Biblio

Designing an efficient difficulty control algorithm is an essential problem in Proof-of-Work (PoW) based blockchains because the network hash rate is randomly changing. This paper proposes a general difficulty control algorithm and provides insights for difficulty adjustment rules for PoW based blockchains. The proposed algorithm consists a two-layer neural network. It has low memory cost, meanwhile satisfying the fast-updating and low volatility requirements for difficulty adjustment. Real data from Ethereum are used in the simulations to prove that the proposed algorithm has better performance for the control of the block difficulty.

The concept of social networking is integrated into Internet of things (IoT) to socialize smart objects by mimicking human behaviors, leading to a new paradigm of Social Internet of Things (SIoT). A crucial problem that needs to be solved is how to establish reliable relationships autonomously among objects, i.e., building trust. This paper focuses on exploring an efficient context-aware trustworthiness inference framework to address this issue. Based on the sociological and psychological principles of trust generation between human beings, the proposed framework divides trust into two types: familiarity trust and similarity trust. The familiarity trust can be calculated by direct trust and recommendation trust, while the similarity trust can be calculated based on external similarity trust and internal similarity trust. We subsequently present concrete methods for the calculation of different trust elements. In particular, we design a kernel-based nonlinear multivariate grey prediction model to predict the direct trust of a specific object, which acts as the core module of the entire framework. Besides, considering the fuzziness and uncertainty in the concept of trust, we introduce the fuzzy logic method to synthesize these trust elements. The experimental results verify the validity of the core module and the resistance to attacks of this framework.

Resilient control systems should efficiently restore control into physical systems not only after the sabotage of themselves, but also after breaking physical systems. To enhance resilience of control systems, given an originally minimal-input controlled linear-time invariant(LTI) physical system, we address the problem of efficient control recovery into it after removing a known system vertex by finding the minimum number of inputs. According to the minimum input theorem, given a digraph embedded into LTI model and involving a precomputed maximum matching, this problem is modeled into recovering controllability of it after removing a known network vertex. Then, we recover controllability of the residual network by efficiently finding a maximum matching rather than recomputation. As a result, except for precomputing a maximum matching and the following removed vertex, the worst-case execution time of control recovery into the residual LTI physical system is linear.

Usually, the air gap will appear inside the composite insulators and it will lead to serious accident. In order to detect these internal defects in composite insulators operated in the transmission lines, a new non-destructive technique has been proposed. In the study, the mathematical analysis model of the composite insulators inner defects, which is about heat diffusion, has been build. The model helps to analyze the propagation process of heat loss and judge the structure and defects under the surface. Compared with traditional detection methods and other non-destructive techniques, the technique mentioned above has many advantages. In the study, air defects of composite insulators have been made artificially. Firstly, the artificially fabricated samples are tested by flash thermography, and this method shows a good performance to figure out the structure or defects under the surface. Compared the effect of different excitation between flash and hair drier, the artificially samples have a better performance after heating by flash. So the flash excitation is better. After testing by different pollution on the surface, it can be concluded that different pollution don't have much influence on figuring out the structure or defect under the surface, only have some influence on heat diffusion. Then the defective composite insulators from work site are detected and the image of defect is clear. This new active thermography system can be detected quickly, efficiently and accurately, ignoring the influence of different pollution and other environmental restrictions. So it will have a broad prospect of figuring out the defeats and structure in composite insulators even other styles of insulators.

Delay and security are both highly concerned in the Internet of Things (IoT). In this paper, we set up a secure analytical framework for IoT networks to characterize the network delay performance and secrecy performance. Firstly, stochastic geometry and queueing theory are adopted to model the location of IoT devices and the temporal arrival of packets. Based on this model, a low-complexity secure on-off scheme is proposed to improve the network performance. Then, the delay performance and secrecy performance are evaluated in terms of packet delay and packet secrecy outage probability. It is demonstrated that the intensity of IoT devices arouse a tradeoff between the delay and security and the secure on-off scheme can improve the network delay performance and secrecy performance. Moreover, secrecy transmission rate is adopted to reflect the delay-security tradeoff. The analytical and simulation results show the effects of intensity of IoT devices and secure on-off scheme on the network delay performance and secrecy performance.

OpenFlow has recently emerged as a powerful paradigm to help build dynamic, adaptive and agile networks. By decoupling control plane from data plane, OpenFlow allows network operators to program a centralized intelligence, OpenFlow controller, to manage network-wide traffic flows to meet the changing needs. However, from the security's point of view, a buggy or even malicious controller could compromise the control logic, and then the entire network. Even worse, the recent attack Stuxnet on industrial control systems also indicates the similar, severe threat to OpenFlow controllers from the commercial operating systems they are running on. In this paper, we comprehensively studied the attack vectors against the OpenFlow critical component, controller, and proposed a cross layer diversity approach that enables OpenFlow controllers to detect attacks, corruptions, failures, and then automatically continue correct execution. Case studies demonstrate that our approach can protect OpenFlow controllers from threats coming from compromised operating systems and themselves.

Risk-control optimization has great significance for security of power system. Usually the probabilistic uncertainties of parameters are considered in the research of risk optimization of power system. However, the method of probabilistic uncertainty description will be insufficient in the case of lack of sample data. Thus non-probabilistic uncertainties of parameters should be considered, and will impose a significant influence on the results of optimization. To solve this problem, a robust optimization operation method of power system risk-control is presented in this paper, considering the non-probabilistic uncertainty of parameters based on information gap decision theory (IGDT). In the method, loads are modeled as the non-probabilistic uncertainty parameters, and the model of robust optimization operation of risk-control is presented. By solving the model, the maximum fluctuation of the pre-specified target can be obtained, and the strategy of this situation can be obtained at the same time. The proposed model is applied to the IEEE-30 system of risk-control by simulation. The results can provide the valuable information for operating department to risk management.