Termite Hill Protocol for Network Lifetime in Wireless Sensor Networks: Review of Selected Techniques

International Journal of Emerging Technology and Advanced Engineering

453

Termite Hill Protocol for Network Lifetime in Wireless Sensor

Networks: Review of Selected Techniques

Amandeep Singh

, Sunny Behal

Department of Computer Science & Engineering, Saheed Bhagat Singh State Technical Campus, Ferozepur, India 2 _{Asst.Prof. Department of Computer Science & Engineering, Saheed Bhagat Singh State Technical Campus, Ferozepur, India}

Abstract - Improving network lifetime and reliability are the fundamental challenges in Wireless Sensor Networks (WSNs). Most of the research in this area has focused on energy-efficient solutions, but has not thoroughly analyzed the network performance, e.g. in terms of data collection rate and time.

A critical aspect of applications with wireless sensor networks is network lifetime. Battery-powered sensors are usable as long as they can communicate captured data to a processing node. Sensing and communications consume energy, therefore judicious power management and scheduling can effectively extend operational time. In this paper, we are reviewing some selected techniques or papers which are best describing the lifetime efficiency of the WSN. Paper provides base to researchers who are new in the field of WSN Specially who are instreated in the network lifetime of WSN.

I. INTRODUCTION

Wireless sensor networks provide new applications for environment monitoring, and military surveillance applications. Recent developments in hardware miniaturization combined with low-cost mass production and advances in wireless communications technologies have made possible applications with large numbers of sensors. In some cases ground access to the area of the objectives to be monitored is difficult or dangerous, so one solution to install the sensors is to deploy them from an aircraft. Without precise positioning, the only way to provide adequate target coverage by sensors is to use more sensors than the optimal number. Large sensor density will increase the probability of target coverage, considering that sensors may be randomly dispersed in the targets‟ proximity.[1]

Wireless sensor networks (WSNs) are collections of compact-size, relatively inexpensive computational nodes that measure local environmental conditions, or other parameters and forward such information to a central point for appropriate processing. Many applications of sensor networks deals with the static nature of nodes which in most cases sense their environment and then send the measured values to a central base station through hop-to-hop (multihop-to-hop) routing, hence leading to rapid exhaustion of energy around the sink (base station).

The issue is that, sensor nodes around the sink tend to deplete faster in energy than those farther away. This is mainly because, besides forwarding their own traffic, they forward traffic on behalf of other sensor nodes that are located farther away from the sink node. Due to the high depletion in energy, sensor nodes closer to the sink will drain their energy resources faster than other nodes which will limit their lifetime. Hence, the lifetime of sensor network can be improved upon if the energy spent in traffic relaying to the sink is reduced. The use of mobile sink that can collect information from the sensor network while moving within the monitored area can lessen energy dissipation of those nodes closer to the sink.[2]

Several researches in the area of routing in WSN towards improving the network lifetime, focus on static applications. Recently, mobile sink was considered by [1] to improve the network lifetime. Other protocols which supports mobile sink are Improved Energy Efficient Ant Based Routing algorithm (IEEABR) [2], Flooded Forward ant routing FF [3], Ad-hoc On-demand Distance Vector (AODV) [3], and Sensor-driven and Cost-aware ant routing (SC) [3] among others.

Literature Review

Hwa Young Lim et al., in this paper, they propose a routing protocol called Maximum Energy routing protocol based on Strong Head (MESH) in order to solve the problems of LEACH. The key idea of our protocol is that the collected data from cluster head is transmitted to SINK by node defined as a „Strong Head‟. They evaluate the performance of protocol through simulations. Simulation results show that protocol outperforms the LEACH in terms of the energy consumption and lifetime of the network. [4]

International Journal of Emerging Technology and Advanced Engineering

454 Chi-Tsun Cheng et al. In this paper, 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. Two network formation algorithms are designed to construct the proposed network structure in a centralized and a decentralized approach. Performances of the proposed network structure are evaluated using computer simulations. Simulation results show that, when comparing with other common network structures in wireless sensor networks, the proposed network structure is able to shorten the delays in the data collection process significantly.[6]

