Energy-Efficient and Delay-Fair Mobile Computation Offloading

Mu, Siqi; Zhang, Zhong; Zhao, Dongmei

doi:10.1109/tvt.2020.3033288

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…A benefit from integrating CLE into the problem formulation is that we no longer require the hard completion time constraints in the problem formulation. The objective in [21] is to minimize the energy consumption of the entire system, and in references [22] and [33], the objective is to minimize the total energy consumption of all MDs. Instead of satisfying delay constraints, the work in [24], [25], [34] optimize a utility function that is a weighted sum of task completion time and energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…Reference [21] studies the problem of task offloading and channel resource allocation for ultra-dense networks and minimizes the total energy consumption of the system with a limited delay tolerance. The work in [22] studies MCO by considering application latency fairness and minimizes MD energy consumption by jointly optimizing the offloading ratio, channel assignments, and channel time allocations. Reference [23] investigates the power minimization problem for meeting the service delay requirements in multi-cell multi-user mobile edge computing networks.…”

Section: Related Workmentioning

confidence: 99%

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Wireless and Service Allocation for Mobile Computation Offloading with Task Deadlines

Chen¹,

Todd²,

Zhao³

et al. 2023

Preprint

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In mobile computation offloading (MCO), mobile devices (MDs) can choose to either execute tasks locally or to have them executed on a remote edge server (ES). This paper addresses the problem of assigning both the wireless communication bandwidth needed, along with the ES capacity that is used for the task execution, so that task completion time constraints are satisfied. The objective is to obtain these allocations so that the average power consumption of the mobile devices is minimized, subject to a cost budget constraint. The paper includes contributions for both soft and hard task completion deadline constraints. The problems are first formulated as mixed integer nonlinear programs (MINLPs). Approximate solutions are then obtained by decomposing the problems into a collection of convex subproblems that can be efficiently solved. Results are presented that demonstrate the quality of the proposed solutions, which can achieve near optimum performance over a wide range of system parameters.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Wireless and Service Allocation for Mobile Computation Offloading with Task Deadlines

Chen¹,

Todd²,

Zhao³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…where N G and N m are the numbers of groups and sensors in each group, respectively. 3) ECM scheme: In the ECM scheme, the bipartite graph is generated to record the Q i,k for all sensor-MEC combinations, then, the KM algorithm is used to find the optimal matching to minimize the energy consumption, as used in [8]- [10]. The computational complexity of this scheme is O[N I (4N 4 + 6N 3 + N ) + 2N 2 I + N 4 I + log(1/ε)].…”

Section: Simulationsmentioning

confidence: 99%

“…However, existing IoT computation offloading schemes have the following two challenges. Firstly, traditional schemes usually focus on the optimization of computation or communication performance, such as computing latency minimization, energy consumption minimization, or network throughput maximization [3], [8]- [10]. The Gas 1 factor only affects the block generation speed and is not considered in computational resource allocation, leading to the dissatisfaction of sensors as better computational resources are not allocated to those sensors under the performance priority strategy even if they pay high Gas.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient and Physical Layer Secure Computation Offloading in Blockchain-Empowered Internet of Things

Liu¹,

Zhou²,

Wang³

2022

Preprint

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This paper investigates computation offloading in blockchain-empowered Internet of Things (IoT), where the task data uploading link from sensors to a base station (BS) is protected by intelligent reflecting surface (IRS)-assisted physical layer security (PLS). After receiving task data, the BS allocates computational resources provided by mobile edge computing (MEC) servers to help sensors perform tasks. Existing blockchain-based computation offloading schemes usually focus on network performance improvements, such as energy consumption minimization or latency minimization, and neglect the Gas fee for computation offloading, resulting in the dissatisfaction of high Gas providers. Also, the secrecy rate during the data uploading process can not be measured by a steady value because of the time-varying characteristics of IRS-based wireless channels, thereby computational resources allocation with a secrecy rate measured before data uploading is inappropriate. In this paper, we design a Gas-oriented computation offloading scheme that guarantees a low degree of dissatisfaction of sensors, while reducing energy consumption. Also, we deduce the ergodic secrecy rate of IRS-assisted PLS transmission that can represent the global secrecy performance to allocate computational resources. The simulations show that the proposed scheme has lower energy consumption compared to existing schemes, and ensures that the node paying higher Gas gets stronger computational resources.

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“…With this, the problem OPT.1 formulated in Chapter 3 can be transformed into a geometric programming (GP) problem and solved optimally using commercial software, such as matlab. However, this approach requires high computation complexity because Successive Convex Approximation (SCA) [22] is needed in order to transform the posynomials in the constraint functions into monomials to fit the general format of the GP. The complexity becomes prohibitively high when the number of sensor nodes is large.…”

Section: Proposed Solutionmentioning

confidence: 99%

Joint Charging, Routing, and Power Allocations in Rechargeable Wireless Sensor Networks

Guo

Zhao

2021

2021 International Wireless Communications and Mobile Computing (IWCMC)

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In a wireless sensor network (WSN), sensor nodes monitor the physical environment and forward the collected data to a data sink for further processing. Sensors are battery powered and, therefore, prolonging the lifetime of their batteries is critically important. In a rechargeable WSN (RWSN), prolonging the battery lifetime of sensors is achieved through reducing communication energy and recharging the batteries periodically. Reducing the communication energy consumption is done through choosing the best forwarding sensors (i.e., routing) for data collected by each sensor and deciding the transmission power of each sensor (i.e., power allocation). Recharging the batteries is achieved through harvesting energy from external sources. In this thesis, we consider a RWSN that uses wireless power transfer as the energy harvesting technology and jointly optimizes charging and communications in order to minimize the power consumption of the RWSN.

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Energy-Efficient and Delay-Fair Mobile Computation Offloading

Cited by 11 publications

References 37 publications

Wireless and Service Allocation for Mobile Computation Offloading with Task Deadlines

Wireless and Service Allocation for Mobile Computation Offloading with Task Deadlines

Energy-Efficient and Physical Layer Secure Computation Offloading in Blockchain-Empowered Internet of Things

Joint Charging, Routing, and Power Allocations in Rechargeable Wireless Sensor Networks

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