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Forward to the special issue of the 9th international Symposium on Cyberspace Safety and Security (CSS 2017)

Forward to the special issue of the 9th International Sympos...

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作者： Wen, Sheng Castiglione, Aniello Wang, Tian Bhuiyan, Md Zakirul Alam School of Software and Electrical Engineering Swinburne University of Technology HawthornVIC Australia Department of Science and Technology University of Naples Parthenope Italy National Huaqiao University College of Computer Science and Technology Xiamen China Department of Computer and Information Sciences Fordham University New YorkNY United States

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An MQTT-based Resource Management Framework for Edge computing systems

An MQTT-based Resource Management Framework for Edge Computi...

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ieee international Symposium on Innovations in Intelligent systems and Applications (INISTA)

作者： Saša Pešić Miloš Radovanović Mirjana Ivanović University of Novi Sad Faculty of Sciences

ISBN: (数字)9781728167992

ISBN: (纸本)9781728168005

The complexity of IoT systems and tasks that are put before them require shifts in the way resources and service provisioning are managed. The concept of edge computing is introduced to enhance IoT systems' scalability, reactivity, efficiency, and privacy. In this paper, we present an edge computing solution for resource management of context-aware decision-making processes distributed between IoT gateways. The solution performs decision-making process management for smart actuation, based on analysis of sensory data streams, and context-informed edge computing resource and service provisioning management based on topology and operational changes. Our architectural solution showcases the first version of a Resource Management Framework - a generic framework for software resource orchestration best-suited to IoT platforms with event-driven, publish-subscribe communication mechanisms. Proof of concept experiments that are executed in a simulated edge computing testbed validate our solution's performance in improving the resilience and responsiveness of the edge computing system when there are operational and topology changes. Furthermore, the framework addresses the recovery of failed decision-making processes, impacting the overall health of the underlying IoT system.

关键词： Software Resource management Edge computing Computer architecture sensor systems Cloud computing

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Real-time machine learning for early detection of heart disease using big data approach 5

Real-time machine learning for early detection of heart dise...

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international conference on Wireless Technologies, Embedded and Intelligent systems (WITS)

作者： Ed-daoudy, Abderrahmane Maalmi, Khalil Univ Sidi Mohamed Ben Abdellah LTTI ESTF Route ImouzzerBP 2427 Fes 30000 Morocco

ISBN: (纸本)9781538678503

Over the last few decades, heart disease is the most common cause of global death. So early detection of heart disease and continuous monitoring can reduce the mortality rate. The exponential growth of data from different sources such as wearable sensor devices used in Internet of Things health monitoring, streaming system and others have been generating an enormous amount of data on a continuous basis. The combination of streaming big data analytics and machine learning is a breakthrough technology that can have a significant impact in healthcare field especially early detection of heart disease. This technology can be more powerful and less expensive. To overcome this issue, this paper propose a real-time heart disease prediction system based on apache Spark which stand as a strong large scale distributed computing platform that can be used successfully for streaming data event against machine learning through in-memory computations. The system consists of two main sub parts, namely streaming processing and data storage and visualization. The first uses Spark MLlib with Spark streaming and applies classification model on data events to predict heart disease. The seconds uses Apache Cassandra for storing the large volume of generated data.

关键词： real-time distributed machine learning big data spark heart disease MLlib

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CHAINMAIL: distributed coordination for multi-task κ-Assignment using autonomous mobile IoT devices 14

CHAINMAIL: Distributed coordination for multi-task κ-Assign...

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14th Annual international conference on distributed computing in sensor systems, dcoss 2018

作者： Esterle, Lukas Aston University Birmingham United Kingdom

ISBN: (纸本)9781538654705

The Internet-of-Things (IoT) becomes more and more pervasive and supports us in our daily activities. However, when individual devices struggle in accomplishing certain tasks, they have to cooperate in order to achieve desired outcomes. In the absence of a central controller, devices have to coordinate autonomously within the network in order to attain and complete as many tasks as possible. We propose CHAINMAIL, a novel, distributed approach to coordinate sensors to attain tasks that cannot be accomplished by single, but only by cooperation of multiple devices. We demonstrate our approach with an IoT case study on multi-object k-coverage with autonomously operating mobile cameras and show that our approach does not over-provision tasks, allowing the remaining devices to attain other duties. This enables our network to provision more tasks in the same time as other comparable solutions. © 2018 ieee.

关键词： Internet of things

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Stargazer: A Deep Learning Approach for Estimating the Performance of Edge- Based Clustering Applications

Stargazer: A Deep Learning Approach for Estimating the Perfo...

