Offloading decision methods for multiple users with structured tasks in edge computing for smart cities

Kuang, Li; Gong, Tao; OuYang, Shuyin; Gao, Honghao; Deng, Shuiguang

doi:10.1016/j.future.2019.12.039

Cited by 81 publications

(47 citation statements)

References 26 publications

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“…We also reviewed the LA applications as follows. There are some researches about multiuser offloading 15‐17 . Here, we mention these articles based on their objectives and methods.…”

Section: Related Workmentioning

confidence: 99%

Multiuser context‐aware computation offloading in mobile edge computing based on Bayesian learning automata

Farahbakhsh

Shahidinejad

Ghobaei‐Arani

2020

Trans Emerging Tel Tech

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Today a lot of data sensed from the environment by the Internet of things applications. These data need to process with the lowest delay. Mobile devices (MDs) as ubiquitous tools are end devices in the network. These devices with limited resources cannot process all computations locally. Mobile edge computing (MEC) is a good architecture for processing computations in the network's edge. It solves the cloud challenges such as delay, energy, and cost. If MDs could not process the computations, they will offload tasks to the edge or cloud. Research shows that ignoring context information of application, requests, sensors, resources, and network tools cause to not complete the offloading method. In this article, we consider Bayesian learning automata (BLA) with considering context‐aware offloading in MEC with multiuser. BLA learns all states and actions in the network and helps us to improve the offloading algorithm. The contexts are collected using autonomous management as the monitor‐analysis‐plan‐execution loop in all offloading processes. The simulation results indicate that our method is superior to local computing and offload without considering context‐aware algorithms in some metrics such as energy consumption, execution cost, network usage, delay, and fairness.

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“…We also reviewed the LA applications as follows. There are some researches about multiuser offloading 15‐17 . Here, we mention these articles based on their objectives and methods.…”

Section: Related Workmentioning

confidence: 99%

Multiuser context‐aware computation offloading in mobile edge computing based on Bayesian learning automata

Farahbakhsh

Shahidinejad

Ghobaei‐Arani

2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…In respect of objective, some other works address the VNF placement regarding load balancing, service resilience and monitoring overhead [13][14][15][16]. Authors in [17] presented an offloading decision problem as a costminimization problem. In [18], the authors presented an energy-aware placement for NFV environments using a game-theory approach.…”

Section: Related Workmentioning

confidence: 99%

Service Function Chain Placement for Joint Cost and Latency Optimization

et al. 2020

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Network Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are served through performing two tasks: VNF placement and link embedding on the substrate networks. Reducing deployment cost is a desired objective for all service providers in cloud/edge environments to increase their profit form demanded services. However, increasing resource utilization in order to decrease deployment cost may lead to increase the service latency and consequently increase SLA violation and decrease user satisfaction. To this end, we formulate a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. We, then solve the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. Since the optimization model involves conflicting objectives, we also investigate pareto optimal solution so that it optimizes multiple objectives as much as possible. The efficiency of proposed algorithms is evaluated using both simulation and emulation. The evaluation results show that the proposed optimization approach succeed in minimizing both cost and latency while the results are as accurate as optimal solution obtained by Gurobi (5%).

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“…In the arm skeleton rigid body model, the arm and forearm lengths are assumed to be constant. Given the degree of freedom, the wrist and elbow positions of the watch can be calculated according to (5). According to this idea, we can construct the mapping relationship between the watch posture and the 5 degrees of freedom of the arm, elbow joint and wrist joint position.…”

Section: ) Mathematical Modeling To Bone Of Armmentioning

confidence: 99%

“…W ITH the rapid development of smart hardware and mobile computing, Internet of Things (IoT) is wildly applied in multiple domains, e.g., Vehicular ad hoc networks (VANETs) [1], edge computing [2]- [5], quality of service (QoS) prediction [6], intention detection [7], and smart home or intelligent appliances interactions and controlling [8]- [13]. A smart home system will control lighting, climate, entertainment systems, and appliances, e.g., smart air conditioner, smart refrigerator and smart TV.…”

Section: Introductionmentioning

confidence: 99%

Control Your Home With a Smartwatch

Zhao

Duan

et al. 2020

IEEE Access

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With the wide applications of smart devices and mobile computing, smart home becomes a hot issue in the household appliance industry. The controlling and interaction approach plays a key role in users' experience and turns into one of the most important selling points for profit growth. Considering the robustness and privacy protection, wearable devices equipped with MEMS, e.g., smartphones, smartwatches, or smart wristbands, are thought of one of the most feasible commercial solutions for interaction. However, the low-cost built-in MEMS sensors do not perform well in capturing finely grained human activity directly. In this paper, we propose a method that leverages the arm constraint and historical information recorded by MEMS sensors to estimate the maximum likelihood action in a two-phases model. First, in the arm posture estimation phase, we leverage the kinematics model to analyze the maximum likelihood position of users' arms. Second, in the trajectory recognition phase, we leverage the gesture estimation model to identify the key actions and output the instructions to devices by SVM. Our substantial experiments show that the proposed solution can recognize eight kinds of postures defined for manmachine interaction in the smart home application scene, and the solution implements efficient and effective interaction using low-cost smartwatches, and the interaction accuracy is >87%. The experiments also show that the algorithm proposed in this paper can be well applied to the perceptual control of smart household appliances, and has high practical value for the application design of the perceptual interaction function of household appliances.

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Offloading decision methods for multiple users with structured tasks in edge computing for smart cities

Cited by 81 publications

References 26 publications

Multiuser context‐aware computation offloading in mobile edge computing based on Bayesian learning automata

Multiuser context‐aware computation offloading in mobile edge computing based on Bayesian learning automata

Service Function Chain Placement for Joint Cost and Latency Optimization

Control Your Home With a Smartwatch

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