WorkshopTalks

Sujeet Chand was appointed senior vice president, Advanced Technology and Chief Technical Officer for Rockwell Automation in April 2001. He is responsible for the company’s technology strategy, advanced technology development, and global standards and trade. Chand joined Rockwell International in 1985 as manager, Control Laboratory, followed by an appointment as principal scientist and manager, Software and Signal Processing, for the Rockwell Science Center. He was named director, Information Sciences, Rockwell Science Center, in 1993. He has published more than 50 technical papers and holds four patents. He has also represented the United States as the Head of Delegation to the Intelligent Manufacturing Systems, a worldwide consortium on manufacturing technology. Chand holds a doctor of philosophy degree in electrical and computer engineering from the University of Florida, Gainesville.

Mark Goodman is a Director of Sales at Crossbow Technology. He works closely with customers to develop and deploy ubiquitous portable wireless devices and sensor network solutions for their business needs Mark has extensive experience in wireless and new technology applications. As a founding member of the Cellular Phone team at Motorola in the 1980’s, Mark was instrumental in the adoption and growth of products such as DynaTAC, MicroTAC, and StarTAC. Additionally, he has headed up activities in the e-commerce and global outsourcing. For the last 5 years, Mark has been focused on bringing new technology to the commercial, industrial and government marketplace. Mark has worked with direct sales, dealers, wireless resellers, carriers and retailers Mark has a BA from Ohio State University; MA from Northwestern University, and MBA from Boston University.

"Wireless Sensing Systems: From ecosystems to human systems" Miniaturization and Moore’s law has enabled us to combine sensing, computation and wireless communication in integrated, low-power devices, and to embed networks of these devices in the physical world. By placing sensing devices up close to the physical phenomena we are now able to study details in space and time that were previously unobservable. Across a wide array of applications, the ability to observe physical processes with such high fidelity will allow domain experts to create models, make predictions, and manage critical resources. Looking back over the past few years we have made significant progress toward the vision of programmable, multi-modal, multi-scale, and multi-use observatories. We have made our greatest strides in these applications using multiscale data and models as context for the in situ measurements, and in network processing and mobility to achieve scalability (in terms of communication, energy, latency and coverage). We found that moving a sensor through a space is the only way to actually achieve dense sensing. And in network processing was needed because by processing data near the sensor source we not only make systems last longer (by conserving communication energy), but make the systems more reactive and interactive e so other elements in the systems (including humans) can adapt to the varying physical observations of the system. We are now applying these lessons learned and technical approaches to human as well as natural systems, in particular by exploring use of the installed base of image and acoustic sensors that we all carry around in our pockets or on our belts—cell phones. We see these applications as going well beyond the intentional conversations and postings supported by sites such as Flikr and lifeblog to automated, programmable, and adaptive collection of both physical and social parameters at the personal and community level. There are important overlapping themes with scientific applications, most critically the crucial importance of processing of data close to the source so as to address the varied but persistent needs of individuals to selective share these direct observations of their lives and spaces. Moreover, we see the continued importance of mobility to achieve coverage, and the challenge of verifiable location tagging in the context of mobility.

"From dense nodes to smart networks: randomized signal processing and decentralized codes" As wireless sensor networks continue to profoundly impact the way in which we interact with the physical world around us, we are increasingly being faced with the challenge of scaling the system densely, seamlessly, and robustly. The need for scalability naturally imposes pragmatic constraints on the cost and resources of individual nodes, resulting in nodes that may be imprecise, statistically varying, and unreliable. Yet, as a collection, can they be made to overcome their individual deficiencies, deriving strength from numbers, and realizing a network that is strong, robust, and capable of reliably meeting quantifiable performance guarantees without the luxury of centralized intelligence or global co-ordination? We will explore this vision and highlight how a minimalistic, randomized and distributed approach to signal processing and coding can help tackle the two important system attributes of scale and robustness. We will provide concrete illustrations of this paradigm to some of our recent and ongoing work on (i) reliable communication with cheap, unreliable radios, (ii) high-capacity content distribution in dense vehicular networks, and (iii) distributed data storage and representation in large-scale sensor networks having limited node capabilities.

"When the Sensors Hit the Road: Mobile Sensor Networks" Over the past few years, impressive advances in wireless networking and embedded computing have led to the "first generation" of wireless sensor networks. In general, these are used for monitoring or tracking applications characterized by low data rates and static deployments. In this talk, I will motivate what the "second generation" might look like, focusing on mobile sensor networks gathering heterogeneous "media-rich" data. These networks offer the opportunity to sense the world at much higher scale and finer fidelity than their static counterparts, especially over large areas. With hundreds of millions of automobiles and over a billion cell phone-equipped people in the world, cars and humans may turn out to be the vehicles of the world's largest and most dynamic sensor networks in the coming years. This talk will discuss several network architecture and data management challenges in realizing this vision.