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Smart Data Services (SMDS), ieee international conference on

作者： Breno Dantas Cruz Arnab K. Paul Zheng Song Eli Tilevich Software Innovations Lab Virginia Tech Distributed Systems and Storage Laboratory Virginia Tech

ISBN: (数字)9781728187778

ISBN: (纸本)9781728187785

As a solution to the sensor data deluge, edge computing processes sensor data by means of local devices. Many of these devices are resource-scarce in terms of the available processing capabilities and battery power. To achieve the required design trade-offs of edge applications, developers must be able to understand the performance and resource utilization of data processing algorithms. An increasing number of edge-based applications use machine learning (ML) as their key functionality. However, the performance and resource utilization of ML algorithms remain poorly understood, thus hindering the system design of edge-based ML applications. In addition, developers often cannot access real-world edge-based test beds during the design phase. To address this problem, we present an approach for estimating the performance of edge-based ML applications, with a particular application to clustering. To that end, we first comprehensively evaluate the performance and resource utilization of widely used clustering algorithms deployed in a representative edge environment. Second, we identify which properties of these algorithms are correlated with their performance and resource utilization. Finally, we apply our findings to create Stargazer, a Deep Neural Network that given a clustering algorithm's computational load and input data size, estimates how this algorithm would perform and utilize resources in an edge-based application. Our tool provides viable decision-making support for addressing the multifaceted design challenges of edge-based ML applications.

关键词： Performance evaluation Machine learning algorithms Neural networks Clustering algorithms Tools Resource management System analysis and design

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DiGS: distributed Graph Routing and Scheduling for Industrial Wireless sensor-Actuator Networks 38

DiGS: Distributed Graph Routing and Scheduling for Industria...

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38th ieee international conference on distributed computing systems (ICDCS)

作者： Shi, Junyang Sha, Mo Yang, Zhicheng SUNY Binghamton Dept Comp Sci Binghamton NY 13902 USA Univ Calif Davis Dept Comp Sci Davis CA 95616 USA

ISBN: (纸本)9781538668719

Wireless sensor-Actuator Networks (WSANs) technology is appealing for use in industrial IoT applications because it does not require wired infrastructure. Battery-powered wireless modules easily and inexpensively retrofit existing sensors and actuators in industrial facilities without running cabling for communication and power. ieee 802.15.4 based WSANs operate at low-power and can be manufactured inexpensively, which makes them ideal where battery lifetime and costs are important Almost a decade of real-world deployments of WirelessHART standard has demonstrated the feasibility of using its core techniques including reliable graph routing and Time Slotted Channel Hopping (TSCH) to achieve reliable low-power wireless communication in industrial facilities. Today we are facing the 4th Industrial Revolution as proclaimed by political statements related to the Industry 4.0 Initiative of the German Government There exists an emerging demand for deploying a large number of field devices in an industrial facility and connecting them through a WSAN. However, a major limitation of current WSAN standards is their limited scalability due to their centralized routing and scheduling that enhance the predictability and visibility of network operations at the cost of scalability. This paper decentralizes the network management in WirelessHART and presents the first distributed Graph routing and autonomous Scheduling (DiGS) solution that allows the field devices to compute their own graph routes and transmission schedules. Experimental results from two physical testbeds and a simulation study show our approaches can significantly improve the network reliability, latency, and energy efficiency under dynamics.

关键词： Wireless sensor-Actuator Networks Industrial Internet of Things Graph Routing Transmission Scheduling

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Enabling Edge Intelligence for Activity Recognition in Smart Homes 15

Enabling Edge Intelligence for Activity Recognition in Smart...

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15th ieee international conference on Mobile Ad Hoc and sensor systems (MASS)

作者： Zhang, Shaojun Li, Wei Wu, Yongwei Watson, Paul Zomaya, Albert Y. Univ Sydney Sch Informat Technol Ctr Distributed & High Performance Comp Sydney NSW Australia Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Newcastle Univ Digital Inst Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781538655801

In recent years, Edge computing has emerged as a new paradigm that can reduce communication delays over the Internet by moving computation power from far-end cloud servers to be closer to data sources. It is natural to shift the design of cloud-based IoT applications to Edge-based ones. Activity recognition in smart homes is one of the IoT applications that can benefit significantly from such a shift. In this work, we propose an Edge-based solution for addressing the activity recognition problem in smart homes from multiple perspectives, including: architecture, algorithm design and system implementation. first, the Edge computing architecture is introduced and several critical management tasks are also investigated. Second, a realization of the Edge computing system is presented by using open source software and low-cost hardware. The consistency and scalability of running jobs on Edge devices are also addressed in our approach. Last, we propose a convolutional neural network model to perform activity recognition tasks on Edge devices. Preliminary experiments are conducted to compare our model with existing machine learning methods, and the results demonstrate that the performance of our model is promising.

