With the spread of Internet of Things' (IoT) applications, security has become extremely important. A recent distributed denial-of-service (DDoS) attack revealed the ubiquity of vulnerabilities in IoT, and many IoT devices unwittingly contributed to the DDoS attack. The emerging software-defined anything (SDx) paradigm provides a way to safely manage IoT devices. In this paper, we first present a general framework for software-defined Internet of Things (SD-IoT) based on the SDx paradigm. The proposed framework consists of a controller pool containing SD-IoT controllers, SD-IoT switches integrated with an IoT gateway, and IoT devices. We then propose an algorithm for detecting and mitigating DDoS attacks using the proposed SD-IoT framework, and in the proposed algorithm, the cosine similarity of the vectors of the packet-in message rate at boundary SD-IoT switch ports is used to determine whether DDoS attacks occur in the IoT. Finally, experimental results show that the proposed algorithm has good performance, and the proposed framework adapts to strengthen the security of the IoT with heterogeneous and vulnerable devices.

Nowadays, computation is playing an increasingly more important role in the future generation of computer and communication networks, as exemplified by the recent progress in SDN for wired networks as well as C-RAN and mobile cloud computing (MCC) for wireless networks. This article proposes a unified concept (i.e., computation diversity) to describe the impact and diverse forms of the computation resources on both wired and wireless communications. By linking the computation resources to the communication networks based on quality of service requirements, we can show how computation resources influence the networks. Moreover, by analyzing the different functionalities of computation resources in SDN, C-RAN, and MCC, we can show the diverse and flexible forms that the computation resources present in different networks. The study of computation diversity can provide guidance in future networks design as far as how to allocate the resources jointly between computation (e.g., CPU capacity) and communication (e.g., bandwidth), thereby saving system energy and improving users' experiences.

By offloading computation-intensive tasks to the edge cloud, mobile edge computing (MEC) has been regarded as an effective technology for enhancing computational capacity and extending the battery lifetime of mobile devices (MDs). However, due to the limitation of bandwidth and computing resources in MEC, unreasonable task offloading might lead to intensive resource competition, which recedes the performance gains benefit from offloading. When the tasks are latency-sensitive, a proper task offloading strategy is more important. Considering the heterogeneous delay constraints and resource competition comprehensively, we aim at minimizing the energy consumption of MDs subject to the individual delay constraints of tasks by jointly optimizing the task offloading and resource allocation in terms of wireless channel and remote computation capacity in a multi-MD MEC system in this paper. Due to the complexity of the primal optimization problem, a heuristic algorithm is devised. In the algorithm, a subset of tasks to be offloaded is incrementally constructed, and the corresponding offloading sub-problem is then repeatedly solved for this task subset using a two-stage algorithm until the total energy consumption can no longer be further reduced. The first stage of solving the sub-problem is to find the optimal full offloading scheme for the to-offload tasks, which is proved to be a convex optimization problem. For the task subset without a full offloading solution, an effective iterative algorithm is employed in the second stage where the channel allocation and computing resource allocation are optimized alternately. A great number of experiments are given to verify the performance of the proposed algorithm. We observe that the heuristic algorithm shows different performance when adopting different task ordering schemes. The proposed heuristic algorithm is evaluated against three reference schemes, and the results show that it can save up to 14.20% of energy consumption while guaranteeing the delay requirements of all tasks.

With the increasing popularity of user equipments, the corresponding UE generated big data (UGBD) is also growing substantially, which makes both UEs and current network structures struggle to process those data and applications. This article proposes a near-far computing enhanced C-RAN (NFC-RAN) architecture that can better process big data and its corresponding applications. NFC-RAN is composed of near edge computing (NEC) and far edge computing (FEC) units. NEC is located in the remote radio head,, which can quickly respond to delay-sensitive tasks from the UEs, while FEC sits next to a baseband unit pool, which can do other computation-intensive tasks. Task allocation between NEC and FEC is introduced in this article. Also, WiFi indoor positioning is illustrated as a case study of the proposed architecture. Moreover, simulation and experiment results are provided to show the effectiveness of the proposed task allocation and architecture.

Large-scale sewage treatment plants are one of the typical Industrial Internet of Things systems, where the presence of a large number of sensors generates massive dynamic time series data, and such multivariate time series data are usually time-dependent and random. Therefore, there is a certain risk when fitting the potential anomalies of real-world data, which will bring great challenges to anomaly detection. In this article, we propose a time-series mutual adversarial network (TMAN), a novel reconstruction model for anomaly detection on multivariate time series. It is based on the idea of adversarial learning and consists of two identical subnetworks. During the training process, two subnetworks can independently complete the learning of the time distribution of normal samples of industrial time series data for mutual adversarial. In the process of detecting, we obtain the residual values of TMAN reconstructed for different time series samples to discriminate anomalies. We combine TMAN and anomaly determination mechanisms to build a new industrial time series anomaly detection framework named TMANomaly. In addition, we select the dataset features with a grey correlation algorithm to achieve very high performance with a small number of features. Experimental results show that our proposed TMANomaly outperforms five popular anomaly detection methods and effectively improves the accuracy of industrial multivariate time series anomaly detection.