Local design of distributed H∞‐consensus filtering over sensor networks under multiplicative noises and deception attacks

This paper is concerned with the local design of the distributed H_∞‐consensus filtering problem for a class of discrete time‐varying systems subject to both multiplicative noises and deception attacks over sensor networks. The target plant and the measuring sensors are disturbed by multiplicative noises with known statistics. The malicious signal involved in deception attacks is constrained by a specific sector‐like bounded condition, which reflects certain tolerable bound on the difference between the attack signal and the true signal. Attention is paid to the design of filter gains for guaranteeing a desirable filtering performance that simultaneously characterizes the filtering accuracy and the consensus requirement. To handle the proposed filtering problem, the supply rate function is firstly chosen for each node and then the dissipation matrix is constructed as a column substochastic matrix based on the stochastic vector dissipation theory. Subsequently, sufficient conditions by means of recursive linear matrix inequalities are presented for each node such that the filtering error and the consensus error satisfy the desirable H_∞‐consensus performance index over a finite horizon. Finally, an illustrative simulation is presented to demonstrate the effectiveness of the proposed filter strategy.     

传感器网络中鲁棒状态信息融合抗差卡尔曼滤波器

研究了无线传感器网络中的分布式鲁棒状态信息融合问题. 在局部状态估计层, 基于鲁棒统计学理论提出了适用于噪声相关情况的抗差(扩展)卡尔曼滤波器. 在融合中心层, 针对局部估计相关未知性和不完整性, 给出了不依赖于互协方差阵的稳健航迹融合方法—–内椭球逼近法. 仿真结果证实了算法的有效性: 所提出的抗差卡尔曼滤波器在野值存在情况下, 性能退化远低于传统卡尔曼滤波器(28.6%比428.6%); 所提出的内椭球逼近法获得比协方并交叉法更好的融合估计性能, 且不需要局部估计相关性的先验知识.     

无线传感器网络中的Sybil攻击

无线传感器网络中的Sybil攻击会破坏无线传感器网络的路由算法、公平资源分配等机制,因此研究Sybil攻击的防范和检测方法存在一定的意义.分析了Sybil攻击的破坏性,阐述了目前已有的防范和检测Sybil攻击的方法,并针对目前已有方法的不足,提出了从节点的能量消耗、计算量等性能进行优化出发,设计出有效的Sybil攻击防范和检测机制.     

Distributed linear estimation over sensor networks

We consider a network of sensors in which each node may collect noisy linear measurements of some unknown parameter. In this context, we study a distributed consensus diffusion scheme that relies only on bidirectional communication among neighbour nodes (nodes that can communicate and exchange data), and allows every node to compute an estimate of the unknown parameter that asymptotically converges to the true parameter. At each time iteration, a measurement update and a spatial diffusion phase are performed across the network, and a local least-squares estimate is computed at each node. The proposed scheme allows one to consider networks with dynamically changing communication topology, and it is robust to unreliable communication links and failures in measuring nodes. We show that under suitable hypotheses all the local estimates converge to the true parameter value.     

Reliable fusion estimation over sensor networks with outliers and energy constraints

This paper provides a reliable fusion scheme over sensor networks subject to abnormal measurements and energy constraints. Two kinds of channels are employed to implement the information transmission in order to extend the lifetime. Specifically, the one has the merit of high reliability by sacrificing energy cost and the other reduces the energy cost but could result in packet loss. For the addressed problem, a χ² detection in local state estimator is first designed to remove abnormal measurements, which could come from outliers or a malicious modification by attackers. Then, a new strategy is developed to compensate the lost local estimation transmitted by low‐reliable channels. Furthermore, by view of matrix operation and probability theory, a set of recursive formulas are developed to calculate desired error covariance matrices of local state estimation, compensated state estimation as well as fusion estimation. The optimal fusion weights are obtained analytically and the advantage of fusion estimation is disclosed by resorting to these covariance matrices. Finally, a numerical example is used to illustrate the effectiveness of the proposed method.     

State estimation with remote sensors and intermittent transmissions

This paper deals with the problem of estimating the state of a discrete-time linear stochastic dynamical system on the basis of data collected from multiple sensors subject to a limitation on the communication rate from the remote sensor units. The optimal probabilistic measurement-independent strategy for deciding when to transmit estimates from each sensor is derived. Simulation results show that the derived strategy yields certain advantages in terms of worst-case time-averaged performance with respect to periodic strategies when coordination among sensors is not possible.     

