Because the number of elderly people is predicted to increase quickly in the upcoming years, “aging in place” (which refers to living at home regardless of age and other factors) is becoming an important topic in the area of ambient assisted living. Therefore, in this paper, we propose a human physical activity recognition system based on data collected from smartphone sensors. The proposed approach implies developing a classifier using three sensors available on a smartphone: accelerometer, gyroscope, and gravity sensor. We have chosen to implement our solution on mobile phones because they are ubiquitous and do not require the subjects to carry additional sensors that might impede their activities. For our proposal, we target walking, running, sitting, standing, ascending, and descending stairs. We evaluate the solution against two datasets (an internal one collected by us and an external one) with great effect. Results show good accuracy for recognizing all six activities, with especially good results obtained for walking, running, sitting, and standing. The system is fully implemented on a mobile device as an Android application.

Data dissemination in opportunistic networks poses a series of challenges, since there is no central entity aware of all the nodes' subscriptions. Each individual node is only aware of its own interests and those of a node that it is contact with, if any. Thus, dissemination is generally performed using epidemic algorithms that flood the network, but they have the disadvantage that the network overhead and congestion are very high. In this paper, we propose ONSIDE, an algorithm that leverages a node's online social connections (i.e. friends on social networks such as Facebook or Google+), its interests and the history of contacts, in order to decrease congestion and required bandwidth, while not affecting the overall network's hit rate and the delivery latency. We present the results of testing our algorithm using an opportunistic network emulator and three mobility traces taken in different environments.

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An opportunistic network is composed of human-carried mobile devices that interact in a store-carry-and-forward fashion. A mobile node stores data and carries it around; when it encounters another node, it may decide to forward the data if the encountered node is the destination or has a better chance of bringing the data closer to the destination.In order to obtain efficient routing in such a network, we should be able to predict the future behavior of a node. This would help the algorithm decide if the data contained by the node should be further carried or forwarded, and to which node it is to be forwarded. In this paper, we present a mobile interaction trace collected at the University Politehnica of Bucharest in the spring of 2012, and analyze it in terms of the predictability of encounters and contact durations. We show that there is a regular pattern in the contact history of a node and then we prove that, by modelling the time series as a Poisson distribution, we can efficiently predict the number of contacts per time unit in the future. These assumptions are demonstrated both on the trace presented in this paper, as well as on a different trace recorded in another type of environment, showing that predictability doesn't happen only in strict and controlled situations.

Opportunistic networks are mobile networks that rely on the store-carry-and-forward paradigm, using contacts between nodes to opportunistically transfer data. For this reason, traditional routing mechanisms are no longer suitable. To increase the probability of successfull message delivery, we propose SPRINT, an opportunistic routing algorithm that introduces an additional routing criterion: online social information about nodes. Furthermore, previous results show that, for particular environments, contacts between devices in opportunistic networks are highly predictable. When users follow rare events-based mobility patterns, we show that human mobility can be approximated as a Poisson distribution. Based on this result, we add an additional prediction component into our routing algorithm. Our solution delivers better results compared to traditional social-based routing approaches, for different real-world and synthetic mobility scenarios.