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Wireless Body Area Networks (WBANs) is one of the most suitable technologies for building unobtrusive, scalable, and robust wearable health monitoring systems that enable physicians to remotely monitor vital signs of patients and provide real-time feedback with medical diagnosis and consultations. A Medium Access Control (MAC) layer is effective in addressing energy efficiency in WBANs and providing longer lifetimes for WBANs nodes. In this thesis we study techniques for improving the performance of WBAN MAC protocols and develop techniques for modelling WBAN MAC protocol performance, especially considering that when work in thesis was first started 802.15.6 had not been standardised and transition approaches were needed. When IEEE 802.15.6 was in the process of being standardized the existing IEEE 802.15.4 protocol or a modification of it was considered as an interim solution. This part of the project investigated 802.15.4's suitability for WBANs implant applications. In its unmodified form it was not suitable for in-body communication because it had to be transmitted at too high a power level. We implemented in Network Simulator (NS-2) a low power modification to study its performance. We implemented the 802.15.6 communication link channel model CM1 (implant to implant) and channel model CM2 (between an implant device and an on or out-of body device) in NS-2 and changed the power parameters of IEEE 802.15.4 so that it can be compatible for IEEE 802.15.6. Using simulation we determined path loss and received signal strength for several distances from the transmitter and receiver. We also analyzed packet delivery ratio, energy consumption and transmission delay. The simulation results in NS-2, successfully confirmed that the modified IEEE 802.15.4 protocol could be used in WBANs. Our results showed that CM1 had less path loss and its energy consumption and packet delivery ratio is also much smaller than CM2. Emergency data delivery is an important service for medical WBANs. The utmost importance of emergency message dissemination requires high reliability and low delay. Channel access delay minimization is very important in WBANs because delayed delivery of an emergency message can endanger a human life. IEEE 802.15.6 beacon enabled networks have defined an adjustable superframe structure that consists of contention-free and contention access periods. Short superframes can satisfy the channel access delay requirements of emergency traffic but penalize the energy efficiency of all devices in the network. On the other hand, long superframes increase the energy efficiency but the channel access delay is also increased. To balance this contradicting requirement of energy efficiency and Quality of Service (QoS), we propose the Medical Emergency Body (MEB) MAC protocol that inserts listening windows dynamically within the contention free periods to reduce access delay for emergency data. MEB MAC utilizes idle time slots to insert additional listening window opportunities for emergency traffic, without affecting the network throughput. The frequency of listening window insertion is determined by the minimum delay tolerance. Our analysis shows that MEB MAC is able to reduce channel access delay for emergency traffic especially for long superframe durations. OPNET Modeler is an industry leading discrete-event network modeling and simulation environment, commonly used due to its accuracy and better graphical user interface. It is an important tool for developing wireless protocols. In the last part of the thesis, we implemented the superframe structure and various application traffic models as recommended in IEEE 802.15.6 modifying the OPNET module of OPEN-ZB. To the best of our knowledge, this is the first ever protocol built in OPNET based on IEEE 802.15.6. It was used to implement the MEB MAC protocol. Based on simulation and analysis of results this work can be considered as a guide for researchers in evaluating OPNET for WBANs.

Automation is a process that deals with the aiding of manual tasks with the help of technology.Robotic Process Automation (RPA) has emerged as software that has replaced the mundane and repetitive tasks done by the humans in the back-end of the office with deterministic outcomes and structured data. The research study proves the effectiveness of the Robotic Process Automation in the increase in productivity of the business process with reduced error and improved accuracy. The RPA speeds up the process of the business with the optimization of costs. The major application of the RPA is carried out at the back-end of the business keeping the front-end free for humans. RPA has been found to be productive majorly in IT where the repetitive tasks are more in number when compared to other industries. The RPA has also replaced some traditional automation processes and has speeded up the process of automation.Major industries around the world are opting for the RPA in their business process in order to make the process of business faster and smarter. The research paper has discussed every detail about the RPA and its application in the process automation of business. It has focused on every aspect of the automation process of business and the tools used in the process.

Social media becomes a communicating channel during a natural disaster for detecting disaster events because people share their opinions, feelings, activity during the disaster through the Twitter. Twitter is not simply a platform for broadcasting information, but one of informational interaction. So, we use this platform for mining various disaster relevant tweets during a natural disaster. We examine more than 4,500 tweets during crisis moment. In this paper, we propose a classifier for classifying the disaster phases using social data and identify these types of phases. We use KNN, a machine learning classification algorithm for classifying the disaster relevant tweets. By knowing different phases of a disaster, response teams can detect where disaster will happen; the medical enterprise can be prepared to mitigate the damage after disaster and neighborhood area may also be alert to face the disaster. We classify the disaster-related tweets into three phases that are: pre (preparedness before the disaster event), on (during disaster event), post (impact and recovery after the disaster).We also take the geolocation with latitude and longitude of the disaster event for visualizing it using an earth map which can be useful to emergency response teams and also increase social awareness of the disaster.