The rapid development of unmanned aerial vehicle (UAV) technology and mobile edge computing (MEC) has created new opportunities for efficient data processing and transmission. UAV-assisted MEC enables data transmission from UAVs to a base station (BS) equipped with MEC capabilities. However, ensuring the security of these transmissions is a concern, especially when UAVs operate in open airspace. In this paper, we introduce a coordinated multi-point (CoMP) offloading model aimed at enhancing the secure transmission performance of the network. The airspace is divided into several equal-sized hexagonal cells, with multiple UAVs collaborating to offload data to a BS with MEC. During this process, the locations of potential eavesdroppers are randomized as they attempt to intercept the data transmitted by the UAVs. Based on this model, we first derive the success communication probability (SCP) for a typical BS and an eavesdropper using stochastic geometry. We then introduce the concept of secure transmission rate, precisely the average secrecy rate (ASR). Further, we characterize the ASR in the presence of random eavesdroppers. Finally, we analyze the effects of various parameters on transmission performance. The results of our simulations closely align with our numerical findings, confirming the accuracy of our analysis. Notably, the ASR of the proposed system is nearly four times higher than that of an offloading model without cooperation. Moreover, compared to a user-centric offloading model with CoMP, the ASR increases by 5.37 %, enhancing system performance and reducing search overheads.

As mobile device usage and data traffic increase, the demand for faster data processing becomes crucial. Mobile edge computing (MEC) meets this need by placing servers at the network’s edge for real-time computing. However, fixed terrestrial MEC servers struggle with scalability, limiting their effectiveness. Integrating unmanned aerial vehicles (UAV) with MEC technology offers a promising solution, enhancing communication efficiency and service quality. This paper proposes a joint communication and computation offloading model for coordinated multi-point (CoMP) UAV-assisted MEC networks utilizing hexagonal cell partitioning. Within each cell, a cluster of UAVs, each equipped with its own MEC server and connected to a central server via a reliable backhaul, collaborates to serve terrestrial user equipment. To analyze this system, we develop a unified analytical framework integrating stochastic geometry and queuing theory. Furthermore, we define the success probability of edge computing (SPEC) metric to quantitatively evaluate communication reliability and computational efficiency. Finally, we explore the effects of critical parameters on network performance. Simulation results closely match the theoretical predictions, confirming our proposed model’s validity and our analysis’s accuracy. Notably, our proposed model demonstrates an improvement in SPEC of approximately 57.24% over non-CoMP model and 24.97% over the user-centric CoMP model.

Air-to-ground (A2G) networks, using unmanned aerial vehicles (UAVs) as base stations to serve terrestrial user equipments (UEs), are promising for extending the spatial coverage capability in future communication systems. Coordinated transmission among multiple UAVs significantly improves network coverage and throughput compared to a single UAV transmission. However, implementing coordinated multi-point (CoMP) transmission for UAV mobility requires complex cooperation procedures, regardless of the handoff mechanism involved. This paper designs a novel CoMP transmission strategy that enables terrestrial UEs to achieve reliable and seamless connections with mobile UAVs. Specifically, a computationally efficient CoMP transmission method based on the theory of Poisson-Delaunay triangulation is developed, where an efficient subdivision search strategy for a CoMP UAV set is designed to minimize search overhead by a divide-and-conquer approach. For concrete performance evaluation, the cooperative handoff probability of the typical UE is analyzed, and the coverage probability with handoffs is derived. Simulation results demonstrate that the proposed scheme outperforms the conventional Voronoi scheme with the nearest serving UAV regarding coverage probabilities with handoffs. Moreover, each UE has a fixed and unique serving UAV set to avoid real-time dynamic UAV searching and achieve effective load balancing, significantly reducing system resource costs and enhancing network coverage performance.

To enable fast content delivery for delay-sensitive applications, large content providers build edge servers, Points of Presence (PoPs), and datacenters around the world. They are networked together as an integrated infrastructure via a private wide-area network (WAN), named content delivery network (CDN). To deliver quality services in the CDN, there are two critical decisions that should be properly made: 1) making assignments of PoP and datacenter for user requests, and 2) selecting routing paths from PoP to datacenter. However, with both the network variability and CDN environment complexity, it is challenging to achieve satisfying decisions. In this paper, we propose DeepDelivery, an adaptive deep reinforcement learning approach to intelligently make assignments and routing decisions in real time. Essentially, DeepDelivery adopts the Markov decision process (MDP) model to capture the dynamics of network variation, and the objective is to jointly maximize the infrastructure utilization of providers and minimize the total latency of end users. We conduct extensive trace-driven evaluations spanning various environment dynamics with both real-world and synthetic trace data. The result demonstrates that DeepDelivery can outperform the state-of-the-art scheme by 21.89% higher utilization and 11.27% lower end-to-end latency on average.

