Recently, unmanned aerial vehicles (UAVs) have been widely used in mobile edge computing (MEC) systems to compute part of the computing tasks offloaded from ground equipments (GEs) due to their high mobility and flexibility. In addition, reconfigurable intelligent surfaces (RIS), as an emerging technology, can enhance the wireless propagation environment in wireless networks and improve the computation efficiency of the system. In this paper, we propose a multi-UAV-assisted MEC system supported by RIS, where GEs can partially offload tasks to UAVs for computation. A joint optimization problem is formulated to maximize the weighted computation efficiency and fairness by optimizing the user association state, offloading ratio, computing resource allocation, the trajectory of UAVs and the actual RIS phase shift design. To solve the problem, a Fuzzy C-Means-Multi-Agent Deep Deterministic Policy Gradient Alternating Iterative (FMAI) algorithm is designed. In this algorithm, we firstly design a GE-UAV Association and Variable Initialization Combine Fuzzy C-Means Clustering (GUAIFCM) algorithm to solve GE-UAV association strategy. Then we introduce the multi-agent deep deterministic policy gradient alternating iteration (MADDPGAI) algorithm to solve the computation resource allocation, the trajectory of UAVs, task allocation and RIS phase shift. The simulation results show that the proposed scheme can significantly improve the computation efficiency and fairness of RIS-assisted multi-UAV MEC system compared with the benchmark scheme.

Abstract As autonomous driving and in-vehicle applications develop rapidly, vehicles face significant computing challenges. In order to address this issue, vehicle edge computing (VEC) has emerged. In this paper, we study the issue of time delay and energy consumption for task offloading in VEC systems. Considering varying computing resources and changes in vehicle position due to vehicle mobility, we integrate offloading decisions with resource allocation. The problem is summarized as minimizing the delay and energy consumption of completing vehicle tasks. To solve the multi-objective optimization solution, we propose an improved multi-objective particle swarm optimization algorithm (IMOPSO). At first, we initialize particles by entropy weight method and Roulette Wheel Selection. Then, we introduce the transition probability to control the particles’ exploration in the search space for optimal solutions. Extensive simulation results demonstrate that the IMOPSO algorithm is superior to other alternative algorithms.

Summary In mobile edge computing (MEC), due to the limited computing resources and power of mobile augmented reality (MAR) devices, cache identification results which can reduce power consumption and executing time of mobile devices are the solution to process MAR tasks. In this paper, we study an allocation cache problem in MAR systems. The allocation cache problem is formulated as maximizing the cache utility of cache hit ratio and user preference factor. To solve this problem, a cache resource allocation and cache space adjustment policy for edge computing systems is proposed. We also propose an improved double deep Q‐network (DDQN) algorithm to learn this policy. Simulation results show that the policy greatly improves the cache hit ratio compared with the traditional caching policy.

Recently, Unmanned Aerial Vehicles (UAVs) have attracted much attention due to their flexibility and low cost. However, there are limitations for multiple UAVs such as limited energy and collaborative coverage. To achieve a better coverage performance, each UAV needs to find the optimal position to cover many ground users while saving energy. However, there are trade-offs between coverage utility and energy consumption. In this paper, we study a multi-UAV communication scenario where multi-UAV array is deployed to provide wireless coverage for mobile ground users. Considering the number, 3D positions, and speeds of UAVs, we formulate a Coverage Utility and Energy Multi-objective Optimization Problem (CUEMOP) to simultaneously maximize the total coverage utility and minimize the total energy consumption of UAVs. Due to the complexity and NP-hardness of the formulated CUEMOP, we propose Improved Multi-objective Grey Wolf Optimizer (ImMOGWO) algorithm. In this algorithm, we design the Role Determination (RD) algorithm to cluster the ground users and prepare for initialization of UAV number and position. Hybrid solution initialization (HSI) algorithm is to initialize multi-dimensional variables and overcome algorithm inefficiency caused by random initialization. The Levy flight and Sin Cosine method based on the MOGWO algorithm (LSCMGA) is proposed to increase the diversity of solutions and ensure the convergence effect of the algorithm. Simulation results verify that proposed ImMOGWO algorithm has better performance than some other benchmark methods.

The unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) technology is opening up new opportunities in the integrated space-air-ground in the 5 G era and beyond. However, providing low-latency services solely from an overall perspective cannot ensure a high quality of experience (QoE) for user equipments (UEs). Therefore, we propose a service experience-oriented cooperative caching framework, where the UAVs can effectively serve each UE by providing communication and computing resources. A novel metric called service experience ratio is defined to reflect the experience at the UEs. Under the constraints of UAV's energy budget and delay requirements, we consider jointly optimizing task offloading, resource allocation, trajectory planning, and service caching placement to maximize the service experience ratio. Since the original problem is a mixed- integer non-convex programming problem with a fractional structure, it is challenging to be solved in polynomial time. Based on Dinkelbach's method and convex optimization theory, we simplify the problem model and propose a four-stage alternating iterative service ratio maximization algorithm to solve this problem. Besides, we also analyze the convergence and complexity of our proposed algorithm. Numerical results demonstrate that the service experience ratio achieved by the proposed algorithm is 19%-34% higher than the comparative works.

The application of unmanned aerial vehicles (UAVs) in IoT networks, especially data collection, has received extensive attention. Due to the urgency of the mission and limitation of the network cost, the mission completion time and number of UAVs are research hotspots. Most studies mainly focus on the trajectory optimization of the UAV to shorten the mission completion time. However, under different data collection modes, flying mode (FM) and hovering mode (HM), the collection time will also greatly affect the mission completion time. This paper studies the data collection from ground IoT devices (GIDs) in Multi-UAV enabled IoT networks. The problem of data collection is formulated to minimize the aerial cost and maximum mission completion time of UAVs by optimizing mission allocation, UAV trajectory, and UAVs’ flying speeds. In view of the complexity and non-convexity of the formulated problem, we propose a heuristic-based approximation algorithm to optimize the mission allocation of UAVs. Then, we specifically optimize the trajectory of the UAV for GIDs to minimize the flight time and collection time. Since the UAV’s flying speed affects the mission completion time, the successive convex approximation (SCA) technique is adopted to optimize it. Simulation results show that our scheme achieves the performance of near-optimal solution.

The unmanned aerial vehicle (UAV) is widely used in some scenes with high requirements for information freshness. Due to the limited endurance of the UAV, especially in the scenes with large area and dense sensor nodes (SNs), it is difficult for one UAV to complete the data collection task under the condition of ensuring the freshness of SNs’ information. Therefore, multiple UAVs are required to cooperate to participate in data collection. In this paper, we study the multi-UAV assisted data collection problem to improve information freshness. We use the Age of Information (AoI) to measure the freshness of information, mainly including the SNs’ uploading time, the UAVs’ flight time and the data offloading time. The data collection problem is formulated to minimize the SNs’ peak AoI and average AoI in multi-UAV assisted wireless sensor networks. Since the problem is complex, we introduce a start-to-end strategy comprising of association and planning to minimize two SNs’ AoIs through an iterative three-step process. Firstly, the locations of data collection points (CPs) at which the UAVs hover to collect data and the SN-CP association are determined based on a density-based clustering algorithm. Secondly, the CPs are clustered to form CP clusters, and the CP-UAV association is established. Finally, based on the results of the above two steps, the flight trajectories of the UAVs are optimized by improved ant colony (ACO) algorithm subject to the limited endurance capability. The simulation results show the proposed strategy can optimize the peak-AoI and ave-AoI of SNs to improve the freshness of information.

In recent years, multi-access edge computing (MEC) has become a hot topic. In this paper, we study a task offloading problem combined with bandwidth allocation in multi-access edge computing. Based on alliance game, we formulate bandwidth allocation to minimize the dissatisfaction of alliances. Then, we formulate the task offloading decision-making to minimize the delay. The delay consists of communication delay and execution delay. To solve the offloading problem, we convert the dissatisfaction of alliance into a vector, and obtain the Pareto optimal through multi-objective particle swarm algorithm. Then, we use Branch and Bound method to construct the propagation tree to facilitate decision-making. To evaluate the edge servers in the tree, we build an evaluation matrix and transform the matrix to a set of evaluation index which is used on task offloading decision-making. A large number of experimental results show that our algorithm is better than compared algorithm.

