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REVIEW ON ARTIFICIAL INTELLIGENCE BASED ENERGY EFFICIENT ROUTINGPROTOCOL IN WSN
Yagini Sharma1, Pranjal Khare2
2Asst. Professor
Abstract- The recent technological advancements and communication have caused the significant shift in sensor network research. In Wireless Sensor Networks routing data and information from one node to another and to base station is a major challenges. It shows the commutation between efficiency and responsiveness. Fuzzy logic is one such protocol existing in this category which is used to reduce the overburden cluster head (CH) in Wireless sensor network (WSN), it is a rule based architecture system and with each variation in the network, rules are added in the network. For a network like WSN or MANET, the fuzzy logic may contain hundreds of rules, which makes the architecture more complicated. In this work, a hybrid Power Efficient Gathering in Sensor Information System (PEGASIS) hierarchical protocol is proposed which uses Firefly Optimization technique and artificial neural network for making the lifespan of the Wireless Sensor Network (WSN) enhanced. The Firefly optimization algorithm will work on the grid deployment model.
PEGASIS is best used for routing with other protocols based on sensors lifetime, total energy consumed and shows comparative results with other mainline protocols. In this work the technique used for optimization is Firefly algorithm in firefly algorithm, attractiveness and light intensity are the significant variables used to attract other firefly with more light intensity than them-self. The light intensity is directly dependent on attractiveness of the firefly. PEGASS is considered as one of the best redirecting method which allows better routing technique. In PEGASIS the sensor node location is random and every node has the capability to detect data, blend data, and equally sends the load among the nodes. Chain of nodes is made according to the positioning of the node and the nodes are plotted by using greedy algorithm. Artificial neural network (ANN) is used as a classifier to remove distortion from the network or to overcome the battery discharge problem. The accomplishment of the proposed work can be assessed by measuring the performance parameters named as Total number of packets transmitted, Energy dissipation, Throughput, Network lifetime by using MATLAB simulator.
Keywords: WSN, PEGASIS, ANN, CH.
1 INTRODUCTION
The major challenge for wireless sensor networks is routing. Many protocols exist in this category which provides various routing techniques. Fuzzy logic is one among them which reduces the overburden cluster head in Wireless sensor network (WSN), it is a rule based architecture system and with each variation in the network, rules are added in the network. For a network like WSN or MANET, the fuzzy logic may contain hundreds of rules, which makes the architecture more complicated. WSN comprises of small low-price, low-energy nodes that connects efficiently and gather data to transmit to the destination i.e.
Base Station. Before transmitting the information to the destination the CH collects and compresses the data transmitted by the cluster nodes. Due to small size sensors which are being deployed in the remote areas for sensor
nodes access, there is a requirement of radio communication link for the transmission of data to and from the sensor nodes [1].
2 RELATED WORK
The recent technological advancements and communication have caused a significant shift in sensor network research. Supporting real time communication is a major challenge due to limited energy, dynamic network topology, low node reliability and distributed architecture. Different query optimization techniques become the focus of research for reducing the network energy and redundant data. In WSN, queries are executing continuously and reaching the base station. The research work focuses on improving the lifetime of wireless sensor networks.
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Razaque et al. [2], proposed PEGASIS-LEACH technique, an improvement of the PEGASIS and LEACH protocols. For transmitting information over the wireless network, the P- LEACH protocol used an energy-efficient algorithm. Simulation of P-LEACH protocol is done in NS-2 and MATLAB environments for high-efficiency energy. The simulated network of 600- meter x 600meter rectangular area with 100 sensor nodes was formed5 Joule energy with unlimited data has been propagated from one node to another node. The use of a finitely reserved node during the simulation results in exhaustion and treats the node depleting the remaining energy as a dead node. It was compared with the simple PEGASIS and LEACH protocols and the P-LEACH protocol. It was observed that the P- LEACH protocol performs better than in case of dead nodes and energy consumption.
Rana et al. [3], have represented the field of wireless sensor networks as a rapidly emerging area of science and engineering. It was a special network consisting of tiny nodes with power of sensing, computing and communication wireless capabilities. These tiny nodes were installed in large numbers in the sensor field environment. The main goal of Wireless Sensor Network (WSN) was to recognize valuable information in the system depending on which type of application it is deployed and formulate restorative action while forwarding the information from source to destination.
