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Autonomous wireless sensor networks are subject to power, bandwidth, and resource limitations that can be represented as capacity constraints imposed to their equivalent flow networks. The maximum sustainable workload (i.e., the maximum data flow from the sensor nodes to the collection point which is compatible with the capacity constraints) is the maxflow of the flow network. Although a large number of energy-aware routing algorithms for ad-hoc networks have been proposed, they usually aim at maximizing the lifetime of the network rather than the steady-state sustainability of the workload. Energy harvesting techniques, providing renewable supply to sensor nodes, prompt for a paradigm shift from energy-constrained lifetime optimization to power-constrained workload optimization.This paper presents a self-adapting maximum flow (SAMF) routing strategy which is able to route any sustainable workload while automatically adapting to time-varying operating conditions. The theoretical properties of SAMF routing are formally proved and a simulation model is developed on top of OMNeT++ (http://www.omnetpp.org/) in order to enable simulation-based assessment and design exploration. Simulation results are reported which demonstrate the applicability of the proposed approach.

Abstract-The opportunities to empirically study temporal networks nowadays are immense thanks to Internet of Things technologies along with ubiquitous and pervasive computing that allow a real-time finegrained collection of social network data. This empowers data analytics and data scientists to reason about complex temporal phenomena, such as disease spread, residential energy consumption, political conflicts etc., using systematic methologies from complex networks and graph spectra analysis. However, a misuse of these methods may result in privacyintrusive and discriminatory actions that may threaten citizens' autonomy and put their life under surveillance. This paper studies highly sparse temporal networks that model social interactions such as the physical proximity of participants in conferences. When citizens can self-determine the anonymized proximity data they wish to share via privacy-preserving platforms, temporal networks may turn out to be highly sparse and have low quality. This paper shows that even in this challenging scenario of privacy-by-design, significant information can be mined from temporal networks such as the correlation of events happening during a conference or stable groups interacting over time. The findings of this paper contribute to the introduction of privacy-preserving data analytics in temporal networks and their applications.

This paper examines the opportunities and the economic benefits of exploiting publicly-sourced datasets of road surface quality. Crowdsourcing and crowdsensing initiatives channel the participation of engaged citizens into communities that contribute towards a shared goal. In providing people with the tools needed to positively impact society, crowd-based initiatives can be seen as purposeful drivers of social innovation from the bottom. Mobile crowdsensing (MCS), in particular, takes advantage of the ubiquitous nature of mobile devices with on-board sensors to allow large-scale inexpensive data collection campaigns. This paper illustrates MCS in the context of road surface quality monitoring, presenting results from several pilots adopting a public crowdsensing mobile application for systematic data collection. Evaluation of collected information, its quality, and its relevance to road sustainability and maintenance are discussed, in comparison to authoritative data from a variety of other sources.