining sensors
data. The reliability ðrÞ for the aggregated data from a grid
can be computed as,
r ¼ 1 fg ¼ 1
Nx
X
Nx !
ðfs Þi ð1 fs ÞNx i
i!
N
ð
i
Þ!
x
Nx
ð6Þ
i¼ 2
phase. Hence, DSRP creates lot of communication burden
on resource conservative sensors and results in reducing the
network lifetime. In this paper, the existing DSRP is simulated and analyzed under the IEEE 802.15.4 MAC protocol. The PDR, AEED, and AED in the network for the
existing DSRP under IEEE 802.15.4 MAC protocol are
depicted in Figs. 24, 25, 26 respectively. The values of
AEED and AED increases with the increase in number of
sensors, but PDR is inversely proportional to the number of
sensors. From simulation results, it can be observed that
DSRP does not work properly under node heterogeneity
conditions and mobile actors.
where, fg is the failure probability of grid g on data
aggregation, Nx is the number of nodes in the grid, and fs is
a ratio of the malfunctioned sensors.
The aggregating node may act as a reporting node to
forward the aggregated data to an actor. Each sensor
decides independently whether it will serve as a reporting
node or not according to the number of data reports to be
transferred by it. In the priority based event reporting
module, each sensor is enabled with a priority queue to
assign priorities for the received data. The queue utilization
acts as an index for route selection. An adaptive actor
allocation mechanism forwards the sensor data to a nearest
actor to reduce the packet delay. It computes event frequency freq for every grid. Then, the network area is
equally partitioned into two subfields based on the frequency distribution. Actors are equally allocated to every
subfield. It is a recursive process and repeats till each
subfield contains only one actor.
DSRP is a reliability centric framework and uses fault
tolerant data aggregation mechanism to eliminate the faulty
sensors in the network. DSRP was also simulated using the
IEEE 802.11 MAC protocol, which is a non-realistic in
nature for many WSAN applications. It assumes both
sensors and actors are static. The actors are not used
properly in network establishment and data transmission
GBRP is a distributed approach, where nodes take local
decisions while forwarding data to the destination based on
geometric mechanism [81]. GBRP imposes low communication overhead as it does not require neighborhood
information. The actors are utilized properly to reduce the
energy consumption in sensors. GBRP uses separate protocols to handle the broadcast mechanisms for sensor and
actor respectively. In GBRP, R and k R denotes the
transmission range of a sensor and actor respectively.
In actor broadcast protocol, sensor transfer its data to a
nearest actor. Then actor broadcasts the packet to the
remaining actors and sensors in the network. In sensor
broadcast mechanism, each sensor checks whether the
broadcast packet is already received or not. If it is already
received then sensor discards the packet, else it broadcast
the packet to the remaining sensors. GBRP broadcast
packets in the entire network area instead of concentrating
on a specific region. From Fig. 27, It can be observed that
the value of PDR increases with the increase number of
actors and inversely proportional to the number of
sensors.
Fig. 24 Packet delivery ratio versus number of sensors (DSRP)
Fig. 25 Average energy dissipation versus number of sensors
(DSRP)
123
6.4 Geometric Broadcast Routing Protocol (GBRP)
Int J Wireless Inf Networks
Fig. 26 Average end-to-end delay versus number of sensors (DSRP)
Fig. 28 Packet delivery ratio versus number of sensors (PARP)
receiver. PARP requires lot of memory to store the large
size routing table. It chooses an energy efficient from the
routing table to transfer its data. This process increases
packet delay as shown in Fig. 28. PARP is not feasible for
dense network as the routing table size increases with the
increase in network size.
6.6 Power Controlled Routing Protocol (PCRP)
Fig. 27 Average end-to-end delay versus number of sensors (GBRP)
6.5 Power Aware Routing Protocol (PARP)
Cayirci et al. [82] have proposed a power aware many-tomany routing protocol for WSAN. It has two versions. In
the first version, every node transmits data using same
transmission power. Actors send registration packets to
the sensors consisting of their data interest. The registration message consists of node identification, actor
identification, the minimum number of hops required to
reach a node from an actor, minimum power, total power
available, and task(s). When a sensor receives the registration packet, it builds the registration table contains
actor id, uplink node, echelon, minimum power, total
power, and tasks information. Each sensor constructs a
routing table from the registration table consisting a
record for every unique downstream node and sensing
task. Each sensor forwards its data to its nearest sensor
towards an actor. An actor can de-register from a sensing
task by broadcasting a de-registration packet to the
respective sensor. The task de-registration message consists of node identification, actor identification, and tasks.
