A Dynamic Multi-Mobile Agent Itinerary Planning Approach in Wireless Sensor Networks via Intuitionistic Fuzzy Set
In recent research developments, the application of mobile agents (MAs) has attracted extensive research in wireless sensor networks (WSNs) due to the unique benefits it offers, such as energy conservation, network bandwidth saving, and flexibility of open usage for various WSN applications. The maj...
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| Published in | Sensors (Basel, Switzerland) Vol. 22; no. 20; p. 8037 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.10.2022
MDPI |
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| Online Access | Get full text |
| ISSN | 1424-8220 1424-8220 |
| DOI | 10.3390/s22208037 |
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| Abstract | In recent research developments, the application of mobile agents (MAs) has attracted extensive research in wireless sensor networks (WSNs) due to the unique benefits it offers, such as energy conservation, network bandwidth saving, and flexibility of open usage for various WSN applications. The majority of the proposed research ideas on dynamic itinerary planning agent-based algorithms are efficient when dealing with node failure as a result of energy depletion. However, they generate inefficient groups for MAs itineraries, which introduces a delay in broadcasting data return back to the sink node, and they do not consider the expanding size of the MAs during moving towards a sequence of related nodes. In order to rectify these research issues, we propose a new Graph-based Dynamic Multi-Mobile Agent Itinerary Planning approach (GDMIP). GDMIP works with “Directed Acyclic Graph” (DAG) techniques and distributes sensor nodes into various and efficient group-based shortest-identified routes, which cover all nodes in the network using intuitionistic fuzzy sets. MAs are restricted from moving in the predefined path and routes and are responsible for collecting data from the assigned groups. The experimental results of our proposed work show the effectiveness and expediency compared to the published approaches. Therefore, our proposed algorithm is more energy efficient and effective for task delay (time). |
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| AbstractList | In recent research developments, the application of mobile agents (MAs) has attracted extensive research in wireless sensor networks (WSNs) due to the unique benefits it offers, such as energy conservation, network bandwidth saving, and flexibility of open usage for various WSN applications. The majority of the proposed research ideas on dynamic itinerary planning agent-based algorithms are efficient when dealing with node failure as a result of energy depletion. However, they generate inefficient groups for MAs itineraries, which introduces a delay in broadcasting data return back to the sink node, and they do not consider the expanding size of the MAs during moving towards a sequence of related nodes. In order to rectify these research issues, we propose a new Graph-based Dynamic Multi-Mobile Agent Itinerary Planning approach (GDMIP). GDMIP works with “Directed Acyclic Graph” (DAG) techniques and distributes sensor nodes into various and efficient group-based shortest-identified routes, which cover all nodes in the network using intuitionistic fuzzy sets. MAs are restricted from moving in the predefined path and routes and are responsible for collecting data from the assigned groups. The experimental results of our proposed work show the effectiveness and expediency compared to the published approaches. Therefore, our proposed algorithm is more energy efficient and effective for task delay (time). In recent research developments, the application of mobile agents (MAs) has attracted extensive research in wireless sensor networks (WSNs) due to the unique benefits it offers, such as energy conservation, network bandwidth saving, and flexibility of open usage for various WSN applications. The majority of the proposed research ideas on dynamic itinerary planning agent-based algorithms are efficient when dealing with node failure as a result of energy depletion. However, they generate inefficient groups for MAs itineraries, which introduces a delay in broadcasting data return back to the sink node, and they do not consider the expanding size of the MAs during moving towards a sequence of related nodes. In order to rectify these research issues, we propose a new Graph-based Dynamic Multi-Mobile Agent Itinerary Planning approach (GDMIP). GDMIP works with "Directed Acyclic Graph" (DAG) techniques and distributes