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Most of the QoS routing schemes proposed so far require periodic exchange of QoS state information among routers, imposing both communication overhead on the network and processing overhead on core routers. Furthermore, stale QoS state information causes the performance of these QoS routing schemes to degrade drastically. In order to circumvent these problems, we focus on localized QoS routing schemes where the edge routers make routing decisions using only local information and thus reducing the overhead at core routers. We first describe virtual capacity based routing (vcr), a theoretical scheme based on the notion of virtual capacity of a route. We then propose proportional sticky routing, an easily realizable approximation of vcr and analyze its performance. We demonstrate through extensive simulations that adaptive proportional routing is indeed a viable alternative to the global QoS routing approach.Index Terms-Localized proportional routing, quality-of-service routing.

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A central problem in sensor network security is that sensors are susceptible to physical capture attacks. Once a sensor is compromised, the adversary can easily launch clone attacks by replicating the compromised node, distributing the clones throughout the network, and starting a variety of insider attacks. Previous works against clone attacks suffer from either a high communication/storage overhead or a poor detection accuracy. In this paper, we propose a novel scheme for detecting clone attacks in sensor networks, which computes for each sensor a social fingerprint by extracting the neighborhood characteristics, and verifies the legitimacy of the originator for each message by checking the enclosed fingerprint. The fingerprint generation is based on the superimposed s-disjunct code, which incurs a very light communication and computation overhead. The fingerprint verification is conducted at both the base station and the neighboring sensors, which ensures a high detection probability. The security and performance analysis indicate that our algorithm can identify clone attacks with a high detection probability at the cost of a low computation/communication/storage overhead. To our best knowledge, our scheme is the first to provide realtime detection of clone attacks in an effective and efficient way.

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