A Novel Bio-Inspired Bat Node Scheduling Algorithm for Dependable Safety-Critical Wireless Sensor Network Systems
The multi-objective optimization (MOO) problem in wireless sensor networks (WSNs) is concerned with optimizing the operation of the WSN across three dimensions: coverage, connectivity, and lifetime. Most works in the literature address only one or two dimensions of this problem at a time, except for...
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MDPI AG
2024-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/6/1928 |
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| author | Issam Al-Nader Aboubaker Lasebae Rand Raheem Gerard Ekembe Ngondi |
| author_facet | Issam Al-Nader Aboubaker Lasebae Rand Raheem Gerard Ekembe Ngondi |
| author_sort | Issam Al-Nader |
| collection | DOAJ |
| description | The multi-objective optimization (MOO) problem in wireless sensor networks (WSNs) is concerned with optimizing the operation of the WSN across three dimensions: coverage, connectivity, and lifetime. Most works in the literature address only one or two dimensions of this problem at a time, except for the randomized coverage-based scheduling (RCS) algorithm and the clique-based scheduling algorithm. More recently, a Hidden Markov Model (HMM)-based algorithm was proposed that improves on the latter two; however, the question remains open if further improvement is possible as previous algorithms explore solutions in terms of local minima and local maxima, not in terms of the full search space globally. Therefore, the main contribution of this paper is to propose a new scheduling algorithm based on bio-inspired computation (the bat algorithm) to address this limitation. First, the algorithm defines a fitness and objective function over a search space, which returns all possible sleep and wake-up schedules for each node in the WSN. This yields a (scheduling) solution space that is then organized by the Pareto sorting algorithm, whose output coordinates are the distance of each node to the base station and the residual energy of the node. We evaluated our results by comparing the bat and HMM node scheduling algorithms implemented in MATLAB. Our results show that network lifetime has improved by 30%, coverage by 40%, and connectivity by 26.7%. In principle, the obtained solution will be the best scheduling that guarantees the best network lifetime performance as well as the best coverage and connectedness for ensuring the dependability of safety-critical WSNs. |
| first_indexed | 2024-04-24T17:49:50Z |
| format | Article |
| id | doaj.art-bf5a3e632b454c8185fd18e64e48d3e3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T17:49:50Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bf5a3e632b454c8185fd18e64e48d3e32024-03-27T14:04:09ZengMDPI AGSensors1424-82202024-03-01246192810.3390/s24061928A Novel Bio-Inspired Bat Node Scheduling Algorithm for Dependable Safety-Critical Wireless Sensor Network SystemsIssam Al-Nader0Aboubaker Lasebae1Rand Raheem2Gerard Ekembe Ngondi3Faculty of Science & Technology, Department of Computer Science, Middlesex University, The Burroughs, London NW4 4BT, UKFaculty of Science & Technology, Department of Computer Science, Middlesex University, The Burroughs, London NW4 4BT, UKFaculty of Science & Technology, Department of Computer Science, Middlesex University, The Burroughs, London NW4 4BT, UKComputer Science Department, School of Statistics and Computer Science, Trinity College, Dublin D02 PN40, IrelandThe multi-objective optimization (MOO) problem in wireless sensor networks (WSNs) is concerned with optimizing the operation of the WSN across three dimensions: coverage, connectivity, and lifetime. Most works in the literature address only one or two dimensions of this problem at a time, except for the randomized coverage-based scheduling (RCS) algorithm and the clique-based scheduling algorithm. More recently, a Hidden Markov Model (HMM)-based algorithm was proposed that improves on the latter two; however, the question remains open if further improvement is possible as previous algorithms explore solutions in terms of local minima and local maxima, not in terms of the full search space globally. Therefore, the main contribution of this paper is to propose a new scheduling algorithm based on bio-inspired computation (the bat algorithm) to address this limitation. First, the algorithm defines a fitness and objective function over a search space, which returns all possible sleep and wake-up schedules for each node in the WSN. This yields a (scheduling) solution space that is then organized by the Pareto sorting algorithm, whose output coordinates are the distance of each node to the base station and the residual energy of the node. We evaluated our results by comparing the bat and HMM node scheduling algorithms implemented in MATLAB. Our results show that network lifetime has improved by 30%, coverage by 40%, and connectivity by 26.7%. In principle, the obtained solution will be the best scheduling that guarantees the best network lifetime performance as well as the best coverage and connectedness for ensuring the dependability of safety-critical WSNs.https://www.mdpi.com/1424-8220/24/6/1928IoT sensor systemWSNsmart sensing for safetydependable WSNscheduling algorithmsreal-time systems |
| spellingShingle | Issam Al-Nader Aboubaker Lasebae Rand Raheem Gerard Ekembe Ngondi A Novel Bio-Inspired Bat Node Scheduling Algorithm for Dependable Safety-Critical Wireless Sensor Network Systems Sensors IoT sensor system WSN smart sensing for safety dependable WSN scheduling algorithms real-time systems |
| title | A Novel Bio-Inspired Bat Node Scheduling Algorithm for Dependable Safety-Critical Wireless Sensor Network Systems |
| title_full | A Novel Bio-Inspired Bat Node Scheduling Algorithm for Dependable Safety-Critical Wireless Sensor Network Systems |
| title_fullStr | A Novel Bio-Inspired Bat Node Scheduling Algorithm for Dependable Safety-Critical Wireless Sensor Network Systems |
| title_full_unstemmed | A Novel Bio-Inspired Bat Node Scheduling Algorithm for Dependable Safety-Critical Wireless Sensor Network Systems |
| title_short | A Novel Bio-Inspired Bat Node Scheduling Algorithm for Dependable Safety-Critical Wireless Sensor Network Systems |
| title_sort | novel bio inspired bat node scheduling algorithm for dependable safety critical wireless sensor network systems |
| topic | IoT sensor system WSN smart sensing for safety dependable WSN scheduling algorithms real-time systems |
| url | https://www.mdpi.com/1424-8220/24/6/1928 |
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