10993158
Efficient and Distributed Access Control for Sensor Networks
Sensor networks are often used to sense the physical world and provide observations for various uses. In hostile environments, it is critical to control the network access to ensure the integrity, availability, and at times confidentiality of the sensor data. This paper develops efficient methods for distributed access control in sensor networks. The paper starts with a baseline approach, which provides a more flexible and efficient way to enforce access control when compared with previous solutions. This paper then extends the baseline approach to enable privilege delegation, which allows a user to delegate its privilege to other users without using a trusted server, and broadcast query, which allows a user to access the network at a large scale efficiently. The privilege delegation and broadcast query are very useful in practice; none of the current solutions can achieve these two properties.
1535984
Establishing pairwise keys in distributed sensor networks
Pairwise key establishment is a fundamental security service in sensor networks; it enables sensor nodes to communicate securely with each other using cryptographic techniques. However, due to the resource constraints on sensors, it is infeasible to use traditional key management techniques such as public key cryptography and key distribution center (KDC). To facilitate the study of novel pairwise key predistribution techniques, this paper presents a general framework for establishing pairwise keys between sensors on the basis of a polynomial-based key predistribution protocol [2]. This paper then presents two efficient instantiations of the general framework: a random subset assignment key predistribution scheme and a grid-based key predistribution scheme. The analysis in this paper indicates that these two schemes have a number of nice properties, including high probability (or guarantee) to establish pairwise keys, tolerance of node captures, and low communication overhead. Finally, this paper presents a technique to reduce the computation at sensors required by these schemes.
6546013
Detecting Malicious Beacon Nodes for Secure Location Discovery in Wireless Sensor Networks
Sensors' locations play a critical role in many sensor network applications. A number of techniques have been proposed recently to discover the locations of regular sensors based on a few special nodes called beacon nodes, which are assumed to know their locations (e.g., through GPS receivers or manual configuration). However, none of these techniques can work properly when there are malicious attacks, especially when some of the beacon nodes are compromised. This paper introduces a suite of techniques to detect and remove compromised beacon nodes that supply misleading location information to the regular sensors, aiming at providing secure location discovery services in wireless sensor networks. These techniques start with a simple but effective method to detect malicious beacon signals. To identify malicious beacon nodes and avoid false detection, this paper also presents several techniques to detect replayed beacon signals. This paper then proposes a method to reason about the suspiciousness of each beacon node at the base station based on the detection results collected from beacon nodes, and then revoke malicious beacon nodes accordingly. Finally, this paper provides detailed analysis and simulation to evaluate the proposed techniques. The results show that our techniques are practical and effective in detecting malicious beacon nodes
53235721
Establishing pairwise keys in distributed sensor networks
Pairwise key establishment is a fundamental security service in sensor networks; it enables sensor nodes to communicate securely with each other using cryptographic techniques. However, due to the resource constraints on sensor nodes, it is not feasible to use traditional key management techniques such as public key cryptography and key distribution center (KDC). A number of key predistribution techniques have been proposed for pairwise key establishment in sensor networks recently. To facilitate the study of novel pairwise key predistribution techniques, this paper develops a general framework for establishing pairwise keys between sensor nodes using bivariate polynomials. This paper then proposes two efficient instantiations of the general framework: a random subset assignment key predistribution scheme, and a hypercube-based key predistribution scheme. The analysis shows that both schemes have a number of nice properties, including high probability, or guarantee to establish pairwise keys, tolerance of node captures, and low storage, communication, and computation overhead. To further reduce the computation at sensor nodes, this paper presents an optimization technique for polynomial evaluation, which is used to compute pairwise keys. This paper also reports the implementation and the performance of the proposed schemes on MICA2 motes running TinyOS, an operating system for networked sensors. The results indicate that the proposed techniques can be applied efficiently in resource-constrained sensor networks.
2385789
Efficient self-healing group key distribution with revocation capability
This paper presents group key distribution techniques for large and dynamic groups over unreliable channels. The techniques proposed here are based on the self-healing key distribution methods (with revocation capability) recently developed by Staddon et al. [27]. By introducing a novel personal key distribution technique, this paper reduces (1) the communication overhead of personal key share distribution from O(t2log q) to O(tlogq), (2) the communication overhead of self-healing key distribution with t-revocation capability from O((mt2+tm)log q) to O(mtlog q), and (3) the storage overhead of the self-healing key distribution with $t$-revocation capability at each group member from O(m2log q) to O(mlogq), where $t$ is the maximum number of colluding group members, $m$ is the number of sessions, and $q$ is a prime number that is large enough to accommodate a cryptographic key. All these results are achieved without sacrificing the unconditional security of key distribution. In addition, this paper presents two techniques that allow trade-off between the broadcast size and the recoverability of lost session keys. These two methods further reduce the broadcast message size in situations where there are frequent but short-term disruptions of communication and where there are long-term but infrequent disruptions of communication, respectively.
