A comparative study of energy efficient algorithms for IoT applications based on WSNs

Guiloufi, Awatef Benfradj; Khediri, Salim El; Nasri, Nejah; Kachouri, Abdennaceur

doi:10.1007/s11042-023-14813-3

Cited by 9 publications

(1 citation statement)

References 119 publications

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“…Therefore, the sensor nodes are helpful for sensing the incidents, while sending the alarm packets to the sink in making informed decisions [1]. Nowadays, Wireless Sensor Network (WSN) is an advantageous and hopeful network that is used for the enhancement of network function [2,20,21,27]. WSN is widely used for biomedical applications, inventory management systems, military entreaty and smart home monitoring systems.…”

Section: Introductionmentioning

confidence: 99%

FCWWO: Fuzzy Chaotic Whale Wolf Optimization for Enhancing Network Lifetime and Minimizing Energy Consumption

Kavitha

Usha

et al. 2023

Preprint

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The Wireless Sensor Networks (WSN) include several sensors which collect data from their surroundings and transmit it to the destination node. In WSN, sensor nodes operate individually and build the ad-hoc network infrastructure. The observing data perceives the sensor nodes and the data have been sent to the Base Station (BS) with the utilization of gateway and Cluster Head (CH). The WSNs have limited battery power which diminishes the lifetime of the network. The clustering algorithm is one of the efficient solutions that help in improving the WSNs network. Many algorithms were applied in the clustering process in rectifying the issue of NP-hard optimization. But, these algorithms lead to disadvantages namely delay, slow conveyance rate and weak exploitation stage. Therefore, in this paper, a novel Fuzzy Chaotic Whale Wolf (FCWW) optimization is proposed for selecting the optimal CH. The primary goal of our article is to lessen computational complexity with increased network lifetime. The main operation of the CH is gathering information from the sensor nodes and directly communicating with the BS. As compared to other techniques, the proposed FCWW algorithm provides 712 kbps of throughput, 98.8% of packet delivery ratio, network lifetime of 1652 seconds, 35% of energy consumption and 82 ms of end-to-end latency respectively.

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Section: Introductionmentioning

confidence: 99%

FCWWO: Fuzzy Chaotic Whale Wolf Optimization for Enhancing Network Lifetime and Minimizing Energy Consumption

Kavitha

Usha

et al. 2023

Preprint

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High Performance Magnetic Mass‐Enhanced Triboelectric‐Electromagnetic Hybrid Vibration Energy Harvester Enabling Totally Self‐Powered Long‐Distance Wireless Sensing

Xi,

Yu,

et al. 2024

Adv Materials Technologies

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Wireless sensor networks play a significant role in various fields, and it is promising to construct a totally self‐powered wireless sensor network by harvesting unused mechanical vibration energy. Here, a magnetic mass‐enhanced triboelectric‐electromagnetic hybrid nanogenerator (MM‐HNG) is proposed for harvesting mechanical vibration energy. The additional magnets generate magnetic fields for electromagnetic power generation. As an additional mass effectively increases the membrane's amplitude, thereby enhancing the output performance of the MM‐HNG. The peak power density of TENG in the MM‐HNG reaches 380.4 W m−3, while the peak power density of EMG achieves 736 W m−3, which can charge a 0.1 F capacitor rapidly. In addition, a totally self‐powered wireless sensing system is constructed, with the integrated microcontroller unit (MCU), which detects and processes various sensing parameters and controls wireless transmission. The system features rapid transmission speeds and an extensive transmission range (up to 1 km), and its effectiveness has been validated in a practical application aboard an actual ship. The results illustrate the MM‐HNG's broad applicability across various Internet of Things (IoT) scenarios, including smart machinery, smart transportation, and smart factories.

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Delving into the Dark Side of Artificial Intelligence in Green Marketing

Dalhoum,

Zouari,

Khemakhem

2024

Lecture Notes in Business Information Processing

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A comparative study of energy efficient algorithms for IoT applications based on WSNs

Cited by 9 publications

References 119 publications

FCWWO: Fuzzy Chaotic Whale Wolf Optimization for Enhancing Network Lifetime and Minimizing Energy Consumption

FCWWO: Fuzzy Chaotic Whale Wolf Optimization for Enhancing Network Lifetime and Minimizing Energy Consumption

High Performance Magnetic Mass‐Enhanced Triboelectric‐Electromagnetic Hybrid Vibration Energy Harvester Enabling Totally Self‐Powered Long‐Distance Wireless Sensing

Delving into the Dark Side of Artificial Intelligence in Green Marketing

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