Energy Consumption Minimization in Secure Multi-Antenna UAV-Assisted MEC Networks With Channel Uncertainty

Mao, Weihao; Xiong, Ke; Lu, Yang; Fan, Pingyi; Ding, Zhiguo

doi:10.1109/twc.2023.3248962

Cited by 13 publications

(8 citation statements)

References 46 publications

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“…In this regard, the energy consumption was optimized, under secrecy rate and latency constraints. A MEC network was also presented in [11], where a UAV equipped with a uniform planar array (UPA) antenna acted as an aerial relay, concurrently providing MEC functionalities. To minimize the energy consumption and fulfill security requirements in the presence of multiple GEs, an optimization problem was formulated.…”

Section: A Backgroundmentioning

confidence: 99%

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Optimization of Secure Computation Efficiency in UAV-Enabled RIS-Assisted MEC-IoT Networks With Aerial and Ground Eavesdroppers

Michailidis,

Volakaki,

Miridakis

et al. 2024

IEEE Trans. Commun.

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This paper proposes a security-aware computation offloading framework tailored for mobile edge computing (MEC)enabled Internet of Things (IoT) networks operating in environments with aerial eavesdroppers (AEs) and ground eavesdroppers (GEs). It is envisaged that multiple ground nodes (GNs) should perform computation tasks partly locally and partly remotely by offloading a portion of these tasks to MEC servers. To facilitate this paradigm, an unmanned aerial vehicle (UAV) is deployed, serving as both an aerial MEC server and a relay for forwarding part of the tasks to a ground access point (AP) for computing. The computation offloading is further reinforced by incorporating a reconfigurable intelligent surface (RIS) unit in close proximity to the AP. Within this context, this paper provides an analysis of the secrecy outage probability (SOP) and formulates an optimization problem aimed at maximizing the minimum secure computation efficiency (SCE) by jointly optimizing transmit power allocation, time slot scheduling, task allocation, and RIS's phase shifts. Given the non-convex nature of the problem, an iterative algorithm is introduced to address the fractional objective function and coupled optimization variables by employing Dinkelbach-and block coordinate descent (BCD)-based methods, respectively. The obtained results confirm the efficacy of the optimized scheme.

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Section: A Backgroundmentioning

confidence: 99%

“…of the instantaneous SNR received at the AP (l AE -th AE) can be defined using ( 10) and (11), respectively, and properly replacing the indices. In this paper, the worst-case scenario is considered, where the L AE AEs work cooperatively by utilizing maximum ratio combining (MRC) [34].…”

Section: Wireless Transmission Model a Direct Links Without Ris Unitmentioning

confidence: 99%

Optimization of Secure Computation Efficiency in UAV-Enabled RIS-Assisted MEC-IoT Networks With Aerial and Ground Eavesdroppers

Michailidis,

Volakaki,

Miridakis

et al. 2024

IEEE Trans. Commun.

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“…In a UAV-enabled mobile edge computing system based on device-to-device communication, the overall energy efficiency is maximised by optimising UAV and node transmit power and scheduling strategies in order to improve the balance between different types of nodes [23]. For channel uncertainty during offloading, [24] minimises the energy consumption under constraints such as the user quality of service by optimising the CPU frequency and user transmit power, etc. To effectively support the communication and computation of unmanned surface vehicles, [25] jointly considered the UAV flight speed and offloading decision to minimise the energy consumption of the UAV swarm under the condition of ensuring the time delay constraint.…”

