• Corpus ID: 27784808

Cooperative Particle Swarm Optimization for Layout Optimization in Wireless Sensor Networks

@inproceedings{Fortier2014CooperativePS,
  title={Cooperative Particle Swarm Optimization for Layout Optimization in Wireless Sensor Networks},
  author={Nathan Fortier and Nathan Fortier and Agata Gruza and Russell E. Ericksen},
  year={2014},
  url={https://api.semanticscholar.org/CorpusID:27784808}
}
This work proposes a novel heuristic search technique to solve the problem of selecting the optimal geographical positions of the nodes in Wireless Sensor Networks (WSNs).

Tables from this paper

Energy Efficient Layout for a Wireless Sensor Network using Multi-Objective Particle Swarm Optimization

An energy efficient layout with good coverage based on Multi-objective Particle Swarm Optimization algorithm is proposed here.

Multi-objective Evolutionary Algorithms to Solve Coverage and Lifetime Optimization Problem in Wireless Sensor Networks

The concept of boundary search is investigated and the application of a special evolutionary operator on a multi-objective optimization problem; Coverage and Lifetime Optimization Problem in Wireless Sensor Network (WSN).

Overlapping particle swarms for energy-efficient routing in sensor networks

This work proposes an energy-aware routing protocol, based on overlapping swarms of particles, that offers reliable path selection while reducing the energy consumption for the route selection process and shows promise in extending the life of battery-powered networks while still providing robust routing functionality to maintain network reliability.

Optimal Wireless Sensor Network Coverage with Ant Colony Optimization

This paper prepares an Ant Colony Optimization (ACO) algorithm and compares its results with existing metaheuristic algorithms for wireless sensor network coverage problem.

Layout optimization for a wireless sensor network using a multi-objective genetic algorithm

    D. JourdanO. Weck
    Engineering, Computer Science
  • 2004
This paper examines the optimization of wireless sensor network layouts by benchmarking a multi objective genetic algorithm (MOGA) for the sensor placement, where the two competing objectives considered are the total sensor coverage and the lifetime of the network.

A CLUSTERING ROUTING PROTOCOL FOR ENERGY BALANCE OF WIRELESS SENSOR NETWORK BASED ON SIMULATED ANNEALING AND GENETIC ALGORITHM

Simulations show that the new program could improve Energy Hotspot caused by the uneven distribution of cluster head in LEACH protocol, thus it can balance the wireless sensor network load balance and extend the lifecycle of wireless sensornetwork.

Energy Consumption Estimation for Wireless Sensor Network Layout Optimization

The node energy consumption is estimated with a discrete-event network simulation that provides realistic estimation values by capturing the dynamics of wireless communication protocols to achieve high estimation accuracy and low computational complexity.

Optimal wireless sensor networks (WSNs) deployment: minimum cost with lifetime constraint

This paper explores the problem of the optimal WSN deployment, with an objective of minimizing the network cost with lifetime constraint, and proposes a deterministic deployment strategy based on a recursive algorithm.

Exploiting Multi-Objective Evolutionary Algorithms for Designing Energy-Efficient Solutions to Data Compression and Node Localization in Wireless Sensor Networks

This chapter discusses how multi–objective evolutionary algorithms can successfully be exploited to generate energy–aware data compressors and to solve the node localization problem.

Maximum Lifetime Routing In Wireless Sensor Networks ∗

It is shown that the problem of routing messages in a wireless sensor network so as to maximize network lifetime is NP-hard and an online heuristic is developed, which performs two shortest path computations to route each message, is superior to previously published heuristics for lifetime maximization.