Abolfazl Razi

Date of Award

5-2013

Level of Access Assigned by Author

Campus-Only Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical and Computer Engineering

Advisor

Ali Abedi

Second Committee Member

Mauricio P. Da Cunha

Third Committee Member

Nuri Emanetoglu

Abstract

Robust error recovery and data compression algorithms are desirable in Wireless Sensor Networks (WSN), while pose significant implementation challenges due to the dynamic nature of networks and limited available resources at each node. Several near optimal algorithms have been developed to realize Distributed Joint Source and Channel Coding (D-JSCC) performing both compression and error recovery tasks. However, majority of the reported techniques are too complex to be implemented in tiny sensor nodes.

In this dissertation, a D-JSCC algorithm is proposed for WSN, which to the best of our knowledge, is less complex than previously reported methods. The idea is to exploit the existing correlation among sensors observations to eliminate transmission errors. The algorithm is general in the sense that it is applicable to a wide variety of analog and discrete sources without affecting quantization and digitization blocks. In this distributed algorithm, the sensors compress their data collectively and transmit to a central data fusion center without the need for inter-sensor communications. The algorithm is robust to sensor failures and stays operational even with only one active sensor.

A novel bi-modal decoder is proposed to constantly track the network state; e.g. channel conditions, observation accuracy, and the number of nodes. The decoder switches between two iterative and non-iterative modes based on the network state to maintain the overall data recovery performance at the highest possible level, while reducing the decoding complexity by avoiding unnecessary computations. Sensors observation accuracies can be extracted in real-time from the received data, hence no prior estimation is required at the destination. The algorithm can easily be scaled, since the decoding complexity grows linearly with the number of sensors.

Furthermore, an optimal bundling policy is proposed to combine sensor measurements into transmit packets such that the end to end latency is minimized. This solution considerably reduces data collection delivery in time sensitive sensor applications such as remote surgery and air traffic control systems. The results of this low-complexity algorithm not only improves the performance of data aggregation in sensor networks, but also provides a criterion to determine required sensor density in a data field to achieve a desired reliability level.

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