Search

In the context of the rapid development of the Internet of Vehicles, virtual reality, automatic driving and the industrial Internet, the terminal devices in the network show explosive growth. As a result, more and more information is generated from the edge of the network, which makes the data throughput increase dramatically in the mobile communication network. As the key technology of the fifth-generation mobile communication network, mobile edge caching technology which caches popular data to the edge server deployed at the edge of the network avoids the data transmission delay of the backhaul link and the occurrence of network congestion. With the growing scale of the network, distributing hot data from cloud servers to edge servers will generate huge energy consumption. To realize the green and sustainable development of the communication industry and reduce the energy consumption of distribution of data that needs to be cached in edge servers, we make the first attempt to propose and solve the problem of edge caching data distribution with minimum energy consumption (ECDDMEC) in this paper. First, we model and formulate the problem as a constrained optimization problem and then prove its NP-hardness. Subsequently, we design a greedy algorithm with computational complexity of O (n 2) to solve the problem approximately. Experimental results show that compared with the distribution strategy of each edge server directly requesting data from the cloud server, the strategy obtained by the algorithm can significantly reduce the energy consumption of data distribution. [ABSTRACT FROM AUTHOR]

With the development of intelligent transportation and the rapid growth of application data, the tasks of offloading vehicles in vehicle-to-vehicle communication technology are continuously increasing. To further improve the service efficiency of the computing platform, energy-efficient and low-latency mobile-edge-computing (MEC) offloading methods are urgently needed, which can solve the insufficient computing capacity of vehicle terminals. Based on an improved gray-wolf algorithm designed, an adaptive joint offloading strategy for vehicular edge computing is proposed, which does not require cloud-computing support. This strategy first establishes an offloading computing model, which takes task computing delays, computing energy consumption, and MEC server computing resources as constraints; secondly, a system-utility function is designed to transform the offloading problem into a constrained system-utility optimization problem; finally, the optimal solution to the computation offloading problem is obtained based on an improved gray-wolf optimization algorithm. The simulation results show that the proposed strategy can effectively reduce the system delay and the total energy consumption. [ABSTRACT FROM AUTHOR]

Mobile edge computing is proposed as a promising computing paradigm to relieve the excessive burden of data centers and mobile networks, which is induced by the rapid growth of Internet of Things (IoT). This work introduces the cloud-assisted multi-cloudlet framework to provision scalable services in cloudlet-based mobile edge computing. Due to the constrained computation resources of cloudlets and limited communication resources of wireless access points (APs), IoT sensors with identical computation offloading decisions interact with each other. To optimize the processing delay and energy consumption of computation tasks, theoretic analysis of the computation offloading decision problem of IoT sensors is presented in this paper. In more detail, the computation offloading decision problem of IoT sensors is formulated as a computation offloading game and the condition of Nash equilibrium is derived by introducing the tool of a potential game. By exploiting the finite improvement property of the game, the Computation Offloading Decision (COD) algorithm is designed to provide decentralized computation offloading strategies for IoT sensors. Simulation results demonstrate that the COD algorithm can significantly reduce the system cost compared with the random-selection algorithm and the cloud-first algorithm. Furthermore, the COD algorithm can scale well with increasing IoT sensors. [ABSTRACT FROM AUTHOR]