In the second version, sensor dynamically adjusts its
transmission power based on the intended distance to the
Yangfan et al. [83] have proposed a real-time power aware
routing protocol. It forwards the packets in a stateless
manner. The intermediate nodes do not maintain any path
to a nearest actor. Each sensor sets its power level based on
the distance to the intended neighbor. PCRP is a cross
layered approach which requires physical layer information. In PCRP, sensor selects a neighbor according to the
packet delay deadline and energy required to forward the
packet. The packet delay at a sensor u is computed as,
DelayðuÞ ¼ tprop þ tproc þ tq þ ttran
ð7Þ
where, tprop is the propagation delay from sensor u to its
intended neighbor, tproc is the processing delay of u, tq is
the queuing delay of u during which the packet is waiting
to be sent out, and ttran is the transmission delay which is
related to the channel bandwidth and packet size. The
intermediate sensor can estimate the future hop-by-hop
delay based on the delay estimation of the upstream hops.
It does not require feedback from downstream sensors to
estimate the future hop-by-hop packet delay. The average
number of hops that a packet can go through per second is
computed using the following equation:
s¼
1
davg hop
where, davg
davg
hop
ð8Þ
hop
is denoted as
¼ b davg
hop
þ ð1 bÞdðsi Þ
ð9Þ
123
Int J Wireless Inf Networks
where, b is a constant, dðsi Þ is the delay calculated at
sensor si . PCRP requires 2 to 3 hop neighborhood
information to compute the packet delay, that produces
control packet overhead in dynamic networks. The delay is
estimated only from the upstream sensors. PCRP does not
consider feedback from the recently visited downstream
nodes. This type of estimation performs well only for
periodic data transfer mode. Due to the transmitter power
control, a sensor uses small transmitting power to the
nearest node. This information may not be sensed by other
neighbors that are far away and want to transfer the packets
at the same time. This scenario is called as a hidden terminal problem.
PCRP has been simulated using IEEE 802.11 MAC
protocol which is specifically designed for WLAN. IEEE
802.11 MAC protocol is not feasible for energy constrained
sensor–actor networks. In this paper, PCRP is simulated
using IEEE 802.15.4 MAC protocol, that is specially
designed for dense and energy constrained sensor networks. The values of AEED, PDR, AED for the PCRP is
analyzed under IEEE 802.15.4 MAC protocol and depicted
in Figs. 29, 30, 31 respectively. It can be observed that
PCRP does not perform well under IEEE 802.15.4 MAC
protocol.
Fig. 30 Average energy dissipation versus number of sensors (PCRP)
6.7 Scalable Source Routing Protocol (SSRP)
Fuhrmann has proposed a scalable source routing protocol
for sensor–actor networks [84]. The working principle of
SSRP is similar to overlay routing in a virtual network
structure. SSRP is a reactive protocol and uses a proactive
mechanism for the virtual ring construction. Each node has a
location independent address and consists of physical
neighbors. The physical network and virtual ring are constructed using distance metric. In the physical network,
distance is computed based on the hop count, while in the
virtual ring it is measured from the difference between
absolute address of two nodes. If a sensor cannot retrieve
Fig. 31 Packet delivery ratio versus number of sensors (PCRP)
Fig. 32 Average energy dissipation versus number of sensors (SSRP)
Fig. 29 Average end-to-end delay versus number of sensors (PCRP)
123
entire source route to the destination from its own cache,
then it dynamically constructs a route to the intermediate
node. The source selects an intermediate node that is near to
the destination. This type of routing may not always produce
shortest paths and also increases packet end-to end delay.
From Fig. 32, it can be observed that SSRP consumes
lot of energy to transfer the data. SSRP also not specified
which actor is selected as a destination to transfer the data
Int J Wireless Inf Networks
architecture for communication, where the sink collects all
the sensor data and takes decision. The semi-automated
architecture incurs high end-to-end delay and rapid energy
depletion on the sensors closer to the server called as
funneling problem. The inclusion of backhaul nodes may
increase network design complexity. Figure 34 depicts the
value of AEED for the existing LMCA under node
heterogeneity conditions and mobile actors. To reduce the
delay the sensors should forward the event information
directly to the nearest actor instead of transferring via a
sink.
Fig. 33 Average end-to-end delay versus number of sensors (SSRP)
7 Analysis of Non-cluster Based Routing Protocols
Fig. 34 Average end-to-end delay versus number of sensors (LMCA)
and it may leads to wrong selection of actor that is far away
from the event area. It can be observed that SSRP degrades
the average end-to-end delay parameter as shown in
Fig. 33.
6.8 Routing Protocol for Light Monitoring
and Control Application (LMCA)
In LMCA, sensor-sensor coordination and actor–actor
coordination is performed in separate channels with different capacity, cost, and reliability [85]. Both sensor and
actor networks are connected to a central server, that
handle the user request and also provides coordination
between both the networks. The backhaul nodes are
resource rich nodes, act as a mediator between sensor and
actor networks. The sensor network uses data-centric
routing architecture.
