Energy-efficient strategies for wireless sensor networks

Mohammed Bakhtawar Ahmed*, Sandeep Gonnade, Deepak Xaxa

Department of Computer Science and Engineering, MATS University, Aarang, Raipur

*Corresponding Author Email: bakhtawar229@gmail.com

ABSTRACT:

Design of large scale wireless sensor networks WSN has become cost effective with the advances in inexpensive sensor technology and wireless communications and attracts the attention of a wide range of applications such as health and environmental monitoring and battlefields surveillance. Wireless Sensor Networks (WSNs) consists of huge number of Sensor Nodes (SNs) with sensing, communication and processing capabilities. SNs have limited energy supply, storage and computational capacity. In recent years energy efficient computation is a major concern in WSN. The critical aspects include reduction in the energy consumption of SNs so that the network lifetime can be extended to reasonable times. For this purpose many novel innovative techniques based on energy efficient computation have been proposed. In this paper, we present a brief analysis on energy efficient computation protocols. We have also presented a comparison of these protocols The main challenge in the design of wireless sensor networks is the limited battery power of the sensors and the difficulty of replacing and/or recharging these batteries due to the nature of the monitoring field and cost to ease this problem it is necessary that the sensors be densely deployed and appropriate protocols be designed in order to identify this redundancy while maximizing the lifetime of the network .Several protocols have been proposed in the literature with a goal to save the sensors energy. All those proposed algorithms aim to design energy efficient protocol for WSN .However these protocols consider the various issues of WSN separately.

KEYWORDS: WSN, AEC, Energy management, MPM,GAF

INTRODUCTION:

Energy management is a critical issue in Wireless Sensor Networks that need urgent attention. An energy source supplies the energy needed by the device to achieve the programmed assignment. This energy source often consists of a battery with a limited energy plan. In addition, it could be unfeasible or difficult to recharge the battery, because nodes may be deployed in a hostile or unpractical environment. On the other hand, the sensor network should have a life span long enough to fulfill the application requirements. Therefore, the crucial issue is to prolong the network’s lifetime. This paper has paid careful attention to the energy management in wireless sensor networks. The work has attempted to maximize the energy conservation. More specifically, the work dealt with only on energy management. This study recapitulates the effectiveness of the proposed systems. As energy management is executed from several distinct points, it is difficult to guarantee that a single point energy management would give a better solution. Energy management at multiple points needs to interface and cooperate with one another and appropriate methods have been attempted through this study [1].

Performance Analysis:

In this research, Wireless Sensor Networks have been established and various energy efficient schemes have been analyzed. Then, the clxii performance analysis of energy efficient node deployment has been done using single and multi robot scheme. Energy consumption has been measured for various node densities. The energy conservation of multi robot scheme is observed to be better than that of single robot scheme. Energy conservation of multi robot deployment scheme has been found to be 4% better than the single robot deployment scheme. A limitation of this scheme is the deployment cost incurred on using many robots. In order to improve the energy conservation further, a novel scheme called aggregation has been proposed. The analyses have been performed for different input samples. Aggregation routing scheme for Mobile relay has been found to be 21% better than the static sensor nodes. A constraint of this scheme is the mobile relay which needs to stay only within a two-hop radius of the sink. To improve the energy conservation further, a scheme called Rays based approach has been proposed. Energy conservation of Diagonal area and copies coverage based Increasing Ray Search scheme has been found to be 26% better than the area and copies coverage based Increasing Ray Search scheme. Increasing ray search searches rays sequentially one after the other and hence the latency incurred will be very high. This is the limitation of this scheme. The voting schemes have been applied to reduce the energy consumption in WSN. It is evident from the results that consumption has been reduced. Energy conservation of witness based voting scheme has been found to be 34% better than the one round voting scheme. A limitation of this scheme is that it has a notable amount of delay .The research has further investigated the energy conservation of WSN by providing the polling scheme. The analyses have been performed for clxiii different input samples. Based on the results obtained for the different test cases, the polling scheme for sector partitioning is 51 % better than the clustering scheme in energy conservation.

As per the analysis from the five schemes, polling scheme is very much effective in terms of reducing the energy consumption in Wireless Sensor Networks. Figure 1 depicts the performance of various energy efficient schemes that are analyzed for Wireless sensor networks. The research has further investigated the energy conservation of WSN by providing the polling scheme. The analyses have been performed for clxiii different input samples Works.

