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Abs tract. Semantic clustering is a recent technique for saving energy in wireless sensor networks. Its mechanism of action consists in dividing the network into groups (clusters) formed by semantically related nodes and at least one semantic collector, which acts as a bridge between its internal nodes and the sink node. Since semantic collector nodes need to perform more tasks than normal nodes, they deplete their energy budget faster, so it is necessary to use efficient mechanisms for electing semantic collectors to prolong the network lifetime. Our hypothesis is that an effective choice of semantic collectors allows a longer network lifetime. To test it, we start from a previous work of the authors of this article and we propose an algorithm for electing semantic collectors in a distributed way based on a fuzzy inference engine. The inputs of the inference engine are the residual energy of nodes and their received signal strength indicator (RSSI). Simulation results confirm our hypothesis, since the algorithm provides (i) an improvement of 17.4% in relation to another proposal of the related literature, and (ii) a gain of 68.8% over the time life of the network's original work.

In sheep farming, the process of body measurement of animals is of fundamental importance, as they are hereditary characteristics, which reflect on meat production and body development. However, breeders still perform body measurements on animals, mostly by manual methods. Theobjective of this article is to present a computational solution, composed of a mobile device for automating data collection in sheep, through sensors, in addition to a software to process and find body measurements. For the proposed solution, the mobile device was built using Arduinotechnology with sensors, and the software was developed in RubyOnRails framework. To validate the computational solution, measurements were made with manual equipment (current solution employed by the producers) and compared using the error to identify the noise caused. Thus, the average relative errors of 7.44 % for withers height, 7.61 % for rump height, 7.19 % for chest girth, 6.45 % for body length, 13.48 % for Weight and 10.76 % for Depth, presenting the mean and standard deviation of automatic measurements close to manual measurements.  It is concluded that the body measurements performed in an automated way, allow greater agility in the measurements, in relation to the traditional (manual) measurements, which require a lot of work with the animal in the correct measurement posture and demands time.