Optimal resource allocation in ultra-low power fog-computing SWIPT-based networks

Janatian, Nafiseh; Stupia, Ivan; Vandendorpe, Luc

doi:10.1109/wcnc.2018.8376974

Cited by 21 publications

(25 citation statements)

References 13 publications

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“…In [45], the power minimization problem was studied in a SWIPT-aided fog computing networks with dog offloading, where however, the fog server was just used to assign tasks rather than participate in computing. In [46], the authors studied the optimal resource allocation in ultra-low power fog-computing SWIPT-based networks, where however, only the TS receiver architecture was adopted and only single-user was considered. In [47], the authors extended the work in [46] to a multi-user scenario, but it still only studied the TS receiver architecture.…”

Section: B Related Workmentioning

confidence: 99%

“…In [46], the authors studied the optimal resource allocation in ultra-low power fog-computing SWIPT-based networks, where however, only the TS receiver architecture was adopted and only single-user was considered. In [47], the authors extended the work in [46] to a multi-user scenario, but it still only studied the TS receiver architecture. In [48], the power minimization problem was investigated in SWIPTaware fog computing system with PS receiver architecture, where however, only single-user was considered.…”

Section: B Related Workmentioning

confidence: 99%

“…According to [50] and [55], the value of K depends on the specific algorithms, but at high bit rate, the computing operation with linear complexity is often expected to reduce the computational complexity and power consumption. Hence, similar to [46], [50], we also adopt the linear model in (9) to characterize the local computing energy consumption in this paper. R…”

Section: System Modelmentioning

confidence: 99%

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Fog-Assisted Multiuser SWIPT Networks: Local Computing or Offloading

Zheng

Xiong

Fan

et al. 2019

IEEE Internet Things J.

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This paper investigates a fog computing-assisted multi-user simultaneous wireless information and power transfer (SWIPT) network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors' required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multi-user scheduling, the time assignment, the sensors' transmit powers and the PS ratios. Since the problem is a mixed integer programming (MIP) problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions and theoretical analysis. For a scheduled sensor, the closed-form and semi-closedform solutions to achieve its minimal required energy are derived, and then an efficient multi-user scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server (FS), the fog offloading mode is the better choice; otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors.

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Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

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Fog-Assisted Multiuser SWIPT Networks: Local Computing or Offloading

Zheng

Xiong

Fan

et al. 2019

IEEE Internet Things J.

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“…A few recent works began to study SWIPTaware fog computing systems. In [10] and [11], the energy consumption was minimized by adopting time switching (TS) mode and power splitting (PS) mode, respectively, where a user was powered by an energy access point and then the tasks were offloaded to a fog server. In [12], the energy consumption was minimized by optimizing power, time and data allocation, where multiple users were considered.…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, in existing works, see e.g. [10]- [11], only single type of users or single user were studied. That is, only EH users, PS users or TS users were considered in their works.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of SWIPT-Aware Fog Computing Networks

Liu

Xiong

Fan

et al. 2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing network, where a multiple antenna fog function integrated hybrid access point (F-HAP) transfers information and energy to multiple heterogeneous single-antenna sensors and also helps some of them fulfill computing tasks. By jointly optimizing energy and information beamforming designs at the F-HAP, the bandwidth allocation and the computation offloading distribution, an optimization problem is formulated to minimize the required energy under communication and computation requirements, as well as energy harvesting constraints. Two optimal designs, i.e., fixed offloading time (FOT) and optimized offloading time (OOT) designs, are proposed. As both designs get involved in solving non-convex problems, there are no known solutions to them. Therefore, for the FOT design, the semidefinite relaxation (SDR) is adopted to solve it. It is theoretically proved that the rank-one constraints are always satisfied, so the global optimal solution is guaranteed. For the OOT design, since its non-convexity is hard to deal with, a penalty dual decomposition (PDD)-based algorithm is proposed, which is able to achieve a suboptimal solution. The computational complexity for two designs are analyzed. Numerical results show that the partial offloading mode is superior to binary benchmark modes. It is also shown that if the system is with strong enough computing capability, the OOT design is suggested to achieve lower required energy; Otherwise, the FOT design is preferred to achieve a relatively low computation complexity.

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A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

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Optimal resource allocation in ultra-low power fog-computing SWIPT-based networks

Cited by 21 publications

References 13 publications

Fog-Assisted Multiuser SWIPT Networks: Local Computing or Offloading

Fog-Assisted Multiuser SWIPT Networks: Local Computing or Offloading

Optimal Design of SWIPT-Aware Fog Computing Networks

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

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