Visual sensor network lifetime maximization by prioritized scheduling of nodes

Hooshmand, Mohsen; Soroushmehr, S. M. Reza; Khadivi, Pejman; Samavi, Shadrokh; Shirani, Shahram

doi:10.1016/j.jnca.2012.04.017

Cited by 22 publications

(9 citation statements)

References 25 publications

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“…For these challenges, a sensing model has to be designed according to the process of gathering data by MSNs from the region. As MSNs have a direct view in limited sensing angle, the circle-based strategies used to solve the coverage problem for WSNs are not suitable for WMSNs [23][24][25].…”

Section: Problem Overviewmentioning

confidence: 99%

Sensor network node scheduling for preserving coverage of wireless multimedia networks

Osamy¹,

Khedr

Salim

et al. 2019

IET wirel. sens. syst.

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Wireless multimedia sensor networks (WMSNs) are receiving increasing consideration in the deployment of unmanned surveillance systems for applications such as monitoring battle field and security surveillance. However, ensuring high levels of coverage of the monitored area and concurrently maximising the WMSN lifetime remains a challenging issue. The authors introduce a novel distributed algorithm in this study, for scheduling multimedia sensor nodes (MSNs) activity that maximises coverage and minimises energy consumption. The proposed activity scheduling algorithm incorporates an approach that enables each MSN in WMSN to efficiently extract its cover sets to ensure that the critical points are monitored, and thereby minimise and effectively balance the network energy utilisation. They introduce a new way to select the most suitable cover set of an MSN by assigning priority for each cover set based on the correlation of visual information from the camera observations. The efficacy of the proposed approach is studied through rigorous simulations. Their simulation work and results comparison with existing approaches demonstrate that their proposed scheme exceeds current results in terms of network lifetime, capture rate and percentages of dead and active nodes.

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Section: Problem Overviewmentioning

confidence: 99%

Sensor network node scheduling for preserving coverage of wireless multimedia networks

Osamy¹,

Khedr

Salim

et al. 2019

IET wirel. sens. syst.

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“…The benchmark algorithms include (1) the proposed GATPC, (2) the proposed GA with the Boolean disk model which is commonly used in literature (GABD) [14,15], (3) the proposed GA without repair mechanism (GAW), (4) a discrete version of particle swarm optimization (PSO) algorithm [39], and (5) an ant colony optimization (ACO) algorithm [40] which were used in two similar wireless coverage problems, as well as (6) the former PSO with the repair mechanism proposed in this paper (Algorithm 2), which is named PSOR.…”

Section: Performance Comparison With Benchmark Algorithmsmentioning

confidence: 99%

An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments

Gong

David

Tanghe

et al. 2018

Applied Soft Computing

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The industrial wireless local area network (IWLAN) is increasingly dense, due to not only the penetration of wireless applications to shop floors and warehouses, but also the rising need of redundancy for robust wireless coverage. Instead of simply powering on all access points (APs), it becomes an unavoidable challenge to dynamically control the transmit power of APs on a large scale, in order to minimize interference and adapt the coverage to the latest shadowing effects of dominant obstacles in an industrial indoor environment. To tackle this challenge, this paper formulates a transmit power control (TPC) model that enables both powering on/off APs and transmit power calibration of each AP that is powered on. This TPC model uses an empirical one-slope path loss model considering three-dimensional obstacle shadowing effects, to enable accurate yet simple coverage prediction. An efficient genetic algorithm (GA), named GATPC, is designed to solve this TPC model even on a large scale. To this end, it leverages repair mechanism-based population initialization, crossover and mutation, parallelism as well as dedicated speedup measures. The GATPC was experimentally validated in a small-scale IWLAN that is deployed a real industrial indoor environment. It was further numerically demonstrated and benchmarked on both small-and large-scales, regarding the effectiveness and the scalability of TPC. Moreover, sensitivity analysis was performed to reveal the produced interference and the qualification rate of GATPC according to varying target coverage percentage as well as number and placement direction of dominant obstacles.

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“…The simulation results exhibit the proposed deployment strategy achieves enhanced network lifetime. Most recently, Hooshmand et al [22] proposed three algorithms to generate maximum coverage area while enhancing the network lifetime. Each of these algorithms is different from each other in terms of degree of overlapping coverage area.…”

Section: B State-of-the-art Strategies In Vsnsmentioning

confidence: 99%

A Location-Wise Predetermined Deployment for Optimizing Lifetime in Visual Sensor Networks

Halder¹,

Ghosal²

2016

IEEE Trans. Circuits Syst. Video Technol.

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Visual sensor networks are receiving significant attention due to their potential applications ranging from surveillance to tracking domains. Nevertheless, due to the funneling effect, the unbalanced energy usage among visual sensor nodes (SNs) increases and leads to premature decrease in network lifetime. Firstly, considering Rayleigh fading channel and routing models, we analyze the optimization of network lifetime by balancing the energy consumption among different SNs. From analysis it is revealed that the number of SNs and relay nodes, and their locations has significant influence on limiting the energy hole problem and optimization of network lifetime. Based on the derived principle of energy balancing, we develop a heterogeneous SNs deployment strategy leading to optimization in network lifetime. Exhaustive simulation is performed, primarily to measure the extent of achieving our design goal of optimizing network lifetime while attaining energy balancing. We also measure the effect of placement errors on the performance and robustness of the scheme. The results show that even in presence of placement error the performance is comparable and provides better robustness compared to the competing scheme. Further, the simulation results show that our scheme does not compromise with other performance metrics e.g. end-to-end delay and throughput, while achieving the design goal. Finally, all the comparative results confirm our scheme's supremacy over the competing schemes.

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Visual sensor network lifetime maximization by prioritized scheduling of nodes

Cited by 22 publications

References 25 publications

Sensor network node scheduling for preserving coverage of wireless multimedia networks

Sensor network node scheduling for preserving coverage of wireless multimedia networks

An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments

A Location-Wise Predetermined Deployment for Optimizing Lifetime in Visual Sensor Networks

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