Internet of Things Based Smart Greenhouse: Remote Monitoring and Automatic Control

Computer network communication is quickly growing in this pandemic situation. Phone conferencing, video streaming and sharing file/printing are all made easier with communications technologies. Data transmitted in time with little interruption become a significant achievement of wireless sensor networks (WSNs). A massive network is interconnection computer networks in the globe connected by the Internet, and the Internet plays a critical role in WSNs. Data access is a key element of any enterprise network, and the routing protocol is used to transmit data or access data. Due to the growing use of WSNs, it is essential to know about the network structure, the routing protocol. The routing protocols must be used to route all data sent over the Internet between the source and the destination. Which chooses the optimum routes between any two nodes in an enterprise network. This research focused on how the routing table will determine the optimum path/route of data packets to be transmitted from source to destination. The performance of three routing protocols, Routing Information Protocol (RIP), Enhanced Interior Gateway Routing Protocol (EIGRP) and Open Shortest Path First (OSPF), is investigated in this research for the massive mesh based enterprise wireless sensor network. We also investigated the behaviors of end-to-end packet latency, convergence time on flapping connections and average point-to-point throughput (bits/sec) between network links. Finally, the simulation results are compared to the efficacy and performance of these protocols implemented in the wireless LAN and internet-based wireless sensor network.

The Purpose of this work is to improve the availability and remote access for secure enterprise network infrastructure by using dual hub dual DMVPN (Dynamic Multipoint VPN). Using multipoint GRE (mgre) over IPsec data transmission in the enterprise network is highly reliable secured. DMVPN is a technology that can be associated with different protocols concept such as IPsec encryption, next hop resolution protocol (NHRP), generic routing encapsulation (GRE) and it provides dynamic and static IPsec tunnel between spoke to the hub, spoke to spoke communication. In this paper, we implement the DMVPN technique to constructs a secure enterprise network for an organization and employing hot standby routing protocol (HSRP) to overcome the unavailability and failure of a certain network. The simulation was done by GNS3 and packets were captured by Wireshark software. It was revealed by the test that, DMVPN technology with HSRP protocols completely fulfills the demand of availability that is vital for any enterprises. It offers a faster mode, highly efficient and practically valued venture and also provides accessibility by keeping the network always in upstate thereby facilities for building a safer and highly dependable network infrastructure.

The swift development of Internet of Things (IoT) technology has led to the expanded application of sensor networks in smart cities. Streetlights, as a vital aspect of smart city infrastructure, serve as a reflection of a town’s development. While streetlights are crucial for ensuring optimal visibility, road security, and public space utilization at night, they contribute significantly to electricity consumption. Governments across the globe are committed to reducing residential and industrial electricity usage. Regarding electricity usage, the Intelligent Street Lights System based on LED lights is a crucial concept today. This system functions by switching on the LED lights when a vehicle approaches and adjusting them to a dimmed or off state when no vehicle is present. The primary focus of this research paper, implementing an Intelligent Street Lights System based on LED lights, has emerged as a critical approach to achieving energy efficiency and cost reduction. Experimental findings demonstrate the potential for up to 80% energy savings compared to traditional streetlamp systems. Additionally, the system enables remote monitoring and intelligent management of urban street light conditions through terminal devices.

Rolling element bearing is the most important and critical mechanical device of rotating machinery. Identifying and trouble-shooting bearing faults in an early stage is necessary to prevent possible damage. The traditional intelligent bearing fault diagnosis method usually need preprocess the signal, manually extracting the features and pattern classification. The process of feature extraction requires much professional knowledge and complex feature extraction for this reason the model does not achieve satisfactory result. To overcome the complexity of traditional method, In this paper, the authors propose a deep learning algorithm for detection and classification of bearing faults based on Convolutional Neural Network, which does not need any feature extraction method. To evaluate the proposed CNN model, the bearing fault diagnosis experiment were carried out using transfer learning method and also compared with four other models. Finally, on CWRU's bearing dataset, we implemented the proposed model. The achieved accuracy indicates that the proposed CNN model is highly reliable in bearing fault diagnosis.