Maciej Nikodem et al. This paper focuses on the theoretical aspects of clustering in wireless sensor networks, as a mean to improve network lifetime. We investigate whether clustering itself (with no data aggregation) can improve network lifetime in particular application when compared to non-clustered networks. We use integer linear programming to analyze 1D and 2D networks, taking into account capabilities of real-life nodes. Our results show that clustering itself cannot improve network lifetime so additional techniques and means are required to be used in synergy with clustering.[7]

Lıliam Barroso Leal et al. The approach proposed by this paper presents the application of Genetic Fuzzy System (GFS) for the selection of routes in WNSs, in order to make the communication between multiple sensor nodes and multiple sink nodes. Fuzzy Inference System of Mamdani are used to determine the most appropriate sink node through consideration of some characteristics of the sensors network, such as energy and number of hops. Genetic Algorithms are employed to obtain the optimal adjustment of Mamdani‟s fuzzy inference system parameters. By applying GAs, we intend to achieve both a fuzzy database and a fuzzy rules base to maximize performance of the application of Mamdani‟s inference system in the selection of routes in Wireless Sensor Networks.[8]

Yan Shen, Hui Ju et al. In this paper a new energy-saving task assignment method is proposed. This method describes a cost function based on entropy theory according to the nodes status, such as computing resources, the residual energy and the number of neighbors.

Meanwhile, particle swarm optimization algorithm is used to optimize dynamic task assignment. The tasks can be dynamic adjusted due to sensor network changing. The simulation results show that the execution time and energy consumption can be decreased and the lifetime of wireless sensor network is prolonged with the proposed method.[9]

F.J. Atero et al. In this paper they propose a new architecture called HARP, a Hierarchical Adaptive and Reliable Routing Protocol, a clustering algorithm which builds intercluster and intra-cluster hierarchical trees, which are optimized to save power. This architecture is scalable and can be used in both homogeneous and heterogeneous wireless sensor networks. By means of the addition of a recovery slot in the scheduling scheme, HARP provides efficient link fault tolerance and also supports node mobility management. The same process can additionally function as a joining mechanism for newly deployed nodes. This architecture is highly adaptive to specific application requirements and provides bounded-time data transmissions. Furthermore, a new cluster head election formulation and its associated data gathering protocol (s-HARP) is proposed. This protocol optimizes and balances the energy consumption in the network.[10]

Nagarajan. M , et al. In this paper we introduce a new algorithm to increase life time of the sensor nodes in the network. Only few sensors are in active state in the covered regions and the remaining are in ideal. All the nodes change their status from active to ideal and ideal to active state periodically. Meantime the nodes which are in ideal state enable for a short period to check whether the active nodes are still active or not. If there is any failure nodes in the region ideal sensor get active and sense the data. As all the nodes changes their status periodically, few nodes only in active state and start to sense the data using its energy. So the energy of ideal nodes is saved and it will be used only when it gets active. The proposed algorithm provides close to optimal enhancement in the network life time and the output performs six times better than existing algorithm.[11]

Adamu Murtala Zungeru et al. This paper presents a biological inspired self-organized routing protocol for WSN which is based on termite colony optimization meta heuristic termed Termite-hill. The main objective of the proposed algorithm is to efficiently relay all the traffic destined for the sink, and also balance the network energy. The results of our extensive experiments on Routing Modeling Application Simulation Environment (RMASE) demonstrated that with sink mobility, our proposed routing algorithm was able to balance the network traffic load and prolong the network lifetime without performance degradation.[12]

International Journal of Emerging Technology and Advanced Engineering

455

This scheme can be transformated to the Disjoint Set Covers (DSC) problem, which is proved to be NP-complete. Existing heuristic algorithms either get barely satisfactory solutions or take exponential time complexity. In this paper, we present a genetic algorithm to solve the DSC problem. Simulation results show that the proposed genetic algorithm can improve the most constrained-minimum constraining heuristic algorithm (MCMCC) in solution quality by $99\%$ with only polynomial computation time complexity.[13]

Mihaela Cardei et al this paper describes a critical aspect of applications with wireless sensor networks is network lifetime. Battery-powered sensors are usable as long as they can communicate captured data to a processing node. Sensing and communications consume energy, therefore judicious power management and scheduling can effectively extend operational time. To monitor a set of targets with known locations when ground access in the monitored area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The loss of precise sensor placement would then be compensated by a large sensor population density in the drop zone that would improve the probability of target coverage.

