M-Connected Coverage Problem in Wireless Sensor Networks

Abstract

Solving coverage problem alone is not adequate in a wireless sensor network, since data has to be transmitted to the base station. This leads to the lookout for an energy efficient method to solve connected coverage problem. This paper addresses M-connected (each sensor node will have at least M other sensor nodes within its communication range) target coverage problem in wireless sensor networks, where the required level of connectivity and coverage may be high or low as required. We propose a heuristic for M-connected target coverage problem, where initially a cover is decided and later on it is checked for M-connectivity. M-connectivity for simple coverage, k-coverage, and Q-coverage is focussed on in this paper. We use a Low-Energy Adaptive Clustering Hierarchy (LEACH) inspired model, where a cluster is considered as a set of sensor nodes satisfying M-connectivity and required level of coverage. It is enough if one among these nodes transmits the monitored information to the base station. When the required level of coverage are high, chances of nodes being connected in the cover is high. Simulation results show that our proposed method can achieve better results than Communication Weighted Greedy Cover (CWGC).

Optimization for connectivity and coverage issue in target‐based wireless sensor networks using an effective multiobjective hybrid tunicate and salp swarm optimizer

“…Scenario-I Scenario-II To prove the effectiveness of the proposed approach, we compared with five existing methods such as standard TSO, Gupta et al, 18 Jehan and Shalini, 9 Mini et al, 17 and Rebai et al 42 Rebai et al 42 have developed the GA-based node consumption algorithm, and the k-coverage and m-connectivity algorithm was developed by Mini et al 17 Coverage and connectivity problems are analyzed using the proposed algorithms of other studies. 9,17,18,42 Furthermore, inadequacies in solving these problems are handled through executing GA by Rebai et al 42 F I G U R E 6 Comparative analysis of sensor nodes selected in scenario-I F I G U R E 7 Comparative analysis of sensor nodes selected in scenario-II For both the network scenarios (I and II), the k and m containing various k-coverage and m-connectivity values are provided through separating number of selected possible locations against the sensor node through executing our proposed algorithm. Considering the possible locations in both the scenarios (I and II), the comparison done for the proposed methods of Gupta et al, 18 Jehan and Shalini, 9 Rebai et al, 42 and Mini et al 17 is illustrated in Tables 6 and 7.…”

“…15,16 A designated data/ gateway sink reached by a sensor node through finding a reliable path defines the WSNs connectivity. [17][18][19][20] It is impossible to process the data collected by the sensor node due to the nonavailability of routes to the data sink. 21,22 Hence, the implementation of deployed WSN systems turns to be a difficult process during network connectivity maintenance.…”

Binary Monkey-King Evolutionary Algorithm for single objective target based WSN

“…The need for this modification is for efficiently handling the solutions without further offspring repair at the end of the iteration. The proposed algorithm has been compared with the Conventional GA, GREEDY Approach, and the algorithm proposed by Mini et al [10]. The parameters used in the proposed algorithm include Total no.…”

Cluster Head Selection in a Homogeneous Wireless Sensor Network Ensuring Full Connectivity with Minimum Isolated Nodes

“…In the research article [10], the authors talk aboutconnectivity in which each mote is connected to at least other motes in the same cover. But it may happen that a node in the network is not connected to any other node at all which does not ensure a completely connected network.…”

“…The related researches in the literature for cluster-head selection in WSN are many. The brief details of some of them [2,[6][7][8][9][10][11] are depicted in Table 1. Table 1 inspires the authors to come up with a CHS algorithm that selects any CH on the basis of any unique note connected to it, the residual energy, and the number of nodes connected to it.…”