15 research outputs found
Position Estimation of Robotic Mobile Nodes in Wireless Testbed using GENI
We present a low complexity experimental RF-based indoor localization system
based on the collection and processing of WiFi RSSI signals and processing
using a RSS-based multi-lateration algorithm to determine a robotic mobile
node's location. We use a real indoor wireless testbed called w-iLab.t that is
deployed in Zwijnaarde, Ghent, Belgium. One of the unique attributes of this
testbed is that it provides tools and interfaces using Global Environment for
Network Innovations (GENI) project to easily create reproducible wireless
network experiments in a controlled environment. We provide a low complexity
algorithm to estimate the location of the mobile robots in the indoor
environment. In addition, we provide a comparison between some of our collected
measurements with their corresponding location estimation and the actual robot
location. The comparison shows an accuracy between 0.65 and 5 meters.Comment: (c) 2016 IEEE. Personal use of this material is permitted. Permission
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this work in other work
Building the Future Internet through FIRE
The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate
Towards Autonomous Computer Networks in Support of Critical Systems
L'abstract è presente nell'allegato / the abstract is in the attachmen
Building the Future Internet through FIRE
The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate
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Architectures and algorithms for dynamic overlay networks
Most of today’s Internet of Things (IoT) applications assume that data will be moved offdevices into centralized cloud platforms. While existing IoT systems leverage cloud-based analytics for meaningful data reasoning, the assumption that data should always be moved off the devices is problematic. The amount of data to be moved from devices over Internet gateways to cloud platforms is huge which potentially make it cost inefficient. In other scenarios, privacy concerns of customers or organizational rules complicate the process of transferring data to third-party data centers.This dissertation proposes architectures and dynamic overlay network algorithms for in-networkand edge processing of data offered by the globally available IoT devices and provides a global platform for meaningful and responsive data analysis and decision making. The proposed techniques shift IoT analytics from a ”collect data now and analyze it later” scenario to directlyproviding meaningful information from the in-network processing of devices data at or near thedevices. The techniques serve future IoT use cases including distributed context awareness, on-demand data analysis, and in-network decision making. The dissertation comprises three main components.The first component is a device management protocol for cloning devices’ data in proximateEdge Computing platforms. Unlike existing application-layer IoT management protocols theproposed protocol uses the LTE LTE-A radio frame structure, device-to-device communication,and IoT data properties to avoid excessive network access latency in existing technologies.The second component realizes distributed IoT analytics as overlay networks of devices clones. By means of virtual network embedding, it selects and interconnects devices’ clones to efficiently realize applications’ virtual topologies to achieve goals such as minimum latency, minimum infrastructure cost, or maximum infrastructure utilization.Finally, the dissertation presents a communication middleware that allows autonomous discovery, self-deployment, and online migration of devices’ clones across heterogeneous Edge computing platforms. The middleware ensures that communication latency between clones is kept minimum despite the uncontrolled variability of the network and hosting platforms conditions.We evaluate the proposed architectures and algorithms through simulations and prototypeimplementation of various components in controlled testbed environments, which we evaluateusing real user applications. We explore the feasibility of the proposed techniques from boththeoretical and practical perspectives.Keywords: Cloud Computing, Internet of Things, Algorithmic Game Theory, Compressive Sensin
Cyber physical approach and framework for micro devices assembly
The emergence of Cyber Physical Systems (CPS) and Internet-of-Things (IoT) based principles and technologies holds the potential to facilitate global collaboration in various fields of engineering. Micro Devices Assembly (MDA) is an emerging domain involving the assembly of micron sized objects and devices. In this dissertation, the focus of the research is the design of a Cyber Physical approach for the assembly of micro devices. A collaborative framework comprising of cyber and physical components linked using the Internet has been developed to accomplish a targeted set of MDA life cycle activities which include assembly planning, path planning, Virtual Reality (VR) based assembly analysis, command generation and physical assembly. Genetic algorithm and modified insertion algorithm based methods have been proposed to support assembly planning activities. Advanced VR based environments have been designed to support assembly analysis where plans can be proposed, compared and validated. The potential of next generation Global Environment for Network Innovation (GENI) networking technologies has also been explored to support distributed collaborations involving VR-based environments. The feasibility of the cyber physical approach has been demonstrated by implementing the cyber physical components which collaborate to assemble micro designs. The case studies conducted underscore the ability of the developed Cyber Physical approach and framework to support distributed collaborative activities for MDA process contexts
Doctor of Philosophy
dissertationNetwork emulation has become an indispensable tool for the conduct of research in networking and distributed systems. It offers more realism than simulation and more control and repeatability than experimentation on a live network. However, emulation testbeds face a number of challenges, most prominently realism and scale. Because emulation allows the creation of arbitrary networks exhibiting a wide range of conditions, there is no guarantee that emulated topologies reflect real networks; the burden of selecting parameters to create a realistic environment is on the experimenter. While there are a number of techniques for measuring the end-to-end properties of real networks, directly importing such properties into an emulation has been a challenge. Similarly, while there exist numerous models for creating realistic network topologies, the lack of addresses on these generated topologies has been a barrier to using them in emulators. Once an experimenter obtains a suitable topology, that topology must be mapped onto the physical resources of the testbed so that it can be instantiated. A number of restrictions make this an interesting problem: testbeds typically have heterogeneous hardware, scarce resources which must be conserved, and bottlenecks that must not be overused. User requests for particular types of nodes or links must also be met. In light of these constraints, the network testbed mapping problem is NP-hard. Though the complexity of the problem increases rapidly with the size of the experimenter's topology and the size of the physical network, the runtime of the mapper must not; long mapping times can hinder the usability of the testbed. This dissertation makes three contributions towards improving realism and scale in emulation testbeds. First, it meets the need for realistic network conditions by creating Flexlab, a hybrid environment that couples an emulation testbed with a live-network testbed, inheriting strengths from each. Second, it attends to the need for realistic topologies by presenting a set of algorithms for automatically annotating generated topologies with realistic IP addresses. Third, it presents a mapper, assign, that is capable of assigning experimenters' requested topologies to testbeds' physical resources in a manner that scales well enough to handle large environments
