Data dissemination in VANETs using clustering and probabilistic forwarding based on adaptive jumping multi-objective firefly optimization
Data dissemination in a VANETs network requires a meticulous process to ensure a high quality of service and eliminate hazardous conditions due to congestion or a broadcast storm. Considering multi-metric approaches and their implicit conflicting nature, it is necessary to handle this through effe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers
2022
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/6891/ http://eprints.uthm.edu.my/6891/1/J13941_d030937920e7d0e2c7563679fea0ec65.pdf |
| _version_ | 1848888940909559808 |
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| author | Hamdi, Mustafa Maad Audah, Lukman Rashid, Sami Abduljabbar |
| author_facet | Hamdi, Mustafa Maad Audah, Lukman Rashid, Sami Abduljabbar |
| author_sort | Hamdi, Mustafa Maad |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Data dissemination in a VANETs network requires a meticulous process to ensure a high
quality of service and eliminate hazardous conditions due to congestion or a broadcast storm. Considering
multi-metric approaches and their implicit conflicting nature, it is necessary to handle this through effective
multi-objective optimization algorithms. An effective optimization can be handled using a meta-heuristic
approach with a high level of solution interactions. For this purpose, firefly was selected, which is a type of
meta-heuristic search algorithm. Several developments of the firefly optimization were added to increase its
capability to find more dominating solutions, namely, objective decomposition, archive management, and
controlled mutation for exploration and exploitation balance. This developed multi-objective optimization
was designated as adaptive jumping multi-objective firefly algorithm (AJ-MOFA). Afterwards, AJ-MOFA
was integrated with a clustering and forwarding mechanism (CFM). This mechanism includes three main
components. The first is clustering, which uses arbitration based on the cluster head score; the second
is a forwarding mechanism that uses probabilistic forwarding and the third is AJ-MOFA. The solution
space design in CFM combined two variables: the first is the probability of forwarding and the second
is the maximum number of nodes within one cluster. The metrics to be incorporated in the multi-objective
optimizations are the packet delivery ratio (PDR), the end-to-end delay (E2E-delay) and the number of
dropped packets. Comparing both AJ-MOFA and CFM with benchmarks using multi-objective optimization
and networking metrics reveals the superiority in most evaluation measures, which makes them promising
algorithms for data dissemination in VANETs. The results showed an accomplished PDR of 60% and an
E2E delay of 6.6 seconds, while the number of dropped packets was almost nine for the entire running time
of the experiment, comparing a similar or lower performance of the benchmarks for these metrics. |
| first_indexed | 2025-11-15T20:18:16Z |
| format | Article |
| id | uthm-6891 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:18:16Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-68912022-04-07T02:34:47Z http://eprints.uthm.edu.my/6891/ Data dissemination in VANETs using clustering and probabilistic forwarding based on adaptive jumping multi-objective firefly optimization Hamdi, Mustafa Maad Audah, Lukman Rashid, Sami Abduljabbar TK7885-7895 Computer engineering. Computer hardware Data dissemination in a VANETs network requires a meticulous process to ensure a high quality of service and eliminate hazardous conditions due to congestion or a broadcast storm. Considering multi-metric approaches and their implicit conflicting nature, it is necessary to handle this through effective multi-objective optimization algorithms. An effective optimization can be handled using a meta-heuristic approach with a high level of solution interactions. For this purpose, firefly was selected, which is a type of meta-heuristic search algorithm. Several developments of the firefly optimization were added to increase its capability to find more dominating solutions, namely, objective decomposition, archive management, and controlled mutation for exploration and exploitation balance. This developed multi-objective optimization was designated as adaptive jumping multi-objective firefly algorithm (AJ-MOFA). Afterwards, AJ-MOFA was integrated with a clustering and forwarding mechanism (CFM). This mechanism includes three main components. The first is clustering, which uses arbitration based on the cluster head score; the second is a forwarding mechanism that uses probabilistic forwarding and the third is AJ-MOFA. The solution space design in CFM combined two variables: the first is the probability of forwarding and the second is the maximum number of nodes within one cluster. The metrics to be incorporated in the multi-objective optimizations are the packet delivery ratio (PDR), the end-to-end delay (E2E-delay) and the number of dropped packets. Comparing both AJ-MOFA and CFM with benchmarks using multi-objective optimization and networking metrics reveals the superiority in most evaluation measures, which makes them promising algorithms for data dissemination in VANETs. The results showed an accomplished PDR of 60% and an E2E delay of 6.6 seconds, while the number of dropped packets was almost nine for the entire running time of the experiment, comparing a similar or lower performance of the benchmarks for these metrics. Institute of Electrical and Electronics Engineers 2022 Article PeerReviewed text en http://eprints.uthm.edu.my/6891/1/J13941_d030937920e7d0e2c7563679fea0ec65.pdf Hamdi, Mustafa Maad and Audah, Lukman and Rashid, Sami Abduljabbar (2022) Data dissemination in VANETs using clustering and probabilistic forwarding based on adaptive jumping multi-objective firefly optimization. IEEE Access, 10. pp. 14624-14642. |
| spellingShingle | TK7885-7895 Computer engineering. Computer hardware Hamdi, Mustafa Maad Audah, Lukman Rashid, Sami Abduljabbar Data dissemination in VANETs using clustering and probabilistic forwarding based on adaptive jumping multi-objective firefly optimization |
| title | Data dissemination in VANETs using clustering and probabilistic forwarding based on adaptive jumping multi-objective firefly optimization |
| title_full | Data dissemination in VANETs using clustering and probabilistic forwarding based on adaptive jumping multi-objective firefly optimization |
| title_fullStr | Data dissemination in VANETs using clustering and probabilistic forwarding based on adaptive jumping multi-objective firefly optimization |
| title_full_unstemmed | Data dissemination in VANETs using clustering and probabilistic forwarding based on adaptive jumping multi-objective firefly optimization |
| title_short | Data dissemination in VANETs using clustering and probabilistic forwarding based on adaptive jumping multi-objective firefly optimization |
| title_sort | data dissemination in vanets using clustering and probabilistic forwarding based on adaptive jumping multi-objective firefly optimization |
| topic | TK7885-7895 Computer engineering. Computer hardware |
| url | http://eprints.uthm.edu.my/6891/ http://eprints.uthm.edu.my/6891/1/J13941_d030937920e7d0e2c7563679fea0ec65.pdf |