10 research outputs found
A Trust-based Recruitment Framework for Multi-hop Social Participatory Sensing
The idea of social participatory sensing provides a substrate to benefit from
friendship relations in recruiting a critical mass of participants willing to
attend in a sensing campaign. However, the selection of suitable participants
who are trustable and provide high quality contributions is challenging. In
this paper, we propose a recruitment framework for social participatory
sensing. Our framework leverages multi-hop friendship relations to identify and
select suitable and trustworthy participants among friends or friends of
friends, and finds the most trustable paths to them. The framework also
includes a suggestion component which provides a cluster of suggested friends
along with the path to them, which can be further used for recruitment or
friendship establishment. Simulation results demonstrate the efficacy of our
proposed recruitment framework in terms of selecting a large number of
well-suited participants and providing contributions with high overall trust,
in comparison with one-hop recruitment architecture.Comment: accepted in DCOSS 201
Agent-based modeling of a price information trading business
We describe an agent-based simulation of a fictional (but feasible)
information trading business. The Gas Price Information Trader (GPIT) buys
information about real-time gas prices in a metropolitan area from drivers and
resells the information to drivers who need to refuel their vehicles.
Our simulation uses real world geographic data, lifestyle-dependent driving
patterns and vehicle models to create an agent-based model of the drivers. We
use real world statistics of gas price fluctuation to create scenarios of
temporal and spatial distribution of gas prices. The price of the information
is determined on a case-by-case basis through a simple negotiation model. The
trader and the customers are adapting their negotiation strategies based on
their historical profits.
We are interested in the general properties of the emerging information
market: the amount of realizable profit and its distribution between the trader
and customers, the business strategies necessary to keep the market operational
(such as promotional deals), the price elasticity of demand and the impact of
pricing strategies on the profit.Comment: Extended version of the paper published at Computer and Information
Sciences, Proc. of ISCIS-26, 201
Deep-Gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning
Mobile crowdsourcing has become easier thanks to the widespread of
smartphones capable of seamlessly collecting and pushing the desired data to
cloud services. However, the success of mobile crowdsourcing relies on
balancing the supply and demand by first accurately forecasting spatially and
temporally the supply-demand gap, and then providing efficient incentives to
encourage participant movements to maintain the desired balance. In this paper,
we propose Deep-Gap, a deep learning approach based on residual learning to
predict the gap between mobile crowdsourced service supply and demand at a
given time and space. The prediction can drive the incentive model to achieve a
geographically balanced service coverage in order to avoid the case where some
areas are over-supplied while other areas are under-supplied. This allows
anticipating the supply-demand gap and redirecting crowdsourced service
providers towards target areas. Deep-Gap relies on historical supply-demand
time series data as well as available external data such as weather conditions
and day type (e.g., weekday, weekend, holiday). First, we roll and encode the
time series of supply-demand as images using the Gramian Angular Summation
Field (GASF), Gramian Angular Difference Field (GADF) and the Recurrence Plot
(REC). These images are then used to train deep Convolutional Neural Networks
(CNN) to extract the low and high-level features and forecast the crowdsourced
services gap. We conduct comprehensive comparative study by establishing two
supply-demand gap forecasting scenarios: with and without external data.
Compared to state-of-art approaches, Deep-Gap achieves the lowest forecasting
errors in both scenarios.Comment: Accepted at CloudCom 2019 Conferenc
From MANET to people-centric networking: Milestones and open research challenges
In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications
Ensuring high quality public safety data in participatory crowdsourcing used as a smart city initiative
The increase in urbanisation is making the management of city resources a difficult task. Data collected through observations of the city surroundings can be used to improve decision-making in terms of manage city resources. However, the data collected must be of quality in order to ensure that effective and efficient decisions are made. This study is focused on improving emergency and non-emergency services (city resources) by using Participatory Crowdsourcing as a data collection method (collect public safety data) utilising voice technology in the form of an advanced IVR system known as the Spoken Web. The study illustrates how Participatory Crowdsourcing can be used as a Smart City initiative by illustrating what is required to contribute to the Smart City, and developing a roadmap in the form of a model to assist decision-making when selecting the optimal Crowdsourcing initiative. A Public Safety Data Quality criteria was also developed to assess and identify the problems affecting Data Quality. This study is guided by the Design Science methodology and utilises two driving theories: the characteristics of a Smart City, and Wang and Strong’s (1996) Data Quality Framework. Five Critical Success Factors were developed to ensure high quality public safety data is collected through Participatory Crowdsourcing utilising voice technologies. These Critical Success Factors include: Relevant Public Safety Data, Public Safety Reporting Instructions, Public Safety Data Interpretation and Presentation Format, Public Safety Data Integrity and Security, and Simple Participatory Crowdsourcing System Setup