David Chu

Approximate Data Collection in Sensor Networks using Probabilistic Models

Wireless sensor networks are proving to be useful in a variety of settings. A core challenge in these networks is to minimize energy consumption. Prior database research has proposed to achieve this by pushing data-reducing operators like aggregation and selection down into the network. This approach has proven unpopular with early adopters of sensor network technology, who typically want to extract complete dumps of the sensor readings, i.e., to run SELECT * queries. Unfortunately, because these queries do no data reduction, they consume significant energy in current sensornet query processors. In this paper we attack the SELECT problem for sensor networks. We propose a robust approximate technique called Ken that uses replicated dynamic probabilistic models to minimize communication from sensor nodes to the network’s PC base station. In addition to data collection, we show that Ken is well suited to anomaly- and event-detection applications. A key challenge in this work is to intelligently exploit spatial correlations across sensor nodes without imposing undue sensor-to-sensor communication burdens to maintain the models. Using traces from two real-world sensor network deployments, we demonstrate that relatively simple models can provide significant communication (and hence energy) savings without undue sacrifice in result quality or frequency. Choosing optimally among even our simple models is NPhard, but our experiments show that a greedy heuristic performs nearly as well as an exhaustive algorithm.

Securing the deluge Network programming system

A number of multi-hop, wireless, network programming systems have emerged for sensor network retasking but none of these systems support a cryptographically-strong, public-key-based system for source authentication and integrity verification. The traditional technique for authenticating a program binary, namely a digital signature of the program hash, is poorly suited to resource-contrained sensor nodes. Our solution to the secure programming problem leverages authenticated streams, is consistent with the limited resources of a typical sensor node, and can be used to secure existing network programming systems. Under our scheme, a program binary consists of several code and data segments that are mapped to a series of messages for transmission over the network. An advertisement, consisting of the program name, version number, and a hash of the very first message, is digitally signed and transmitted first. The advertisement authenticates the first message, which in turn contains a hash of the second message. Similarly, the second message contains a hash of the third message, and so on, binding each message to the one logically preceding it in the series through the hash chain. We augmented the Deluge network programming system with our protocol and evaluated the resulting system performance

The design and implementation of a declarative sensor network system

Sensor networks are notoriously difficult to program, given that they encompass the complexities of both distributed and embedded systems. To address this problem, we present the design and implementation of a declarative sensor network platform, DSN: a declarative language, compiler and runtime suitable for programming a broad range of sensornet applications. We demonstrate that our approach is a natural fit for sensor networks by specifying several very different classes of traditional sensor network protocols, services and applications entirely declaratively -- these include tree and geographic routing, link estimation, data collection, event tracking, version coherency, and localization. To our knowledge, this is the first time these disparate sensornet tasks have been addressed by a single high-level programming environment. Moreover, the declarative approach accommodates the desire for architectural flexibility and simple management of limited resources. Our results suggest that the declarative approach is well-suited to sensor networks, and that it can produce concise and flexible code by focusing on what the code is doing, and not on how it is doing it.

Mobile OGSI.NET: grid computing on mobile devices

The problem with the grid is that it does not currently extend completely to devices, because these devices are not viewed as having sufficient capability to be both clients and services. We design, implement and evaluate Mobile OGSI.NET, which extends an implementation of grid computing, OGSI.NET, to mobile devices. Mobile OGSI.NET addresses the mobile devices resource limitations and intermittent network connectivity, factors which differentiate them from traditional computers. Because Mobile OGSI.NET uniquely supports the hosting of grid services on the device, Mobile OGSI.NET is an important step toward making the mobile device a first-class entity in grids based on OGSI or the Web Services Resource Framework (WSRF).

