Dynamic deployment of randomly deployed mobile sensor nodes in the presence of obstacles

Rout, Mrutyunjay; Roy, Rajarshi

doi:10.1016/j.adhoc.2016.03.004

Cited by 36 publications

(14 citation statements)

References 20 publications

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“…The coverage is affected by the sensitivity of the sensors represented by the detection range (noted Rs), while the connectivity is guaranteed by the communication range (noted Rc). According to [8], the degradation of the coverage probability in some WSN applications is tolerable, whereas the degradation of the probability of connectivity could be fatal for the network.…”

Section: D Deployment 3d Deploymentmentioning

confidence: 99%

3D node deployment strategies prediction in wireless sensors network

Nasri

Mnasri

Val

2019

International Journal of Electronics

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3D Deployment represents a fundamental role in setting up an efficient wireless sensor network (WSNs) and IoT network. In general, WSN are widely used in a variety of applications ranging from monitoring a smart house to monitoring forest fires with parachuted sensors. In this paper, we focus on planned 3D deployment, which the sensor nodes must be accurately positioned at predetermined locations to optimize one or more design objectives under some given constraints. The purpose of planned deployment is to determine the type, number, and locations of nodes to optimize coverage, connectivity and network lifetime. There have been a large number of studies, which proposed algorithms for solving the premeditated 3D deployment problem. The objective of this paper is twofold. The first one is to present the complexity of 3D deployment and then detail the types of sensors, objectives, applications and recent research that concerns the strategy used to solve this problem. The second one is to present a comparative survey between recent optimization approaches used to resolve the deployment problem in WSN. Based on our extensive review, we discuss the strengths and limitations of each proposed solutions and compare them in terms of the WSN design factors.

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Section: D Deployment 3d Deploymentmentioning

confidence: 99%

3D node deployment strategies prediction in wireless sensors network

Nasri

Mnasri

Val

2019

International Journal of Electronics

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“…Consequently, the final network cannot satisfy the requirements for connectivity, cooperation and coverage, no matter how many devices are dropped. Dropping sensors in groups and a multi-stage strategy, where sensors are deployed iteratively taking into account the quality of previous deployments, are proposed for coverage improvement [ 7 , 8 , 9 ]. However, in general, random dropping can be used at initiation, but is not always a sufficient solution.…”

Section: Deployment Strategies In a Wsnmentioning

confidence: 99%

A Movement-Assisted Deployment of Collaborating Autonomous Sensors for Indoor and Outdoor Environment Monitoring

Niewiadomska-Szynkiewicz

Sikora

Marks

2016

Sensors

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Using mobile robots or unmanned vehicles to assist optimal wireless sensors deployment in a working space can significantly enhance the capability to investigate unknown environments. This paper addresses the issues of the application of numerical optimization and computer simulation techniques to on-line calculation of a wireless sensor network topology for monitoring and tracking purposes. We focus on the design of a self-organizing and collaborative mobile network that enables a continuous data transmission to the data sink (base station) and automatically adapts its behavior to changes in the environment to achieve a common goal. The pre-defined and self-configuring approaches to the mobile-based deployment of sensors are compared and discussed. A family of novel algorithms for the optimal placement of mobile wireless devices for permanent monitoring of indoor and outdoor dynamic environments is described. They employ a network connectivity-maintaining mobility model utilizing the concept of the virtual potential function for calculating the motion trajectories of platforms carrying sensors. Their quality and utility have been justified through simulation experiments and are discussed in the final part of the paper.

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“…The effectiveness of WSNs depends strongly on the network coverage and connectivity provided by the sensor deployment [23,24]. Even if sensor nodes are usually easy to install, once they have been placed they only way to improve the information they acquire is to add new nodes, or to re-deploy the existing ones.…”

Section: Introductionmentioning

confidence: 99%

Integration of a Mobile Node into a Hybrid Wireless Sensor Network for Urban Environments

Bertiz

Lozano

Gómez-Ruiz

et al. 2019

Sensors

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Robots, or in general, intelligent vehicles, require large amounts of data to adapt their behavior to the environment and achieve their goals. When their missions take place in large areas, using additional information to that gathered by the onboard sensors frequently offers a more efficient solution of the problem. The emergence of Cyber-Physical Systems and Cloud computing allows this approach, but integration of sensory information, and its effective availability for the robots or vehicles is challenging. This paper addresses the development and implementation of a modular mobile node of a Wireless Sensor Network (WSN), designed to be mounted onboard vehicles, and capable of using different sensors according to mission needs. The mobile node is integrated with an existing static network, transforming it into a Hybrid Wireless Sensor Network (H-WSN), and adding flexibility and range to it. The integration is achieved without the need for multi-hop routing. A database holds the data acquired by both mobile and static nodes, allowing access in real-time to the gathered information. A Human–Machine Interface (HMI) presents this information to users. Finally, the system is tested in real urban scenarios in a use-case of measurement of gas levels.

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Dynamic deployment of randomly deployed mobile sensor nodes in the presence of obstacles

Cited by 36 publications

References 20 publications

3D node deployment strategies prediction in wireless sensors network

3D node deployment strategies prediction in wireless sensors network

A Movement-Assisted Deployment of Collaborating Autonomous Sensors for Indoor and Outdoor Environment Monitoring

Integration of a Mobile Node into a Hybrid Wireless Sensor Network for Urban Environments

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