关键词： Edge computing Edge intelligence smart home activity recognition data parallelism model parallelism convolutional neural network deep learning

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Multi-hop Routing Implementation in Spatially distributed Wireless sensor Networks for Large-Scale Environmental Monitoring Applications

Multi-hop Routing Implementation in Spatially Distributed Wi...

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ieee international conference on Intelligent Data Acquisition and Advanced computing systems

作者： Afzal Ahmad Muhammad Adeel Pasha Shahid Masud Manuel Schappacher Assila Belhouichet Axel Sikora Department of Electrical Engineering Lahore University of Management Sciences (LUMS) Institute of Reliable Embedded Systems and Communication Electronics

ISBN: (纸本)9781728140704

With the surge in global data consumption with proliferation of Internet of Things (IoT), remote monitoring and control is increasingly becoming popular with a wide range of applications from emergency response in remote regions to monitoring of environmental parameters. Mesh networks are being employed to alleviate a number of issues associated with single-hop communication such as low area coverage, reliability, range and high energy consumption. Low-power Wireless Personal Area Networks (LoWPANs) are being used to help realize and permeate the applicability of IoT. In this paper, we present the design and test of ieee 802.15.4-compliant smart IoT nodes with multi-hop routing. We first discuss the features of the software stack and design choices in hardware that resulted in high RF output power and then present field test results of different baseline network topologies in both rural and urban settings to demonstrate the deployability and scalability of our solution.

关键词： Wireless sensor networks Routing protocols Environmental monitoring Wireless sensor networks environmental monitoring Routing protocols Spread spectrum communication remote regions REMOTE DETECTION AND CONTROL Output power Wireless personal area networks Network routing

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Predictive Maintenance Using GPU-Accelerated Partially Observable Markov Decision Process

Predictive Maintenance Using GPU-Accelerated Partially Obser...

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international conference on Parallel and distributed systems (ICPADS)

作者： Naman Sharma Chen-Khong Tham Dept. of ECE National University of Singapore Singapore

ISBN: (纸本)9781728125848

The Industrial IoT era has seen an outburst of areas benefiting from collecting more data. This includes Industry 4.0 and predictive maintenance, which have benefited from advancements in edge and fog computing. Predictive maintenance aims to minimize the downtime due to maintenance of machinery, while simultaneously minimizing the risk of unforeseen failures. This paper proposes a method to aid industries to make maintenance scheduling decisions that can be adopted in a distributed factory environment. The Partially Observable Markov Decision Process (POMDP) approach is used to determine the optimal time for maintenance for a machine. We first put forward an offline method for learning the Markov model parameters using historical sensor data. To allow for continual learning, an algorithm based on particle filters is proposed to provide online estimation of parameters of a Partially Observable MDP model. The particle filter algorithm allows the framework to adapt uniquely to each machine. The relative benefits of the POMDP model over a standard MDP model in the presence of noisy sensor data are evaluated through simulations which show significant improvements in revenue and reduced downtime. The POMDP and particle filter computations are executed on GPU-accelerated edge devices which achieve a speed-up of around 4 times compared to the CPU implementation.

关键词： Predictive maintenance Industrial IoT Partially observable MDP Particle filter GPU Predictive maintenance Particle filters Markov chain sensor Data downtime GRAPPER PICK UP Graphics Processing Unit Particle filtering algorithm

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AirBeam: Experimental Demonstration of distributed Beamforming by a Swarm of UAVs

AirBeam: Experimental Demonstration of Distributed Beamformi...

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ieee Internatonal conference on Mobile Adhoc and sensor systems (MASS)

作者： Subhramoy Mohanti Carlos Bocanegra Jason Meyer Gokhan Secinti Mithun Diddi Hanumant Singh Kaushik Chowdhury Northeastern University Boston MA USA Istanbul Technical University Istanbul Turkey

ISBN: (数字)9781728146010

ISBN: (纸本)9781728146027

We propose AirBeam, the first complete algorithmic framework and systems implementation of distributed air-to-ground beamforming on a fleet of UAVs. AirBeam synchronizes software defined radios (SDRs) mounted on each UAV and assigns beamforming weights to ensure high levels of directivity. We show through an exhaustive set of the experimental studies on UAVs why this problem is difficult given the continuous hovering-related fluctuations, the need to ensure timely feedback from the ground receiver due to the channel coherence time, and the size, weight, power and cost (SWaP-C) constraints for UAVs. AirBeam addresses these challenges through: (i) a channel state estimation method using Gold sequences that is used for setting the suitable beamforming weights, (ii) adaptively starting transmission to synchronize the action of the distributed radios, (iii) a channel state feedback process that exploits statistical knowledge of hovering characteristics. Finally, AirBeam provides insights from a systems integration viewpoint, with reconfigurable B210 SDRs mounted on a fleet of DJI M100 UAVs, using GnuRadio running on an embedded computing host.

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