A Distributed Energy-Harvesting-Aware Routing Algorithm for Heterogeneous IoT Networks

Nguyen, Thien Duc; Khan, Jamil Y.; Ngo, Duy T.

doi:10.1109/tgcn.2018.2839593

Cited by 117 publications

(69 citation statements)

References 27 publications

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“…Edge servers harvest the renewable energy during different time intervals from the solar panels which are installed in the deployment site. We consider the time-varying solar energy harvesting during one day time duration by approximating the amount of periodical harvested solar energy pattern presented in [27] using the Gaussian-shape function. In other words, the harvested energy by edge server k at the beginning of time slot t is given using the following function:…”

Section: Edge Server Energy Modelmentioning

confidence: 99%

“…The size of the cache at each edge server is fixed at Q k = 3Gb, ∀1 ≤ k ≤ K and the capacity of B e = 1KJoule is considered for the energy storage of edge servers. We adopt from [27] the Gaussian-shape solar energy harvesting profile with mean time slot chosen from the uniform interval µ ∼ U nif [11am, 2pm] and the harvesting variance of σ 2 = 5Joule. According to [27], this energy harvesting profile corresponds to the maximum feasible harvesting (h max ) between approximately 60 to 80 Joule at each time slot.…”

Section: A Simulation Setupmentioning

confidence: 99%

“…We adopt from [27] the Gaussian-shape solar energy harvesting profile with mean time slot chosen from the uniform interval µ ∼ U nif [11am, 2pm] and the harvesting variance of σ 2 = 5Joule. According to [27], this energy harvesting profile corresponds to the maximum feasible harvesting (h max ) between approximately 60 to 80 Joule at each time slot. We also assume that the available grid energy to each edge servers in one round of scheduling (300 seconds) is chosen from the uniform distribution U nif [1.5KJ, 2KJ].…”

Section: A Simulation Setupmentioning

confidence: 99%

See 2 more Smart Citations

Energy-Aware QoE and Backhaul Traffic Optimization in Green Edge Adaptive Mobile Video Streaming

Mehrabi

Siekkinen

Ylä-Jääski

2019

IEEE Trans. on Green Commun. Netw.

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Collaborative caching and processing at the network edges through mobile edge computing (MEC) helps to improve the quality of experience (QoE) of mobile clients and alleviate significant traffic on backhaul network. Due to the challenges posed by current grid powered MEC systems, the integration of time-varying renewable energy into the MEC known as green MEC (GMEC) is a viable emerging solution. In this paper, we investigate the enabling of GMEC on joint optimization of QoE of the mobile clients and backhaul traffic in particularly dynamic adaptive video streaming over HTTP (DASH) scenarios. Due to intractability, we design a greedy-based algorithm with self-tuning parameterization mechanism to solve the formulated problem. Simulation results reveal that GMEC-enabled DASH system indeed helps not only to decrease grid power consumption but also significantly reduce backhaul traffic and improve average video bitrate of the clients. We also find out a threshold on the capacity of energy storage of edge servers after which the average video bitrate and backhaul traffic reaches a stable point. Our results can be used as some guidelines for mobile network operators (MNOs) to judge the effectiveness of GMEC for adaptive video streaming in next generation of mobile networks. Index Terms-Green mobile edge computing (GMEC), DASH, Quality of experience (QoE), Fairness, Greedy-based algorithm. Antti Ylä-Jääski received the Ph.D. degree from ETH Zurich in 1993. From 1994 to 2009, he was with Nokia in several research and research management positions, with a focus on future Internet, mobile networks, applications, services, and service architectures. Since 2004, he has been a tenured Professor with the Department of Computer Science, Aalto University. His current research interests include mobile cloud computing, mobile multimedia systems, pervasive computing and communications, indoor positioning and navigation, energy efficient communications and computing, and Internet of Things.

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Section: Edge Server Energy Modelmentioning

confidence: 99%

Section: A Simulation Setupmentioning

confidence: 99%

Section: A Simulation Setupmentioning

confidence: 99%

See 1 more Smart Citation

Energy-Aware QoE and Backhaul Traffic Optimization in Green Edge Adaptive Mobile Video Streaming

Mehrabi

Siekkinen

Ylä-Jääski

2019

IEEE Trans. on Green Commun. Netw.

