The Mobile Ad-hoc Network is a self-configuring decentralized network, where the network topology is dynamically modifiable. The IoT (Internet of Things) based Wireless Sensor Network contains more sensors and shares information over the Internet to a cloud server. However, the IoT-based wireless sensor network channel has moderate security is poor compared to MANET and packet loss is increased due to attackers. In IoT, all the sensors forward the detected data frequently to the internet gateway, so the energy saving in the network is low compared to MANET. In this work, the smart environment of IoT, Wireless Sensor Networks (WSN) and MANET make a great heterogeneous network in IT Technology; the combination of this heterogeneous network has new challenging issues. In this heterogeneous network, MANET provides a trusted route between the sensor to gateway nodes into the IoT environment using Energy Saving Optimization Techniques [MANET-ESO in IoT]. It saves energy for each node and reduces the economic level. The results of the ns-3 simulation show that the proposed method provides better results in Alive node counts, residual Energy, throughput, packet delivery ratio and routing overhead.

This paper presents a control strategy for a grid-connected solar photovoltaic (PV) system that effectively addresses power quality issues arising from various grid conditions and load types. The proposed controller integrates an ADAptive LInear NEuron (ADALINE) and a Sixth-Order Generalized Integrator-Frequency Locked Loop (SiOGI-FLL) to enhance system performance under non-linear loads, unbalanced loads, and fluctuating solar PV generation. The system is tested under multiple challenging grid voltage scenarios, including unbalanced grid voltage, grid voltage with harmonics, and grid voltage with DC offset conditions. The ADALINE-based control mechanism accurately detects and compensates for harmonics, while the SiOGI-FLL ensures precise extraction of the positive sequence component of the grid voltage. Simulation results demonstrate that the proposed controller effectively mitigates power quality issues, ensuring improved power factor, reduced total harmonic distortion, and stable grid operation, thus making it a robust solution for modern grid-connected PV systems.

An important and modern trend in wireless network is managing and accessing in heterogeneous wireless networks structures effectively. Vertical handover or handoff technology is major and acting vital role in wireless communication, it has key challenging issues in wireless network, while handover gets call drop kind of latency and call quality minimization. So the communication networks needs an efficient and dynamic services and knowledge monitoring management to select network for handover of different network environments (example: WiMAX, WiFi, WLAN, etc.). In this paper we proposed the concepts of knowledgeable monitoring (KM-method), cognitive advisor (CA) and Advanced Handover Optimization (AHO) technique to improve effective network selection for seamless communication, while travels over heterogeneous wireless networks (HWN). KM-method monitoring and manage effective network selection and holding connected mobile nodes inform header files also maintaining re-initiate to next network information (network IP address, user ID, new path, etc.). CAVHO-Cognitive Advisor based Vertical Handover (VSH and V2H) and KM-methods perform and measures velocity and direction of mobility nodes through SRSS sequential Received Strength Signal. Also CA receives the nearest networks details of each another using nearest neighbouring concept with SDT ratio Algorithm. Optimization technique (AHO) performs dynamically balancing the threshold level of speed, load and quality based on Receiving signal strength and user preference. Simulation results of CAVHO Shows it secure and increases the handover probability of access also proposed system reduce delay and improve quality of services and throughput.

In this modern world, the number of working mothers has risen significantly. Before even going to outside work, she is working for the family in all aspects of cooking, washing, cleaning the room, etc. Consequently, childcare becomes a daily challenge for many mothers. However, mothers cannot constantly observe their infant's activity and childcare to family conditions and workload. In this work, baby care with smart cradle is proposed in which infant activities and health status are observed with the help of various sensors, activator, internet of things (IoT) and the cloud called as baby care robot (BC-Robot). The proposed method helps mothers to monitor and take care of the infants' activities and health status from remote location.