Ions Motion Optimization-Based Clustering Routing Protocol for Cognitive Radio Sensor Network

Wang, Jihong; Li, Shuo; Ge, Yiyang

doi:10.1109/access.2020.3030808

Cited by 12 publications

(15 citation statements)

References 34 publications

(52 reference statements)

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“…According to the analysis above, TD-IMOCRP and IMOCRP have similar performance in serving time-triggered traffic. As we have verified in our previous work [ 6 ], IMOCRP performs better than majority of current time-triggered clustering protocols for CRSNs. Therefore, we can obtain the conclusion that TD-IMOCRP is superior to majority of current time-triggered clustering protocols for CRSNs in serving time-triggered traffic.…”

Section: Resultssupporting

confidence: 71%

“…The event radius is 30m, which means that CRSNs nodes within 30m around the event occurrence place can detect the event. In order to evaluate its performance in serving time-triggered traffic, we compare TD-IMOCRP with IMOCRP [ 6 ], as IMOCRP performs the best among current time-triggered clustering protocols for CRSNs. In addition, by comparing TD-IMOCRP with current event-driven clustering protocols such as ESAC [ 24 ], mESAC [ 25 ] and ERP [ 26 ], we test its capability in serving event-driven traffic.…”

Section: Resultsmentioning

confidence: 99%

“…In other words, cluster radius increases as the distance from the sink increases, which can help balance the residual energy among CHs. In our previous work [ 6 ], IMOCRP is proposed which aims at minimizing the average node energy consumption and the standard deviation of node residual energy. It can determine CHs and clustering relationship automatically.…”

Section: Related Workmentioning

confidence: 99%

“…The sink determines node identity by using IMO algorithm, and then it informs each CRSNs node about the clustering results. The whole process is the same as that in IMOCRP, please refer to [ 6 ] for details.…”

Section: Traffic-driven Ions Motion Optimization-based Clustering Rou...mentioning

confidence: 99%

“…How to form clusters? Our previous work in [ 6 ] applies swarm intelligence algorithm, i.e., ions motion optimization (IMO) algorithm to determine the optimal number of clusters and CHs automatically while minimizing the total control overhead incurred during clustering process. Ions motion optimization-based clustering routing protocol (IMOCRP) is proposed and the simulation results show that IMOCRP gains obvious advantages over existing time-triggered clustering protocols proposed for CRSNs in terms of network lifetime and network surveillance capability.…”

Section: Introductionmentioning

confidence: 99%

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Traffic-driven ions motion optimization-based clustering routing protocol for cognitive radio sensor networks

Wang

Hao

et al. 2022

PLoS ONE

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In cognitive radio sensor networks, single clustering protocol cannot simultaneously satisfy the various requirements of time-triggered and event-driven traffic, as a result, different kinds of clustering protocols are designed to serve them separately. In addition, for event-driven traffic, the long delay incurred by clustering and searching for available routes after events results in poor timeliness of information transmission. In order to solve above problems, a traffic-driven ions motion optimization-based clustering routing protocol (TD-IMOCRP) is proposed in this paper. For the first time, time-triggered and event-driven traffic can be served by a single clustering protocol. To be specific, ions motion optimization algorithm is leveraged to automatically determine the optimal number of clusters and form basic clustering structure. In this case, time-triggered traffic can be periodically served. Priority-based schedule and corresponding frame structure are designed to ensure priority delivery of event-driven information. The clustering architecture built for time-triggered traffic is leveraged, and there is no cluster construction and route selection after emergent events. Only the CRSNs nodes which discover emergent events and corresponding CHs participate in data transmission, which means that TD-IMOCRP covers fewer nodes, especially when the sink is located at the corner. Therefore, it can help reduce node energy consumption and delay. Simulation results demonstrate that compared with representative event-driven clustering protocols, TD-IMOCRP can decrease the average number of covered nodes and the total energy consumption by more than 66.3% and 25%, respectively. In addition, when serving time-triggered traffic, TD-IMOCRP can achieve almost the same performance as its basic version IMOCRP which is better than majority of current time-triggered clustering protocols. In a word, TD-IMOCRP can guarantee in-time delivery of event-driven information while guaranteeing its performance of serving time-triggered traffic.

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Section: Resultssupporting

confidence: 71%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Traffic-driven Ions Motion Optimization-based Clustering Rou...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Traffic-driven ions motion optimization-based clustering routing protocol for cognitive radio sensor networks

Wang

Hao

et al. 2022

PLoS ONE

Self Cite

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AFHO‐based link reliable routing and power‐assisted channel allocation protocol in cognitive radio networks for healthcare applications

Sekar,

Appranchi

2024

Trans Emerging Tel Tech

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In the realm of cognitive networks, accommodating the increasing need for radio spectrum to facilitate both secondary and primary users stands as an intricate and demanding task. This study introduces a tailored framework for healthcare applications in Cognitive Radio (CR) networks for link reliable routing and power‐assisted channel allocation. Through the integration of Adaptive Fire Hawk Optimization for routing, the strategy addresses optimization complexities while prioritizing robust link quality via optimal hop selection for effective communication. A new routing optimization challenge is formulated, aiming to establish reliable communication pathways and extend link reliability. Moreover, the framework incorporates power‐assisted channel allocation to strategically enhance resource management and overall network performance. The rigorous evaluation demonstrates significant enhancements across key performance metrics, which showcase percentage improvements of 9.86%, 14.56%, 3.85%, 5.06%, 6.94%, and 17.80% for active nodes, delay, Packet Delivery Ratio (PDR), residual energy, and throughput, respectively. These advancements underscore the proposed approach's efficacy in enhancing CR network performance, particularly for healthcare applications.

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Energy and Trust Efficient Cluster Head Selection in Wireless Sensor Networks Under Meta-Heuristic Model

Dattatraya

Ananthakumaran

2022

Expert Clouds and Applications

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Ions Motion Optimization-Based Clustering Routing Protocol for Cognitive Radio Sensor Network

Cited by 12 publications

References 34 publications

Traffic-driven ions motion optimization-based clustering routing protocol for cognitive radio sensor networks

Traffic-driven ions motion optimization-based clustering routing protocol for cognitive radio sensor networks

AFHO‐based link reliable routing and power‐assisted channel allocation protocol in cognitive radio networks for healthcare applications

Energy and Trust Efficient Cluster Head Selection in Wireless Sensor Networks Under Meta-Heuristic Model

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