The rapid development of edge computing has an impact on the Internet of Vehicles (IoV). However, the high-speed mobility of vehicles makes the task offloading delay unstable and unreliable. Hence, this paper studies the task offloading problem to provide stable computing, communication and storage services for user vehicles in vehicle networks. The offloading problem is formulated to minimize cost consumption under the maximum delay constraint by jointly considering the positions, speeds and computation resources of vehicles. Due to the complexity of the problem, we propose the vehicle deep Q-network (V-DQN) algorithm. In V-DQN algorithm, we firstly propose a vehicle adaptive feedback (VAF) algorithm to obtain the priority setting of processing tasks for service vehicles. Then, the V-DQN algorithm is implemented based on the result of VAF to realize task offloading strategy. Specially, the interruption problem caused by the movement of the vehicle is formulated as a return function as part of evaluating the task offloading strategy. The simulation results show that our proposed scheme significantly reduces cost consumption and improves Quality of Service (QoS).

Summary In cloud datacenter network, deadline‐aware disaster evacuation transfers the endangered data out of disaster zone using limited residual network resources. Previous work has not jointly considered the selection of safe datacenter and reasonable allocation of bandwidth proportion in time‐varying postdisaster network environment. Therefore, they cannot make full use of network transmission capability. Based on our earlier work, we propose a new time‐varying disaster evacuation strategy with flexible traffic scheduling. We aim to maximize disaster evacuation capability in the disaster spread scenario. We construct a new disaster‐aware time‐expanded network model to divide time slots according to progressive disaster spread, and optimize the utilization of evacuation capability in the current disaster stage. In each time slot, we carry out two‐step optimization including safe datacenter selection and proportional bandwidth allocation. Especially, we select store‐and‐forward node to ensure the safety of evacuated data in the next time slot, and use marked evacuation routing search based on transmission requirement to improve the utilization of evacuation capability. Through extensive simulations we demonstrate that our strategy achieves better performance with higher evacuation transmission efficiency in the disaster spread scenario.

Summary To leverage periodic disaster backup in a cloud data center (DC) network, previous studies employ disjoint unicast paths for bulk data transfers among multiple geographically distributed DCs, causing massive unnecessary traffic duplication. This not only adds the overhead but also may result in severe network congestion. With flexible network resource management in software‐defined networks and powerful traffic aggregation capability of multicast, we propose capacity‐constrained multicast to realize cost‐efficient disaster backup. First, considering limited backup storage capacity and essential redundancy guarantee, we construct a capacity‐constrained multicasting backup model. Then, we formulate the disaster backup problem as capacity‐constrained multiple Steiner tree problem, which is NP‐hard. To solve this problem, we design a new multicasting backup ant colony optimization algorithm based on requirement‐aware growth. It directly optimizes every disaster‐backup multicast tree (DBMT) from its root node to cover enough destination nodes guaranteeing sufficient redundancy and then expands them into the forest under the guidance of a multicast tree shared degree, the ratio of available storage capacity, and backup load distribution offset. We introduce unique edge fitness evaluation and pheromones for every DBMT to reduce mutual influences among multiple trees. Extensive simulations demonstrate that our strategy performs with less bandwidth consumption cost and relatively good backup load distribution fairness simultaneously.

Mobile crowdsourcing takes advantage of mobile devices such as smart phones and tablets to process data for a lot of applications (e.g., geotagging for mobile touring guiding monitoring and spectrum sensing). In this paper, we propose a mobile crowdsourcing paradigm to make a task requester exploit encountered mobile workers for high-quality results. Since a task may be too complex for a single worker, it is necessary for a task requester to divide a complex task into several parts so that a mobile worker can finish a part of the task easily. We describe the task crowdsourcing process and propose the worker arrival model and task model. Furthermore, the probability that all parts of the complicated task are executed by mobile workers is introduced to evaluate the result of task crowdsourcing. Based on these models, considering computing capacity and rewards for mobile workers, we formulate a task partition problem to maximize the introduced probability which is used to evaluate the result of task crowdsourcing. Then, using a Markov chain, a task partition policy is designed for the task requester to realize high-quality mobile crowdsourcing. With this task partition policy, the task requester is able to divide the complicated task into precise number of parts based on mobile workers’ arrival, and the probability that the total parts are executed by mobile workers is maximized. Also, the invalid number of task assignment attempts is analyzed accurately, which is helpful to evaluate the resource consumption of requesters due to probing potential workers. Simulations show that our task partition policy improves the results of task crowdsourcing.

