Offloading Optimization in Edge Computing for Deep-Learning-Enabled Target Tracking by Internet of UAVs

The unmanned aerial vehicles (UAVs) have been extensively used in providing intelligence such as target tracking. In our field experiments, a pre-trained deep neural network (DNN) is deployed on the UAV to identify a target from the captured video frames and enable UAV to keep tracking. However, tracking in real time by the DNN requires a lot of computational resources. This motivates us to consider offloading this type of machine learning (ML) tasks to a mobile edge computing (MEC) server. Specifically, we propose a novel hierarchical ML tasks distribution framework for the UAV tracking system, where the UAV is embedded with lower layers of the pre-trained convolutional neural network (CNN) model due to its limited computing capability, while the MEC server with rich computing resources will handle the higher layers of the CNN model. An optimization problem is formulated to minimize the CNN inference delay while taking into account the communications delay, computing time, and ML error. Insights are provided to understand the tradeoff between communications and ML computing in offloading decisions. Numerical results demonstrate the effectiveness of the proposed ML tasks distribution framework with the optimized offloading strategy.

In this paper, we investigate a new mobile blockchain-enabled edge computing (MBEC) network, where mobile users can join the empowered process of public blockchains and meanwhile offload computation-intensive mining tasks to the mobile edge computing (MEC) server. However, the trustiness of the MEC server and the fairness of computation resources allocated by the MEC server for each user become key challenges. To tackle these challenges, we consider an untrusted MEC server and propose a nonce hash computing ordering (HCO) mechanism in MBEC networks. Then we formulate nonce hash computing demands of an individual user as a non-cooperative game that maximizes the personal revenue. Moreover, we also analyze the existence of Nash equilibrium of the non-cooperative game and design an alternating optimization algorithm to achieve the optimal nonce selection strategies for all users. With the proposed HCO mechanism, the MEC server can provide much fairer computation resources for all users, and we can achieve the optimal nonce strategies of hash computing demands by using the proposed alternating optimization algorithm. Numerical results demonstrate that the proposed HCO mechanism can provide fairer computation resource allocation than the traditional weighted round-robin mechanism, and further verify the effectiveness of this alternating optimization algorithm.

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.

Aiming at the problem that fixed mobile edge computing (MEC) server is difficult to meet the needs of mobile users and temporary computing services, this study proposes a network resource allocation strategy based on unmanned aerial vehicle (UAV) cooperative edge computing. First, a UAV-aided MEC scene is designed, and a single UAV with an MEC server is used to provide auxiliary computing services for ground multiusers. Then, an optimization model aiming at total system delay is constructed by considering the system communication model and calculation model. Finally, Deep Q-Network is used to solve the optimization problem to obtain the best resource allocation scheme. Based on the experimental platform, the proposed strategy is demonstrated and analyzed. The results show that when the number of user equipment is 40, the total delay is about 33s, which is 35.29%, 31.25%, and 15.38% lower than other comparison strategies and effectively reduces the computing delay of users.

This paper designs a novel energy-efficient hybrid federated and centralized learning (HFCL) framework for training wireless traffic prediction models in aerial networks over distributed multi-access edge computing (MEC) servers where multiple network data analytics functions (NWDAFs) are embedded in the unmanned aerial vehicle (UAV)-aided MEC servers according to the framework standardized by the 3rd Generation Partnership Project (3GPP) specification. Here, UAVs can use federated learning (FL) with their part of local datasets, while the remaining local datasets can be offloaded to the centralized server for centralized learning (CL). To achieve such HFCL energy-efficiently for battery-constrained UAVs, we propose an energy-efficient computation offloading (ECO) scheme for the HFCL. We rigorously formulate analytical models for the overall energy consumption at MEC servers on UAVs and total latency for HFCL process based on the amount of offloaded datasets at each UAV. Here the energy consumption includes i) the transmission energy consumption for offloading the local dataset and ii) the energy consumption for FL processing, which includes local training and local model uploading. To balance between the overall energy consumption and total latency during HFCL process while preventing overload at UAVs, we propose a theoretical framework for the ECO problem that optimizes the amount of offloaded local datasets over multiple UAVs on aerial networks. Here, the ECO problem is formulated as the convex optimization problem under the continuous domain of the amount of offloaded datasets. Our numerical and simulation results show that our proposed framework can construct wireless traffic prediction models with an acceptable training accuracy in an energy-efficient manner over various benchmarks by striking a balance between energy consumption and overall training latency.

