A Green Routing Protocol with Wireless Power Transfer for Internet of Things

Chiti, Francesco; Fantacci, R.; Pierucci, Laura

doi:10.3390/jsan10010006

Cited by 10 publications

(8 citation statements)

References 36 publications

(56 reference statements)

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“…Moreover, these works assumed a constant replenishment rate for all nodes, which makes them unsuitable to deal with the stochastic characteristics of the ambient energy sources. Motivated by the significant benefits of WPT to wirelessly charge devices' batteries through a dedicated and fully controlled RF power source, the authors of [16] studied the use of WPT technology in low-power and lossy network (RPL) model, where they proposed a new objective function for RPL, which jointly considers the energy consumption of intermediate nodes and the amount of recharging energy available from the received power of a child node. Simulation results demonstrated that the proposed algorithm efficiently minimizes the energy loss and extends the network lifetime by selecting the most efficient path via the sink.…”

Section: A Related Workmentioning

confidence: 99%

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Reconfigurable Intelligent Surface-Assisted Routing for Power-Constrained IoT Networks

Derbas,

Naser,

Bariah

et al. 2024

IEEE Open J. Commun. Soc.

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In power-constrained wireless networks, extending network lifespan can be achieved through effective energy harvesting techniques. Developing energy-efficient routing protocols in such networks is complex due to the unstable and unpredictable harvested energy. In this regard, reconfigurable Intelligent Surfaces (RIS), capable of manipulating the RF signals, offer enhanced energy harvesting, which in turn minimizes the nodes' battery recharging time (BRT). Our study presents a novel RIS-assisted routing protocol for multi-hop energy harvesting-based wireless networks. The protocol assigns channels to each hop on the chosen path. It incorporates a rate calculation algorithm that takes into consideration the harvested power, BRT, and channel conditions. More specifically, it assigns a specific rate to every available channel, taking into account the energy available at each node, the estimated time required for battery recharging, and the link status between nodes. By selecting the highest rate per hop, the protocol enhances network throughput while considering energy limits and channel quality. The proposed approach is evaluated and compared with the benchmark, the Min hop scheme, using Matlab simulations, demonstrating its superior performance.INDEX TERMS Battery recharging time, energy-efficient routing, IoT, reconfigurable intelligent surface (RIS), wireless power transfer (WPT).

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Section: A Related Workmentioning

confidence: 99%

“…In this paper, we assume that the battery parameters remain constant over time. The PDF of battery recharging time can be obtained using (11) through the Jacobian approach using (16). Hence, the PDF of the BRT can be written as follows…”

Section: ) Statistical Characterization Of the Battery Recharging Timementioning

confidence: 99%

Reconfigurable Intelligent Surface-Assisted Routing for Power-Constrained IoT Networks

Derbas,

Naser,

Bariah

et al. 2024

IEEE Open J. Commun. Soc.

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“…Authors in [16] suggested a unique method for RPL protocol trying to develop the IoT network lifetime. The suggested method combined the consumed and recharged energy to choose the most suitable route to send data information.…”

Section: Related Workmentioning

confidence: 99%

Security-Aware Routing Protocol Based on Artificial Neural Network Algorithm and 6LoWPAN in the Internet of Things

Lu¹,

Li²,

Wang³

et al. 2022

Wireless Communications and Mobile Computing

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Today, with increasing information technology such as the Internet of Things (IoT) in human life, interconnection and routing protocols need to find optimal solution for safe data transformation with various smart devices. Therefore, it is necessary to provide an enhanced solution to address routing issues with respect to new interconnection methodologies such as the 6LoWPAN protocol. The artificial neural network (ANN) is based on the structure of intelligent systems as a branch of machine interference, has shown magnificent results in previous studies to optimize security-aware routing protocols. In addition, IoT devices generate large amounts of data with variety and accuracy. Therefore, higher performance and better data handling can be achieved when this technology incorporates data for sending and receiving nodes in the environment. Therefore, this study presents a security-aware routing mechanism for IoT technologies. In addition, a comparative analysis of the relationship between previous approaches discusses with quality of service (QoS) factors such as throughput and accuracy for improving routing mechanism. Experimental results show that the use of time-division multiple access (TDMA) method to schedule the sending and receiving of data and the use of the 6LoWPAN protocol when routing the sending and receiving of data can carry out attacks with high accuracy.

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“…In Varga et al, 20 the author proposed a stack of network protocols specially meant for energy harvesting enabled WSNs, the author presented results was the power consumption using his proposed stack of protocols compared to the network power consumption when using standard protocols. Similarly, Chiti et al, 21 Sarwar et al, 22 and Liu et al 23 focused on proposing greener RPL protocol, for which nodes power consumption is more efficient and thus can be suitable for autonomous and EH enabled WSNs. Other works which may be considered relevant to ours are the studies by Liu et al, Lu et al, and Clerckx et al, [24][25][26] which all proposed RF energy harvesting methods, this may be relevant since WSNs' nodes do have an RF module which is the module having the highest power consumption.…”

Section: Related Workmentioning

confidence: 99%

Energy harvesting effect on prolonging low-power lossy networks lifespan

Habaebi

Basaloom

Islam

et al. 2021

International Journal of Distributed Sensor Networks

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Low-power lossy networks performance relies heavily on the wireless node battery status. Furthermore, Routing Protocol for Low-Power and Lossy Network routing protocol was not optimally designed with sustainable energy consumption in mind to suit these networks. Prolonging the lifespan of these networks is of utmost priority. This article introduces a solar energy harvesting module to power energy-constrained network devices and quantifies the effect of using harvested energy on prolonging their network lifetime when Routing Protocol for Low-Power and Lossy Network routing protocol is used. Simulation of the new developed module is conducted in three different scenarios using Contiki Cooja simulator sporting Zolertia Z1 motes. Furthermore, the harvested energy used was fed from a Cooja-based Simulation model of actual PV supercapacitor circuit design. All battery levels were set to 1% of their total capacity for all nodes in the network to speed up observing the energy harvesting effect. The performance evaluation results showed that the network with no-energy harvesting operated for time duration of 4:08:04 time units (i.e. hour:minute:second) with a dramatic decrease in connection between nodes in the network. However, the same network, when using the harvested energy to back up the battery operation, lasted for 6:40:01 in time units with improved connectivity, a total extended network lifetime of 2:31:97-time units. Furthermore, for the Routing Protocol for Low-Power and Lossy Network routing metrics, OF0 outperformed ETX in term of throughput, packet delivery ratio, energy consumption, and network connectivity. Results indicate that the developed harvested energy module fits perfectly for any Cooja-based simulation and mimics actual photovoltaic-based supercapacitor battery. It should also help researchers introduce and quantify accurately new energy consumption-based routing metrics for Routing Protocol for Low-Power and Lossy Network.

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A Green Routing Protocol with Wireless Power Transfer for Internet of Things

Cited by 10 publications

References 36 publications

Reconfigurable Intelligent Surface-Assisted Routing for Power-Constrained IoT Networks

Reconfigurable Intelligent Surface-Assisted Routing for Power-Constrained IoT Networks

Security-Aware Routing Protocol Based on Artificial Neural Network Algorithm and 6LoWPAN in the Internet of Things

Energy harvesting effect on prolonging low-power lossy networks lifespan

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