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The purpose of this paper is to improve the lifetime ratio of wireless sensor networks for maintaining the battery level at a desired point for better improvement of network health.

Sensor point network (SPN) is used for variety of applications like weather check, tracking of undesirable vehicles and delivery of data to end points. The proposed special high health sensing point (SHHSP) scheme will overcome several limitations of existing game theory approaches with respect to delay, health and overall throughput.

The simulation results of the proposed SHHSP scheme confirms the excellence over the existing works examined with respect to delay, hops, energy consumed, nutrition SP, harmful SP, throughput and overhead.

It is proposed for a smart communication system in IoT, where in the communication between the sensing point network to its neighbouring sensing network is carried out by selection of SHHSP, this is implemented by using the remaining energy and distance vector with respect to control station. The system is applicable to weather check and can also be used in tracking of vehicles in a vehicle ad hoc networks.

It is subsidized to the IoT system and vehicle-to-vehicle communication system where in the safety is of utmost concern. The system is concentrated on the battery concern of SPN in a pool of SPNs.

The purpose of the paper is to make use of multiple parameters namely; residual energy, closeness to centre and mobility of detection point (DP) for the selection of detection point network (DPN). In the novel method proposed, the path will have less number of DPs participating in the entire DPN.

The proposed novel method will find out the special detection point (SDP) based on three criteria, namely, the amount of mobility for DP, the amount of remaining energy and the amount of distance between two DPs. This proposed method is an attempt to resolve the network lifetime problems during the communication of DPs over a period of time. It is developed for increasing the lifetime ratio, throughput, residual energy, number of alive nodes.

The simulation results of the novel method show the improvement over the existing methods investigated based on the lifetime ratio, throughput, remaining energy and alive nodes.

In the proposed method, the communication is done between different DPs in the network. The commutation is done using SDPs only from one cluster to another cluster. It is proposed for the implementation of energy efficient data sensing in mobile communication networks.

It is a significant mechanism for energy efficient data sensing of one DP to another DP of different clusters in the network. The total energy consumed for a period of time by the network is significantly reduced from the novel method.

This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection.

The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance.

A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead.

The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks.

This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.