Optimally Approximating the Coverage Lifetime of Wireless Sensor Networks

Pananjady, Ashwin; Bagaria, Vivek Kumar; Vaze, Rahul

doi:10.1109/tnet.2016.2574563

Cited by 30 publications

(7 citation statements)

References 27 publications

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“…Pananjady et al focused on the problem of energy efficiency in sensor networks, and aimed to solve the issue of maximizing the lifetime of coverage of targets in a wireless sensor network with battery-limited sensors. Extensive simulation experiments prove the effectiveness of the proposed algorithm [18].…”

Section: Related Workmentioning

confidence: 76%

Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm

Chen¹,

Xiao-guang²,

Wang³

2019

DCDS-S

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As a basic and fundamental problem in wireless sensor network (WSN), the network coverage greatly reflects the performance of information transmission in WSN. In order to achieve a good balance between target coverage and energy consumption, in this paper, we propose a novel wireless sensor network energy efficient coverage method based on genetic algorithm. Particularly, the goal of this work is cover a 2D sensing area via selecting a minimum number of sensors. Moreover, the deployed wireless sensors should be connected to let each sensor be connected a path to the base station. Afterwards, genetic algorithm is used to compute the minimum number of potential position to let all target be k-covered and all sensor nodes be m-connected, and each chromosome is set to be the number of potential positions. Finally, we provide a simulation to test the performance of the proposed method, and simulation results demonstrate that the proposed method can achieve high degree of target coverage without wasting extra energy.

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Section: Related Workmentioning

confidence: 76%

Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm

Chen¹,

Xiao-guang²,

Wang³

2019

DCDS-S

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show abstract

“…YOLO (you only look once) [ 48 , 49 , 50 , 51 ] is a real-time object detector based on convolutional neural networks introduced by Redmon et al After some time, Joseph Redmon and Ali Farhadi published YOLO v2 [ 52 ], a new version with improved performance and speed. The most recent version is YOLO v3 [ 53 ], which was proposed by Joseph Redmon and Ali Farhadi to enhance speed and accuracy by increasing the number of layers in the design.…”

Section: Methodsmentioning

confidence: 99%

Automatic Target Detection from Satellite Imagery Using Machine Learning

Tahir

Munawar

Akram

et al. 2022

Sensors

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Object detection is a vital step in satellite imagery-based computer vision applications such as precision agriculture, urban planning and defense applications. In satellite imagery, object detection is a very complicated task due to various reasons including low pixel resolution of objects and detection of small objects in the large scale (a single satellite image taken by Digital Globe comprises over 240 million pixels) satellite images. Object detection in satellite images has many challenges such as class variations, multiple objects pose, high variance in object size, illumination and a dense background. This study aims to compare the performance of existing deep learning algorithms for object detection in satellite imagery. We created the dataset of satellite imagery to perform object detection using convolutional neural network-based frameworks such as faster RCNN (faster region-based convolutional neural network), YOLO (you only look once), SSD (single-shot detector) and SIMRDWN (satellite imagery multiscale rapid detection with windowed networks). In addition to that, we also performed an analysis of these approaches in terms of accuracy and speed using the developed dataset of satellite imagery. The results showed that SIMRDWN has an accuracy of 97% on high-resolution images, while Faster RCNN has an accuracy of 95.31% on the standard resolution (1000 × 600). YOLOv3 has an accuracy of 94.20% on standard resolution (416 416) while on the other hand SSD has an accuracy of 84.61% on standard resolution (300 × 300). When it comes to speed and efficiency, YOLO is the obvious leader. In real-time surveillance, SIMRDWN fails. When YOLO takes 170 to 190 milliseconds to perform a task, SIMRDWN takes 5 to 103 milliseconds.

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“…This solution, however, has been shown to be NP-complete. As a result, authors in [27][28][29] have suggested a centralised heuristic method to achieve coverage. Cover sets are generated by this method, which aid in the monitoring of all targets.…”

Section: Related Workmentioning

confidence: 99%

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Akram

Munawar

Kouzani

et al. 2022

Sensors

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Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

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Optimally Approximating the Coverage Lifetime of Wireless Sensor Networks

Cited by 30 publications

References 27 publications

Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm

Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm

Automatic Target Detection from Satellite Imagery Using Machine Learning

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

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