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An efficient strategy for reducing message transmission in a wireless sensor network (WSN) is to group sensors by means of an abstraction denoted cluster. The key idea behind the cluster formation process is to identify a set of sensors whose sensed values present some data correlation. Nowadays, sensors are able to simultaneously sense multiple different physical phenomena, yielding in this way multidimensional data. This paper presents three methods for clustering sensors in WSNs whose sensors collect multidimensional data. The proposed approaches implement the concept of multidimensional behavioral clustering. To show the benefits introduced by the proposed methods, a prototype has been implemented and experiments have been carried out on real data. The results prove that the proposed methods decrease the amount of data flowing in the network and present low root-mean-square error (RMSE).

In this work, we present a framework, denoted ADAGA -- P *, for processing complex queries and for managing sensor-field regression models. The proposed mechanism builds and instantiates sensor-field models. Thus ADAGA -- P * makes query engines able to answer complex queries such as give the probability of rain for the next two days in the city of Fortaleza . On the other hand, it is well known that minimizing energy consumption in a Wireless Sensor Network (WSN) is a critical issue for increasing the network lifetime. An efficient strategy for saving power in WSNs is to reduce the data volume injected into the network. For that reason, ADAGA -- P * implements an in-network data prediction mechanism in order to avoid that all sensed data have to be sent to fusion center node (or base station). Thus, sensor nodes only transmit data which are novelties for a regression model applied by ADAGA -- P *. Experiments using real data have been executed to validate our approach. The results show that ADAGA -- P * is quite efficient regarding communication cost and the number of executed float-point operations. In fact, the energy consumption rate to run ADAGA -- P * is up to 14 times lower than the energy consumed by kernel distributed regression for an RMSE difference of 0.003.

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We live in a world where demand for monitoring natural and artificial phenomena is growing. The practical importance of Sensor Networks is continuously increasing in our society due to their broad applicability to tasks such as traffic and air-pollution monitoring, forest-fire detection, agriculture, and battlefield communication. Furthermore, we have seen the emergence of sensor technology being integrated in everyday objects such as cars, traffic lights, bicycles, phones, and even being attached to living beings such as dolphins, trees, and humans. The consequence of this widespread use of sensors is that new sensor network infrastructures may be built out of static (e.g., traffic lights) and mobile nodes (e.g., mobile phones, cars). The use of smart devices carried by people in sensor network infrastructures creates a new paradigm we refer to as Social Networks of Sensors (SNoS). This kind of opportunistic network may be fruitful and economically advantageous where the connectivity, the performance, of the scalability provided by cellular networks fail to provide an adequate quality of service. This paper delves into the issue of understanding the impact of human mobility patterns to the performance of sensor network infrastructures with respect to four different metrics, namely: detection time, report time, data delivery rate, and network coverage area ratio. Moreover, we evaluate the impact of several other mobility patterns (in addition to human mobility) to the performance of these sensor networks on the four metrics above. Finally, we propose possible improvements to the design of sensor network infrastructures