Yanfei Sun

Green Industrial Internet of Things Architecture: An Energy-Efficient Perspective

The Internet of Things (IoT) can support collaboration and communication between objects automatically. However, with the increasing number of involved devices, IoT systems may consume substantial amounts of energy. Thus, the relevant energy efficiency issues have recently been attracting much attention from both academia and industry. In this article we adopt an energy-efficient architecture for Industrial IoT (IIoT), which consists of a sense entities domain, RESTful service hosted networks, a cloud server, and user applications. Under this architecture, we focus on the sense entities domain where huge amounts of energy are consumed by a tremendous number of nodes. The proposed framework includes three layers: the sense layer, the gateway layer, and the control layer. This hierarchical framework balances the traffic load and enables a longer lifetime of the whole system. Based on this deployment, a sleep scheduling and wake-up protocol is designed, supporting the prediction of sleep intervals. The shifts of states support the use of the entire system resources in an energy-efficient way. Simulation results demonstrate the significant advantages of our proposed architecture in resource utilization and energy consumption.

Wireless Big Data Computing in Smart Grid

The development of smart grid brings great improvement in the efficiency, reliability, and economics to power grid. However, at the same time, the volume and complexity of data in the grid explode. To address this challenge, big data technology is a strong candidate for the analysis and processing of smart grid data. In this article, we propose a big data computing architecture for smart grid analytics, which involves data resources, transmission, storage, and analysis. In order to enable big data computing in smart grid, a communication architecture is then described consisting of four main domains. Key technologies to enable big-data-aware wireless communication for smart grid are investigated. As a case study of the proposed architecture, we introduce a big-data- enabled storage planning scheme based on wireless big data computing. A hybrid approach is adopted for the optimization including GA for storage planning and a game theoretic inner optimization for daily energy scheduling. Simulation results indicate that the proposed storage planning scheme greatly reduce

Strategic Honeypot Game Model for Distributed Denial of Service Attacks in the Smart Grid

Advanced metering infrastructure (AMI) is an important component for a smart grid system to measure, collect, store, analyze, and operate users consumption data. The need of communication and data transmission between consumers (smart meters) and utilities make AMI vulnerable to various attacks. In this paper, we focus on distributed denial of service attack in the AMI network. We introduce honeypots into the AMI network as a decoy system to detect and gather attack information. We analyze the interactions between the attackers and the defenders, and derive optimal strategies for both sides. We further prove the existence of several Bayesian-Nash equilibriums in the honeypot game. Finally, we evaluate our proposals on an AMI testbed in the smart grid, and the results show that our proposed strategy is effective in improving the efficiency of defense with the deployment of honeypots.

Attack Detection and Distributed Forensics in Machine-to-Machine Networks

The advanced idea of machine-to-machine technology has attracted a new period of network revolution, evolving into a method to monitor and control global industrial user assets, machines, and the production process. M2M networks are considered to be the intelligent connection and communication between machines. However, the security issues have been further amplified with the development of M2M networks. Consequently, it is essential to pour attention into attack detection and forensics problems in M2M networks. This article puts forward the hybrid attack detection and forensics model in M2M networks. It contains two modules: the attack detection module and the forensics analysis module. In addition, we present a distributed anti-honeypot- based forensics strategy to cope with DDoS attacks in the forensics analysis module. Finally, we also discuss some challenges in M2M network security and forensics.

A Hybrid Security and Compressive Sensing-Based Sensor Data Gathering Scheme

The use of cryptographic techniques such as encryption and hashing largely increases the energy consumption of sensors, which aggravates the original critical energy constraint problem of wireless sensor networks (WSNs). To reduce the burden of sensors, compression can be utilized. Since the traditional chaos-based schemes are not directly applicable for WSNs, we present a hybrid security solution. The hybrid security consists of 8-bit integer chaotic block encryption and a chaos-based message authentication codes. It aims to promote the security and performance of data gathering. In this paper, a hybrid security and compressive sensing-based scheme for multimedia sensor data gathering is presented. It has light security mechanism and thus decreases the complexity and energy consumption of system. Performance analysis about security and compression is carried out. The results show that our scheme is more applicable for WSNs multimedia data gathering from security and compression efficiency.

