Modified Growth Codes: Enhancing data persistence in sparse sensor networks

Wireless sensor networks are often deployed to work in harsh or disaster and other special environments, such as earthquakes, floods, fires, other outer space and the battlefield. Owing to the lack of energy or disaster scenarios, sensor nodes may fail easily. This severe reduce the data persistence in the network and the efficiency of the sensed data acquisition. Growth Codes (GC) can work effectively and enhance the data persistence simultaneously. However, the performance of GC decreases significantly when deployed in the sparse sensor networks. Uneven sensor data distribution may happen at the beginning of the encoding due to GC exchanges codewords in a completely random way which may also do no good to the data collection in the later period. Furthermore, in the catastrophic scenarios, the nodes continue to failure, which may lead to the network become sparse. To solve this problem, in this paper, we propose an improved GC algorithm-MGC (Modified Growth Codes) from the perspective of making the sensed data distribute uniformly. Later, a more efficient data collection algorithm MGC TYPE ? is proposed. Simulation results show that the performance of MGC and MGC TYPE II is better than GC, especially in the sparse networks.