Mobile edge computing has risen as a promising technology for augmenting the computational capabilities of mobile devices Meanwhile, in-network caching has become a natural trend of the solution of handling exponentially increasing Internet traffic The important issues in these two networking paradigms are computation offloading and content caching strategies, respectively In order to jointly tackle these issues in wireless cellular networks with mobile edge computing, we formulate the computation offloading decision, resource allocation and content caching strategy as an optimization problem, considering the total revenue of the network Furthermore, we transform the original problem into a convex problem and then decompose it in order to solve it in a distributed and efficient way Finally, with recent advances in distributed convex optimization, we develop an alternating direction method of multipliers-based algorithm to solve the optimization problem The effectiveness of the proposed scheme is demonstrated by simulation results with different system parameters

Computation Offloading and Resource Allocation in Wireless Cellular Networks With Mobile Edge Computing

Next-generation wireless networks will integrate multiple wireless access technologies to provide seamless mobility to mobile users with high-speed wireless connectivity. This will give rise to a heterogeneous wireless access environment where network selection becomes crucial for load balancing to avoid network congestion and performance degradation. We study the dynamics of network selection in a heterogeneous wireless network using the theory of evolutionary games. The competition among groups of users in different service areas to share the limited amount of bandwidth in the available wireless access networks is formulated as a dynamic evolutionary game, and the evolutionary equilibrium is considered to be the solution to this game. We present two algorithms, namely, population evolution and reinforcement-learning algorithms for network selection. Although the network-selection algorithm based on population evolution can reach the evolutionary equilibrium faster, it requires a centralized controller to gather, process, and broadcast information about the users in the corresponding service area. In contrast, with reinforcement learning, a user can gradually learn (by interacting with the service provider) and adapt the decision on network selection to reach evolutionary equilibrium without any interaction with other users. Performance of the dynamic evolutionary game-based network-selection algorithms is empirically investigated. The accuracy of the numerical results obtained from the game model is evaluated by using simulations.

Dynamics of Network Selection in Heterogeneous Wireless Networks: An Evolutionary Game Approach

This paper provides an overview of the Industrial Internet with the emphasis on the architecture, enabling technologies, applications, and existing challenges. The Industrial Internet is enabled by recent rising sensing, communication, cloud computing, and big data analytic technologies, and has been receiving much attention in the industrial section due to its potential for smarter and more efficient industrial productions. With the merge of intelligent devices, intelligent systems, and intelligent decisioning with the latest information technologies, the Industrial Internet will enhance the productivity, reduce cost and wastes through the entire industrial economy. This paper starts by investigating the brief history of the Industrial Internet. We then present the 5C architecture that is widely adopted to characterize the Industrial Internet systems. Then, we investigate the enabling technologies of each layer that cover from industrial networking, industrial intelligent sensing, cloud computing, big data, smart control, and security management. This provides the foundations for those who are interested in understanding the essence and key enablers of the Industrial Internet. Moreover, we discuss the application domains that are gradually transformed by the Industrial Internet technologies, including energy, health care, manufacturing, public section, and transportation. Finally, we present the current technological challenges in developing Industrial Internet systems to illustrate open research questions that need to be addressed to fully realize the potential of future Industrial Internet systems.

Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges

Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with MEC. In this integrated framework, we formulate the computation offloading decision, physical resource block (PRB) allocation, and MEC computation resource allocation as optimization problems. The MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using the graph coloring method. The outcomes of the offloading decision and PRB allocation are then used to distribute the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters.

Joint Computation Offloading and Interference Management in Wireless Cellular Networks with Mobile Edge Computing

Remote patient monitoring is an eHealth service, which is used to collect and transfer biosignal data from the patients to the eHealth service provider (e.g., healthcare center). A heterogeneous wireless access-based remote patient monitoring system is presented in which multiple wireless technologies are integrated to support continuous biosignal monitoring in presence of patient mobility. A patient-attached monitoring device with a heterogeneous wireless transceiver collects biosignal data from the sensors and transmits the data through the radio access network (RAN) to the eHealth service provider. In this system, the eHealth service provider reserves wireless bandwidth (or connections) from a network service provider in a proactive manner as well as in an on-demand basis. To determine the optimal number of connections to be reserved pro-actively so that the network access cost is minimized, a stochastic programming problem is formulated considering the randomness of service demand due to the mobility of the patients. Since different biosignal data can have different quality-of-service (QoS) requirements, traffic scheduling is used in the patient-attached device which determines whether to transmit and what to transmit over an available wireless connection. To make the optimal scheduling decision, an optimization problem is formulated as a constrained Markov decision process (CMDP). The objective of this formulation is to minimize the connection cost. The proposed system architecture and the optimization formulations will be useful for the eHealth service provider to provide flexible and cost-effective monitoring service to remote/mobile patients.

Yu

Papers

Optimal Joint Session Admission Control in Integrated WLAN and CDMA Cellular Networks with Vertical Handoff