sensor nodes into various and efficient group-based shortest-identified routes, which cover all nodes in the network using intuitionistic fuzzy sets. MAs are restricted from moving in the predefined path and routes and are responsible for collecting data from the assigned groups. The experimental results of our proposed work show the effectiveness and expediency compared to the published approaches. Therefore, our proposed algorithm is more energy efficient and effective for task delay (time).In recent research developments, the application of mobile agents (MAs) has attracted extensive research in wireless sensor networks (WSNs) due to the unique benefits it offers, such as energy conservation, network bandwidth saving, and flexibility of open usage for various WSN applications. The majority of the proposed research ideas on dynamic itinerary planning agent-based algorithms are efficient when dealing with node failure as a result of energy depletion. However, they generate inefficient groups for MAs itineraries, which introduces a delay in broadcasting data return back to the sink node, and they do not consider the expanding size of the MAs during moving towards a sequence of related nodes. In order to rectify these research issues, we propose a new Graph-based Dynamic Multi-Mobile Agent Itinerary Planning approach (GDMIP). GDMIP works with "Directed Acyclic Graph" (DAG) techniques and distributes sensor nodes into various and efficient group-based shortest-identified routes, which cover all nodes in the network using intuitionistic fuzzy sets. MAs are restricted from moving in the predefined path and routes and are responsible for collecting data from the assigned groups. The experimental results of our proposed work show the effectiveness and expediency compared to the published approaches. Therefore, our proposed algorithm is more energy efficient and effective for task delay (time). |
| Audience | Academic |
| Author | Riaz, Muhammad Hill, Richard Alsboui, Tariq Iram, Shamaila Shakeel, Hafiz Muhammad Hussain, Muhammad Al-Aqrabi, Hussain Farid, Hafiz Muhammad Athar |
| AuthorAffiliation | 1 Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK 2 Department of Mathematics, University of the Punjab, Lahore 54590, Pakistan |
| AuthorAffiliation_xml | – name: 1 Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK – name: 2 Department of Mathematics, University of the Punjab, Lahore 54590, Pakistan |
| Author_xml | – sequence: 1 givenname: Tariq surname: Alsboui fullname: Alsboui, Tariq – sequence: 2 givenname: Richard orcidid: 0000-0003-0105-7730 surname: Hill fullname: Hill, Richard – sequence: 3 givenname: Hussain orcidid: 0000-0003-1920-7418 surname: Al-Aqrabi fullname: Al-Aqrabi, Hussain – sequence: 4 givenname: Hafiz Muhammad Athar orcidid: 0000-0002-8318-0750 surname: Farid fullname: Farid, Hafiz Muhammad Athar – sequence: 5 givenname: Muhammad orcidid: 0000-0001-8115-9168 surname: Riaz fullname: Riaz, Muhammad – sequence: 6 givenname: Shamaila orcidid: 0000-0003-0217-500X surname: Iram fullname: Iram, Shamaila – sequence: 7 givenname: Hafiz Muhammad orcidid: 0000-0003-2307-2763 surname: Shakeel fullname: Shakeel, Hafiz Muhammad – sequence: 8 givenname: Muhammad surname: Hussain fullname: Hussain, Muhammad |
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| Cites_doi | 10.1016/j.eswa.2022.118832 10.3390/math9161922 10.3233/JIFS-191819 10.1109/ACCESS.2018.2814064 10.1155/2007/36871 10.1016/S0165-0114(86)80034-3 10.1007/s12652-022-04348-0 10.1007/s41066-021-00253-7 10.3934/math.2021739 10.1016/j.eswa.2022.117430 10.1016/j.comnet.2014.06.019 10.1007/3-540-36978-3 10.1109/TKDE.2004.12 10.1016/j.compeleceng.2017.10.020 10.1007/978-3-319-11973-1_25 10.1109/TFUZZ.2006.890678 10.1016/j.adhoc.2006.07.004 10.1145/332833.332838 10.24200/sci.2022.59601.6326 10.1016/j.jnca.2014.01.003 10.1016/j.comcom.2012.09.017 10.3182/20090909-4-JP-2010.00055 10.1002/int.22587 10.1016/j.engappai.2021.104203 10.1016/S0019-9958(65)90241-X 10.1007/s40314-022-01806-5 10.3390/s17061280 10.1007/s12652-020-02667-8 10.1109/TKDE.2013.46 10.1109/TITS.2022.3202111 10.1002/int.22684 |
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| SubjectTerms | Algorithms dynamic itinerary Energy Energy conservation Fuzzy logic Fuzzy sets human–robot interaction intuitionistic fuzzy set itinerary planning mobile agent Planning Radio communications Robots Sensors Wireless sensor networks |
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| Title | A Dynamic Multi-Mobile Agent Itinerary Planning Approach in Wireless Sensor Networks via Intuitionistic Fuzzy Set |
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