Collaborative Sensing is an important enabling technique for realizing opportunistic spectrum access in white space (cognitive radio) networks. We consider the security ramifications of crowdsourcing of spectrum sensing in presence of malicious users that report false measurements. We propose viewing the area of interest as a grid of square cells and using it to identify and disregard false measurements. The proposed mechanism is based on identifying outlier measurements inside each cell, as well as corroboration among neighboring cells in a hierarchical structure to identify cells with significant number of malicious nodes. We provide a framework for taking into consideration inherent uncertainties, such as loss due to distance and shadowing, to reduce the likelihood of inaccurate classification of legitimate measurements as outliers. We use simulations to evaluate the effectiveness of the proposed approach against attackers with varying degrees of sophistication. The results show that depending on the attacker-type and location parameters, in the worst case we can nullify the effect of up to 41% of attacker nodes in a particular region. This figure is as high as 100% for a large subset of scenarios.This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE DySPAN 2010 proceedings 978-1-4244-5188-3/10/$26.00 ©2010 IEEE

Abstract-We investigate the use of white spaces in the TV spectrum for Advanced Meter Infrastructure (AMI) communications. We provide a design for using white spaces for AMI and show its benefits in terms of bandwidth, deployment, and cost. We also discuss ongoing work on applying machine learning classification techniques to improve the attack resilience of spectrum data fusion in the proposed architecture.

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Abstract-Denial-of-service (DoS) attacks are considered within the province of a shared channel model in which attack rates may be large but are bounded and client request rates vary within fixed bounds. In this setting, it is shown that clients can adapt effectively to an attack by increasing their request rate based on timeout windows to estimate attack rates. The server will be able to process client requests with high probability while pruning out most of the attack by selective random sampling. The protocol introduced here, called Adaptive Selective Verification (ASV), is shown to use bandwidth efficiently and does not require any server state or assumptions about network congestion. The main results of the paper are a formulation of optimal performance and a proof that ASV is optimal.Index Terms-Bandwidth, distributed denial of service (DDoS), performance analysis, selective verification, shared channel model, theorem.

Participatory sensing services based on mobile phones constitute an important growing area of mobile computing. Most services start small and hence are initially sparsely deployed. Unless a mobile service adds value while sparsely deployed, it may not survive conditions of sparse deployment. The paper offers a generic solution to this problem and illustrates this solution in the context of GreenGPS; a navigation service that allows drivers to find the most fuel-efficient routes customized for their vehicles between arbitrary end-points. Specifically, when the participatory sensing service is sparsely deployed, we demonstrate a general framework for generalization from sparse collected data to produce models extending beyond the current data coverage. This generalization allows the mobile service to offer value under broader conditions. GreenGPS uses our developed participatory sensing infrastructure and generalization algorithms to perform inexpensive data collection, aggregation, and modeling in an end-to-end automated fashion. The models are subsequently used by our backend engine to predict customized fuel-efficient routes for both members and non-members of the service. GreenGPS is offered as a mobile phone application and can be easily deployed and used by individuals. A preliminary study of our green navigation idea was performed in [1], however, the effort was focused on a proof-of-concept implementation that involved substantial offline and manual processing. In contrast, the results and conclusions in the current paper are based on a more advanced and accurate model and extensive data from a real-world phone-based implementation and deployment, which enables reliable and automatic end-to-end data collection and route recommendation. The system further benefits from lower cost and easier deployment. To evaluate the green navigation service efficiency, we conducted a user subject study consisting of 22 users driving different vehicles over the course of several months in Urbana-Champaign, IL. The experimental results using the collected data suggest that fuel savings of 21.5% over the fastest, 11.2% over the shortest, and 8.4% over the Garmin eco routes can be achieved by following GreenGPS green routes. The study confirms that our navigation service can survive conditions of sparse deployment and at the same time achieve accurate fuel predictions and lead to significant fuel savings.