Empowered by computing resources at the network edge, data sensed from Internet of Things (IoT) devices can be processed and stored in their nearby cloudlets to reduce the traffic load in the core network, while various IoT applications can be run in cloudlets to reduce the response time between IoT users (e.g., user equipment in mobile networks) and cloudlets. Considering the spatial and temporal dynamics of each application's workloads among cloudlets, the workload allocation among cloudlets for each IoT application affects the response time of the application's requests. While assigning IoT users' requests to their nearby cloudlets can minimize the network delay, the computing delay of a type of requests may be unbearable if the corresponding virtual machine of the application in a cloudlet is overloaded. To solve this problem, we design an application aware workload allocation scheme for edge computing-based IoT to minimize the response time of IoT application requests by deciding the destination cloudlets for each IoT user's different types of requests and the amount of computing resources allocated for each application in each cloudlet. In this scheme, both the network delay and computing delay are taken into account, i.e., IoT users' requests are more likely assigned to closer and lightly loaded cloudlets. Meanwhile, the scheme will dynamically adjust computing resources of different applications in each cloudlet based on their workloads, thus reducing the computing delay of all requests in the cloudlet. The performance of the proposed scheme has been validated by extensive simulations.

Vehicular edge computing (VEC) can learn and cache most popular contents for vehicular users (VUs) in the roadside units (RSUs) to support real-time vehicular applications. Federated learning (FL) can protect VUs' privacy by sharing vehicles' local models instead of data. In traditional FL, the global model is periodically updated by aggregating all vehicles' local models. However, vehicles may frequently been driven out of the coverage area of VEC before they finish the local model training and thus the traditional FL cannot upload all local models as expected, which would degrade the accuracy of global model. The asynchronous FL can be performed without aggregating all vehicles' local models, thus more local models can be uploaded to improve the accuracy of global model. The vehicle mobility significantly impacts the asynchronous FL. There is no published work considering the vehicle mobility to design the cooperative caching in VEC based on asynchronous FL. In addition, the caching capacity of RSU is limited and the size of the predicted popular contents usually exceeds the cache capacity of RSU. Hence, VEC should cache the predicted popular contents in different RSUs while considering content transmission delay. In this paper, we consider vehicle mobility and propose a cooperative caching scheme in the VEC based on asynchronous federated and deep reinforcement learning (CAFR). We first consider vehicle mobility and propose an asynchronous FL algorithm to obtain an accurate global model, and then propose an algorithm to predict popular contents based on the global model. In addition, we consider vehicle mobility and propose a deep reinforcement learning (DRL) algorithm to obtain the optimal cooperative caching location for the predicted popular contents in order to optimize the content transmission delay. Extensive experimental results have demonstrated that CAFR scheme outperforms other baseline caching schemes.

As latency is the key performance metric for IoT applications, fog nodes co-located with cellular base stations can move the computing resources close to IoT devices. Therefore, data flows of IoT devices can be offloaded to fog nodes in their proximity, instead of the remote cloud, for processing. However, the latency of data flows in IoT devices consist of both the communications latency and computing latency. Owing to the spatial and temporal dynamics of IoT device distributions, some BSs and fog nodes are lightly loaded, while others, which may be overloaded, may incur congestion. Thus, the traffic load allocation among base stations (BSs) and computing load allocation among fog nodes affect the communications latency and computing latency of data flows, respectively. To solve this problem, we propose a workload balancing scheme in a fog network to minimize the latency of data flows in the communications and processing procedures by associating IoT devices to suitable BSs. We further prove the convergence and the optimality of the proposed workload balancing scheme. Through extensive simulations, we have compared the performance of the proposed load balancing scheme with other schemes and verified its advantages for fog networking.

Vehicular edge computing (VEC) is a promising technology to support real-time vehicular applications, where vehicles offload intensive computation tasks to the nearby VEC server for processing. However, the traditional VEC that relies on single communication technology cannot well meet the communication requirement for task offloading, thus the heterogeneous VEC integrating the advantages of dedicated short-range communications (DSRC), millimeter-wave (mmWave) and cellular-based vehicle to infrastructure (C-V2I) is introduced to enhance the communication capacity. The communication resource allocation and computation resource allocation may significantly impact on the ultra-reliable low-latency communication (URLLC) performance and the VEC system utility, in this case, how to do the resource allocations is becoming necessary. In this paper, we consider a heterogeneous VEC with multiple communication technologies and various types of tasks, and propose an effective resource allocation policy to minimize the system utility while satisfying the URLLC requirement. We first formulate an optimization problem to minimize the system utility under the URLLC constraint which modeled by the moment generating function (MGF)-based stochastic network calculus (SNC), then we present a Lyapunov-guided deep reinforcement learning (DRL) method to convert and solve the optimization problem. Extensive simulation experiments illustrate that the proposed resource allocation approach is effective.

Multi unmanned aerial vehicle (UAV) network is a promising solution to providing wireless coverage to ground users in challenging rural areas (such as Internet of Things (IoT) devices in farmlands), where the traditional cellular networks are sparse or unavailable. A key challenge in such networks is the 3-D placement of all UAV base stations (BSs) such that the formed multi-UAV network: 1) utilizes a minimum number of UAVs while ensuring—2) backhaul connectivity directly (or via other UAVs) to the nearby terrestrial BS; and 3) wireless coverage to all ground users in the area of operation. This joint backhaul-and-coverage-aware drone deployment (BoaRD) problem is largely unaddressed in the literature and, thus, is the focus of this article. We first formulate the BoaRD problem as integer linear programming (ILP). However, the problem is NP-hard and, therefore, we propose a low complexity algorithm with a provable performance guarantee to solve the problem efficiently. Our simulation study shows that the Proposed algorithm performs very close to that of the Optimal algorithm (solved using ILP solver) for smaller scenarios, where the area size and the number of users are relatively small. For larger scenarios, where the area size and the number of users are relatively large, the proposed algorithm greatly outperforms the baseline approaches—Backhaul-aware Greedy and random algorithm, respectively, by up to 17% and 95% in utilizing fewer UAVs while ensuring 100% ground-user coverage and backhaul connectivity for all deployed UAVs across all considered simulation setting.

Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles’ local models instead of local data. The gradients of vehicles’ local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient quantization has been proposed as one effective approach to reduce the per-round latency in FL enabled VEC through compressing gradients and reducing the number of bits, i.e., the quantization level, to transmit gradients. The selection of quantization level and thresholds determines the quantization error, which further affects the model accuracy and training time. To do so, the total training time and quantization error (QE) become two key metrics for the FL enabled VEC. It is critical to jointly optimize the total training time and QE for the FL enabled VEC. However, the time-varying channel condition causes more challenges to solve this problem. In this paper, we propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize the long-term reward in terms of the total training time and QE. Extensive simulations identify the optimal weighted factors between the total training time and QE, and demonstrate the feasibility and effectiveness of the proposed scheme.