The data collected from the sensors is sent to a central node for processing. In this paper we propose an efficient method to extend the sensor network operational time by organizing the sensors into a maximal number of disjoint set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while nodes from all other sets are in a low-energy sleep mode. In this paper we address the maximum disjoint set covers problem and we design a heuristic that computes the sets. Theoretical analysis and performance evaluation results are presented to verify our approach.[14]

Tal Anker et al. The main objective of this paper is to provide a useful fully-distributed inference algorithm for clustering, based on belief propagation. The algorithm selects cluster heads, based on a unique set of global and local parameters, which finally achieves, under the energy constraints, improved network performance. Evaluation of the algorithm implementation shows an increase in throughput in more than 40% compared to HEED scheme. This advantage is expressed in terms of network reliability, data collection quality and transmission cost.[15]

Comparison Analysis

Sr. no

Author Title & Year Technique used Limitations/Results

1 Hwa Young Lim et al

Maximum Energy Routing Protocol based on Strong Head in Wireless Sensor Networks,2007

Maximum Energy routing protocol based on Strong Head

Provides better results in terms of the energy consumption and lifetime of the network

2 Bilal Abu Bakr et al.

A Quantitative Comparison of Energy Consumption and WSN Lifetime for LEACH and LEACH-SM,2011

LEACHSM protocol and LEACH Protocol

Presents a quantitative comparison of energy consumption and WSN lifetime for both protocols

3 Chi-Tsun Cheng et al.

A Delay-Aware Data Collection Network Structure for Wireless Sensor Networks,2011

A delay-aware data collection network structure for wireless sensor networks is proposed

International Journal of Emerging Technology and Advanced Engineering

456 4 Maciej

Nikodem et al.

Upper Bounds on Network Lifetime for Clustered Wireless Sensor Networks,2011

Uses theoretical aspects of clustering in wireless sensor networks, as a mean to improve network lifetime

Results show that clustering itself cannot improve network lifetime so additional techniques and means are required to be used in synergy with clustering.[

5 Lıliam Barroso Leal et al.

A Hybrid Approach Based on Genetic Fuzzy Systems for Wireless Sensor Networks,2011

Genetic Fuzzy System (GFS) for the selection of routes in WNSs,

They particular achieve both a fuzzy database and a fuzzy rules base to maximize performance of the application of Mamdani‟s inference system in the selection of routes in Wireless Sensor Networks

6 Yan Shen, Hui Ju et al.

Energy-Efficient Task Assignment Based on Entropy Theory and Particle Swarm Optimization Algorithm for Wireless Sensor Networks,2011

energy-saving task assignment

They show that the execution time and energy consumption can be decreased and the lifetime of wireless sensor network is prolonged with the proposed method

7 F.J. Atero et al.

A Low Energy and Adaptive Architecture for Efficient Routing and Robust Mobility Management in Wireless Sensor Networks,2011

Hierarchical Adaptive and Reliable Routing Protocol

Efficient link fault tolerance and also supports node mobility management. This architecture is highly adaptive to specific application requirements and provides bounded-time data transmissions

8 Nagarajan. M , et al.

A New Approach to Increase the Life Time and Efficiency of Wireless Sensor Network,2012

new algorithm proposed to increase life time of the sensor nodes in the network

Provides close to optimal enhancement in the network life time and the output performs six times better than existing algorithm

9 Adamu Murtala Zungeru et al.

Performance of Termite-Hill Routing Algorithm on Sink Mobility in Wireless Sensor Networks,2012

biological inspired self-organized routing protocol for WSN

proposed routing algorithm was able to balance the network traffic load and prolong the network lifetime without performance degradation

10 Chih-Chung Lai et al.

An effective genetic algorithm for improving wireless sensor network lifetime,2007

Proposed a scheme divide sensor nodes into disjoint groups such that each group covers all targets and works alternatively

International Journal of Emerging Technology and Advanced Engineering

457 11 Mihaela

Cardei et al

Improving Wireless Sensor Network Lifetime through Power Aware Organization” Springer Science,2005

Propose an efficient method to extend the sensor network operational time by organizing the sensors into a maximal number of disjoint set covers that are activated successively

They address the maximum disjoint set covers problem and design a heuristic that computes the sets. The theoretical analysis and performance evaluation results presented here.

