Utilizing distributed learning automata to solve the connected target coverage problem in directional sensor networks
Sensor networks have been employed in a variety of applications. Directional sensor networks (DSNs) are a class of sensor networks that have emerged more recently and received noticeable attention from scholars. One of the most significant challenges associated with DSNs is designing an effective al...
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| Format: | Article |
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Elsevier B.V.
2013
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| _version_ | 1796859477054455808 |
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| author | Mohamadi, Hosein Ismail, Abdul Samad Salleh, Shaharuddin |
| author_facet | Mohamadi, Hosein Ismail, Abdul Samad Salleh, Shaharuddin |
| author_sort | Mohamadi, Hosein |
| collection | ePrints |
| description | Sensor networks have been employed in a variety of applications. Directional sensor networks (DSNs) are a class of sensor networks that have emerged more recently and received noticeable attention from scholars. One of the most significant challenges associated with DSNs is designing an effective algorithm to cover all the targets and, at the same time, retain connectivity with the sink. As sensors are often densely deployed, employing scheduling algorithms can be considered as a promising approach. In this paper, we use distributed learning automata (DLA) to design a new scheduling algorithm for solving the connected target coverage problem in DSNs. The proposed algorithm employs DLA to determine the sensors that should be activated at each stage for monitoring all the targets and transmitting the sensing data to the sink. In addition, we devise several pruning rules in order to maximize network lifetime. Extensive simulation experiments were carried out to evaluate the performance of the proposed algorithm. Simulation results demonstrated the superiority of the proposed algorithm over a greedy-based algorithm in terms of extending network lifetime |
| first_indexed | 2024-03-05T19:27:45Z |
| format | Article |
| id | utm.eprints-50297 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:27:45Z |
| publishDate | 2013 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | utm.eprints-502972018-10-21T04:33:25Z http://eprints.utm.my/50297/ Utilizing distributed learning automata to solve the connected target coverage problem in directional sensor networks Mohamadi, Hosein Ismail, Abdul Samad Salleh, Shaharuddin Q Science (General) Sensor networks have been employed in a variety of applications. Directional sensor networks (DSNs) are a class of sensor networks that have emerged more recently and received noticeable attention from scholars. One of the most significant challenges associated with DSNs is designing an effective algorithm to cover all the targets and, at the same time, retain connectivity with the sink. As sensors are often densely deployed, employing scheduling algorithms can be considered as a promising approach. In this paper, we use distributed learning automata (DLA) to design a new scheduling algorithm for solving the connected target coverage problem in DSNs. The proposed algorithm employs DLA to determine the sensors that should be activated at each stage for monitoring all the targets and transmitting the sensing data to the sink. In addition, we devise several pruning rules in order to maximize network lifetime. Extensive simulation experiments were carried out to evaluate the performance of the proposed algorithm. Simulation results demonstrated the superiority of the proposed algorithm over a greedy-based algorithm in terms of extending network lifetime Elsevier B.V. 2013 Article PeerReviewed Mohamadi, Hosein and Ismail, Abdul Samad and Salleh, Shaharuddin (2013) Utilizing distributed learning automata to solve the connected target coverage problem in directional sensor networks. Sensors and Actuators A-Physical, 198 . pp. 21-30. ISSN 0924-4247 http://dx.doi.org/10.1016/j.sna.2013.03.034 DOI: 10.1016/j.sna.2013.03.034 |
| spellingShingle | Q Science (General) Mohamadi, Hosein Ismail, Abdul Samad Salleh, Shaharuddin Utilizing distributed learning automata to solve the connected target coverage problem in directional sensor networks |
| title | Utilizing distributed learning automata to solve the connected target coverage problem in directional sensor networks |
| title_full | Utilizing distributed learning automata to solve the connected target coverage problem in directional sensor networks |
| title_fullStr | Utilizing distributed learning automata to solve the connected target coverage problem in directional sensor networks |
| title_full_unstemmed | Utilizing distributed learning automata to solve the connected target coverage problem in directional sensor networks |
| title_short | Utilizing distributed learning automata to solve the connected target coverage problem in directional sensor networks |
| title_sort | utilizing distributed learning automata to solve the connected target coverage problem in directional sensor networks |
| topic | Q Science (General) |
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