Optimizing the Lifetime of Heterogeneous Sensor Networks Under Coverage Constraint : MILP and Genetic Based Approaches

Charr, Jean‐Claude; Deschinkel, Karine; Mansour, Rania Haj; Hakem, Mourad

doi:10.1109/wimob.2019.8923327

Cited by 5 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most popular evolutionary techniques are genetic algorithms (GA) (see [19]). They were used recently by [4] to solve the target coverage problem, and by [20] for lifetime optimization by applying a method combining mixed integer programming with GA. In [21], the authors proposed an energy-efficient coverage method, where GA is used to compute the minimum number of potential positions to allow all targets to be k-covered and all sensor nodes to be m-connected.…”

Section: Related Workmentioning

confidence: 99%

Coverage and Lifetime Optimization by Self-Optimizing Sensor Networks

Seredyński

Kulpa

Hoffmann

et al. 2023

Sensors

View full text Add to dashboard Cite

We propose an approach to self-optimizing wireless sensor networks (WSNs) which are able to find, in a fully distributed way, a solution to a coverage and lifetime optimization problem. The proposed approach is based on three components: (a) a multi-agent, social-like interpreted system, where the modeling of agents, discrete space, and time is provided by a 2-dimensional second-order cellular automata, (b) the interaction between agents is described in terms of the spatial prisoner’s dilemma game, and (c) a local evolutionary mechanism of competition between agents exists. Nodes of a WSN graph created for a given deployment of WSN in the monitored area are considered agents of a multi-agent system that collectively make decisions to turn on or turn off their batteries. Agents are controlled by cellular automata (CA)-based players participating in a variant of the spatial prisoner’s dilemma iterated game. We propose for players participating in this game a local payoff function that incorporates issues of area coverage and sensors energy spending. Rewards obtained by agent players depend not only on their personal decisions but also on their neighbor’s decisions. Agents act in such a way to maximize their own rewards, which results in achieving by them a solution corresponding to the Nash equilibrium point. We show that the system is self-optimizing, i.e., can optimize in a distributed way global criteria related to WSN and not known for agents, provide a balance between requested coverage and spending energy, and result in expanding the WSN lifetime. The solutions proposed by the multi-agent system fulfill the Pareto optimality principles, and the desired quality of solutions can be controlled by user-defined parameters. The proposed approach is validated by a number of experimental results.

show abstract

Section: Related Workmentioning

confidence: 99%

Coverage and Lifetime Optimization by Self-Optimizing Sensor Networks

Seredyński

Kulpa

Hoffmann

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

A Learning Automata-Based Approach to Lifetime Optimization in Wireless Sensor Networks

Gąsior

Seredyński

2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Cellular automata rules solving the wireless sensor network coverage problem

2022

View full text Add to dashboard Cite

The problem of an optimal coverage of a wireless sensor network area is considered. To solve this problem, a Cellular Automata (CA) approach is proposed. More specifically, the objective is to find CA rules which are able to cover the 2D space by a minimum number of so–called “Sensor Tiles”. A sensor tile consists of a von Neumann neighborhood of range 2 centered at sensor “point” and surrounded by 12 sensing “pixels”. Two probabilistic CA rules were designed that can perform this task. Results of an experimental study show that the first rule evolves very fast stable sub-optimal coverings, starting from a random configuration. The second rule finds optimal coverings, however it needs much more time for their evolution. The results are supported by a theoretical study on von Neumann neighborhoods and borrowing either from heuristics or from the spectral theory of circulant graphs.

show abstract

Optimizing the Lifetime of Heterogeneous Sensor Networks Under Coverage Constraint : MILP and Genetic Based Approaches

Cited by 5 publications

References 18 publications

Coverage and Lifetime Optimization by Self-Optimizing Sensor Networks

Coverage and Lifetime Optimization by Self-Optimizing Sensor Networks

A Learning Automata-Based Approach to Lifetime Optimization in Wireless Sensor Networks

Cellular automata rules solving the wireless sensor network coverage problem

Contact Info

Product

Resources

About