Multiobjective Optimization in Wireless Sensor Networks

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (19 September 2022) | Viewed by 2900

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Department of Chemical Engineering, Norwegian University of Science and Technology, 7034 Trondheim, Norway
Interests: artificial intelligence; scientific machine learning; process system engineering; systems control & optimization; cyber-physical systems; digital twins
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Dear Colleagues,

Modern society is living a moment of paradigm changes. These changes have been instigated by new technologies such as high-performance computing, cloud computing, and internet of things. Nowadays, we are observing an almost immediate exchange of information worldwide, creating big data pipelines that enable the emergence of complex artificial intelligence systems. This digital revolution promotes the emergence of a complex system where conflicting goals need to be considered. This can be specially observed in wireless networks (WSNs), where there are several tradeoffs, such as energy consumption and coverage area. In this scenario, the multiobjective optimization (MOO) technique allows the determination of possible conditions portrayed in a set of known solutions. Therefore, developments in this optimization field might play an essential role in digital transformation.

Hence, this Special Issue aims to discuss advances in mathematical scalarization methods, heuristics/metaheuristics-based optimization algorithms, and other advanced optimization techniques to address WSNs problems. However, it seeks to provide a broader discussion in the optimization field. Thus, any work in a related optimization field is welcome. Furthermore, developments in the sensors field are welcome, including soft sensors.

Dr. Idelfonso B. R. Nogueira
Guest Editor

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Keywords

  • multiobjective optimization
  • wireless sensor networks
  • meta-heuristic algorithms
  • Pareto optimal solutions
  • artificial intelligence
  • cyber-physical systems
  • digital twins

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Published Papers (1 paper)

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Research

17 pages, 3342 KiB  
Article
Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks Using Aquila Optimizer Algorithm
by Ashraf A. Taha, Hagar O. Abouroumia, Shimaa A. Mohamed and Lamiaa A. Amar
Future Internet 2022, 14(12), 365; https://doi.org/10.3390/fi14120365 - 7 Dec 2022
Cited by 10 | Viewed by 2297
Abstract
As sensors are distributed among wireless sensor networks (WSNs), ensuring that the batteries and processing power last for a long time, to improve energy consumption and extend the lifetime of the WSN, is a significant challenge in the design of network clustering techniques. [...] Read more.
As sensors are distributed among wireless sensor networks (WSNs), ensuring that the batteries and processing power last for a long time, to improve energy consumption and extend the lifetime of the WSN, is a significant challenge in the design of network clustering techniques. The sensor nodes are divided in these techniques into clusters with different cluster heads (CHs). Recently, certain considerations such as less energy consumption and high reliability have become necessary for selecting the optimal CH nodes in clustering-based metaheuristic techniques. This paper introduces a novel enhancement algorithm using Aquila Optimizer (AO), which enhances the energy balancing in clusters across sensor nodes during network communications to extend the network lifetime and reduce power consumption. Lifetime and energy-efficiency clustering algorithms, namely the low-energy adaptive clustering hierarchy (LEACH) protocol as a traditional protocol, genetic algorithm (GA), Coyote Optimization Algorithm (COY), Aquila Optimizer (AO), and Harris Hawks Optimization (HHO), are evaluated in a wireless sensor network. The paper concludes that the proposed AO algorithm outperforms other algorithms in terms of alive nodes analysis and energy consumption. Full article
(This article belongs to the Special Issue Multiobjective Optimization in Wireless Sensor Networks)
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