Finite-Time Distributed $H_{\infty }$ Filtering in Sensor Networks With Switching Topology and Two-Channel Stochastic Attacks

Distributed event-triggered [formula omitted] consensus filtering in sensor networks

This study attempts to analyze and design multi-agent systems in the spatial frequency domain and demonstrates that the spatial frequency-based approach is useful for distributed spatial filtering in sensor networks. First, we take the consensus of multi-agent systems (i.e., letting the states of all agents converge to an identical value) as an example and analyze it using the concept of spatial frequencies. We then show that consensus by typical controllers corresponds to lowpass filtering in the spatial frequency domain. This demonstrates that spatial frequencies can characterize the behavior of multi-agent systems. Second, we present a controller design method in the spatial frequency domain. The designed controllers provide the feedback system with a desired spatial frequency characteristic given in advance. We further derive a sufficient condition for the spatial frequency characteristic to ensure that the designed controllers are distributed. Finally, the effectiveness and applicability of our design method are demonstrated through an example of distributed denoising in a sensor network.

This paper is concerned with the energy-efficient distributed filtering in sensor networks, and a unified switched system approach is proposed to achieve this goal. For the system under study, the measurement is first sampled under nonuniform sampling periods, then the local measurement elements are selected and quantized for transmission. Then, the transmission rate is further reduced to save constrained power in sensors. Based on the switched system approach, a unified model is presented to capture the nonuniform sampling, the measurement size reduction, the transmission rate reduction, the signal quantization, and the measurement missing phenomena. Sufficient conditions are obtained such that the filtering error system is exponentially stable in the mean-square sense with a prescribed H∞ performance level. Both simulation and experiment studies are given to show the effectiveness of the proposed new design technique.

Consensus algorithms for networked dynamic systems provide scalable algorithms for sensor fusion in sensor networks. This paper introduces a distributed filter that allows the nodes of a sensor network to track the average of n sensor measurements using an average consensus based distributed filter called consensus filter. This consensus filter plays a crucial role in solving a data fusion problem that allows implementation of a scheme for distributed Kalman filtering in sensor networks. The analysis of the convergence, noise propagation reduction, and ability to track fast signals are provided for consensus filters. As a byproduct, a novel critical phenomenon is found that relates the size of a sensor network to its tracking and sensor fusion capabilities. We characterize this performance limitation as a tracking uncertainty principle. This answers a fundamental question regarding how large a sensor network must be for effective sensor fusion. Moreover, regular networks emerge as efficient topologies for distributed fusion of noisy information. Though, arbitrary overlay networks can be used. Simulation results are provided that demonstrate the effectiveness of consensus filters for distributed sensor fusion.

One of the fundamental problems in sensor networks is to estimate and track the target states of interest that evolve in the sensing field. Distributed filtering is an effective tool to deal with state estimation in which each sensor only communicates information with its neighbors in sensor networks without the requirement of a fusion center. However, in the majority of the existing distributed filters, it is assumed that typically all sensors possess unlimited field of view to observe the target states. This is quite restrictive since practical sensors have limited sensing range. In this article, we consider distributed filtering based on linear minimum mean square error criterion in sensor networks with limited sensing range. To achieve the optimal filter and consensus, two types of strategies based on linear minimum mean square error criterion are proposed, that is, linear minimum mean square error filter based on measurement and linear minimum mean square error filter based on estimate, according to the difference of the neighbor sensor information received by the sensor. In linear minimum mean square error filter based on measurement, the sensor node collects measurement from its neighbors, whereas in linear minimum mean square error filter based on estimate, the sensor node collects estimate from its neighbors. The stability and computational complexity of linear minimum mean square error filter are analyzed. Numerical experimental results further verify the effectiveness of the proposed methods.

Sensor networks frequently deploy many tiny and inexpensive devices over large regions to detect events of interest. It can be easy to compromise sensors, enabling attackers to use the keys and other information stored at the sensors to inject false reports, forging fake events. Existing approaches do not localize the impact of such node compromises, so that compromises in one sensing region may compromise other parts of the system. In this paper, we propose two fault localized schemes for false report filtering. In our basic scheme, sensors signal events using one-way hash chains, which allows en-route nodes to verify the authenticity of received reports based on commitments of detecting sensors, but prevents them from forging events. We extend this basic scheme to a collaborative filtering scheme using commitment predistribution, making it more adaptable for mobile sensor networks and high-density sensor networks. Our scheme can also provide localized protection for areas that require special protection. Our security analysis shows that our schemes can offer stronger security protection than existing schemes, and are efficient.

Given the extremely limited hardware resources on sensor nodes and the inclement deploying environment, the adversary Denial-of-Service (DoS) attack becomes a serious security threat toward wireless sensor networks. Without adequate defense mechanism, the adversary can simply inundate the network by flooding the bogus data packets, and paralyze the partial or whole sensor network by depleting node battery power. Prior work on false packet filtering in sensor networks are mostly based on symmetric key schemes, with the concern that the public key operations are too expensive for the resource constrained sensors. Recent progress in public key implementations on sensors, however, has shown that public key is already feasible for sensors. In this paper, we present PDF, a Public-key based false Data Filtering scheme that leverages Shamir's threshold cryptography and Elliptic Curve Cryptography (ECC), and effectively rejects 100% of false data packets. We evaluate PDF by real world implementation on MICAz motes. Our experiment results support the conclusion that PDF is practical for real world sensor deployment.