On the other hand, the actor network uses point-to-point
communication to improve the network performance. The
centralized server gathers data from both sensor and actor
networks to take decisions. LMCA uses semi-automated
In the previous section, non-cluster based routing protocols are analyzed individually to study their performance
in isolation. To derive an overall inference, the performance of all the non-cluster based routing protocols are
analyzed together.
The simulation results of the non-cluster based protocols
presented in the previous section. DEARP uses a greedy
mechanism and assures loop free path selection while transferring the data. It provides reliable data transmission and
each sensor uses periodic wake-up mechanism to improve the
network lifetime. The PCRP, DSRP, and ATCM protocols
have been simulated using IEEE 802.11 MAC protocol. It is
specially designed for WLAN and does not require any energy
efficient mechanisms. SSRP may not always produce the
shortest paths and requires complete topological information.
It does not select destination actor properly, which may cause
delay in data transmission. GBRP is useful for only query
based applications. But, it biases the energy consumption and
delay as it uses broadcast mechanism to transfer the data.
LMCA uses a semi-automated architecture, which produces
high delay in the network. DEARP did not specify how to
select an destination for border sensors. It requires MAC layer
information for calculating the sleep schedule of a sensor and
actors mobility is also not considered properly. To visualize
the merits and demerits of all non-cluster based routing protocols, we have listed them together in Table 3. It may be
observed that with respect to all the three parameters under
consideration, DEARP outperforms other non-cluster based
routing protocols.
8 Comparison of HEROP and DEARP
In the cluster based routing protocols HEROP outperforms
others with respect to all the three parameters packet
delivery ratio, average end-to-end delay, and average
123
Int J Wireless Inf Networks
Table 3 Comparison of non-cluster based routing protocols
Year
First
author
Approach
Merits
Demerits
2005
Arjan
Durresi
Delay energy aware routing protocol
(DEARP)
Sensor sleep-active mechanism is used to
improve network lifetime
It does not specify how to select an actor
for border sensors
It uses a greedy mechanism to transfer
the data
Requires MAC layer information for
calculating the traffic load of a sensor
It assure no loops in data forwarding
Actor mobility is not considered
Reliable data transmission
2005
Wen Hu
Anycast tree based communication
mechanism (ATCM)
It uses directed diffusion mechanism
Complex routing table updating
The anycast table size is controlled by
storing only nearest sink information
It was simulated using IEEE 802.11
MAC protocol designed for WLANs
It uses broadcasting mechanism to
transfer the data
It is useful only for query based
applications
It imposes low communication overhead
It does not address packet delay properly
It does not provide data reliability
2005
Arjan
Durresi
Geometric broadcast routing protocol
(GBRP)
2005
cayirci
Power aware routing protocol (PARP)
2006
Ngai
Delay sensitive routing protocol
(DSRP)
It employs transmitter power control
It produces traffic load
It constructs many-to-many multi-cast
routing tree
High overhead for updating routing table
Actors are used properly in
communication
It creates hidden terminal problem
It is a reliability centric framework
It was simulated using IEEE 802.11
MAC protocol designed for WLANs
It create virtual grids
It assumes that both the sensors and
actors are static
It has designed priority based event
reporting
Actor nodes are not used properly in
data communication
Poor scalability
A fault tolerant data aggregation model is
designed to remove faulty sensors
2006
Thomas
Scalable source routing protocol
(SSRP)
It is a reactive protocol
It does not always produce shortest path
It uses proactive mechanism for virtual
ring construction
It does not consider delay parameter
The physical network and virtual ring are
constructed using distance metric
Complete topological information is
required
Actor selection is not specified properly
in sensor–actor coordination
2006
Suet-Fei
Li
Routing protocol for light monitoring
and control application (LMCA)
It uses heterogeneous architecture
Backhaul nodes increases design
complexity
It uses semi-automated mechanism
It produces high end-to-end delay
Backhaul nodes are used as mediators
between two networks
2007
Yangfan
Zhou
Power controlled routing protocol
(PCRP)
It is a power aware mechanism
It creates hidden terminal problem
It forwards the packet in a stateless
manner
It was simulated using IEEE 802.11
MAC protocol designed for WLANs
It is a cross layered mechanism
energy dissipation in the network. Similarly, DEARP is
shown to have superior performance among non-cluster
based routing protocols. A comparative analysis among
these two protocols is shown in Table 4 for all the three
123
parameters under consideration. It may be observed that
HEROP performs better as compared to DEARP. Hence,
cluster based routing protocols have better scope of
application in WSAN.
