SIMOF: swarm intelligence multi-objective fuzzy thermal-aware routing protocol for WBANs

Aryai, Pouya; Khademzadeh, Ahmad; Jassbi, Somayyeh Jafarali; Hosseinzadeh, Mehdi

doi:10.1007/s11227-023-05102-9

Cited by 11 publications

(2 citation statements)

References 64 publications

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“…The node with the utmost priority was chosen to transmit the next step. In [ 21 ], an intelligent multi-objective fuzzy protocol, i.e., “SIMOF: swarm intelligence multi-objective fuzzy thermal-aware routing protocol for WBANs”, was introduced like a configurable network routing protocol within the intra-body networks. “SIMOF” consisted of two phases: (I) the Fuzzy Inference System (FIS) and (II) the Whale Optimization Algorithm (WOA) which automatically adjusts the rules.…”

Section: Review Of Literaturementioning

confidence: 99%

Enhancing healthcare IoT systems for diabetic patient monitoring: Integration of Harris Hawks and grasshopper optimization algorithms

Hosseinzadeh,

Arabi,

Ali

et al. 2024

PLoS ONE

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The integration of the Internet of Things (IoT) in healthcare, especially for people with diabetes, allows for constant health monitoring. This means that doctors can watch over patients’ health more closely, making sure they catch any issues early on. With this technology, healthcare workers can be more accurate and effective when keeping an eye on how patients are doing. This not only helps in keeping track of patients’ health in real-time but also makes the whole process more reliable and efficient.By implementing appropriate routing techniques, the transmission of diabetic patients’ data to medical centers will facilitate real-time and timely responses from healthcare professionals. The grasshopper optimization algorithm is employed in the proposed approach to cluster network nodes, resulting in the formation of a network tree that facilitates the establishment of connections between the cluster head and the base station. After identifying the cluster head and establishing the clusters, the second stage of routing is implemented by employing the Harris Hawks optimization algorithm. This algorithm ensures that the data pertaining to diabetic patients is transmitted to the treatment centers and hospitals with minimal delay. For node routing, the optimal next step is selected based on the parameters such as the residual energy of the node, the ratio of delivered data packages, and the number of the neighbors of the node. To continue, first, the MATLAB software is utilized to simulate the proposed method, and then, it is compared with other similar methods. This comparison is conducted based on various parameters, including delay, energy consumption, network throughput, and network lifespan. Compared to other methods, the proposed method demonstrates a significant 33% improvement in the average point-to-point delay parameter in the subsequent iterations or rounds.

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Section: Review Of Literaturementioning

confidence: 99%

Enhancing healthcare IoT systems for diabetic patient monitoring: Integration of Harris Hawks and grasshopper optimization algorithms

Hosseinzadeh,

Arabi,

Ali

et al. 2024

PLoS ONE

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“…Over the past years, there have been significant strides in the progress of routing algorithms designed for terrestrial Wireless Sensor Networks (WSNs) [5][6][7][8][9][10]. However, the aquatic environment presents a markedly different set of challenges for routing protocols in underwater settings.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Monitoring in Underwater Wireless Sensor Networks using Energy-efficient Artificial Fish Swarm-based Clustering Protocol (EAFSCP)

Kaur,

Singh

et al. 2023

Preprint

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Underwater wireless sensor networks (UWSNs) represent a specialized category of WSNs with versatile applications including acoustic monitoring, oil and gas exploration, and military surveillance. UWSNs face formidable challenges such as limited energy resources, extended propagation delays, and harsh conditions. Existing clustering and multi-hop routing protocols often unevenly distribute nodes geographically, causing network fragmentation and disproportionately draining the battery life of nodes near the sink due to higher data transmission demands. In this paper, we introduce an Energy-efficient Artificial Fish Swarm-based Clustering Protocol (EAFSCP), inspired by the collective behavior of fish swarms. EAFSCP is a decentralized clustering algorithm designed for acoustic monitoring in UWSNs. Its decentralized nature makes it particularly well-suited for large-scale UWSNs, where centralized algorithms may not be feasible. Through comprehensive comparisons with existing cluster-based routing protocols, our findings indicate that EAFSCP consistently outperforms them across multiple key performance metrics, including network lifetime, energy consumption, packet delivery ratio, packet loss rate, and throughput. According to the results, EAFSCP represents an effective clustering algorithm that enhances network performance, prolongs network lifespan by reducing energy consumption, promotes scalability, and provides valuable guidance for emergency response efforts.

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Energy-efficient aware and predicting bandwidth estimation routing protocol for hybrid communication in wireless body area networks

Abdullah

2024

Cluster Comput

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SIMOF: swarm intelligence multi-objective fuzzy thermal-aware routing protocol for WBANs

Cited by 11 publications

References 64 publications

Enhancing healthcare IoT systems for diabetic patient monitoring: Integration of Harris Hawks and grasshopper optimization algorithms

Enhancing healthcare IoT systems for diabetic patient monitoring: Integration of Harris Hawks and grasshopper optimization algorithms

Acoustic Monitoring in Underwater Wireless Sensor Networks using Energy-efficient Artificial Fish Swarm-based Clustering Protocol (EAFSCP)

Energy-efficient aware and predicting bandwidth estimation routing protocol for hybrid communication in wireless body area networks

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