A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks
Wireless Sensor Networks (WSN) have lately been gaining momentum thanks to the hardware improvements and standardization software efforts. Moreover, the appearance of Internet of Things (IoT) and its reliance on sensors are helping to widely extend the usage of WSNs. However, such networks present d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/21/4564 |
| _version_ | 1818859714244509696 |
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| author | David Sembroiz Behnam Ojaghi Davide Careglio Sergio Ricciardi |
| author_facet | David Sembroiz Behnam Ojaghi Davide Careglio Sergio Ricciardi |
| author_sort | David Sembroiz |
| collection | DOAJ |
| description | Wireless Sensor Networks (WSN) have lately been gaining momentum thanks to the hardware improvements and standardization software efforts. Moreover, the appearance of Internet of Things (IoT) and its reliance on sensors are helping to widely extend the usage of WSNs. However, such networks present drawbacks, mainly because of limited sensor batteries and their vulnerability against physical attacks due to the lack of protection and security. Additionally, not all the sensors inside the network have the same responsibility in terms of traffic handling. In this paper, we firstly analyze the fact that some nodes are more <i>critical</i> than others, considering the most <i>critical node</i> the one that, once incapacitated, causes the most deterioration on the network performance. Such performance is analyzed using two metrics, namely network latency and lifetime. We present a result comparison between a Mixed Integer Programming (MIP) model and a Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic for small networks. For bigger networks, GRASP meta-heuristic results are presented to understand how the network degrades as the number of both <i>critical</i> and network nodes increase, by distributing them into two different areas: fixed and incremental to maintain node density. |
| first_indexed | 2024-12-19T09:16:34Z |
| format | Article |
| id | doaj.art-8fa8244d1cce4a08af7fb1ec949503fc |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-19T09:16:34Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8fa8244d1cce4a08af7fb1ec949503fc2022-12-21T20:28:06ZengMDPI AGApplied Sciences2076-34172019-10-01921456410.3390/app9214564app9214564A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor NetworksDavid Sembroiz0Behnam Ojaghi1Davide Careglio2Sergio Ricciardi3Department of Computer Architecture, Technical University of Catalonia—BarcelonaTech (UPC), 08034 Barcelona, SpainDepartment of Computer Engineering, TOBB University of Economics and Technology, 06560 Ankara, TurkeyDepartment of Computer Architecture, Technical University of Catalonia—BarcelonaTech (UPC), 08034 Barcelona, SpainDepartment of Computer Architecture, Technical University of Catalonia—BarcelonaTech (UPC), 08034 Barcelona, SpainWireless Sensor Networks (WSN) have lately been gaining momentum thanks to the hardware improvements and standardization software efforts. Moreover, the appearance of Internet of Things (IoT) and its reliance on sensors are helping to widely extend the usage of WSNs. However, such networks present drawbacks, mainly because of limited sensor batteries and their vulnerability against physical attacks due to the lack of protection and security. Additionally, not all the sensors inside the network have the same responsibility in terms of traffic handling. In this paper, we firstly analyze the fact that some nodes are more <i>critical</i> than others, considering the most <i>critical node</i> the one that, once incapacitated, causes the most deterioration on the network performance. Such performance is analyzed using two metrics, namely network latency and lifetime. We present a result comparison between a Mixed Integer Programming (MIP) model and a Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic for small networks. For bigger networks, GRASP meta-heuristic results are presented to understand how the network degrades as the number of both <i>critical</i> and network nodes increase, by distributing them into two different areas: fixed and incremental to maintain node density.https://www.mdpi.com/2076-3417/9/21/4564internet of thingsmipwsnilpoptimizationgraspmeta-heuristicslatencylifetime |
| spellingShingle | David Sembroiz Behnam Ojaghi Davide Careglio Sergio Ricciardi A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks Applied Sciences internet of things mip wsn ilp optimization grasp meta-heuristics latency lifetime |
| title | A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks |
| title_full | A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks |
| title_fullStr | A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks |
| title_full_unstemmed | A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks |
| title_short | A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks |
| title_sort | grasp meta heuristic for evaluating the latency and lifetime impact of critical nodes in large wireless sensor networks |
| topic | internet of things mip wsn ilp optimization grasp meta-heuristics latency lifetime |
| url | https://www.mdpi.com/2076-3417/9/21/4564 |
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