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Design and development of an SDN robotic system with intelligent openflow IOT testbeds for power assessment, prediction and fault management
This thesis was submitted for the award of Docctor of Philosophy and was awarded by Brunel University LondonCurrent wind turbine and power grid industry have relatively little research and
development with regards to implementing novel communication network and intel-
ligent system to overcome issues that pertain to network failure and lack of monitor-
ing. Wind turbine location could be a big concern when it comes to identifying an
efficient location for future wind turbine and the impact of a site with non-efficient
meteorological parameters can result in relocation of a wind turbine and revenue-
loss. Unplanned wind turbine shutdowns that are considered to be one of the major
revenue-loss factors of a modern wind farm business. Typically, the unplanned wind
turbine shutdown is a result of sensors fail due to harsh environment challenges that
prevent hardware status from being available on the monitoring system. The above
mentioned research problems pertain to wind turbine site assessment and predic-
tion of power. In this thesis, a novel programmable software-defined robotics and
IoT testbeds are proposed with the fusion of Artificial Intelligence and optimiza-
tion methods to solve specific problems related to wind turbine site assessment and
fault management. The site selection process is implemented using proposed aerial
and ground robotic systems that are incorporated with Software-Defined Networks
and OpenFlow switching capabilities. A second stage development of the system is
proposing a prediction platform that run on the aerial robot cluster using neural net-
works optimization regression techniques. To overcome the unplanned wind turbine
network outage, an IoT micro cloud cluster system is proposed that act as immedi-
ate fail-over platform to provide continuous health readings of the wind turbine to
ensure the turbine in question will not get shutdown unnecessarily. The proposed
system help in minimizing revenue-loss caused by stopping a wind turbine from op-
eration and help maintain generated power stability on the grid. Additionally, since
large wind farms require an agile and scalable management of selecting the most
efficient wind turbine location install. Thus, a softwarized cognitive routing proto-
col is proposed. The group of quadcopters is a redundant failover Software-Defined
Network/OpenFlow system that can cover every single way point of the farm land.
Although, power consumption is essential for the continuity the service, a Software-
Defined charging system testbed is proposed that uses inductive power transfer wit
Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS 1994), volume 1
The AIAA/NASA Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS '94) was originally proposed because of the strong belief that America's problems of global economic competitiveness and job creation and preservation can partly be solved by the use of intelligent robotics, which are also required for human space exploration missions. Individual sessions addressed nuclear industry, agile manufacturing, security/building monitoring, on-orbit applications, vision and sensing technologies, situated control and low-level control, robotic systems architecture, environmental restoration and waste management, robotic remanufacturing, and healthcare applications
System Abstractions for Scalable Application Development at the Edge
Recent years have witnessed an explosive growth of Internet of Things (IoT) devices, which collect or generate huge amounts of data. Given diverse device capabilities and application requirements, data processing takes place across a range of settings, from on-device to a nearby edge server/cloud and remote cloud. Consequently, edge-cloud coordination has been studied extensively from the perspectives of job placement, scheduling and joint optimization. Typical approaches focus on performance optimization for individual applications. This often requires domain knowledge of the applications, but also leads to application-specific solutions. Application development and deployment over diverse scenarios thus incur repetitive manual efforts. There are two overarching challenges to provide system-level support for application development at the edge. First, there is inherent heterogeneity at the device hardware level. The execution settings may range from a small cluster as an edge cloud to on-device inference on embedded devices, differing in hardware capability and programming environments. Further, application performance requirements vary significantly, making it even more difficult to map different applications to already heterogeneous hardware. Second, there are trends towards incorporating edge and cloud and multi-modal data. Together, these add further dimensions to the design space and increase the complexity significantly. In this thesis, we propose a novel framework to simplify application development and deployment over a continuum of edge to cloud. Our framework provides key connections between different dimensions of design considerations, corresponding to the application abstraction, data abstraction and resource management abstraction respectively. First, our framework masks hardware heterogeneity with abstract resource types through containerization, and abstracts away the application processing pipelines into generic flow graphs. Further, our framework further supports a notion of degradable computing for application scenarios at the edge that are driven by multimodal sensory input. Next, as video analytics is the killer app of edge computing, we include a generic data management service between video query systems and a video store to organize video data at the edge. We propose a video data unit abstraction based on a notion of distance between objects in the video, quantifying the semantic similarity among video data. Last, considering concurrent application execution, our framework supports multi-application offloading with device-centric control, with a userspace scheduler service that wraps over the operating system scheduler