12,771 research outputs found
Earthquake Early Warning and Beyond: Systems Challenges in Smartphone-based Seismic Network
Earthquake Early Warning (EEW) systems can effectively reduce fatalities,
injuries, and damages caused by earthquakes. Current EEW systems are mostly
based on traditional seismic and geodetic networks, and exist only in a few
countries due to the high cost of installing and maintaining such systems. The
MyShake system takes a different approach and turns people's smartphones into
portable seismic sensors to detect earthquake-like motions. However, to issue
EEW messages with high accuracy and low latency in the real world, we need to
address a number of challenges related to mobile computing. In this paper, we
first summarize our experience building and deploying the MyShake system, then
focus on two key challenges for smartphone-based EEW (sensing heterogeneity and
user/system dynamics) and some preliminary exploration. We also discuss other
challenges and new research directions associated with smartphone-based seismic
network.Comment: 6 pages, conference paper, already accepted at hotmobile 201
Towards new methods for mobility data gathering: content, sources, incentives
Over the past decade, huge amounts of work has been done in mobile and opportunistic networking research. Unfortunately, much of this has had little impact as the results have not been applicable to reality, due to incorrect assumptions and models used in the design and evaluation of the systems.
In this paper, we outline some of the problems of the assumptions of early research in the field, and provide a survey of some initial work that has started to take place to alleviate this through more realistic modelling and measurements of real systems. We do note that there is still much work to be done in this area, and then go on to identify some important properties of the network that must be studied further. We identify the types of data that are important to measure, and also give some guidelines on finding existing and potentially new sources for such data and incentivizing the holders of the data to share it
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
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
A New Protocol for Cooperative Spectrum Sharing in Mobile Cognitive Radio Networks
To optimize the usage of limited spectrum resources, cognitive radio (CR) can be used as a viable solution. The main contribution of this article is to propose a new protocol to increase throughput of mobile cooperative CR networks (CRNs). The key challenge in a CRN is how the nodes cooperate to access the channel in order to maximize the CRN's throughput. To minimize unnecessary blocking of CR transmission, we propose a so-called new frequency-range MAC protocol (NFRMAC). The proposed method is in fact a novel channel assignment mechanism that exploits the dependence between signal's attenuation model, signal's frequency, communication range, and interference level. Compared .to the conventional methods, the proposed algorithm considers a more realistic model for the mobility pattern of CR nodes and also adaptively selects the maximal transmission range of each node over which reliable transmission is possible. Simulation results indicate that using NFRMAC leads to an increase of the total CRN's throughput by 6% and reduces the blocking rate by 10% compared to those of conventional methods
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