Predictability and Fairness in Social Sensing
- URL: http://arxiv.org/abs/2007.16117v3
- Date: Tue, 25 May 2021 16:14:29 GMT
- Title: Predictability and Fairness in Social Sensing
- Authors: Ramen Ghosh and Jakub Marecek and Wynita M. Griggs and Matheus Souza
and Robert N. Shorten
- Abstract summary: We consider the design of distributed algorithms that govern the manner in which agents contribute to a social sensing platform.
We show how the IFS framework can be used to realise systems that deliver a predictable quality of service to agents.
- Score: 6.457260875902829
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We consider the design of distributed algorithms that govern the manner in
which agents contribute to a social sensing platform. Specifically, we are
interested in situations where fairness among the agents contributing to the
platform is needed. A notable example are platforms operated by public bodies,
where fairness is a legal requirement. The design of such distributed systems
is challenging due to the fact that we wish to simultaneously realise an
efficient social sensing platform, but also deliver a predefined quality of
service to the agents (for example, a fair opportunity to contribute to the
platform). In this paper, we introduce iterated function systems (IFS) as a
tool for the design and analysis of systems of this kind. We show how the IFS
framework can be used to realise systems that deliver a predictable quality of
service to agents, can be used to underpin contracts governing the interaction
of agents with the social sensing platform, and which are efficient.
To illustrate our design via a use case, we consider a large, high-density
network of participating parked vehicles. When awoken by an administrative
centre, this network proceeds to search for moving missing entities of interest
using RFID-based techniques. We regulate which vehicles are actively searching
for the moving entity of interest at any point in time. In doing so, we seek to
equalise vehicular energy consumption across the network. This is illustrated
through simulations of a search for a missing Alzheimer's patient in Melbourne,
Australia. Experimental results are presented to illustrate the efficacy of our
system and the predictability of access of agents to the platform independent
of initial conditions.
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