The wireless sensor network (WSN) is a growing sector in the network domain. By implementing it many industries developed smart task for different purposes. Sensor nodes interact with each other and this interaction technique are handled by different routing protocol. Extending the life of the network in WSN is a challenging issue because energy in sensor nodes are quickly drained. So the overall performance of WSN are degraded by this limitation. To resolve this unreliable low power link, many researches have provided various routing protocols to make the network as dependable and sustainable as possible. While speeding up the data delivery is also considered to be an effective approach to save energy. To achieve this objective, we propose a new energy efficient routing protocol using genetic fuzzy logic system. Our primary objective is to save energy by sending data packets
In this decade, wireless sensor network (WSN) is an emerging field equally in industry and research due to advancement in wireless technology. WSN has numerous applications in military, healthcare, agriculture, surveillance, environment, mines, community, work place, home and elsewhere [
In conventional networks, the design and architecture of the network is focused on achieving high quality. Therefore, focus must be given on design and deployment of WSN protocols to make the best use of devices, which will lead the network to sustain energy and perform for a longer duration as compared to the previous version [
The deployment of WSN routing protocol has different challenging task. At the outset, in a multi hop network, the intermediate node has to transmit packets from one source to destination node [
In this paper we propose energy efficient routing protocol using genetic fuzzy logic system which will select optimal routing path based on energy efficiency of sensor node. The overall network throughput can be maintained by reducing delay in data transmission and by increasing the packet delivery to the target.
The following Section 2 outlines related work and motivation regard to study of various cluster based energy efficient routing protocol and limitations identified in existing research work. Proceeded as motivation towards cluster based energy efficient routing protocol with added concept GA based shorted route finding. Section 3 clarifies the system design parameters, algorithm and protocol steps of genetic fuzzy logic system for optimal routing and cluster head selection. Section 4 shows the result and discussion of proposed work. Performance analysis of proposed work in comparison with fuzzy base energy efficient routing protocol and other protocol is given in Section 5. Section 6 concludes conclusion of the paper and future scope.
In this section, comprehensive literature work designing an energy efficient routing based on clustering technology to support hierarchy in network topology proposed by researchers in the area of WSN to prolong the network lifetime is discussed.
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From the literature review, many researchers proposed cluster based energy efficient routing protocols and different parameters like packet delivery, delay in transmission, energy efficiency and throughput are taken to measure the performance. But more research is required in optimal routing path selection, fault tolerance, self-organizing capacity, spatial coverage ability etc. There is still a lot of research needed as the level of energy efficiency in real time applications varies. As the performance of energy efficiency in real time applications varies, still a lot of research required.
This paper proposes two main process to find optimal shortest path using genetic fitness function and selection of CH based on fuzzy rule based logic with genetic base optimization and cluster formation using Fuzzy membership logic.
Creating another virtual cluster within this cluster is likely to reduce the routing cost if the node has the opportunity to be a qualified head near the cluster boundaries. So the CH can be elected from the cluster region and also from the virtual region which will minimize the routing cost.
The proposed approach sets sensor node in the cluster’s center is whished for CH for the clusters [
Electing node as CH is considered as a constrained factor. The optimal power control mechanism is essential to reduce the power consumption in every network transmission so that the lifetime of the network may have chance to be extended. The energy consumption of the CH is needed to aggregate the data for the transmission to the neighboring cluster and to the BS. The total residual energy is described between the total initial energy and consumed energy by the sensor node by ERes = EIni – ECons, where EIni is initial total energy and ECons is consumed energy in every transmission. The listening sensor node sends link request to the CH within the cluster. The cluster head generates the routing tables for the newly formed cluster. Based on the minimum link cost the inter cluster communication will be established. If the CH energy level is below the threshold level, and the distance to inter cluster is maximum, the sensor nodes near to the same cluster’s edge that is in the virtual cluster region is elected as temporary CH. This temporary CH will make the link to the neighbour cluster. Let k is the cluster, then dist (Ki, Kj) = max (tip, tjq). The temporary apriori CH probability [
where
The temporary probability of CH is the based on the total energy level consumed by the sensor node which is near to the cluster edge. The total energy consumption of a node will be impacted by the data transfer from source to the target. So the goal is to find the optimal route to minimize the energy usage per packets. The total expected energy consumed by the nodes for one transmission in a cluster per round is total energy of a cluster which is computed by
where ECH is the energy consumption of CH [
where d is data size in bits and n is number of node in the link and EReq is energy required per bit.
where TStart is the starting time EIni is the initial energy, as d is number of bit, DDrate is data delivery rate, PElect is maximum efficiency in output and EDT is the energy utilization in data transmission. The expected energy for inter cluster consumption is computed by
where ETr is energy consumption for n bit is calculated by
where PStart is average power at Time start (TStart). DRate is data delivery rate PElec is highest efficiency at maximum output power and Pamp is amplifier power for WSN.
The genetic fitness function finds the minimum distance to BS and fuzzy system is constructed to maximize the network throughput and reduce the energy consumption in WSN as like LEACH protocol [
The genetic learning approach is the principles inspired by natural population genetics to find solution to the problem. The fitness function optimize the output parameter of rule based fuzzy system with the
Three network parameters such as residual energy, distance to BS, expected efficiency are given as a input to the fuzzy system is to make the network active for more time [
According to these three levels, fuzzy inference system generates at the maximum of 27 possible chances using fuzzy if—then rules to make decision.
