A Sustainable Scheme for Minimizing Energy in Visual Sensor Network using Disjoint Set Cover Approach

2020 ◽  
Vol 38 (12A) ◽  
pp. 1818-1831
Author(s):  
Shuruq A. Hashim ◽  
Muna M. Jawaad Alnayar ◽  
Bassam M.S Wheedd
Keyword(s):  
Network Lifetime ◽  
Set Cover ◽  
Moving Targets ◽  
Visual Sensor ◽  
Visual Sensor Network ◽  
Promising Tool ◽  
Directional Sensors ◽  
Entry Points ◽  
Minimum Number ◽  
Random Manner

Directional sensors in wireless visual sensor networks attract growing attention as a promising tool for monitoring the real world; directional sensors consume energy for two main tasks: sensing and communication. Since a VSN contains a number of configurable visual sensors with changeable spherical sectors of restricted angle known as a field of view that is intended to monitor a number of targets located in a random manner over a given area. Therefore maximizing the network lifetime through minimizing power consumption while covering the targets remains a challenge. In this paper, the problem of obtaining a disjoint set cover includes a minimum number of camera sensors is solved. The problem is known to be NP-complete. The sustainable design is improving an existing Iterative Target Oriented Algorithm (ITOA) to cover moving targets move randomly over a given area of deployment starting from entry points reaching to exit ones in a realistic simulation. To evaluate the performance of the modified algorithm, a comparison is provided with three existing algorithms (Iterative centralized Greedy Algorithm (ICGA), Iterative Centralized Forced-directed Algorithm ICFA, and Iterative Target Oriented Algorithm ITOA). Simulation results revealed that the sustainable scheme can find a disjoint set with a minimum number of sensors covers the maximum number of moving targets in an energy-efficient way and extended network lifetime.

Visual sensor network lifetime maximization by prioritized scheduling of nodes

2013 ◽  
Vol 36 (1) ◽  
pp. 409-419 ◽  
Author(s):  
M. Hooshmand ◽  
S.M.R. Soroushmehr ◽  
P. Khadivi ◽  
S. Samavi ◽  
S. Shirani
Keyword(s):  
Sensor Network ◽  
Network Lifetime ◽  
Visual Sensor ◽  
Lifetime Maximization ◽  
Visual Sensor Network

A learning automata-based algorithm to solve imbalanced k-coverage in visual sensor networks

2020 ◽  
Vol 39 (3) ◽  
pp. 2817-2829
Author(s):  
Ahmad Javan Bakht ◽  
Homayun Motameni ◽  
Hosein Mohamadi
Keyword(s):  
Sensor Networks ◽  
Learning Automata ◽  
Coverage Problem ◽  
Visual Sensor ◽  
Directional Sensor Networks ◽  
Algorithm Performance ◽  
Visual Sensor Networks ◽  
Directional Sensors ◽  
Minimum Number ◽  
Directional Sensor

One of the most important problems in directional sensor networks is k-coverage in which the orientation of a minimum number of directional sensors is determined in such a way that each target can be monitored at least k times. This problem has been already considered in two different environments: over provisioned where the number of sensors is enough to cover all targets, and under provisioned where there are not enough sensors to do the coverage task (known as imbalanced k-coverage problem). Due to the significance of solving the imbalanced k-coverage problem, this paper proposes a learning automata (LA)-based algorithm capable of selecting a minimum number of sensors in a way to provide k-coverage for all targets in a balanced way. To evaluate the efficiency of the proposed algorithm performance, several experiments were conducted and the obtained results were compared to those of two greedy-based algorithms. The results confirmed the efficiency of the proposed algorithm in terms of solving the problem.

Dynamic Resource Allocation for Compressive-Sensing-Based Wireless Visual Sensor Networks with Energy Harvesting

2013 ◽  
Vol 303-306 ◽  
pp. 187-190
Author(s):  
Lei You ◽  
Xin Su ◽  
Yu Tong Han
Keyword(s):  
Resource Allocation ◽  
Energy Harvesting ◽  
Compressive Sensing ◽  
Information Acquisition ◽  
Visual Information ◽  
Sensor Nodes ◽  
Visual Sensor ◽  
Visual Sensor Network ◽  
Available Bandwidth ◽  
Potential Applications

Wireless visual sensor network (WVSN) is emerging with many potential applications. The lifetime of a WVSN is seriously dependent on the energy shored in the battery of its sensor nodes as well as the adopted compression and resource allocation scheme. In this paper, we use the energy harvesting to provide almost perpetual operation of the networks and compressed-sensing-based encoding to decrease the power consumption of acquiring visual information at the front-end sensors. We propose a dynamic algorithm to jointly allocate power for both compressive-sensing-based visual information acquisition and data transmission, as well as the available bandwidth under energy harvesting and stability constraints. A virtual energy queue is introduced to control the resource allocation and the measurement rate in each time slot. The algorithm can guarantee the stability of the visual data queues in all sensors and achieve near-optimal performance.

scholarly journals Fast contrast enhancement by adaptive pixel value stretching

2018 ◽  
Vol 14 (8) ◽  
pp. 155014771879380
Author(s):  
Gang Cao ◽  
Huawei Tian ◽  
Lifang Yu ◽  
Xianglin Huang
Keyword(s):  
Contrast Enhancement ◽  
Dynamic Range ◽  
Computational Cost ◽  
Computer Applications ◽  
Visual Sensor ◽  
Visual Sensor Network ◽  
Image Contrast Enhancement ◽  
Quantitative Performance ◽  
Pixel Value ◽  
Adaptive Gamma Correction

In this article, we propose a fast and effective method for digital image contrast enhancement. The gray-level dynamic range of contrast-distorted images is extended maximally via adaptive pixel value stretching. The quantity of saturated pixels is set intelligently according to the perceptual brightness of global images. Adaptive gamma correction is also novelly used to recover the normal luminance in enhancing dimmed images. Different from prior methods, our proposed technique could be enforced automatically without complex manual parameter adjustment per image. Both qualitative and quantitative performance evaluation results show that, comparing with some recent influential contrast enhancement techniques, our proposed method achieves comparative or better enhancement quality at a surprisingly lower computational cost. Besides general computer applications, such merit should also be valuable in low-power scenarios, such as the imaging pipelines used in small mobile terminals and visual sensor network.

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