10,337 research outputs found
Middleware Technologies for Cloud of Things - a survey
The next wave of communication and applications rely on the new services
provided by Internet of Things which is becoming an important aspect in human
and machines future. The IoT services are a key solution for providing smart
environments in homes, buildings and cities. In the era of a massive number of
connected things and objects with a high grow rate, several challenges have
been raised such as management, aggregation and storage for big produced data.
In order to tackle some of these issues, cloud computing emerged to IoT as
Cloud of Things (CoT) which provides virtually unlimited cloud services to
enhance the large scale IoT platforms. There are several factors to be
considered in design and implementation of a CoT platform. One of the most
important and challenging problems is the heterogeneity of different objects.
This problem can be addressed by deploying suitable "Middleware". Middleware
sits between things and applications that make a reliable platform for
communication among things with different interfaces, operating systems, and
architectures. The main aim of this paper is to study the middleware
technologies for CoT. Toward this end, we first present the main features and
characteristics of middlewares. Next we study different architecture styles and
service domains. Then we presents several middlewares that are suitable for CoT
based platforms and lastly a list of current challenges and issues in design of
CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268,
Digital Communications and Networks, Elsevier (2017
Middleware Technologies for Cloud of Things - a survey
The next wave of communication and applications rely on the new services
provided by Internet of Things which is becoming an important aspect in human
and machines future. The IoT services are a key solution for providing smart
environments in homes, buildings and cities. In the era of a massive number of
connected things and objects with a high grow rate, several challenges have
been raised such as management, aggregation and storage for big produced data.
In order to tackle some of these issues, cloud computing emerged to IoT as
Cloud of Things (CoT) which provides virtually unlimited cloud services to
enhance the large scale IoT platforms. There are several factors to be
considered in design and implementation of a CoT platform. One of the most
important and challenging problems is the heterogeneity of different objects.
This problem can be addressed by deploying suitable "Middleware". Middleware
sits between things and applications that make a reliable platform for
communication among things with different interfaces, operating systems, and
architectures. The main aim of this paper is to study the middleware
technologies for CoT. Toward this end, we first present the main features and
characteristics of middlewares. Next we study different architecture styles and
service domains. Then we presents several middlewares that are suitable for CoT
based platforms and lastly a list of current challenges and issues in design of
CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268,
Digital Communications and Networks, Elsevier (2017
Just-in-Time Memoryless Trust for Crowdsourced IoT Services
We propose just-in-time memoryless trust for crowdsourced IoT services. We
leverage the characteristics of the IoT service environment to evaluate their
trustworthiness. A novel framework is devised to assess a service's trust
without relying on previous knowledge, i.e., memoryless trust. The framework
exploits service-session-related data to offer a trust value valid only during
the current session, i.e., just-in-time trust. Several experiments are
conducted to assess the efficiency of the proposed framework.Comment: 8 pages, Accepted and to appear in 2020 IEEE International Conference
on Web Services (ICWS). Content may change prior to final publicatio
A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd
Mobile CrowdSensing (MCS), through employing considerable workers to sense
and collect data in a participatory manner, has been recognized as a promising
paradigm for building many large-scale applications in a cost-effective way,
such as combating COVID-19. The recruitment of trustworthy and high-quality
workers is an important research issue for MCS. Previous studies assume that
the qualities of workers are known in advance, or the platform knows the
qualities of workers once it receives their collected data. In reality, to
reduce their costs and thus maximize revenue, many strategic workers do not
perform their sensing tasks honestly and report fake data to the platform. So,
it is very hard for the platform to evaluate the authenticity of the received
data. In this paper, an incentive mechanism named Semi-supervision based
Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve
the recruitment problem of multiple unknown and strategic workers in MCS.
First, we model the worker recruitment as a multi-armed bandit reverse auction
problem, and design an UCB-based algorithm to separate the exploration and
exploitation, considering the Sensing Rates (SRs) of recruited workers as the
gain of the bandit. Next, a Semi-supervised Sensing Rate Learning (SSRL)
approach is proposed to quickly and accurately obtain the workers' SRs, which
consists of two phases, supervision and self-supervision. Last, SCMABA is
designed organically combining the SRs acquisition mechanism with multi-armed
bandit reverse auction, where supervised SR learning is used in the
exploration, and the self-supervised one is used in the exploitation. We prove
that our SCMABA achieves truthfulness and individual rationality. Additionally,
we exhibit outstanding performances of the SCMABA mechanism through in-depth
simulations of real-world data traces.Comment: 18 pages, 14 figure
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
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