Wireless sensor networks (WSN) has been extensively used in many real time wireless sensor networks applications. Due to limitations of hardware resources and restricted communication capabilities of sensor nodes, it is very challenging to use wireless sensor networks in real time data transmission. Data collection and routing is the main issue in such applications. To enhance the performance under such real time transmission scenario, it is essential to make the protocol intelligent to choose the appropriate path with change in network scenario. Now a days, many machine learning and deep learning algorithms are used for improving the real time data transmissions in WSNs. From the survey on existing methods, it is clear that using machine learning makes the computational methods more reliable, powerful and economical. This paper proposes a machine learning based Medium Access Control (MAC) protocol to handle real time traffic in wireless sensor networks. To deal with the limitations of WSN in real time application, the proposed scheme can help to increase the performance of time-critical wireless sensor network applications. Simulation results authorize our work and confirm the accuracy of the proposed ML-MAC protocol strategy is higher than the existing work.

Leaf diseases pose a significant threat to the productivity and quality of mango crops, necessitating effective detection and management strategies. This study presents an automated system for the detection of mango leaf diseases using machine learning techniques. Using image processing methods to extract relevant features from leaf images, various machine learning models were trained to accurately classify common mango leaf diseases. This approach involved using a comprehensive dataset of diseased and healthy mango leaves, preprocessing images, and extracting features such as color, texture, and shape. Features were extracted using MobileNetV2 and EfficientNetV2. Feature fusion was performed using a dense layer. Principal component analysis was used to reduce dimensionality. These reduced features were then fed to a support vector classifier to classify the mango leaf images. Eight different classes were considered, including the seven most common diseases in mango leaves and one class for healthy ones. The proposed model achieved a remarkable accuracy of 99.83 %. These results demonstrate that machine learning models can achieve high accuracy in the early detection of mango leaf diseases. Implementing this system in agricultural practices can significantly help farmers in timely disease management, reducing crop losses, and improving mango production.

Over the past few years, wireless sensor networks (WSN) have emerged as one of the most exciting fields in Computer Science research. A Wireless Sensor Network (WSN) is a set of sensors that are integrated with a physical environment. These sensors are small in size, and capable of sensing physical phenomenon and processing them. The main aim of deploying applications based on WSNs is to make use of the data sensed by the sensors to raise the real-time decisions. The main limitations of WSNs are characteristics of sensor nodes and nature of sensor data generated by networks. Due to these limitations, traditional data mining techniques are not suitable to WSNs. As the data generated by WSNs is highly resource-constrained, huge in volume, fast changing, it is very challenging to design suitable data mining techniques for WSNs. This inspires to explore a novel and appropriate data mining technique capable of extracting knowledge from huge volume and variety of continuously arriving data from WSNs. In this paper different existing data mining techniques adopted for WSNs are examined with different classification, evaluation approaches, Finally, some research challenges related to adopting data mining techniques in WSNs are also pointed out.

Wireless Sensor Network (WSN) is highly distributed network of small and light weight node. The node has the limited battery lifetime. Packet scheduling is important in WSN to maintain fairness based on priority of the data and to reduce the end to end delay. Existing packet scheduling algorithm used were First Come First Served (FCFS), Preemptive, Non-Preemptive.In this paper Dynamic Multilevel Priority (DMP) Packet Scheduling Scheme with the Bit Rate classification is proposed. The threshold value check mechanism is also proposed to prevent the deadlock situation. To provide security we will be implementing the RC6 security algorithm

According to some estimates, 50 percent of accidents are caused by poor road conditions and the failure to wear a helmet. A smart interactive robotic helmet with features such as a traffic adaptive music player, wireless bike authentication, and road hazard warning playing can be offered to prevent accidents and encourage people to wear helmets. When a traffic hazard is coming, the helmet will inform the rider, communicate with him if he is not wearing it, and perform wireless bike authentication to avoid theft. Because two-wheelers are used more frequently in India than four-wheelers, it necessitates chevalier care in terms of safety. In today's culture, safety, like security, is extremely vital. The goal of this anti-theft mechanism system is to create a vehicle safety and security system that is implanted integrating and altering existing components. This system is primarily comprised of three modules: gas sensing, obstacle detection, and anti-theft warning system, all of which are connected to an ATmega16 microprocessor. This paper presents an overview of a smart helmet having various security and safety-related features.