ChapterPDF Available

Overview of Wireless Sensor Network

Authors:

Figures

Content may be subject to copyright.
Chapter 1
© 2012 Matin and Islam, licensee InTech. This is an open access chapter distributed under the terms of the
Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Overview of Wireless Sensor Network
M.A. Matin and M.M. Islam
Additional information is available at the end of the chapter
http://dx.doi.org/10.5772/49376
1. Introduction
Wireless Sensor Networks (WSNs) can be defined as a self-configured and infrastructure-
less wireless networks to monitor physical or environmental conditions, such as
temperature, sound, vibration, pressure, motion or pollutants and to cooperatively pass
their data through the network to a main location or sink where the data can be observed
and analysed. A sink or base station acts like an interface between users and the network.
One can retrieve required information from the network by injecting queries and gathering
results from the sink. Typically a wireless sensor network contains hundreds of thousands
of sensor nodes. The sensor nodes can communicate among themselves using radio signals.
A wireless sensor node is equipped with sensing and computing devices, radio transceivers
and power components. The individual nodes in a wireless sensor network (WSN) are
inherently resource constrained: they have limited processing speed, storage capacity, and
communication bandwidth. After the sensor nodes are deployed, they are responsible for
self-organizing an appropriate network infrastructure often with multi-hop communication
with them. Then the onboard sensors start collecting information of interest. Wireless sensor
devices also respond to queries sent from a “control site” to perform specific instructions or
provide sensing samples. The working mode of the sensor nodes may be either continuous
or event driven. Global Positioning System (GPS) and local positioning algorithms can be
used to obtain location and positioning information. Wireless sensor devices can be
equipped with actuators to “act” upon certain conditions. These networks are sometimes
more specifically referred as Wireless Sensor and Actuator Networks as described in
(Akkaya et al., 2005).
Wireless sensor networks (WSNs) enable new applications and require non-conventional
paradigms for protocol design due to several constraints. Owing to the requirement for low
device complexity together with low energy consumption (i.e. long network lifetime), a
proper balance between communication and signal/data processing capabilities must be
found. This motivates a huge effort in research activities, standardization process, and
Wireless Sensor Networks – Technology and Protocols
4
industrial investments on this field since the last decade (Chiara et. al. 2009). At present
time, most of the research on WSNs has concentrated on the design of energy- and
computationally efficient algorithms and protocols, and the application domain has been
restricted to simple data-oriented monitoring and reporting applications (Labrador et. al.
2009). The authors in (Chen et al., 2011) propose a Cable Mode Transition (CMT) algorithm,
which determines the minimal number of active sensors to maintain K-coverage of a terrain
as well as K-connectivity of the network. Specifically, it allocates periods of inactivity for
cable sensors without affecting the coverage and connectivity requirements of the network
based only on local information. In (Cheng et al., 2011), a delay-aware data collection
network structure for wireless sensor networks is proposed. The objective of the proposed
network structure is to minimize delays in the data collection processes of wireless sensor
networks which extends the lifetime of the network. In (Matin et al., 2011), the authors have
considered relay nodes to mitigate the network geometric deficiencies and used Particle
Swarm Optimization (PSO) based algorithms to locate the optimal sink location with respect
to those relay nodes to overcome the lifetime challenge. Energy efficient communication has
also been addressed in (Paul et al., 2011; Fabbri et al. 2009). In (Paul et al., 2011), the authors
proposed a geometrical solution for locating the optimum sink placement for maximizing
the network lifetime. Most of the time, the research on wireless sensor networks have
considered homogeneous sensor nodes. But nowadays researchers have focused on
heterogeneous sensor networks where the sensor nodes are unlike to each other in terms of
their energy. In (Han et al., 2010), the authors addresses the problem of deploying relay
nodes to provide fault tolerance with higher network connectivity in heterogeneous wireless
sensor networks, where sensor nodes possess different transmission radii. New network
architectures with heterogeneous devices and the recent advancement in this technology
eliminate the current limitations and expand the spectrum of possible applications for WSNs
considerably and all these are changing very rapidly.
Figure 1. A typical Wireless Sensor Network
2. Applications of wireless sensor network
Wireless sensor networks have gained considerable popularity due to their flexibility in
solving problems in different application domains and have the potential to change our lives
Overview of Wireless Sensor Network 5
in many different ways. WSNs have been successfully applied in various application
domains (Akyildiz et al. 2002; Bharathidasan et al., 2001), (Yick et al., 2008; Boukerche, 2009),
(Sohraby et al., 2007), and ( Chiara et al., 2009;Verdone et al., 2008), such as:
Military applications: Wireless sensor networks be likely an integral part of military
command, control, communications, computing, intelligence, battlefield surveillance,
reconnaissance and targeting systems.
Area monitoring: In area monitoring, the sensor nodes are deployed over a region where
some phenomenon is to be monitored. When the sensors detect the event being monitored
(heat, pressure etc), the event is reported to one of the base stations, which then takes
appropriate action.
Transportation: Real-time traffic information is being collected by WSNs to later feed
transportation models and alert drivers of congestion and traffic problems.
Health applications: Some of the health applications for sensor networks are supporting
interfaces for the disabled, integrated patient monitoring, diagnostics, and drug
administration in hospitals, tele-monitoring of human physiological data, and tracking &
monitoring doctors or patients inside a hospital.
Environmental sensing: The term Environmental Sensor Networks has developed to cover
many applications of WSNs to earth science research. This includes sensing volcanoes,
oceans, glaciers, forests etc. Some other major areas are listed below:
Air pollution monitoring
Forest fires detection
Greenhouse monitoring
Landslide detection
Structural monitoring: Wireless sensors can be utilized to monitor the movement within
buildings and infrastructure such as bridges, flyovers, embankments, tunnels etc enabling
Engineering practices to monitor assets remotely with out the need for costly site visits.
Industrial monitoring: Wireless sensor networks have been developed for machinery
condition-based maintenance (CBM) as they offer significant cost savings and enable new
functionalities. In wired systems, the installation of enough sensors is often limited by the
cost of wiring.
Agricultural sector: using a wireless network frees the farmer from the maintenance of
wiring in a difficult environment. Irrigation automation enables more efficient water use
and reduces waste.
3. Design issues of a wireless sensor network
There are a lot of challenges placed by the deployment of sensor networks which are a
superset of those found in wireless ad hoc networks. Sensor nodes communicate over
wireless, lossy lines with no infrastructure. An additional challenge is related to the limited,
Wireless Sensor Networks – Technology and Protocols
6
usually non-renewable energy supply of the sensor nodes. In order to maximize the lifetime
of the network, the protocols need to be designed from the beginning with the objective of
efficient management of the energy resources (Akyildiz et al., 2002). Wireless Sensor
Network Design issues are mentioned in (Akkaya et al., 2005), (Akyildizet al., 2002),
(SensorSim; Tossim, Younis et al., 2004), (Pan et al., 2003) and different possible platforms
for simulation and testing of routing protocols for WSNs are discussed in ( NS-2, Zeng et al.,
1998, SensorSim, Tossiim ). Let us now discuss the individual design issues in greater detail.
