On Increasing the Energy Efficiency of Wireless Rechargeable Sensor Networks for Cyber-Physical Systems

Orumwense, Efe Francis; Abo-Al-Ez, Khaled M.

doi:10.3390/en15031204

Cited by 10 publications

(3 citation statements)

References 60 publications

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“…To maximize the sum of sensor survival times, Xu et al [132] considered how to schedule MCs to charge sensors multiple times in one charging cycle. Examples of single charging schemes are studied and explained in [124,128,90].…”

Section: Number Of Mobile Chargersmentioning

confidence: 99%

Wireless Power Transfer Technologies, Applications, and Future Trends: A Review

Alabsi,

Hawbani,

Wang

et al. 2024

IEEE Trans. Sustain. Comput.

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Wireless Power Transfer (WPT) is a disruptive technology that allows wireless energy provisioning for energylimited IoT devices, thus decreasing the over-reliance on batteries and wires. WPT could replace conventional energy provisioning (e.g., energy harvesting) and expand for deployment in many of our daily-life applications, including but not limited to healthcare, transportation, automation, and smart cities. As a new rising technology, WPT has attracted many researchers from academia and industry in terms of technologies and charging scheduling within a plethora of services and applications. Thus, in this paper, we review the most recent studies related to WPT, including the classifications, advantages, disadvantages, and main application domains. Furthermore, we review the recently designed wireless charging scheduling algorithms and schemesfor wireless sensor networks. Our study provides a detailed survey of wireless charging scheduling schemes covering the main scheme classifications, evaluation metrics, application domains, advantages, and disadvantages of each charging scheme. We further summarize trends and opportunities for applying WPT at some intersections.Index Terms-wireless power transfer, wireless sensor networks, smart homes, healthcare, industrial, and charging schemes. I. INTRODUCTIONS IXTH Generation (6G) wireless networks seek to enhance the dependability, speed, and bandwidth of their forerunners, the 5G networks, in order to meet the expanding demands of the Internet of Everything (IoE) applications and accommodate cutting-edge technological trends like decentralized and pervasive artificial intelligence (AI). Energy efficiency and power provisioning are critical problems in future wireless networking, including Wireless Sensor Networks (WSN), IoE, Beyond 5G (B5G), and 6G [70], [144]. Many power-saving (energy conservation) solutions are used to improve the power usage in wireless communications, such as in [44], dynamic routing techniques [80], [146], [145], efficient construction of multi-cast trees [27], [25], mobile data collection, low power hardware architecture [61, 33], and resource allocation. Despite the fact that these solutions

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Section: Number Of Mobile Chargersmentioning

confidence: 99%

Wireless Power Transfer Technologies, Applications, and Future Trends: A Review

Alabsi,

Hawbani,

Wang

et al. 2024

IEEE Trans. Sustain. Comput.

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“…Authors in [27] present a plan that arranges sensor nodes for charging, in order to enhance systems for extending lifetime. To determine the sensor elements that require charging, an inspection algorithm is first developed to visit and inspect sensor nodes.…”

Section: Clustering Based On Partitionsmentioning

confidence: 99%

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

V.,

2023

IJRITCC

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The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime.

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“…To address this challenge, the practice of clustering networks has emerged as a popular mechanism for prolonging the operational longevity of WSNs while facilitating efficient data transmission. [1]. Clustering networks is a commonly employed technique to optimize energy consumption and mitigate the issues of rapid and erratic energy depletion to a specific degree.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor Networks

Jebur

2024

JECNAM

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Wireless sensor networks (WSN) find applications in diverse fields such as environmental monitoring, healthcare, and industrial control systems. The pivotal components of these networks are the sensor nodes, which, unfortunately, consume a substantial amount of energy when transmitting information directly to the base station (BS). To mitigate energy consumption associated with direct transmission, this paper proposes a two-phase approach utilizing hybrid clustering and routing algorithms. The proposed approach incorporates fuzzy and seagull techniques for clustering and adopts optimal CH (cluster head) selection, CBRP (Cluster-Based Routing Protocol), and AES (Advanced Encryption Standard) for secure routing. The system employs rule-based fuzzy logic to correlate input values in both clustering and routing algorithms. Decision-making is based on factors such as the residual energy of sensor nodes, distance from the BS, and the number of nodes within the communication range. Input variables' crisp values are transformed into diverse fuzzy values, and the fuzzy output values are converted back to crisp values using the centroid defuzzification method. Selection of cluster heads and routers is determined by the output values, with sensor nodes being allocated to respective cluster heads based on their load-handling capacity. The routing path is then generated considering the capacity of routers. Simulations are conducted to evaluate energy consumption, active sensor nodes per round, and the sustainability period of the network. This proposed hybrid clustering and routing system aim to enhance the overall efficiency of wireless sensor networks by optimizing energy consumption and ensuring secure data transmission. The optimization model identifies the most suitable nodes in the routing cycle, starting with chosen cluster heads. The overarching goal is to enhance network indicators, including network lifespan, power consumption per node, and packet delivery percentage. The proposed solution achieved a network lifetime of 100 hours and a data delivery rate of 98%. additionally, it consumed the least amount of energy, measuring at 95,000 joules.

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On Increasing the Energy Efficiency of Wireless Rechargeable Sensor Networks for Cyber-Physical Systems

Cited by 10 publications

References 60 publications

Wireless Power Transfer Technologies, Applications, and Future Trends: A Review

Wireless Power Transfer Technologies, Applications, and Future Trends: A Review

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor Networks

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