Opportunistic sensing is a novel approach that exploits the sensing capabilities offered by smartphones and users' mobility to sense large scale areas without requiring the deployment of sensors in-situ. In this work, we propose a novel framework for fully distributed, opportunistic sensing which coherently integrates two main components that operate in DTN mode: i. participant recruitment and ii. data collection. We evaluate our approach by considering alternative implementations of the framework, and by measuring their performance via extensive trace-based simulations. Our results show how the performances of the considered protocols vary, depending on the particular scenario, and suggest guidelines for future development of distributed opportunistic sensing systems.

Tactical edge networks provide one of the most challenging environments for communications, which significantly complicates the development of efficient and robust information dissemination solutions. In our previous work, we found that exploiting highly mobile nodes, such as Unmanned Air Vehicles, with cyclic mobility patterns, as message ferries can significantly improve the performance of information dissemination solutions. However, our experience demonstrated that robust forecasting mechanisms are essential in order to withstand frequent changes in the mobility patterns of message ferrying nodes. This paper presents an extension of the adaptive node presence forecasting component developed for DisService, a Peer-to-peer information dissemination system, that provides estimates of tolerance and accuracy of node mobility forecasts. We tested the extended forecasting mechanism in a simulation environment and found that it can lead to significant improvements in the timeliness and reliability of information dissemination.

People-centric sensing is a novel apporach that exploits the sensing capabilities offered by smartphones and the mobility of users to sense large scale areas without requiring the deployment of sensors in-situ. Given the ubiquitous nature of smartphones, people-centric sensing is a viable and efficient solution for crowdsourcing data. In this work, we propose a fully distributed, opportunistic sensing framework that involves two main components which both work in an ad hoc fashion: Recruitment and Data Collection. We analyzed the feasibility of our distributed approach for both components through preliminary simulations. The results show that our recruitment method is able to select 66% of the nodes that are appropriate for the sensing activity and 88% of the messages sent by these selected nodes reach the sink by using our data collection method.