Memetic Algorithm based Energy Efficient Wake-up Scheduling Scheme for Maximizing the Network Lifetime, Coverage and Connectivity in Three-Dimensional Wireless Sensor Networks

Abstract

In wireless sensor networks (WSNs), energy efficient wakeup scheduling of sensor nodes is one of an efficient approach for saving the energy consumption of the network. Determining an optimal wakeup schedule of sensor nodes with satisfactory coverage and connectivity requirements is very challenging issue and known as NP-hard problem. In literature, several evolutionary or meta-heuristic algorithm-based schemes are proposed for solving this problem. Most of the existing wakeup scheduling schemes consider only either coverage or connectivity constraint. Only very few proposed schemes consider both coverage and connectivity constraints for determining an optimal wakeup schedule. These existing schemes do not guarantee optimal solution and sometimes struck in local minima. In this paper, an improved Memetic Algorithm based energy efficient wakeup scheduling scheme is proposed where four constraints are considered such as energy consumption, coverage, connectivity, and optimal length of wakeup schedule list. The proposed scheme devises a novel mutation, crossover, and local search operators. An extensive simulation experiments are done in different network scenarios to prove the performance of the proposed scheme and compare its performance with two latest existing schemes. The results confirm that the proposed scheme performs better than the existing schemes in terms of coverage ratio, optimal number of active sensor nodes and network lifetime.

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

“…The study in [59] proposed four heuristic algorithms to solve the connectivity and coverage problems in wireless networks. In [60], an energy efficient wakeup scheduling scheme based on an improved Memetic algorithm was proposed, where four constraints, namely energy consumption, coverage, connectivity, and optimal length of wakeup schedule list were considered. The work in [61] ensured the coverage and connectivity of nodes in the network by miming a spider canvas.…”

“…[30] 2-D Heuristic Algorithm [31] Network uniformity, deployment time 2-D Heuristic Algorithm [32] Energy consumption 2-D Mathematical optimization [33] Time to calculate the fitness function 2-D Heuristic Algorithm [34] Power consumption of SNs 2-D Heuristic Algorithm [35] 2-D Graph theory, Heuristic Algorithm [36] Coverage duration 2-D Heuristic Algorithm [37] Target detection 2-D Mathematical optimization [38] Target detection 2-D Mathematical optimization [39] 2-D Heuristic Algorithm [40] The scheme of broadcast,unicast 3-D Mathematical optimization [41] Indicator of availability,time,reliability 2-D Mathematical optimization [42] 2-D Path coloring, Mathematical optimization [43] 2-D Graph theory [44] Fixed budget 2-D Heuristic Algorithm [45] Residual energy of the SNs 2-D Heuristic Algorithm [46] Sent Bytes, movement cost of SNs 2-D Mathematical optimization [47] Power assignment 2-D Heuristic Algorithm [48] The number of relay nodes 2-D Graph theory [49] The number of sensors to activate 2-D Mathematical optimization [50] 2-D Heuristic Algorithm [51] 2-D Heuristic Algorithm [52] The number of SNs 2-D Heuristic Algorithm [53] 2-D Mathematical optimization [54] 2-D Heuristic Algorithm [55] 2-D Mathematical optimization [56] 2-D Heuristic Algorithm [57] 2-D Mathematical optimization [58] The number of SNs 2-D Graph theory, Heuristic Algorithm [59] 2-D Heuristic Algorithm [60] The wakeup scheduling scheme of SNs 3-D Heuristic Algorithm [61] 3-D Heuristic Algorithm Ours…”

Modern Energy Optimization Approach for Efficient Data Communication in IoT-Based Wireless Sensor Networks

“…When the network's connectedness rises to a value of "CC," the change in communication radius may be calculated using Equations ( 1) and (2) [26]. MWSN's redundancy rises when nodes occur outside of the set range or limit of nodes, putting exceptional stress on the communication network [22,28]. As a result, in addition to…”

VKECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and K-Means Algorithm

“…According to the idea of clustering and computational geometry, the target area is partitioned into different clusters, and a method that calculates the optimal sensing radius of different structures is used to form HWSNs clusters in which nodes move to the center–of–mass position, which can reduce the network average movement distance. For a redundant node dormancy mechanism, a common approach is to use more than one sensor node working together to monitor the common goal, and then to establish a rotational dormancy and wake-up mechanism between sensor nodes to enhance the node energy efficiency, resulting in lower network energy consumption [ 10 ]. Zhao et al [ 11 ] used the Vampire Bat Optimizer (VBO) to redeploy the nodes and effectively enhance the network coverage.…”