Toward Energy-Oriented Optimization for Green Communication in Sensor Enabled IoT Environments

Kumar, Sushil; Kaiwartya, Omprakash; Rathee, Manisha; Kumar, Neeraj; Lloret, Jaime

doi:10.1109/jsyst.2020.2975823

Cited by 46 publications

(20 citation statements)

References 31 publications

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“…Many key areas of smart sensor networks are worth studying, such as energy management [2,3], data privacy protection, node location monitoring, and network routing settings. Although the application prospect is very impressive, many problems are still exposed.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Chaotic Ant Colony Optimization for Energy Optimization in Smart Sensor Networks

2021

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Smart sensor network has the characteristics of low cost, low power consumption, real time, strong adaptability, etc., and it has a wide range of application prospects in the agricultural field. However, the smart sensor node is limited by its own energy; it also faces many bottlenecks in agricultural applications. Therefore, balancing the energy consumption of nodes and extending the life of the network are important considerations in the design of efficient routing for smart sensor networks. Aiming at the problem of energy constraints, this paper proposes an intelligent sensor network clustering algorithm based on adaptive chaotic ant colony optimization (ACACO). ACACO introduces logical chaotic mapping to interfere with the pheromone on the initial path and uses the adaptive strategy to improve the transition probability formula. After selecting the best next hop node, the advancing ants are released to update the local pheromone, and the current pheromone content is adjusted by the chaos factor. When the ants determine the path, they release subsequent ants to update the global pheromone. The simulation results show that ACACO has obvious advantages over genetic algorithm (GA) and particle swarm optimization (PSO).

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Section: Introductionmentioning

confidence: 99%

Adaptive Chaotic Ant Colony Optimization for Energy Optimization in Smart Sensor Networks

2021

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“…According to a recent report by Mckinsey, the number of connected IoT devices worldwide is projected to increase up to 43 billion by 2023 [3]. In the IoT enabled connected environment, balancing the usage of sensor's energy for longevity and maximizing the throughput of the sensor-enabled smart services is major concern [4,5]. In next generation wireless systems, relay assisted wireless communication along with optimal transmit power has come out to be a favorable solution [6].…”

Section: Introductionmentioning

confidence: 99%

Quantum Learning-Enabled Green Communication for Next-Generation Wireless Systems

Jaiswal

Kumar

Kaiwartya

et al. 2021

IEEE Trans. on Green Commun. Netw.

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Next generation wireless systems have witnessed significant R&D attention from academia and industries to enable wide range of applications for connected environment around us. The technical design of next generation wireless systems in terms of relay and transmit power control is very critical due to the ever-reducing size of these sensor enabled systems. The growing demand of computation capability in these systems for smart decision making further diversified the significance of relay and transmit power control. Towards harnessing the benefits of Quantum Reinforcement Leaning (QRL) in the design of next generation wireless systems, this paper presents a framework for joint optimal Relay and transmit Power Selection (QRL-RPS). In QRL-RPS, each sensor node learns using its present and past local state's knowledge to take optimal decision in relay and transmit power selection. Firstly, RPS problem is modelled as a Markov Decision Process (MDP), and then QRL optimization aspect of the MDP problem is formulated focusing on joint optimization of energy consumption and throughput as network utility. Secondly, a QRL-RPS algorithm is developed based on Grover's iteration to solve the MDP problem. The comparative performance evaluation attests the benefit of the proposed framework as compared to the stateof-the-art techniques.

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“…The new advancements in energy harvesting techniques for WSNs have become a prominent research field. But, in the case of energy harvesting, the energy output may be insufficient due to streaky and spatial variations, and some mechanisms are needed for effective utilization of the harvested energy; further, these mechanisms are collectively termed as energy optimization strategies [12,13].…”

Section: Introductionmentioning

confidence: 99%

Green Communication for Next‐Generation Wireless Systems: Optimization Strategies, Challenges, Solutions, and Future Aspects

Rathore

Sangwan

Kaiwartya

et al. 2021

Wireless Communications and Mobile Computing

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Wireless sensor networks (WSNs) have emerged as a backbone technology for the wireless communication era. The demand for WSN is rapidly increasing due to their major role in various applications with a wider deployment and omnipresent nature. The WSN is rapidly integrated into a large number of applications such as industrial, security, monitoring, tracking, and applications in home automation. The widespread use in many different areas attracts research interest in WSNs. Therefore, researchers are taking initiatives in exploring innovation day by day particularly towards the Internet of Things (IoT). But, WSN is having lots of challenging issues that need to be addressed, and the inherent characteristics of WSN severely affect the performance. Energy constraints are one of the primary issues that require urgent attention from the research community. Optimal energy optimization strategies are needed to counter the issue of energy constraints. Although one of the most appropriate schemes for handling energy constraints issues is the appropriate energy harvesting technique, the optimal energy optimization strategies should be coupled together for effectively utilizing the harvested energy. In this high-level systematic and taxonomical survey, we have organized the energy optimization strategies for EH-WSNs into eleven factors, namely, radio optimization schemes, optimizing the energy harvesting process, data reduction schemes, schemes based on cross-layer optimization, schemes based on cross-layer optimization, sleep/wake-up policies, schemes based on load balancing, schemes based on optimization of power requirement, optimization of communication mechanism, schemes based on optimization of battery operations, mobility-based schemes, and finally energy balancing schemes. We have also prepared the summarized view of various protocols/algorithms with their remarkable details. This systematic and taxonomy survey also provides a progressive detailed overview and classification of various optimization challenges for the EH-WSNs that require attention from the researcher followed by a survey of corresponding solutions for corresponding optimization issues. Further, this systematic and taxonomical survey also provides a deep analysis of various emerging energy harvesting technologies in the last twenty years of the era.

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Toward Energy-Oriented Optimization for Green Communication in Sensor Enabled IoT Environments

Cited by 46 publications

References 31 publications

Adaptive Chaotic Ant Colony Optimization for Energy Optimization in Smart Sensor Networks

Adaptive Chaotic Ant Colony Optimization for Energy Optimization in Smart Sensor Networks

Quantum Learning-Enabled Green Communication for Next-Generation Wireless Systems

Green Communication for Next‐Generation Wireless Systems: Optimization Strategies, Challenges, Solutions, and Future Aspects

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