Advances in wireless networking and embedded microprocessor designs have enabled the creation of dense low-power sensor networks. These sensor networks consist of nodes endowed with a multitude of sensing modalities such as temperature, pressure, light, magnetometer, infrared, audio, video, etc. The nodes are typically of small physical dimensions and operated by battery power, making energy consumption a major concern. For example, failure of a set of nodes in the sensor network due to energy depletion can lead to a partition of the sensor network and loss of potentially critical information. Motivated by this, there has been considerable recent interest in the area of energy-aware routing for ad hoc and sensor networks [1, 2 and 3] and efficient information processing [4 and 5] to reduce the energy usage of sensor nodes. For example, one method of conserving energy in a sensor node is to aggregate packets along the sensor paths to reduce header overhead. In this paper, we propose a fundamental new method of conserving energy in sensor networks that is mutually exclusive and complementary to the above approaches, and can be used in combination with them to increase energy reduction.
Our approach is based on judiciously exploiting existing sensor data correlations in a distributed manner. Correlations in sensor data are brought about by the spatio-temporal characteristics of the physical medium being sensed. Dense sensor networks are particularly rich in correlations, where spatially dense nodes are typically needed to acquire fine spatial resolution in the data being sensed, and for fault tolerance from individual node failures. Examples of correlated sensors include temperature and humidity sensors in a similar geographic region, or magnetometric sensors tracking a moving vehicle. Another interesting example of correlated sensor data involves audio field sensors (microphones) that sense a common event such as a concert or whale cries. Audio data is particularly interesting in that it is rich in spatial correlation structure due to the presence of echoes, causing multiple sensors to pick up attenuated and delayed versions of a common sound origin.
We propose to remove the redundancy caused by these inherent correlations in the sensor data through a distributed compression algorithm which obviates the need for the sensors to exchange their data among each other in order to strip their common redundancy. Rather surprisingly, we will show that compression can be effected in a fully blind manner without the sensor nodes ever knowing what the other correlated sensor nodes have measured. This enables a simple and inexpensive architecture for each sensor node and is in fact preferable to an architecture based on each sensor knowing the other sensors’ measurements. Our proposed paradigm is particularly effective for sensor network architectures having two types of nodes: sensing nodes and data-gathering nodes. The sensing nodes gather data of a specific type and transmit this data upon being queried. The data gathering node queries specific sensors in order to gather information in which it is interested (see Fig. 1). We will assume the above architecture (Fig. 1) for the rest of the paper and show that for such an architecture, we can devise compression algorithms that have very lightweight encoders, yet can achieve significant savings. Note, that we target very lightweight encoders in this paper because we assume that the sensors have limited compute power, but the constructions introduced in this paper can be easily strengthened given greater compute power at the sensors. The savings are achieved by having the data gathering node track the correlation structure among nodes and then use this information to effect distributed sensor data compression. The correlation structure is determined by using an adaptive prediction algorithm. The sensors, however, do not need to know the correlation structure; they need to know only the number of bits that they should use for encoding their measurements. As a result, each sensor node is required to perform very few operations in order to encode its data. The decoder, however, is considerably more complex, but it resides on the data gathering node, which is not assumed to be energy constrained. Preliminary results based on our distributed compression and adaptive prediction algorithms perform well in realistic scenarios, achieving 10–65% energy savings for each sensor in typical cases. In addition, our distributed compression architecture can be combined with other energy saving methods such as packet/data aggregation to achieve further gains [6].
In this paper we (1) devise a computationally inexpensive encoder that can support multiple compression rates and (2) present an adaptive correlation-tracking algorithm based on least-mean-square (LMS) filtering that can continuously track and exploit both spatial and temporal correlation in the sensors’ data. In the next section, we start by devising a computationally inexpensive compression algorithm for the sensor nodes. In Section 3, we will present the correlation tracking algorithm. In Section 4, we will integrate the above components into a complete system. Simulation results are given in Section 5 and we conclude with some remarks in Section 6.
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