1,396 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
Network Topology and Protocol Design for Efficient Consensus in Sensor Networks
Doktorgradsavhandling ved Fakultet for teknologi og naturvitenskap, Universitetet i Agder, 2016In the new era of Internet of Things, complex sensor networks are becoming
crucial to link the physical world to the Internet. These sensor networks,
composed by hundreds or thousands of nodes, provide many important services
to promote a heightened level of awareness about the area of interest
such as in predictive maintenance, intelligent buildings, enhanced security
systems, etc. In order to make these services possible, several signal processing
tasks are needed to support their operation, some widely used examples
of these tasks are parameter estimation, signal detection and target tracking.
These tasks allow to improve the services by inferring missing data, reducing
samples noise, etc., at the cost of some collaboration of the network nodes
that implies their repeated communication over time. Most of these solutions
are consensus-based strategies, which have recently attracted a great deal of
research work because of its simplicity. These are in-network algorithms,
where each node only exchanges information with its immediate neighbors
and these are able to obtain global information as a function of some sensed
data. A relevant example is the average consensus algorithm, which its goal
is to obtain, in a distributed way, the average of the initial data. These algorithms
avoid the need of performing all the computations at one or more sink
nodes, thus, reducing congestion around them and incrementing the robustness
of the network.
In this dissertation, we focus on improving consensus algorithms in terms
of different parameters and under different types of communications and network
configurations. Each setting considered requires its own assumptions
and methodologies, since the convergence conditions for each of them are
related but different in general. In particular, in this work, all of the methodologies
proposed are based on designing how the underlying communications
are performed.
In static networks, where the asymptotic convergence to the average value
is easily ensured, a topology optimization can be a priori performed in terms
of several relevant parameters. In particular, we optimize the network topology
to make consensus algorithms fast and energy efficient. In this setting
and for continuous systems, we derive a general framework to minimize several
energy related functions under different network and nodes constraints To solve it, we propose a fractional convex-concave optimization problem
with different constraints that leads to obtain the optimal topology in terms
of the energy function considered. As a significant variation of the previous
results, we also optimize the network topology in discrete systems. The
discretization of the system introduces a weight matrix and certain step-size
(related to the discrete increments of time) in the process. We show that if
this step-size is small enough, the energy related problems stated before can
be still casted as convex-concave fractional problems with the weight matrix
as a unique optimization variable. As the step-size of the process increases
in size, a discrete system requires a different approach. To solve it, we aim to
find another formulation based on adding a constraint on the connectivity and
solving the problem several times (for different values of the step-size). In
addition, two low-complex methodologies with different computational requirements
are proposed to a posteriori redesign an existing topology by the
collaboration of the network nodes.
On the contrary, in time-varying (random) networks, it is needed to guarantee
a minimum accuracy of the algorithm, while maximizing the number of
simultaneous exchanges of data to ensure fast convergence. Regarding random
and asymmetric communications, we propose a novel gossip algorithm,
which is based on the residual information that is generated when an asymmetric
communication is performed. We exploit this information to preserve
the summation of the process and accelerate it. Moreover, our proposal is
useful in the case of having both unicast and broadcast communications, presenting
faster convergence in both schemes than existing approaches in the related
literature. When the problem of wireless interferences constraining the
communications is additionally taken into account, we propose a novel and
computationally efficient link scheduling protocol that correctly operates in
the presence of secondary interference. Our protocol is easily implementable
and does not require global knowledge of the network. The main objective
of this new protocol is to be suitable for a cross-layer scheme in which the
execution of the average consensus algorithm is favoured, ensuring necessary
conditions for its convergence with certain accuracy. Additionally, the
number of simultaneous links is additionally considered in order to make the
convergence of the consensus process as fast as possible
Synchronization in complex networks
Synchronization processes in populations of locally interacting elements are
in the focus of intense research in physical, biological, chemical,
technological and social systems. The many efforts devoted to understand
synchronization phenomena in natural systems take now advantage of the recent
theory of complex networks. In this review, we report the advances in the
comprehension of synchronization phenomena when oscillating elements are
constrained to interact in a complex network topology. We also overview the new
emergent features coming out from the interplay between the structure and the
function of the underlying pattern of connections. Extensive numerical work as
well as analytical approaches to the problem are presented. Finally, we review
several applications of synchronization in complex networks to different
disciplines: biological systems and neuroscience, engineering and computer
science, and economy and social sciences.Comment: Final version published in Physics Reports. More information
available at http://synchronets.googlepages.com
Distributed Robotic Vision for Calibration, Localisation, and Mapping
This dissertation explores distributed algorithms for calibration, localisation, and mapping in the context of a multi-robot network equipped with cameras and onboard processing, comparing against centralised alternatives where all data is transmitted to a singular external node on which processing occurs. With the rise of large-scale camera networks, and as low-cost on-board processing becomes increasingly feasible in robotics networks, distributed algorithms are becoming important for robustness and scalability. Standard solutions to multi-camera computer vision require the data from all nodes to be processed at a central node which represents a significant single point of failure and incurs infeasible communication costs. Distributed solutions solve these issues by spreading the work over the entire network, operating only on local calculations and direct communication with nearby neighbours.