Given the extremely limited hardware resources on sensor nodes and the inclement deploying environment, the adversary denial-of-service (DoS) attack becomes a serious security threat toward wireless sensor networks. Without adequate defense mechanism, the adversary can simply inundate the network by flooding the bogus data packets, and paralyze the partial or whole sensor network by depleting node battery power. Prior work on false packet filtering in sensor networks are mostly based on symmetric key schemes, with the concern that the public key operations are too expensive for the resource constrained sensors. Recent progress in public key implementations on sensors, however, has shown that public key is already feasible for sensors. In this paper, we present PDF, a Public-key based false data filtering scheme that leverages Shamir's threshold cryptography and elliptic curve cryptography (ECC), and effectively rejects 100% of false data packets. We evaluate PDF by real world implementation on MICAz motes. Our experiment results support the conclusion that PDF is practical for real world sensor deployment.

Distributed consensus filtering in sensor networks considering correlated estimation errors

This paper studies the distributed secure estimation problem of sensor networks (SNs) in the presence of eavesdroppers. In an SN, sensors communicate with each other through digital communication channels, and the eavesdropper overhears the messages transmitted by the sensors over fading wiretap channels. The increasing transmission rate plays a positive role in the detectability of the network while playing a negative role in the secrecy. Two types of SNs under two cooperative filtering algorithms are considered. For networks with collectively observable nodes and the Kalman filtering algorithm, by studying the topological entropy of sensing measurements, a sufficient condition of distributed detectability and secrecy, under which there exists a code–decode strategy such that the sensors’ estimation errors are bounded while the eavesdropper’s error grows unbounded, is given. For collectively observable SNs under the consensus Kalman filtering algorithm, by studying the topological entropy of the sensors’ covariance matrices, a necessary condition of distributed detectability and secrecy is provided. A simulation example is given to illustrate the results.

Given the extremely limited hardware resources on sensor nodes and the inclement deploying environment, the adversary denial-of-service (DoS) attack becomes a serious security threat toward wireless sensor networks. Without adequate defense mechanism, the adversary can simply inundate the network by flooding the bogus data packets, and paralyze the partial or whole sensor network by depleting node battery power. Prior work on false packet filtering in sensor networks are mostly based on symmetric key schemes, with the concern that the public key operations are too expensive for the resource constrained sensors. Recent progress in public key implementations on sensors, however, has shown that public key is already feasible for sensors. In this paper, we present PDF, a Public-key based false data filtering scheme that leverages Shamir's threshold cryptography and elliptic curve cryptography (ECC), and effectively rejects 100% of false data packets. We evaluate PDF by real world implementation on MICAz motes. Our experiment results support the conclusion that PDF is practical for real world sensor deployment.

This paper considers a distributed H∞ sampled‐data filtering problem in sensor networks with stochastically switching topologies. It is assumed that the topology switching is triggered by a Markov chain. The output measurement at each sensor is first sampled and then transmitted to the corresponding filters via a communication network. Considering the effect of a transmission delay, a distributed filter structure for each sensor is given based on the sampled data from itself and its neighbor sensor nodes. As a consequence, the distributed H∞ sampled‐data filtering in sensor networks under Markovian switching topologies is transformed into H∞ mean‐square stability problem of a Markovian jump error system with an interval time‐varying delay. By using Lyapunov Krasovskii functional and reciprocally convex approach, a new bounded real lemma (BRL) is derived, which guarantees the mean‐square stability of the error system with a desired H∞ performance. Based on this BRL, the topology‐dependent H∞ sampled‐data filters are obtained. An illustrative example is given to demonstrate the effectiveness of the proposed method.

A Markovian system approach to distributed H∞ filtering for sensor networks with stochastic sampling

A data collection problem in sensor networks is formulated in which the number of channel uses per source sample is greater than one. An example of this problem is given in which the objective of the data collector is to compute a filtered and downsampled version of the sensor field. In this regime, it is shown that uncoded transmission is not appropriate and that strategies based on separating source and channel coding perform poorly. By using a novel coding strategy based on computation codes, the power-distortion tradeoff becomes more favorable than that from separation.

SUMMARY This paper is concerned with the distributed filtering problem for a class of nonlinear systems with randomly occurring sensor saturations (ROSS) and successive packet dropouts in sensor networks. The issue of ROSS is brought up to account for the random nature of sensor saturations in a networked environment of sensors, and accordingly, a novel sensor model is proposed to describe both the ROSS and successive packet dropouts within a unified framework. Two sets of Bernoulli distributed white sequences are introduced to govern the random occurrences of the sensor saturations and successive packet dropouts. Through available output measurements from not only the individual sensor but also its neighboring sensors, a sufficient condition is established for the desired distributed filter to ensure that the filtering dynamics is exponentially mean-square stable and the prescribed performance constraint is satisfied. The solution of the distributed filter gains is characterized by solving an auxiliary convex optimization problem. Finally, a simulation example is provided to show the effectiveness of the proposed filtering scheme. Copyright © 2013 John Wiley & Sons, Ltd.