Fault Tolerance: Sensor nodes are vulnerable and frequently deployed in dangerous
environment. Nodes can fail due to hardware problems or physical damage or by
exhausting their energy supply. We expect the node failures to be much higher than the one
normally considered in wired or infrastructure-based wireless networks. The protocols
deployed in a sensor network should be able to detect these failures as soon as possible and
be robust enough to handle a relatively large number of failures while maintaining the
overall functionality of the network. This is especially relevant to the routing protocol
design, which has to ensure that alternate paths are available for rerouting of the packets.
Different deployment environments pose different fault tolerance requirements.
Scalability: Sensor networks vary in scale from several nodes to potentially several hundred
thousand. In addition, the deployment density is also variable. For collecting high-
resolution data, the node density might reach the level where a node has several thousand
neighbours in their transmission range. The protocols deployed in sensor networks need to
be scalable to these levels and be able to maintain adequate performance.
Production Costs: Because many deployment models consider the sensor nodes to be
disposable devices, sensor networks can compete with traditional information gathering
approaches only if the individual sensor nodes can be produced very cheaply. The target
price envisioned for a sensor node should ideally be less than $1.
Hardware Constraints: At minimum, every sensor node needs to have a sensing unit, a
processing unit, a transmission unit, and a power supply. Optionally, the nodes may have
several built-in sensors or additional devices such as a localization system to enable
location-aware routing. However, every additional functionality comes with additional cost
and increases the power consumption and physical size of the node. Thus, additional
functionality needs to be always balanced against cost and low-power requirements.
Sensor Network Topology: Although WSNs have evolved in many aspects, they continue to
be networks with constrained resources in terms of energy, computing power, memory, and
communications capabilities. Of these constraints, energy consumption is of paramount
importance, which is demonstrated by the large number of algorithms, techniques, and
protocols that have been developed to save energy, and thereby extend the lifetime of the
network. Topology Maintenance is one of the most important issues researched to reduce
energy consumption in wireless sensor networks.
Transmission Media: The communication between the nodes is normally implemented
using radio communication over the popular ISM bands. However, some sensor networks
Overview of Wireless Sensor Network 7
use optical or infrared communication, with the latter having the advantage of being robust
and virtually interference free.
Power Consumption: As we have already seen, many of the challenges of sensor networks
revolve around the limited power resources. The size of the nodes limits the size of the
battery. The software and hardware design needs to carefully consider the issues of efficient
energy use. For instance, data compression might reduce the amount of energy used for
radio transmission, but uses additional energy for computation and/or filtering. The energy
policy also depends on the application; in some applications, it might be acceptable to turn
off a subset of nodes in order to conserve energy while other applications require all nodes
operating simultaneously.
4. Structure of a wireless sensor network
Structure of a Wireless Sensor Network includes different topologies for radio
communications networks. A short discussion of the network topologies that apply to
wireless sensor networks are outlined below:
4.1. Star network (single point-to-multipoint) (Wilson, 2005)
A star network is a communications topology where a single base station can send and/or
receive a message to a number of remote nodes. The remote nodes are not permitted to send
messages to each other. The advantage of this type of network for wireless sensor networks
includes simplicity, ability to keep the remote node’s power consumption to a minimum. It
also allows low latency communications between the remote node and the base station. The
disadvantage of such a network is that the base station must be within radio transmission
range of all the individual nodes and is not as robust as other networks due to its
dependency on a single node to manage the network.
Figure 2. A Star network topology
Wireless Sensor Networks – Technology and Protocols
8
4.2. Mesh network (Wilson, 2005)
A mesh network allows transmitting data to one node to other node in the network that is
within its radio transmission range. This allows for what is known as multi-hop
communications, that is, if a node wants to send a message to another node that is out of
radio communications range, it can use an intermediate node to forward the message to the
desired node. This network topology has the advantage of redundancy and scalability. If an
individual node fails, a remote node still can communicate to any other node in its range,
which in turn, can forward the message to the desired location. In addition, the range of the
network is not necessarily limited by the range in between single nodes; it can simply be
extended by adding more nodes to the system. The disadvantage of this type of network is
in power consumption for the nodes that implement the multi-hop communications are
generally higher than for the nodes that don’t have this capability, often limiting the battery
life. Additionally, as the number of communication hops to a destination increases, the time
to deliver the message also increases, especially if low power operation of the nodes is a
requirement.
Figure 3. A Mesh network topology
4.3. Hybrid star – Mesh network (Wilson, 2005)
A hybrid between the star and mesh network provides a robust and versatile
communications network, while maintaining the ability to keep the wireless sensor nodes
power consumption to a minimum. In this network topology, the sensor nodes with lowest
power are not enabled with the ability to forward messages. This allows for minimal power
consumption to be maintained. However, other nodes on the network are enabled with
multi-hop capability, allowing them to forward messages from the low power nodes to
other nodes on the network. Generally, the nodes with the multi-hop capability are higher
power, and if possible, are often plugged into the electrical mains line. This is the topology
implemented by the up and coming mesh networking standard known as ZigBee.
Overview of Wireless Sensor Network 9
Figure 4. A Hybrid Star – Mesh network topology
5. Structure of a wireless sensor node
A sensor node is made up of four basic components such as sensing unit, processing unit,
transceiver unit and a power unit which is shown in Fig. 5. It also has application dependent
additional components such as a location finding system, a power generator and a
mobilizer. Sensing units are usually composed of two subunits: sensors and analogue to
digital converters (ADCs) (Akyildiz et al., 2002). The analogue signals produced by the
sensors are converted to digital signals by the ADC, and then fed into the processing unit.
The processing unit is generally associated with a small storage unit and it can manage
the procedures that make the sensor node collaborate with the other nodes to carry out
the assigned sensing tasks. A transceiver unit connects the node to the network. One of
the most important components of a sensor node is the power unit. Power units can be
supported by a power scavenging unit such as solar cells. The other subunits, of the node
are application dependent.
A functional block diagram of a versatile wireless sensing node is provided in Fig. 6.
Modular design approach provides a flexible and versatile platform to address the needs of
a wide variety of applications. For example, depending on the sensors to be deployed, the
signal conditioning block can be re-programmed or replaced. This allows for a wide variety
Wireless Sensor Networks – Technology and Protocols
10
of different sensors to be used with the wireless sensing node. Similarly, the radio link may
be swapped out as required for a given applications’ wireless range requirement and the
need for bidirectional communications.
Figure 5. The components of a sensor node
Figure 6. Functional block diagram of a sensor node
Using flash memory, the remote nodes acquire data on command from a base station, or by
an event sensed by one or more inputs to the node. Moreover, the embedded firmware can
be upgraded through the wireless network in the field.
