The recent development of communication and sensor technology results in the growth of a new attractive and challenging area â€" wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes’ resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network’s operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks.
The main goal of wireless sensor networks is to gather information from the region of interest through a large number of micro sensor nodes. This gathering is based on a communication architecture such as client/server which consumes a lot of power and doesn't take in consideration the information properties. In this paper, we propose a new communication architecture for wireless sensor networks based on agents' cooperation. This architecture uses techniques from multi-agent systems and networks in order to ensure an optimal information gathering. It benefits from AODV not only for route discovery but also to define the basic list of cooperating agents, using the RREP control message. These agents create cooperatively a simple message summarizing the important information of multiple nodes, where it is widely known that sending a one big message consumes less energy than sending several small messages of the same quantity of information. In order to appreciate our contribution, we discuss its advantages and its limitations by comparing it to client/server and mobile agent architectures.
This paper addresses the problem of forecasting daily stock trends. The key consideration is to predict whether a given stock will close on uptrend tomorrow with reference to today’s closing price. We propose a forecasting model that comprises a features selection model, based on the Genetic Algorithm (GA), and Random Forest (RF) classifier. In our study, we consider four international stock indices that follow the concept of distributed lag analysis. We adopted a genetic algorithm approach to select a set of helpful features among these lags’ indices. Subsequently, we employed the Random Forest classifier, to unveil hidden relationships between stock indices and a particular stock’s trend. We tested our model by using it to predict the trends of 15 stocks. Experiments showed that our forecasting model had 80% accuracy, significantly outperforming the dummy forecast. The S&P 500 was the most useful stock index, whereas the CAC40 was the least useful in the prediction of daily stock trends. This study provides evidence of the usefulness of employing international stock indices to predict stock trends.
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