As an enhancement of cellular networks, the future-generation 5G network can be considered an ultra-high-speed technology. The proposed 5G network might include all types of advanced dominant technologies to provide remarkable services. Consequently, new architectures and service management schemes for different applications of the emerging technologies need to be recommended to solve issues related to data traffic capacity, high data rate, and reliability for ensuring QoS. Cloud computing, Internet of things (IoT), and software-defined networking (SDN) have become some of the core technologies for the 5G network. Cloud-based services provide flexible and efficient solutions for information and communications technology by reducing the cost of investing in and managing information technology infrastructure. In terms of functionality, SDN is a promising architecture that decouples control planes and data planes to support programmability, adaptability, and flexibility in ever-changing network architectures. However, IoT combines cloud computing and SDN to achieve greater productivity for evolving technologies in 5G by facilitating interaction between the physical and human world. The major objective of this study provides a lawless vision on comprehensive works related to enabling technologies for the next generation of mobile systems and networks, mainly focusing on 5G mobile communications.

In smart Industrial Wireless Sensor Networks (IWSNs), sensor nodes usually adopt a programmable technology. These smart devices can obtain new or special functions by reprogramming: they upgrade their soft systems through receiving new version of program codes. If sensor nodes need to be upgraded, the sink node will propagate program code packets to them through “one-to-many” broadcasting, and therefore new capabilities can be obtained, forming the so-called Software Defined Network (SDN). However, due to the high volume of code packet, the constraint energy of sensor node, and the unreliable link quality of wireless network, rapidly broadcasting the code packets to all nodes in network can be a challenge issue. In this paper, a novel Energy-efficient Broadcast scheme with adjustable broadcasting radius is proposed aiming to improve the performance of network upgrade. In our scheme, the nonhotspots sensor nodes take full advantage of their residual energy caused in data collection period to improve the packet reception probability and reduce the broadcasting delay of code packet transmission by enlarging the broadcasting radius, that is, the transmitting power. The theoretical analyses and experimental results show that, compared with previous work, our approach can averagely reduce the Network Upgrade Delay (NUD) by 14.8%–45.2% and simultaneously increase the reliability without harming the lifetime of network.

Cognitive femtocell has emerged as one of the promising ways to solve the indoor coverage and spectrum shortage problems in cellular networks. However, it is a challenge to motivate both macro base station (MBS) and femtocell access points (FAPs) to adopt this hybrid access policy. In this paper, we propose a systematic dynamic spectrum sharing framework for hybrid access in orthogonal frequency-division multiple-access-based cognitive macro-femtocell networks. In this framework, the MBS offloads some macro user equipments (MUEs) to FAP to improve the transmission performance and save energy. In return, the MBS leases a portion of its spectrum to FAP. Then, the FAP can use a part of spectrum for its own transmission and use another part to serve MUEs. We design an auction-based approach where each FAP receives additional MUE as bidders, while MBS acts as auctioneer. We formulate the dynamic resource sharing as a sum utility maximization problem. Numerical results demonstrate that both the macro and femtocell could get benefit from our framework.

Rendezvous is the fundamental challenge in cognitive radio networks to find each other on a specific channel and establish a communication link. The primary focus of this paper is to design an algorithm for blind rendezvous, i.e., a rendezvous without a coordinator or common control channel. Channels to jump are assumed to be homogeneous in prior work; however, in the real world, channels exhibit different conditions. Therefore, we propose an adaptive jumping pattern such that a superior channel is considered more frequently based on its noise level and users have a greater possibility to rendezvous on such a channel. Furthermore, in this paper, we investigate the rendezvous problem with multiple radio interfaces. When multiple radio interfaces are available for users, the possibility of rendezvous increases; however, the number of channels to jump for each interface may not be the same as other users' interfaces. We propose an adaptive rendezvous algorithm that can function for multiple interfaces and different sizes of channel lists and an adaptive jumping pattern for different channel conditions. We prove that the proposed algorithm provides guaranteed rendezvous and derive the maximum time-to-rendezvous (TTR), which is also verified via simulation results. Further, our algorithm outperforms jump-and-stay in terms of TTR while enabling users to rendezvous on a better channel.

VCC is a computing paradigm which consists of vehicles cooperating with each other to realize a lot of practical applications, such as delivering packages. Security cooperation is a fundamental research topic in Vehicular Cloud Computing (VCC). Because of the existence of malicious vehicles, the security cooperation has become a challenging issue in VCC. In this paper, a trust-based model for security cooperating, named DBTEC, is proposed to promote vehicles’ security cooperation in VCC. DBTEC combines the indirect trust estimation in Public board and the direct trust estimation in Private board to compute the trust value of vehicles when choosing cooperative partners; a trustworthy cooperation path generating scheme is proposed to ensure the safety of cooperation and increase the cooperation completion rates in VCC. Extensive experiments show that our scheme improves the overall cooperation completion rates by 6~7%.

Machine learning enables intrusion detection systems to detect network attacks adaptively and intelligently. Recently deep neural network has been investigated as such a solution owing to its high accuracy but it has limitation in real-time performance. To enhance the learning time, in this paper, we propose to use feature selection and layer configuration. We use the NSL-KDD data set, which is a refined version of the KDD CUP 99 data set and analyzed the associations between features using WEKA, a data mining tool. Our experimental results confirm that proper feature selection and layer configuration can reduce learning time while maintaining high average accuracy.