The microprocessor has a number of functions including:
Overview of Wireless Sensor Network 11
Managing data collection from the sensors
performing power management functions
interfacing the sensor data to the physical radio layer
managing the radio network protocol
A key aspect of any wireless sensing node is to minimize the power consumed by the
system. Usually, the radio subsystem requires the largest amount of power. Therefore, data
is sent over the radio network only when it is required. An algorithm is to be loaded into the
node to determine when to send data based on the sensed event. Furthermore, it is
important to minimize the power consumed by the sensor itself. Therefore, the hardware
should be designed to allow the microprocessor to judiciously control power to the radio,
sensor, and sensor signal conditioner (Akyildiz et al., 2002).
6. Communication structure of a wireless sensor network
The sensor nodes are usually scattered in a sensor field as shown in Fig. 1. Each of these
scattered sensor nodes has the capabilities to collect data and route data back to the sink and
the end users. Data are routed back to the end user by a multi-hop infrastructure-less
architecture through the sink as shown in Fig. 1. The sink may communicate with the task
manager node via Internet or Satellite.
Figure 7. Wireless Sensor Network protocol stack
The protocol stack used by the sink and the sensor nodes is given in Fig. 7. This protocol
stack combines power and routing awareness, integrates data with networking protocols,
communicates power efficiently through the wireless medium and promotes cooperative
efforts of sensor nodes. The protocol stack consists of the application layer, transport layer,
Wireless Sensor Networks – Technology and Protocols
12
network layer, data link layer, physical layer, power management plane, mobility
management plane, and task management plane (Akyildiz et al., 2002). Different types of
application software can be built and used on the application layer depending on the
sensing tasks. This layer makes hardware and software of the lowest layer transparent to the
end-user. The transport layer helps to maintain the flow of data if the sensor networks
application requires it. The network layer takes care of routing the data supplied by the
transport layer, specific multi-hop wireless routing protocols between sensor nodes and
sink. The data link layer is responsible for multiplexing of data streams, frame detection,
Media Access Control (MAC) and error control. Since the environment is noisy and sensor
nodes can be mobile, the MAC protocol must be power aware and able to minimize collision
with neighbours’ broadcast. The physical layer addresses the needs of a simple but robust
modulation, frequency selection, data encryption, transmission and receiving techniques.
In addition, the power, mobility, and task management planes monitor the power,
movement, and task distribution among the sensor nodes. These planes help the sensor
nodes coordinate the sensing task and lower the overall energy consumption.
7. Energy consumption issues in wireless sensor network
Energy consumption is the most important factor to determine the life of a sensor network
because usually sensor nodes are driven by battery. Sometimes energy optimization is more
complicated in sensor networks because it involved not only reduction of energy
consumption but also prolonging the life of the network as much as possible. The
optimization can be done by having energy awareness in every aspect of design and
operation. This ensures that energy awareness is also incorporated into groups of
communicating sensor nodes and the entire network and not only in the individual nodes
(Bharathidasan et al. 2001).
A sensor node usually consists of four sub-systems (Bharathidasan et al. 2001):
a computing subsystem : It consists of a microprocessor(microcontroller unit, MCU)
which is responsible for the control of the sensors and implementation of
communication protocols. MCUs usually operate under various modes for power
management purposes. As these operating modes involves consumption of power, the
energy consumption levels of the various modes should be considered while looking at
the battery lifetime of each node.
a communication subsystem: It consists of a short range radio which communicate with
neighboring nodes and the outside world. Radios can operate under the different
modes. It is important to completely shut down the radio rather than putting it in the
Idle mode when it is not transmitting or receiving for saving power.
a sensing subsystem : It consists of a group of sensors and actuators and link the node
to the outside world. Energy consumption can be reduced by using low power
components and saving power at the cost of performance which is not required.
Overview of Wireless Sensor Network 13
a power supply subsystem : It consists of a battery which supplies power to the node. It
should be seen that the amount of power drawn from a battery is checked because if
high current is drawn from a battery for a long time, the battery will die faster even
though it could have gone on for a longer time. Usually the rated current capacity of a
battery being used for a sensor node is less than the minimum energy consumption.
The lifetime of a battery can be increased by reducing the current drastically or even
turning it off often.
To minimize the overall energy consumption of the sensor network, different types of
protocols and algorithms have been studied so far all over the world. The lifetime of a
sensor network can be increased significantly if the operating system, the application layer
and the network protocols are designed to be energy aware. These protocols and algorithms
have to be aware of the hardware and able to use special features of the micro-processors
and transceivers to minimize the sensor node’s energy consumption. This may push toward
a custom solution for different types of sensor node design. Different types of sensor nodes
deployed also lead to different types of sensor networks. This may also lead to the different
types of collaborative algorithms in wireless sensor networks arena.
8. Protocols & algorithms of wireless sensor network
In WSN, the main task of a sensor node is to sense data and sends it to the base station in
multi hop environment for which routing path is essential. For computing the routing path
from the source node to the base station there is huge numbers of proposed routing
protocols exist (Sharma et al., 2011). The design of routing protocols for WSNs must
consider the power and resource limitations of the network nodes, the time-varying quality
of the wireless channel, and the possibility for packet loss and delay. To address these
design requirements, several routing strategies for WSNs have been proposed in (Labrador
et al., 2009), (Akkaya et al., 2005), ( Akyildiz et al. 2002), (Boukerche, 2009, Al-karaki et al.,
2004, Pan et al., 2003) and (Waharte et al., 2006).
The first class of routing protocols adopts a flat network architecture in which all nodes are
considered peers. Flat network architecture has several advantages, including minimal
overhead to maintain the infrastructure and the potential for the discovery of multiple
routes between communicating nodes for fault tolerance.
A second class of routing protocols imposes a structure on the network to achieve energy
efficiency, stability, and scalability. In this class of protocols, network nodes are organized in
clusters in which a node with higher residual energy, for example, assumes the role of a
cluster head. The cluster head is responsible for coordinating activities within the cluster
and forwarding information between clusters. Clustering has potential to reduce energy
consumption and extend the lifetime of the network.
A third class of routing protocols uses a data-centric approach to disseminate interest within
the network. The approach uses attribute-based naming, whereby a source node queries an
attribute for the phenomenon rather than an individual sensor node. The interest
Wireless Sensor Networks – Technology and Protocols
14
dissemination is achieved by assigning tasks to sensor nodes and expressing queries to
relative to specific attributes. Different strategies can be used to communicate interests to the
sensor nodes, including broadcasting, attribute-based multicasting, geo-casting, and any
casting.
A fourth class of routing protocols uses location to address a sensor node. Location-based
routing is useful in applications where the position of the node within the geographical
coverage of the network is relevant to the query issued by the source node. Such a query
may specify a specific area where a phenomenon of interest may occur or the vicinity to a
specific point in the network environment.
In the rest of this section we discuss some of the major routing protocols and algorithms to
deal with the energy conservation issue in the literatures.
