Wireless Energy Harvesting for Cognitive Multihop Wireless Sensor Networks

Nguyen, Van‐Dinh; Nguyen, Hieu V.; Shin, Oh‐Soon

doi:10.1155/2015/613165

Cited by 6 publications

(11 citation statements)

References 25 publications

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“…We assume that all the relay nodes can correctly decode the signal from a relay in the preceding cluster. Energy harvested at each node from the surrounding environment can be expressed as [13,14] …”

Section: System Modelmentioning

confidence: 99%

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Energy Harvesting for Throughput Enhancement of Cooperative Wireless Sensor Networks

Nguyen

Shin

2016

International Journal of Distributed Sensor Networks

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We consider an energy harvesting for cooperative wireless sensor networks with a nonlinear power consumption model. The information transmission from the source node to the destination node is assumed to occur via several clusters of decode-andforward relay nodes. We assume that all the source and relay nodes have the ability to harvest energy from the environment and use that harvested energy to transmit and forward the information to the next hop. Under such assumption, the objective of our design is to improve the total throughput of the end-to-end link over a number of transmission blocks subject to constraints on energy causality, battery overflow, and time duration for energy harvesting. The optimization problem is found to be a nonconvex maximin fractional program, which is difficult to solve in general. We present an efficient iterative algorithm to solve the optimization problem. Specifically, by introducing novel transformations, we apply an approximate convex technique to obtain a convex problem at each iteration. We then propose an iterative power allocation algorithm which converges to a locally optimal solution at a Karush-Kuhn-Tucker point. Numerical results are provided to evaluate the proposed scheme.

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Section: System Modelmentioning

confidence: 99%

“…The energy harvesting refers to a process by which energy is derived from surrounding environment (e.g., wind, wave, solar, and thermal energy), captured, and stored for later use [13,14]. The harvested energy can be extracted even from a radio frequency (RF) signal [15].…”

Section: Introductionmentioning

confidence: 99%

Energy Harvesting for Throughput Enhancement of Cooperative Wireless Sensor Networks

Nguyen

Shin

2016

International Journal of Distributed Sensor Networks

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“…Energy is often stored in super-capacitors, which have the ability to store energy for only a limited timeduration. Since these super-capacitors cannot be charged and discharged at the same time, power splitting (PS) and time switching (TS) protocols were developed [4]. In the PS protocol, a node harvests energy from a fraction of the received signal power, while the remaining signal power is used to decode information.…”

Section: Introductionmentioning

confidence: 99%

Performance off cluster‐based multi‐hop underlay networks with energy harvesting nodes

2020

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“…Hence, a promising advancement to WSNs is the proposition of combining WSNs with Cognitive Radio Networks (CRNs). This avoids the aforementioned spectrum access limitation and also provides the extra benefit of energy harvesting to WSN nodes [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…The combination of WSNs and CRNs into Cognitive Radio Sensor Networks (CRSNs) is quite an attractive proposal, because when the spectrum bands of a PU are not being utilized, CRSN nodes can transmit their data over such high-bandwidth bands. Conversely, when the PU starts utilizing such spectrum, CRSN nodes can switch to energy harvesting mode to obtain their energy needs (for free) from the PU itself [17,18].…”

Section: Introductionmentioning

confidence: 99%

A Self-Learning MAC Protocol for Energy Harvesting and Spectrum Access in Cognitive Radio Sensor Networks

et al. 2016

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The fusion of Wireless Sensor Networks (WSNs) and Cognitive Radio Networks (CRNs) into Cognitive Radio Sensor Networks (CRSNs) is quite an attractive proposal, because it allows a distributed set of low-powered sensor nodes to opportunistically access spectrum bands that are underutilized by their licensed owners (called primary users (PUs)). In addition, when the PUs are actively transmitting in their own bands, sensor nodes can switch to energy harvesting mode to obtain their energy needs (for free), to achieve almost perpetual life. In this work, we present a novel and fully distributed MAC protocol, calledS-LEARN, that allows sensor nodes in a CRSN to entwine their RF energy harvesting and data transmission activities, while intelligently addressing the issue of disproportionate difference between the high power necessary for the node to transmit data packets and the small amount of power it can harvest wirelessly from the environment. The presented MAC protocol can improve both the network throughput and total harvested energy, while being robust to changes in the network configuration. Moreover, S-LEARN can keep the cost of the system low, and it avoids the pitfalls from which centralized systems suffer.

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Wireless Energy Harvesting for Cognitive Multihop Wireless Sensor Networks

Cited by 6 publications

References 25 publications

Energy Harvesting for Throughput Enhancement of Cooperative Wireless Sensor Networks

Energy Harvesting for Throughput Enhancement of Cooperative Wireless Sensor Networks

Performance off cluster‐based multi‐hop underlay networks with energy harvesting nodes

A Self-Learning MAC Protocol for Energy Harvesting and Spectrum Access in Cognitive Radio Sensor Networks

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