The exponential growth of devices connected to the network has resulted in the development of new Internet of Things (IoT) applications and on-line services, which may have diverse and dynamic requirements on received quality. Although, the emerging Software-Defined Networking (SDN) approach can be leveraged for the IoT environment, to dynamically achieve differentiated quality levels for different IoT tasks in very heterogeneous wireless networking scenarios, the open interfaces in SDN introduces new network attacks, which may make SDN-based IoT malfunctioned. The challenges lies in securely using SDN for IoT systems. To address this challenge, we design a SDN-based data transfer security model Middlebox-Guard (M-G). M-G aims at reducing network latency, and properly manage dataflow to ensure the network run safely. First, according to different security policies, middleboxes related to the defined secure policies, are placed at the most appropriate locations, using dataflow abstraction and a heuristic algorithm. Next, to avoid any middlebox becoming a hot-spot, an offline Integer Linear Program (ILP) pruning algorithm is proposed in M-G, to tackle switch volume constraints. In addition, an online Linear Program (LP) formulation is come up to handle load balance. Finally, secure mechanisms are proposed to handle different attacks. And network routing is solved flexibly, through dataflow management protocol, which are formulated via combining tunnels and tags. Experimental results demonstrate that this model can improve security performance and manage dataflow effectively in SDN-based IoT system.

As Vehicular Ad hoc Network (VANET) features random topology and accommodates freely connected nodes, it is important that the cooperation among the nodes exists. This paper proposes a trust model called Implicit Web of Trust in VANET (IWOT-V) to reason out the trustworthiness of vehicles. Such that untrusted nodes can be identified and avoided when we make a decision regarding whom to follow or cooperate with. Furthermore, the performance of Cooperative Intelligent Transport System (C-ITS) applications improves. The idea of IWOT-V is mainly inspired by web page ranking algorithms such as PageRank. Although there does not exist explicit link structure in VANET because of random topology and dynamic connections, social trust relationship among vehicles exists and an implicit web of trust can be derived. To accomplish the derivation, two algorithms are presented, i.e. BayesTrust and VehicleRank. They are responsible for deriving the local and global trust relationship respectively. The simulation results show that IWOT-V can accurately identify trusted and untrusted nodes if enough local trust information is collected. The performance of IWOT-V affected by five threat models is demonstrated, and the related discussions are also given.

The dissemination of false messages in Internet of Vehicles (IoV) has a negative impact on road safety and traffic efficiency. Therefore, it is critical to quickly detect fake news considering news timeliness in IoV. We propose a network computing framework Quick Fake News Detection (QcFND) in this paper, which exploits the technologies from Software-Defined Networking (SDN), edge computing, blockchain, and Bayesian networks. QcFND consists of two tiers: edge and vehicles. The edge is composed of Software-Defined Road Side Units (SDRSUs), which is extended from traditional Road Side Units (RSUs) and hosts virtual machines such as SDN controllers and blockchain servers. The SDN controllers help to implement the load balancing on IoV. The blockchain servers accommodate the reports submitted by vehicles and calculate the probability of the presence of a traffic event, providing time-sensitive services to the passing vehicles. Specifically, we exploit Bayesian Network to infer whether to trust the received traffic reports. We test the performance of QcFND with three platforms, i.e., Veins, Hyperledger Fabric, and Netica. Extensive simulations and experiments show that QcFND achieves good performance compared with other solutions.

Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.