Cluster-Based Systematic Data Aggregation Model (CSDAM) for Real-Time Data Processing in Large-Scale WSN

In present decade, wireless sensor networks is applied in a variety of applications such as health monitoring, agriculture, traffic management, security domains, pollution management, and so on. Owing to the node density, the same data are collected by multiple sensors that introduce redundancy, which should be avoided by means of proper data aggregation methodology. With that note, this paper presents a cluster-based systematic data aggregation model (CSDAM) for real-time data processing. First, the network is formed into a cluster with active and sleep state nodes and cluster-head (CH) is selected based on ranking given to sensors with two criteria: existing energy level (EEL) and geographic-location (GL) to base station (BS), [i.e., Rank(EEL,GL)]. Here, the CH is the aggregator. Second, Aggregation is carried out in 3 levels where the data processing of level 3 has been reduced by aggregating the data at level 1 and level 2. If the energy of aggregator goes below the threshold, we choose another aggregator. Third, Real time application should be given more precedence than other applications, so additionally an application type field is added to each sensor node from which the priority of data processing is given first to real time applications. The simulation results show that CSDAM minimizes the consumption of energy and transmission delay effectively, thereby increasing the network lifespan.