• From the theoretical results on correlation and need of a simplified protocol stack, we propose a complete communication suite for energy efficient transport of correlated data from sensors to a centralized location. Accordingly, we design XLM, a cross-layer module, as the communication backbone of our cross-layer protocol suite. Our research melts common protocol layer functionalities into a cross-layer module (Figure 1). The design principle of XLM is complete unified cross-layering such that both the information and the functionalities of traditional communication layers are melted in a single module.




Figure 1. Our Cross Layer Approach



• A node initiates transmission by broadcasting an RTS packet to indicate its neighbors that it has a packet to send. Upon receiving an RTS packet, each neighbor of a node decides to participate in the communication through initiative determination. The preference or initiative shown by a node to participate in the forwarding process depends on the following conditions: 1) Reliable links should constructed for communication, 2) The congestion areas of the network should be avoided by limiting the traffic routed through a node and also preventing buffer overflow, and 3) The remaining energy of a node is above a minimum value.

• The received signal is a harmonical random process that is a sum of a number of complex exponentials. First, a simple way of using the spectral average of the received signal for estimation was proposed and tested through Monte-Carlo simulations. These simulations addressed different cases of network density and field correlation levels.
• A new sensor transceiver energy model was devised to reflect the latest hardware developments. The proposed model has a linear formulation of the dependence of transmitter energy cost on a radiated output energy. Furthermore, it was extended by inspecting a commercial node transceiver and identifying the model parameters in terms of its actual device parameters.
• The CSF approach is also shown to use less energy than conventional cluster-based, multi-hop or direct transmission protocols. The energy savings are in the order of minimum 50-90% savings for the earlier and the newly-proposed experimental transceiver configurations.

Publications

• A. Akanser and M. A. Ingram, ``MAC-free reading of a network of correlated sensors,'' Proc. IEEE Conference on Military Communications (MILCOM), 2007.
• A. Akanser and M. A. Ingram, ``MAC-free Cooperative Spectrum Fusion (CSF) in Wireless Sensor Networks (WSN),'' submitted to the IEEE Transactions on Aerospace and Electronic Systems (AES), 2008.

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We focus on two different types of correlation in this project - near and far correlation. We define near correlation as the correlation between content sent by sensors in the same vicinity. For example, the detection of the same event by multiple sensors in a region will result in near correlation. Similarly, we define far correlation as the correlation between content sent by sensors that are far apart. Such correlation can happen due to either large range events, or semantic correlation between data sent by far apart sensors.

We argue in this work that near and far correlation can be, and should be, handled differently in terms of the aggregation process for optimal performance. Specifically, in the case of near correlation, it is easier to predict in advance the existence of correlation between data sent by sensors in the vicinity, and hence a more aggressive aggregation scheme can be employed. At the same time, we argue that such prediction is infeasible for far correlation, and hence alternative strategies need to be employed. In this context, we consider two different, but complimentary, schemes for aggregating correlated data in wireless sensor networks that specifically target near and far term correlation respectively. The two schemes broadly fall under the classification of distributed source coding (DSC) and data gathering techniques.

We first assume that we have full knowledge of the correlation values before sensor deployment. This prior information is used to optimally design the source coding. Each node compresses its data without communicating with the other node and sends the compressed data to the next node that is closer to the sink. We propose a scheme for distributed source coding of correlated signals of two nodes. We show that our approach reaches the Slepian-Wolf limit, if we use a channel code that achieves the capacity of the equivalent channel. Next, we propose a method for compression of two correlated sources at every arbitrary rate on the Slepian-Wolf rate region using a single channel code. To describe the procedure, first we assume that each source uses a separate systematic LDPC code. Then, we show how the same procedure compresses both sources at rates close to the theoretical limit using only a single systematic channel code. We also extend our results for two sources to three sources by source coding of two signals together and the third one is compressed with the rate as close as possible to the theoretical limit. Assuming a large number of sensor nodes randomly deployed in a circular ¥å?eld, we propose a novel clustering scheme called Annular Slicing-based Clustering, and show that the proposed scheme performs near-optimally. Thus, we demonstrate that a judicious choice of the cluster size and distribution could result in better energy effciency, which is very valuable to the design of distributed WSNs.

Key Features and Results