Building Reliable Routing Infrastructure for Green IoT Network

Lenka, Rakesh Kumar; Rath, Amiya Kumar; Sharma, Suraj

doi:10.1109/access.2019.2939883

Cited by 66 publications

(17 citation statements)

References 35 publications

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“…SNs produce a vast volume of data and transfer of the enormous volume of data may lead to collision. A multi-channel communication method was used for reducing disputes amongst SNs [12]. Numerous conventional routing plans leveraged composite event recognition for diminishing power consumption.…”

Section: Introductionmentioning

confidence: 99%

Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

Rajesh¹,

Acharya²,

Hajiyev³

et al. 2023

Computers, Materials &Amp; Continua

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Recently, Internet of Things (IoT) has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications. Wireless sensor network (WSN) is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management, intelligent transportation, home automation, smart buildings, and other such IoT-based application. The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is, for balancing the energy amongst sensor nodes. The clustering and routing procedures assumed that Non-Polynomial (NP) hard problems but bio-simulated approaches are utilized to a recognized time for resolving such problems. With this motivation, this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning (BCEHGS-RP) scheme for IoT assisted WSN. The presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supported WSN environment. In addition, an effective multihop route planning approach was designed by the use of EHGS technique. The proposed EHGS technique is derived from the concept of Hill Climbing strategy (HCS) and HGS algorithm. Moreover, a fitness function with two parameters namely residual energy (RE) and intercluster distance to elect optimal routes. The performance validation of the BCEHGS-RP model is experimented with under diverse number of nodes.

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Section: Introductionmentioning

confidence: 99%

Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

Rajesh¹,

Acharya²,

Hajiyev³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…5 Clustering is one of the typical ways for node stability and energy management in sensor networks. 6 Clustering divides the sensor network into clusters, each of which contains a coordinator or head node called the cluster head. 7 Cluster members are few among the nodes in a cluster that are not CH.…”

Section: Introductionmentioning

confidence: 99%

Designing green IoT communication by adaptive spotted hyena tunicate swarm optimization‐based cluster head selection

Das¹

2022

Trans Emerging Tel Tech

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The Internet of Things (IoT) has completely transformed the digital as well as virtual worlds of interconnected objects. In today's environment, the IoT describes a critical enabler for a broader variety of applications. Energy is at the center of the smart IoT application that allows the sensors to function. The sensors' ability to perform efficiently is hampered by rapid energy loss. To avoid the rapid loss of energy from sensors in the IoT, and energy‐effective method is necessary. Because heuristic approaches are not ideal for these issues and may turn into NP‐hard problems, meta‐heuristic techniques are mainly successful in solving various problems with near‐optimal solutions. This paper intends to develop a novel clustering protocol in Green IoT using a well‐performing novel hybrid adaptive meta‐heuristic algorithm. Here, an energy‐efficient clustering is performed by the hybrid spotted hyena optimization (SHO) and tunicate swarm algorithm (TSA) referred to as adaptive spotted hyena tunicate swarm optimization (ASHTSO) by deriving a multi‐objective function with “distance, energy, delay, security, and QoS”. The simulation findings show that the suggested technique outperforms existing algorithms significantly.

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“…The Internet of Things (IoT) can connect almost all smart devices with the Internet in order to realize intelligent identification and applications through sensing devices [1]. The IoT has many applications in any fields, such as transportation, medicine, logistics management or even smart homes.…”

Section: Introductionmentioning

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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Building Reliable Routing Infrastructure for Green IoT Network

Cited by 66 publications

References 35 publications

Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

Designing green IoT communication by adaptive spotted hyena tunicate swarm optimization‐based cluster head selection

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

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