This brief presents a parallel single-rail self-timed adder. It is based on a recursive formulation for performing multibit binary addition. The operation is parallel for those bits that do not need any carry chain propagation. Thus, the design attains logarithmic performance over random operand conditions without any special speedup circuitry or look-ahead schema. A practical implementation is provided along with a completion detection unit. The implementation is regular and does not have any practical limitations of high fanouts. A high fan-in gate is required though but this is unavoidable for asynchronous logic and is managed by connecting the transistors in parallel. Simulations have been performed using an industry standard toolkit that verify the practicality and superiority of the proposed approach over existing asynchronous adders.

Location awareness remains the key for many potential future applications of distributed wireless ad hoc sensor networks (WSNs). While the location of a WSN node can be estimated by incorporating Global Positioning System (GPS) devices, it is not suitable to embed GPS receivers in every node considering the cost and size of these devices as well as from an optimization point of view. However, a small number of WSNs nodes called anchor nodes are able to resolve their location either through fixed deployment or using GPS receivers, and thereby provide the reference framework for localization of other nodes. The measurement devices in individual nodes are often erroneous for tiny WSNs nodes, and hence, robustness is a major issue for localization. In this paper, a theoretical localization framework in the presence of noise is postulated, which achieves accurate positioning compared to the existing theoretical approaches. The paired measurement localization (PML) strategy is evaluated through simulations under various noise conditions and environmental modeling, and practically verified by a testbed implementation with real motes. The results corroborate the improved positioning as well as the robustness of PML for ad hoc wireless sensor networks in the presence of noise.

Geographic routing a.k.a. location aided routing for mobile ad hoc networks is popular for being able to meet the challenging issue of scalability and efficiency in dynamic networks. In the analysis of geographic routing, it is assumed that all the nodes know their location. On the other hand, localization for WSNs is another challenging research area. As localization error can hinder geographic routing performance significantly, it is essential to have a combined evaluation of localization and geographic routing to understand the tradeoffs among different self-localization schemes for location based routing in WSNs. In this paper, several practical localization methods are evaluated for their suitability to geographic routing by extensive simulations.

Maximum likelihood (ML) is a popular and widely used statistical method, and while it is effective, its major short-comings are that it is a biased and non robust estimator. This paper proposes a formal establishment of an optimisation of ML (OML) by approximating the true distribution minimising the bias, and exploiting the underlying relationship between ML and the maximum entropy method. OML exposes the inefficiency of the classical ML in the orthogonal least square error minimisation sense, for a number of finite sample datasets. The robustness of the proposed OML method in finding an estimate within the boundaries of the parameter space is also proven. Under the same conditions, OML consistently provides a more global and efficient estimation, so both theoretically and empirically establishing its superiority over ML in terms of efficiency and robustness