Energy Efficient Localization in Wireless Sensor Networks Using Computational Intelligence

Akram, Junaid; Najam, Zeeshan; Rizwi, Haider

doi:10.1109/honet.2018.8551332

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…Faster RCNN is purely used CNN for feature extraction rather than handcrafted features ( Figure 4 ). With VGG16 [ 44 ] model, faster RCNN gives 5 fps on GPU and achieves object detection accuracy on PASCAL VOC [ 45 , 46 ] dataset using three hundred proposals per image. With the rapid development of faster RCNN, Lenc et al [ 47 ] studied the role of generation of region proposals through selective search and generation of region proposals through CNN and claimed that CNN based RPN contains less geometric information for object detection in the CONV Layers rather than FC layers.…”

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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Section: Methodsmentioning

confidence: 99%

Automatic Target Detection from Satellite Imagery Using Machine Learning

Tahir

Munawar

Akram

et al. 2022

Sensors

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“…The estimated distances are then determined using the calculated time difference, offering a precise method of localization. ToF techniques are less energy-efficient since they frequently need specialised hardware and can be sensitive to nonline-of-sight situations [11].…”

Section: Review Of Literaturementioning

confidence: 99%

Energy-Efficient Localization Techniques for Wireless Sensor Networks in Indoor IoT Environments

Suresh Limkar

2024

jes

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For Wireless Sensor Networks (WSNs) to operate as efficiently as possible in Indoor Internet of Things (IoT) environments, energy-efficient localization approaches are essential. We investigate several localization approaches, such as trilateration based on Received Signal Strength Indicator (RSSI), Proximity Based Technique, Inertial Navigation, Ultrasound-based, and Magnetic Field-based approaches, in the context of energy efficiency. RSSI-based trilateration, which provides good accuracy with little energy consumption, uses measurements of signal intensity to infer device positions. In cases where there are limitations on line of sight, technologies based on ultrasound measure signal travel durations. Although calibration and sensitivity to interference are taken into account, magnetic field-based approaches use magnetic field anomalies to determine positions. Accuracy, energy usage, scalability, robustness, and calibration effort are some of the factors that these techniques are evaluated against in order to fulfil the demands of indoor IoT environments. A thoughtful choice of localization methods can increase energy efficiency, increase the lifespan of sensor networks, and enable precise location-aware IoT applications. In order to meet the increasing demand for energy-efficient localisation in Indoor IoT environments, more research in this field is still being conducted.

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“…In [34][35][36], you may find the solution to the issue of how to manage the schedule of a big number of sensor nodes installed. The maximum lifetime coverage issue is presented in this article as a big sensor deployment.…”

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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Energy Efficient Localization in Wireless Sensor Networks Using Computational Intelligence

Cited by 12 publications

References 12 publications

Automatic Target Detection from Satellite Imagery Using Machine Learning

Automatic Target Detection from Satellite Imagery Using Machine Learning

Energy-Efficient Localization Techniques for Wireless Sensor Networks in Indoor IoT Environments

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

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