A key application for IoT based technologies in the field of healthcare is wireless medical sensors that can be used to monitor patients’ physiological information such as heartbeat, bowel activity and lung sounds. Real-time detection of bowel motility after major abdominal surgery has significant importance for the patients’ healing process. Due to temporal cessation of intestinal motility after the surgery, a period of fasting is commonly practiced, and patients are fed with fluids following the recovery of bowel motility. Many studies have been conducted to monitor intestinal motility and automatically detect bowel activity. Detection and identification are challenging because of the ambient noise in clinical environments. Active noise cancellation methods remove unwanted signals by using adaptive filters. In this paper, active noise cancellation simulations were performed in order to remove ambient noise from gastrointestinal auscultation recordings. The simulation setup was created based on a previously developed IoT-driven electronic stethoscope by our group. Five widely used adaptive filter algorithms: Least Mean Squares, Normalized Least Mean Squares, Affine Projection, Recursive Least Squares, and Adaptive Lattice were tested, and performance evaluations are reported.

The accessibility of accurate location information for operators in mission-critical scenarios would considerably increase their mission success. In order to obtain precise location information, numerous algorithms and technologies have been suggested. These methods and systems show varying performances under different conditions, and with the help of machine learning techniques, their reliability can be enhanced dramatically. In this paper, we overview the state-of-the-art in emerging algorithms and technologies employing cog-nitive solutions in mission critical localization applications. We compare these algorithms in terms of different localization parameters such as scalability, power consumption, availability , service quality and accuracy. Consequently, this survey will assist researchers who are working in the area of RF-based localization to achieve better performance in mission critical scenarios that can be experienced in smart city applications.

One of the applications of big data research is to utilize inexpensive and unobtrusive Internet of Things- (IoT) driven devices for monitoring hospitalized patients whose physiological status requires close attention. This type of solution employs sensors to collect physiological information and uses gateways to send the data or warnings to caregivers for further analysis. Unfortunately, real-world applications of health monitoring for mobile users were so far poor mainly due to the energy constraints imposed by the batteries. Edge computing aims to process data produced by devices to be closer to its origin instead of sending it to data centers.

With the advent of IoT-based technologies, communication of everyday objects with the Internet and with each other has become a reality which resulted in the creation of new research areas such as location-aware routing and location-based services. These concepts are also described as the ability to locate a mobile user geographically and offer services to the user based on their location. Besides, accurate positioning of network components in wireless networks is desirable for building intelligent networks, energy preserving routing, and providing improved network lifetime. In this study, we have conducted empirical studies on positioning of ZigBee-based sensor nodes. Results indicate that time of flight-based positioning provides better distance estimates than receiver signal strength-based positioning. Findings of this study can be used to develop more realistic data routing algorithms and improve node positioning. Tables 2.1 shows the list of abbreviations used in this chapter.