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The Internet of Things (IoT) systems create a large amount of sensing information. The consistency of this information is an essential problem for ensuring the quality of IoT services. The IoT data, however, generally suffers due to a variety of factors such as collisions, unstable network communication, noise, manual system closure, incomplete values and equipment failure. Due to excessive latency, bandwidth limitations, and high communication costs, transferring all IoT data to the cloud to solve the missing data problem may have a detrimental impact on network performance and service quality. As a result, the issue of missing information should be addressed as soon as feasible by offloading duties like data prediction or estimations closer to the source. As a result, the issue of incomplete information must be addressed as soon as feasible by offloading duties such as predictions or assessment to the network’s edge devices. In this work, we show how deep learning may be used to offload tasks in IoT applications.

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Wireless sensor network (WSN) is composed of multiple sensors that are connected through a communication channel and communicate with each other. As these sensor nodes are battery-operated, therefore, as a consequence, battery life or energy is always an issue of concern. Therefore, researchers focus their work on optimizing the routing strategies to save energy wastage in WSNs. Among all routing strategies, cluster-based techniques proved to be quite able to successfully manage propagation from sender to receiver. Because it must gather all data and send it to the base station, each cluster's elected head is responsible for bearing the complete load. A cluster-based routing mechanism is established under this paper and termed a Heterogeneous Cluster Prediction and Formation Routing Protocol (HCPFR) in which the algorithm first creates the cluster and predicts the energy utilization or network lifetime, and then provides energy-efficient optimized clustering. In this method, the proposed HCPFR model is compared with different methods; LEACH PSO, LEACH-GWO, LEACH-EEGWO, and FZR, and the performance is compared with different parameters mainly First Dead Node (FDN), Network Longevity (NL) and throughput (THP) in term of packet delivered and residual energy. The result shows that the HCPFR model outperforms better over these approaches. The FDN, NL, and THP of the proposed HCPFR are nearly 8000,10000, and 30000. Also, the suggested model shows that as the number of rounds increases the residual energy drops to 0.1 from 3.8 as the rounds increases to 10000 from 2000.