In the current fifth-generation (5G) and Beyond 5G (B5G) era, the Unmanned Aerial Vehicles (UAVs) have been playing a vital role and attracting interest in different application areas in the military, and civil applications such as communications, disaster management, search and rescue, security, control, agriculture, Internet of things (IoT), etc. In these networks, ultra-heterogeneous IoT devices generate time-sensitive traffic. However, those devices have limited resources to compute tasks. Recently, Mobile Edge Computation Offloading (MECO) has been considered as an encouraging model to enable the computation tasks of IoT devices to be performed by MEC servers and support ultra-low latency IoT applications to ensure Quality of services (QoS). However, terrestrial network failure due to natural and human-made disasters has been increasing, and difficult to provide reliable computation offloading and resource allocation services to IoT networks. Nowadays, UAVs have been promising technology to quickly deploy and recover the system to provide efficient services to edge nodes. The offloading and resource allocation problems in current network technology are complex, and offloading task to edge server is vulnerable to security risks. Hence, we utilize a deep reinforcement learning method to handle a complex problem for computation offloading and resource allocations in a dynamic environment. And also, we explore a blockchain-based multi-UAV-assisted MEC architecture in securing and optimizing the offloading problems.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent strategy where a UAV equipped with an MEC server is deployed to serve terminal devices. This paper considers a multi-UAV assisted network in which multiple UAVs and a terrestrial base station (BS) are deployed to provide MEC services to mobile users. The objective is to minimize an energy and latency-based cost function by jointly optimizing task offloading and MEC server selection decision, transmission power, UAV trajectory, and CPU frequency allocation. An alternating iterative approach based on the block descent method is proposed to solve this problem. In the first layer, task offloading and server selection decision subproblem is solved using a game theoretic approach. The second layer handles offloading and downloading transmission power allocations by utilizing a simplistic geometric waterfilling (GWF) technique, and the UAV trajectory by successive convex approximation (SCA). Whereas, the third layer solves the computation resource subproblem by performing CPU frequency allocation using a gradient descent method. The proposed method uses a segment-by-segment approach, which divides the entire UAV flight trajectory into shorter timeframe segments to reduce the computation time. Simulation results are presented to show that the proposed approach outperforms various benchmark schemes.

A game incentive mechanism for energy efficient federated learning in computing power networks

The offloading policy has attracted much attention in the study of Device-to-Device (D2D) Communication underlaying cellular network integrated with Mobile Edge Computing (MEC) service. D2D communication can improve energy efficiency and prolong the battery life of user equipment (UEs) by offloading computation tasks from a UE to a nearby UE via a D2D link. How to coordinate UEs to offload tasks in D2D underlay communication integrated with MEC Service for reducing interference and improving network energy efficiency is a very important subject. In this paper, we study a D2D underlay communication integrated with MEC service system that provides three offloading policies that are: (i) executing tasks in local device, (ii) offloading tasks to servers in local region, (iii) offloading tasks to a nearby UE. Furthermore, we formulate the computation offloading problem as a potential game and propose a D2D communication integrated with MEC service offloading computation algorithm for solving the computation offloading problem in a short period. The simulation results show that our proposed algorithm can reduce overall system energy consumption under time constraints.

Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with mobile edge computing. In this integrated framework, the MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using graph coloring. The outcomes of the offloading decision and PRB allocation are then used to allocate the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters.

