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.
In recent year's computational capability of the mobile nodes have been greatly improved. The mobile nodes have the capability of running different applications. Implementation of services in Mobile Ad Hoc Networks (MANETs) increases the flexibility of using mobile devices for running a wide variety of applications. Single service cannot satisfy the user needs. The complex needs of the users can be satisfied by the service composition. Service composition means, combining the atomic services into a complex service. In this paper we propose QoS constraint service composition in MANETs. We considered both service QoS parameters as well node parameters. Response time and throughput as parameters for services and energy and hop count as node parameters. These four QoS parameters are optimized using a mathematical model Hammerstein model to generate a single output. Based on generated output, max valued (optimal) services are considered in service composition path. The simulation results shown that, our proposed method outperforms than the traditional AODV method of service composition.
Due to the open nature of wireless data transmission, routing and data security pose an important research challenge in the Internet of Things (IoT)-enabled networks. Also, the characteristic features, like constrained resources, heterogeneity, uncontrolled environment, and scalability requirement, make the security issues even more challenging. Hence, an effective and secure routing protocol named modified Energy Harvesting Trust-aware Routing Algorithm (mod-EHTARA) is proposed to increase the energy efficiency and the lifespan of the nodes. The proposed mod-EHTARA is designed by adopting the Link Lifetime (LLT) model with the traditional EHTARA. The optimal secure routing path is effectively selected by the proposed mod-EHTARA using the cost metric, which considers the factors like delay, LLT, energy, and trust. The big data classification process is carried out at the Base Station (BS) using the MapReduce framework. Accordingly, the big data classification is progressed using a stacked autoencoder, which is trained by the Adaptive E-Bat algorithm. The Adaptive E-Bat algorithm is developed by integrating the adaptive concept with the Bat Algorithm (BA) and Exponential Weighted Moving Average (EWMA). The proposed mod-EHTARA showed better performance by obtaining a maximal energy of 0.9855.
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