A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges

In the early months of the COVID-19 pandemic with no designated cure or vaccine, the only way to break the infection chain is self-isolation and maintaining the physical distancing. In this article, we present a potential application of the Internet of Things (IoT) in healthcare and physical distance monitoring for pandemic situations. The proposed framework consists of three parts: a lightweight and low-cost IoT node, a smartphone application (app), and fog-based Machine Learning (ML) tools for data analysis and diagnosis. The IoT node tracks health parameters, including body temperature, cough rate, respiratory rate, and blood oxygen saturation, then updates the smartphone app to display the user health conditions. The app notifies the user to maintain a physical distance of 2 m (or 6 ft), which is a key factor in controlling virus spread. In addition, a Fuzzy Mamdani system (running at the fog server) considers the environmental risk and user health conditions to predict the risk of spreading infection in real time. The environmental risk conveys from the virtual zone concept and provides updated information for different places. Two scenarios are considered for the communication between the IoT node and fog server, 4G/5G/WiFi, or LoRa, which can be selected based on environmental constraints. The required energy usage and bandwidth (BW) are compared for various event scenarios. The COVID-SAFE framework can assist in minimizing the coronavirus exposure risk.

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COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life

The Internet of Things (IoT) can enable smart infrastructures to provide advanced services to the users. New technological advancement can improve our everyday life, even simple tasks as a visit to the museum. In this article, an indoor localization system is presented, to enhance the user experience in a museum. In particular, the proposed system relies on Bluetooth Low Energy (BLE) beacons proximity and localization capabilities to automatically provide the users with cultural contents related to the observed artworks. At the same time, a received signal strength-based technique is used to estimate the location of the visitor in the museum. An Android application is developed to estimate the distance from the exhibits and collect useful analytics regarding each visit and provide a recommendation to the users. Moreover, the application implements a simple Kalman filter in the smartphone, without the need of the cloud, to improve localization, precision and accuracy. Experimental results on distance estimation, location, and detection accuracy show that BLE beacon is a promising solution for an interactive smart museum. The proposed system has been designed to be easily extensible to the IoT technologies and its effectiveness has been evaluated through experimentation.

BLE Beacons for Indoor Positioning at an Interactive IoT-Based Smart Museum

An increasing number of objects (things) are being connected to the Internet as they become more advanced, compact, and affordable. These Internet-connected objects are paving the way toward the emergence of the Internet of Things (IoT). The IoT is a distributed network of low-powered, low-storage, light-weight and scalable nodes. Most low-power IoT sensors and embedded IoT devices are powered by batteries with limited lifespans, which need replacement every few years. This replacement process is costly, so smart energy management could play a vital role in enabling energy efficiency for communicating IoT objects. For example, harvesting of energy from naturally or artificially available environmental resources removes IoT networks’ dependence on batteries. Scavenging unlimited amounts of energy in contrast to battery-powered solutions makes IoT systems long-lasting. Thus, here we present energy-harvesting and sub-systems for IoT networks. After surveying the options for harvesting systems, distribution approaches, storage devices and control units, we highlight future design challenges of IoT energy harvesters that must be addressed to continuously and reliably deliver energy.

Design architectures for energy harvesting in the Internet of Things

The rapid growth of the Internet of Things (IoT) has accelerated strong interests in the development of low-power wireless sensors. Today, wireless sensors are integrated within IoT systems to gather information in a reliable and practical manner to monitor processes and control activities in areas such as transportation, energy, civil infrastructure, smart buildings, environment monitoring, healthcare, defense, manufacturing, and production. The long-term and self-sustainable operation of these IoT devices must be considered early on when they are designed and implemented. Traditionally, wireless sensors have often been powered by batteries, which, despite allowing low overall system costs, can negatively impact the lifespan and the performance of the entire network they are used in. Energy Harvesting (EH) technology is a promising environment-friendly solution that extends the lifetime of these sensors, and, in some cases completely replaces the use of battery power. In addition, energy harvesting offers economic and practical advantages through the optimal use of energy, and the provisioning of lower network maintenance costs. We review recent advances in energy harvesting techniques for IoT. We demonstrate two energy harvesting techniques using case studies. Finally, we discuss some future research challenges that must be addressed to enable the large-scale deployment of energy harvesting solutions for IoT environments.