Users in heterogeneous wireless networks may generate massive amounts of data that are delay-sensitive or require computation-intensive processing. Owing to computation ability and battery capacity limitations, wireless users (WUs) cannot easily process such data in a timely manner, and mobile edge computing (MEC) is increasingly being used to resolve this issue. Specifically, data generated by WUs can be offloaded to the MEC server for processing, which has greater computing power than WUs. However, as the location of MEC servers is fixed, unmanned aerial vehicles (UAVs) have been considered a promising solution in heterogeneous wireless networks. In this study, we design an UAV-assisted computation offloading scheme in an MEC framework with renewable power supply. The proposed model considers the instability of energy arrival, stochastic computation tasks generated by WUs, and a time-varying channel state. Owing to the complexity of the state, it is difficult to use traditional Markov decision process (MDP) with complete prior knowledge for offloading optimization. Accordingly, we propose UAV-assisted computation offloading for MEC based on deep reinforcement learning (UACODRL) to minimize the total cost, which is the weighted sum of the delay, energy consumption, and bandwidth cost. We first use the K-Means algorithm for classification to reduce the dimension of the action space. Subsequently, we use UACODRL to find the near-optimal offloading scheme to minimize the total cost. Simulations demonstrate that UACODRL converges satisfactorily and performs better than four baseline schemes with different parameter configurations.

For mobile devices (MDs) and Internet of Things (IoT) devices with limited computing capacity and battery, offloading part of tasks to the mobile edge computing (MEC) server is attractive. In this paper, we propose a joint partial computation offloading and pricing scheme in a multi-user MEC system. Firstly, we establish MD’s cost model and MEC server’s revenue model in terms of money. Secondly, we investigate MD’s cost minimization partial offloading strategy to jointly control MD’s task allocation, local CPU frequency and the amount of computational resource blocks (CRBs) requested. Finally, we formulate the revenue maximization problem for MEC server with limited computing capacity, a heuristic algorithm is proposed for MEC server to find the optimal service price. Numerical results verify the effectiveness of our proposed scheme in cost saving and pricing.

Internet of things (IoT), mobile edge computing (MEC), and unmanned aerial vehicle (UAV) have attracted significant attention in both industry and academic research. By consolidating these technologies, IoT can be facilitated with improved connectivity, better data transmission, energy saving, and other advantages. However, the communication between these entities is subject to potential cyber threats. In addition, the integrity of the data must be maintained after storing into local storage. Blockchain is a data structure that supports features like pseudonymity, data integrity etc. This paper represents a blockchain based data acquisition process in which information is gathered from IoTs using UAV as a relay and is securely kept in blockchain at MEC server. In the proposed scheme, data are encrypted prior to transfer to MEC server with the assistance of a UAV. Upon receiving the data, MEC server validates the data and the identity of the sender. Successful validation is followed by stocking of the data into blockchain, subsequent to obtaining consent from the validators. Security analysis is conducted in order to show the feasibility of the proposed secure scheme. Finally, the performance of the proposed scheme is analyzed via simulation and implementation.

With the emergence of increasingly computing-intensive and delay-sensitive tasks, the processing of computing tasks on cloud servers cannot meet the current needs any longer. The emergence of mobile edge computing (MEC) technology and the popularity of 5G applications can solve these demands. Offloading tasks to the MEC server reduces the energy consumption of local devices, and also has a lower latency than offloading to the cloud server. In this paper, an MEC–edge cloud server collaborative system model with energy harvesting technology is designed to minimize the processing delay of computing tasks by allocating computing resources. We propose an optimal integer linear programming (OILP) algorithm with two steps. Firstly, we propose a Lyapunov stability optimization algorithm based on task priority. With the constraints of local mobile device power stability, the divide-and-conquer idea is used for solving the target values of the processing tasks locally, and the MEC and edge cloud servers separately. Therefore, the objective problem is transformed into an integer linear programming problem, and then an integer linear programming algorithm based on CPU utilization optimization is proposed to obtain a resource allocation scheme. Simulation results show that the proposed OILP algorithm can further reduce the delay, improve the CPU’s utilization of the MEC server, and reduce the number of the tasks that cannot be processed under the condition of the energy stability of the local device.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with a MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things (IoT) in a finite period. In this paper, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point (AP) via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV's flight energy by optimizing the bits allocation, time slot scheduling and power allocation as well as UAV trajectory design. As the formulated problem is non-convex and difficult to find the optimal solution, we solve the problem by two parts, and obtain the near optimal solution with within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.