Abstract
Precision viticulture (PV) and precision agriculture (PA) requires the acquisition and processing of a vast collection of data coming typically from large scale and heterogeneous sensor networks. Unfortunately, sensor integration is far from being simple due to the number of incompatible network specifications and platforms. The adoption of a common, standard communication interface would allow the engineer to abstract the relation between the sensor and the network. This would reduce the development efforts and emerge as an important step towards the adoption of "plug-and-play" technology in PA/PV sensor networks. This paper explores this need and introduces a framework for smart data acquisition in PA/PV that relies on the IEEE 1451 family of standards, which addresses the transducer-to-network interoperability issues. The framework includes a ZigBee end device (sMPWiNodeZ),,as an IEEE 1451 WTIM (Wireless Transducer Interface Module), and an IEEE 1451 NCAP (Network Capable Application Processor) that acts as gateway to an information service provider and WSN (Wireless Sensor Network) coordinator. The paper discusses the proposed IEEE 1451 system architecture and its benefits in PA/PV and closes with results/lessons learned from in-field trials towards smarter WSN.

Abstract
In this work, we address a study of the spectral reflectance of silver nanowire metamaterials in the visible and near-infrared regions. To this end, several samples were fabricated with different fill-ratios and lattice constants, and their respective optical responses characterized in terms of these parameters. We perform a direct comparison between the collected experimental data with the values predicted by different analytical homogenization models to provide a better understanding of the effective optical behavior of this kind of metamaterials.

Abstract
The rising fuel costs is disallowing random cruising strategies for passenger finding. Hereby, a recommendation model to suggest the most passengerprofitable urban area/stand is presented. This framework is able to combine the 1) underlying historical patterns on passenger demand and the 2) current network status to decide which is the best zone to head to in each moment. The major contribution of this work is on how to combine well-known methods for learning from data streams (such as the historical GPS traces) as an approach to solve this particular problem. The results were promising: 395.361/506.873 of the services dispatched were correctly predicted. The experiments also highlighted that a fleet equipped with such framework surpassed a fleet that is not: they experienced an average waiting time to pick-up a passenger 5% lower than its competitor.

Abstract

Abstract
Wireless Sensor Networks (WSNs) consist of small devices capable of sensing various variables in the environment, process and communicate them through the network. These devices interact together to carry out monitoring tasks. A photo-voltaic (PV) power plant is an example of such network, where each solar panel has a sensor connected to it. The number of interconnected solar panels can become very large in order to cover a large area. Each sensor senses the output of the panel and sends this value to a central node for processing. In this paper we study and compare the performance of a multi-hop wireless sensor network, employing a polling based data collecting technique with data aggregation, against the performance of a one hop network employing two different data collecting techniques. The study considers a wireless network with fixed number of nodes using different values of the offered load, estimating the network throughput for each technique and offered load. The use of a multi-hop setup was chosen in order to reduce transmission power and interference among nodes. Results show that the multi-hop network, using the polling based data collecting technique with data aggregation, performs close to the one hop network using the other two techniques. The study involves both simulation and testbed experimentation.

Abstract
Distributed transaction processing has benefited greatly from optimistic concurrency control protocols thus avoiding costly fine-grained synchronization. However, the performance of these protocols degrades significantly when the workload increases, namely, by leading to a substantial amount of aborted transactions due to concurrency conflicts. Our approach stems from the observation that when the abort rate increases with the load as already executed transactions queue for longer periods of time waiting for their turn to be certified and committed. We thus propose an adaptive algorithm for judiciously scheduling transactions to minimize the time during which these are vulnerable to being aborted by concurrent transactions, thereby reducing the overall abort rate. We do so by throttling transaction execution using an adaptive mechanism based on the locally known state of globally executing transactions, that includes out-of-order execution. Our evaluation using traces from the industry standard TPC-E workload shows that the amount of aborted transactions can be kept bounded as system load increases, while at the same time fully utilizing system resources and thus scaling transaction processing throughput. © 2013 IFIP International Federation for Information Processing.