As sensor networks edge closer towards wide-spread deployment, security issues become a central concern. So far, much research has focused on making sensor networks feasible and useful, and has not concentrated on security.We present a suite of security building blocks optimized for resource-constrained environments and wireless communication. SPINS has two secure building blocks: SNEP and mTESLA SNEP provides the following important baseline security primitives: Data confidentiality, two-party data authentication, and data freshness. A particularly hard problem is to provide efficient broadcast authentication, which is an important mechanism for sensor networks. mTESLA is a new protocol which provides authenticated broadcast for severely resource-constrained environments. We implemented the above protocols, and show that they are practical even on minimal hardware: the performance of the protocol suite easily matches the data rate of our network. Additionally, we demonstrate that the suite can be used for building higher level protocols.

http://www.csl.mtu.edu/cs5461/www/Reading/Perrig02.pdf

SPINS: security protocols for sensor networks

Wireless sensor networks will be widely deployed in the near future. While much research has focused on making these networks feasible and useful, security has received little attention. We present a suite of security protocols optimized for sensor networks: SPINS. SPINS has two secure building blocks: SNEP and μTESLA. SNEP includes: data confidentiality, two-party data authentication, and evidence of data freshness. μTESLA provides authenticated broadcast for severely resource-constrained environments. We implemented the above protocols, and show that they are practical even on minimal hardware: the performance of the protocol suite easily matches the data rate of our network. Additionally, we demonstrate that the suite can be used for building higher level protocols.

Wireless ad-hoc sensor networks have recently emerged as a premier research topic. They have great long-term economic potential, ability to transform our lives, and pose many new system-building challenges. Sensor networks also pose a number of new conceptual and optimization problems. Some, such as location, deployment, and tracking, are fundamental issues, in that many applications rely on them for needed information. We address one of the fundamental problems, namely coverage. Coverage in general, answers the questions about quality of service (surveillance) that can be provided by a particular sensor network. We first define the coverage problem from several points of view including deterministic, statistical, worst and best case, and present examples in each domain. By combining the computational geometry and graph theoretic techniques, specifically the Voronoi diagram and graph search algorithms, we establish the main highlight of the paper-optimal polynomial time worst and average case algorithm for coverage calculation. We also present comprehensive experimental results and discuss future research directions related to coverage in sensor networks.

/pdf/coverage-problems-in-wireless-ad-hoc-sensor-networks-3nq5fgloph.pdf

Coverage problems in wireless ad-hoc sensor networks

Systems composed of interacting autonomous agents offer a promising software engineering approach for developing applications in complex domains. However, this multiagent system paradigm introduces a number of new abstractions and design/development issues when compared with more traditional approaches to software development. Accordingly, new analysis and design methodologies, as well as new tools, are needed to effectively engineer such systems. Against this background, the contribution of this article is twofold. First, we synthesize and clarify the key abstractions of agent-based computing as they pertain to agent-oriented software engineering. In particular, we argue that a multiagent system can naturally be viewed and architected as a computational organization, and we identify the appropriate organizational abstractions that are central to the analysis and design of such systems. Second, we detail and extend the Gaia methodology for the analysis and design of multiagent systems. Gaia exploits the aforementioned organizational abstractions to provide clear guidelines for the analysis and design of complex and open software systems. Two representative case studies are introduced to exemplify Gaia's concepts and to show its use and effectiveness in different types of multiagent system.

/pdf/developing-multiagent-systems-the-gaia-methodology-3qm0mwov2h.pdf

Developing multiagent systems: The Gaia methodology

Wireless sensor networks have emerged recently as an effective way of monitoring remote or inhospitable physical environments. One of the major challenges in devising such networks lies in the constrained energy and computational resources available to sensor nodes. These constraints must be taken into account at all levels of the system hierarchy. The deployment of sensor nodes is the first step in establishing a sensor network. Since sensor networks contain a large number of sensor nodes, the nodes must be deployed in clusters, where the location of each particular node cannot be fully guaranteed a priori. Therefore, the number of nodes that must be deployed in order to completely cover the whole monitored area is often higher than if a deterministic procedure were used. In networks with stochastically placed nodes, activating only the necessary number of sensor nodes at any particular moment can save energy. We introduce a heuristic that selects mutually exclusive sets of sensor nodes, where the members of each of those sets together completely cover the monitored area. The intervals of activity are the same for all sets, and only one of the sets is active at any time. The experimental results demonstrate that by using only a subset of sensor nodes at each moment, we achieve a significant energy savings while fully preserving coverage.

/pdf/power-efficient-organization-of-wireless-sensor-networks-48bb9g1in3.pdf

Power efficient organization of wireless sensor networks

F or the past 40 years, most of the IT research community has focused on interactive computing, J.C.R. Licklider’s powerful and human-centered vision of human-computer symbiosis [3]. In tandem with this research has come the creation of an IT industry that is hurtling toward the human/machine/network breakpoint—the point at which the number of networked interactive computers will surpass the number of people on the planet. We still have a long way to go before Licklider’s vision is attained—and are many years from extending per-capita penetration to most parts of the world. However, “missing science” may no longer be the factor limiting progress toward these long-cherished goals. It is reasonable, though perhaps heretical, to suggest that refinements of the existing science base will be sufficient to drive these efforts forward. It is time for a change. The computer science research community now enjoys a rare and exciting opportunity to redefine its agenda and establish the new goals that will propel society beyond interactive computing and the human/machine breakpoint. In lifting our sights toward a world in which networked computers outnumber human beings by a hundred or thousand to one, we should consider what these “excess” computers will be doing and craft a research agenda that can lead to increased human productivity and quality of life.

Proactive computing

Wireless network devices can obtain their geographical location by triangulating with access points that have precise time information. In response to a location prompt, a wireless device can send a transmission to multiple access points that are within its range. The different times at which the transmission is received at different access points can be collected and forwarded to a server, which can compute the location of the wireless device using triangulation techniques. The access points can calibrate their own time bases from time services received from global position satellites.

Determining wireless device locations

The volunteer search for Jim Gray, lost at sea in 2007, highlights the challenges of computer-aided emergency response.

/pdf/searching-for-jim-gray-a-technical-overview-544yqnehe0.pdf

Searching for Jim Gray: a technical overview

Embodiments utilize analog sub-threshold circuits to perform Boolean logic and soft-gate logic. These analog circuits may be grouped into configurable logic blocks that are locally asynchronous, but block-level synchronous. The Boolean logic, or function, performed by these blocks may be configured by programming bits. Other embodiments are described and claimed.

David Tennenhouse

Papers

Proactive computing

Determining wireless device locations

Searching for Jim Gray: a technical overview

Locally asynchronous, block-level synchronous, configurable logic blocks with sub-threshold analog circuits