Location aware sensor routing (LASeR) protocol for mobile wireless sensor networks

Location aware sensor routing (LASeR) protocol is a novel solution to the challenges of routing in mobile wireless sensor networks (MWSNs). It addresses the high reliability and low latency requirements of emerging applications. The protocol uses location information to maintain a gradient field even in highly mobile environments, whilst reducing the routing overhead. This allows the protocol to utilise a blind forwarding technique to propagate packets towards the sink. The protocol inherently utilises multiple paths simultaneously to create route diversity and increase its robustness. LASeR is designed for use in a high variety of MWSN applications with autonomous land, sea or air vehicles. Analytical expressions are derived and evaluated against the simulations. Extensive modelling and simulation of the proposed routing protocol has shown it to be highly adaptable and robust. It is compared with the recent MWSN proactive highly ambulatory sensor routing protocol, the high performance mobility adaptive cross-layer routing protocol, as well as ad-hoc on-demand distance vector and optimised link state routing. Protocols are evaluated on packet delivery ratio, end-to-end delay, overhead, throughput and energy consumption. The results highlight both the high performance of LASeR in various challenging environments and its superiority over the state-of-the-art

Prioridade Dinâmica de Mensagens Aplicada a Redes de Sensores Corporais Sem-Fio

The Wireless Body Area Networks (WBANs) are a special case ofthe Wireless Sensor Networks (WSNs) and, in general, are responsiblefor gathering and transmitting biometric data from a givenpatient. One of the features of the WSNs is the self–configuration;i.e., the capability that a given parameter can be adjusted regardingsome occurrence in execution time. As stated, the objective of theWBANs is the gathering, through several sensors, of biometric dataof a patient. In general, because of possible daily activities and/orpathologies, some sensors can be more demanded (or required) incertain periods of the day. In these situations, information of thesementioned sensors are more relevant and require larger data deliverypriority. Thus, we propose QoSBody-AODV, a variation of theAODV routing protocol, which performs the dynamic adjustmentof the message’s priority, turning the network functioning to besensitive to the physical activities performed by the patient, as wellas pathological information

Matching Misaligned Two-Resolution Metrology Data

Multi-resolution metrology devices co-exist in today's manufacturing environment, producing coordinate measurements complementing each other. Typically, the high-resolution device produces a scarce but accurate dataset, whereas the low-resolution one produces a dense but less accurate dataset. Research has shown that combining the two datasets of different resolutions makes better predictions of the geometric features of a manufactured part. A challenge, however, is how to effectively match each high-resolution data point to a low-resolution point that measures approximately the same physical location. A solution to this matching problem appears a prerequisite to a good final prediction. This dissertation solves this metrology matching problem by formulating it as a quadratic integer programming, aiming at minimizing the maximum inter-point-distance difference (maxIPDdiff) among all potential correspondences. Due to the combinatorial nature of the optimization model, solving it to optimality is computationally prohibitive even for a small problem size. In order to solve real-life sized problems within a reasonable amount of time, a two-stage matching framework (TSMF) is proposed. The TSMF approach follows a coarse-to-fine search strategy and consists of down-sampling the full size problem, solving the down-sampled problem to optimality, extending the solution of the down-sampled problem to the full size problem, and refining the solution using iterative local search. Many manufactured parts are designed with symmetric features; that is, many part surfaces are invariant (are mapped to themselves) to certain intrinsic reflections and/or rotations. Dealing with parts surfaces with symmetric features makes the metrology matching problem even more challenging. The new challenge is that, due to this symmetry, alignment performance metrics such as maxIPDdiff and root mean square error are not able to differentiate between (a) correct solutions/correspondences that are orientationally consistent with the underlying true correspondences and (b) incorrect but seemingly correct solutions that can be obtained by applying the surface's intrinsic reflections and/or rotations to a correct set of correspondences. To address this challenge, a filtering procedure is proposed to supplement the TSMF approach. Specifically, the filtering procedure works by generating a solution pool that contains a group of plausible candidate sets of correspondences and subsequently filtering this pool in order to select a correct set of correspondences from the pool. Numerical experiments show that the TSMF approach outperforms two widely-used point set registration alternatives, the iterative closest point (ICP) and coherent point drift methods (CPD), in terms of several performance metrics. Moreover, compared to ICP and CPD, the TSMF approach scales very well as the instance size increases, and is robust with respect to the initial misalignment degree between the two datasets. The numerical results also show that, when enhanced with the proposed filtering procedure, TSMF exhibits much better alignment performance than TSMF without filtering, CPD and ICP in terms of both orientation correctness of the selected solution and several other performance metrics. Furthermore, in terms of computational performance, TSMF (with and without filtering) can solve real-life sized metrology data matching problems within a reasonable amount of time. Therefore, they are both well suitable to serve as an off-line tool in the manufacturing quality control process