MULTI-CHANNEL MAC PROTOCOL FOR ENERGY SAVING IN WIRELESS SENSOR NETWORKS

Wireless Sensor Network (WSN) is a self-organizing and distributed collection of small sensor nodes with limited energy are connected wirelessly to the sink, where the information is needed. The significant trait for any Wireless Sensor Network is power consumption since WSNs finds its most of the applications in unsafe, risky areas like Volcano eruption identification, Warfield monitoring, where human intervention is less or not possible at all. Hence designing a protocol with minimum energy consumption as a concern is an important challenge in increasing the lifetime of the sensor networks. Medium Access Control (MAC) Layer of WSN consumes much of the energy as it contains the radio component. Energy problems in MAC layer include collision, idle listening, and protocol overhead. Our Proposed MAC protocol provides solution for the problem of: collision by providing multiple channels; idle listening by providing sleeping mechanism for the nodes other than the active node; overhead by reducing the number of control messages. Avoiding collision results in the decrease in number of retransmissions which consumes more energy, avoiding idle listening problem will fairly increase the lifetime of the sensor node as well as the network’s lifetime and reducing overhead in turn consumes less energy

IoT-Based Decision Support System for Health Monitoring of Induction Motors

An electrical motor is a common device that is used for a variety of electrical purposes. Because of their wide range of applications, motors that are both reliable and long-lasting are in high demand. Motors are prone to a variety of faults, including rotor bar breaking faults, short turn faults, bearing outrace faults, and so on. Unexpected faults or failures in these motors reduce workplace productivity. The time it takes to resolve the issues reduces the organization’s profit. Bearing failures account for approximately 42% of all faults. Due to continuous operation, the shape of the majority of electrical motors with rolling bearings becomes disproportional. This causes the motor’s elastic limit to be exceeded, as well as fractures, vibrations, and a rise in temperature. A good solution is to switch from scheduled maintenance to predictive maintenance, which is based on monitoring the motor’s operating condition. This chapter proposes an Internet of Things (IoT)-based solution that continuously monitors and records the vibration from the induction motor. A decision support system analyzes the impact of vibration using log data and the Naïve Bayes classifier. The proposed decision support system detects the critical level of vibration and notifies the user of the motor’s abnormal working condition