Journal ArticleDOI
UAV-Aided Ultra-Reliable Low-Latency Computation Offloading in Future IoT Networks
TLDR
This paper investigates the novel problem of UAV-aided ultra-reliable low-latency computation offloading which would enable future IoT services with strict requirements and proposes a two-stage approximate algorithm where the two problems are transformed into approximate convex programs.Abstract:
Modern 5G services with stringent reliability and latency requirements such as smart healthcare and industrial automation have become possible through the advancement of Multi-access Edge Computing (MEC). However, the rigidity of ground MEC and its susceptibility to infrastructure failure would prevent satisfying the resiliency and strict requirements of those services. Unmanned Aerial Vehicles (UAVs) have been proposed for providing flexible edge computing capability through UAV-mounted cloudlets, harnessing their advantages such as mobility, low-cost, and line-of-sight communication. However, UAV-mounted cloudlets may have failure rates that would impact mission-critical applications, necessitating a novel study for the provisioned reliability considering UAV node reliability and task redundancy. In this paper, we investigate the novel problem of UAV-aided ultra-reliable low-latency computation offloading which would enable future IoT services with strict requirements. We aim at maximizing the rate of served requests, by optimizing the UAVs’ positions, the offloading decisions, and the allocated resources while respecting the stringent latency and reliability requirements. To do so, the problem is divided into two phases, the first being a planning problem to optimize the placement of UAVs and the second an operational problem to make optimized offloading and resource allocation decisions with constrained UAVs’ energy. We formulate both problems associated with each phase as non-convex mixed-integer programs, and due to their non-convexity, we propose a two-stage approximate algorithm where the two problems are transformed into approximate convex programs. Further, we approach the problem considering the task partitioning model which will be prevalent in 5G networks. Through numerical analysis, we demonstrate the efficiency of our solution considering various scenarios, and compare it to other baseline approaches.read more
Citations
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Journal ArticleDOI
Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
TL;DR: A comprehensive study on UAVs, swarms, types, classification, charging, and standardization is presented, highlighting the importance of drones, goals and functionality problems, and identifying the research gap.
Journal ArticleDOI
Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS.
Saeed H. Alsamhi,Saeed H. Alsamhi,Faris A. Almalki,Hatem Al-Dois,Soufiene Ben Othman,Soufiene Ben Othman,Jahan Hassan,Ammar Hawbani,Radyah Sahal,Brian Lee,Hager Saleh +10 more
TL;DR: In this paper, a survey on the use of ML for enhancing IoT applications is presented, and an in-depth overview of the various IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare.
Journal ArticleDOI
QoS-aware placement of microservices-based IoT applications in Fog computing environments
TL;DR: In this paper , the authors proposed a scalable QoS-aware application scheduling policy for batch placement of microservices-based IoT applications within fog environments, which aims at maximising the satisfaction of multiple QoS parameters (makespan, budget and throughput) while focusing on the utilisation of limited fog resources.
Journal ArticleDOI
Secure Transmission for Multi-UAV-Assisted Mobile Edge Computing Based on Reinforcement Learning
TL;DR: This paper proposes two secure transmission methods for multi-UAV-assisted mobile edge computing based on the single-agent and multi-agent reinforcement learning, respectively, and results indicate that compared with thesingle-agent method and the benchmark, the multi- agent method can optimize the of-�oading in a better manner and achieve larger system utility.
Journal ArticleDOI
Secure Transmission for Multi-UAV-Assisted Mobile Edge Computing Based on Reinforcement Learning
TL;DR: In this paper , the authors proposed two secure transmission methods for multi-UAV-assisted mobile edge computing based on the single-agent and multi-agent reinforcement learning, respectively, to reduce the information eavesdropping by a flying eavesdropper.
References
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Journal ArticleDOI
A Survey on Mobile Edge Computing: The Communication Perspective
TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management is provided in this paper, where a set of issues, challenges, and future research directions for MEC are discussed.
Journal ArticleDOI
Optimal LAP Altitude for Maximum Coverage
TL;DR: An analytical approach to optimizing the altitude of LAPs to provide maximum radio coverage on the ground shows that the optimal altitude is a function of the maximum allowed pathloss and of the statistical parameters of the urban environment, as defined by the International Telecommunication Union.
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Industrial Internet of Things: Challenges, Opportunities, and Directions
TL;DR: The concepts of IoT, Industrial IoT, and Industry 4.0 are clarified and the challenges associated with the need of energy efficiency, real-time performance, coexistence, interoperability, and security and privacy are focused on.
Journal ArticleDOI
On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration
TL;DR: This paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties, and elaborates further on open research challenges.
Journal ArticleDOI
Energy Minimization for Wireless Communication With Rotary-Wing UAV
Yong Zeng,Jie Xu,Rui Zhang +2 more
TL;DR: This paper derives a closed-form propulsion power consumption model for rotary-wing UAVs, and proposes a new path discretization method to transform the original problem into a discretized equivalent with a finite number of optimization variables, for which the proposed designs significantly outperform the benchmark schemes.