Extracting the overlapped sub-regions in wireless sensor networks

Hawbani, Ammar; Wang, Xingfu; Kuhlani, Hassan; Ghannami, Aiman; Farooq, Muhammad Umar; Al-sharabi, Yaser

doi:10.1007/s11276-018-1755-7

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…A sensor node contains several small, low-cost, and low-power electronic devices such as a battery, sensing unit, a storage unit, a processing unit, and a communication unit [125]. These nodes suffer from scarce resources (i.e., limited sensing range, battery power, communication range, storage, process, and computing resources) [126]. Every sensor can read data from its surroundings such as sound, temperature, pressure, acceleration, humidity, etc.…”

Section: ) Wireless Sensor Networkmentioning

confidence: 99%

Green internet of things using UAVs in B5G networks: A review of applications and strategies

et al. 2021

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Section: ) Wireless Sensor Networkmentioning

confidence: 99%

Green internet of things using UAVs in B5G networks: A review of applications and strategies

et al. 2021

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“…Moreover, CSI can play an important role in the IoT; therefore, hybrid methods to apply CSI in multimedia communications and IoT applications [159,160,161,162] can be addressed. Furthermore, Wireless Sensor Network (WSN) schemes [163,164,165] can be studied.…”

Section: Limitations and Challengesmentioning

confidence: 99%

Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey

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Elaziz

Kim

et al. 2019

Sensors

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Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology.

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POWER: probabilistic weight-based energy-efficient cluster routing for large-scale wireless sensor networks

et al. 2022

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Extracting the overlapped sub-regions in wireless sensor networks

Cited by 8 publications

References 21 publications

Green internet of things using UAVs in B5G networks: A review of applications and strategies

Green internet of things using UAVs in B5G networks: A review of applications and strategies

Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey

POWER: probabilistic weight-based energy-efficient cluster routing for large-scale wireless sensor networks

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