Wearable sensing technologies are having a worldwide impact on the creation of novel business opportunities and application services that are benefiting the common citizen. By using these technologies, people have transformed the way they live, interact with each other and their surroundings, their daily routines, and how they monitor their health conditions. We review recent advances in the area of wearable sensing technologies, focusing on aspects such as sensor technologies, communication infrastructures, service infrastructures, security, and privacy. We also review the use of consumer wearables during the coronavirus disease 19 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and we discuss open challenges that must be addressed to further improve the efficacy of wearable sensing systems in the future.

This paper presents P-Sense (Pollution-Sense), a PS system for air pollution monitoring and control. The ultimate goal of this system is to allow government officials, international organizations, communities, and individuals access to the pollution data to address their particular problems and needs. P-Sense should provide large amounts of pollution data in time and space with different granularities. Government officials will have data to monitor and control the Air Quality Index (AQI) [1] of a city, state, or country; doctors will be able to correlate respiratory problems of their patients to the AQI they are exposed to during their daily activities, in the places they work and live; county officials, community developers, and realtors will have data to determine the best place where to build a new school or community and advertise properties according to the AQI where they are located.

The pervasiveness of cellular phones combined with Internet connectivity, GPS embedded chips, location information, and integrated sensors provide an excellent platform to collect data about the individual and its surrounding environment. As a result, new applications have recently appeared to address large-scale societal problems as well as improve the quality of life of the individual. However, these new applications, recently called location-based services, participatory sensing, and human-centric sensing, bring many new challenges, one of them being the management of the huge amount of traffic (data) they generate. This article presents G-Sense, for Global-Sense, an architecture that integrates mobile and static wireless sensor networks in support of location-based services, participatory sensing, and human-centric sensing applications. G-Sense includes specific mechanisms to control the amount of data generated by these applications while meeting the application requirements. Furthermore, it creates a network of servers organized in a peer-to-peer architecture to address scalability and reliability issues. An example prototype application is presented along with some basic results and open research issues.

Consumer wearables have emerged as disrupting devices that benefit citizens in areas such as mobile health, fitness, security, and entertainment. The mass adoption of these devices not only generates high revenues but also exposes important privacy issues. The authors identify some of the major privacy issues associated with consumer wearables and explore possible solutions to address privacy concerns.

Blockchain is a developing technology that can be utilized for secure data storage and sharing. In this work, we examine the cost of Blockchain-based data storage for constrained Internet of Things (IoT) devices. We had two phases in the study. In the first phase, we stored data retrieved from a temperature/humidity sensor connected to an Ethereum testnet blockchain using smart contracts in two different ways: first, appending the new data to the existing data, storing all sensor data; and second, overwriting the new data onto the existing data, storing only a recent portion of the data. In the second phase, we stored simulated data from several sensors on the blockchain assuming sensor data is numeric. We proposed a method for encoding the data from the sensors in one variable and compared the costs of storing the data in an array versus storing the encoded data from all sensors in one variable. We also compared the costs of carrying out the encoding within the smart contract versus outside the smart contract. In the first phase, our results indicate that overwriting data points is more cost-efficient than appending them. In the second phase, using the proposed encoding method to store the data from several sensors costs significantly less than storing the data in an array, if the encoding is done outside the smart contract. If the encoding is carried out in the smart contract, the cost is still less than storing the data in an array, however, the difference is not significant. The study shows that even though expensive, for applications where the integrity and transparency of data are crucial, storing IoT sensor data on Ethereum could be a reliable solution.