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The expansion in the Internet of Things (IoT) has led to a shift towards smart technologies. IoT focuses on integrating networks to facilitate smooth services to humans. The interface between the mobility patterns and the routing protocols is considered to increase the performance of the network. However, incorporating security in the IoT network has been a major issue that continues to nurture with increasing IoT devices. This article addresses this issue by developing a novel technique, namely energy harvesting trust aware routing algorithm (EHTARA) for initiating a trust‐based routing model in the IoT network in the presence of ambient energy sources. The cost metric is newly devised by considering energy, distance, and trust parameters for determining the best path. At the base station, big data classification is performed using the adaptive exponential‐Bat (adaptive E‐Bat) algorithm based deep belief network (DBN). The training of DBN is performed using the adaptive E‐Bat algorithm, which is the combination of adaptive concept, exponential weighted moving average (EWMA), and Bat algorithm (BA). Here, the optimization‐based map‐reduce framework helps to deal with the imbalanced data by adapting the deep learning in classification. The proposed EHTARA outperformed other methods with a maximal energy of 0.927.

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Despite the rapid growth in popularity and hardware capacity in mobile devices, they suffer from resource poverty, which limits their ability to meet increasing mobile users' demands. Computation offloading may give a prominent solution. But it relies on the connection to the remote cloud and may fail in situations where there is poor or no connectivity. Cloudlet was introduced to cover this problem, but mobile users miss free mobility when using cloudlets. Offloading to the cloud or cloudlet is not always the preferred solution. An alternative is to utilize the nearby mobile devices as local resource suppliers and pull their capabilities as a mobile device cloud. In this paper, the authors present such an approach known as ad hoc computing as a service (AhCaaS) model for computation offloading in an ad hoc manner by connecting to nearby mobile devices. They define a multi-attribute selection strategy to determine the optimal computation offloadee. They evaluated the proposed model, and the result shows that AhCaaS reduces execution time, battery consumption, and avoids task reassignment.