A Scalable Energy vs. Latency Trade-Off in Full-Duplex Mobile Edge Computing Systems

Kabir, Mahmoud T.; Masouros, Christos

doi:10.1109/tcomm.2019.2915833

Cited by 32 publications

(13 citation statements)

References 41 publications

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“…Researchers implemented a low-cost framework for large-scale IoT devices to achieve heterogeneity, scalability, and resiliency. 21,22 The studies facilitate resource-constrained IoT devices to offload complex tasks to the edge server to achieve low latency and energy consumption. Despite this, computation offload incurs communication delay among IoT devices and consumes more energy, and this is where the study lacks in proposing an efficient solution.…”

Section: Related Workmentioning

confidence: 99%

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ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems

Babar

Khan

2021

International Journal of Distributed Sensor Networks

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Edge computing brings down storage, computation, and communication services from the cloud server to the network edge, resulting in low latency and high availability. The Internet of things (IoT) devices are resource-constrained, unable to process compute-intensive tasks. The convergence of edge computing and IoT with computation offloading offers a feasible solution in terms of performance. Besides these, computation offload saves energy, reduces computation time, and extends the battery life of resource constrain IoT devices. However, edge computing faces the scalability problem, when IoT devices in large numbers approach edge for computation offloading requests. This research article presents a three-tier energy-efficient framework to address the scalability issue in edge computing. We introduced an energy-efficient recursive clustering technique at the IoT layer that prioritizes the tasks based on weight. Each selected task with the highest weight value offloads to the edge server for execution. A lightweight client–server architecture affirms to reduce the computation offloading overhead. The proposed energy-efficient framework for IoT algorithm makes efficient computation offload decisions while considering energy and latency constraints. The energy-efficient framework minimizes the energy consumption of IoT devices, decreases computation time and computation overhead, and scales the edge server. Numerical results show that the proposed framework satisfies the quality of service requirements of both delay-sensitive and delay-tolerant applications by minimizing energy and increasing the lifetime of devices.

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Section: Related Workmentioning

confidence: 99%

“…However, there is a trade-off between energy consumption to make computation offload and reducing latency. 29,30 The proposed work scalable edge computing 22 considers the implementation of multiobjective optimization over edge servers for efficient resource allocation.…”

Section: Related Workmentioning

confidence: 99%

ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems

Babar

Khan

2021

International Journal of Distributed Sensor Networks

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“…In [18], Zhao et al studied full task offloading for minimizing the energy consumption of smart mobile devices by jointly allocating of radio resources and computational resources of edge servers. In [19], Kabir and Masouros studied full task offloading to investigate the energy consumption and latency tradeoff in a MEC system with fullduplex. They formulated two optimization problems for the two objectives.…”

Section: Related Workmentioning

confidence: 99%

Optimal Resource Allocation for Multimedia Applications Offloading in Mobile Edge Computing

Chen¹,

Zhao²,

Li³

et al. 2021

IEEE Open J. Comput. Soc.

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Thanks to the development of the technologies in the wireless communications and Internet of Things (IoT), the adoption of mobile devices is growing rapidly. Accordingly, the number of multimedia applications like face recognition and augmented reality generated from various mobile devices is growing at an unprecedented rate. The processing of these multimedia applications needs a lot of computation resources and has to be processed as quickly as possible. However, as these mobile devices have limited computation resources, the undesirable response delay will occur. By offloading the multimedia applications to the edge cloud close to the access point (AP) or the cellular base station (BS), mobile edge computing (MEC) is considered as a prospective approach to improve the quality of service (QoS) and enhance the computing capacity of mobile devices. Multimedia applications offloading in a MEC system are studied in this paper. The objective of the studied problem is to minimize the execution delay of multimedia applications of all mobile devices by allocating both the communication resource and the computing resource in the edge server. An optimization problem is formulated and an efficient multimedia applications offloading scheme is proposed to get the solution. Simulation results are conducted to verify the proposed application offloading method, which show that there is a significant execution delay reduction.

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“…, where r 0 = y − e 0 is the undistorted received signal, where y is given in (5). Note that (3) extends the transmit LDR noise model [12] to the case of hybrid FD transceiver at the transmit side.…”

Section: System Modelmentioning

confidence: 99%

“…Full-duplex (FD) communication in mmWave can enable simultaneous transmission and reception in the same frequency band, which theoretically doubles the spectral efficiency compared to half-duplex (HD) systems. By avoiding using two independent channels for bi-directional communication, it can be beneficial for efficient management of the available spectrum, reduce end-to-end delays/latency, solve the hidden node problem [3]- [5], and enable joint communication and sensing [6].…”

Section: Introductionmentioning

confidence: 99%

Practical Hybrid Beamforming for Millimeter Wave Massive MIMO Full Duplex with Limited Dynamic Range

Sheemar¹,

Thomas²,

Slock³

2021

Preprint

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Full-Duplex (FD) communication can revolutionize wireless communications as it doubles spectral efficiency and offers numerous other advantages over a half-duplex (HD) system. In this paper, we present a novel and practical joint hybrid beamforming (HYBF) and combining scheme for millimeter-wave (mmWave) massive MIMO FD system for weighted sum-rate (WSR) maximization with multi-antenna HD uplink and downlink users with non-ideal hardware.<br>Moreover, we present a novel interference and self-interference (SI) aware optimal power allocation scheme for the optimal beamforming directions. The analog processing stage is assumed to be quantized, and both the unit-modulus and unconstrained cases are considered.<br>Moreover, compared to the traditional sum-power constraints, the proposed algorithm is designed under the joint sum-power and the practical per-antenna power constraints. To model the non-ideal hardware of a hybrid FD transceiver, we extend the traditional limited dynamic range (LDR) noise model to mmWave. Our HYBF design relies on alternating optimization based on the minorization-maximization method. <br>We investigate the maximum achievable gain of a hybrid FD system with different levels of the LDR noise variance and with different numbers of radio-frequency (RF) chains over a HD system. Simulation results show that the mmWave massive MIMO FD systems can significantly outperform the fully digital HD systems with only a few RF chains if the LDR noise generated from the limited number of RF chains available is low. If the LDR noise variance dominates, FD communication with HYBF results to be disadvantageous than a HD system. <br>

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A Scalable Energy vs. Latency Trade-Off in Full-Duplex Mobile Edge Computing Systems

Cited by 32 publications

References 41 publications

ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems

ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems

Optimal Resource Allocation for Multimedia Applications Offloading in Mobile Edge Computing

Practical Hybrid Beamforming for Millimeter Wave Massive MIMO Full Duplex with Limited Dynamic Range

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