Consistent Data Collection and Assortment in the Progression of Continuous Objects in IoT

In Internet of Things (IoT)-based applications, one of the foremost application is the localization and detection of continuous objects, such as mud flow, forest fire, toxic gases, biochemical materials, and so forth. The localization and detection of continuous objects require massive communication which may cause congestion, exhaust extensive amount of energy, and cause severe packet loss. In this paper, we proposed consistent data collection and assortment in the progression of continuous objects in IoT (CDCAPC) to tackle traffic congestion, continuous objects detection and throughput maximization problem, which employs the link capacity diversity, congested boundary node selection and node remaining power during the scheduling policy construction to reach maximum data transmission rate. The proposed algorithm is based on efficient utilization of network resources and variable data rates. The congestion is mitigated in hotspots by considering the differences of the link capacities in the sending process. The CDCAPC performance has been evaluated with promising results against equivalent scheme in term of data loss, high priority data packets delivery, end-to-end delay, hop-by-delay, and percentage of successfully received packets.