A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSN) have lately been gaining momentum thanks to the hardware improvements and standardization software efforts. Moreover, the appearance of Internet of Things (IoT) and its reliance on sensors are helping to widely extend the usage of WSNs. However, such networks present drawbacks, mainly because of limited sensor batteries and their vulnerability against physical attacks due to the lack of protection and security. Additionally, not all the sensors inside the network have the same responsibility in terms of traffic handling. In this paper, we firstly analyze the fact that some nodes are more critical than others, considering the most critical node the one that, once incapacitated, causes the most deterioration on the network performance. Such performance is analyzed using two metrics, namely network latency and lifetime. We present a result comparison between a Mixed Integer Programming (MIP) model and a Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic for small networks. For bigger networks, GRASP meta-heuristic results are presented to understand how the network degrades as the number of both critical and network nodes increase, by distributing them into two different areas: fixed and incremental to maintain node density.

Fault‐tolerance based on augmenting approach in wireless sensor networks

“…Centrality 49,50,51 Centralized 37,40,44 Distributed 20,41,42,43 Data transmission 39 Energy 38,51 k-connected graph 14,45,46,47 F I G U R E 2 Classification of critical nodes variants on WSN…”

“…37,40 The cut vertex detection algorithms available in the literature can be divided into two categories: distributed algorithms 20,[41][42][43] and centralized algorithms. 37,40,44 In Reference 14, the authors consider a k-connected graph where a graph (G) has k connectivity if the minimum vertex cut of G has at least k vertices, that is, it remains whenever fewer than k vertices are removed. Decreasing the k value of the network can have a significant impact on the performance of the network.…”

“…Although the MIP and LP models provide accurate results, they have some drawbacks due to their high execution time and complexity especially when the number of nodes increases. To overcome these limitations, Sembroiz et al 37 proposed two greedy randomized adaptive search metaheuristics to identify critical nodes when the targeted performance criteria are latency and lifetime. Liu et al 38 proposed an approach to determinate important nodes by considering energy consumption and node lifetime.…”

Average Consensus over Mobile Wireless Sensor Networks: Weight Matrix Guaranteeing Convergence without Reconfiguration of Edge Weights

“…When compared to static WSNs, MWSNs find a wider variety of applications, since the mobility of entities results in many advantages, such as reliability, cost, improved coverage, connectivity, energy efficiency, dynamic topology, increased channel capacity, etc. [ 26 , 27 , 28 ]. Moreover, MWSNs can easily monitor also moving targets (e.g., animals, people, chemical clouds, etc.)…”

A Lightweight Secure and Energy-Efficient Fog-Based Routing Protocol for Constraint Sensors Network

“…Therefore, appropriate routing decisions should be determined for optimizing network connectivity and stability. The main aim of the optimal decision is to choose the best and most maintainable path among the others with efficient utilization of node resources and reliable data delivery to application users [4][5][6][7][8]. Also, due to the lower cost and robust features of sensor networks, most researchers have integrated it with IoT for better network performance in terms of cost-effectiveness, better network coverage, and scalability [9][10][11][12].…”