Energy Harvesting for Self-Sustainable Wireless Body Area Networks

Akhtar, Fayaz; Rehmani, Mubashir Husain

doi:10.1109/mitp.2017.34

Cited by 79 publications

(45 citation statements)

References 14 publications

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“…functions and the interference constraints, the OP in is a nonlinear optimization problem, and thus, it is nontrivial to convert it to a convex optimization problem. It was shown in Akhtar and Rehmani that such optimization problems are NP hard, and the optimal solution cannot be found in polynomial time. We convert the OP to a matching graph and solve it through a graph theory‐based algorithm that gives the solution to the graph problem.…”

Section: Relay and Subchannel Selection Under Energy Constraints And mentioning

confidence: 99%

Improving physical layer security via cooperative diversity in energy‐constrained cognitive radio networks with multiple eavesdroppers

Shah

Koo

2019

Int J Communication

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In this paper, a scheme that exploits cooperative diversity of multiple relays to provide physical layer security against an eavesdropping attack is concerned. Relay-based cognitive radio network (CRN) faces issues multiple issues other than the same as faced by conventional wireless communications. If the nodes in a CRN are able to harvest energy and then spend less energy than the total energy available, we can ensure a perpetual lifetime for the network. In this paper, an energy-constrained CRN is considered where relay nodes are able to harvest energy. A cooperative diversity-based relay and subchannel-selection algorithm is proposed, which selects a relay and a subchannel to achieve the maximum secrecy rate while keeping the energy consumed under a certain limit. A transmission power factor is also selected by the algorithm, which ensures long-term operation of the network. The power allocation problem at the selected relay and at the source also satisfies the maximum-interference constraint with the primary user (PU). The proposed scheme is compared with a variant of the proposed scheme where the relays are assumed to have an infinite battery capacity (so maximum transmission power is available in every time slot) and is compared with a scheme that uses jamming for physical layer security. The simulation results show that the infinite battery-capacity scheme outperforms the jamming-based physical layer security scheme, thus validating that cooperative diversity-based schemes are suitable to use when channel conditions are better employed, instead of jamming for physical layer security. KEYWORDS cognitive radio networks, energy constrained networks, energy harvesting, graph theory, physical layer security, security threat Int J Commun Syst. 2019;32:e4008.wileyonlinelibrary.com/journal/dac

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Section: Relay and Subchannel Selection Under Energy Constraints And mentioning

confidence: 99%

Improving physical layer security via cooperative diversity in energy‐constrained cognitive radio networks with multiple eavesdroppers

Shah

Koo

2019

Int J Communication

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“…These SNs are conventionally powered by batteries, which are needed to be replaced once the energy is consumed. Therefore, wireless energy harvesting serves as an alternative approach that enables self-sustained SNs operations by scavenging energy from biomechanical, biochemical, and ambient sources (e.g., thermal, electromagnetic radiations) [2].Due to limited battery life, saving energy of the SNs is of significant importance. Therefore, WBAN has to provide sustainable battery lifetime, high energy efficiency (EE), and quality-of-service (QoS) of the data stream.…”

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confidence: 99%

“…1 shows the two state Markov chain with transition probabilities P 10 , and P 01 from state S 1 to S 2 , and from state S 2 to S 1 , respectively. The states in A i are arranged in an increasing order as g (1) i ≤ g (2) i · · · ≤ g |A i | i , where |A i | is the cardinality of A i and represents the number of the states in A i . Let Π i be the steady-probability vector at SN i, that can be calculated as follows:…”

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confidence: 99%

Energy-Efficiency Maximization of Self-Sustained Wireless Body Area Sensor Networks