This research considers a framework for a distributed robotic vision platform for calibration, localisation, mapping tasks where three main stages are identified: an initialisation stage where calibration and localisation are performed in a distributed manner, a local tracking stage where visual odometry is performed without inter-robot communication, and a global mapping stage where global alignment and optimisation strategies are applied. In consideration of this framework, this research investigates how algorithms can be developed to produce fundamentally distributed solutions, designed to minimise computational complexity whilst maintaining excellent performance, and designed to operate effectively in the long term. Therefore, three primary objectives are sought aligning with these three stages
A Survey on Virtualization of Wireless Sensor Networks
Wireless Sensor Networks (WSNs) are gaining tremendous importance thanks to their broad range of commercial applications such as in smart home automation, health-care and industrial automation. In these applications multi-vendor and heterogeneous sensor nodes are deployed. Due to strict administrative control over the specific WSN domains, communication barriers, conflicting goals and the economic interests of different WSN sensor node vendors, it is difficult to introduce a large scale federated WSN. By allowing heterogeneous sensor nodes in WSNs to coexist on a shared physical sensor substrate, virtualization in sensor network may provide flexibility, cost effective solutions, promote diversity, ensure security and increase manageability. This paper surveys the novel approach of using the large scale federated WSN resources in a sensor virtualization environment. Our focus in this paper is to introduce a few design goals, the challenges and opportunities of research in the field of sensor network virtualization as well as to illustrate a current status of research in this field. This paper also presents a wide array of state-of-the art projects related to sensor network virtualization
Unsupervised Anomaly Detection of High Dimensional Data with Low Dimensional Embedded Manifold
Anomaly detection techniques are supposed to identify anomalies from loads of seemingly homogeneous data and being able to do so can lead us to timely, pivotal and actionable decisions, saving us from potential human, financial and informational loss. In anomaly detection, an often encountered situation is the absence of prior knowledge about the nature of anomalies. Such circumstances advocate for ‘unsupervised’ learning-based anomaly detection techniques. Compared to its ‘supervised’ counterpart, which possesses the luxury to utilize a labeled training dataset containing both normal and anomalous samples, unsupervised problems are far more difficult. Moreover, high dimensional streaming data from tons of interconnected sensors present in modern day industries makes the task more challenging. To carry out an investigative effort to address these challenges is the overarching theme of this dissertation.
In this dissertation, the fundamental issue of similarity measure among observations, which is a central piece in any anomaly detection techniques, is reassessed. Manifold hypotheses suggests the possibility of low dimensional manifold structure embedded in high dimensional data. In the presence of such structured space, traditional similarity measures fail to measure the true intrinsic similarity. In light of this revelation, reevaluating the notion of similarity measure seems more pressing rather than providing incremental improvements over any of the existing techniques. A graph theoretic similarity measure is proposed to differentiate and thus identify the anomalies from normal observations. Specifically, the minimum spanning tree (MST), a graph-based approach is proposed to approximate the similarities among data points in the presence of high dimensional structured space. It can track the structure of the embedded manifold better than the existing measures and help to distinguish the anomalies from normal observations. This dissertation investigates further three different aspects of the anomaly detection problem and develops three sets of solution approaches with all of them revolving around the newly proposed MST based similarity measure.
In the first part of the dissertation, a local MST (LoMST) based anomaly detection approach is proposed to detect anomalies using the data in the original space. A two-step procedure is developed to detect both cluster and point anomalies. The next two sets of methods are proposed in the subsequent two parts of the dissertation, for anomaly detection in reduced data space. In the second part of the dissertation, a neighborhood structure assisted version of the nonnegative matrix factorization approach (NS-NMF) is proposed. To detect anomalies, it uses the neighborhood information captured by a sparse MST similarity matrix along with the original attribute information. To meet the industry demands, the online version of both LoMST and NS-NMF is also developed for real-time anomaly detection. In the last part of the dissertation, a graph regularized autoencoder is proposed which uses an MST regularizer in addition to the original loss function and is thus capable of maintaining the local invariance property. All of the approaches proposed in the dissertation are tested on 20 benchmark datasets and one real-life hydropower dataset. When compared with the state of art approaches, all three approaches produce statistically significant better outcomes.
“Industry 4.0” is a reality now and it calls for anomaly detection techniques capable of processing a large amount of high dimensional data generated in real-time. The proposed MST based similarity measure followed by the individual techniques developed in this dissertation are equipped to tackle each of these issues and provide an effective and reliable real-time anomaly identification platform
Collaborative autonomy in heterogeneous multi-robot systems
As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition.
This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems.
Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots
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