DATA COMPRESSION USING EFFICIENT DICTIONARY SELECTION METHOD

With the increase in silicon densities, it is becoming feasible for compression systems to be implemented in chip. A system with distributed memory architecture is based on having data compression and decompression engines working independently on different data at the same time. This data is stored in memory distributed to each processor. The objective of the project is to design a lossless data compression system which operates in high-speed to achieve high compression rate. By using the architecture of compressors, the data compression rates are significantly improved. Also inherent scalability of architecture is possible. The main parts of the system are the data compressors and the control blocks providing control signals for the Data compressors, allowing appropriate control of the routing of data into and from the system. Each Data compressor can process four bytes of data into and from a block of data in every clock cycle. The data entering the system needs to be clocked in at a rate of 4 bytes in every clock cycle. This is to ensure that adequate data is present for all compressors to process rather than being in an idle state

SOFTWARE UPDATE MANAGEMENT IN WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) have recently emerged as a promising platform for many non-traditional applications, such as wildfire monitoring and battlefield surveillance. Due to bug fixes, feature enhancements and demand changes, the code running on deployed wireless sensors often needs to be updated, which is done through energy-consuming wireless communication. Since the energy supply of battery-powered sensors is limited, the network lifetime is reduced if more energy is consumed for software update, especially at the early stage of a WSN’s life when bug fixes and feature enhancements are frequent, or in WSNs that support multiple applications, and frequently demand a subset of sensors to fetch and run different applications. In this dissertation, I propose an energy-efficient software update management framework for WSNs. The diff-based software update process can be divided into three phases: new binary generation, diff-patch generation, and patch distribution. I identify the energy-saving opportunities in each phase and develop a set of novel schemes to achieve overall energy efficiency. In the phase of generating new binary after source code changes, I design an update-conscious compilation approach to improve the code similarity between the new and old binaries. In the phase of generating update patch, I adopt simple primitives in the literature and develop a set of advanced primitives. I then study the energy-efficient patch distribution in WSNs and develop a multicast-based code distribution protocol to effectively disseminate the patch to individual sensors. In summary, this dissertation successfully addresses an important problem in WSNs. Update-conscious compilation is the first work that compiles the code with the goal of improving code similarity, and proves to be effective. The other components in the proposed framework also advance the state of the art. The proposed software update management framework benefits all WSN users, as software update is indispensable in WSNs. The techniques developed in this framework can also be adapted to other platforms such as the smart phone network