Privacy and security are among the significant challenges of the Internet of Things (IoT). Improper device updates, lack of efficient and robust security protocols, user unawareness, and famous active device monitoring are among the challenges that IoT is facing. In this work, we are exploring the background of IoT systems and security measures, and identifying (a) different security and privacy issues, (b) approaches used to secure the components of IoT-based environments and systems, (c) existing security solutions, and (d) the best privacy models necessary and suitable for different layers of IoT driven applications. In this work, we proposed a new IoT layered model: generic and stretched with the privacy and security components and layers identification. The proposed cloud/edge supported IoT system is implemented and evaluated. The lower layer represented by the IoT nodes generated from the Amazon Web Service (AWS) as Virtual Machines. The middle layer (edge) implemented as a Raspberry Pi 4 hardware kit with support of the Greengrass Edge Environment in AWS. We used the cloud-enabled IoT environment in AWS to implement the top layer (the cloud). The security protocols and critical management sessions were between each of these layers to ensure the privacy of the users’ information. We implemented security certificates to allow data transfer between the layers of the proposed cloud/edge enabled IoT model. Not only is the proposed system model eliminating possible security vulnerabilities, but it also can be used along with the best security techniques to countermeasure the cybersecurity threats facing each one of the layers; cloud, edge, and IoT.

With the rapidly aging population, there is a need to detect elderly’s activity, monitor their health, alert health care personnel, and provide real-time and long-term access to the generated sensor data. With the advances in sensor technologies, communication protocols, computing power, and cloud and edge services, it is now possible to build smart assistive living systems to improve people’s lives. In this research, we present a Context-aware and private real-time reporting aging in place system. The proposed system, we call it smart carpet, consists of a sensor pad placed under a carpet; the electronics reads walking activity to provide an automated health monitoring and alert system. We extended the system’s functionalities to improve its ability to detect falls, measure gait, and count the number of people traversing the carpet (socializing). In an urgent and time-sensitive situation, we need to provide a real-time notification. We propose a cooperative cloudlet model, where the sensors’ data will be sent to the nearest Cloudlet for analysis and extracting real-time decisions in minimal delay. Results showed that our system could assist the elderly in detecting falls with 95% sensitivity and 85% specificity. Measuring and estimating their gait with a mean percentage error difference to GAITRite 1.43% in walking speed; hence, predicting a fall risk and counting people’s plurality socializing with the elderly with an average accuracy of 100%. We evaluated our system’s improvements in a controlled environment.

With all these smart devices around us, and the spread of the Internet and emerging technologies, our life became easier and busier. The un-controlled excessive use of the mobile applications and social media made many of the people around the world feel they are alone, and socializing with electronic devices rather than human beings. This problem is more sever when it comes to the elderly people who are already in excessive need for healthcare and socializing. In this research, we present a Context-aware and private real-time fall detection system (FDS) for elderly people. The proposed system consists of sensor pad placed under a carpet; the electronics reads walking activity to provide an automated health monitoring and alert system. We extended the functionalities of the smart carpet to improve its ability to detect falls, alert health care personnel, by providing a real-time, long-term access, and monitoring event system. The sensed data is stored in the cloud, and analyzed using data mining tools to enhance the system accuracy in detecting falls and monitor elderly people activities. The user can obtain these results and make decisions by accessing the cloud through his/her mobile devices and in real time manner. Results showed that our system detects falls using computational intelligence techniques with 96.2% accuracy, 95% sensitivity and 85% Specificity.

This paper explores the cyber security threat of Implanted Medical Devices. The widely use of Internet of Thing capable devices in healthcare like pacemakers, infusion pumps, and insulin pumps support patients, at the same time they are considered a point of security threats. All of these devices are susceptible to hacking and exploitation, which has been demonstrated at various cyber security conventions for nearly the past decade. Although some progress in security measures seems to have been made, recent discoveries and interviews with researchers show a battle is still being fought with some manufacturers. Through pressure applied by some of these researchers, The Food and Drug Administration (FDA) has taken notice and issued safety warnings for some of these devices. We will begin by defining what these devices are and explore why people need them. Next, we will briefly give a history of the Internet of Things, explore prior security issues, and then introduce the security researchers and groups involved with finding the exploits of healthcare devices.

We have developed a floor based personnel detection system to extract gait parameters including walking speed, stride length and stride time, we call it smart carpet. These parameters are validated with a GAITRite Electronic mat. The smart carpet is laid over the GAITRite mat, and subjects walked across the mat for 9 trials each. The data acquisition system of the smart carpet recorded the location of the active sensors, which were later used to extract the gait parameters. An excellent agreement for walking speed, stride length and stride time between the two systems is achieved. The mean percentage error difference for walking speed is 1.43%, stride length is -4.32% and stride time is -5.73%. For walking speed, the standard deviation is 4.39, which emphasizes that 68% of the error is within a 5%. We compared our work to the work done by a research group who used a vision based Kinect and web cameras system with excellent agreement.

Current advances in sensor design technologies and computing power (computational and artificial intelligence) have made it possible to build smart assistive living systems that can improve the lives of people. Because older adults want to age in place at home, there is a need to monitor their health status, detect emergency situations, and notify health care providers. We have improved a floor based monitoring system, which we call the smart carpet, originally to detect falls, but we can take advantage of the continual 24/7 monitoring capability to get important information on gait, fall detection, counting the number of people traversing the carpet and studied the waveform for useful information. Recently, we studied the characteristics of the waveform of the scavenged signal from the sensors and used computational intelligence and feature extractions and classifications to separate people. In this paper, we used Dynamic Time Warping (DTW) to help improve on walk identification, compared with the MFCC feature extraction methods. Results showed that our system identifies walks using a dynamic time warping algorithm and KNN classifier with 86% precision, 76% recall, 81% accuracy. We also present a cooperative cloudlet mobile computing model for eldercare and medical applications where the decisions are very time sensitive. The sensors data will be sent to the nearest cloudlet for analysis and extracting real-time decisions in minimal delay. Users can obtain these results and make decisions by accessing the cloud through their mobile devices and in a real time manner.