In wireless body area network (BAN), node authentication is essential for trustworthy and reliable gathering of patient's critical health information. Traditional authentication solutions depend on prior trust among nodes whose establishment would require either key pre-distribution or non-intuitive participation by inexperienced users. Most existing non-cryptographic authentication schemes require advanced hardware or significant modifications to the system software, which are impractical for BANs.In this paper, for the first time, we propose a lightweight body area network authentication scheme BANA. Different from previous work, BANA does not depend on prior-trust among nodes and can be efficiently realized on commercial off-the-shelf low-end sensors. We achieve this by exploiting a unique physical layer characteristic naturally arising from the multi-path environment surrounding a BAN, i.e., the distinct received signal strength (RSS) variation behaviors among on-body channels and between on-body and off-body communication channels. Based on distinct RSS variations, BANA adopts clustering analysis to differentiate the signals from an attacker and a legitimate node. We also make use of multi-hop on-body channel characteristics to enhance the robustness of our authentication mechanism. The effectiveness of BANA is validated through extensive real-world experiments under various scenarios. It is shown that BANA can accurately identify multiple attackers with minimal amount of overhead.

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In this paper, we analyze the bit error rate (BER) of the diffusive molecular communication (DMC) systems employing on-off keying (OOK) modulation. We also analyze the BER of the OOK-modulated DMC systems with inter-symbol interference cancellation (ISIC). Our main motivation is to introduce alternative tools for analyzing and efficiently computing the BER of the DMC systems without or with ISIC. Specifically, for the OOK-modulated DMC systems without ISIC, we first derive an exact BER expression based on the Poisson modeling of DMC systems. Then, the Gaussian-and Gamma-approximation approaches are introduced to approximate the discrete Poisson distribution, and based on the approximation approaches, the corresponding BER expressions are derived. Furthermore, in order to reduce the computation complexity imposed by long ISI, we propose the Monte-Carlo, simplified Poisson, simplified Gaussian and the simplified Gamma approaches for BER computation. In the context of the OOK-modulated DMC systems with ISIC, we consider both the Poisson and Gaussian-approximation approaches for BER analysis. Again, exact and approximate BER expressions are derived under the Poisson, Gaussianapproximation, simplified Poisson and simplified Gaussian approaches. Finally, the considered approaches are compared and validated by a range of performance results obtained from evaluation of the derived expressions or by simulations. Our studies show that the alternative approaches are in general effective for providing near-accurate BER estimation.

Reverberation had a more significant effect on aided speech perception than AT/RT, but fast and slow AT/RT resulted in improved speech intelligibility over linear amplification.

Abstract-Lacking trusted central authority, distributed systems have received serious security threats from Sybil attack, where an adversary forges identities of more than one node and attempts to control the system. By utilizing the real-world trust relationships between users, social network-based defense schemes have been proposed to mitigate the impact of Sybil attacks. These solutions are mostly built on the assumption that the social network graph can be partitioned into two loosely linked regions -a tightly connected non-Sybil region and a Sybil region. Although such an assumption may hold in certain settings, studies have shown that the real-world social connections tend to divide users into multiple inter-connected small worlds instead of a single uniformly connected large region. Given this fact, the applicability of existing schemes would be greatly undermined for inability to distinguish Sybil users from valid ones in the small non-Sybil regions. This paper addresses this problem and presents SybilShield, the first protocol that defends against Sybil attack utilizing multicommunity social network structure in real world. Our scheme leverages the sociological property that the number of cutting edges between a non-Sybil community and a Sybil community, which represent human-established trust relationships, is much smaller than that among non-Sybil communities. With the help of agent nodes, SybilShield greatly reduces false positive rate of non-Sybils among multiple communities, while effectively identifying Sybil nodes. Analytical results prove the superiority of SybilShield. Our experiments on a real-world social network graph with 100,000 nodes also validate the effectiveness of SybilShield.