1. Flooding: Flooding is a common technique frequently used for path discovery and
information dissemination in wired and wireless ad hoc networks which has been
discussed in (Akyildiz et al., 2002). The routing strategy of flooding is simple and does
not rely on costly network topology maintenance and complex route discovery
algorithms. Flooding uses a reactive approach whereby each node receiving a data or
control packet sends the packet to all its neighbors. After transmission, a packet follows
all possible paths. Unless the network is disconnected, the packet will eventually reach
its destination. Furthermore, as the network topology changes, the packet transmitted
follows the new routes. Fig. 8 illustrates the concept of flooding in data communications
network. As shown in the figure, flooding in its simplest form may cause packets to be
replicated indefinitely by network nodes.
Figure 8. Flooding in data communication networks
Overview of Wireless Sensor Network 15
1. Gossiping:
To address the shortcomings of flooding, a derivative approach, referred to as gossiping, has
been proposed in ( Braginsky et al., 2002). Similar to flooding, gossiping uses a simple
forwarding rule and does not require costly topology maintenance or complex route
discovery algorithms. Contrary to flooding, where a data packet is broadcast to all
neighbors, gossiping requires that each node sends the incoming packet to a randomly
selected neighbor. Upon receiving the packet, the neighbor selected randomly chooses one
of its own neighbors and forwards the packet to the neighbor chosen. This process continues
iteratively until the packet reaches its intended destination or the maximum hop count is
exceeded.
2. Protocols for Information via Negotiation (SPIN):
Sensor protocols for information via negotiation (SPIN), is a data-centric negotiation-based
family of information dissemination protocols for WSNs (Kulik et al., 2002). The main
objective of these protocols is to efficiently disseminate observations gathered by individual
sensor nodes to all the sensor nodes in the network. Simple protocols such as flooding and
gossiping are commonly proposed to achieve information dissemination in WSNs. Flooding
requires that each node sends a copy of the data packet to all its neighbors until the
information reaches all nodes in the network. Gossiping, on the other hand, uses
randomization to reduce the number of duplicate packets and requires only that a node
receiving a data packet forward it to a randomly selected neighbor.
Figure 9. SPIN basic protocol operation
Wireless Sensor Networks – Technology and Protocols
16
3. Low-Energy Adaptive Clustering Hierarchy (LEACH)
Low-energy adaptive clustering hierarchy (LEACH) is a routing algorithm designed to
collect and deliver data to the data sink, typically a base station (Heinzelman et. al. 2000).
The main objectives of LEACH are:
Extension of the network lifetime
Reduced energy consumption by each network sensor node
Use of data aggregation to reduce the number of communication messages
To achieve these objectives, LEACH adopts a hierarchical approach to organize the network
into a set of clusters. Each cluster is managed by a selected cluster head. The cluster head
assumes the responsibility to carry out multiple tasks. The first task consists of periodic
collection of data from the members of the cluster. Upon gathering the data, the cluster head
aggregates it in an effort to remove redundancy among correlated values. The second main
task of a cluster head is to transmit the aggregated data directly to the base station over
single hop. The third main task of the cluster head is to create a TDMA-based schedule
whereby each node of the cluster is assigned a time slot that it can use for transmission. The
cluster head announces the schedule to its cluster members through broadcasting. To reduce
the likelihood of collisions among sensors within and outside the cluster, LEACH nodes use
a code-division multiple access–based scheme for communication.
The basic operations of LEACH are organized in two distinct phases. The first phase, the
setup phase, consists of two steps, cluster-head selection and cluster formation. The second
phase, the steady-state phase, focuses on data collection, aggregation, and delivery to the
base station. The duration of the setup is assumed to be relatively shorter than the steady-
state phase to minimize the protocol overhead.
At the beginning of the setup phase, a round of cluster-head selection starts. To decide
whether a node to become cluster head or not a threshold T(s) is addressed in (Heinzelman
et. al. 2000) which is as follows:
,
'
1
1.(.mod.)
()
0,
opt
opt
opt
pifs G
pr
Ts p
otherwise

(1)
Where r is the current round number and G is the set of nodes that have not become cluster
head within the last 1/popt rounds. At the beginning of each round, each node which belongs
to the set G selects a random number 0 or 1. If the random number is less than the threshold
T(s) then the node becomes a cluster head in the current round.
4. Threshold-sensitive Energy Efficient Protocols (TEEN and APTEEN):
Two hierarchical routing protocols called TEEN (Threshold-sensitive Energy Efficient sensor
Network protocol), and APTEEN (Adaptive Periodic Threshold-sensitive Energy Efficient
sensor Network protocol) are proposed in (Manjeshwar et al., 2001) and (Manjeshwar et al.,
Overview of Wireless Sensor Network 17
2002) , respectively. These protocols were proposed for time-critical applications. In
TEEN, sensor nodes sense the medium continuously, but the data transmission is done
less frequently. A cluster head sensor sends its members a hard threshold, which is the
threshold value of the sensed attribute and a soft threshold, which is a small change in the
value of the sensed attribute that triggers the node to switch on its transmitter and
transmit. Thus the hard threshold tries to reduce the number of transmissions by allowing
the nodes to transmit only when the sensed attribute is in the range of interest. The soft
threshold further reduces the number of transmissions that might have otherwise
occurred when there is little or no change in the sensed attribute. A smaller value of the
soft threshold gives a more accurate picture of the network, at the expense of increased
energy consumption. Thus, the user can control the trade-off between energy efficiency
and data accuracy. When cluster-heads are to change, new values for the above
parameters are broadcast. The main drawback of this scheme is that, if the thresholds are
not received, the nodes will never communicate, and the user will not get any data from
the network at all.
5. Power-Efficient Gathering in Sensor Information Systems (PEGASIS):
Power-efficient gathering in sensor information systems (PEGASIS) (Lindsey et al., 2002)
and its extension, hierarchical PEGASIS, are a family of routing and information-gathering
protocols for WSNs. The main objectives of PEGASIS are twofold. First, the protocol aims at
extending the lifetime of a network by achieving a high level of energy efficiency and
uniform energy consumption across all network nodes. Second, the protocol strives to
reduce the delay that data incur on their way to the sink.
The network model considered by PEGASIS assumes a homogeneous set of nodes deployed
across a geographical area. Nodes are assumed to have global knowledge about other
sensors’ positions. Furthermore, they have the ability to control their power to cover
arbitrary ranges. The nodes may also be equipped with CDMA-capable radio transceivers.
The nodes’ responsibility is to gather and deliver data to a sink, typically a wireless base
station. The goal is to develop a routing structure and an aggregation scheme to reduce
energy consumption and deliver the aggregated data to the base station with minimal delay
while balancing energy consumption among the sensor nodes. Contrary to other protocols,
which rely on a tree structure or a cluster-based hierarchical organization of the network for
data gathering and dissemination, PEGASIS uses a chain structure.