Various routing protocols have been used to make the communication possible between the sensor nodes. Roughly in WSN routing protocols are categorized into three categories Hierarchical, Location-Based and Planar. The author describes, effective collection of sensor information systems, hierarchical routing protocols, and different versions of the PEGASIS have been compared.
3 PROBLEM FORMULATION
In wireless sensor network a routing chain where all the sensor nodes are ordered in sequence for the formation of a chain like structure to receive and forward the data to the base station.
Clustering technique can make contribution to the overall system
scalability, network lifetime and energy efficiency. It enhances the power control of the network to a larger extent and aided to make the bandwidth reusable for the best resource allocation. Lowering the amount of energy consumption in the network at the cluster level is the major aim of this algorithm and formation of an optimal data gathering chain for detection, recover and distortion occurs due to battery discharge problem or due to any other issue in the network.
Previous researchers [10] have attempted fuzzy logic for this purpose, which has demonstrated an efficient outcome. Firstly the problem with fuzzy logic is that, it is a rule based architecture system and with each variation in the network, rules are added in the network. For a network like WSN or MANET, the fuzzy logic may contain hundreds of rules, which makes the architecture more complicated.
The proposed hybrid model simplifies it by using Firefly Optimization algorithm [11] along with PEGASIS for efficient routing and ANN for detection and recovery of distortion occurs due to battery discharge problems or in data transmission. The major problem in WSN is to optimize the path in case of distortion in the network. If a cluster head faces any distortion due to network issue or battery discharge problem, then there is no significant way to choose other cluster head. The proposed architecture has taken this issue seriously and has opened many future possibilities. Keeping the modern frame in mind the proposed architecture set will utilize the architecture of Artificial Neural Network (ANN) [12] for optimal cluster head selection other than the existing cluster head keeping the lifetime in mind. ANN is the classification of decision groups.
Which consists of simple elements of parallel operation. ANN works on the patterns that has been processed by it and produce a „guess‟ for the processed pattern, if it found the produced results different it will make necessary adjustments to its connection weights. In the proposed work a hybrid approach is used to enhance the lifespan of the network and also to minimize distortion due to battery discharge problem. The time and energy used in the CH selection is very much less than the energy and
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time consumed by the Type-2 Fuzzy Logicalgorithm. As a result the overall network lifespan of the WSN gets enhanced by generating more alive nodes. The work conduct of the proposed hybrid model is validated through Total number of packet transmitted, Energy dissipation, Throughput, Network lifetime.
4 PROPOSED METHODOLOGY
The routing series in the WSN is an efficient sequence of each node in the network, developing a chained structure for delivering the message to Base station.
As discussed previously clustering methods can significantly give scalability to the system, energy effectiveness and network lifespan. It enhances power control and assists for reusing bandwidth for enhanced resource allocation. In contrast, as the density of sensors increases, single-hop communication overloads the gateway. Appropriate selection of cluster head is as important as the selection of the area.
Wireless sensor network contains very limited source of energy. The proposed approach is a hybrid model for enhancing the lifespan of the network.
The proposed hybrid approach take the advantage of PEGASIS, ANN and firefly algorithm to improve the active lifespan of the network and if distortion occurs due to battery discharge problem or due to any other issues in the network, apply ANN to classify distortion. In cluster head selection, if the cluster head current energy is more than the energy required for cluster head selection then only that node will be considered for cluster head.
The steps of proposed hybrid approach are discussed below:
A. Grid Based Deployment
In this approach, the wireless sensor network deployed over a geographical area is divided in the form of grids. Grid based routing is mainly depends upon the position of the node. In this protocol, every node uses information based on GPS location itself with “virtual grid” so that the entire area is divided into a number of square grids that are related to the node's maximum remaining energy.
Grid based routing protocol divides 2D WSN area into equally square shapes. The grid may consist of some nodes or may be empty. The energy consumed by each
node can be saved with grid based clustering algorithm, while knowing the location of sensor nodes and grids. To avoid overloading of cluster head, the routing algorithm is used to determine the optimized route within the cluster and thus nodes transmit data to the CH instead of approaching the BS directly [13],[14].
