A Lossless Distributed Data Compression and Aggregation Methods for Low Resources Wireless Sensors Platforms

Wireless Multimedia Sensor Networks (WMSN) are undoubtedly one of the technologies that will transform the world around all of us. Still, they have been as useful and beneficial as resource-constrained distributed event-based system for several scenarios. Yet, in WMSN, optimisation of limited resources (energy, computing memory, bandwidth, storage and so on) during data collection, processing and communication process is a major challenge to guarantee the high performance of the system. Unfortunately, data redundancy involves a large consumption of sensor resources during processing and transferring information to an analysis centre. As a matter of fact, most of energy consumption (as much as 80%) for standard WSN applications lies in the radio module where receiving and sending packets is necessary to communicate between stations. To tackle this issue, this paper proposes an approach to achieve optimal sensor resources by data compression and aggregation regarding integrity of raw data. Then, the main objective is to reduce this redundancy by discarding a certain number of packets of information and keeping only the most meaningful and informative ones for the reconstruction. Data aggregation discarded a certain sensing data packet, which lead to low data-rate communication and low likelihood of packet collisions on the wireless medium. Data compression reduces a redundancy in keeping aggregated data, which leads to storage saving and sending only a small data stream in the bandwidth of communication. The performances of the proposed approach are qualified using experimental simulation on OMNeT + + /Castalia. The performance metrics were evaluated in terms of data Aggregation Rate (AR), Compression Ratio (CR), Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Energy Consumption (EC).The obtained results have significantly increased the life span of the sensors and thus the lifetime of the network. Furthermore, the integrity (quality) of the raw data is guaranteed.