Optimizing Compression for Power Efficient Wireless Multimedia Sensor Networks

Main focus of our study here is how to reach optimum compression in the wireless Multimedia Sensor Network (WMSN) so as to improve power allocation between compression and transmission for maximizing the battery life of sensors. We know that there exists a large redundancy in sensor's data. So data need to be compressed before being transmitted to the sink/receiver/customer for reducing the heavy amount of energy consumed in transmission, but wireless sensor are very limited to computational ability, memory and energy. Due to these limitations, it becomes a sincere issue while processing any compression algorithm on the data because this also requires a significant amount of energy to perform these operations but even so, for a single bit, the energy used for compression is usually less than that of transmission. But if algorithm is highly complex and requires execution of a very large number of operations then the high degree of compression may also consume more energy than that of transmission. Here we can clearly see that there must be balance between power consumption of data transmission and that of compression. The power of compression depends on many factors such as amount of data to be reduced, compression algorithm, compression ratio, processor's architecture, etc. Our scheme is primarily focused on the video sensor networks which optimizes the balance between compression and transmission; and consequently, the overall power consumption will be reduced. In our results, we have discussed the variation of optimum compression ratio with respect to various model parameters and shown the power consumption behaviour for a typical model and achieved the minimum power consumption on optimum value of compression.