Int J Wireless Inf Networks
Table 4 Comparative analysis
of HEROP and DEARP
Protocol/No. of sensors
HEROP
DEARP
PDR
AEED (ms)
AED (J)
PDR
AEED (ms)
100
97
200
94
300
2.1
0.48
78
4.7
2.5
0. 52
76
5
0.83
92
2.8
0.6
73
5.3
0.87
400
89
3.2
0.65
71
5.6
0.9
500
85
3.7
0.72
68
5.8
0.94
600
82
4
0.79
66
6.2
0.99
700
80
4.5
0.87
63
6.7
1.02
800
77
4.9
0.98
61
7
1.1
900
75
5.5
1.07
58
7.2
1.21
1000
72
6.1
1.16
54
7.6
1.3
9 Conclusion
In this paper, we have made a comparative analysis of
wireless sensor–actor network (WSAN) routing protocols.
The protocols are divided into two categories i.e., cluster
based and non-cluster based. Simulation has been carried
out in NS2 for each protocol under similar environment.
Each protocol is analyzed in isolation with respect to
average end-to-end delay, packet delivery ratio, and average energy dissipation varying the number of sensors and
actors. It is observed that among cluster based protocols
hierarchical, reliable, and energy efficient routing protocol
(HEROP) outperforms others. Similarly delay energy
aware routing protocol (DEARP) has superior performance
in non-cluster based protocols. Comparative analysis
between HEROP and DEARP reveals that HEROP performs well with respect to all the three parameters under
consideration. Hence, it can inferred that cluster based
protocols are more popular among the research community
of WSAN.
References
1. A. Rezgui and M. Eltoweissy, ‘‘Service-oriented sensor–actuator
networks: Promises, challenges, and the road ahead,’’ Computer
Communications, vol. 30, no. 13, pp. 2627–2648, 2007.
2. T. Melodia, D. Pompili, V. Gungor, and I. Akyildiz, ‘‘Communication and coordination in wireless sensor and actor networks,’’
IEEE Transactions on Mobile Computing, vol. 6, no. 10,
pp. 1116–1129, 2007.
3. R. Jafari, A. Encarnacao, A. Zahoory, F. Dabiri, H. Noshadi, and
M. Sarrafzadeh, ‘‘Wireless sensor networks for health monitoring,’’ in The Second Annual International Conference on Mobile
and Ubiquitous Systems: Networking and Services, 2005,
pp. 479–481.
4. D. Trossen and D. Pavel, ‘‘Sensor networks, wearable computing, and healthcare applications,’’ IEEE Pervasive Computing,
vol. 6, no. 2, pp. 58–61, 2007.
AED (J)
0.81
5. I. Akyildiz, S. Weilian, Y. Sankarasubramaniam, and E. Cayirci,
‘‘A survey on sensor networks,’’ IEEE Communications Magazine, vol. 40, no. 8, pp. 102–114, 2002.
6. J. Al-Karaki and A. Kamal, ‘‘Routing techniques in wireless
sensor networks: a survey,’’ IEEE Wireless Communications,
vol. 11, no. 6, pp. 6–28, 2004.
7. X. Wang, L. Ding, B. Dao-Wei, and S. Wang, ‘‘Energy-efficient
optimization of reorganization-enabled wireless sensor networks,’’ Sensors, vol. 7, no. 9, pp. 1793–1816, 2007.
8. N. Vasanthi and S. Annadurai, ‘‘Sleep schedule for fast and
efficient control of parameters in wireless sensor–actor networks,’’ in First International Conference on Communication
System Software and Middleware. IEEE, 2006, pp. 1–6.
9. R. Rajagopalan and P. Varshney, ‘‘Data-aggregation techniques
in sensor networks: a survey,’’ IEEE Communications Surveys
Tutorials, vol. 8, no. 4, pp. 48–63, 2006.
10. G. Shah and M. Hassan, ‘‘A reliable event response framework
for wireless sensor and actor networks,’’ in IEEE Workshops of
International Conference on Advanced Information Networking
and Applications, 2011, pp. 396–401.
11. S.-L. Wu, Y.-C. Tseng, and S. Ivan, Eds., Energy conservation in
sensor and sensor–actuator networks. Auerbach Publications,
2007.
12. V. Gungora, M. Vurana, and O. Akanb, ‘‘On the cross-layer
interactions between congestion and contention in wireless sensor
and actor networks,’’ Ad Hoc Networks, vol. 5, no. 6, pp. 897 –
909, 2007.
13. A. Zamanifar, M. Sharifi, and O. Kashefi, ‘‘Self actor–actor
connectivity restoration in wireless sensor and actor networks,’’
in First Asian Conference on Intelligent Information and Database Systems. IEEE, 2009, pp. 442–447.
14. Y. Gao, J. Wang, and X. Song, ‘‘Data collection scheme of
mobile sink in wireless sensor and actor networks,’’ in 11th
World Congress on Intelligent Control and Automation. IEEE,
2014, pp. 2505–2508.
15. Z. Cai, X. Ren, G. Hao, B. Chen, and Z. Xue, ‘‘Survey on
wireless sensor and actor network,’’ in 9th World Congress on
Intelligent Control and Automation. IEEE, 2011, pp. 788–793.