399636
Resilient Cluster Formation for Sensor Networks
Sensor nodes are often organized into clusters to facilitate many network operations such as data aggregation. Clustering has to be protected in hostile environments. Otherwise, an attacker can easily mislead a cluster-based application by attacking the clustering protocol. This paper proposes three techniques for resilient cluster formation. The simple neighbor validation provides a simple yet effective way to validate a sensor's neighbors; the priority-based selection organizes clusters basedon the sensor's priority of being a cluster head; and the centralized detection further enhances the security by detecting misbehaving nodes. Another appealing benefit of this protocol is that a sensor node can make a clustering decision immediately once the neighborhood information is available. This further increases the difficulty of attacking the clustering protocol. The analysis also shows that the proposed protocol is efficient and effective in dealing with attacks.
11260681
Mining Multi-dimensional Data with Visualization Techniques
This paper describes a method to generate classification rules by using an interactive multidimensional data visualization and classification tool, called PolyCluster. PolyCluster is a system that adopts state-of-the-art algorithms for data visualization and integrates human domain knowledge into the construction process of classification rules. In addition, PolyCluster proposes a pair of novel and robust measurements, called the Average External Connecting Distance and the Average Internal Connecting Distance to evaluate the quality of the induced clusters. Experimental evaluation shows that PolyCluster is a visual-based approach that offers numerous improvements over previous visual-based techniques.
84347
Latent Layout Analysis for Discovering Objects in Images
Latent layout analysis (LLA) is a novel unsupervised learning technique to discover objects in unseen images using a set of un-annotated training images. LLA defines a generative model that associates latent aspects to local appearances. The dependency between aspects and position is captured by a spatial sensitive aspect model. This dependency distinguishes LLA from probabilistic latent semantic analysis (PLSA). The latent aspects together with the latent layout constitute a compact scene representation. We demonstrate that the proposed LLA significantly outperforms probabilistic latent semantic analysis in two tasks: object discovery (detection) and object localization
215202
Neural network-based model reference adaptive control system
In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.
13943173
Coordinated Static and Mobile Sensing for Environmental Monitoring
Distributed embedded sensor networks are now being successfully deployed in environmental monitoring of natural phenomena as well as for applications in commerce and physical security. While substantial progress in sensor network performance has appeared, new challenges have also emerged as these systems have been deployed in the natural environment. First, in order to achieve minimum sensing fidelity performance, the rapid spatiotemporal variation of environmental phenomena requires impractical deployment densities. The presence of obstacles in the environment introduces sensing uncertainty and degrades the performance of sensor fusion systems in particular for the many new applications of image sensing. The physical obstacles encountered by sensing may be circumvented by a new mobile sensing method or Networked Infomechanical Systems (NIMS). NIMS integrates distributed, embedded sensing and computing systems with infrastructure-supported mobility. NIMS now includes coordinated mobility methods that exploits adaptive articulation of sensor perspective and location as well as management of sensor population to provide the greatest certainty in sensor fusion results. The architecture, applications, and implementation of NIMS will be discussed here. In addition, results of environmentally-adaptive sampling, and direct measurement of sensing uncertainty will be described.
12746246
JRV: an interactive tool for data mining visualization
In this paper, we demonstrate JRV, a new data mining visualization tool for the knowledge discovery process where the user and computer can cooperate with each other. First, the computer can be instructed by the user interactively to compute values of several evaluation functions. Then, the user can take advantage of domain knowledge and assess the intermediate results obtained. Furthermore, by providing effective and efficient data visualization, the pattern recognition capacities of users can be greatly improved. Instead of being limited to two attributes at a given time in independence diagrams, this novel tool will allow simultaneous analyses of multiple attribute dependencies using four different drawing panels. Also, by utilizing the existing techniques of data visualization, we design a general model which can handle both categorical and numerical attributes in an intuitive way. With this model, we can identify patterns of interests efficiently. Through actual examples, we show that it might help users to find novel attribute relationships. This work is supported by NIH grant #RO1-CA98932-01.