Abstract — In this work we propose the use of vehicles as traffic sensors to quasi-synchronously measure both velocity and position of the probe. Those sensor vehicles wirelessly cooperate to relay that distributed information to a Data Fusion Center. That Data Fusion Center, in turn, calculates

Cognitive radio technology has been proposed to improve spectrum efficiency by having the cognitive radios act as secondary users to opportunistically access under-utilized frequency bands. Spectrum sensing, as a key enabling functionality in cognitive radio networks, needs to reliably detect

The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting congestion hotspots. This paper presents a decentralized data fusion and active sensing (D 2 FAS) algorithm for mobile

of the processing and fusion of spectrum sensing information of cognitive radio systems. The use of cyclic featurebased methods for distributed signal detection and classification is discussed and recent results are presented. I.

Abstract—This paper presents an overview of a novel multimodal system being developed at UC San Diego for vehicle detection and traffic flow analysis. A Distributed Multimodal Array (DiMMA) framework is presented for sensory data acquisition, processing, analysis, fusion, and “active ” control

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Spectrum sensing is the most important technique used to implement cognitive radio; this approach allows opportunistic and dynamic allocation of spectral bands. Among the methods used for detection, there are Artificial Neural Networks (ANN) and Energy Detection (ED); those exploit the signals

degrading the primary systems. Yet in practice, monitoring the spectrum resources, detecting available resources for opportunistic communication, and transmit-ting over the resources are hard tasks. This thesis addresses the tasks of monitoring, de-

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Messaging is a basic service in sensornets. Yet the unique system and application prop-erties of sensornets pose substantial challenges for the messaging design: Firstly, dynamic wireless links, constrained resources, and application diversity challenge the architecture and protocol design of sensornet messaging; Secondly, complex faults and large system scale introduce new challenges to the design of fault-tolerant protocols. The objective of this dissertation is to address the aforementioned challenges of sensornet messaging. Despite the extensive effort in studying sensornet messaging, the lack of a basic un-derstanding of its essential components has been an obstacle for reliable, efcient, and reusable messaging services in sensornets. To address this problem, one task of this dis-sertation is to identify the basic components of sensornet messaging and to study the re-lated algorithmic design issues. More specically, we propose the messaging architecture SMA that consists of three components: trafc-adaptive link estimation and routing (TLR), application-adaptive structuring (AST), and application-adaptive scheduling (ASC). TLR deals with dynamic wireless links as well as the impact of application trafc patterns on