6. Directed Diffusion:
Directed diffusion (Intanagonwiwat et al., 2000) is a data-centric routing protocol for
information gathering and dissemination in WSNs. The main objective of the protocol is to
achieve substantial energy savings in order to extend the lifetime of the network. To achieve
this objective, directed diffusion keeps interactions between nodes, in terms of message
exchanges, localized within limited network vicinity. Using localized interaction, direct
diffusion can still realize robust multi-path delivery and adapt to a minimal subset of
network paths. This unique feature of the protocol, combined with the ability of the nodes to
aggregate response to queries, results into significant energy savings.
Wireless Sensor Networks – Technology and Protocols
18
Figure 10. Chain-based data gathering and aggregation scheme
The main elements of direct diffusion include interests, data messages, gradients, and
reinforcements. Directed diffusion uses a publish-and-subscribe information model in which
an inquirer expresses an interest using attribute–value pairs. An interest can be viewed as a
query or an interrogation that specifies what the inquirer wants.
7. Geographic Adaptive Fidelity (GAF):
GAF (Xu et al., 2001) is an energy-aware location-based routing algorithm designed mainly
for mobile ad hoc networks, but may be applicable to sensor networks as well. The network
area is first divided into fixed zones and forms a virtual grid. Inside each zone, nodes
collaborate with each other to play different roles. For example, nodes will elect one sensor
node to stay awake for a certain period of time and then they go to sleep. This node is
responsible for monitoring and reporting data to the BS on behalf of the nodes in the zone.
Hence, GAF conserves energy by turning off unnecessary nodes in the network without
affecting the level of routing fidelity.
9. Security issues in wireless sensor network
Security issues in sensor networks depend on the need to know what we are going to protect.
In (Zia et al., 2006), the authors defined four security goals in sensor networks which are
Confidentiality, Integrity, Authentication and Availability. Another security goal in sensor
network is introduced in (Sharma et al., 2011).Confidentiality is the ability to conceal message
from a passive attacker, where the message communicated on sensor networks remain
confidential. Integrity refers to the ability to confirm the message has not been tampered,
altered or changed while it was on the network. Authentication Need to know if the messages
are from the node it claims to be from, determining the reliability of message’s origin.
Availability is to determine if a node has the ability to use the resources and the network is
available for the messages to move on. Freshness implies that receiver receives the recent and
fresh data and ensures that no adversary can replay the old data. This requirement is
Overview of Wireless Sensor Network 19
especially important when the WSN nodes use shared-keys for message communication,
where a potential adversary can launch a replay attack using the old key as the new key is
being refreshed and propagated to all the nodes in the WSN ( Sen, 2009). To achieve the
freshness the mechanism like nonce or time stamp should add to each data packet.
Having built a foundation of security goals in sensor network, the major possible security
attacks in sensor networks are identified in (Undercoffer et al., 2002) . Routing loops attacks
target the information exchanged between nodes. False error messages are generated when
an attacker alters and replays the routing information. Routing loops attract or repel the
network traffic and increases node to node latency. Selective forwarding attack influences
the network traffic by believing that all the participating nodes in network are reliable to
forward the message. In selective forwarding attack malicious nodes simply drop certain
messages instead of forwarding every message. Once a malicious node cherry picks on the
messages, it reduces the latency and deceives the neighboring nodes that they are on a
shorter route. Effectiveness of this attack depends on two factors. First the location of the
malicious node, the closer it is to the base stations the more traffic it will attract. Second is
the percentage of messages it drops. When selective forwarder drops more messages and
forwards less, it retains its energy level thus remaining powerful to trick the neighboring
nodes. In sinkhole attacks, adversary attracts the traffic to a compromised node. The
simplest way of creating sinkhole is to place a malicious node where it can attract most of
the traffic, possibly closer to the base station or malicious node itself deceiving as a base
station. One reason for sinkhole attacks is to make selective forwarding possible to attract
the traffic towards a compromised node. The nature of sensor networks where all the traffic
flows towards one base station makes this type of attacks more susceptible. Sybil attacks are
a type of attacks where a node creates multiple illegitimate identities in sensor networks
either by fabricating or stealing the identities of legitimate nodes. Sybil attacks can be used
against routing algorithms and topology maintenance; it reduces the effectiveness of fault
tolerant schemes such as distributed storage and disparity. Another malicious factor is
geographic routing where a Sybil node can appear at more than one place simultaneously.
In wormhole attacks an adversary positioned closer to the base station can completely
disrupt the traffic by tunneling messages over a low latency link. Here an adversary
convinces the nodes which are multi hop away that they are closer to the base station. This
creates a sinkhole because adversary on the other side of the sinkhole provides a better route
to the base station. In Hello flood attacks a Broadcasted message with stronger transmission
power is pretending that the HELLO message is coming from the base station. Message
receiving nodes assume that the HELLO message sending node is the closest one and they
try to send all their messages through this node. In this type of attacks all nodes will be
responding to HELLO floods and wasting the energies. The real base station will also be
broadcasting the similar messages but will have only few nodes responding to it. Denial of
service (DoS) attacks occur at physical level causing radio jamming, interfering with the
network protocol, battery exhaustion etc. An specific type of DoS attack, Denial-of-service
attack has been explored in (Raymond et al., 2009), in which a sensor node’s power supply is
targeted. Attacks of this type can reduce the sensor lifetime from years to days and have a
devastating impact on a sensor network.
Wireless Sensor Networks – Technology and Protocols
20
1. Layering based security approach:
Application layer
Data is collected and managed at application layer therefore it is important to ensure the
reliability of data. Wagner (Wanger, 2004) has presented a resilient aggregation scheme
which is applicable to a cluster based network where a cluster leader acts as an aggregator
in sensor networks. However this technique is applicable if the aggregating node is in the
range with all the source nodes and there is no intervening aggregator between the
aggregator and source nodes. To prove the validity of the aggregation, cluster leaders use
the cryptographic techniques to ensure the data reliability.
Network layer
Network layer is responsible for routing of messages from node to node, node to cluster
leader, cluster leaders to cluster leaders, cluster leaders to the base station and vice versa.
Data link layer
Data link layer does the error detection and correction, and encoding of data. Link layer is
vulnerable to jamming and DoS attacks. TinySec (Karlof et al., 2004) has introduced link
layer encryption which depends on a key management scheme. However, an attacker
having better energy efficiency can still rage an attack. Protocols like LMAC (Hoesel et al.,
2004) have better anti-jamming properties which are viable countermeasure at this layer.
Physical Layer
The physical layer emphasizes on the transmission media between sending and receiving
nodes, the data rate, signal strength, frequency types are also addressed in this layer. Ideally
FHSS frequency hopping spread spectrum is used in sensor networks.
10. Conclusion & future work
The aim of this chapter is to discuss few important issues of WSNs, from the application,
design and technology points of view. For designing a WSN, we need to consider different
factors such as the flexibility, energy efficiency, fault tolerance, high sensing fidelity, low-
cost and rapid deployment, above all the application requirements. We hope the wide range
of application areas will make sensor networks an integral part of our lives in the future.
