17 research outputs found
A gap analysis of Internet-of-Things platforms
We are experiencing an abundance of Internet-of-Things (IoT) middleware
solutions that provide connectivity for sensors and actuators to the Internet.
To gain a widespread adoption, these middleware solutions, referred to as
platforms, have to meet the expectations of different players in the IoT
ecosystem, including device providers, application developers, and end-users,
among others. In this article, we evaluate a representative sample of these
platforms, both proprietary and open-source, on the basis of their ability to
meet the expectations of different IoT users. The evaluation is thus more
focused on how ready and usable these platforms are for IoT ecosystem players,
rather than on the peculiarities of the underlying technological layers. The
evaluation is carried out as a gap analysis of the current IoT landscape with
respect to (i) the support for heterogeneous sensing and actuating
technologies, (ii) the data ownership and its implications for security and
privacy, (iii) data processing and data sharing capabilities, (iv) the support
offered to application developers, (v) the completeness of an IoT ecosystem,
and (vi) the availability of dedicated IoT marketplaces. The gap analysis aims
to highlight the deficiencies of today's solutions to improve their integration
to tomorrow's ecosystems. In order to strengthen the finding of our analysis,
we conducted a survey among the partners of the Finnish IoT program, counting
over 350 experts, to evaluate the most critical issues for the development of
future IoT platforms. Based on the results of our analysis and our survey, we
conclude this article with a list of recommendations for extending these IoT
platforms in order to fill in the gaps.Comment: 15 pages, 4 figures, 3 tables, Accepted for publication in Computer
Communications, special issue on the Internet of Things: Research challenges
and solution
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis
arXiv:1912.06384 [eess.SP]The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.Peer reviewe
Toward Massive Scale Air Quality Monitoring
Dangers associated with poor air quality are driving deployments of air quality monitoring technology. These deployments rely either on professional-grade measurement stations or a small number of low-cost sensors integrated into urban infrastructure. In this article, we present a research vision of real-time massive scale air quality sensing that integrates tens of thousands or even millions of air quality sensors to monitor air quality at fine spatial and temporal resolution. We highlight opportunities and challenges of our vision by discussing use cases, key requirements and reference technologies in order to establish a roadmap on how to realize this vision. We address the feasibility of our vision, introducing a testbed deployment in Helsinki, Finland, and carrying out controlled experiments that address collaborative and opportunistic sensor calibration, a key research challenge for our vision.Peer reviewe
Bio-Inspired Routing and Resource Management for Future Networks
Communication systems have continually played a major role in our lives. In particular
in recent years, the Internet has transformed the way people interact and
communicate, which has led to increased number of services and functionalities.
However, with the increase in changes in network environments, more adaptive,
exible, efficient and scalable techniques are needed to enhance the operation of
the network, and at the same time minimise human intervention. The communication
network management community have addressed this problem, through the
notion of \autonomic network management". One particular approach of addressing
autonomic mechanisms is by borrowing mechanisms and processes that are
exhibited by biological systems (e.g. reaction to changes in their environments).
This thesis has investigated new bio-inspired solutions that are able to address
the challenges of the Future Internet. The thesis will present new bio-inspired
mechanisms to provide (i) efficient routing, (ii) energy-aware networking, (iii) adaptive
bandwidth allocation and (iv) support of multiple service providers. In the
case of (i), the bio-inspired routing protocol is scalable and supports complex and
highly dynamic services environments efficiently and robustly (e.g. dynamic traf-
fic, large scale networks). The bio-inspired energy awareness in (ii) is maximising
the benefits of the solution in (i) to reduce dramatically the energy consumption
of infrastructure networks without disrupting the delivery of services. In the case
of (iii), a bio-inspired bandwidth allocation mechanism adapts to new traffic conditions
in order to maintain the quality of delivery for prioritised traffic in the
event of bandwidth starvation. Lastly in (iv), the new architecture of the Internet,
iii
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requiring efficient and fair resource allocation between multiple service providers
sharing common physical resources, will be provided by an adaptable and
exible
bio-inspired model