Evita raced: metacompilation for declarative networks

Declarative languages have recently been proposed for many new applications outside of traditional data management. Since these are relatively early research efforts, it is important that the architectures of these declarative systems be extensible, in order to accommodate unforeseen needs in these new domains. In this paper, we apply the lessons of declarative systems to the internals of a declarative engine. Specifically, we describe our design and implementation of Evita Raced, an extensible compiler for the OverLog language used in our declarative networking system, P2. Evita Raced is a metacompiler: an OverLog compiler written in OverLog. We describe the minimalist architecture of Evita Raced, including its extensibility interfaces and its reuse of P2s data model and runtime engine. We demonstrate that a declarative language like OverLog is well-suited to expressing traditional and novel query optimizations as well as other query manipulations, in a compact and natural fashion. Finally, we present initial results of Evita Raced extended with various optimization programs, running on both Internet overlay networks and wireless sensor networks.

Data gathering tours in sensor networks

A basic task in sensor networks is to interactively gather data from a subset of the sensor nodes. When data needs to be gathered from a selected set of nodes in the network, existing communication schemes often behave poorly. In this paper, we study the algorithmic challenges in efficiently routing a fixed-size packet through a small number of nodes in a sensor network, picking up data as the query is routed. We show that computing the optimal routing scheme to visit a specific set of nodes is NP-complete, but we develop approximation algorithms that produce plans with costs within a constant factor of the optimum. We enhance the robustness of our initial approach to accommodate the practical issues of limited-sized packets as well as network link and node failures, and examine how different approaches behave with dynamic changes in the network topology. Our theoretical results are validated via an implementation of our algorithms on the TinyOS platform and a controlled simulation study using Matlab and TOSSIM

Robust message-passing for statistical inference in sensor networks

Large-scale sensor network applications require in-network processing and data fusion to compute statistically relevant summaries of the sensed measurements. This paper studies distributed message-passing algorithms, in which neighboring nodes in the network pass local information relevant to a global computation, for performing statistical inference. We focus on the class of reweighted belief propagation (RBP) algorithms, which includes as special cases the standard sum-product and max-product algorithms for general networks with cycles, but in contrast to standard algorithms has attractive theoretical properties (uniqueness of fixed points, convergence, and robustness). Our main contribution is to design and implement a practical and modular architecture for implementing RBP algorithms in real networks. In addition, we show how intelligent scheduling of RBP messages can be used to minimize communication between motes and prolong the lifetime of the network. Our simulation and Mica2 mote deployment indicate that the proposed algorithms achieve accurate results despite real- world problems such as dying motes, dead and asymmetric links, and dropped messages. Overall, the class of RBP provides an ideal fit for sensor networks due to their distributed nature, requiring only local knowledge and coordination, and little requirements on other services such as reliable transmission.

A declarative sensornet architecture

Increased code reuse, independent development, and interoperability are three key missing characteristics of sensornet programming today that motivate the need for an overall sensornet architecture. Architecture traditionally implies a componentized modular framework in which narrow interfaces provide the only form of communication between layers encapsulating specific functions and services. The Internet architecture provides the classic example of the modular approach.

Sdlib: a sensor network data and communications library for rapid and robust application development

Sensor network applications tend to exhibit significant high-level commonalities along several major dimensions that have heretofore been underexposed, particularly in the areas of collection and dissemination. We have developed a component library, Sdlib, which presents the fundamental abstractions of collection and dissemination as part of a dataflow system. This allows application developers to rapidly develop applications at the nesC level. This means that Sdlib maintains significant expressivity while operating efficiently. We have built four applications, each faithful to a mature monolithic application, on top of Sdlib to compare its performance to that of original. We find that applications implemented with Sdlib are much simpler to write, just as resource efficient, and perform comparably to monolithic implementations

Que: a sensor network rapid prototyping tool with application experiences from a data center deployment

Several considerable impediments stand in the way of sensor network prototype applications that wish to realize sustained deployments. These are: scale, longevity, data of interest, and infrastructure integration. We present a tool, Que, which assists those sensor network deployments transitioning from prototypes to early production environments by addressing these issues. Que is able to simulate realistic deployments with faithful data, provide fast and iterative feedback on operations, and compose applications quickly in a platform-independent manner. We demonstrate Ques applicability via tests against our new data center environment-monitoring deployment, DataCenter.NET.