Index Terms-Community networks, sustainability, incentive mechanisms.Abstract-Community network (CN) initiatives have been around for roughly two decades, evangelizing a distinctly different paradigm for building, maintaining, and sharing network infrastructure but also defending the basic human right to Internet access. Over this time they have evolved into a mosaic of systems that vary widely with respect to their network technologies, their offered services, their organizational structure, and the way they position themselves in the overall telecommunications' ecosystem. Common to all these highly differentiated initiatives is the sustainability challenge. We approach sustainability as a broad term with an economical, political, and cultural context. We first review the different perceptions of the term. These vary both across and within the different types of stakeholders involved in CNs and are reflected in their motivation to join such initiatives. Then, we study the diverse ways that CN operators pursue the sustainability goal. Depending on the actual context of the term, these range all the way from mechanisms to fund their activities and synergistic approaches with commercial service providers, to organizational structures and social activities that serve as incentives to maximize the engagement of their members. Finally, we iterate and discuss theoretical concepts of incentive mechanisms that have been proposed in the literature for these networks as well as implemented tools and processes designed to set the ground for CN participation. While, theoretical mechanisms leverage game theory, reputation frameworks, and social mechanisms, implemented mechanisms focus on organizational matters, education and services, all aiming to motivate the active and sustained participation of users and other actors in the CN.
We focus on the problem of contributor-task matching in mobile crowd-sourcing. The idea is to identify existing social media users who possess domain expertise (e.g., photography) and incentivize them to perform some tasks (e.g., take quality pictures). To this end, we propose a framework that extracts the potential contributors' expertise based on their social media activity and determines incentives for them within the constraint of a budget. This framework does so by preferentially targeting contributors who are likely to offer quality content. We evaluate our framework on Flickr data for the entire city of Barcelona and show that it ensures high levels of task quality and wide geographic coverage, all without compromising fairness.
Mobile crowdsensing applications rely on crowds of contributors who are willing to carry out certain tasks of interest. This willingness varies widely across users and tasks and (monetary) incentives are often engaged to strengthen it or make up for its total absence. These incentives need to carefully account for the highly nonhomogeneous response of users to external motivation. We propose a framework that explicitly accounts for the implicit uncertainty about the eventual participation and contributions of users. In the framework, the impact of incentives on the user choice to contribute or not is modeled probabilistically. First, we formulate a convex optimization problem for incentive allocation with the goal of achieving maximum expected quality while taking into account task budget limitations and constraints related to the physical locations of users and tasks. We then propose an iterative algorithm to alleviate the complexity of the original problem with two basic steps: an allocation step that applies incentive allocation to a set of available contributors; and a refinement step that revokes portions of the allocated incentives. We study the performance characteristics of our algorithm by comparing it to a default solver for convex optimization problems. CCS Concepts ❼Human-centered computing → Ubiquitous and mobile computing; Ubiquitous and mobile computing theory, concepts and paradigms;
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