14,786 research outputs found
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Survey of Inter-satellite Communication for Small Satellite Systems: Physical Layer to Network Layer View
Small satellite systems enable whole new class of missions for navigation,
communications, remote sensing and scientific research for both civilian and
military purposes. As individual spacecraft are limited by the size, mass and
power constraints, mass-produced small satellites in large constellations or
clusters could be useful in many science missions such as gravity mapping,
tracking of forest fires, finding water resources, etc. Constellation of
satellites provide improved spatial and temporal resolution of the target.
Small satellite constellations contribute innovative applications by replacing
a single asset with several very capable spacecraft which opens the door to new
applications. With increasing levels of autonomy, there will be a need for
remote communication networks to enable communication between spacecraft. These
space based networks will need to configure and maintain dynamic routes, manage
intermediate nodes, and reconfigure themselves to achieve mission objectives.
Hence, inter-satellite communication is a key aspect when satellites fly in
formation. In this paper, we present the various researches being conducted in
the small satellite community for implementing inter-satellite communications
based on the Open System Interconnection (OSI) model. This paper also reviews
the various design parameters applicable to the first three layers of the OSI
model, i.e., physical, data link and network layer. Based on the survey, we
also present a comprehensive list of design parameters useful for achieving
inter-satellite communications for multiple small satellite missions. Specific
topics include proposed solutions for some of the challenges faced by small
satellite systems, enabling operations using a network of small satellites, and
some examples of small satellite missions involving formation flying aspects.Comment: 51 pages, 21 Figures, 11 Tables, accepted in IEEE Communications
Surveys and Tutorial
Hearing the clusters in a graph: A distributed algorithm
We propose a novel distributed algorithm to cluster graphs. The algorithm
recovers the solution obtained from spectral clustering without the need for
expensive eigenvalue/vector computations. We prove that, by propagating waves
through the graph, a local fast Fourier transform yields the local component of
every eigenvector of the Laplacian matrix, thus providing clustering
information. For large graphs, the proposed algorithm is orders of magnitude
faster than random walk based approaches. We prove the equivalence of the
proposed algorithm to spectral clustering and derive convergence rates. We
demonstrate the benefit of using this decentralized clustering algorithm for
community detection in social graphs, accelerating distributed estimation in
sensor networks and efficient computation of distributed multi-agent search
strategies
Space-Time Hierarchical-Graph Based Cooperative Localization in Wireless Sensor Networks
It has been shown that cooperative localization is capable of improving both
the positioning accuracy and coverage in scenarios where the global positioning
system (GPS) has a poor performance. However, due to its potentially excessive
computational complexity, at the time of writing the application of cooperative
localization remains limited in practice. In this paper, we address the
efficient cooperative positioning problem in wireless sensor networks. A
space-time hierarchical-graph based scheme exhibiting fast convergence is
proposed for localizing the agent nodes. In contrast to conventional methods,
agent nodes are divided into different layers with the aid of the space-time
hierarchical-model and their positions are estimated gradually. In particular,
an information propagation rule is conceived upon considering the quality of
positional information. According to the rule, the information always
propagates from the upper layers to a certain lower layer and the message
passing process is further optimized at each layer. Hence, the potential error
propagation can be mitigated. Additionally, both position estimation and
position broadcasting are carried out by the sensor nodes. Furthermore, a
sensor activation mechanism is conceived, which is capable of significantly
reducing both the energy consumption and the network traffic overhead incurred
by the localization process. The analytical and numerical results provided
demonstrate the superiority of our space-time hierarchical-graph based
cooperative localization scheme over the benchmarking schemes considered.Comment: 14 pages, 15 figures, 4 tables, accepted to appear on IEEE
Transactions on Signal Processing, Sept. 201
PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms
Mobile phones provide a powerful sensing platform that researchers may adopt
to understand proximity interactions among people and the diffusion, through
these interactions, of diseases, behaviors, and opinions. However, it remains a
challenge to track the proximity-based interactions of a whole community and
then model the social diffusion of diseases and behaviors starting from the
observations of a small fraction of the volunteer population. In this paper, we
propose a novel approach that tries to connect together these sparse
observations using a model of how individuals interact with each other and how
social interactions happen in terms of a sequence of proximity interactions. We
apply our approach to track the spreading of flu in the spatial-proximity
network of a 3000-people university campus by mobilizing 300 volunteers from
this population to monitor nearby mobile phones through Bluetooth scanning and
to daily report flu symptoms about and around them. Our aim is to predict the
likelihood for an individual to get flu based on how often her/his daily
routine intersects with those of the volunteers. Thus, we use the daily
routines of the volunteers to build a model of the volunteers as well as of the
non-volunteers. Our results show that we can predict flu infection two weeks
ahead of time with an average precision from 0.24 to 0.35 depending on the
amount of information. This precision is six to nine times higher than with a
random guess model. At the population level, we can predict infectious
population in a two-week window with an r-squared value of 0.95 (a random-guess
model obtains an r-squared value of 0.2). These results point to an innovative
approach for tracking individuals who have interacted with people showing
symptoms, allowing us to warn those in danger of infection and to inform health
researchers about the progression of contact-induced diseases
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
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