16. C. Konstantopoulos, I. Venetis, G. Pantziou, and D. Gavalas,
‘‘An efficient event handling protocol for wireless sensor and
actor networks,’’ in IEEE Symposium on Computers and Communication. IEEE, 2014, pp. 1–6.
17. A. Boukerche, R. Araujo, and L. Villas, ‘‘A wireless actor and
sensor networks qos-aware routing protocol for the emergency
preparedness class of applications,’’ in 31st IEEE Conference on
Local Computer Networks. IEEE, 2006, pp. 832–839.
123
Int J Wireless Inf Networks
18. H. Peng, W. Huafeng, and G. Chuanshan, ‘‘Elrs: an energy-efficient layered routing scheme for wireless sensor and actor networks,’’ in 20th International Conference on Advanced Information
Networking and Applications, vol. 2. IEEE, 2006, pp. 5–pp.
19. H. Shibo, J. Chen, P. Cheng, Y. Gu, H. Tian, and Y. Sun,
‘‘Maintaining quality of sensing with actors in wireless sensor
networks,’’ IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 9, pp. 1657–1667, 2012.
20. W. Li, E. Chan, M. Hamdi, S. Lu, and D. Chen, ‘‘Communication cost minimization in wireless sensor and actor networks for
road surveillance,’’ IEEE Transactions on Vehicular Technology,
vol. 60, no. 2, pp. 618–631, 2011.
21. W. Abbas, H. Jaleel, and M. Egerstedt, ‘‘Energy-efficient data
collection in heterogeneous wireless sensor and actor networks,’’
in IEEE 52nd Annual Conference on Decision and Control.
IEEE, 2013, pp. 4164–4169.
22. M. Kamali, S. Sedighian, and M. Sharifi, ‘‘A distributed recovery
mechanism for actor–actor connectivity in wireless sensor actor
networks,’’ in International Conference on Intelligent Sensors,
Sensor Networks and Information Processing. IEEE, 2008,
pp. 183–188.
23. K. Muazzam, G. Shah, M. Ahsan, and M. Sher, ‘‘An efficient
and reliable clustering algorithm for wireless sensor actor networks (wsans),’’ in 53rd IEEE International Midwest Symposium
on Circuits and Systems. IEEE, 2010, pp. 332–338.
24. K. Selvaradjou, H. Nikhil, A. Franklin, and C. Siva, ‘‘Energyefficient directional routing between partitioned actors in wireless
sensor and actor networks,’’ IET communications, vol. 4, no. 1,
pp. 102–115, 2010.
25. S. Sedighian, M. Sharifi, S. Azhari, and H. Momeni, ‘‘Service
requirements for actor–actor coordination through sensor nodes
in wireless sensor actor networks,’’ in International Conference
on Innovations in Information Technology. IEEE, 2008,
pp. 475–479.
26. N. Sabri, S. Aljunid, R. Ahmad, M. Malik, A. Yahya, R. Kamaruddin, and M. Salim, ‘‘Towards smart wireless sensor actor
networks: design factors and applications,’’ in IEEE Symposium
on Industrial Electronics and Applications. IEEE, 2011,
pp. 704–708.
27. S. Kashi and M. Sharifi, ‘‘Connectivity weakness impacts on
coordination in wireless sensor and actor networks,’’ IEEE
Communications Surveys and Tutorials, vol. 15, no. 1,
pp. 145–166, 2013.
28. F. Senel, K. Akkaya, and M. Younis, ‘‘An efficient mechanism
for establishing connectivity in wireless sensor and actor networks,’’ in IEEE Global Telecommunications Conference. IEEE,
2007, pp. 1129–1133.
29. K. Ozaki, K. Watanabe, S. Itaya, N. Hayashibara, T. Enokido,
and M. Takizawa, ‘‘A fault-tolerant model of wireless sensor–
actor network,’’ in Ninth IEEE International Symposium on
Object and Component-Oriented Real-Time Distributed Computing. IEEE, 2006, pp. 8–pp.
30. A. Abbasi, M. Younis, and U. Baroudi, ‘‘Restoring connectivity
in wireless sensor–actor networks with minimal node movement,’’ in 7th International Wireless Communications and Mobile
Computing Conference. IEEE, 2011, pp. 2046–2051.
31. S. Zhang, X. Wu, and H. Wang, ‘‘Length-aware topology
reconfiguration in wireless sensor–actor networks to recover from
an actor failure,’’ in 33rd Chinese Control Conference. IEEE,
2014, pp. 304–309.
32. A. Abbasi, F. Younis, and U. Baroudi, ‘‘Recovering from a node
failure in wireless sensor-actor networks with minimal topology
changes,’’ IEEE Transactions on Vehicular Technology, vol. 62,
no. 1, pp. 256–271, 2013.