Figure 1: Performance analysis of energy efficient schemes

WSN is typically expected to work for a long period of time. Sensors in some regions might fail because of energy exhaustion. The main area of future work lies in developing more energy efficient algorithms. Another area of future work may focus on transfer energy by wireless means using diverse mechanisms like Laser beam, piezoelectric clxiv principle, radio waves and microwaves, Inductive coupling and electromagnetic resonance[2]. This would be of more practical interest. Careful attention may be paid on further reduction of energy consumption in wireless sensor networks. In summary, this study introduced an energy efficient scheme that reduces the energy consumption and leads to increase the lifetime of wireless sensor networks. The possibilities are endless in this field of study.

Minimum Power Management:

Initially, Minimum Power Management (MPM) approach is proposed to enhance the lifetime of a WSN. The objective of MPM is to reduce the energy consumed in each of the radio states (transmission/ reception/ idle/ sleep) so that the average energy consumption of all nodes will be minimized [2]. In this approach Geographic Adaptive Fidelity (GAF) is used for scheduling of nodes to sleep, and for further optimization Minimum Power Management Protocol (MPMP) and Minimum Active Subnet Protocol (MASP) are used for routing of packets in an energy efficient manner[3]. The main objectives of MPM are: (1) When network activities are low, the idle power dominates the total energy consumption of a network. In this case, scheduling nodes to sleep saves the most energy. MPM uses the long communication range between any two nodes. (2) When network activities are high, the transmission energy dominates the total energy consumption of a network. Since transmission power increases quickly with distance, MPM uses shorter communication ranges and transmits data through multiple nodes to save energy.

Energy consumption model:

Energy model specifies the energy consumption by a node during various operations such as radio transmission, reception, sensing, and computing. Energy spent for sensing or computation in wireless networks is much lesser than the energy spent for transmission and reception. Therefore, we adopt the First Order Radio model considering energy spent for radio transmission, reception and a distance square energy loss for channel transmission [4]. In this radio model, energy consumed by a node for transmitting m-bit data over a distance Rc is Et x (m, Rc) = m(eelec + eamp × R2 c ) = m×et where et = eelec+eamp×R2 c is energy spent for transmitting one bit of data, The corresponding energy consumed for receiving m-bit data is Er x (m) = m × eelec = m × er, where er = eelec is energy required to receive one bit of data.

NS-2

Performance of the proposed energy conservation schemes were evaluated using NS-2, the most widely used network simulator. Simulation result shows that MPM provides the satisfactory performance under different radio states i.e., MPMP conserves significantly more energy than existing approaches since existing approaches treat different radio states as separate issue. MASP has a lower overhead than MPMP since MASP does not depend on information about the current set of sources and their data rates; it depends only on node state. But, its energy performance depends on the power states of the radio. Hence, MASP is only suitable for radios with high idle power.

Simulation result shows that high coverage efficiency was achieved with minimized set of relay nodes[5]. Even when the number of relay nodes is being decreased, packet transmission takes place without large number of packet drops. It has been found that IPSD scheme is able to have delivery rate of more than 98% with varying coverage ratio, thus ensuring connectivity. Simulation result also shows that IPSD scheme ensures enhanced lifetime with minimum number of relay nodes in the network.

REFERENCES :

1. Aziz, A. A., Sekercioglu, Y. A., Fitzpatrick, P., and Ivanovich, M. (2013). A survey on distributed topology control techniques for extending the lifetime of battery powered wireless sensor networks. IEEE Communications Surveys and Tutorials, 15, 121–144.

2. Cotuk, H., Bicakci, K., Tavli, B., and Uzun, E. (2014). The impact of transmission power control strategies on lifetime of wireless sensor networks. IEEE Transactions on Computers, 63, 2866–2879.

3. Liu, X. (2015). An optimal-distance-based transmission strategy for lifetime maximization of wireless sensor networks. IEEE Sensors Journal, 15, 3484–3491.

4. Liu, X. (2016). A novel transmission range adjustment strategy for energy hole avoiding in wireless sensor networks. Journal of Network and Computer Applications. doi:10.1016/j.jnca.2016.02. 018.

5. Wang, Y., and Tan, H. (2016). Distributed probabilistic routing for sensor network lifetime optimization. Wireless Networks. doi:10. 1007/s11276-015-1012-2.

Received on 21.05.2016 Accepted on 04.06.2016

Int. J. Tech. 2016; 6(1): 01-03

DOI: 10.5958/2231-3915.2016.00001.8