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Energy Harvesting Techniques for Internet of Things (IoT)

The interconnectedness of all things is continuously expanding which has allowed every individual to increase their level of interaction with their surroundings. Internet of Things (IoT) devices are used in a plethora of context-aware application, such as proximity-based services (PBSs), and location-based services (LBSs). For these systems to perform, it is essential to have reliable hardware and predict a user’s position in the area with high accuracy in order to differentiate between individuals in a small area. A variety of wireless solutions that utilize received signal strength indicators (RSSIs) have been proposed to provide PBS and LBS for indoor environments, though each solution presents its own drawbacks. In this article, Bluetooth low energy (BLE) beacons are examined in terms of their accuracy in proximity estimation. Specifically, a mobile application is developed along with three Bayesian filtering techniques to improve the BLE beacon proximity estimation accuracy. This includes a Kalman filter, a particle filter, and a nonparametric information (NI) filter. Since the RSSI is heavily influenced by the environment, experiments were conducted to examine the performance of beacons from three popular vendors in two different environments. The error is compared in terms of mean absolute error (MAE) and root mean squared error (RMSE). According to the experimental results, Bayesian filters can improve proximity estimation accuracy up to 30% in comparison with traditional filtering, when the beacon and the receiver are within 3 m.

Improving BLE Beacon Proximity Estimation Accuracy Through Bayesian Filtering

Enforced by the concept of Internet of Things, indoor location services using Bluetooth Low Energy beacons has become a growing reality. Many systems of which, propose the use of Received Signal Strength Indicator based location methods. The unpredictable propagation of these signals, in combination with surrounding signal noise and variable environmental conditions, makes it difficult to implement such systems as a simple entity. With the multitude of beacons on the market, it may be difficult to develop an optimal indoor positioning system for the desired application. To help address this issue, this paper compares three popular beacons, presents a simple mobile application based Kalman filter, and explores the correlation between transmit power and desired Kalman filter parameters.

Performance evaluation of beacons for indoor localization in smart buildings

Urban centers and dense populations are expanding, hence, there is a growing demand for novel applications to aid in planning and optimization. In this article, a smart parking system that operates both indoor and outdoor is introduced. The system is based on Bluetooth low energy (BLE) beacons and uses particle filtering to improve its accuracy. Through simple BLE connectivity with smartphones, an intuitive parking system is designed and deployed. The proposed system pairs each spot with a unique BLE beacon, providing users with guidance to free parking spaces and a secure and automated payment scheme based on real-time usage of the parking space. Three sets of experiments were conducted to examine different aspects of the system. A particle filter is implemented in order to increase the system performance and improve the credence of the results. Through extensive experimentation in both indoor and outdoor parking spaces, the system was able to correctly predict which spot the user has parked in, as well as estimate the distance of the user from the beacon.

Smart Parking System Based on Bluetooth Low Energy Beacons With Particle Filtering

Energy efficiency and accuracy of solar powered BLE beacons

The introduction of Global Positioning Systems (GPS) to the public has provided millions of people with navigation and positioning services around the globe. However, it is known that no matter the improvements to accuracy, fundamentally, GPS cannot provide location information in extreme environments, such as underwater and underground. Hence, there is a growing need for alternative technologies and methodologies of providing localization in such extreme or GPS-less environments. This paper introduces a low-power and low-cost substitute founded on Long Range (LoRa) to realize similar localization capabilities, based on the Received Signal Strength Indicator (RSSI) techniques. LoRa transceivers support swift deployment in GPS-less emergency scenarios, providing emergency teams with critical location and sensing data. According to outdoor experimental results, LoRa is a promising solution for wireless localization systems at GPS-less environments.

Andrew Mackey

Papers

Improving BLE Beacon Proximity Estimation Accuracy Through Bayesian Filtering

Performance evaluation of beacons for indoor localization in smart buildings

Smart Parking System Based on Bluetooth Low Energy Beacons With Particle Filtering

Energy efficiency and accuracy of solar powered BLE beacons

LoRa-based Localization System for Emergency Services in GPS-less Environments