Intelligent Task Offloading and Collaborative Computation in Multi-UAV-Enabled Mobile Edge Computing

This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions,while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution(DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network(DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity.

Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

Demand-aware mobile bike-sharing service using collaborative computing and information fusion in 5G IoT environment

Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a promising way to build smart cities,especially in the new era of 5G and Internet-of-Things(IoT)environments.A mobile bike-sharing service makes commuting convenient for people and imparts new vitality to urban transportation systems.In the real world,the problems of no docks or no bikes at bike-sharing stations often arise because of several inevitable reasons such as the uncertainty of bike usage.In addition to pure manual rebalancing,in several works,attempts were made to predict the demand for bikes.In this paper,we devised a bike-sharing service with highly accurate demand prediction using collaborative computing and information fusion.We combined the information of bike demands at different time periods and the locations between stations and proposed a dynamical clustering algorithm for station clustering.We carefully analyzed and discovered the group of features that impact the demand of bikes,from historical bike-sharing records and 5G IoT environment data.We combined the discovered information and proposed an XGBoost-based regression model to predict the rental and return demand.We performed sufficient experiments on two real-world datasets.The results confirm that compared to some existing methods,our method produces superior prediction results and performance and improves the availability of bike-sharing service in 5G IoT environments.

Optimal edge-cloud collaboration based strategies for minimizing valid latency of railway environment monitoring system

Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable,and the latency contribution of each task to the system is discrepant.Hence,two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper.First,the processing resources are allocated to the tasks based on the priorities,and the tasks are processed parallly with the allocated resources to minimize the system valid latency.Furthermore,considering the differences in the data volume of the tasks,which will induce the waste of the resources for the tasks finished in advance.Thus,the tasks with similar priorities are graded into the same group,and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously.Compared with the other four strategies in four railway monitoring scenarios,the proposed strategies proved latency efficiency to the high-priority tasks,and the system valid latency is reduced synchronously.The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.

Temporal pattern mining from user-generated content

Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content;however, they mainly focus on textual data, thus undermining the importance of metadata. Considering this gap, we provide a temporal pattern mining framework to model and utilize user-generated content's metadata. First, we scrap 2.1 million tweets from Twitter between Nov-2020 to Sep-2021 about 100 hashtag keywords and present these tweets into 100 User-Tweet-Hashtag (UTH) dynamic graphs. Second, we extract and identify four time-series in three timespans (Day, Hour, and Minute) from UTH dynamic graphs. Lastly, we model these four time-series with three machine learning algorithms to mine temporal patterns with the accuracy of 95.89%, 93.17%, 90.97%, and 93.73%, respectively. We demonstrate that user-generated content's metadata contains valuable information, which helps to understand the users' collective behavior and can be beneficial for business and research. Dataset and codes are publicly available;the link is given in the dataset section.

A Cloud-Based Virtualized Execution Environment for Mobile Applications

Smartphones and cloud computing technologies have enabled the development of sophisticated mobile applications. Still, many of these applications do not perform well due to limited computation, data storage, network bandwidth, and battery capacity in a mobile phone. While applications can be redesigned with client-server models to benefit from cloud services, users are no longer in full control of the application. This is also a serious concern. We propose an innovative framework for executing mobile applications in a virfualized cloud environment. With encryption and isolation, this environment is controlled by the user and protected against eavesdropping from cloud providers. We have developed efficient schemes for migrating applications and synchronizing data between execution environments. Performance and power issues within a virtualized execution environment are also addressed using power saving and scheduling techniques that enable automatic, seamless application migration.