Energy harvesting techniques for routing issues in wireless sensor networks

Ahmed, Sheeraz; Khan, Muhammad Ayub; Ishtiaq, Atif; Khan, Zahoor Ali; Ali, Malik Taimur

doi:10.1504/ijguc.2019.097225

Cited by 24 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Excessive battery consumption necessitates disposal, which adversely pollutes the environment [9]. Utilizing RF-EH, power may be constantly provided and node lifespan can be increased [10]. Due to its sustainability, RF-EH and passive energy scavenging nodes without batteries will power the next generation of WSNs [11].…”

Section: Concept and Review Of Related Workmentioning

confidence: 99%

Energy Harvesting From Television Frequency: Case Study of Rectifying Antennas

Nicholas,

Ojo,

Oghenenyoreme Emakpo

2023

IJEAST

View full text Add to dashboard Cite

Various research trends have tended to investigate the viability of powering these circuits using either a dedicated RF source from television frequencies (TF) or free energy harvested from ambient electromagnetic space due to the rapid development of wireless systems and demands for low-power integrated electronic circuits. In order to enable battery-free sustainable wireless networks, radio frequency energy harvesting (RFEH) and radiative wireless power transfer (WPT) have gained a lot of attention. Rectenna is primarily a combination of a receiving antenna and a rectifier circuit used to convert RF energy into usable DC electrical energy. The foundation of WPT and RFEH systems, rectifying antennas (rectenna), play a crucial role in the quantity of direct current power delivered to the load. The radiation to AC harvesting efficiency of the rectenna directly affects the gathered power, which can vary by orders of magnitude. The television frequency in the study area is taken into account in this essay so that it can be converted to usable dc voltages and currents.

show abstract

Section: Concept and Review Of Related Workmentioning

confidence: 99%

Energy Harvesting From Television Frequency: Case Study of Rectifying Antennas

Nicholas,

Ojo,

Oghenenyoreme Emakpo

2023

IJEAST

View full text Add to dashboard Cite

show abstract

“…They utilised genetic algorithm technique to accomplish the bi-objective optimisation process of high-output voltage and circuit efficiency with the energy harvesting capabilities. Ahmed et al [24] analysed and compared various routing protocols in the context of energy harvesting sensor systems with improved network stability and elevated lifetime capacity, aimed at minimal computation overhead. Likewise, Cheng et al [25] proposed an opportunistic routing protocol for wireless sensor networks with a dynamic transmission power implementation scheme to comprehend efficient energy harvesting and consumption for packet forwarding mechanisms.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi‐objective design of energy harvesting enabled wireless networks based on evolutionary genetic optimisation

Mehta¹

2020

IET Networks

View full text Add to dashboard Cite

Sustainable operation of energy-restrained wireless network services requires multiple objectives to be satisfied synchronously. Among these objectives, reduced spectrum outage, energy conservation, and minimal packet transmission failures considerably affect the energy harvesting operation of these networks. These three objectives are associated with disparate protocol layers incorporating the transport, medium access control, and physical layers of traditional networking architecture. The authors investigate energy harvesting wireless communications by formulating the multi-objective optimisation problem comprising these global networking criteria, which are simultaneously optimised with the heuristic design procedure. For this, they employ a Pareto-based evolutionary genetic algorithm technique built in the wireless network design and operation to find the optimal set of all non-dominated solutions traversing the entire design search space. Besides, iterative implementation of the presented genetic optimisation model with distinct feasible integrations of crossover and mutation operations is performed to evaluate the proficiency of the proposed scheme for evaluating the Pareto-optimal frontier set. The influence of different combinations of these operations is examined and adaptively applied with appropriate genetic parameters tuning for efficient meta-heuristic search through the candidate solution space. Simulation results demonstrate that the proposed hybrid genetic mechanism outperforms the existing methods in terms of throughput, energy efficiency, and loss rate.

show abstract

“…One of the techniques that is being used to prolong the lifetime of a WSN is the energy harvesting (EH) technique [12][13][14][15][16][17][18]. Although the EH technique can be used to extend the lifetime of the network and solve the node energy restriction problem to some extent, it still has many deficiencies, bordering on environmental limitations [1,9].…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence Techniques for Mobile Wireless Charging

2022

View full text Add to dashboard Cite

This paper proposes energy-efficient swarm intelligence (SI)-based approaches for efficient mobile wireless charging in a distributed large-scale wireless sensor network (LS-WSN). This approach considers the use of special multiple mobile elements, which traverse the network for the purpose of energy replenishment. Recent techniques have shown the advantages inherent to the use of a single mobile charger (MC) which periodically visits the network to replenish the sensor-nodes. However, the single MC technique is currently limited and is not feasible for LS-WSN scenarios. Other approaches have overlooked the need to comprehensively discuss some critical tradeoffs associated with mobile wireless charging, which include: (1) determining the efficient coordination and charging strategies for the MCs, and (2) determining the optimal amount of energy available for the MCs, given the overall available network energy. These important tradeoffs are investigated in this study. Thus, this paper aims to investigate some of the critical issues affecting efficient mobile wireless charging for large-scale WSN scenarios; consequently, the network can then be operated without limitations. We first formulate the multiple charger recharge optimization problem (MCROP) and show that it is N-P hard. To solve the complex problem of scheduling multiple MCs in LS-WSN scenarios, we propose the node-partition algorithm based on cluster centroids, which adaptively partitions the whole network into several clusters and regions and distributes an MC to each region. Finally, we provide detailed simulation experiments using SI-based routing protocols. The results show the performance of the proposed scheme in terms of different evaluation metrics, where SI-based techniques are presented as a veritable state-of-the-art approach for improved energy-efficient mobile wireless charging to extend the network operational lifetime. The investigation also reveals the efficacy of the partial charging, over the full charging, strategies of the MCs.

show abstract

Energy harvesting techniques for routing issues in wireless sensor networks

Cited by 24 publications

References 11 publications

Energy Harvesting From Television Frequency: Case Study of Rectifying Antennas

Energy Harvesting From Television Frequency: Case Study of Rectifying Antennas

Multi‐objective design of energy harvesting enabled wireless networks based on evolutionary genetic optimisation

Swarm Intelligence Techniques for Mobile Wireless Charging

Contact Info

Product

Resources

About