The impact of spatial correlation on routing with compression in wireless sensor networks

Abstract

The efficacy of data aggregation in sensor networks is a function of the degree of spatial correlation in the sensed phenomenon. The recent literature has examined a variety of schemes that achieve greater data aggregation by routing data with regard to the underlying spatial correlation. A well known conclusion from these papers is that the nature of optimal routing with compression depends on the correlation level. In this article we show the existence of a simple, practical, and static correlation-unaware clustering scheme that satisfies a min-max near-optimality condition. The implication for system design is that a static correlation-unaware scheme can perform as well as sophisticated adaptive schemes for joint routing and compression.

Energy-Balancing, Local Data Correlation-Aware Clustering Algorithm for Wireless Sensor Networks

“…WSNs include a large number of small, cheap, wireless sensor nodes with low power in a monitored environment [1]- [3]. The sensor nodes collect lots of similar data, which results in redundancy [4], [5]. In general, sensors have limited resources, especially energy, but data transmission consumes about 80% of the power [6].…”

An incentive-based evolutionary algorithm for participatory sensing

“…However locations of the mobile devices cannot be controlled in participatory sensing. In addition, usually if sensors are deployed closely each other, the sensory data is spatially correlated [7]. Since the service providers have the limited budget, collecting spatially redundant data may result in wasting the budget.…”

Spatial signal processing in wireless sensor networks

“…In large-scale sensor networks, sensors will be densely deployed on the field of interest to gain high spatial and temporal resolution. However, big density also renders the nodes' measurements highly correlated in space [7,[34][35][36][37][38]. Here, we propose a distributed ML method to estimate the mean of the spatially correlated field; we then propose a sequential detector to test whether the mean of the field is greater than or equal to 0, without losing generality.…”

“…We apply the proposed calibration algorithms to the rainfall precipitation data set [94] which has been used in recent sensor-networks literature, see e.g. [36,37]. In particular, we analyze annual precipitation in the Pacific Northwest region of the United States, averaged over the period 1949-94.…”

“…Due to the high node density, the physical phenomena monitored by sensor networks (e.g. concentration of a chemical, temperature, or water contamination) usually yield observations that are highly correlated in space [34,36,37]. This spatial correlation has been utilized for (i) data aggregation, compression, routing, and querying, (ii) localization, and (iii) MAC protocol design, see [34,36,37] and references therein.…”