Code updates, such as those for debugging purposes, are frequent and expensive in the early development stages of wireless sensor network applications. We propose AdapCode, a reliable data dissemination protocol that uses adaptive network coding to reduce broadcast traffic in the process of code updates. Packets on every node are coded by linear combination and decoded by Gaussian elimination. The core idea in AdapCode is to adaptively change the coding scheme according to the link quality. Our evaluation shows that AdapCode uses up to 40% less packets than Deluge in large networks. In addition, AdapCode performs much better in terms of load balancing, which prolongs the system lifetime, and has a slightly shorter propagation delay. Finally, we show that network coding is doable on sensor networks in that (i) it imposes only a 3 byte header overhead, (ii) it is easy to find linearly independent packets, and (3) Gaussian elimination needs only 1 KB of memory.

Web-based social networks such as Linkedln, FaceBook and MySpace have gained wide popularity in recent years. With the advent of ubiquitous sensing, future social networks will be cyber-physical, combining measured elements of the physical world with manual human input. Microsoft SensorMap is an early example of a cyber-physical network in that it allows users to browse the physical world. In contrast to social networks, however, the main abstractions exported by SensorMap are those of physical objects (such as sensors), not people. This paper extends the concept of SensorMap to incorporate social entities such as individuals and special-interest communities to which they belong. In the extended network, called SenseWorld, SensorMap offers a geographical index into a cyber-physical social network with its own logical topology derived from social connections.

This paper develops mathematical foundations and architectural components for providing privacy guarantees on stream data in grassroots participatory sensing applications, where groups of participants use privately-owned sensors to collectively measure aggregate phenomena of mutual interest. Grassroots applications refer to those initiated by members of the community themselves as opposed to by some governing or official entities. The potential lack of a hierarchical trust structure in such applications makes it harder to enforce privacy. To address this problem, we develop a privacy-preserving architecture, called PoolView, that relies on data perturbation on the client-side to ensure individuals' privacy and uses community-wide reconstruction techniques to compute the aggregate information of interest. PoolView allows arbitrary parties to start new services, called pools, to compute new types of aggregate information for their clients. Both the client-side and server-side components of PoolView are implemented and available for download, including the data perturbation and reconstruction components. Two simple sensing services are developed for illustration; one computes traffic statistics from subscriber GPS data and the other computes weight statistics for a particular diet. Evaluation, using actual data traces collected by the authors, demonstrates the privacy-preserving aggregation functionality in PoolView.

Sensor networks introduce a new application domain and set of challenges in distributed computing including new network-level programming languages, global system abstractions, and general-purpose communication protocols. These challenges are brought about by the tight integration of computation, communication, and distributed real-time interaction with the physical world. With the growing interest in interconnecting different sensor networks across a wide-area communication infrastructure, an overarching challenge becomes one of arriving at an agreed-upon global sensor network architecture that ensures interoperability. Unlike the Internet, where a layered communication stack (namely, the TCP/IP stack) defines the network architecture, a sensor network architecture must unify not only communication interfaces but also programming interfaces, since network communication and computation functions are tightly intertwined. In that sense, the sensor network architecture refers to a layered stack of distributed computing abstractions. This paper presents an architecture and key considerations in designing and interconnecting local and global sensor networks. Candidate protocols and middleware instantiations are described from the authors' ongoing work that meet the discussed considerations