61 research outputs found
Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting
Modern urbanization is demanding smarter technologies to improve a variety of
applications in intelligent transportation systems to relieve the increasing
amount of vehicular traffic congestion and incidents. Existing incident
detection techniques are limited to the use of sensors in the transportation
network and hang on human-inputs. Despite of its data abundance, social media
is not well-exploited in such context. In this paper, we develop an automated
traffic alert system based on Natural Language Processing (NLP) that filters
this flood of information and extract important traffic-related bullets. To
this end, we employ the fine-tuning Bidirectional Encoder Representations from
Transformers (BERT) language embedding model to filter the related traffic
information from social media. Then, we apply a question-answering model to
extract necessary information characterizing the report event such as its exact
location, occurrence time, and nature of the events. We demonstrate the adopted
NLP approaches outperform other existing approach and, after effectively
training them, we focus on real-world situation and show how the developed
approach can, in real-time, extract traffic-related information and
automatically convert them into alerts for navigation assistance applications
such as navigation apps.Comment: This paper is accepted for publication in IEEE Technology Engineering
Management Society International Conference (TEMSCON'20), Metro Detroit,
Michigan (USA
A Stochastic Geometry-based Demand Response Management Framework for Cellular Networks Powered by Smart Grid
In this paper, the production decisions across multiple energy suppliers in
smart grid, powering cellular networks are investigated. The suppliers are
characterized by different offered prices and pollutant emissions levels. The
challenge is to decide the amount of energy provided by each supplier to each
of the operators such that their profitability is maximized while respecting
the maximum tolerated level of CO2 emissions. The cellular operators are
characterized by their offered quality of service (QoS) to the subscribers and
the number of users that determines their energy requirements. Stochastic
geometry is used to determine the average power needed to achieve the target
probability of coverage for each operator. The total average power requirements
of all networks are fed to an optimization framework to find the optimal amount
of energy to be provided from each supplier to the operators. The generalized
-fair utility function is used to avoid production bias among the
suppliers based on profitability of generation. Results illustrate the
production behavior of the energy suppliers versus QoS level, cost of energy,
capacity of generation, and level of fairness.Comment: 6 pages, 4 figure
A Stochastic Team Formation Approach for Collaborative Mobile Crowdsourcing
Mobile Crowdsourcing (MCS) is the generalized act of outsourcing sensing
tasks, traditionally performed by employees or contractors, to a large group of
smart-phone users by means of an open call. With the increasing complexity of
the crowdsourcing applications, requesters find it essential to harness the
power of collaboration among the workers by forming teams of skilled workers
satisfying their complex tasks' requirements. This type of MCS is called
Collaborative MCS (CMCS). Previous CMCS approaches have mainly focused only on
the aspect of team skills maximization. Other team formation studies on social
networks (SNs) have only focused on social relationship maximization. In this
paper, we present a hybrid approach where requesters are able to hire a team
that, not only has the required expertise, but also is socially connected and
can accomplish tasks collaboratively. Because team formation in CMCS is proven
to be NP-hard, we develop a stochastic algorithm that exploit workers knowledge
about their SN neighbors and asks a designated leader to recruit a suitable
team. The proposed algorithm is inspired from the optimal stopping strategies
and uses the odds-algorithm to compute its output. Experimental results show
that, compared to the benchmark exponential optimal solution, the proposed
approach reduces computation time and produces reasonable performance results.Comment: This paper is accepted for publication in 2019 31st International
Conference on Microelectronics (ICM
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