The main advantage of Grid Based routing is that the routing table is not maintained while performing the operation. The routing operations are performed by knowing the location of base station. The example of grid based routing protocol is shown in Figure 1.
Fig. 1 Grid based deployment model B. Firefly Algorithm
The Firefly Algorithm is globally optimized nature inspired meta-heuristic algorithm.
The Firefly Algorithm(FA) is inventive by the flicker nature of firefly insects. The FA is mostly effective algorithm that imitate the natural behaviour of fireflies. The algorithm was proposed by Yang X in she in 2008. Fireflies make use of their flickering behaviour to communicate with other fireflies, by sending signals to the opposite sex [15]. A fitness function is calculated based on the objective function as a result the fitter fireflies will attract the other fireflies that are less fit. The fireflies swarm frequently flies in the brightest way. All another firefly with less light intensities flies towards the ones having enhanced less intensities. The distance among the fireflies increases with the increment in the light intensity.
We start constructing the chain from the most distant CH node from the base station (BS). As shown in figure 2 we start from
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cluster head node C1 to make sure thatC1 cluster head is the farthest CH node from the destination i.e. BS and have adjacent neigh bours. Distance between the neigh bour CH nodes will increase in greedy algorithm as nodes rooted in the chain cannot be revisited again. The cluster head node C1 connecting to node C2, node C2 connecting to node C3, node C3 connecting to node C4, C4 connecting to node C5 as shown in figure 2. The chain gets reconstructed in the same manner when a node dies.
The position of the leader node will be at random on the chain in each round of communication. In figure 3, a simple token passing mechanism is used by the C5 leader node to schedule the data transmission throughout the chain starting from the farthest node C1.
NodeC5 is the leader node, and circulate the token throughout the chain to node C1. The farthest node C1 forward its data towards node C2 node C2 receives data from node Cl, C2 forwarded the data to C3, C3forwarded the data to C4 and so on till the data finally reaches the leader node C5. The leader node will next pass the token to node C8, and node C8will pass its data towards node C5.
C. Artificial Neural Network (ANN) Artificial neural network (ANN) is a center-based efficient computing system that borrows the idea from the biological neural networks. ANN is also known as Artificial Neuron System or Parallel Distributed Processing System (PDPS) or connector System. ANN acquires large number of elements that are related to each other in a certain pattern to allow communication among them. These elements are also called nodes or neurons, which are operated in parallel.
The major problem in WSN is to optimize the path in case of distortion in the network. If a cluster head faces any distortion due to network issue or battery discharge problem, then there is no significant way to choose other cluster head. The proposed architecture has taken this issue seriously and has opened many future possibilities. Keeping the modern frame in mind the proposed architecture set will utilize the architecture of Artificial Neural Network (ANN) [5] for optimal cluster head
selection other than the exiting cluster head keeping the lifetime in mind. ANN is the classification of decision groups.
Which consists of simple elements of parallel operation. Utilization of Artificial Neural Network (ANN) in WSN is to optimize the path in case of distortion in the network.
5 CONCLUSION
In this work an overview of WSN is studied followed by the functionality of the sensor node with its application in real- time environment. In our proposed work the network area is partitioned into rectangular blocks called grids, the sensor nodes are randomly deployed in the wireless sensor network. The major advantage of making such type of arrangement is that all the sensor nodes will belongs to a unique grid in the network, as a result no more than one sensor node will belongs to a particular grid. The major drawback with fuzzy logic is that, it is rule based architecture system and with each variation in the network, rules are added in the network to make it more accurate but with network like WSN or MANET or VANET or WBAN, the fuzzy logic may contain hundreds of rules, which makes the architecture more complicated. The proposed work simplifies it by using optimized Firefly Algorithm. This algorithm is very suitable for limited area optimization. Artificial Neural Network (ANN) in WSN provide way to optimize the path in case of distortion in the network or due to any other reason. The intermediate results shows that the proposed hybrid model will improve the life cycle of the wireless sensor network and reduces the amount of energy consumption in the network, while making the system more secure and less prone to threads like distortion and attacks from malicious nodes.
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