Ensuring authentication in the Internet of Things (IoT) environment is a crucial task because of its unique characteristics which include sensing, intelligence, large scale, selfconfiguring, connectivity, heterogeneity, open and dynamic environment. Besides, every object in the IoT environment should trust other devices with no recommendation or prior knowledge for any network operations. Hence, those characteristics and blindness in communication make security violations in the form of various attacks. Therefore, a trustbased solution is necessary for ensuring security in the IoT environment. Trust is considered as a computational measure represented through a relationship between trustor and trustee, explained in a particular context valued through trust metrics and evaluated by a trust mechanism. The proposed logistic regression-based trust model provides an efficient way to identify and isolate the misbehaving nodes in the RPL (Routing Protocol for Low Power Lossy Networks) based IoT network. It is one of the popularly used routing protocols in IoT, that builds a path especially for the constrained nodes in IoT environments. However, it is vulnerable to many attacks. The proposed model classifies and predicts the node’s behavior (trusted or malicious). This model uses the logistic regression model to predict the node’s behavior based on the integrated trust value which is computed from the direct trust, reputation score, and experience trust. It is primarily designed to address the black hole attack in the IoT environment. The mathematical analysis shows the possibility of the proposed work and the simulation results show the proposed model is better than the existing similar work.
Many application domains gain considerable advantages with the internet of things (IoT) network. It improves our lifestyle towards smartness in smart devices. IoT devices are mostly resource-constrained such as memory, battery, etc. So it is highly vulnerable to security attacks. Traditional security mechanisms can't be applied to these devices due to their restricted resources. A trust-based security mechanism plays an important role to ensure security in the IoT environment because it consumes only fewer resources. Thus, it is essential to evaluate the trustworthiness among IoT devices. The proposed model improves trusted routing in the IoT environment by detecting and isolating malicious nodes. This model uses reinforcement learning (RL) where the agent learns the behavior of the node and isolates the malicious nodes to improve the network performance. The model focuses on IoT with the routing protocol for low power and lossy network (RPL) and counters the blackhole attack.
Energy storing has turned into a notable statement for the society of nowadays. This paper displays a Wireless Sensor and Actuator Network (WSN) intended to offer help to a programmed framework, based on the GSM, which empowers a mindful utilization of vitality. The proposed system plays out a proficient administration of devices, machines and procedures, upgrading their activity to accomplish a decrease in their general vitality use at some random time. For this reason, significant information is gathered from smart sensors, which are placed at the required areas, just as from the vitality showcase through the wireless communication. This data analysis is to give learning about load use, and to enhance proficiency.
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