Computing Aggregate Quantities in Large-Scale and Dense Sensor Networks
Maryam Vahabi · Open Repository of the University of Porto (University of Porto) · 2016
Although the information technology transformation of the 20th century appeared revolutionary, a bigger change is on the horizon. The term cyber-physical system (CPS)has come to describe the research and technological efforts that will ultimately allow theinterlinking of the real-world physical objects and the cyber-space efficiently.Technological advances in hardware design enable the emergence of low-cost singleembedded computer equipped with sensing, processing and communication capabilities.This makes it economically feasible to densely deploy networks with very large quantitiesof such nodes. Accordingly, it is possible to take a very large number of sensor readingsfrom the physical world, compute quantities and take decisions out of them.Very dense networks offer a better resolution of the physical world and therefore abetter capability of detecting the occurrence of an event; this is of paramount importancefor a number of foreseeable applications. Environmental monitoring and structural healthmonitoring (SHM) are two examples of such applications. In this Thesis, we considerdensely instrumented CPS applications.Considering the typical limited capabilities of sensor nodes (in terms of communication and processing), computing an estimate of the state of the physical world is challenging. In general, the main challenge of dense sensing networks is categorized into twodistinct classes: data transmission and data aggregation.Assume a large-scale dense networked sensor system, whose nodes have a commonsensing goal: to measure a physical phenomenon and also to compute different featuresfrom the distribution of sensor readings in order to trigger a set of control commands forthe actuators in a timely manner. Based on the aforementioned challenges, we formulatethe Thesis hypothesis as follows: we believe that it is possible to extract certain featuresof a physical phenomenon in a dense sensor network, in a reliable and timely way, andwith a time complexity that is essentially independent of the number of sensor nodes.Therefore, the primary objective of this Thesis is to devise technologies and methodologies that enable extracting certain features of a physical phenomenon monitored bya dense sensing network in a timely and reliable manner. To reach this primary objective, a set of scientific and technical objectives have been identified. The first objectiveis to devise an error recovery scheme for the slotted WiDOM protocol in a noisy wireless channel. This scheme should enable reliable data transmission in harsh environmentswhere sensor nodes are more prone to interference. The second objective is to devisean aggregation mechanism that captures the dynamics of the physical quantities and selfadapts according to the physical changes. This mechanism allows obtaining an accurateinterpolation for a dynamic physical quantity. The third objective is to devise algorithmsto identify various features in a distribution of a physical phenomenon. Feature extraction algorithms enable identifying the location and the boundaries of events in dense sensing applications. FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO Computing Aggregate Quantities in Large-Scale and Dense Sensor Networks