However, realization of sensor networks needs to satisfy several constraints such as
scalability, cost, hardware, topology change, environment and power consumption. Since
these constraints are highly tight and specific for sensor networks, new wireless ad hoc
networking protocols are required. To meet the requirements, many researchers are
engaged in developing the technologies needed for different layers of the sensor networks
protocol stack.
Future research on WSN will be directed towards maximizing area throughput in clustered
Wireless Sensor Networks designed for temporal or spatial random process estimation,
accounting for radio channel, PHY, MAC and NET protocol layers and data aggregation
Overview of Wireless Sensor Network 21
techniques, simulation and experimental verification of lifetime-aware routing, sensing
spatial coverage and the enhancement of the desired sensing spatial coverage evaluation
methods with practical sensor model.
The advances of wireless networking and sensor technology open up an interesting
opportunity to manage human activities in a smart home environment. Real-life activities
are often more complex than the case studies for both single and multi-user. Investigating
such complex cases can be very challenging while we consider both single- and multi-user
activities at the same time. Future work will focus on the fundamental problem of
recognizing activities of multiple users using a wireless body sensor network.
Wireless Sensor Networks hold the promise of delivering a smart communication paradigm
which enables setting up an intelligent network capable of handling applications that evolve
from user requirements. We believe that in near future, WSN research will put a great
impact on our daily life. For example, it will create a system for continual observation of
physiological signals while the patients are at their homes. It will lower the cost involved
with monitoring patients and increase the efficient exploitation of physiological data and the
patients will have access to the highest quality medical care in their own homes. Thus, it will
avoid the distress and disruption caused by a lengthy inpatient stay.
Author details
M.A. Matin
Institut Teknologi Brunei, Brunei Darussalam
M.M. Islam
North South University, Dhaka, Bangladesh
11. References
A. Boukerche. Algorithms and Protocols for Wireless, Mobile Ad Hoc Networks, John Wiley
& Sons, Inc., 2009.
A. Manjeshwar and D. P. Agarwal, "APTEEN: A hybrid protocol for efficient routing and
comprehensive information retrieval in wireless sensor networks," Parallel and
Distributed Processing Symposium., Proceedings International, IPDPS 2002, pp. 195-
202.
A. Manjeshwar and D. P. Agarwal, "TEEN: a routing protocol for enhanced efficiency in
wireless sensor networks," In 1st International Workshop on Parallel and Distributed
Computing Issues in Wireless Networks and Mobile Computing, April 2001.
B. Paul, M. A. Matin,” Optimal Geometrical Sink Location Estimation for Two Tiered
Wireless Sensor Networks” IET Wireless Sensor Systems, vol.1, no.2, pp.74-84, June
2011,doi: 10.1049/iet-wss.2010.0073, IET UK.
Bharathidasan, A., Anand, V., Ponduru, S. (2001), Sensor Networks: An Overview,
Department of Computer Science, University of California, Davis 2001. Technical
Report
Wireless Sensor Networks – Technology and Protocols
22
C. Intanagonwiwat, R. Govindan, D. Estrin, ‘‘Directed Diffusion: A Scalable and Robust
Communication Paradigm for Sensor Networks,’’ Proceedings of the 6th ACM 226
ROUTING PROTOCOLS FOR WIRELESS SENSOR NETWORKS International
Conference on Mobile Computing and Networking (MobiCom’00), Boston, MA, Aug.
2000, pp. 56–67.
C. karlof, N. Shastry and D. Wagner, TinySec: A link layer security architecture for wireless
sensor networks, SenSys’04, November 3-5 2004, Baltimore, Maryland, USA
Chiara, B.; Andrea, C.; Davide, D.; Roberto, V. An Overview on Wireless Sensor Networks
Technology and Evolution. Sensors 2009, 9, 6869-6896.
Chi-Tsun Cheng, Chi K. Tse, and Francis C. M. Lau, “A Delay-Aware Data Collection
Network Structure for Wireless Sensor Networks”, IEEE Sensors Journal, Vol. 11, No. 3,
pp. 699-710, March 2011.
D. Braginsky, D. Estrin, ‘‘Rumor Routing Algorithm for Sensor Networks,’’ Proceedings of
the 1st Workshop on Sensor Networks and Applications (WSNA’02), Atlanta, GA, Oct.
2002.
D. Wagner, Resilient aggregation in sensor networks, In Proceedings of the 2nd ACM
workshop on Security of ad hoc and sensor networks. ACM Press, 2004, pp. 78-87.
D.R. Raymond, R.C. Marchany, M.I. Brownfield, and S.F. Midkiff. Effects of Denial-of-Sleep
Attacks on Wireless Sensor Network MAC Protocols. IEEE Transactions on Vehicular
Technology, vol. 58, no. 1, pp. 367-380, 2009.
Fabbri, F.; Buratti, C.; Verdone, R.; Riihij¨arvi, J.; M¨ah¨onen, P. Area Throughput and
Energy Consumption for Clustered Wireless Sensor Networks. In Proceedings of IEEE
WCNC 2009, Budapest, Hungary, 2009
Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication
protocol for wireless micro-sensor networks. In: Proceedings of the 33rd International
Conference on System Sciences (HICSS), pp. 1–10 (2000)
I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. Wireless sensor networks: A
survey. Computer Networks, 38(4):393–422, 2002.
I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci. A survey on sensor networks. IEEE
Communications Magazine. 40 (8) (2002) 102–114.
J. Kulik, W. R. Heinzelman, H. Balakrishnan, ‘‘Negotiation-Based Protocols for
Disseminating Information in Wireless Sensor Networks,’’ Wireless Networks, Vol. 8,
2002, pp. 169–185.
J. Pan, Y. Hou, L. Cai, Y. Shi and S. X. Shen, ‘Topology Control for Wireless Sensor
Networks’ Proc. 9th ACM Int. Conf. on Mobile Computing and Networking, San Diego,
USA, September, 2003, pp. 286-29.
J. Sen. A survey on wireless sensor network security. International Journal of
Communication Networks and Information Security (IJCNIS), 1(2):59–82, August 2009.
J. Undercoffer, S. Avancha, A. Joshi, and J. Pinkston, Security for sensor networks, 2002
CADIP Research Symposium.
J. Yick, B. Mukherjee, D. Ghosal. Wireless sensor network survey, Computer Networks 52
(12) (2008) 2292–2330.
Overview of Wireless Sensor Network 23
J.N. Al-Karaki, A.E. Kamal, Routing techniques in wireless sensor networks: a survey, IEEE
Wireless Communications (2004).
K. Akkaya, M. Younis, A survey on routing protocols for wireless sensor networks, Elsevier
Journal of Ad Hoc Networks 3 (3) (2005) 325–349.
L.V. Hoesel and P. Havinga, A Lightweight Medium Access Protocol (LMAC) for wireless
sensor networks: reducing preamble transmissions and transceiver state switches, in the
proceedings of INSS, June 2004.
M A Matin, and Md. Nafees Rahman, “Lifetime improvement of Wireless Sensor Networks”
3rd IEEE International conference on Communication Software and Networks (ICCSN)
2011, Xi'an, China, May 27-29, 2011, pp.475-479.