33. N. Sabri, S. Aljunid, R. Ahmad, M. Malik, A. Yahya, R. Kamaruddin, and M. Salim, ‘‘Wireless sensor actor networks,’’ in
123
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
IEEE Symposium on Wireless Technology and Applications.
IEEE, 2011, pp. 90–95.
J. Barbar, M. Diaz, I. Esteve, D. Garrido, L. Llopis, B. Rubio,
and J. Troya, ‘‘Tc-wsans: A tuple channel based coordination
model for wireless sensor and actor networks,’’ in 12th IEEE
Symposium on Computers and Communications. IEEE, 2007,
pp. 173–178.
A. Ian and H. Ismail, ‘‘Wireless sensor and actor networks: research
challenges,’’ Ad Hoc Networks, vol. 2, no. 4, pp. 351–367, 2004.
A. Boukerche and A. Martirosyan, ‘‘An efficient algorithm for
preserving events’ temporal relationships in wireless sensor actor
networks,’’ in 32nd IEEE Conference on Local Computer Networks. IEEE, 2007, pp. 771–780.
A. Martirosyan and A. Boukerche, ‘‘Preserving temporal relationships of events for wireless sensor actor networks,’’ IEEE
Transactions on Computers, vol. 61, no. 8, pp. 1203–1216, 2012.
S. Habib, M. Safar, and N. ElSayed, ‘‘Automatic placement of
actors within wireless sensor–actor networks,’’ in Telecommunication Networks and Applications Conference. IEEE, 2008,
pp. 224–229.
V. Rafe, H. Momeni, and M. Sharifi, ‘‘Energy-aware task allocation in wireless sensor actor networks,’’ in Second International
Conference on Computer and Electrical Engineering, vol. 1.
IEEE, 2009, pp. 145–148.
L. Barolli, T. Yang, M. Ikeda, A. Durresi, and F. Xhafa, ‘‘A
simulation system for routing efficiency in wireless sensor–actor
networks: a case study for semi-automated architecture,’’ in
Parallel and Distributed Systems, 2008. ICPADS’08. 14th IEEE
International Conference on. IEEE, 2008, pp. 567–574.
Z. Li and H. Shen, ‘‘A kautz-based real-time and energy-efficient
wireless sensor and actuator network,’’ in 32nd International Conference on Distributed Computing Systems. IEEE, 2012, pp. 62–71.
N. Trivedi, G. Elangovan, S. Iyengar, and N. Balakrishnan, ‘‘A
message-efficient, distributed clustering algorithm for wireless
sensor and actor networks,’’ in IEEE International Conference on
Multisensor Fusion and Integration for Intelligent Systems. IEEE,
2006, pp. 53–58.
M. Akba, M. Brust, and D. Turgut, ‘‘Sofrop: Self-organizing and
fair routing protocol for wireless networks with mobile sensors
and stationary actors,’’ Computer Communications, vol. 34,
no. 18, pp. 2135–2146, 2011.
S. Chinnappen-Rimer and G. Hancke, ‘‘Actor coordination in
wireless sensor–actor networks,’’ in INDICON Conference.
IEEE, 2009, pp. 1–4.
H. Momeni, M. Sharifi, and S. Sedighian, ‘‘A new approach to
task allocation in wireless sensor actor networks,’’ in First
International Conference on Computational Intelligence, Communication Systems and Networks. IEEE, 2009, pp. 73–78.
V. Narasimhan, A. Arvind, and K. Bever, ‘‘Greenhouse asset
management using wireless sensor–actor networks,’’ in International Conference on Mobile Ubiquitous Computing, Systems,
Services and Technologies. IEEE, 2007, pp. 9–14.
A. Zamanifar, M. Sharifi, and S. Sedighian, ‘‘A distributed
algorithm for restoring actor–actor connectivity in wireless sensor
and actor networks,’’ in International Conference on Electronic
Design. IEEE, 2008, pp. 1–6.
H. Kim and J. A. Cobb, ‘‘Optimization trade-offs in the design of
wireless sensor and actor networks,’’ in 37th IEEE Conference on
Local Computer Networks, 2012, pp. 559–567.
A. Zamanifar, M. Sharifi, and O. Kashefi, ‘‘A hybrid approach to
actor–actor connectivity restoration in wireless sensor and actor
networks,’’ in Eighth International Conference on Networks.
IEEE, 2009, pp. 76–81.
A. Alamuti, ‘‘Three protocols for actor selection in wireless
sensor and actor networks,’’ in International Conference on
Education and e-Learning Innovations. IEEE, 2012, pp. 1–3.
Int J Wireless Inf Networks
51. V. Ranga, M. Dave, and V. Anil, ‘‘A distributed approach for
selection of optimal actor nodes in wireless sensor and actor
networks,’’ in International Conference on Contemporary Computing and Informatics. IEEE, 2014, pp. 312–319.
