A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

This paper studies an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) data collection system, where a UAV is employed as a data collection platform for a group of ground IoT devices. Our objective is to minimize the energy consumption of this system by optimizing the UAV's deployment, including the number and locations of stop points of the UAV. When using evolutionary algorithms to solve this UAV's deployment problem, each individual usually represents an entire deployment. Since the number of stop points is unknown a priori, the length of each individual in the population should be varied during the optimization process. Under this condition, the UAV's deployment is a variable-length optimization problem and the traditional fixed-length mutation and crossover operators should be modified. In this paper, we propose a differential evolution algorithm with a variable population size, called DEVIPS, for optimizing the UAV's deployment. In DEVIPS, the location of each stop point is encoded into an individual, and thus the whole population represents an entire deployment. Over the course of evolution, differential evolution is employed to produce offspring. Afterward, we design a strategy to adjust the population size according to the performance improvement. By this strategy, the number of stop points can be increased, reduced, or kept unchanged adaptively. In DEVIPS, since each individual has a fixed length, the UAV's deployment becomes a fixed-length optimization problem and the traditional fixed-length mutation and crossover operators can be used directly. The performance of DEVIPS is compared with that of five algorithms on a set of instances. The experimental studies demonstrate its effectiveness.

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“…Therefore, if the ith IoT device sends data to the UAV at the jth stop point, the data rate is given by [32]…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Differential Evolution With a Variable Population Size for Deployment Optimization in a UAV-Assisted IoT Data Collection System

Huang

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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“…The i D indicates the amount of data that the device i needs to transmit. The , i j R represents the transmission rate of data, and the calculation method is shown in (6) [26].…”

Section: Figure 1 Schematic Diagram Of Uav Collecting Data Of Iot Dementioning

confidence: 99%

Swarm Intelligence Application to UAV Aided IoT Data Acquisition Deployment Optimization

Chen

Mohamed

et al. 2020

IEEE Access

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It is feasible and safe to use unmanned aerial vehicle (UAV) as the data collection platform of the Internet of things (IoT). In order to save the energy loss of the platform and make the UAV perform the collection work effectively, it is necessary to optimize the deployment of UAV. The objective problem is to minimize the sum of the lost energy of UAV and the loss of data transmission of Internet of things devices. The key to solving the problem is to calculate the location of the docking points and the number of docking points when the UAV is working to collect data. This paper proposes a coding scheme based on swarm intelligence optimization, which encapsulates the docking position of UAV into a dimension, so the number of docking points to be calculated is the dimension number of optimization objective. This problem is considered as a dynamic dimension optimization problem. Each individual in swarm intelligence algorithm is a solution. When adjusting the dimension, the best individual is added or deleted to achieve dynamic search in the evolutionary process. Collaborative search among multiple individuals can improve the local optimal limit of search to a certain extent. Finally, the validity of the swarm intelligence-based coding approach is verified by simulation experiments under seven different IoT device distribution scenarios. INDEX TERMS UAV deployment optimization, Internet of things data collection, Swarm intelligence, Dynamic dimension optimization.

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“…In each iteration, ACO assigns higher pheromone values to good solution components probabilistically. ACO has been applied to address job scheduling and resource allocation in CC and EC [78]- [81]. • Bee algorithm (BA), introduced by Pham [82] in 2005, is inspired by the natural foraging behavior of honey bees.…”

Section: B Siasmentioning

confidence: 99%

“…Different rules are adopted to provide services to a large number of service requests. 4) SIAs for Joint Issues: Huang et al [81] studied an ACO-based bilevel optimization approach for joint offloading decision and resource allocation in cooperative MEC to attain energy-efficient task execution under delay constraints. The effectiveness of the proposed approach is demonstrated by comparing it with four other algorithms.…”

Section: B Sias In CC and Ecmentioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

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A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

Cited by 105 publications

References 42 publications

Differential Evolution With a Variable Population Size for Deployment Optimization in a UAV-Assisted IoT Data Collection System

Differential Evolution With a Variable Population Size for Deployment Optimization in a UAV-Assisted IoT Data Collection System

Swarm Intelligence Application to UAV Aided IoT Data Acquisition Deployment Optimization

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

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