传感器网络一致性分布式滤波算法

为了改善分布式传感器网络的估计性能,提出了一种基于状态预测一致的滤波算法.在对局部估计值进行一致化处理的基础上,重点研究了利用邻居节点前一时刻的估计值对当前局部状态预测值进行修正来提高估计精度.给出了一种一致性增益的选择方法,利用李雅普诺夫方法得到了算法收敛的充分条件,并讨论了影响算法收敛速度的因素.仿真结果表明了算法的有效性,并发现节点度较大的传感器在网络估计中发挥着重要作用,可通过调整这类节点的一致性系数来改善算法性能.     

Optimal interval estimation fusion based on sensor interval estimates with confidence degrees

The interval estimation fusion method based on sensor interval estimates and their confidence degrees is developed. When sensor estimates are independent of each other, a combination rule to merge sensor estimates and their confidence degrees is proposed. Moreover, two optimization criteria: minimizing interval length with an allowable minimum confidence degree, or maximizing confidence degree with an allowable maximum interval length are suggested. In terms of the two criteria, an optimal interval estimation fusion can be obtained based on the combined intervals and their confidence degrees. Then we can extend the results on the combined interval outputs and their confidence degrees to obtain a conditional combination rule and the corresponding optimal fault-tolerant interval estimation fusion in terms of the two criteria. It is easy to see that Marzullo's fault-tolerant interval estimation fusion [Marzullo, (1990). Tolerating failures of continuous-valued sensors. ACM Transactions on Computer System, 8(4), 284-304] is a special case of our method.     

无线传感器网络中的虫洞攻击防护机制

虫洞攻击能够随意制造“热点”区域以加速消耗特定区域能量,对依赖连接的无线传感器网络来说影响最大：直接导致获得的数据混乱,结果远远偏离实际情况。现有的措施虽然有一定的抵御作用,但是它们仍然存在很多不足。针对这种情况,提出了基于信誉认证的虫洞攻击抵御机制,利用了自反馈的信誉认证机制,不需要任何额外硬件。仿真实验结果表明,基于信誉认证的虫洞抵御机制能够有效地抵御无线传感器网络中各种类型的虫洞攻击。     

Detection and mitigation of sinkhole attacks in wireless sensor networks

With the advances in technology, there has been an increasing interest in the use of wireless sensor networks (WSNs). WSNs are vulnerable to a wide class of attacks among which sinkhole attack puts severe threats to the security of such networks. This paper proposes two approaches to detect and mitigate such attack in WSNs. It provides a centralized approach to detect suspicious regions in the network using geostatistical hazard model. Furthermore, a distributed monitoring approach has been proposed to explore every neighborhood in the network to detect malicious behaviors. Our simulation experiments validate the correctness and efficiency of the proposed approaches.     

Event-triggered filtering and fault estimation for nonlinear systems with stochastic sensor saturations

This paper is concerned with the filtering problem for a class of nonlinear systems with stochastic sensor saturations and event-triggered measurement transmissions. An event-triggered transmission scheme is proposed with hope to ease the traffic burden and improve the energy efficiency. The measurements are subject to randomly occurring sensor saturations governed by Bernoulli-distributed sequences. Special effort is made to obtain an upper bound of the filtering error covariance in the presence of linearisation errors, stochastic sensor saturations as well as event-triggered transmissions. A filter is designed to minimise the obtained upper bound at each time step by solving two sets of Riccati-like matrix equations, and thus the recursive algorithm is suitable for online computation. Sufficient conditions are established under which the filtering error is exponentially bounded in mean square. The applicability of the presented method is demonstrated by dealing with the fault estimation problem. An illustrative example is exploited to show the effectiveness of the proposed algorithm.     

高效的分簇传感器网络广播认证方案*

广播是无线传感器网络的重要通信手段,广播消息的安全性应得到保障。在分簇传感器网络结构基础上提出了一种无线传感器网络广播认证方案,簇头节点对广播进行签名认证,簇内使用消息认证码进行消息认证,网关节点负责广播消息在簇间的传递。分析表明,与部分现有的传感器网络广播认证方案相比,该方案以较低的能耗提供了较高的安全性。     

Distributed resilient estimation over sensor networks for nonlinear time‐delayed systems with stochastic perturbations