In this article, we investigate the worker (i.e., vehicle) selection problem in vehicle-based crowdsourcing (VBC), where vehicles in a specific area are recruited by the crowdsourcing platform to collect geographical information in real driving scenarios for autonomous driving. Given a limited recruitment budget, we formulate a cumulative platform utility maximization problem (CMP) to obtain the optimal worker set. The CMP is unsolvable directly as the platform has no prior information of workers at the initial stage (also known as “cold start”) and the cost of collecting all workers' information is prohibitive. To solve the problem, we first conduct a comprehensive data analytics on two real-world vehicle traces and obtain two crucial observations: 1) trajectory of individual vehicle is highly uncertain that it is difficult to make accurate prediction and 2) the overall distribution of vehicular trajectory penetration (measured by collection quantity and coverage) has a diurnal pattern and varies with weekly periodicity. Inspired by the insights, we propose the performance transfer-based online worker selection (POSE) scheme, which works independently from trajectory prediction with two components, i.e., transfer learning-based performance estimation and online worker selection (OWS). Based on the diurnal pattern, the former component collects a short-period trajectory penetration data of vehicles for model fitting, which can output a specific numerical distribution. With the fitting model, we can identify and select vehicles with high trajectory penetration at the initial stage to cope with the “cold start” problem. Then, we map the worker selection problem into a multiarmed bandit problem and develop upper confidence bound-based approach to solve it. Extensive trace-driven simulations are carried out and the results demonstrate the efficiency of POSE in terms of cumulative platform utility.

Edge computing is a promising computing paradigm by pushing the cloud service to the network edge. To this end, edge infrastructure providers (EIPs) need to bring computation and storage resources to the network edge and allow edge service providers (ESPs) to provision latency-critical services for end users. Currently, EIPs prefer to establish a series of private edge-computing environments to serve specific requirements of users. This kind of resource provisioning mechanism severely limits the development and spread of edge computing in serving diverse user requirements. In this paper, we propose an integrated resource provisioning model, namededge federation, to seamlessly realize the resource cooperation and service provisioning across standalone edge computing providers and clouds. To efficiently schedule and utilize the resources across multiple EIPs, we systematically characterize the provisioning process as a large-scale linear programming (LP) problem and transform it into an easily solved form. Accordingly, we design a dynamic algorithm to tackle the varying service demands from users. We conduct extensive experiments over the base station networks in Toronto. Compared with the fixed contract model and multihoming model, edge federation can reduce the overall costs of EIPs by 23.3% to 24.5%, and 15.5% to 16.3%, respectively.

Vehicle-based crowdsourcing is expected to be an economic yet efficient solution to build and maintain an accurate, fine-grained, and up-to-date environment map (i.e., high-definition map) for autonomous vehicles, which is an essential building block for safe and intelligent autonomous driving. However, how to select crowdsourcing workers with performance maximization is prudent and quite challenging since vehicles are highly dynamic and have unpredictable routes. In this paper, we study the worker selection problem for crowdsourced on-route map collection where the trade- off between the real-time worker exploration and exploitation is the main focus. Specifically, by adopting the multi-armed bandit model, we formulate a cumulative platform utility maximization problem. To solve this problem, we propose an Online Worker Selection (OWS) scheme, to learn drivers' performance and make worker selection decisions in real time. Essentially, two key designs are integrated in OWS: 1) performance transfer. If a new driver joins the crowdsourcing, we will initialize the new driver's performance based on the knowledge transferred from the existing drivers' records; and 2) marginal utility. Particularly, we carefully incorporate the platform utility to embody the marginal effect, i.e., repeated coverage by multiple vehicles on a certain road will undermine the utility. Based on the real-world vehicular GPS trace, we conduct extensive trace- driven simulations, and results demonstrate that our scheme can effectively obtain high-quality environment map, with on average 40.5% crowdsourcing utility gain over other benchmark schemes.

In multi-tenant shared clouds, applications of different tenants compete for the shared network and thus suffer significant unpredictability of performance. The progress is an essential metric to measure the overall data transfer rate of a tenant and to indicate how fast a tenant can complete data transfer. Most previous work of network allocation or schedule focus on achieving the tradeoff among the fairness, performance, and efficiency. They, however, often do not make each tenant achieve noticeable progress. In this paper, we motivate to maximize tenant's progress by using information about the underlying network and traffic patterns of tenants' applications. To illustrate the feasibility of maximizing progress, we develop a network sharing framework for multi-tenant datacenter, by exploiting the benefits of careful placement of tenant's applications. Furthermore, the tenants' progress and network utilization would be further improved without impacting performance isolation, if the bandwidth demand of each application can be rescheduled. Our evaluation results show that our method provides higher tenant progress and better tenant-level performance than HUG.

Summary Large‐scale online services and distributed execution engines (i.e., MapReduce and Dryad) generate large volumes of traffic in data center networks. As a consequence, significant congestion can occur in the data center network. To the best of our knowledge, most existing approaches either focus on local congestion‐aware mechanisms, which have only a poor ability to handle asymmetry or use explicit congestion notification packets, which are difficult to implement directly in switch hardware. These methods are insufficient to solve the congestion problem. In this paper, we focus on a congestion‐free routing strategy, resorting to the global view of the data center network in a software‐defined networking controller. Specifically, a timeslot allocation was first conducted for the coming packets, and then the corresponding routing paths were computed for each packet. In view of the efficiency, the timeslot allocation algorithm follows a heuristic pattern, and the path selection is modeled as a bin‐packing problem. Simulation results showed that the congestion‐free routing strategy proposed here performs well in throughput, queuing, and end‐to‐end round‐trip time. Copyright © 2015 John Wiley & Sons, Ltd.