52. M. Akbas, E. M., and D. Turgut, ‘‘Localization for wireless sensor
and actor networks with meandering mobility,’’ IEEE Transactions
on Computers, vol. 64, no. 4, pp. 1015–1028, April 2015.
53. X. Li, X. Liang, R. Lu, S. He, J. Chen, and X. Shen, ‘‘Toward
reliable actor services in wireless sensor and actor networks,’’ in
8th International Conference on Mobile Adhoc and Sensor Systems. IEEE, 2011, pp. 351–360.
54. M. Alaiwy, F. Alaiwy, and S. Habib, ‘‘Optimization of actors
placement within wireless sensor–actor networks,’’ in 12th IEEE
Symposium on Computers and Communications, 2007, pp. 179–184.
55. G. Shah, M. Bozyigit, and F. Hussain, ‘‘Cluster-based coordination and routing framework for wireless sensor and actor networks,’’ Wireless Communications and Mobile Computing,
vol. 11, no. 8, 2011.
56. M. Imran, N. Haider, and M. Alnuem, ‘‘Efficient movement
control actor relocation for honing connected coverage in wireless sensor and actor networks,’’ in 37th Conference on Local
Computer Networks Workshops. IEEE, 2012, pp. 710–717.
57. M. Dong, K. Ota, S. Du, H. Zhu, and S. Guo, ‘‘Ants: Pushing the
rapid event notification in wireless sensor and actor networks,’’ in
International Joint Conference on Awareness Science and Technology and Ubi-Media Computing. IEEE, 2013, pp. 753–758.
58. N. Dinh and Y. Kim, ‘‘Directional anycast routing in wireless
sensor and actor networks,’’ in International Symposium on
Communications and Information Technologies. IEEE, 2012,
pp. 251–255.
59. C. Tuan and Y. Wu, ‘‘Event ordering by double confirmation in
wireless sensor and actor networks,’’ IEEE Sensors Journal,
vol. 11, no. 3, pp. 829–836, 2011.
60. T. Melodia, D. Pompili, and I. Akyldiz, ‘‘Handling mobility in
wireless sensor and actor networks,’’ IEEE Transactions on
Mobile Computing, vol. 9, no. 2, pp. 160–173, 2010.
61. T. Issariyakul and H. E, ‘‘Introduction to network simulator 2
(ns2),’’ in Introduction to Network Simulator NS2. Springer US,
2009, pp. 1–18.
62. H. Kim and J. Cobb, ‘‘Optimal transmission range for multi-hop
communication in wireless sensor and actor networks,’’ in 36th
Conference on Local Computer Networks. IEEE, 2011, pp. 223–226.
63. P. Lameski, E. Zdravevski, A. Kulakov, and D. Davcev, ‘‘Architecture for wireless sensor and actor networks control and data
acquisition,’’ in International Conference on Distributed Computing in Sensor Systems and Workshops. IEEE, 2011, pp. 1–3.
64. P. Bose, P. Morin, and J. Urrutia, ‘‘Routing with guaranteed
delivery in ad hoc wireless networks,’’ Wireless Networks, vol. 7,
no. 6, pp. 609–616, 2001.
65. W. Chen, J. Hou, and L. Sha, ‘‘Dynamic clustering for acoustic
target tracking in wireless sensor networks,’’ IEEE Transactions
on Mobile Computing, vol. 3, no. 3, pp. 258–271, 2004.
66. C. Eduardo, D. Manuel, L. Luis, and R. Bartolom, ‘‘Hero: A
hierarchical, efficient and reliable routing protocol for wireless
sensor and actor networks,’’ Computer Communications, vol. 35,
no. 11, pp. 1392 – 1409, 2012.
67. Y. Haidong, M. Huadong, and L. Hongyu, ‘‘Coordination
mechanism in wireless sensor and actor networks,’’ in First
International Multi-Symposiums on Computer and Computational Sciences, vol. 2, 2006, pp. 627–634.
68. A. Boukerche, R. Araujo, and L. Villas, ‘‘A wireless actor and
sensor networks qos-aware routing protocol for the emergency
preparedness class of applications,’’ in 31st IEEE Conference on
Local Computer Networks, 2006, pp. 832–839.
69. M. Tommaso, P. Dario, C. Vehbi, and I. F. Akyildiz, ‘‘A distributed coordination framework for wireless sensor and actor
networks,’’ in Proceedings of the 6th ACM international conference, 2005, pp. 99–110.
70. H. Fei, C. Xiaojun, S. Kumar, and K. Sankar, ‘‘Trustworthiness
in wireless sensor and actuator networks: towards low-complexity reliability and security,’’ in IEEE Global Telecommunications
Conference, vol. 3, 2005.
71. L. Yen-Ting and S. Megerian, ‘‘Low cost distributed actuation in
large-scale ad hoc sensor–actuator networks,’’ in International
Conference on Wireless Networks, Communications and Mobile
Computing, vol. 2, 2005, pp. 975–980.
