3 research outputs found
When Social Sensing Meets Edge Computing: Vision and Challenges
This paper overviews the state of the art, research challenges, and future
opportunities in an emerging research direction: Social Sensing based Edge
Computing (SSEC). Social sensing has emerged as a new sensing application
paradigm where measurements about the physical world are collected from humans
or from devices on their behalf. The advent of edge computing pushes the
frontier of computation, service, and data along the cloud-to-things continuum.
The merging of these two technical trends generates a set of new research
challenges that need to be addressed. In this paper, we first define the new
SSEC paradigm that is motivated by a few underlying technology trends. We then
present a few representative real-world case studies of SSEC applications and
several key research challenges that exist in those applications. Finally, we
envision a few exciting research directions in future SSEC. We hope this paper
will stimulate discussions of this emerging research direction in the
community.Comment: This manuscript has been accepted to ICCCN 201
DASC: Towards A Road Damage-Aware Social-Media-Driven Car Sensing Framework for Disaster Response Applications
While vehicular sensor networks (VSNs) have earned the stature of a mobile
sensing paradigm utilizing sensors built into cars, they have limited sensing
scopes since car drivers only opportunistically discover new events.
Conversely, social sensing is emerging as a new sensing paradigm where
measurements about the physical world are collected from humans. In contrast to
VSNs, social sensing is more pervasive, but one of its key limitations lies in
its inconsistent reliability stemming from the data contributed by unreliable
human sensors. In this paper, we present DASC, a road Damage-Aware
Social-media-driven Car sensing framework that exploits the collective power of
social sensing and VSNs for reliable disaster response applications. However,
integrating VSNs with social sensing introduces a new set of challenges: i) How
to leverage noisy and unreliable social signals to route the vehicles to
accurate regions of interest? ii) How to tackle the inconsistent availability
(e.g., churns) caused by car drivers being rational actors? iii) How to
efficiently guide the cars to the event locations with little prior knowledge
of the road damage caused by the disaster, while also handling the dynamics of
the physical world and social media? The DASC framework addresses the above
challenges by establishing a novel hybrid social-car sensing system that
employs techniques from game theory, feedback control, and Markov Decision
Process (MDP). In particular, DASC distills signals emitted from social media
and discovers the road damages to effectively drive cars to target areas for
verifying emergency events. We implement and evaluate DASC in a reputed vehicle
simulator that can emulate real-world disaster response scenarios. The results
of a real-world application demonstrate the superiority of DASC over current
VSNs-based solutions in detection accuracy and efficiency.Comment: Elsevier Pervasive and Mobile Computing (accepted for publication
Towards Privacy-aware Task Allocation in Social Sensing based Edge Computing Systems
With the advance in mobile computing, Internet of Things, and ubiquitous
wireless connectivity, social sensing based edge computing (SSEC) has emerged
as a new computation paradigm where people and their personally owned devices
collect sensor measurements from the physical world and process them at the
edge of the network. This paper focuses on a privacy-aware task allocation
problem where the goal is to optimize the computation task allocation in SSEC
systems while respecting the users' customized privacy settings. It introduces
a novel Game-theoretic Privacy-aware Task Allocation (G-PATA) framework to
achieve the goal. G-PATA includes (i) a bottom-up game-theoretic model to
generate the maximum payoffs at end devices while satisfying the end user's
privacy settings; (ii) a top-down incentive scheme to adjust the rewards for
the tasks to ensure that the task allocation decisions made by end devices meet
the Quality of Service (QoS) requirements of the applications. Furthermore, the
framework incorporates an efficient load balancing and iteration reduction
component to adapt to the dynamic changes in status and privacy configurations
of end devices. The G-PATA framework was implemented on a real-world edge
computing platform that consists of heterogeneous end devices (Jetson TX1 and
TK1 boards, and Raspberry Pi3). We compare G-PATA with state-of-the-art task
allocation schemes through two real-world social sensing applications. The
results show that G-PATA significantly outperforms existing approaches under
various privacy settings (our scheme achieved as much as 47% improvements in
delay reduction for the application and 15% more payoffs for end devices
compared to the baselines.)