Cognitive radio technology allows unlicensed users to utilize licensed wireless spectrum if the wireless spectrum is unused by licensed users. Therefore, spectrum sensing should be carried out before unlicensed users access the wireless spectrum. Since mobile terminals such as smartphones are more and more intelligent, they can sense the wireless spectrum. The method that spectrum sensing task is assigned to mobile intelligent terminals is called crowdsourcing. For a large-scale region, we propose the crowdsourcing paradigm to assign mobile users the spectrum sensing task. The sensing task assignment is influenced by some factors including remaining energy, locations, and costs of mobile terminals. Considering these constraints, we design a precise sensing effect function with a local constraint and aim to maximize this sensing effect to address crowdsensing task assignment. The problem of crowdsensing task assignment is difficult to solve since we prove that it is NP-hard. We design an optimal algorithm based on particle swarm optimization to solve this problem. Simulation results show our algorithm achieves higher performance than the other algorithms.

Spectrum sensing is an important issue in cognitive radio networks. The unlicensed users can access the licensed wireless spectrum only when the licensed wireless spectrum is sensed to be idle. Since mobile terminals such as smartphones and tablets are popular among people, spectrum sensing can be assigned to these mobile intelligent terminals, which is called crowdsourcing method. Based on the crowdsourcing method, this paper studies the distributed scheme to assign spectrum sensing task to mobile terminals such as smartphones and tablets. Considering the fact that mobile terminals’ positions may influence the sensing results, a precise sensing effect function is designed for the crowdsourcing-based sensing task assignment. We aim to maximize the sensing effect function and cast this optimization problem to address crowdsensing task assignment in cognitive radio networks. This problem is difficult to be solved because the complexity of this problem increases exponentially with the growth in mobile terminals. To assign crowdsensing task, we propose four distributed algorithms with different transition probabilities and use a Markov chain to analyze the approximation gap of our proposed schemes. Simulation results evaluate the average performance of our proposed algorithms and validate the algorithm’s convergence.

The energy cost is one of the major concerns for the cloud providers. Virtual machine placement has been demonstrated as an effective method for energy saving. In addition to constraints caused by the physical machine resources such as CPU and memory (PM-constraints), the constraints caused by the network resource such as bandwidth (Net-constraints) are also crucial, since virtual machines are not isolated and require communication with each other to exchange data. However, most current research on data center power optimization only focuses on server resource. As a result, the optimization results are often inferior, because server consolidation without considering the network may cause traffic congestion and thus degraded network performance. We take the traffic demands between virtual machines into consideration and formulate the virtual machine placement problem under both PM-constraints and Net-constraints to minimize the energy cost, and propose an approach based on ant colony optimization to solve the problem. We evaluate the expected performance of our proposed algorithm through a simulation study, providing strong indications to the superiority of our proposed solution.

Software Defined Network (SDN) has been envisioned as the next generation network infrastructure, which simplify network management by decoupling the control plane and data plane. It is becoming the leading technology behind many traffic engineering solutions, since it allows a central controller to globally plan the paths of the flows. However, Ternary Content Addressable Memory (TCAM), as a critical hardware storing rules in SDN-enabled devices, can be supplied to each device with very limited quantity because it is expensive and energy-consuming. To efficiently use TCAM resources, we address the routing rule space occupation problem for multiple unicastSessions with Quality-of-Service (QoS) constraints. To our best knowledge, this is the first work to joint routing rule optimization with traffic engineering for multipath flows. We formulate the problem using Mixed Integer Linear Programing (MILP) and propose an approach based on ant colony algorithm to solve it. Finally, we evaluate the expected performance of our proposed algorithm through a simulation study.