Clustering is the process of finding groups in unlabeled dataset based on a similarity measure between the data. A cluster contains similar patterns placed together. The fuzzy clustering technique generates fuzzy partitions of the data instead of hard partitions. Given a set of nodes, at any point of set has coefficients, X = x1,…,xk,…,xn, the fuzzy clustering technique minimizes the objective function, O(U, C).
where xk is the k-th D-dimensional data vector, ci is the center of cluster i,
The spatial membership function
where,
where
In the proposed approach, two kinds of spatial information are included in the membership function of FCM algorithm. They are given as follows:
In order to assign a false node to a cluster that contains a majority of the false node’s neighbourhood as its members, this parameter is incorporated in the membership function. This probability will make the benefit of minimum path cost and reduce energy consumption of a node while routing.
The second term in the denominator of
The proposed ISFCM method forms the clustering when the sensor nodes are deployed in the wireless sensor network. The satellite signal is transmitted from the sink node to the sensor node, the distance between any two sensor nodes are computed and at the same time the distance between the sensor nodes and the sink nodes are computed. The inter cluster communication is established with CH. After the formation of cluster in the network the cluster is further clustered based on the sensor node which is near to the edge of the cluster Ki along with the spatial neighbourhood cluster KSj. The cluster edge is exactly the coverage area of the clustered network with the clustering diameter. The new cluster Kp formed and its centroid Cpj is measured. The inner cluster radius is the distance between these two centroids computed by dist (Ki, Kpi) = dist (Ci, Cpi). With the dist (Ki, Kpj), the virtual layer is formed and the sensor nodes which are in between the virtual cluster and the cluster has a chance to be a CH which will be an intermediate node for inter cluster transmission depicted in the
The three input parameters are given to fuzzy inference system. The fuzzy logic system classifies the input parameters into three different level of output like low, medium and high so that the sensor nodes are identified for the selection of cluster head. The trapezoidal membership function is used for low and high, whereas triangular membership is used for medium level.
It is observed that
The inputs for distance to BS are close, average and far. The output variable for this parameter is classified into nine levels. That are, Very low, Low, above low, medium, below medium, above medium, below high, high and very high. This input parameters and its corresponding output for fuzzification is given in
Rule no. | Residual energy | Expected efficiency | Distance to BS | Chance |
---|---|---|---|---|
1 | Low | Low | Far | Very low |
2 | Low | Low | Average | Low |
3 | Low | Low | Close | Below low |
4 | Low | Medium | Far | Low |
5 | Low | Medium | Average | Below low |
6 | Low | Medium | Close | Above low |
7 | Low | High | Far | Very low |
8 | Low | High | Average | Above low |
9 | Low | High | Close | Below medium |
10 | Medium | Low | Far | Below medium |
11 | Medium | Low | Average | Above medium |
12 | Medium | Low | Close | Medium |
13 | Medium | Medium | Far | Below medium |
14 | Medium | Medium | Average | Above medium |
15 | Medium | Medium | Close | Medium |
16 | Medium | High | Far | Below medium |
17 | Medium | High | Average | Medium |
18 | Medium | High | Close | Above medium |
19 | High | Low | Far | Below medium |
20 | High | Low | Average | Medium |
21 | High | Low | Close | Above medium |
22 | High | Medium | Far | Above medium |
23 | High | Medium | Average | Below high |
24 | High | Medium | Close | High |
25 | High | High | Far | Below high |
26 | High | High | Average | High |
27 | High | High | Close | Very high |
This algorithm steps for finding the chance of node to be a CH and also the best route to the BS is given below. The
Step-1: The source node transmit HELLO message to its neighbour and collect the neighbourhood information. The genetic learning algorithm creates a population and constructs a solution based on the parameter distance to BS. The routing link is discovered from the routing list.
Step-2: Residual energy, distance to BS and expected efficiency are choosen as crisp fuzzy input parameters. The fuzzification creats a crisp input to make a membership of the node as low, medium and high chance category.
Step-3: These parameter values are combined using if-then rules with AND logic and evaluating the exact values for low, high, medium, close, average and far. The aggregation combines all the output evaluation using fuzzy OR logic and generates new fuzzy sets.
Step 4: The defuzzification constructs the chance of node to be CH.
Step 5: The fitness function takes the optmization based on the cost from source to the BS and finds best route for data transmission.
In order to develop the WSN network, MatLab is used for the proposed approach. In this network scenario the maximum of 500 nodes randomly dispersed in 5 cluster with the network area of 1000 m2 is developed. The performance measure is observed in the term of overall throughput, delayed transmission, energy efficiency and packet delivery ratio in each round. The deployed WSN network is shown in
The source node is selected to send the data to base station and the cost effective route established is shown in below
The
The network throughput can be measured over rounds by sending more packets to the base station. It can be observed in
This research work proposes an optimal energy efficient routing protocol based on genetic fuzzy logic system. In this proposed approach it traces a shortest link for data transmission. The higher energy nodes are identified and selected for data transmission from source to the base station and low energy node is sensing the information. An optimal routing is established with proposed approach GFLS, the genetic algorithm finds the optimal routing and the fuzzy logic system finds the cluster head node based on its energy. It gives better performance than the existing FBECS and LEACH algorithm in comparing metrics packet delivery and network’s overall throughput. In future we like to extend the research to increase the speed of network, improving the network throughput, and increase the speed of data delivery.
We would like to express our heartfelt thanks to all the good souls who helped us to bring out this research article. We thank our institution for their encouragement and consistent support in doing research.