Sensors of various modalities and capabilities, especially cameras, have become ubiquitous in our environment. Their intended use is wide ranging and encompasses surveillance, transportation, entertainment, education, healthcare, emergency response, disaster recovery, and the like. Technological advances and the low cost of such sensors enable deployment of large-scale camera networks in large metropolises such as London and New York. Multimedia algorithms for analyzing and drawing inferences from video and audio have also matured tremendously in recent times. Despite all these advances, large-scale reliable systems for media-rich sensor-based applications, often classified as situation-awareness applications, are yet to become commonplace. Why is that? There are several forces at work here. First, the system abstractions are just not at the right level for quickly prototyping such applications on a large scale. Second, while Moore's law has held true for predicting the growth of processing power, the volume of data that applications are called upon to handle is growing similarly, if not faster. Enormous amount of sensing data is continually generated for real-time analysis in such applications. Further, due to the very nature of the application domain, there are dynamic and demanding resource requirements for such analyses. The lack of right set of abstractions for programing such applications coupled with their data-intensive nature have hitherto made realizing reliable large-scale situation-awareness applications difficult. Incidentally, situation awareness is a very popular but ill-defined research area that has attracted researchers from many different fields. In this paper, we adopt a strong systems perspective and consider the components that are essential in realizing a fully functional situation-awareness system.

Wireless sensor networks (WSN) built using current Berkeley Mica motes exhibit low reliability for packet delivery. There is anecdotal evidence of poor packet delivery rates from several field trials of WSN deployment. All-to-one communication pattern is a dominant one in many such deployments. As we scale up the size of the network and the traffic density in this communication pattern, improving the reliability of packet delivery performance becomes very important. This study is aimed at two things. Firstly, it aims to understand the factors limiting reliable packet delivery for all-to-one communication pattern in dense wireless sensor networks. Secondly, it aims to suggest enhancements to well-known protocols that may help boost the performance to acceptable levels. We first postulate the potential reasons hampering packet delivery rates with current CSMA-based MAC layer used by the radios deployed in WSN. We then propose a set of enhancements that are aimed to mitigate the ill-effects of these factors. We pick three protocols, namely, Flooding, AODV, and Geographic routing as candidates for this study. Using TOSSIM, we perform a detailed study of these protocols and the proposed enhancements. This study serves several purposes. First, it helps us to quantify the detrimental effects of these factors. Second, it helps us to quantify the extent to which our proposed enhancements improves packet delivery performance. Concretely, we show that using Geographic routing in a WSN with 225 nodes spread over 150 feet times 150 feet, the proposed enhancements yield a 23-fold improvement in packet delivery performance over the baseline. Further, the enhancements result in fairness (measured by the number of messages received from each node at the destination). Lastly, we show that the overhead (in terms of retransmissions, acknowledgement messages, and control messages) is reasonable.

DFuse is an architectural framework for dynamic application-specified data fusion in sensor networks. It bridges an important abstraction gap for developing advanced fusion applications that takes into account the dynamic nature of applications and sensor networks. Elements of the DFuse architecture include a fusion API, a distributed role assignment algorithm that dynamically adapts the placement of the application task graph on the network, and an abstraction migration facility that aids such dynamic role assignment. Experimental evaluations show that the API has low overhead, and simulation results show that the role assignment algorithm significantly increases the network lifetime over static placement.

Simple in-network data aggregation (or fusion) techniques for sensor networks have been the focus of several recent research efforts, but they are insufficient to support advanced fusion applications. We extend these techniques to future sensor networks and ask two related questions: (a) what is the appropriate set of data fusion techniques, and (b) how do we dynamically assign aggregation roles to the nodes of a sensor network. We have developed an architectural framework, DFuse, for answering these two questions. It consists of a data fusion API and a distributed algorithm for energy-aware role assignment. The fusion API enables an application to be specified as a coarse-grained dataflow graph, and eases application development and deployment. The role assignment algorithm maps the graph onto the network, and optimally adapts the mapping at run-time using role migration. Experiments on an iPAQ farm show that, the fusion API has low-overhead, and the role assignment algorithm with role migration significantly increases the network lifetime compared to any static assignment.