Energy-Optimal and Delay-Bounded Computation Offloading in Mobile Edge Computing with Heterogeneous Clouds

By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.

ZTE Communications Special Issue on Recent Advances in Smart Grid

The smart grid is the next generatiou electric： grid that enables effi- cient, intelligent, and economical power generation, transmission, and distribution. It has attracted significant attentions and become a global trend due to the immense potential benefits including en- hanced reliability and resilience, higher operational efficiency, more efficient energy consumption, and better power quality. This special issue expects to address smart grid issues related to data sensing, data communications and data networking, including high-level ideology/methodology, concrete smart grid inspired data communications and networking technolngies, smart grid system ar- chitecture, QoS, energy-efficiency, and fault tolerance in smart grid systems, management of smart grid systems, and real-world deploy- ment experiences.

Radio Resource Allocation for Bidirectional Offloading in Space-Air-Ground Integrated Vehicular Network

Aerial platforms and edge servers have been recognized as two promising building blocks to improve the quality of service(QoS)in space-air-ground integrated vehicular networks(SAGIN).Communication intensive tasks can be offloaded to aerial platforms via broadcasting,while computation intensive tasks can be offloaded to ground edge servers.However,the key issues including how to allocate radio resources and how to determine the task offloading strategy for the two types of tasks,are yet to be solved.In this paper,the joint optimization of radio resource allocation and bidirectional offloading configuration is investigated.To deal with the non-convex nature of the original problem,we decouple it into a two-step optimization problem.In the first step,we optimize the bidirectional offloading configuration in the case of the radio resource allocation known in advance,which is proved to be a convex optimization problem.In the second step,we optimize the radio resource allocation through a brute-force search method.We use queuing theories to analyze the average delay of the two tasks with respect to the broadcasting capacity and task arrival rate.The offloading strategies with closed-form expressions of communication intensive tasks are proposed.We then propose a heuristic algorithm which is shown to perform better than interior point algorithm in simulations.The numerical results also demonstrate that the aerial platforms and edge servers can significantly reduce the average delay of the tasks under different network conditions.

Space-air-ground integrated vehicular network for connected and automated vehicles:Challenges and solutions

Unlimited and seamless coverage as well as ultra-reliable and low-latency communications are vital for connected vehicles,in particular for new use cases like autonomous driving and vehicle platooning.In this paper,we propose a novel Space-Air-Ground integrated vehicular network(SAGiven)architecture to gracefully integrate the multi-dimensional and multi-scale context-information and network resources from satellites,High-Altitude Platform stations(HAPs),low-altitude Unmanned Aerial Vehicles(UAVs),and terrestrial cellular communication systems.One of the key features of the SAGiven is the reconfigurability of heterogeneous network functions as well as network resources.We first give a comprehensive review of the key challenges of this new architecture and then provide some up-to-date solutions on those challenges.Specifically,the solutions will cover the following topics:(1)space-air-ground integrated network reconfiguration under dynamic space resources constraints;(2)multi-dimensional sensing and efficient integration of multi-dimensional context information;(3)real-time,reliable,and secure communications among vehicles and between vehicles and the SAGiven platform;and(4)a holistic integration and demonstration of the SAGiven.Finally,it is concluded that the SAGiven can play a key role in future autonomous driving and Internet-of-Vehicles applications.

Data-Driven User Complaint Prediction for Mobile Access Networks

In this paper,we present a user-complaint prediction system for mobile access networks based on network monitoring data.By applying machine-learning models,the proposed system can relate user complaints to network performance indicators,alarm reports in a data-driven fashion,and predict the complaint events in a fine-grained spatial area within a specific time window.The proposed system harnesses several special designs to deal with the specialty in complaint prediction;complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events.A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations.Furthermore,we combine up-sampling with down-sampling to combat the severe skewness towards negative samples.The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator,in which,events due to complaint bursts account approximately for only 0:3%of all recorded events.Re-sults show that our system can detect 30%of complaint bursts 3 h ahead with more than 80%precision.This will achieve a corresponding proportion of quality of experi-ence improvement if all predicted complaint events can be handled in advance through proper network maintenance.