The process of determining the physical locations of nodes in a wireless sensor network is known as localization . Self-localization is critical for large-scale sensor networks, because manual or assisted localization is often impractical due to time requirements, economic constraints, or inherent limitations of the deployment scenarios. We propose scalable solutions for reliably localizing wireless sensor networks in environments conducive to several types of ranging errors. We follow a hybrid hardware-software approach for acoustic ranging or radio interferometry to acquire internode distance measurements, and a resilient self-localization algorithm to compute the node location estimates. The acoustic ranging method improves on previous work, extending the practical measurement range up to 35 m in grassy outdoor environments, achieving a distance-invariant median measurement error of about 1% (33 cm). The localization algorithm is based on least-squares scaling with soft constraints. Empirical evaluation using ranging results obtained from sensor network field experiments and simulations confirms that our approach is more resilient than multidimensional scaling (MDS) algorithms against large-magnitude ranging errors and sparse range measurements: conditions that are common in large-scale outdoor sensor network deployments.

The process of computing the physical locations of nodes in a wireless sensor network is known as localization. Self-localization is critical for large-scale sensor networks because manual or assisted localization is often impractical due to time requirements, economic constraints or inherent limitations of deployment scenarios. We have developed a service for reliably localizing wireless sensor networks in environments conducive to ranging errors by using a custom hardware-software solution for acoustic ranging and a family of self-localization algorithms. The ranging solution improves on previous work, extending the practical measurement range threefold (20-30m) while maintaining a distance-invariant median measurement error of about 1% of maximum range (33cm). The localization scheme is based on least squares scaling with soft constraints. Evaluation using ranging results obtained from sensor network field experiments shows that the localization scheme is resilient against large-magnitude ranging errors and sparse range measurements, both of which are common in large-scale outdoor sensor network deployments.

We consider the use of binary proximity sensors for tracking targets. Such sensors provide only 1-bit information regarding a target's presence or absence in their vicinity, albeit with less than 100% reliability. A novel tracking method employing such binary sensors is proposed and its performance in different deployment scenarios evaluated. For a given target, the method utilizes the sensor outputs to estimate individual positions in the path of the target in the near past and finds the line which best fits the path points. This line is then used to estimate the target's current position. A performance study has been conducted through comprehensive simulation using parameters collected from a prototype deployment consisting of wireless micro-sensors with binary acoustic detectors.

We present a framework for developing ontologies suitable for a dynamic environment, such as that for web services, and describe itsuse in a commercial system for resource discovery. This framework recognizes the importance of standards but allows for evolution in away that doesn't disrupt those adhering to the standards. The framework is based on the notion of discoverable resources which offer extensibility and security. The specific ontology we use in thesystem includes some salient features, such as attribute based matching rules and the corresponding constraint based search, attributes with dynamic values, and active queries.

Online deal forums are public places where participants share with each other news and information regarding "deals" such as sales promotion events by online stores. The large number of messages in the forums and their inherent uncertainty make it difficult for even seasoned users to identify useful deal information from the forums. We develop an intelligent deal alert service which assists ordinary Web surfers to find useful deals by mining online deal forums. It periodically crawls relevant deal forums to collect fresh message posts and responses, and evaluate them using a form of probabilistic text classification. Users may be notified of new, "potentially" useful deal messages via emails or they may browse them using their favorite Web browser. We train and evaluate the service using deal posts and responses collected from actual deal forums in the Web. The preliminary evaluation results show that the service is quite effective in reducing the time to find useful deals.

Concurrent call/return communication (CCRC) allows programmers to conveniently express a communication pattern where a sender invokes a remote operation and uses the result to continue its computation. The blocking semantics requires context switching for efficient utilization of computation resource. We present a compilation technique which allows programmers to use CCRC with the cost of non-blocking asynchronous communication plus minimum context switch cost. The technique transforms CCRCs into non-blocking asynchronous sends and encapsulates continuations into separate objects. A data flow analysis is used to guarantee that only necessary context is cached in continuation objects.

We describe the design of a runtime system for a fine-grained concurrent object-oriented (actor) language and its performance. The runtime system provides considerable flexibility to users; specifically, it supports location transparency, actor creation and dynamic placement, and migration. The runtime system includes an efficient distributed name server, a latency hiding scheme for remote actor creation, and a compiler-controlled intra-node scheduling mechanism for local messages and dynamic load balancing. Our preliminary evaluation results suggest that the efficiency that is lost by the greater flexibility of actors can be restored by an efficient runtime system which provides an open interface that can be used by a compiler to allow optimizations. On several standard algorithms, the performance results for our system are comparable to efficient C implementations.