M. A. Labrador, P. M. Wightman. Topology Control in Wireless Sensor Networks. Springer
Science + Business Media B.V. 2009.
Ns-2 [Online]. Available: http://www.isi.edu/nsnam/ns/
S Sharma and S K Jena, "A Survey on Secure Hierarchical Routing Protocols in Wireless
Sensor Networks", ICCCS’11 February 2011.
S. Lindsey, C. Raghavendra, ‘‘PEGASIS: Power-Efficient Gathering in Sensor Information
Systems,’’ IEEE Aerospace Conference Proceedings, 2002, Vol. 3, No. 9–16 pp. 1125–
1130.
S. Waharte, R. Boutaba, Y. Iraqi, and B. Ishibashi, “Routing protocols in wireless mesh
networks: challenges and design considerations,” Multimedia Tools Appl., vol. 29, no.
3, pp. 285–303, 2006.
SensorSim [Online]: Available: http://nesl.ee.ucla.edu/projects/sensorsim/
Sohraby, K.; Minoli, D.; Znati, T. Wireless Sensor Networks: Technology, Protocols and
Applications; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2007.
TOSSIM [Online]. Available: http://docs.tinyos.net/index.php/TOSSIM
Verdone, R.; Dardari, D.; Mazzini, G.; Conti, A. Wireless Sensor and Actuator Networks;
Elsevier: London, UK, 2008.
Wilson, J. Sensor Technology Handbook; Elsevier/Newnes: Burlington, MA, USA, 2005.
X. Chen and N. Rowe, "An Energy-Efficient Communication Scheme in Wireless Cable
Sensor Networks", Proc. of IEEE International Conference on Communications (IEEE
ICC), June 2011
X. Han, X. Cao, E. L. Lloyd and C. Shen, “Fault-Tolerant Relay Node Placement in
Heterogeneous Wireless Sensor Networks”, IEEE Transaction on Mobile Computing,
Vol. 9, No. 5, May 2010
X. Zeng, R. Bagrodia, and M. Gerla, “GloMoSim: A library for parallel simulation of large-
scale wireless networks,” SIGSIM Simulation Digest, vol. 28, no. 1, pp. 154-161, 1998.
Y. Xu, J. Heidemann, D. Estrin, ‘‘Geography-informed Energy Conservation for Ad-hoc
Routing," In Proceedings of the Seventh Annual ACM/IEEE International Conference
on Mobile Computing and Networking 2001, pp. 70-84.
Younis, O., Fahmy, S. HEED: a hybryd, energy-efficient, distributed clustering approach for
ad hoc sensor networks. IEEE Trans. Mobile Comput. 3(4), 366–379 (2004)
Wireless Sensor Networks – Technology and Protocols
24
Zia, T.; Zomaya, A., “Security Issues in Wireless Sensor Networks”, Systems and Networks
Communications (ICSNC) Page(s):40 – 40, year 2006.
... Even though WSNs have advanced a lot in recent years, they are still limited in what they can do because of the challenges they face. WSNs transmit their data with wireless links with no infrastructure and are prone to many failures, such as hardware failure or physical damage due to harsh environment [4]. There are many hardware constraints that WSNs face, such as limited power supply, low processing, and below average transmission units. ...
... 3. Find data points in the dataset that fall outside the calculated upper and lower limits. 4. Create a subset containing only the data points that are inside the calculated upper and lower limits. ...
... In range-based techniques, the unknown nodes' position is determined by calculating the distance between the anchor and the unknown sensor nodes (Singh et al. 2020a). They employ a variety of measuring methods, such as Received Signal Strength Indication (RSSI), time of arrival, and angle of arrival (Matin et al. 2012). On the other hand, range-free algorithms like centroid use basic operations and ad-hoc positioning systems that are connected to the local network ...
Article
Full-text available
This article provides a useful way for figuring out the ideal network parameters that deliver a low Average Localization Error (ALE) using a machine learning (ML) approach according to the models of Support Vector Regression (SVR), CatBoost Regression (CAT), and Random Forest Regression (RFR). One of the key issues in Wireless Sensor Networks (WSNs) is node localization. The coordinates of unknown nodes are estimated using anchor nodes, which are sensors with known coordinates. Numerous algorithms with bioinspired designs have been put forward to accurately estimate the unknown nodes. Nevertheless, applying bio-inspired algorithms takes a lot of time. As a result, it is still challenging to quickly and accurately identify the optimal network configuration for node localization during the network setup process. For quick and precise ALE prediction, an optimization technique based on Giant Trevally Optimizer (GTO) is provided. Iterations, node density, transmission range, anchor ratio, and transmission range are features for ALE forecasting and training. These attributes are taken from simulations of Cuckoo Search (CS) that have been altered. Based on a root mean square error (RMSE) of 0.037 and a correlation coefficient (R²) of 0.995 m, the research shows that CAGT performs very well compared to all other approaches.
... WSNs (Wireless Sensor Networks) are self-configured interconnected sensor nodes that communicate with each other to gather data about the surrounding setting and monitor physical and environmental conditions for further analysis and interpretation [38]. Every sensor node is equipped with at least one or more than one sensor, a processor, a power supply, and a radio transceiver section [10]. ...
Article
Soil moisture plays a major role in agricultural yield, hydrological, and environmental monitoring studies. Soil-moisture’s accurate estimation is important for successfully improving crop yield efficiently using agricultural resources and effectively managing water resources. The current article aims to list different approaches to estimating soil moisture such as using microwave, radar, and sensor-based methods of electronic and communication engineering and also IoT-based methods from computer engineering fields to find the journey of use of state-of-the-art technology methods for measuring soil moisture. Out of the mentioned methods, Sensor & IoT-based method is playing a vital role and is currently being used in advanced countries. Therefore this research is focused on experimenting the water flow control and measurement of soil moisture using sensors on micro-controller-based prototypes. Experiments showed that it is feasible to estimate the requirement of water more accurately than traditional methods. Thus more water savings can be benefited.
... Wireless sensor networks (WSNs) are found in different areas of our lives, including medicine, engineering, industry, monitoring, and military purposes [1,2]. WSNs have many challenges that must be addressed to improve the network's overall performance. ...
Article
Full-text available
Metaheuristic algorithms have wide applicability, particularly in wireless sensor networks (WSNs), due to their superior skill in solving and optimizing many issues in different domains. However, WSNs suffer from several issues, such as deployment, localization, sink node placement, energy efficiency, and clustering. Unfortunately, these issues negatively affect the already limited energy of the WSNs; therefore, the need to employ metaheuristic algorithms is inevitable to alleviate the harm imposed by these issues on the lifespan and performance of the network. Some associated issues regarding WSNs are modelled as single and multi-objective optimization issues. Single-objective issues have one optimal solution, and the other has multiple desirable solutions that compete, the so-called non-dominated solutions. Several optimization strategies based on metaheuristic algorithms are available to address various types of optimization concerns relating to WSN deployment, localization, sink node placement, energy efficiency, and clustering. This review reports and discusses the literature research on single and multi-objective metaheuristics and their evaluation criteria, WSN architectures and definitions, and applications of metaheuristics in WSN deployment, localization, sink node placement, energy efficiency, and clustering. It also proposes definitions for these terms and reports on some ongoing difficulties linked to these topics. Furthermore, this review outlines the open issues, challenge paths, and future trends that can be applied to metaheuristic algorithms (single and multi-objective) and WSN difficulties, as well as the significant efforts that are necessary to improve WSN efficiency.
