Efficiency Evaluation of Strategies for Dynamic Management of Wireless Sensor Networks

Abstract

This paper presents and evaluates dynamic management strategies to improve efficiency in event-triggered wireless sensor networks. We are considering mobility, where nodes move themselves to maximize the coverage, and load balancing state-of-the-art techniques, by which the number of nodes sensing the same area is reduced. To explore mobility, we present a simple method by which nodes can dynamically reorganize themselves based on the force fields approach of mobile robotics. Firstly, the strategies are evaluated separately through experiments with different network configurations and, afterwards, a joint evaluation has been conducted to observe the impact of mobility on the efficiency of load balancing techniques. We show that mobile nodes significantly contribute to keeping the coverage as nodes die in mesh and powerfully improving it in random deployments. Load balancing techniques achieve important results, increasing lifetime and the number of sensed events. However, in random deployments, these techniques lose efficiency and become unsuitable strategies. Combining these strategies with mobility, we observe that PS-based technique keeps its contribution in mesh and random deployments, as well as improving its performance for not so dense networks. Ant-based technique when combined with mobile nodes loses performance significantly in mesh and keeps its good performance in random deployed and less dense networks.

Modeling and analyzing malware diffusion in wireless sensor networks based on cellular automaton

“…Wireless sensor networks (WSNs) are extensively used in the real deployments, such as military and civil applications. 1,2 WSNs are characterized by self-organization, dynamic topology, and limited capacity, and each sensor node (SN) has a limited communication range, 3,4 so they are vulnerable to malware diffusion because of their low configuration and weak defense mechanism. 5,6 Once WSNs are targeted by malware by exploiting network vulnerabilities, SN systems, or hardware and software vulnerabilities, the malware will quickly diffuse from one SN to another within communication range via communication.…”

Wireless Sensor Network Deployment Optimization Based on Two Flower Pollination Algorithms

“…1, the sensors are deployed in urban residential areas, nodes can be charged by solar energy, and coverage is the optimization objective of network deployment. In this circumstance, most researchers have discussed the deployment of homogeneous sensor nodes in obstacle-free monitoring areas, and they considered the entire monitoring area to be fully covered with a minimum number of nodes [8]- [12]. Fig.…”

“…For the second deployment circumstance, in [10], a model of node radiation overflow rate was proposed, but it only discusses the area of the node radiation overflow. Reference [12] proposed a network model with network coverage and energy consumption, but only considered a part of energy consumption. In recent years, even if FPA is used to solve multi-objective optimization problems, most of them are converted into single-objective optimization by linear weighted sum.…”

An Adaptive Fish Swarm‐Based Mobile Coverage in WSNs

“…Such mobile coverage uses inordinate amounts of energy, and many scholars [3][4][5] have presented important constructive achievements in this area. Andrea [6] examined node mobility and posited that dynamic mobile coverage is valuable in improving efficiency in event-triggered WSNs, especially in extending node lifetime and reducing the number of nodes that sense the same area. Similar to [6], [7] regarded coverage quality and lifetime as two key parameters for mobile WSNs.…”

“…Andrea [6] examined node mobility and posited that dynamic mobile coverage is valuable in improving efficiency in event-triggered WSNs, especially in extending node lifetime and reducing the number of nodes that sense the same area. Similar to [6], [7] regarded coverage quality and lifetime as two key parameters for mobile WSNs. Reasonable coverage density was discussed with the presented threshold expressions as nodes moved.…”