Energy and Collision Aware WSN Routing Protocol for Sustainable and Intelligent IoT Applications

Patel, Nileshkumar R.; Kumar, Shishir; Singh, Sanjay Kumar

doi:10.1109/jsen.2021.3076192

Cited by 34 publications

(13 citation statements)

References 36 publications

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“…Agarwal et al [23] proposed a scheme for smart data routing in WSNs based on mobile sinks and validated it in terms of stability period and network performance metrics such as network lifetime and throughput. Patel et al [24] proposed a cross-layer variant of AODV based on energy conservation, replacing the hop count metric with link quality and conflict counting. However, these schemes are not applicable to E-VANETs.…”

Section: Related Workmentioning

confidence: 99%

Connectivity Enhancement of E-VANET Based on QL-mRSU Self-Learning Energy-Saving Algorithm

Feng

Huang

et al. 2023

IEEE Access

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With the development of smart cities and smart electric vehicles (EVs), the problem of improving the performance of Vehicular Ad-hoc Networks (VANETs) is gradually being emphasized. To improve the network performance of VANETs, some scholars have considered parked vehicles as roadside units, but have not paid attention to the energy consumption characteristics of vehicles, especially electric vehicles. Therefore, in this paper, we propose a QL-mRSU series artificial intelligence energy saving method to optimize the energy consumption of parked electric vehicles during communication. The method is based on electric vehicle self-organizing networks (E-VANETs), which dynamically cluster electric vehicles parked in parking lots by parameters such as traffic flow, number of service demands, and charging index in reinforcement learning, select the most suitable vehicles as mobile roadside units (mRSUs), and adjust the working mode according to environmental changes such as the number of service demands to achieve the effects of self-learning and energy saving. The simulation experimental results show that compared with other energy-based routing algorithms, the method is able to make optimal choices through self-learning with guaranteed communication quality and is more adaptable to traffic flow changes on the road, thus ensuring the stability of energy-saving efficiency. In addition, the method significantly improves the energy structure of electric vehicle parking clusters.

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Section: Related Workmentioning

confidence: 99%

Connectivity Enhancement of E-VANET Based on QL-mRSU Self-Learning Energy-Saving Algorithm

Feng

Huang

et al. 2023

IEEE Access

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“…Moreover, they do not consider the location of nodes. Once the CH was dead, the cluster member nodes deplete energy 43 . While different nodes have separate energy levels, the unreasonable CH selection is noticed.…”

Section: Novel Routing Model In Wsn and Iot Using Ch Selection And Cc...mentioning

confidence: 99%

“…Once the CH was dead, the cluster member nodes deplete energy. 43 While different nodes have separate energy levels, the unreasonable CH selection is noticed. At a similar time, different clusters may vary in terms of size, and thus, CH selection must be reasonable.…”

Section: Ch Selection Versus Cch Selectionmentioning

confidence: 99%

Network energy optimization and intelligent routing in WSN applicable for IoT using self‐adaptive coyote optimization algorithm

Naveen

Prathap²

2023

Int J Communication

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Summary The Internet of Things (IoT) is a recent wireless telecommunications platform, which contains a set of sensor nodes linked by wireless sensor networks (WSNs). These approaches split the sensor nodes into clusters, in which each cluster consists of an exclusive cluster head (CH) node. The major scope of this task is to introduce a novel CH selection in WSN applicable to IoT using the self‐adaptive meta‐heuristic algorithm. This paper aids in providing the optimal routing in the network based on direct node (DN) selection, CH selection, and clone cluster head (CCH) selection. DNs are located near the base station, and it is chosen to avoid the load of CH. The adoption of the novel self‐adaptive coyote optimization algorithm (SA‐COA) is used for the DN selection and CCH selection. When the nodes are assigned in the network, DN and CCH selection is performed by the proposed SA‐COA. Then, the computation of residual energy helps to select the CH, by correlating with the threshold energy. CCH is proposed to copy the data from the CH to avoid the loss of data in transmitting. By forming the CCH, the next CH can be easily elected with the optimal CCH using SA‐COA. From the simulation findings, the best value of the designed SA‐COA‐LEACH model is secured at 1.14%, 3.17%, 1.18%, and 7.33% progressed than self‐adaptive whale optimization algorithm (SAWOA), cyclic rider optimization algorithm (C‐ROA), krill herd algorithm (KHA), and COA while taking several nodes 50. The proposed routing of sensor networks specifies better performance than the existing methods.

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“…According to [134] presented energy and collision aware WSN routing method for IoT networks. The proposed algorithm is based on AODV protocol; however, it replaces the hop count metric with the link quality and collision count.…”

Section: • Dynamic Network Topologymentioning

confidence: 99%

Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey

Farhan

Hameed

Ahmed

et al. 2021

Network

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The last decade has witnessed the rise of the proliferation of Internet-enabled devices. The Internet of Things (IoT) is becoming ever more pervasive in everyday life, connecting an ever-greater array of diverse physical objects. The key vision of the IoT is to bring a massive number of smart devices together in integrated and interconnected heterogeneous networks, making the Internet even more useful. Therefore, this paper introduces a brief introduction to the history and evolution of the Internet. Then, it presents the IoT, which is followed by a list of application domains and enabling technologies. The wireless sensor network (WSN) is revealed as one of the important elements in IoT applications, and the paper describes the relationship between WSNs and the IoT. This research is concerned with developing energy-efficiency techniques for WSNs that enable the IoT. After having identified sources of energy wastage, this paper reviews the literature that discusses the most relevant methods to minimizing the energy exhaustion of IoT and WSNs. We also identify the gaps in the existing literature in terms of energy preservation measures that could be researched and it can be considered in future works. The survey gives a near-complete and up-to-date view of the IoT in the energy field. It provides a summary and recommendations of a large range of energy-efficiency methods proposed in the literature that will help and support future researchers. Please note that the manuscript is an extended version and based on the summary of the Ph.D. thesis. This paper will give to the researchers an introduction to what they need to know and understand about the networks, WSNs, and IoT applications from scratch. Thus, the fundamental purpose of this paper is to introduce research trends and recent work on the use of IoT technology and the conclusion that has been reached as a result of undertaking the Ph.D. study.

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Energy and Collision Aware WSN Routing Protocol for Sustainable and Intelligent IoT Applications

Cited by 34 publications

References 36 publications

Connectivity Enhancement of E-VANET Based on QL-mRSU Self-Learning Energy-Saving Algorithm

Connectivity Enhancement of E-VANET Based on QL-mRSU Self-Learning Energy-Saving Algorithm

Network energy optimization and intelligent routing in WSN applicable for IoT using self‐adaptive coyote optimization algorithm

Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey

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