Date Published: July 18, 2017
Publisher: Public Library of Science
Author(s): Haleem Farman, Huma Javed, Bilal Jan, Jamil Ahmad, Shaukat Ali, Falak Naz Khalil, Murad Khan, Yongtang Shi.
Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of different components involved in the evaluation process.
Wireless sensors are widely used for health and environmental monitoring, smart homes, smart transportation, and rescue operations . Wireless sensor nodes are usually operated on batteries often in unattended environments where batteries cannot be recharged and battery drainage can cause network disconnection. Sensor nodes consume most of their energy during communication with other nodes . It is highly desirable to optimize communications which can lead to effective and efficient usage of limited resources, thereby enhancing network lifetime.
In literature authors have addressed the issue of energy efficiency through different approaches such as classical clustering, chain-based clustering and grid based clustering. The aim of all approaches is to efficiently use available resources to prolong network life time. Few of them are discussed here.
ANP was developed by Saaty  on the basis of Analytical Hierarchy Process (AHP). ANP can deal with the qualitative and quantitative information of the network. In addition it also handles interaction and feedback relationships between the criteria/sub-criteria and alternatives. The ANP model has been used as the multi-criteria decision tool for different purposes such as, project selection, component selection e. The generalized steps [27, 28] of ANP are discussed as:
The performance of cluster-based WSN directly depends on CH, therefore it is very important to select the optimum node that will ensure efficient resource utilization, thereby improving network lifetime. In this paper, the topology for CH selection is based on grid-based hybrid network deployment (GHND) as in . The constructed topology is shown in Fig 1, where the network is partitioned in multiple zones. Each zone has a CH responsible for data aggregation and forwarding. Cluster head plays a very important role in network stability and prolonging network life time, therefore it should be intelligently selected. The problem of selecting the best node as CH based on certain parameters and can be easily tackled as a multi criteria decision system. ANP has been widely used as a multi criteria decision tool, in which dependencies among elements and feedback are dealt with. After the network deployment, the base station (BS) will run the ANP based CH selection for all clusters. The steps involved in ANP model for cluster head selection are explained in detail as follows:
Sensitivity analysis is highly recommended to check the stability of alternatives ranking. This is used to check the results and ranking of alternatives obtained through ANP model. According to weighted matrix, it should be considered that the elements in alternatives are influenced by the elements in criteria and vice versa. To start with the sensitivity analysis, elements having highest weights are identified first. The impact of these weights must be observed on all other elements (alternatives).
In the proposed method, role of CH is rotated to minimize energy consumption and to avoid early depletion of node. Instead of periodic reselection of CH that leads to network overhead and high energy consumption, the proposed method initiates the process when required. Moreover, the reselection process is zone dependent thus the entire network is not disturbed in the reselection process. The reselection process is initiated based on experimentally obtained optimum threshold value (TV) . If energy level of a CH is less than TV then the process of reselection will be initiated as shown in Algorithm 1. The reselection score (ReSS) is calculated based on three distinct weighted parameters such as Node_weight (fetched from APL), NodeEL (energy level of a node) and PrTCH (Priority of a node to be CH) as shown in Eq (22). The PrTCH is basically the number of times a node has been CH with it priority. Moreover, TCH = 0 means the node has not been cluster head and will have high priority. Furthermore, a high score of TCH will decrease the priority as shown in Eq (23). The weights w1, w2 and w3 are assigned to Node_weight, NodeEL and PrTCH respectively as shown in Eq (22). We recommend weights to be assigned as w2 > w1 > w3 and w1 + w2 + w3 = 1. This criteria for parameter selection is decided after empirical evaluation. It assigns highest weight to energy level of the node as it is the most important factor. The second important being the weight assigned to it by ANP and third important weight is assigned that indicates the frequency of being selected as CH.
The performance of the proposed method using ANP approach is compared with existing state-of-the art energy efficient protocols. The proposed approach is evaluated in terms of network lifetime using first node die (FND), half nodes die (HND) and all nodes die (AND). In the proposed method, a CH is selected based on ANP approach. After certain number of rounds the CH is rotated to preserve the energy, prolonging network stability, and to avoid network partitioning which typically results in unreachable segments. If energy level of a CH decreases below the defined threshold the reselection of CH is initiated for a specific zone. The CH selection based on the parameters mentioned in sub section 4.1 and the reselection process particular to the zone improves energy consumption and subsequently prolongs the network lifetime as shown in Figs 13–15.
This paper has attempted to solve CH selection problem in WSN by using ANP which is a multi-criteria decision analysis tool. CH selection in WSN involves tuning of several inter-related parameters such as residual energy level, distance from center of zone and others, which were taken as criteria parameters for the ANP process. Mathematical framework was provided for applying ANP model on CH selection. The mathematical framework was then tested for three different scenarios. In first scenario, a cluster of 5 nodes was considered for ANP based cluster head selection. The criteria parameters; REL, DistCent, DistNodes, TCH and MN were taken into account. Limit matrix (Table 6) shows that REL parameter has the highest value 0.1923. As initially REL is same for all nodes, therefore the second highest parameter (DistNodes with value 0.1035) is selected for sensitivity analysis. Node 3 was evaluated to be the best node for CH selection with priority value of approximately 40%. For second scenario, Node 3 was moved a bit closer to Node 4 and 5 and sensitivity analysis was checked. The result showed that Node 3 is still the best node to be the CH. In scenario 3 in order to check the stability of alternatives ranking, Node 1 was moved to the center of the zone. Sensitivity analysis showed that node 3 was still the best choice for CH selection.