7,869 research outputs found
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
Map++: A Crowd-sensing System for Automatic Map Semantics Identification
Digital maps have become a part of our daily life with a number of commercial
and free map services. These services have still a huge potential for
enhancement with rich semantic information to support a large class of mapping
applications. In this paper, we present Map++, a system that leverages standard
cell-phone sensors in a crowdsensing approach to automatically enrich digital
maps with different road semantics like tunnels, bumps, bridges, footbridges,
crosswalks, road capacity, among others. Our analysis shows that cell-phones
sensors with humans in vehicles or walking get affected by the different road
features, which can be mined to extend the features of both free and commercial
mapping services. We present the design and implementation of Map++ and
evaluate it in a large city. Our evaluation shows that we can detect the
different semantics accurately with at most 3% false positive rate and 6% false
negative rate for both vehicle and pedestrian-based features. Moreover, we show
that Map++ has a small energy footprint on the cell-phones, highlighting its
promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on
Sensing, Communication, and Networking (IEEE SECON 2014
Trust-Based Fusion of Untrustworthy Information in Crowdsourcing Applications
In this paper, we address the problem of fusing untrustworthy reports provided from a crowd of observers, while simultaneously learning the trustworthiness of individuals. To achieve this, we construct a likelihood model of the userss trustworthiness by scaling the uncertainty of its multiple estimates with trustworthiness parameters. We incorporate our trust model into a fusion method that merges estimates based on the trust parameters and we provide an inference algorithm that jointly computes the fused output and the individual trustworthiness of the users based on the maximum likelihood framework. We apply our algorithm to cell tower localisation using real-world data from the OpenSignal project and we show that it outperforms the state-of-the-art methods in both accuracy, by up to 21%, and consistency, by up to 50% of its predictions. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved
Event-triggered robust distributed state estimation for sensor networks with state-dependent noises
This paper is concerned with the event-triggered distributed state estimation problem for a class of uncertain stochastic systems with state-dependent noises and randomly occurring uncertainties over sensor networks. An event-triggered communication scheme is proposed in order to determine whether the measurements on each sensor should be transmitted to the estimators or not. The norm-bounded uncertainty enters into the system in a random way. Through available output measurements from not only the individual sensor but also its neighbouring sensors, a sufficient condition is established for the desired distributed estimator to ensure that the estimation error dynamics are exponentially mean-square stable. These conditions are characterized in terms of the feasibility of a set of linear matrix inequalities, and then the explicit expression is given for the distributed estimator gains. Finally, a simulation example is provided to show the effectiveness of the proposed event-triggered distributed state estimation scheme.This work was supported in part by the Deanship of Scientific Research (DSR) at King Abdulaziz University of Saudi Arabia under Grant 16-135-35-HiCi, the National Natural Science Foundation of China under Grants 61374127 and 61329301, the Scientific and Technology Research Foundation of Heilongjiang Education Department of China under Grant 12541061 and 12511014, and the Alexander von Humboldt Foundation of
Germany
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