Link evaluation of uniform grid based wireless sensor networks to base station with leveling and clustering

Swamy, Tata Jagannadha; Sandhya, T.; Ramamurthy, Garimella

doi:10.1109/iccci.2013.6466146

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…The source node then sends data to the sink along this path. LEUGB [21] proposes two different types of base station positions, and discusses the effects of different base station placements in a uniformly divided grid for WSNs. The base station is placed at the center and corner positions of the sensing area, and then the network is divided into different numbers of grids to solve the problem of base station placement and grid division.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Two-Tier Data Dissemination Based on Q-Learning for Wireless Sensor Networks

Wang

Hsu

2020

IEEE Access

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Green communication for different kinds of wireless networks has begun to receive significant research attention recently. Green communication focuses mainly on the issue of improving energy efficiency substantially. A wireless sensor network (WSN) consists of a large number of randomly and widely deployed sensor nodes, and these nodes themselves have the ability to wireless communicate, detect and process data. Sensor nodes can thus detect their surrounding environment, and transmit related data to a sink via wireless communication. This study proposes two two-tier data dissemination schemes based on Q-learning for wireless sensor networks. In the proposed schemes, a source node uses Q-learning to find the most energy efficient data dissemination path from the source node to the sink. The first scheme is called TTDD-QL, and the second scheme is called TTDD-QL-A which is an advanced version of TTDD-QL. In TTDD-QL, the reward is determined by the distance between the current dissemination node and the sink. In each iteration, the proposed scheme will update the Q values. After multiple learning iterations, the Q values are converged, and the data dissemination path is found according to the Q values. In TTDD-QL-A, the reward is determined not only by the distance between the current dissemination node and the sink but also by the remaining energy of the current dissemination node. Simulation results show that TTDD-QL and TTDD-QL-A can reduce sensor node energy consumption and extend the lifetime of the WSN.

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Section: Related Workmentioning

confidence: 99%

Energy Efficient Two-Tier Data Dissemination Based on Q-Learning for Wireless Sensor Networks

Wang

Hsu

2020

IEEE Access

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“…There exist two main models for this kind of transmission [3]. In the first model, a proxy system (a base station [4] or a sink [5]), is needed. In the second model, IP protocol is used for WSNs connecting to the Internet [6].…”

Section: Introductionmentioning

confidence: 99%

A Secure Trust-Based Key Distribution With Self-Healing for Internet of Things

Han

Yang

et al. 2019

IEEE Access

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Internet of things (IoT) can enable cyber-physical objects to communicate with one another and realize real-time living-needs control such as for vehicles, smart phones, refrigerators, healthcare gadgets and air-conditioners. Of the applications of IoT, collecting and receiving health-related data securely is the most crucial and significant use in the fields of community and hospital healthcare, where any single communication failure or data loss might cause a life damage risk. To address this problem, self-healing mechanism can be used for facilitating secure communications and recovering from the lost data. In this paper, a new key distribution with self-healing for IoT objects of community healthcare is proposed, and the underlying system is composed of two layers. In the top layer, the new scheme implements both deterministic security link and access control which are based on polynomial-based methods. In the bottom layer, we propose a trust-based key distribution scheme with self-healing and a singular value decomposition (SVD) based authentication method. Security and performance analysis show that our protocol can be performed more efficiently in data communication. In addition, results obtained from both security analysis and simulations indicate that our scheme is more suitable for IoT networks.

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