04-08-2014, 11:12 AM
Energy Efficient Clustering Approaches for Wireless Sensor Networks: A Comprehensive Survey
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Abstract
WSNs consists of a large number of sensing devices each capable of sensing, processing and transmitting environmental information and all the information are conveyed by means of wireless signals. As WSNs gaining more popularity because of widely used in various military and civilian applications, intrusion detection, event detection, home and engineering applications etc. But WSNs consists of thousand numbers of nodes so they suffer from lack of battery power. In this paper, we look at the energy efficiency, security, network lifetime and different formulation of these problems with the help of routing based protocols, game theory, genetic algorithm and swarm intelligence based approaches. The suitability of using all the above approaches using WSNs stems from the nature of strategic interaction between the nodes. We, survey the use of all the above approaches to formulate problem relate to energy efficiency, network lifetime, load distribution in the wireless sensor networks.
Introduction
Nowadays wireless sensor networks (WSNs) are widely used in a vast variety of environments for commercial, civil, and military application such as surveillance vehicle tracking, climate etc. Typically, sensor nodes are grouped into clusters and each cluster has a cluster head (CH). All nodes forward data to CH, which in turns route this data to sink (base station) via multi-hop wireless communication this is called clustering. There are several challenges in designing WSNs because sensor nodes have limited resources of energy, processing power, network lifetime and memory. Several energy-efficient routing protocols have been proposed in literature to deal with limited battery life sensor nodes in order to increase network lifetime.
Nowadays Game Theory (GT) is widely used in WSNs. Most of game formulations surveyed in this paper are cooperative games where nodes agree on pre-mediated to maximize their payoff but in case of non-cooperative where nodes act selfishly to maximize their payoff. To obtain clustering schemes in which network lifetime is optimized and to get the most efficient topology for data transmission from sensor nodes to CH Genetic algorithm (GT) is used. Swarm Intelligence (SI) belongs to an artificial intelligence (AI) that became increasing popular over the last decades. Because of their coordinated behavior swarms achieve their desired goals. Examples of notable swarm- intelligence optimization methods are ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC).
Another important problem in WSNs is security. Since, sensor nodes are deployed in diverse geographical environments, so they are more prone to various kinds of attacks. To deal with this problem there are number to intrusion detection systems are in networks. The main objective of this work is to provide a common framework for the art of study of this research is (energy efficient clustering approaches) and compared proposed approaches with different performance parameters used in recovery process. The remainder of this paper is organized as follows. Section 2 outlines the design issues. Section 3 provides brief explanation of current and future applications. Section 4 explains future research challenges. Section 5 provides a novel taxonomy of state-of-the-art approaches based on their approach types. Section 6 provides a comparative study to understand various performance metrics to improve energy efficiency in present scenarios. Section 7 describes research gaps in present work and Finally, Section 8 concludes and gives future direction to our work.
Design Issues in WSNs
There are number of design issues that determine the performance of wireless network. These metrics include energy efficiency, latency, accuracy, fault tolerance and scalability.
• Energy efficiency: In many scenarios, nodes have to depend on a limited supply of energy. When sensors are battery operated, it is impossible to replace or recharge these energy sources in the field. It is necessary to manage energy to extend the lifetime of the network.
• Network lifetime: As the energy of sensor nodes are not uniformly distributed, this cause’s first node dies earlier. Hence, causes reduction in network lifetime of sensor network.
• Latency: Many sensor applications (e.g., multimedia networks) require delay guaranteed service. In these applications, sensed data must be delivering to the user with in certain delay.
• Accuracy: Any sensor application needs to obtain accurate information. The accuracy information can be improved through detection and estimation of multiple sensors.
• Fault tolerance: When nodes or routing links failure happens, then there is need of some protocols that provide robustness to the WSNs. The feasible methods are through collaborative processing and communication.
• Scalability: Scalable routing algorithms can operated efficiently in wide range sensor network. As network density and network size increases network performance not significantly degrades. How to design such routing protocols is very important to future of sensor networks.
• Node Deployment: Network topology also matters a lot in WSNs. This factor affects the performance of routing protocol. The position of sink or cluster-head also crucial in terms of energy efficiency and performance. When distribution of nodes is not uniform, then clustering plays a significant role in energy efficiency.
• Data Fusion: To eliminate redundant or duplicate data during data transmission data fusion technique is used in which data is aggregated from different sources. Because of heterogeneity nature of the network data fusion technique is a big issue.
Challenges and Open Research Issues in WSNs
In order to develop a platform which provides energy efficient clustering, many challenges and open research issues need to be faced and solved. Some of them are:
• Energy efficiency: There are thousands number of nodes in WSNs and energy is not distributed uniformly among these nodes. Some nodes die very earlier and cause effect on network lifetime.
Hence, it is impossible to recharge or replace nodes in sensor network because it is time-consuming and complex in nature.
• Resources constrained at each sensor node: Sensor nodes are designed with mostly simple devices of low cost according to the economy of scale. Economic factor dictate that sensor networks must cover a wide area with lowest cost sensing devices. As a result, sensor tends to have limited battery energy, a relatively low performance CPU and constrained memory size. Therefore, it is critical importance to use the available resources in WSNs in very efficient way.
• Robustness: Robustness of the network resources requires tolerance of sensor nodes that nodes may fail, lose battery power or may be temporarily unable to communicate due to environmental factors.
