Location awareness is a key enabling feature and fundamental challenge in present and future wireless networks.
Most existing localization methods rely on existing infrastructure and thus lack the flexibility and robustness necessary for large ad hoc networks. In this paper, we build upon SPAWN (sum-product algorithm over a wireless network), which determines node locations through iterative message passing, but does so at a high computational cost. We compare different message representations for SPAWN in terms of performance and complexity and investigate several types of cooperation based on censoring. Our results, based on experimental data with ultra-wideband (UWB) nodes, indicate that parametric message representation combined with simple censoring can give excellent performance at relatively low complexity.
In this letter, a new tree-based anti-collision protocol for RFID systems is proposed to achieve a very high tag identification efficiency. The proposed algorithm works in two phases. In the first phase, the number of competing tags are estimated through the proposed Bayesian estimation technique, while in the second phase tags are identified using our modified dynamic tree algorithm. The system efficiency is mathematically derived and verified through simulation. Numerical results show that the proposed algorithm achieves a tag identification system efficiency of 45% and time system efficiency of 78.5%, thus outperforming any existing collision resolution algorithms.
Abstract-This paper presents a compact and effective chaosbased keyed hash function implemented by a cross-coupled topology of chaotic maps, which employs absolute-value of sinusoidal nonlinearity, and offers robust chaotic regions over broad parameter spaces with high degree of randomness through chaoticity measurements using the Lyapunov exponent.
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