Performance Investigation of Immune Plasma Algorithm on Solving Wireless Sensor Deployment Problem

Tasdemir, Adnan; Demirci, Sercan; Aslan, Selçuk

doi:10.1109/iceee55327.2022.9772539

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Heuristic approaches are appropriate for near-optimal solutions, especially in NP-complete problems. To this end, a metaheuristic algorithm is proposed in [ 19 ], which determines the sensors’ positions for area coverage maximization. As a part of the solution, the authors utilized the immune plasma algorithm (IPA), a unique metaheuristic algorithm that is based on the immune plasma therapy concept and the transfer of an antibody-rich fraction of blood from previously recovered patients to others who are considered critically ill.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration

Xenakis

Chaikalis

Kosmanos

et al. 2023

Sensors

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In dense IoT deployments of wireless sensor networks (WSNs), sensor placement, coverage, connectivity, and energy constraints determine the overall network lifetime. In large-size WSNs, it is difficult to maintain a trade-off among these conflicting constraints and, thus, scaling is difficult. In the related research literature, various solutions are proposed that attempt to address near-optimal behavior in polynomial time, the majority of which relies on heuristics. In this paper, we formulate a topology control and lifetime extension problem regarding sensor placement, under coverage and energy constraints, and solve it by applying and testing several neural network configurations. To do so, the neural network dynamically proposes and handles sensor placement coordinates in a 2D plane, having the ultimate goal to extend network lifetime. Simulation results show that our proposed algorithm improves network lifetime, while maintaining communication and energy constraints, for medium- and large-scale deployments.

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Section: Related Work and Motivationmentioning

confidence: 99%

Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration

Xenakis

Chaikalis

Kosmanos

et al. 2023

Sensors

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“…If the main operations executed by the IPA are investigated, it can be easily seen that the IPA differs from the most representative meta-heuristics including ACO, ABC, PSO, BA, FA, FWA, DE, and GA [53]. While the exploitation-dominant operations of the IPA are designed in a semi-adaptive manner and adjust the convergence dynamically without changing the initial values of the control parameters, a quasi-deterministic approach is responsible for managing the important part of exploration, and its promising performance has been validated recently for engineering problems such as channel assignment [54], time series prediction [55], wireless sensor deployment [56], signal noise minimization [57], neural network training [58], and also UAV or UCAV path planning [59]. In this study, the plasma transfer scheme of the IP algorithm was changed with a newly introduced approach called extended treatment, and an extended IP algorithm, or ExtIPA, was designed to solve the path-planning problem.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of an Unmanned Combat Aerial Vehicle with an Extended-Treatment-Approach-Based Immune Plasma Algorithm

Aslan

Oktay

2023

Aerospace

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The increasing usage of unmanned aerial vehicles (UAVs) and their variants carrying complex weapon systems, known as unmanned combat aerial vehicles (UCAVs), has triggered a global revolution in complex military and commercial operations and has attracted researcher attention from different engineering disciplines in order to solve challenging problems regarding these modern vehicles. Path planning is a challenging problem for UAV and UCAV systems that requires the calculation of an optimal solution by considering enemy threats, total flight length, fuel or battery consumption, and some kinematic properties such as turning or climbing angles. In this study, the immune plasma (IP or IPA) algorithm, one of the most recent nature-inspired intelligent optimization methods, was modified by changing the default plasma transfer operations with a newly proposed technique called the extended treatment approach; extended IPA (ExtIPA) was then introduced as a path planner. To analyze the solving capabilities of the ExtIPA, 16 cases from five battlefield scenarios were tested by assigning different values to the algorithm-specific control parameters. The paths calculated with ExtIPA were compared with the paths found by planners on the basis of other intelligent optimization techniques. Comparative studies between ExtIPA and other techniques allowed for stating that the extended treatment approach significantly contributes to both the convergence speed and qualities of the obtained solutions and helps ExtIPA in performing better than its rivals in most cases.

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Optimal selection of benchmarking datasets for unbiased machine learning algorithm evaluation

Pereira,

Smith-Miles,

Muñoz

et al. 2023

Data Min Knowl Disc

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Performance Investigation of Immune Plasma Algorithm on Solving Wireless Sensor Deployment Problem

Cited by 7 publications

References 10 publications

Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration

Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration

Path Planning of an Unmanned Combat Aerial Vehicle with an Extended-Treatment-Approach-Based Immune Plasma Algorithm

Optimal selection of benchmarking datasets for unbiased machine learning algorithm evaluation

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