Chapter
Many agriculture industries in Malaysia are small and have a low adoption level of modern information technologies and automation, which restricts data analysis and its use. The traditional data collection method implemented in current farming practices is hardly integrated, and the data could not be analyzed promptly. This research presents an integrated approach to track and trace overall agricultural production through a web-based IoT monitoring application. Two experiment setups were conducted in this work. One is a customized-developed IoT system prototype at the Tenth College (K10) of Universiti Putra Malaysia, and another is an IoT-equipped weather station at the College of Bioresources, National Ilan University Taiwan. The data collected from these two study areas were displayed in a web dashboard with interactive functions using a Python web analytics framework called Dash. It visualizes the collected real-time environmental data through the Dash platform, including humidity, temperature, soil moisture content, wind rate, and rainfall parameters. The web dashboard can be updated dynamically and loaded onto any type of web server. The web applications showed an average overall performance score in Google PageSpeed Insights and Google Lighthouse in terms of speed, connectivity, and accessibility. The statistical analysis of the T-test showed no significant difference (P > 0.05) in performance for the web applications developed for both IoT systems.
Conference Paper
In this paper, the optimization strategies of routing protocols are analysed with respect to energy utilization of sensor nodes in Wireless Sensor Networks (WSNs). Routing Protocols are in charge of discovering and maintaining the routes in the network. Different routing mechanisms have been proposed to address energy optimization problem in sensor nodes. Clustering mechanism is one of the most efficient mechanisms which cater to the requirements of energy conservation in wireless sensor networks. To check the efficiency of different clustering scheme against modelled constraints, we select five cluster based routing protocols; Low Energy Adaptive Clustering Hierarchy (LEACH), Threshold Sensitive Energy Efficient sensor Network (TEEN), Stable Election Protocol (SEP), Distributed Energy Efficient Clustering (DEEC), and Hybrid Energy Efficient Distributed protocol. To validate our mathematical framework, we perform analytical simulations in MATLAB by choosing number of alive nodes, number of dead nodes, number of packets, number of cluster heads, as performance metrics.
Book
Learn the fundamental algorithms and protocols for wireless and mobile ad hoc networks. Advances in wireless networking and mobile communication technologies, coupled with the proliferation of portable computers, have led to development efforts for wireless and mobile ad hoc networks. This book focuses on several aspects of wireless ad hoc networks, particularly algorithmic methods and distributed computing with mobility and computation capabilities. It covers everything readers need to build a foundation for the design of future mobile ad hoc networks: Establishing an efficient communication infrastructure. Robustness control for network-wide broadcast. The taxonomy of routing algorithms. Adaptive backbone multicast routing. The effect of inference on routing. Routing protocols in intermittently connected mobile ad hoc networks and delay tolerant networks. Transport layer protocols. ACK-thinning techniques for TCP in MANETs. Power control protocols. Power saving in solar powered WLAN mesh networks. Reputation and trust-based systems. Vehicular ad hoc networks. Cluster interconnection in 802.15.4 beacon enabled networks. The book is complemented with a set of exercises that challenge readers to test their understanding of the material. Algorithms and Protocols for Wireless and Mobile Ad Hoc Networks is appropriate as a self-study guide for electrical engineers, computer engineers, network engineers, and computer science specialists. It also serves as a valuable supplemental textbook in computer science, electrical engineering, and network engineering courses at the advanced undergraduate and graduate levels.
Article
When choosing the technology options to develop a wireless sensor network (WSN), it is vital that their performance level can be assessed for the type of application intended. This book describes the different technology options MAC protocols, routing protocols, localisation and data fusion techniques and provides the means to numerically measure their performance, whether by simulation, mathematical models or experimental test beds. The book seeks to answer vital questions when developing a WSN: how long will my network remain alive given the amount of sensing required of it?; how long should I set the sleeping state of my motes?; how many sensors should I distribute per square meter to meet the expected requirements of the application?; what type of throughput should I expect as a function of the number of nodes deployed and the radio interface chosen (whether it be Bluetooth or Zigbee)?; how is the Packet Error Rate of my Zigbee motes affected by the selection of adjacent frequency sub bands in the ISM 2.4GHz band?; how is the localisation precision dependant on the number of nodes deployed in a corridor? Case studies based on the authors experience of implementing WSNs describe the design methodology and the type of measurements carried out, and give samples of the performance measurements attained. The capabilities of commercially available tools for protocol design are also presented. *Only book to examine wireless sensor network technologies and assess their performance capabilities against possible applications *Enables the engineer to choose the technology that will give the best performance for the intended application *Case studies, based on the authors direct experience of implementing wireless sensor networks, describe the design methodology and the type of measurements used, together with samples of the performance measurements attained.
Book
Without sensors most electronic applications would not existthey perform a vital function, namely providing an interface to the real world. The importance of sensors, however, contrasts with the limited information available on them. Today's smart sensors, wireless sensors, and microtechnologies are revolutionizing sensor design and applications. This volume is an up-to-date and comprehensive sensor reference guide to be used by engineers and scientists in industry, research, and academia to help with their sensor selection and system design. It is filled with hard-to-find information, contributed by noted engineers and companies working in the field today. The book will offer guidance on selecting, specifying, and using the optimum sensor for any given application. The editor-in-chief, Jon Wilson, has years of experience in the sensor industry and leads workshops and seminars on sensor-related topics. In addition to background information on sensor technology, measurement, and data acquisition, the handbook provides detailed information on each type of sensor technology, covering: technology fundamentals sensor types, w/ advantages/disadvantages manufacturers selecting and specifying sensors applicable standards (w/ urls of related web sites) interfacing information, with hardware and software info design techniques and tips, with design examples latest and future developments The handbook also contains information on the latest MEMS and nanotechnology sensor applications. In addition, a CD-ROM will accompany the volume containing a fully searchable pdf version of the text, along with various design tools and useful software.
Conference Paper
The fundamental challenge of a wireless sensor network is to maximize the network lifetime. To overcome this challenge, one of the possible solutions is to find optimal sink location. In this paper, we have introduced relay nodes to mitigate network geometric deficiencies and the Particle Swarm Optimization (PSO) based algorithm has been used to locate the optimal sink position with respect to those relay nodes to make the network more energy efficient. The relay nodes are responsible to communicate with the sink instead of the sensor nodes. Experimental results show that our current approach further improves the lifetime of the network.