A survey of energy harvesting communications: models and offline optimal policies

He, Yejun; Cheng, Xudong; Peng, Wei; Stuber, G.L.

doi:10.1109/mcom.2015.7120021

Cited by 86 publications

(48 citation statements)

References 13 publications

(35 reference statements)

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“…Energy harvesting devices have been conceived for scavenging energy from the environment, hence they are often referred to as energy harvesting wireless sensor networks (EHWSNs) [119], [120]. Compared to conventional battery powered WSNs, EH-WSNs provide substantial benefits in terms of NL maximization [121]- [123].…”

Section: H Energy Harvestingmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

Yetgin

Cheung

El‐Hajjar

et al. 2017

IEEE Commun. Surv. Tutorials

522

229

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Abstract-Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteria.

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Section: H Energy Harvestingmentioning

confidence: 99%

“…Therefore, Tabassum et al reveals the key challenges of designing energy harvesting cellular networks in [119] in order to guarantee an increased battery-lifetime for wireless devices. By contrast, He et al [120] surveys various methods of harvesting the ambient energy, with an emphasis on optimal offline policies.…”

Section: H Energy Harvestingmentioning

confidence: 99%

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

Yetgin

Cheung

El‐Hajjar

et al. 2017

IEEE Commun. Surv. Tutorials

522

229

View full text Add to dashboard Cite

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“…In particular, the CDF of T ðvÞ k in the HDT scheme can be rewritten by combining (13) with (7) and using (14) as…”

Section: Packet Timeout Probability For a Single Nodementioning

confidence: 99%

“…Recently, wireless power technologies have made a great progress to enable the wireless power transfer (WPT) for real wireless applications [11][12][13][14]. The wireless power can be harvested from natural sources such as solar, wind, TV broadcast signals, or a dedicated power transmitter [11].…”

Section: Introductionmentioning

confidence: 99%

RF energy harvesting: an analysis of wireless sensor networks for reliable communication

et al. 2017

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In this paper, we consider a wireless energy harvesting network consisting of one hybrid access point (HAP) having multiple antennas, and multiple sensor nodes each equipped with a single antenna. In contrast to conventional uplink wireless networks, the sensor nodes in the considered network have no embedded energy supply. They need to recharge the energy from the wireless signals broadcasted by the HAP in order to communicate. Based on the point-to-point and multipoints-to-point model, we propose two medium access control protocols, namely harvesting at the header of timeslot (HHT) and harvesting at the dedicated timeslot (HDT), in which the sensor nodes harvest energy from the HAP in the downlink, and then transform its stored packet into bit streams to send to the HAP in the uplink. Considering a deadline for each packet, the cumulative distribution functions of packet transmission time of the proposed protocols are derived for the selection combining and maximal ratio combining (MRC) techniques at the HAP. Subsequently, analytical expressions for the packet timeout probability and system reliability are obtained to analyze the performance of proposed protocols. Analytical results are validated by numerical simulations. The impacts of the system parameters, such as energy harvesting efficiency coefficient, sensor positions, transmit signal-to-noise ratio, and the length of energy harvesting time on the packet timeout probability and the system reliability are extensively investigated. Our results show that the performance of the HDT protocol outperforms the one using the HHT protocol, and the HDT protocol with the MRC technique has the best performance and it can be a potential solution to enhance the reliability for wireless sensor networks.

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“…In prior work, delay-minimization as well as the related rate-maximization problems have been considered for a small number of nodes [1]- [5], [7]- [9], [15], [12]- [14], [16] such as point-to-point, a single unicast with multiple relays, MAC channel with multiple transmit nodes etc. Prior work primarily addresses the offline setting, while fewer results are known in the online setting [5], [7]- [9], [12].…”

Section: Introductionmentioning

confidence: 99%

Online Energy Harvesting Problem Over an Arbitrary Directed Acyclic Graph Network

Vaze

Pillai

2019

IEEE Trans. on Green Commun. Netw.

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A communication network modelled by a directed acyclic graph (DAG) is considered, over which a source wishes to send a specified number of bits to a destination node. Each node of the DAG is powered by a separate renewable energy source, and the harvested energy is used to facilitate the source destination data flow. The challenge here is to find the optimal rate and power allocations across time for each node on its outgoing edges so as to minimize the time by which the destination receives a specified number of bits. An online setting is considered where an algorithm only has causal information about the energy arrivals. Using the competitive ratio as the performance metric, i.e. the ratio of the cost of the online algorithm and the optimal offline algorithm, maximized over all inputs, a lazy online algorithm with a competitive ratio of 2 + δ for any δ > 0 is proposed. Incidentally, 2 is also a lower bound to the competitive ratio of any online algorithm for this problem. Our lazy online algorithm is described and analyzed via defining a novel max-flow problem over a DAG, where the rate on the subset of outgoing edges of any node are related/constrained. An optimal algorithm to find max-flow with these constraints is also provided, which may be of independent interest.

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A survey of energy harvesting communications: models and offline optimal policies

Cited by 86 publications

References 13 publications

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

RF energy harvesting: an analysis of wireless sensor networks for reliable communication

Online Energy Harvesting Problem Over an Arbitrary Directed Acyclic Graph Network

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