2019

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Electronic health monitoring is one of the major applications of wireless body area networks (WBANs) that helps with early detection of any abnormal physiological symptoms. In this paper, we propose and solve an optimization problem that maximizes the energy efficiency (EE) of WBAN consisting of sensor nodes (SNs) equipped with energy harvesting capabilities communicating with an aggregator. We exploit the structure of the optimization problem to provide a suboptimal solution at a lower computational complexity and derive the mathematical expressions of upper and lower bounds of the source rates of the SN. The simulation results reveal that the optimal allocation of the source rate to energy critical SNs improves the system performance of WBAN in terms of energy efficiency during different everyday activities.Index Terms electronic health monitoring, energy harvesting, energy efficiency optimization, wireless body area network. I. INTRODUCTIONElectronic Health (eHealth) monitoring systems with wireless body area networks (WBANs) help integrate the patient's data processing and communications technologies into traditional medical facilities and serves as a promising approach to boost the health-care efficiency. In WBAN, the sensor nodes (SNs) monitor the patient's vital signs and send the data wirelessly to the aggregator [1]. These SNs are conventionally powered by batteries, which are needed to be replaced once the energy is consumed. Therefore, wireless energy harvesting serves as an alternative approach that enables self-sustained SNs operations by scavenging energy from biomechanical, biochemical, and ambient sources (e.g., thermal, electromagnetic radiations) [2].Due to limited battery life, saving energy of the SNs is of significant importance. Therefore, WBAN has to provide sustainable battery lifetime, high energy efficiency (EE), and quality-of-service (QoS) of the data stream. In [3], an efficient power QoS control scheme for energy harvesting WBAN is proposed that ensures the best possible QoS by efficiently transmitting the data packets. Stochastic modeling of wirelessly powered wearables proposed in [4] provides an analytical framework for the SNs ability to notify the medical staff about the patient's condition promptly. In [5], a medium access control (MAC) layer protocol for WBAN is proposed that utilizes the CSMA/CA-TDMA hybrid schemes to extend the lifetime and EE of SNs by saving energy. In [6], a MAC protocol for WBAN is proposed that ensures QoS and EE in the power constrained network by dynamically optimizing the transmission slot such as the energy consumption of the SNs is minimized. Cooperative energy harvesting-adaptive MAC protocol proposed in [7] improves the WBAN performance in terms of delay, EE, and throughput by changing its operation based on the energy harvesting conditions.Compared to the existing work, this paper aims to maximize the overall EE of the energy harvesting WBAN. In particular, we formulate and solve a novel optimization problem that optimally allocates each SN sour...

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“…To the best of our knowledge, the research on resource allocation for EH-WBANs is in its infancy, despite having some pioneering studies [26][27][28][29][30][31][32][33][34][35]. Mohammadi et al [26] proposed a link adaption mechanism to maximize energy efficiency in Institute of Electrical and Electronics Engineers (IEEE) 802.15.6 impulse radio ultra-wideband (IR-UWB) WBANs.…”

mentioning

confidence: 99%

“…These human body bio-energy sources can be categorized into bio-chemical and bio-mechanical energy sources. The bio-chemical energy sources convert electrochemical to electricity for invasive body sensors, while bio-mechanical energy can be obtained from the locomotion of the human body [33]. In the work of Quwaider et al [34], the weighted sum of the outage probabilities was the objective function to be minimized.…”

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confidence: 99%

Reinforcement Learning (RL)-Based Energy Efficient Resource Allocation for Energy Harvesting-Powered Wireless Body Area Network

Xie

Zhang

et al. 2019

Sensors

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Wireless body area networks (WBANs) have attracted great attention from both industry and academia as a promising technology for continuous monitoring of physiological signals of the human body. As the sensors in WBANs are typically battery-driven and inconvenient to recharge, an energy efficient resource allocation scheme is essential to prolong the lifetime of the networks, while guaranteeing the rigid requirements of quality of service (QoS) of the WBANs in nature. As a possible alternative solution to address the energy efficiency problem, energy harvesting (EH) technology with the capability of harvesting energy from ambient sources can potentially reduce the dependence on the battery supply. Consequently, in this paper, we investigate the resource allocation problem for EH-powered WBANs (EH-WBANs). Our goal is to maximize the energy efficiency of the EH-WBANs with the joint consideration of transmission mode, relay selection, allocated time slot, transmission power, and the energy constraint of each sensor. In view of the characteristic of the EH-WBANs, we formulate the energy efficiency problem as a discrete-time and finite-state Markov decision process (DFMDP), in which allocation strategy decisions are made by a hub that does not have complete and global network information. Owing to the complexity of the problem, we propose a modified Q-learning (QL) algorithm to obtain the optimal allocation strategy. The numerical results validate the effectiveness of the proposed scheme as well as the low computation complexity of the proposed modified Q-learning (QL) algorithm.

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Energy Harvesting for Self-Sustainable Wireless Body Area Networks

Cited by 79 publications

References 14 publications

Improving physical layer security via cooperative diversity in energy‐constrained cognitive radio networks with multiple eavesdroppers

Improving physical layer security via cooperative diversity in energy‐constrained cognitive radio networks with multiple eavesdroppers

Energy-Efficiency Maximization of Self-Sustained Wireless Body Area Sensor Networks

Reinforcement Learning (RL)-Based Energy Efficient Resource Allocation for Energy Harvesting-Powered Wireless Body Area Network

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