Optimal multiobjective placement of distributed sensors against moving targets

Wettergren, Thomas A.; Costa, Russell

doi:10.1145/2240092.2240095

Cited by 9 publications

(5 citation statements)

References 31 publications

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“…GA gives alternative strategies to tackle problems that are hard to unravel utilizing conventional techniques. For instance, in [ 123 ], GA has been suggested to take care of the issue of optimal deployment of WSN for increasing probability of searching an moving object in the field. In [ 124 ], the authors utilized GA to tackle the multi-objective optimization formulation utilized to attain the ideal stationing of sensors at the point of port entry to inspect the vessels and identify the movement of illicit freight.…”

Section: Network Optimizationmentioning

confidence: 99%

A Survey and Tutorial on Network Optimization for Intelligent Transport System Using the Internet of Vehicles

Panigrahy¹,

Emany²

2023

Sensors

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The Internet of Things (IoT) has risen from ubiquitous computing to the Internet itself. Internet of vehicles (IoV) is the next emerging trend in IoT. We can build intelligent transportation systems (ITS) using IoV. However, overheads are imposed on IoV network due to a massive quantity of information being transferred from the devices connected in IoV. One such overhead is the network connection between the units of an IoV. To make an efficient ITS using IoV, optimization of network connectivity is required. A survey on network optimization in IoT and IoV is presented in this study. It also highlights the backdrop of IoT and IoV. This includes the applications, such as ITS with comparison to different advancements, optimization of the network, IoT discussions, along with categorization of algorithms. Some of the simulation tools are also explained which will help the research community to use those tools for pursuing research in IoV.

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Section: Network Optimizationmentioning

confidence: 99%

A Survey and Tutorial on Network Optimization for Intelligent Transport System Using the Internet of Vehicles

Panigrahy¹,

Emany²

2023

Sensors

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“…For global optimization, differential evolution (DE) has been applied as an optimization approach to compute Pareto trade-off surfaces for sensor placements using coverage, connectivity and lifetime/energy [27]. In our prior work [28], we have used genetic algorithm approaches to determine sensor placement patterns that achieve a trade-off between coverage and false alarms.…”

Section: Background On Sensor Network Configuration Optimizationmentioning

confidence: 99%

Optimal Configuration Planning for Sensor Network Serviceability Under a System Coverage Constraint

Wettergren

Costa

2021

IEEE Access

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We develop an optimization approach for the planning problem of configuring the positions of sensors within a sensor network for minimization of the travel length required to service the sensor locations. This is in contrast to existing approaches whereby the coverage of the sensor network is an objective of the optimization; in this new optimization approach the level of coverage is treated as a constraint. By beginning with an over-populated sensor network and then alternating between sensor repositioning and sensor removal, we create an optimization procedure that solves this difficult practical planning problem. A formal rule for switching between the repositioning and removal components of the optimization strategy is developed. Numerical example problems are presented to illustrate the method and show its performance against a heuristic optimization approach.

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“…The list of acronyms used in this paper is displayed in Table. 1. Most this kind of problems is often solved using meta-heuristic approaches [6,29,30,31,32] and more precisely using nature-inspired algorithms. As can be seen from Table. 2, among these algorithms, GA was frequently used.…”

Section: Related Workmentioning

confidence: 99%

MOONGA: Multi-Objective Optimization of Wireless Network Approach Based on Genetic Algorithm

et al. 2020

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In high-density wireless sensor networks, the quality of service in terms of sensing coverage, connectivity, lifetime, energy consumption and cost is closely linked to the position of the nodes in the network. Consequently, the placement of a large number of nodes while simultaneously optimizing several measurements is considered to be an NP-difficult problem. In this article, we propose a new approach to optimizing the problem of node placement. To achieve this objective, we started by studying the main approaches existing in the literature in order to identify their limits. In order to have accurate solutions, existing physical models are studied, improved, and validated with real measurements. Then, we proposed a new formulation of the deployment optimization problem as a constrained multi-objective optimization problem. This allowed us to develop an optimizer, based on the multi-objective genetic algorithm and the weighted sum optimization method, which we called MOONGA (multi-objective wireless network optimization using the genetic algorithm). This optimizer makes it possible to generate an optimal deployment according to the topology, the environment, the specifications of different applications and the preferences of the network designer users. The algorithms that we have developed and implemented within the framework of experiments carried out on test data in order to prove the effectiveness of our approach. The analysis of the results found confirm well the interest and the superiority of our proposed approach compared to main studied approaches.

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Optimal multiobjective placement of distributed sensors against moving targets

Cited by 9 publications

References 31 publications

A Survey and Tutorial on Network Optimization for Intelligent Transport System Using the Internet of Vehicles

A Survey and Tutorial on Network Optimization for Intelligent Transport System Using the Internet of Vehicles

Optimal Configuration Planning for Sensor Network Serviceability Under a System Coverage Constraint

MOONGA: Multi-Objective Optimization of Wireless Network Approach Based on Genetic Algorithm

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