Publication
Incentive Mechanisms for Community Sensing
Abstract
Sensing and monitoring of our natural environment are important for sustainability. As sensor systems grow to a large scale, it will become infeasible to place all sensors under centralized control. We investigate community sensing, where sensors are controlled by self-interested agents that report their measurements to a center. The center can control the agents only through incentives that motivate them to provide the most accurate and useful reports. We consider different game-theoretic mechanisms that provide such incentives and analyze their properties. As an example, we consider an application of community sensing for monitoring air pollution.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related concepts (33)
Algorithmic mechanism design
Algorithmic mechanism design (AMD) lies at the intersection of economic game theory, optimization, and computer science. The prototypical problem in mechanism design is to design a system for multiple self-interested participants, such that the participants' self-interested actions at equilibrium lead to good system performance. Typical objectives studied include revenue maximization and social welfare maximization. Algorithmic mechanism design differs from classical economic mechanism design in several respects.
Incentive
In general, incentives are anything that persuade a person to alter their behaviour in the desired manner. It is emphasised that incentives matter by the basic law of economists and the laws of behaviour, which state that higher incentives amount to greater levels of effort and therefore higher levels of performance. An incentive is a powerful tool to influence certain desired behaviors or action often adopted by governments and businesses. Incentives can be broadly broken down into two categories: intrinsic incentives and extrinsic incentives.
Sustainability
Sustainability is a social goal for people to co-exist on Earth over a long time. Specific definitions of this term are disputed and have varied with literature, context, and time. Experts often describe sustainability as having three dimensions (or pillars): environmental, economic, and social, and many publications emphasize the environmental dimension. In everyday use, sustainability often focuses on countering major environmental problems, including climate change, loss of biodiversity, loss of ecosystem services, land degradation, and air and water pollution.
Related publications (36)
Ensuring the environmental sustainability of emerging technologies
Marie-Valentine Renée Agnès Florin
This report assumes that something can be done to guide technology developers and others towards producing technology that does not cause indirect and delayed damage to the environment. It explores the extent to which environmental sustainability concerns ...
EPFL2022Seeking methods for improving sustainability in the extractive sector
Extractive resources are indisputably a necessary component of international sustainable development. Despite current advancements in the circular economy, world production needs for raw materials are ever increasing, and still a long way remains for it to ...
EPFL2021From metrics towards meaningful assessments of urban sustainability - exploring visions, frameworks and indicators
Problem statement. Cities hold a central role in global efforts towards sustainability, and integrating sustainability concerns into the governance of cities constitutes an increasingly urgent challenge. One avenue holding promise in this respect concerns ...
EPFL2021Related MOOCs (11)
Digital Signal Processing [retired]
The course provides a comprehensive overview of digital signal processing theory, covering discrete time, Fourier analysis, filter design, sampling, interpolation and quantization; it also includes a
Digital Signal Processing I
Basic signal processing concepts, Fourier analysis and filters. This module can
be used as a starting point or a basic refresher in elementary DSP
Digital Signal Processing II
Adaptive signal processing, A/D and D/A. This module provides the basic
tools for adaptive filtering and a solid mathematical framework for sampling and
quantization