72. D. ZhiCheng, W. Bingwen, L. Zhi, and A. Yin, ‘‘Vdspt: A sensor–actor coordination protocol for wireless sensor and actor
network based on voronoi diagram and shortest path tree,’’ in
International Symposium on Computer Network and Multimedia
Technology, 2009, pp. 1–4.
73. B. McLaughlan and K. Akkaya, ‘‘Coverage-based clustering of
wireless sensor and actor networks,’’ in IEEE International
Conference on Pervasive Services, 2007, pp. 45–54.
74. A. Durresi, V. Paruchuri, and L. Barolli, ‘‘Delay-energy aware
routing protocol for sensor and actor networks,’’ in 11th International Conference on Parallel and Distributed Systems, vol. 1,
2005, pp. 292–298.
75. S. Yahiaoui, M. Omar, A. Bouabdallah, and Y. Challal, ‘‘Multiactuators based anycast routing protocol for wireless sensor and
actuator networks,’’ in International Conference on Advanced
Networking Distributed Systems and Applications. IEEE, 2014,
pp. 31–34.
76. D. Reina, S. Toral, P. Johnson, and F. Barrero, ‘‘An improvement of route duration in wsan based on nodes mobility and rss,’’
in 37th Annual Conference on Industrial Electronics Society.
IEEE, 2011, pp. 2986–2991.
77. H. Wen, N. Bulusu, and J. Sanjay, ‘‘A communication paradigm
for hybrid sensor/actuator networks,’’ in 15th IEEE International
Symposium on Personal, Indoor and Mobile Radio Communications, vol. 1, 2004, pp. 201–205.
78. J. Haapola and N. Bui, ‘‘Towards dynamic application-dependent
protocol stacks for wsans,’’ in Future Network and Mobile
Summit. IEEE, 2010, pp. 1–8.
79. K. Jagadeesh, B. Majhi, and B. Ramesh, ‘‘A voronoi diagram
based efficient coordination mechanism for wsan,’’ in First
International Conference on Networks and Soft Computing.
IEEE, 2014, pp. 226–230.
80. Z. Ngai, E.C.H Yangfan, M. Lyu, , and L. Jiangchuan, ‘‘Reliable
reporting of delay-sensitive events in wireless sensor–actuator
networks,’’ in IEEE International Conference on Mobile Adhoc
and Sensor Systems, 2006, pp. 101–108.
81. A. Durresi and V. Paruchuri, ‘‘Geometric broadcast protocol for
sensor and actor networks,’’ in 19th International Conference on
Advanced Information Networking and Applications, vol. 1,
2005, pp. 343–348.
82. E. Cayirci, T. Coplu, and O. Emiroglu, ‘‘Power aware many to
many routing in wireless sensor and actuator networks,’’ in
Proceeedings of the Second European Workshop on Wireless
Sensor Networks, 2005, pp. 236–245.
83. Z. Yangfan, L. M. Ngai, E.C.H, and L. Jiangchuan, ‘‘Powerspeed: A power-controlled real-time data transport protocol for
wireless sensor–actuator networks,’’ in IEEE Conference on
Wireless Communications and Networking, 2007, pp. 3736–
3740.
84. F. T, ‘‘Scalable routing in sensor actuator networks with churn,’’
in 3rd Annual IEEE Communications Society on Sensor and Ad
Hoc Communications and Networks, vol. 1, 2006, pp. 30–39.
123
Int J Wireless Inf Networks
85. L. SuetFei, ‘‘Wireless sensor actuator network for light monitoring and control application,’’ in 3rd IEEE Conference on
Consumer Communications and Networking, vol. 2, 2006,
pp. 974–978.
Jagadeesh Kakarla is a Ph.D
student, Department of Computer Science, NIT Rourkela,
Rourkela, India. He has
obtained his M.Tech in the field
of Computer Science, Pondicherry University, India. He has
obtained his B.Tech in the field
of Information Technology,
Jawaharlal Nehru Technological
University, India. His research
areas include Wireless Sensor
Networks, Adhoc Networks and
Image Processing.
Banshidhar Majhi is working
as a professor in Computer science department. NIT Rourkela,
India. Has 23 years of teaching
and 3 ears of industry experience. Has published 50 journal
articles in referred journals and
100 articles in reputed international conferences. Research
interests include image processing, computer vision, security protocols and wireless
sensor networks.
123
View publication stats
Ramesh Babu Battula is
working as an assistant professor and pursuing Ph.D in
Department of Computer Science, MNIT Jaipur, India. He
has obtained his M.Tech in the
field of Computer Science, IIT
Guwahati, India. He has
obtained his B.Tech in the field
of Information Technology,
Nagarjuna University, India.
His research areas include Network Security, Computer Networks, Information Security,
Android Security, Wireless
Mesh Networks, and Wireless Sensor Networks.