Reliable network connectivity in wireless sensor networks for remote monitoring of landslides

Kumar, Shine; Duttagupta, Subhasri; Rangan, Venkat; Ramesh, Maneesha Vinodini

doi:10.1007/s11276-019-02059-7

Cited by 45 publications

(26 citation statements)

References 13 publications

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“…As an important part of the operating system, data management plays a fundamental role in a series of operations to collect farmland information [13]. It includes the functions of pattern analysis and processing in farmland monitoring, and it can copy, delete and maintain the data collected by farmland monitoring, and find these data in real time.…”

Section: Data Collection Intervalmentioning

confidence: 99%

Design of remote control device using wireless sensor network and its use in intelligent monitoring of farmland information

Zhang

Ying

2021

J Wireless Com Network

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The purpose is to control the growth state of crops remotely, design intelligent detection application of farmland information and improve the efficiency of agricultural information management. Wireless sensor technology is applied to remote control device. First, through the integration of the system functions that need to be completed in the farmland remote monitoring, the module requirements of the device are further determined. Finally, the overall design is determined. From the aspects of hardware, such as the selection of sensors, RF transceivers and software (such as data processing center module), remote control device is designed, and the detection and collection of farmland real-time information is completed. The results show that the communication distance of wireless sensor nodes can reach 215 m under the test condition of 0.5 m in open space. The research content can help people improve the understanding of crop growth environment and growth situation, and then help people improve work efficiency, and finally contribute to the process of agricultural informatization.

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Section: Data Collection Intervalmentioning

confidence: 99%

Design of remote control device using wireless sensor network and its use in intelligent monitoring of farmland information

Zhang

Ying

2021

J Wireless Com Network

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“…Mohapatra et al [ 39 ] proposed the network of ocean-bottom seismometer sensors for real-time monitoring using combined routing and node replacement approaches to reduce energy consumption and minimize the costs of replacement the sensors. In landslide monitoring, Kumar et al [ 40 ] dealt with a large-scale IoT based WSN deployment problem for remote monitoring landslide prone zones and reporting real-time measurements by considering network connectivity, coverage, the optimal number of nodes, and time synchronization. Giorgetti et al [ 41 ] proposed the WSN deployment process for online analysis and the alerting of landslide supervision to evaluate the risks and provide useful information in Torgiovannetto (Italy) by prolonging the network lifetime.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of IoT-Based Health and Environment WSN Deployment

Shakeri

Sadeghi‐Niaraki

Choi

et al. 2020

Sensors

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With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.

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“…ɛ-N is a commonly used method for sparsifying both MANETs and social networks, where the connected matrix C is often a Pearson's correlation matrix [24]. Kumar et al [25] aimed at network connectivity problems such as dynamic network failure and network link disconnection caused by landslide prone areas and bad weather, and improved network connectivity according to geological attributes and demographic characteristics of nodes. A common way to predict whether two nodes are linked is to measure the interactivity and similarity between two nodes.…”

Section: Network Connectivitymentioning

confidence: 99%

“…The similarity of two k-dimensional Gaussian distributions was calculated using the Bhattacharyya kernel [17,25]. For two multidimensional Gaussian distributions D 1 (x) and D 2 (x), the similarity is:…”

Section: Graph Kernel Calculationmentioning

confidence: 99%

Graph Kernel Based Clustering Algorithm in MANETs

Song

Luo²,

et al. 2020

IEEE Access

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The mobile ad hoc network (MANET) is a kind of dynamic, easy to construct and universal network, which has been widely concerned by a large number of researchers. Graph theory provides an effective theoretical tool for MANETs modeling and analysis. Clustering is one of the most effective methods to measure network performance with different attributes. This paper gives the basic concept of graph kernel and discusses the principle of optimizing graph kernel and multi-graph kernel. In this paper, we propose a Graph Kernel based Clustering Algorithm in MANETs (GKCA). The GKCA algorithm gives the basic concept of graph kernel, discusses the principle of optimizing graph kernel and multi-graph kernel, and proposes the basic principle based on d-hop graph kernel. GKCA algorithm uses shortest path (SP) to connect different cluster head nodes for packet transmission. The performance of GKCA algorithm, such as the control packets ratio, packets loss ratio, and average end-to-end delay are experimentally evaluated using network simulation (NS2) software. Experimental analysis shows that the proposed approach is efficient, and its performance advantage in dynamic mobile networks is promising.

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Reliable network connectivity in wireless sensor networks for remote monitoring of landslides

Cited by 45 publications

References 13 publications

Design of remote control device using wireless sensor network and its use in intelligent monitoring of farmland information

Design of remote control device using wireless sensor network and its use in intelligent monitoring of farmland information

Performance Analysis of IoT-Based Health and Environment WSN Deployment

Graph Kernel Based Clustering Algorithm in MANETs

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