A Broad Learning-Driven Network Traffic Analysis System Based on Fog Computing Paradigm

The development of communication technologies which support traffic-intensive applications presents new challenges in designing a real-time traffic analysis architecture and an accurate method that suitable for a wide variety of traffic types.Current traffic analysis methods are executed on the cloud,which needs to upload the traffic data.Fog computing is a more promising way to save bandwidth resources by offloading these tasks to the fog nodes.However,traffic analysis models based on traditional machine learning need to retrain all traffic data when updating the trained model,which are not suitable for fog computing due to the poor computing power.In this study,we design a novel fog computing based traffic analysis system using broad learning.For one thing,fog computing can provide a distributed architecture for saving the bandwidth resources.For another,we use the broad learning to incrementally train the traffic data,which is more suitable for fog computing because it can support incremental updates of models without retraining all data.We implement our system on the Raspberry Pi,and experimental results show that we have a 98%probability to accurately identify these traffic data.Moreover,our method has a faster training speed compared with Convolutional Neural Network(CNN).

A Real Plug-and-Play Fog: Implementation of Service Placement in Wireless Multimedia Networks

Initially as an extension of cloud computing, fog computing has been inspiring new ideas about moving computing tasks to the edge of networks. In fog, we often repeat the procedure of placing services because of the geographical distribution of mobile users. We may not expect a fixed demand and supply relationship between users and service providers since users always prefer nearby service with less time delay and transmission consumption. That is, a plug-and-play service mode is what we need in fog. In this paper, we put forward a dynamic placement strategy for fog service to guarantee the normal service provision and optimize the Quality of Service (QoS). The simulation results show that our strategy can achieve better performance under metrics including energy consumption and end-to-end latency. Moreover, we design a real Plug-and-Play Fog (PnPF) based on Raspberry Pi and OpenWrt to provide fog services for wireless multimedia networks.

Efficient Certificateless Authenticated Key Agreement for Blockchain-Enabled Internet of Medical Things

Internet of Medical Things(IoMT)plays an essential role in collecting and managing personal medical data.In recent years,blockchain technology has put power in traditional IoMT systems for data sharing between different medical institutions and improved the utilization of medical data.However,some problems in the information transfer process between wireless medical devices and mobile medical apps,such as information leakage and privacy disclosure.This paper first designs a cross-device key agreement model for blockchain-enabled IoMT.This model can establish a key agreement mechanism for secure medical data sharing.Meanwhile,a certificateless authenticated key agreement(KA)protocol has been proposed to strengthen the information transfer security in the cross-device key agreement model.The proposed KA protocol only requires one exchange of messages between the two parties,which can improve the protocol execution efficiency.Then,any unauthorized tampering of the transmitted signed message sent by the sender can be detected by the receiver,so this can guarantee the success of the establishment of a session key between the strange entities.The blockchain ledger can ensure that the medical data cannot be tampered with,and the certificateless mechanism can weaken the key escrow problem.Moreover,the security proof and performance analysis are given,which show that the proposed model and KA protocol are more secure and efficient than other schemes in similar literature.

Event-Oriented Dynamic Security Service for Demand Response in Smart Grid Employing Mobile Networks

Equipped with millions of sensors and smart meters in smart gird,a reliable and resilient wireless communication technology is badly needed.Mobile networks are among the major energy communication networks which contribute to global energy consumption increase rapidly.As one of core technologies of smart grid employing mobile networks,Demand Response(DR) helps improving efficiency,reliability and security for electric power grid infrastructure.Security of DR events is one of the most important issues in DR.However,the security requirements of different DR events are dynamic for variousactual demands.To address this,an event-oriented dynamic security service mechanism is proposed for DR.Three kinds of security services including security access service,security communication service and security analysis service for DR event are composited dynamically by the fine-grained sub services.An experiment prototype of the network of State Grid Corporation of China(SGCC) is established.Experiment and evaluations shows the feasibility and effectiveness of the proposed scheme in smart grid employing mobile network.

SIoTFog:Byzantine-resilient IoT fog networking

The current boom in the Internet of Things(IoT) is changing daily life in many ways, from wearable devices to connected vehicles and smart cities. We used to regard fog computing as an extension of cloud computing, but it is now becoming an ideal solution to transmit and process large-scale geo-distributed big data. We propose a Byzantine fault-tolerant networking method and two resource allocation strategies for IoT fog computing. We aim to build a secure fog network, called "SIoTFog," to tolerate the Byzantine faults and improve the efficiency of transmitting and processing IoT big data. We consider two cases, with a single Byzantine fault and with multiple faults, to compare the performances when facing different degrees of risk. We choose latency, number of forwarding hops in the transmission, and device use rates as the metrics. The simulation results show that our methods help achieve an efficient and reliable fog network.

Energy Efficiency of a Multi-Core Processor by Tag Reduction

We consider the energy saving problem for caches on a multi-core processor. In the previous research on low power processors, there are various methods to reduce power dissipation. Tag reduction is one of them. This paper extends the tag reduction technique on a single-core processor to a multi-core processor and investigates the potential of energy saving for multi-core processors. We formulate our approach as an equivalent problem which is to find an assignment of the whole instruction pages in the physical memory to a set of cores such that the tag-reduction conflicts for each core can be mostly avoided or reduced. We then propose three algorithms using different heuristics for this assignment problem. We provide convincing experimental results by collecting experimental data from a real operating system instead of the traditional way using a processor simulator that cannot simulate operating system functions and the full memory hierarchy. Experimental results show that our proposed algorithms can save total energy up to 83.93% on an 8-core processor and 76.16% on a 4-core processor in average compared to the one that the tag-reduction is not used for. They also significantly outperform the tag reduction based algorithm on a single-core processor.