22,482 research outputs found
Emotions in context: examining pervasive affective sensing systems, applications, and analyses
Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; âsensingâ, âanalysisâ, and âapplicationâ. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing
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
Visualizing Sensor Network Coverage with Location Uncertainty
We present an interactive visualization system for exploring the coverage in
sensor networks with uncertain sensor locations. We consider a simple case of
uncertainty where the location of each sensor is confined to a discrete number
of points sampled uniformly at random from a region with a fixed radius.
Employing techniques from topological data analysis, we model and visualize
network coverage by quantifying the uncertainty defined on its simplicial
complex representations. We demonstrate the capabilities and effectiveness of
our tool via the exploration of randomly distributed sensor networks
Integrated process of images and acceleration measurements for damage detection
The use of mobile robots and UAV to catch unthinkable images together with on-site global automated acceleration measurements easy achievable by wireless sensors, able of remote data transfer, have strongly enhanced the capability of defect and damage evaluation in bridges. A sequential procedure is, here, proposed for damage monitoring and bridge condition assessment based on both: digital image processing for survey and defect evaluation and structural identification based on acceleration measurements. A steel bridge has been simultaneously inspected by UAV to acquire images using visible light, or infrared radiation, and monitored through a wireless sensor network (WSN) measuring structural vibrations. First, image processing has been used to construct a geometrical model and to quantify corrosion extension. Then, the consistent structural model has been updated based on the modal quantities identified using the acceleration measurements acquired by the deployed WSN. © 2017 The Authors. Published by Elsevier Ltd
Efficient Algorithms for Distributed Detection of Holes and Boundaries in Wireless Networks
We propose two novel algorithms for distributed and location-free boundary
recognition in wireless sensor networks. Both approaches enable a node to
decide autonomously whether it is a boundary node, based solely on connectivity
information of a small neighborhood. This makes our algorithms highly
applicable for dynamic networks where nodes can move or become inoperative.
We compare our algorithms qualitatively and quantitatively with several
previous approaches. In extensive simulations, we consider various models and
scenarios. Although our algorithms use less information than most other
approaches, they produce significantly better results. They are very robust
against variations in node degree and do not rely on simplified assumptions of
the communication model. Moreover, they are much easier to implement on real
sensor nodes than most existing approaches.Comment: extended version of accepted submission to SEA 201
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
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