Section: Related Workmentioning

confidence: 99%

Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

Zhang

Jiang

et al. 2023

Drones

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In emergency situations, such as earthquakes, landslides and other natural disasters, the terrestrial communications infrastructure is severely disrupted and unable to provide services to terrestrial IoT devices. However, tasks in emergency scenarios often require high levels of computing power and energy supply that cannot be processed quickly enough by devices locally and require computational offloading. In addition, offloading tasks to server-equipped edge base stations may not always be feasible due to the lack of infrastructure or distance. Since Low Orbit Satellites (LEO) have abundant computing resources, and Unmanned Aerial Vehicles (UAVs) have flexible deployment, offloading tasks to LEO satellite edge servers via UAVs becomes straightforward, which provides computing services to ground-based devices. Therefore, this paper investigates the computational tasks and resource allocation in a UAV-assisted multi-layer LEO satellite network, taking into account satellite computing resources and device task volumes. In order to minimise the weighted sum of energy consumption and delay in the system, the problem is formulated as a constrained optimisation problem, which is then transformed into a Markov Decision Problem (MDP). We propose a UAV-assisted airspace integration network architecture, and a Deep Deterministic Policy Gradient and Long short-term memory (DDPG-LSTM)-based task offloading and resource allocation algorithm to solve the problem. Simulation results demonstrate that the solution outperforms the baseline approach and that our framework and algorithm have the potential to provide reliable communication services in emergency situations.

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“…An iterative algorithm is proposed that minimizes the UAV's total energy consumption by optimizing the processor frequency, offloading data volume, and UAV flight trajectory. In [14], the authors study multi-antenna UAV-assisted mobile edge computing networks, considering channel uncertainty and using successive convex approximation (SCA)-based algorithms to rationally allocate resources and minimize energy consumption. In [15], the authors propose an algorithm based on the Lyapunov optimization theory, the Lagrangian duality theory, alternating optimization, and SCA to minimize the user's latency by considering the cooperation between the UAVs and the central cloud in the wireless-powered MEC network.…”

Section: Introductionmentioning

confidence: 99%

“…In [18], the authors investigate the path planning problem of multiple UAVs in IoT scenarios and propose a multi-agent reinforcement learning method to obtain a near-optimal UAV control policy in the presence of challenging wireless channel characteristics. References [9][10][11][12][13][14][15][16][17][18] address computational offloading in UAV-assisted wireless-powered edge computing scenarios, but none of them consider users communicating using communication sensing integration signals, i.e., they do not incorporate sensing into edge computing.…”

Section: Introductionmentioning

confidence: 99%

A Joint Optimization Algorithm for UAV Location and Offloading Decision Based on Wireless Power Supply

Sun,

Zhu

2024

Electronics

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In this paper, a joint optimization algorithm of offloading decision, energy harvesting time, and unmanned aerial vehicle (UAV) location is proposed for user equipment (UEs)’s task completion latency problem in a communication–sensing–computing integration scenario with wireless energy supply. Under the constraints of causality of energy harvesting consumption by the UEs and conditional mutual information, the total latency minimization problem of the UEs is established. Firstly, the optimization variables of the problem are transformed from three variables of offloading decision, energy harvesting time, and UAV location to two variables of offloading decision and UAV location by means of the derived closed expression, and then the transformed optimization problem is decomposed into the offloading decision optimization sub-problem and the UAV location optimization sub-problem to be solved alternately and iteratively. The genetic algorithm is employed to tackle the optimization sub-problem of offloading decisions, and the successive convex approximation algorithm is applied to the drone positioning optimization sub-problem. Simulation results show that the proposed algorithm in this paper reduces the average task completion latency by 35 percent and 15 percent, respectively, compared to the two baseline algorithms for different numbers of UEs.

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Energy Consumption Minimization in Secure Multi-Antenna UAV-Assisted MEC Networks With Channel Uncertainty

Cited by 13 publications

References 46 publications

Optimization of Secure Computation Efficiency in UAV-Enabled RIS-Assisted MEC-IoT Networks With Aerial and Ground Eavesdroppers

Optimization of Secure Computation Efficiency in UAV-Enabled RIS-Assisted MEC-IoT Networks With Aerial and Ground Eavesdroppers

Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

A Joint Optimization Algorithm for UAV Location and Offloading Decision Based on Wireless Power Supply

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