Richard R. Brooks

Distributed target classification and tracking in sensor networks

The highly distributed infrastructure provided by sensor networks supports fundamentally new ways of designing surveillance systems. In this paper, we discuss sensor networks for target classification and tracking. Our formulation is anchored on location-aware data routing to conserve system resources, such as energy and bandwidth. Distributed classification algorithms exploit signals from multiple nodes in several modalities and rely on prior statistical information about target classes. Associating data to tracks becomes simpler in a distributed environment, at the cost of global consistency. It may be possible to filter clutter from the system by embedding higher level reasoning in the distributed system. Results and insights from a recent field test at 29 Palms Marine Training Center are provided to highlight challenges in sensor networks.

Fault tolerant algorithms for network-on-chip interconnect

As technology scales, fault tolerance is becoming a key concern in on-chip communication. Consequently, this work examines fault tolerant communication algorithms for use in the NoC domain. Two different flooding algorithms and a random walk algorithm are investigated. We show that the flood-based fault tolerant algorithms have an exceedingly high communication overhead. We find that the redundant random walk algorithm offers significantly reduced overhead while maintaining useful levels of fault tolerance. We then compare the implementation costs of these algorithms, both in terms of area as well as in energy consumption, and show that the flooding algorithms consume an order of magnitude more energy per message transmitted.

Self-Organized Distributed Sensor Network Entity Tracking

The sensor network paradigm uses multiple autonomous sensor nodes to cooperatively construct an ad hoc computational entity. The authors have implemented applications using this approach with a network of sensor prototypes. In the paper, entity tracking is described as a two-tier process: (i) clusters of nodes locally estimate parameters used in entity tracking (i.e. time, class, position, and heading), and (ii) local parameter association forms inter-cluster tracks. Ad hoc routing primitives are used. This supports self-organization at all levels. Derivations of entity tracking applications are given along with preliminary results.

Robust distributed computing and sensing algorithm

Sensors that supply data to computer systems are inherently unreliable. When sensors are distributed, reliability is further compromised. How can a system tell good sensor data from faulty? A hybrid algorithm combines proposed solutions to address the problem.

Automatic correlation and calibration of noisy sensor readings using elite genetic algorithms

This paper explores an image processing application of optimization techniques which entails interpreting noisy sensor data. The application is a generalization of image correlation; we attempt to find the optimal gruence which matches two overlapping gray scale images corrupted with noise. Both tabu search and genetic algorithms are used to find the parameters which match the two images. A genetic algorithm approach using an elitist reproduction scheme is found to provide significantly superior results.

Tracking multiple targets with self-organizing distributed ground sensors

This paper describes a fully distributed approach to target tracking that we have implemented and tested in a military setting. The approach uses local sharing of robust statistics that summarize local events. Local collaboration extracts detection information such as time, velocity, position, heading and target type from the summary statistics. Groups of nodes used for local collaboration are determined dynamically at run time. Local collaboration information is compared with a list of tracks in the immediate vicinity. A variation on the nearest-neighbor algorithm associates detections to tracks. This paper extends our previous work by analyzing the ability of our distributed tracker to track multiple targets in a simulated environment. Results from simulations and field tests of the approach are provided.

Tracking targets with self-organizing distributed ground sensors

This paper describes a fully distributed approach to target tracking that we have implemented and tested in a military setting. The approach is built upon local sharing of robust statistics that summarize local events. Local collaboration extracts detection information such as time, velocity, position, heading and target type from the summary statistics. The groups of nodes used for local collaboration are determined dynamically at run time. Local collaboration information is compared with a list of tracks in the immediate vicinity. Associating detections to tracks is currently done using a variation of the nearest-neighbor metric. This paper extends our previous work by using mobile code daemons to support multiple hypothesis tracking methods. This is done in a resourceconstrained environment by using the network to swap sohare modules dynamically. Results from field tests of the approach are provided. This includes a dependability analysis of the distributed approach versus centralized systems

Masking the energy behavior of DES encryption [smart cards]

Smart cards are vulnerable to both invasive and non-invasive attacks. Specifically, non-invasive attacks using power and timing measurements to extract the cryptographic key has drawn a lot of negative publicity for smart card usage. The power measurement techniques rely on the data-dependent energy behavior of the underlying system. Further, power analysis can be used to identify the specific portions of the program being executed to induce timing glitches that may in turn help to bypass key checking. Thus, it is important to mask the energy consumption when executing the encryption algorithms. In this work, we augment the instruction set architecture of a simple five-stage pipelined smart card processor with secure instructions to mask the energy differences due to key-related data-dependent computations in DES encryption. The secure versions operate on the normal and complementary versions of the operands simultaneously to mask the energy variations due to value dependent operations. However, this incurs the penalty of increased overall energy consumption in the data-path components. Consequently, we employ secure versions of instructions only for critical operations; that is we use secure instructions selectively, as directed by an optimizing compiler. Using a cycle-accurate energy simulator, we demonstrate the effectiveness of this enhancement. Our approach achieves the energy masking of critical operations consuming 83% less energy as compared to existing approaches employing dual rail circuits.

Recognition in the wavelet domain: a survey

The use of wavelets has grown enormously since their original inception in the mid-1980s. Since the wavelet data repre- sentation combines spatial, frequency, and scale information in a sparse data representation, they are very useful in a number of image processing applications. This paper discusses current work in applying wavelets to object and pattern recognition. Feature extrac- tion methods and search algorithms for matching images are dis- cussed. Some important issues are the search for invariant repre- sentations, similarities between existing applications and the human visual system, and the derivation of wavelets that match specific targets. Results from several existing systems and areas for future research are presented.

Traffic Model Evaluation of Ad Hoc Target Tracking Algorithms

Recent work in traffic planning uses differential equations and cellular automata to determine qualitative and quantitative characteristics of granular media flow. In this paper we describe a self-organizing framework for distributed target tracking built on ad hoc publish-subscribe data routing techniques. A cellular automata model of sensor network dynamics is derived and used to express the routing framework. Experimental evaluation is performed for alternative target-tracking algorithms using the framework. Evaluation uses discrete cellular automata models.