Considered as a key technology in 5G networks, mobile edge computing (MEC) can support intensive computation for energy-constrained and computation-limited mobile users (MUs) through offloading various computation and service functions to the edge of mobile networks. In addition to MEC, wireless heterogeneous networks will play an important role in providing high transmission capacity for MUs in 5G, where wireless backhaul is a cost-effective and viable solution to solve the expensive backhaul deployment issue. In this paper, we consider a setting, where MUs can offload their computations to the MEC server through a small cell base station (SBS), the SBS connects to the macro BS through a wireless backhaul, and computation resource at the MEC server is shared among offloading MUs. First, we formulate a joint optimization problem with the goal of minimizing the system-wide computation overhead. This is a mixed-integer problem and hard to derive the optimal solution. To solve this problem, we propose to decompose it into two subproblems, namely the offloading decision subproblem and the joint backhaul bandwidth and computation resource allocation subproblem. An algorithm, namely JOBCA, is proposed to obtain a feasible solution to the original problem by solving two subproblems iteratively. Finally, numerical results are conducted to verify the performance improvement of the proposed algorithm over two baseline algorithms and the close performance of the proposed algorithm compared with the centralized exhaustive search. INDEX TERMS Computation offloading, heterogeneous networks, mobile edge computing, resource allocation, wireless backhaul.

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Recently, the Internet of Things technology has rapidly spread and been applied in various fields including smart factories. Smart factory technologies are used for flexible process automation and custom manufacturing; thus, it requires adaptive network management for frequent network fluctuations due to mobility. Moreover, it is very important to ensure the timeliness of the data collected through the sensor nodes. In order to ensure network mobility in industrial WSNs, distributed scheduling algorithms should be supported. In this paper, we evaluated IEEE 802.15.4e-based industrial WSN MAC performances by using various mobility scenarios for smart factory environments. Also we proposed an IEEE 802.15.4e DSME-based distributed scheduling algorithm for mobility support and measured various performance metrics. The proposed algorithm can adaptively assign communication slots by analyzing the network traffic of each node and improve the network reliability and timeliness. The experimental result shows that the throughput of DSME MAC protocol is better than IEEE 802.15.4e TSCH and legacy slotted CSMA/CA in large network with above 30 nodes. Also the proposed algorithm improves the throughput by 15% higher than other MACs including original DSME. The algorithm was experimentally confirmed to reduce power consumption by improving the availability of communication slots.

Network virtualization technology allows the creation of virtual networks that reflect the requirements of various services and distribute resources of a physical network among virtual tenants. Recently, the virtualization of physical networks through software-defined networking technology has become a popular method. OpenVirteX is a softwaredefined networking-based network virtualization platform that can create multiple virtual and programmable networks on top of a single physical infrastructure and provide full isolation of each virtual tenant. However, some shortcomings persist in OpenVirteX, one of which is the lack of a mechanism to distribute physical network resources efficiently among the virtual tenants. In this article, we propose a novel quality-of-service management mechanism, based on the single rate three color marker proposed by the Internet Engineering Task Force. This mechanism employs two new token buckets, called global token buckets, which are used to collect idle tokens from each virtual network. Furthermore, a resource manager can redistribute the collected tokens among the virtual networks according to their weights. Finally, our work achieves a maximum improvement of approximately 1.6 times over the performances of previous mechanisms. KeywordsSoftware-defined networking, network virtualization, quality of service, autonomic network, single rate three color marker Date