• Security: Sensor nodes are deployed in diverse geographical environments; they are more prone to failure from hostile environmental conditions. During transmission there may be any malicious nodes is to disrupt network operation, prevent broadcast messages and other services-availability related messages from reaching other nodes in the network. Some intrusion detection mechanisms are needed to tackle this security related problems.
• Fault Tolerance in WSNs: Most of the applications of WSNs are in harsh environment, where human intervention is negligible or impossible. Large number of nodes may cause extra energy consumption due to increase in communication overhead and more collisions. So, instead of applying some traditional fault tolerant approach used in WSNs, a real time restoration technique will be more appropriate, if node’s in
Routing based Protocols
Shin-nosuke Toyodo and Fumiaki Sato [6] proposed an Energy Efficient Clustering Algorithm Based on Adjacent Nodes and Residual Electric power. This algorithm considering the node that remains large electric power and covers many adjacent nodes not covered by other CHs and consists of four phases: 1. Hello message exchange Phase: During new round sink node broadcast request message containing some information along with it with in specified range R and construct adjacent node list i.e. ANL. 2. Representative node selection phase: Node with largest residual electric power selected as representative node and it is one of the CHs. 3. CH selection of representative node: To become CH, node should evenly consume the electric power and if 2- hop coverage does not exceed the threshold then next CH is repeatedly chosen until threshold exceed. When selection of CH is finished then node creates 3-hop-check response from the parents and transmits it and wait for response fixed amount of time, if time ends and this 3 hop- check-list does not empty, then repeat this process until 3-hop-check-
Genetic Algorithm based approaches
Sajid Hussain et al. [11] proposed intelligent energy-efficient hierarchical clustering protocol performs better than the traditional cluster based protocols. They improve the HCR protocol by using a Genetic Algorithm (GA) to determine the number of clusters, the cluster heads, the cluster members, transmission schedules and create energy-efficient clusters . The GA outcome identifies the suitable cluster heads for the network. The base station assigns member nodes to each cluster head using the minimum distance strategy. The base station broadcasts the complete network details to the sensor nodes. The broadcast message includes: the number of cluster heads, the members associated with each cluster head, and the number of transmissions for this configuration. All the sensor nodes receive these packets transmitted by the base station and clusters are configured accordingly; this completes the cluster formation phase. Next comes the data transfer phase, where nodes transmit messages to their cluster heads. During calculating fitness function some new parameters has been added like direct distance (D) to the sink, cluster distance ©, cluster distance - standard deviation (SD), transfer energy (E), number of transmission (T). Then chromosome fitness, F, which is a function of all the above fitness parameters, is calculated. After every generation, the best fit chromosome is evaluated to assess the improvement in the fitness parameters such as
Swarm Intelligence based approaches
Ankit Sharma and Jawahar Thakur [18] proposed an algorithm which is a combination of Bacterial foraging optimization algorithm (BFO) which is a Bio-Inspired algorithm, LEACH and HEED protocols which enhances the lifetime of a network by dissipating minimum amount of energy. Bio inspired algorithm consists of three phase: Phase 1The sensor nodes are moved within in the cluster so that a proper inter-node distance is maintained and if node move so close then they may accumulate in the same region then the data they sense will be duplicated. Hence a proper inter node distance is maintained. Phase 2 Select the cluster head according to the LEACH protocol and the HEED protocol. The more is the residual energy the more are the chances of a sensor node to become a cluster head. Phase 3 The actual transmission of the data is done by the cluster heads which gather the data sensed by the sensor nodes in their cluster region and then it will send the data to the base station.
Advantages: Enhances the lifetime of a network by dissipating minimum amount of energy.
Disadvantages: Parameters are still pending to check whether the algorithm is enhancing the lifetime of the network or not.
Wei-Lun Chang et al. [26] tried to minimize energy consumption on the travelling of mobile robot. Artificial Bee Colony - based (ABC) path finding algorithm is developed which is significant to plan a data collection path with minimum length to complete data collection task. In ABC algorithm, the ABC contains three groups of bees: employed bees, onlooker bees, scout bees. This algorithm consists of four phases: 1. Initialization phase 2. The employed bees phase: 3. The onlooker bee’s phase and the scout bees phase. Employer bees search for the food sources within the neighbor and share the information regarding new food to the onlooker bees. At the beginning of ABC algorithm generates a large number of food sources randomly means possible solution, the employed bees do some modifications in food sources and then the quality of food source i.e. fitness value is calculated. Then, onlooker bees select the food source with the largest probability value and produce a modification on the position of the food source. The fitness value of the new and old food source is calculated and compared. Then, the food source with the larger fitness value is recorded as the temporarily best solution. The best food source is iteratively updated for certain iterations. In this way shortest route with least cost is discovered but it lack when deal with multiple mobile robots.
Conclusions and Future Work
Energy efficient clustering technique is a key technology and hot topic of WSNs in recent years. We have proposed a comprehensive survey work by focusing on detailed description of energy efficient clustering recovery approaches. This paper dealing with energy efficiency, network lifetime and security in WSNs with the help of routing protocol, game theory, genetic algorithm and swarm optimization. In the future, we will try to propose new optimized approach based on present research gaps and try to evaluate in WSNs simulation environment based on actual network parameters such as node deployment, mobility, intra cluster topology, network model type, location aware, CH selection etc.