Crowdsourcing-Based Content-Centric Network: A Social Perspective

Driven by the rapidly growing demand for multimedia service, CCN is conceived as an important component for the next-generation network to facilitate information distribution and meet individual needs. This article investigates how to integrate the crowdsourcing technique and the CCN into a coherent whole, for the purpose of significantly improving the performance of CCN. Specifically, we first propose a CCCN that mainly contains two parts, i.e., mobile participants and a server cloud. Second, we design a complementary social-enhanced communication strategy that exploits the social characteristics for this framework, in order to develop a systematic understanding of the interplay between social characteristics and crowdsourcing. Extensive simulations have been conducted, and the results demonstrate that our social strategy can achieve significant performance improvement. Finally, we discuss open issues about security and management that may arise in the future CCCN framework.

Antieavesdropping With Selfish Jamming in Wireless Networks: A Bertrand Game Approach

Wireless communications are vulnerable to eavesdropping attacks due to their broadcast nature. To deal with their emerging challenge of physical layer security, in this paper, we study the antieavesdropping problem in the presence of selfish jammers, who desire to achieve maximum profit for themselves. We consider both the single-channel multijammer (SCMJ) model and the multichannel single-jammer (MCSJ) model. We investigate the interaction between the source that transmits secret information and friendly jammer nodes who assist the source by interfering with the eavesdropper. This problem is formulated as an oligopoly market consisting of a few firms and a buyer. By modeling the problem as a Bertrand game based on price competition, we obtain the optimal pricing scheme for the friendly, while for selfish jammers, the utility of those jammers is maximized. For the SCMJ model, we prove the existence of Bertrand Equilibrium by deriving a closed-form expression for the optimal price strategy. For the MCSJ model, a closed-form expression for power allocation is derived, based on which a new algorithm is designed to obtain the optimal strategy of the jammer. Finally, via simulations, we verify our theoretical analysis.

Strategic Antieavesdropping Game for Physical Layer Security in Wireless Cooperative Networks

This paper deals with the secure communication issues of wireless cooperative networks in the presence of multiple friendly but selfish intermediate nodes. On account of the broadcast nature of wireless communications, it is assailable to malicious eavesdropping. To tackle this challenge in the paper, we present a relay and jammer selection strategy that selects the jammer and relay nodes from intermediate nodes to improve the security of the eavesdropping attacks. The jammer is used to broadcast artificial interference noise on the eavesdropper. The relay acts as a traditional relay that retransmits source signal from the source to the intended destination. For achieving the maximum secrecy capacity by the selected nodes, we introduce a power allocation approach of intermediate nodes that is formulated as a Bertrand game based on price competition. We prove that an optimal pricing scheme can maximize the secrecy capacity and achieve the optimal profits for the selfish friendly nodes. Then, a new particle swarm with simulated anneal optimization algorithm is employed to obtain the solution of pricing and node selection. Finally, the simulation results verify our theoretical analysis.

Distributed Energy Management for Vehicle-to-Grid Networks

Making full use of V2G services, EVs with batteries may assist the smart grid in alleviating peaks of energy consumption. Aiming to develop a systematic understanding of the interplay between smart grid and EVs, an architecture for the V2G networks with the EV aggregator is designed to maintain the balance between energy suppliers (the grid side) and consumers (the EV side). We propose a combined control and communication approach considering distributed features and vehicle preferences in order to ensure efficient energy transfer. In our model, the integrated communication and control unit can achieve realtime and intelligent management with the logic controller and collected data. On the consumers side, we theoretically analyze how to satisfy the charging constraints that we incorporate in the form of willingness to pay, and propose a distributed framework to coordinate the energy delivery behaviors for satisfying service demands. Moreover, illustrative results indicate that the proposed approach can yield higher revenue than the conventional pricing mechanism in V2G networks.

A dynamic assignment scheduling algorithm for big data stream processing in mobile Internet services

As the huge number of mobile devices (e.g., smart phones, tablets and netbooks) increases, more and more people choose to use the Internet services financed by mobile Internet service providers (MISPs). To provide better services, it is quite necessary for MISPs to analyze the information hidden in the big data stream generated by users. Therefore, processing the real-time big data stream efficiently has become increasingly important. However, traditional static data storage technology fails to meet the demands of real-time data processing. To improve processing capacity, many parallel processing structures are proposed, which brings up the problem about how the parallel devices can be scheduled to maximize their efficiency. Accordingly, a dynamic assignment scheduling algorithm for big data stream processing in mobile Internet services is proposed, and a stream query graph is built to calculate the weight of every edge. The edge with the minimum weight is selected to send tuples. Simulation results show that the proper number of the logic devices can dramatically reduce system response time. Furthermore, system context switching is reduced by increasing the number of tuples sent each time.