This paper is concerned with the distributed resilient estimation problem for a class of nonlinear time‐delayed systems subject to stochastic perturbations. The plant and the measurements are disturbed by two Gaussian white stochastic processes with known statistical information, respectively. In addition, a resilient estimator is designed for each node by means of the parameter uncertainties and Bernoulli‐distributed random variables. Then, a novel exponential‐bounded performance index is put forward to measure the disturbance rejection level of the distributed estimators against the external disturbances and the impact of the initial values. A new vector dissipation definition including multiple vectors of energy storage functions is established to deal with the time‐delay estimation error dynamics. Within the framework of local performance analysis inspired by this new definition of vector dissipation, sufficient conditions in terms of recursive linear matrix inequalities are constructed for each node to guarantee the desirable performance index. Next, a local optimization problem subject to a set of recursive linear matrix inequalities is presented for each node to minimize the upper bound in the performance index, where the calculations can be conducted on every node in a distributed manner and the estimator gains are also calculated. Finally, an illustrative simulation example is provided to verify the applicability of the proposed estimators.     

Event‐triggered robust state estimation for wireless sensor networks

Robust state estimation problem subject to a communication constraint is investigated in this paper for a class of wireless sensor networks constituted by multiple remote sensor nodes and a fusion node. An analytical robust fusion estimator using local event‐triggered transmission strategies is derived aiming to reduce energy consumption of the sensor nodes and refrain from network traffic congestion. Some conditions are presented guaranteeing the uniformly bounded estimation errors of the robust state estimator. Several numerical simulations are presented to show the validity of the proposed method.     

An adaptive unscented Kalman filter approach to secure state estimation for wireless sensor networks

Wireless sensor networks are vulnerable to false data injection attacks, which may mislead the state estimation. To solve this problem, this paper presents a chi-square test-based adaptive secure state estimation (CTASSE) algorithm for state estimation and attack detection. Taking advantage of Kalman filters, attack signal together with process noise or measurement noise are described as total white Gaussian noise with uncertain covariance matrix. The chi-square test method is used in the adaptation of the total noise covariance and attack detection. Then, a standard adaptive unscented Kalman filter (UKF) is used for the state estimation. Finally, simulation results show that the proposed CTASSE algorithm performs better than other UKFs in state estimation and is also effective in real-time attack detection.     

Optimal estimation of linear discrete-time systems with stochastic parameters

This paper considers optimal linear state estimation in the general case of linear discrete-time systems with stochastic parameters which are statistically independent with respect to time. The estimator is derived by transforming the system to one with deterministic parameters and state dependent additive system and observation noise. It is shown that mean square stability of the system is a sufficient and almost necessary condition for the existence, uniqueness and stability of the time invariant estimator.     

电力系统状态估计多算法融合

为解决复杂电网大数据背景下单一算法难以满足状态估计要求的问题,提出一种电力系统状态估计多算法融合系统。基于数据融合思想,将加权最小二乘法、快速分解法、量测状态变换法与Sigmoid函数法相互融合实现电网状态估计,支持串行融合模式和并行融合模式。实验结果表明,融合算法使得估计结果保留了各自算法的优点,避免了单一算法的缺点,提高了状态估计的精度和效率。     

Security‐guaranteed filtering for discrete‐time stochastic delayed systems with randomly occurring sensor saturations and deception attacks

In this paper, the security‐guaranteed filtering problem is studied for a class of nonlinear stochastic discrete time‐delay systems with randomly occurring sensor saturations (ROSSs) and randomly occurring deception attacks (RODAs). The nonlinearities in systems satisfy the sector‐bounded conditions, and the time‐varying delays are unknown with given lower and upper bounds. A novel measurement output model is proposed to reflect both the ROSSs and the RODAs. A new definition is put forward on the security level with respect to the noise intensity, the energy bound of the false signals, the energy of the initial system state, and the desired security degree. We aim at designing a filter such that, in the presence of ROSSs and RODAs, the filtering error dynamics achieves the prescribed level of security. By using the stochastic analysis techniques, a sufficient condition is first derived under which the filtering error system is guaranteed to have the desired security level, and then, the filter gain is designed by solving a linear matrix inequality with nonlinear constraints. Finally, a numerical example is provided to demonstrate the feasibility of the proposed filtering scheme. Copyright © 2016 John Wiley & Sons, Ltd.     

基于分簇的无线传感器网络克隆攻击检测方案

针对无线传感器网络面临的节点克隆攻击,提出了一种基于分簇的检测方案。该方案给网络中的消息都标记上其源节点的ID,在成簇阶段依据节点ID和RSSI值由簇头判断各个簇内是否存在克隆节点,待全部节点的ID汇聚至基站后,利用分簇算法中各个簇是不相交的子集这一特点,在基站处检测是否存在同一个ID属于多个簇的异常情况。仿真实验表明,该方案不但有较高的检测率,还能降低网络的存储和通信开销。