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show abstract

“…The proposed routing protocol achieved efficient energy consumption, extended network coverage, and the ability to prolong the lifetime of CA-MANETs. To address the challenges of traffic load variations and energy availability conditions, the authors in [34] proposed an effective Energy-Harvesting-Aware Routing Algorithm (EHARA) for heterogeneity of the IoT networks. The EHARA protocol selects the optimal path based on harvested energy, consumed energy, and the residual energy cost metric functions of the intermediate device for data transmission toward the destination device.…”

Section: Related Workmentioning

confidence: 99%

EMBLR: A High-Performance Optimal Routing Approach for D2D Communications in Large-scale IoT 5G Network

et al. 2020

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Coping with the skyrocketing needs for massive amounts of data for the future Fifth Generation (5G) network, Device-to-Device (D2D) communications technology will provide seamless connectivity, high data rates, extended network coverage, and spectral efficiency. The D2D communications are a prevalent emerging technology to achieve the vision of symmetry in the Internet of Things (IoT) services. However, energy resource constraints, network stability, traffic congestion, and link failure of the devices are the crucial impediments to establish an optimal route in the D2D communications based IoT 5G network. These obstacles induced packet drop, rapid energy depletion, higher end-to-end delay, and unfairness across the network, leading to significant route and network performance degradation. Therefore, in this paper, an energy, mobility, queue length, and link quality-aware routing (EMBLR) approach is proposed to overcome the challenges and boost network performance. Moreover, a multicriteria decision making (MCDM) technique is utilized for the selection of the intermediate device in an optimal route. Extensive simulation has been conducted and proven that the proposed routing approach significantly enhances network performance. Overall, results have been carried out in Quality of Service (QoS) performance metrics and compared with other well-known routing approaches.

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“…It estimates the residual energy based on the difference between the accumulated value and prerecorded full charge value of the battery capacity. The proposed scheme utilizes a generic radio energy model [57] to estimate the consumed energy for each state of node such as (E i…”

Section: Energy Consumption Estimation Of Mobile Nodesmentioning

confidence: 99%

Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm

et al. 2019

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To facilitate connectivity to the internet, the easiest way to establish communication infrastructure in areas affected by natural disaster and in remote locations with intermittent cellular services and/or lack of Wi-Fi coverage is to deploy an end-to-end connection over Mobile Ad-hoc Networks (MANETs). However, the potentials of MANETs are yet to be fully realized as existing MANETs routing protocols still suffer some major technical drawback in the areas of mobility, link quality, and battery constraint of mobile nodes between the overlay connections. To address these problems, a routing scheme named Mobility, Residual energy and Link quality Aware Multipath (MRLAM) is proposed for routing in MANETs. The proposed scheme makes routing decisions by determining the optimal route with energy efficient nodes to maintain the stability, reliability, and lifetime of the network over a sustained period of time. The MRLAM scheme uses a Q-Learning algorithm for the selection of optimal intermediate nodes based on the available status of energy level, mobility, and link quality parameters, and then provides positive and negative reward values accordingly. The proposed routing scheme reduces energy cost by 33% and 23%, end to end delay by 15% and 10%, packet loss ratio by 30.76% and 24.59%, and convergence time by 16.49% and 11.34% approximately, compared with other well-known routing schemes such as Multipath Optimized Link State Routing protocol (MP-OLSR) and MP-OLSRv2, respectively. Overall, the acquired results indicate that the proposed MRLAM routing scheme significantly improves the overall performance of the network.

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A Distributed Energy-Harvesting-Aware Routing Algorithm for Heterogeneous IoT Networks

Cited by 117 publications

References 27 publications

Energy-Aware QoE and Backhaul Traffic Optimization in Green Edge Adaptive Mobile Video Streaming

Energy-Aware QoE and Backhaul Traffic Optimization in Green Edge Adaptive Mobile Video Streaming

EMBLR: A High-Performance Optimal Routing Approach for D2D Communications in Large-scale IoT 5G Network

Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm

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