Coverage-enhancing approach in multimedia directional sensor networks for smart transportation

Zhang, Ke; Jia, Haitao; Lv, Haibin

doi:10.1007/s11042-016-3586-9

Cited by 11 publications

(9 citation statements)

References 39 publications

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“…In order to verify the advancement of the proposed method, comparative experiments with article [23], TLBO and PSO are carried out. The results are shown in Table 6.…”

Section: E Contrast Of Different Methodsmentioning

confidence: 99%

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Differential Evolution Based Regional Coverage-Enhancing Algorithm for Directional 3D Wireless Sensor Networks

et al. 2019

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Wireless sensor networks (WSNs) are adopted in a variety of fields where coverage enhancing is a critical challenge because of the requirements of service quality, cost, and energy consumption. Coverage-enhancing approaches have currently attracted a lot of interest owing to their superior abilities in the deployment of the WSNs, e.g., maximum coverage, minimum sensors, and minimum energy. In this paper, a differential evolution-based regional coverage-enhancing algorithm is proposed for directional 3D WSNs, which is able to maximizing coverage while minimize the number of sensors. First, a directional cone perception model is designed to better display the actual sensing performance of sensor nodes. Subsequently, the coverage region is established to describe the perceptual range of nodes. Thereafter, a three-stage coverage-enhancing method is derived, which includes the pitch angle optimization, the deflection angle optimization and the redundant node sleeping. These strategies are designed to maximize the perception range of a single sensor node, maximize the coverage rate, and minimize the number of nodes, respectively. Finally, simulation results show that our method is able to ensure better performance compared to the stateof-the-art frameworks.

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“…In order to verify the advancement of the proposed method, comparative experiments with article [23], TLBO and PSO are carried out. The results are shown in Table 6.…”

Section: E Contrast Of Different Methodsmentioning

confidence: 99%

“…Fuyou et al designed a seventuple directional wireless sensing model based on 2D grid strategy [22]. Zhang et al proposed a rotation direction model with node position and perceptual radius in [23]. Yet above 2D perception model does not conform to physical environment, some studies have developed 3D models.…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution Based Regional Coverage-Enhancing Algorithm for Directional 3D Wireless Sensor Networks

et al. 2019

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“…Ramar and Shanmugasundaram presented a connected k-coverage topology control model based on the consideration of coverage and connectivity between sensors with circular coverage area in [20]. Zhang et al developed a rotation direction model that integrated position information and perceptual radius of sensor in [21]. Since the height information of sensors is not considered, the above models mismatch actual physical environment, so the researchers make their great efforts to design the coverage models in 3D case.…”

Section: And Liu Proposed a Sectormentioning

confidence: 99%

Coverage Control Algorithm-Based Adaptive Particle Swarm Optimization and Node Sleeping in Wireless Multimedia Sensor Networks

Jiao

Zhang

et al. 2019

IEEE Access

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Wireless multimedia sensor networks (WMSNs) are widely used in various fields where coverage control is a critical difficulty because multiple requirements need to be considered such as service quality and energy consumption. In this paper, we focus on the issues existing in coverage model and coverage control method, and propose an adaptive particle swarm optimization algorithm for solving the coverage control problem in the WMSNs. First of all, a 3D coverage model is developed with introducing the perceptual radius of sensors, while the optimal projection area of a single sensor is deduced. Hereafter, an adaptive particle swarm optimization is suggested to optimize the location information of sensors for reducing both perceptual overlapping areas and perceptual blind areas in the monitoring area. At last, a redundant node sleeping strategy is presented to reduce the number of working sensors. Simulation results have show that we can ensure better coverage as well as fewer sensors compared to the state-of-the-art frameworks.

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“…The main purpose of this framework is to support different smart city applications to reduce energy consumption in computationally complex tasks, e.g., video capturing, packetization, and transmission. A framework based on virtual streaming was proposed in [116]. In this framework, the streaming is based on the artificial fish-swarm technique, and is used to increase the coverage of WMSNs in the smart transport systems.…”

Section: Table 1 Communication Network Benefits and Challengesmentioning

confidence: 99%

“…In this framework, the streaming is based on the artificial fish-swarm technique, and is used to increase the coverage of WMSNs in the smart transport systems. The frameworks proposed in [3,73,101,116] use both fix and mobile nodes and can be helpful in improving the performance of various smart city applications.…”

Section: Table 1 Communication Network Benefits and Challengesmentioning

confidence: 99%

A Survey on Big Multimedia Data Processing and Management in Smart Cities

Usman

Jan

et al. 2019

ACM Comput. Surv.

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Integration of embedded multimedia devices with powerful computing platforms, e.g., machine learning platforms, helps to build smart cities and transforms the concept of Internet of Things into Internet of Multimedia Things (IoMT). To provide different services to the residents of smart cities, the IoMT technology generates big multimedia data. The management of big multimedia data is a challenging task for IoMT technology. Without proper management, it is hard to maintain consistency, reusability, and reconcilability of generated big multimedia data in smart cities. Various machine learning techniques can be used for automatic classification of raw multimedia data and to allow machines to learn features and perform specific tasks. In this survey, we focus on various machine learning platforms that can be used to process and manage big multimedia data generated by different applications in smart cities. We also highlight various limitations and research challenges that need to be considered when processing big multimedia data in real-time.

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Coverage-enhancing approach in multimedia directional sensor networks for smart transportation

Cited by 11 publications

References 39 publications

Differential Evolution Based Regional Coverage-Enhancing Algorithm for Directional 3D Wireless Sensor Networks

Differential Evolution Based Regional Coverage-Enhancing Algorithm for Directional 3D Wireless Sensor Networks

Coverage Control Algorithm-Based Adaptive Particle Swarm Optimization and Node Sleeping in Wireless Multimedia Sensor Networks

A Survey on Big Multimedia Data Processing and Management in Smart Cities

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