Download Evaluation of Wireless Sensor Networks (WSN) for various

Evaluation of Wireless Sensor Networks (WSN)
for various applications: A Practical approach
Objective
The design of sustainable wireless sensor networks (WSNs) is a very challenging issue. On the
one hand, energy-constrained sensors are expected to run autonomously for long periods.
However, it may be cost-prohibitive to replace exhausted batteries or even impossible in hostile
environments. On the other hand, unlike other networks, WSNs are designed for specific
applications which range from small-size healthcare surveillance systems to large-scale
environmental monitoring. Thus, any WSN deployment has to satisfy a set of requirements that
differs from one application to another. A large number of scheduling algorithms have been
proposed to reduce the energy consumption at all levels of the wireless sensor networks. In this
context, this work is related to optimization of energy efficiency in consideration between
application requirements and lifetime extension by using energy saving algorithms.
Literature Review
Wireless sensor networks (WSNs) is an emerging technology that has many current and future
envisioned applications, such as environment monitoring, battlefield surveillance, health care,
and home automation. A wireless sensor network is composed of a large number of
geographically distributed sensor nodes. Though each sensor is characterized by low power
constraint and limited computation and communication capabilities due to various design
considerations such as small size battery, bandwidth and cost, potentially powerful networks can
be constructed to accomplish various high-level tasks via sensor cooperation [1], such as
distributed estimation, distributed detection, and target localization and tracking.
Recent advances in micro-electro-mechanical systems (MEMS), low power and highly
integrated digital electronics have led to the development of micro sensors. A wireless sensor
network consists of sensor nodes deployed over a geographical area for monitoring physical
phenomena like temperature, humidity, vibrations, seismic events, and so on [2]. Typically, a
sensor node is a tiny device that includes three basic components: a sensing subsystem for data
acquisition from the physical surrounding environment, a processing subsystem for local data
processing and storage, and also a wireless communication subsystem for data transmission. In
addition, a power source supplies the energy needed by the device to perform the programmed
tasks.
This power source often consists of a battery with a limited energy budget. The development of
wireless sensor network was originally motivated by military applications like battlefield
surveillance. However, WSNs are now used in many civilian application areas including the
environment and habitat monitoring due to various limitations arising from their inexpensive
nature, limited size, weight and ad hoc method of deployment; each sensor has limited energy.
Moreover, it could be inconvenient to recharge the battery, because nodes may be deployed in a
hostile or impractical environment. At the network layer, the intention is to find ways for energy
efficient route setup and reliable relaying of data from the sensor nodes to the sink, in order to
maximize the lifetime of the network [3]. The major differences between the wireless sensor
network and the traditional wireless network sensors are very sensitive to energy consumption.
Moreover, the performance of the sensor network applications highly depends on the lifetime of
the network [4].We adopt as a common lifetime definition the time; when the first sensor dies.
This lifetime definition, proposed in [5], is widely utilized in the sensor network research field.
An alternative lifetime definition that has been used is the time at which a certain percentage of
total nodes run out of energy. This definition is actually quite similar in nature to the one we use
here. In a well-designed network, the sensors in a certain area exhibit similar behaviors to
achieve energy balance. In other words, when one sensor dies, it can be expected the neighbors
of this node will run out of energy very soon, since they will have to take over the
responsibilities of that sensor and we expect the lifetime of several months to be several years.
Thus, energy saving is crucial in designing life time wireless sensor networks [6].
Proposed research work
Energy is a very scarce resource for such sensor systems and has to be managed wisely in order
to extend the life of the sensor nodes for the duration of a particular mission. Energy
consumption in a sensor node could be due to either “useful” or “wasteful” sources. Useful
energy consumption can be due to transmitting or receiving data, processing query requests, and
forwarding queries and data to neighboring nodes. Wasteful energy consumption can be due to
one or more of the following facts. One of the major sources of energy waste is idle listening,
that is, (listening to an idle channel in order to receive possible traffic) and secondly reason for
energy waste is collision (When a node receives more than one packet at the same time, these
packets are termed collided), even when they coincide only partially. All packets that cause the
collision have to be discarded and retransmissions of these packets are required which increase
the energy consumption. The next reason for energy waste is overhearing (a node receives
packets that are destined to other nodes). The fourth one occurs as a result of control-packet
overhead (a minimal number of control packets should be used to make a data transmission).
Finally, for energy waste is over-emitting, which is caused by the transmission of a message
when the destination node is not ready. Considering the above-mentioned facts, a correctly
designed protocol must be considered to prevent these energy wastes.
Based on the above issue and power breakdown, several approaches have to be exploited, even
simultaneously, to reduce the power consumption in wireless sensor networks. At a very general
level, we identify two main energy efficient techniques namely: Data reduction and Sleep/wake
up schemes.
Methodology
Improving lifetime is directly related to Energy efficiency which is the most required quality in a
sensor network where each node consumes some energy with each transmission over the
network. The proposed work defined the same direction to improve the network life. This work
is about to perform the energy effective routing so that the network life and network throughput
will be improved. In this work the effects of acquiring, processing, and communicating
Compressive Sensing-based measurements on WSN lifetime will be analyzed in comparison to
conventional approaches. The energy dissipation models for both CS and conventional
approaches are built and used to construct a mixed integer programming framework that jointly
captures the energy costs for computation and communication for both CS and conventional
approaches. The numerical analysis is performed by systematically sampling the parameter space
(i.e., sparsity levels, network radius and number of nodes). The problem taken for this research
work is divided into some objectives which are as follows.
• Study of Wireless Sensor Network.
• Study of different energy efficient protocols in WSN.
• To minimize the energy consumption of sensors.
• To improve network lifetime and network throughput.
References
[1] J. Yick, B. Mukherjee, D. Ghosal, Wireless sensor network survey, Computer networks 52
(2008) 2292–2330.
[2] Tifenn Rault, Abdelmadjid Bounabdllah, Yacine Challah, Energy efficiency in wireless
sensor networks: a top –down survey, Computer Networks, Elsevier, 2014, 67 (4), pp.104-122.
[3] Daisuke Takaishi, Hiroki Nishiyama, Nei Kato, and Ryu Miura, “Towards Energy Efficient
Big
Data
Gathering
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Networks”,
DOI
10.1109/TETC.2014.2318177, IEEE Transactions on Emerging Topics in Computing, 2014.
[4] Harb, H., MAKHOUL, A. , Tawil, R. , Jaber, A., “Energy-efficient data aggregation and
transfer in periodic sensor networks” IEEE Sensor journal, VOL. 15, NO. 6, JUNE 2015,
Page(s): 684 – 692
[5] Mini, S., Udgata S.K. , Sabat, S.L., “ Sensor Deployment and Scheduling for Target coverage
Problem in Wireless Sensor Networks”,IEEE Sensor journal , 2014 , Page(s): 636 - 644
[6] Farouk, F. ; Rizk, R. ; Zaki, F.W. ,” Multi-level stable and energy-efficient clustering
protocol in heterogeneous wireless sensor networks”, IEEE Sensor journal ,2014 , Page(s): 159 169