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Joint offloading and beamforming design in full-duplex multi-hop SWIPT MEC systems under non-linear energy harvesting model

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IET COMMUNICATIONS 2022年第18期16卷 2171-2182页

作者： Xu, Zhiguo Lv, You Liu, Jianxin Zou, Junwei Wen, Zhigang Beijing Univ Posts & Telecommun Dept Elect Engn Beijing Key Lab Work Safety Intelligent Monitorin Beijing Peoples R China

In Internet of Things scenarios, the problem of restricted energy and limited computation capability of user node usually exists. To address the problem, a multiple-input-multiple-output full-duplex multi-hop relay simultaneous wireless information and power transfer (SWIPT) mobile-edge computing (MEC) system is proposed in this paper, where energy harvesting model is non-linear. With the aid of MEC and SWIPT, user node equipped with power splitting receiver can locally execute computation tasks or offload partial or all of it to access point associated with an MEC server by using its battery energy, and harvest energy to replenish its battery while downloading the computation results. In addition, the system introduces full-duplex multi-hop relay and non-linear energy model to improve network coverage and model practical energy harvesting circuit. Aiming at minimising the system energy consumption, an energy efficient optimisation problem is formulated while satisfying the latency and energy constraints. Since the original problem is non-linear and non-convex, it is converted into two subproblems and successive convex approximation-based algorithm and geometric programming-based algorithm based alternating optimisation technique are adopted to solve them. Numerical results verify the superiority of the proposed optimisation scheme compared with other benchmark schemes.

关键词： Internet of Things restricted energy beamforming design energy efficient optimisation problem battery energy computation results Things scenarios radiofrequency power transmission mobile-edge computing system power splitting receiver energy harvesting MIMO communication convex programming relay networks (telecommunication) resource allocation energy constraints edge computing concave programming energy consumption optimisation telecommunication power management nonlinear energy model nonconvex array signal processing limited computation capability network coverage system energy consumption computation tasks nonorthogonal multiple access mobile computing energy conservation joint offloading nonlinear energy harvesting model user node geometric programming full-duplex multihop relay power transfer MEC server model practical energy harvesting circuit full-duplex multihop SWIPT MEC systems

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Fractional Greedy and Partial Restreaming Partitioning : New Methods For Massive Graph Partitioning 2

Fractional Greedy and Partial Restreaming Partitioning : New...

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IEEE International Conference on Big Data

作者： Echbarthi, Ghizlane Kheddouci, Hamamache Univ Lyon 1 Lyon France

ISBN: (纸本)9781479956661

Graph partitioning is an important challenging problem when performing computation tasks over large distributed graphs;the reason is that a good partitioning leads to faster computations. In this work, we first introduce a new heuristic for streaming partitioning and show that it outperforms the state-of-the-art heuristics for streaming partitioning, leading to exact balance and lower cut. Secondly, we introduce the partial restreaming partitioning which is a hybrid streaming model allowing only several portions of the graph to be restreamed while the rest is to be partitioned on a single pass of the data stream. We show that our method yields partitions of similar quality than those provided by methods restreaming the whole graph (e.g ReLDG, ReFENNEL), while incurring lower cost in running time and memory since only several portions of the graph will be restreamed.

关键词： computational complexity graph theory greedy algorithms ReFENNEL ReLDG computation tasks data stream fractional Greedy partitioning graph restreaming heuristics hybrid streaming model large-distributed graphs massive-graph partitioning memory cost partial-restreaming partitioning running time cost Adaptation models Linear programming Load modeling NP-hard problem Partitioning algorithms Runtime Silicon Runtime Partitioning algorithms data flow greedy algorithm Load modeling Nondeterministic polynomial-time hard Heuristics partitions Graph theory linear programming complexity classes Adaptation models Line graph Silicon new methods

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Practical issues for configuring carbon nanotube composite materials for computation

Practical issues for configuring carbon nanotube composite m...

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IEEE International Conference on Evolvable Systems (ICES)

作者： Clegg, Kester Dean Miller, Julian Francis Massey, Mark Kieran Petty, Michael C. Univ York York YO10 5DD N Yorkshire England Univ Durham Durham DH1 3HP England

ISBN: (纸本)9781479944798

We report our experiences of attempting to configure a single-walled carbon nanotube (SWCNT) / polymer composite material deposited on a micro-electrode array to carry out two classification tasks based on data sets from University of California, Irvine (UCI)[1]. The tasks are attempted using hybrid "in materio" computation: a technique that uses machine search to configure materials for computation. The SWCNT / polymer composite materials are configured using static voltages so that voltage output readings from the material predict which class the data samples belong to. Our initial results suggest that the configured SWCNT materials are able to achieve good levels of predictive accuracy. However, we are in no doubt that the time and effort required to configure the samples could be improved. The parameter space when dealing with physical systems is large, often unknown and slow to test, making progress in this field difficult. Our purpose is not demonstrate the accuracy of configured samples to perform a certain classification, but to showcase the potential of configuring very small material samples with analogue voltages to solve stand alone computation tasks. Such SWCNT devices would be cheap to manufacture and require only low precision assembly, yet if correctly configured would be able to function as multipurpose, single task computational devices.

关键词： carbon nanotubes composite materials data acquisition evolutionary computation intelligent sensors materials testing microelectrodes polymer blends C SWCNT-polymer composite material analogue voltages carbon nanotube composite materials configuration computation tasks configured SWCNT materials data samples data sets machine search microelectrode array multipurpose computational devices parameter space physical systems predictive accuracy levels single task computational devices single-walled carbon nanotube small material sample configuration static voltages voltage output readings Accuracy Arrays Electrodes Iris Materials Performance evaluation Switches Data sampling Microelectrodes parameter space methods Single-Walled Nanotube Materials intelligent sensors materials testing evolutionary algorithm Polymer blends Switches Data Collection Methods Dataset Performance Evaluation Composites Electrodes

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