12 Tal Anker et al.

Efficient Clustering for Improving Network Performance in Wireless Sensor Networks,2008

fully-distributed inference algorithm for clustering, based on belief propagation

Evaluation of the algorithm implementation shows an increase in throughput in more than 40% compared to HEED scheme. This advantage is expressed in terms of network reliability, data collection quality and transmission cost.

II. CONCLUSION

A survey on selected techniques on research of WSN in area of improving the lifetime of the given Network. We have taken these techniques because this technique provides better results for improvement the network lifetime using different protocols like LEACH, TERMITE HILL etc. The given papers are selected according to their better results which are justified through various parameters.

The research in WSN has taken a good progress since last decade but still the given research at its initial steps, there is a lot of need to improve the Network lifetime so that the network longs last to provide the better results as all the other activities related to the routing, caching are only possible if the lifetime of the network, So our main goal is to provide better lifetime for the network using the termite hill protocol, we will try to include the more efficient results compare to the previous research work.

REFERENCES

[1 ] MIHAELA CARDEI et al.,” Improving Wireless Sensor Network Lifetime through Power Aware Organization” Springer Science Business Media, Inc. Manufactured in The Netherlands, Wireless Networks 11, 333–340, 2005

[2 ] R. Shah, S. Roy, S. Jain, and W. Brunette, "Data mules: Modeling a three-tier architecture for sparse sensor networks," in Proc. IEEE SNPA, May 2003, pp. 30-41.

[3 ] S.Anandamurugan et al. “Increasing the Lifetime of Wireless Sensor Networks by usingg AR (Aggregation Routing) Algorithm” , IJCA Special Issue on “Mobile Ad-hoc Networks” MANETs, 2010

[4 ] Hwa Young Lim, Sung Soo Kim, Hyun Jun Yeo, Seung Woon Kim, and Kwang Seon Ahn “Maximum Energy Routing Protocol based on Strong Head in Wireless Sensor Networks” 2007 IEEE

[5 ] Bilal Abu Bakr, Leszek Lilie “A Quantitative Comparison of Energy Consumption and WSN Lifetime for LEACH and LEACH-SM” 2011 IEEE

[6 ] Chi-Tsun Cheng, Chi K. Tse and Francis C. M. Lau “A Delay-Aware Data Collection Network Structure for Wireless Sensor Networks” 2011 IEEE

[7 ] Maciej Nikodem and Bartosz Wojciechowski “Upper Bounds on Network Lifetime for Clustered Wireless Sensor Networks” 2011 IEEE

[8 ] Lıliam Barroso Leal, Raimir Holanda Filho, Ricardo A. L. Rabelo, Fabio A. S. Borges “A Hybrid Approach Based on Genetic Fuzzy Systems for Wireless Sensor Networks” 2011 IEEE

[9 ] Yan Shen, Hui Ju “Energy-Efficient Task Assignment Based on Entropy Theory and Particle Swarm Optimization Algorithm for Wireless Sensor Networks” 2011 IEE

[10 ]F.J. Atero, J.J. Vinagre, E. Morgado, M.R. Wilby “A Low Energy and Adaptive Architecture for Efficient Routing and Robust Mobility Management in Wireless Sensor Networks” 2011 IEEE [11 ]Nagarajan. M , Dr. S. Karthikeyan “ A New Approach to Increase

the Life Time and Efficiency of Wireless Sensor Network” 2012 IEEE

[12 ]Adamu Murtala Zungeru, Li-Minn Ang, Kah Phooi Seng “Performance of Termite-Hill Routing Algorithm on Sink Mobility in Wireless Sensor Networks”,SPRINGER Third International Conference, ICSI 2012, Shenzhen, China, June 17-20, 2012 Proceedings, Part II, pp 334-343,2012

International Journal of Emerging Technology and Advanced Engineering

458 [14 ]MIHAELA CARDEI et al.,” Improving Wireless Sensor Network

Lifetime through Power Aware Organization” Springer Science Business Media, Inc. Manufactured in The Netherlands, Wireless Networks 11, 333–340, 2005