Adaptability and energy-efficient sensing are essential properties to sustain the easy deployment and lifetime of WSNs. These properties assume a stronger role in autonomous sensing environments where the application objectives or the parameters under measurement vary, and human intervention is not viable. In this context, this paper proposes LiteSense, a selfadaptive sampling scheme for WSNs, which aims at capturing accurately the behavior of the physical parameters of interest in each WSN context yet reducing the overhead in terms of sensing events and, consequently, the energy consumption. For this purpose, a set of low-complexity rules auto-regulates the sensing frequency depending on the observed parameter variation. Resorting to real environmental datasets, we provide statistical results showing the ability of LiteSense in reducing sensing activity and power consumption, while keeping the estimation accuracy of sensing events.
Wireless sensor networks (WSNs) are made up of nodes with limited resources, such as processing, bandwidth, memory and, most importantly, energy. For this reason, it is essential that WSNs always work to reduce the power consumption as much as possible in order to maximize its lifetime. In this context, this paper presents SITRUS (semantic infrastructure for wireless sensor networks), which aims to reduce the power consumption of WSN nodes using ontologies. SITRUS consists of two major parts: a message-oriented middleware responsible for both an oriented message communication service and a reconfiguration service; and a semantic information processing module whose purpose is to generate a semantic database that provides the basis to decide whether a WSN node needs to be reconfigurated or not. In order to evaluate the proposed solution, we carried out an experimental evaluation to assess the power consumption and memory usage of WSN applications built atop SITRUS.
Wireless Sensor Networks (WSNs) have a very large growth in research in recent years. They are essentially adhoc networks, capable of processing, sensing and transmitting wireless information. These are networks that generate a large volume of raw data which possess natural heterogeneity. Besides, energy efficiency is an important performance measure, and one bit transmission over the network can consume as much energy as running thousands of instructions. In order to share information between different networks and decrease the amount of data sent, sensor data need to be enriched with semantic information. Trying to solve this problem, will be presented in this paper an approach to improve the power consumption in WSNs, based on a semantic solution compound by a middleware an ontology. This middleware is able to deal with the heterogeneity of data and applications, giving a semantics formal and unambiguous for data. The results showed that our approach is valid and achieve a satisfactory energy saving, with more than 30% savings between the best and worst case.
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