From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either (a) personalized medicine for individuals or (b) public health for populations, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms – (i) Personal Sensing and (ii) Crowd Sensing paradigms. While both sensing paradigms might incorporate with common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and/or cloudbased data analytics to collect and process sensing data from individuals, we present a novel taxonomy system with two major components that can specify and classify apps/systems from aspects of the life-cycle of mHealth Sensing: (1) Sensing Task Creation & Participation, (2) Health Surveillance & Data Collection, and (3) Data Analysis & Knowledge Discovery. With respect to different goals of the two paradigms, this work systematically reviews this field, and summarizes the design of typical apps/systems in the view of the configurations and interactions between these two components. In addition to summarization, the proposed taxonomy system also helps figure out the potential directions of mobile sensing for health from both personalized medicines and population health perspectives.

Relay-based Cooperation in Mobile Data Offloading

Mobile data offloading has been emerged as a promising solution to manage high data traffic imposed by mobile users. This technique exploits the frequency spectrum of some overlapping networks such as WiFi in cellular networks. Deployment of minimum WiFi access points (APs) is the main challenge in mobile data offloading which leads to the low cost of CAPEX and OPEX. In this letter, we investigate the impact of a relay-based cooperative MAC protocol on the spectrum efficiency of mobile data offloading and consequently on the WiFi APs deployment. Two algorithms are presented to compute the throughput gain and the minimum number of WiFi APs in two modes of cooperative and non-cooperative MAC protocols. The analytical results indicate that the cooperative MAC protocol can provide a reduction of up to 44% in the number of WiFi APs in comparison to non-cooperative mode.

Trust-Based Opportunistic Network Offloaders for Smart Agriculture

With the enormous use of internet of things-based devices for enabling smart agriculture, there is a significant need for efficient systems in order to improve agricultural practices. It can help efficiently to develop optimal web-based information system using the data of field monitoring. But, the collection of such data in the presence of connectivity disruptions poses new challenges for users. This paper targets to determine such offloaders with less infrastructural costs to enable smart agriculture based on network heuristics. Although, few works contribute to the trust established, most of them are applicable only for static networks. This paper explores a trust-based solution for mobile data offloading. This paper identifies the need and impact of trust determination using the trust model algorithm. The proposed algorithm outperforms the hybrid trust-based mobility aware clustering algorithm for trust-based offloaders with up to 13% better offloading potential saving a minimum of 8 pJ energy per user with just 25% contributors with 50% lesser time delay.