Fog computing enabled cost-effective distributed summarization of surveillance videos for smart cities

[EN] Fog computing is emerging an attractive paradigm for both academics and industry alike. Fog computing holds potential for new breeds of services and user experience. However, Fog computing is still nascent and requires strong groundwork to adopt as practically feasible, cost-effective, efficient and easily deployable alternate to currently ubiquitous cloud. Fog computing promises to introduce cloud-like services on local network while reducing the cost. In this paper, we present a novel resource efficient framework for distributed video summarization over a multi-region fog computing paradigm. The nodes of the Fog network is based on resource constrained device Raspberry Pi. Surveillance videos are distributed on different nodes and a summary is generated over the Fog network, which is periodically pushed to the cloud to reduce bandwidth consumption. Different realistic workload in the form of a surveillance videos are used to evaluate the proposed system. Experimental results suggest that even by using an extremely limited resource, single board computer, the proposed framework has very little overhead with good scalability over off-the-shelf costly cloud solutions, validating its effectiveness for IoT-assisted smart cities. (C) 2018 Elsevier Inc. All rights reserved.Nasir, M.; Muhammad, K.; Lloret, J.; Sangaiah, AK.; Sajjad, M. (2019). Fog computing enabled cost-effective distributed summarization of surveillance videos for smart cities. Journal of Parallel and Distributed Computing. 126:161-170. https://doi.org/10.1016/j.jpdc.2018.11.004S16117012

Demo: Design of a Virtualized Smart Car Platform

International audienceIn this demonstration, we present a system that combinesthe use of Single-Board Computer with the lightweight characteristicsof container virtualization technologies for the deploymentof a Smart Car platform. Our approach allows thedefinition of an architecture that exploits the flexibility ofcontainers in terms of dynamic service allocation even onembedded systems. The whole is combined with the designof an inner orchestrator that acts as manager for the schedulingof different virtualized instances according to specificlevels of application priorities. We practically show howthis integrated environment can facilitate the developmentof functional and versatile car On Board Units, even on topof constrained Single-Board Computer with several applicationsinstantiated

Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data

Nowadays, Internet-of-Things (IoT) devices generate data at high speed and large volume. Often the data require real-time processing to support high system responsiveness which can be supported by localised Cloud and/or Fog computing paradigms. However, there are considerably large deployments of IoT such as sensor networks in remote areas where Internet connectivity is sparse, challenging the localised Cloud and/or Fog computing paradigms. With the advent of the Raspberry Pi, a credit card-sized single board computer, there is a great opportunity to construct low-cost, low-power portable cloud to support real-time data processing next to IoT deployments. In this paper, we extend our previous work on constructing Raspberry Pi Cloud to study its feasibility for real-time big data analytics under realistic application-level workload in both native and virtualised environments. We have extensively tested the performance of a single node Raspberry Pi 2 Model B with httperf and a cluster of 12 nodes with Apache Spark and HDFS (Hadoop Distributed File System). Our results have demonstrated that our portable cloud is useful for supporting real-time big data analytics. On the other hand, our results have also unveiled that overhead for CPU-bound workload in virtualised environment is surprisingly high, at 67.2%. We have found that, for big data applications, the virtualisation overhead is fractional for small jobs but becomes more significant for large jobs, up to 28.6%

PENGUJIAN KINERJA MQTT BROKER BERBASIS KONTAINER MENGGUNAKAN DOCKER PADA PERANGKAT RASPBERRY PI

Untuk menguji dan mengetahui kinerja MQTT broker menggunakan teknologi kontainerisasi pada perangkat Raspberry Pi dengan dua basis image yang berbeda dilakukan dengan beberapa parameter pengujian, yaitu pengujian resource usage(cpu dan memory) dengan script python yang penulis buat, pengujian latency dengan MQTT broker latency measure tool, dan pengujian packet loss dengan MQTT broker latency measure tool. Basis image yang digunakan sebagai media pemasangan MQTT broker (Mosquitto) adalah belenalib/raspberry-pi2 dan belenalib/raspberry-pi2-alpine. Docker image dibuat dari Dockerfile. Image dibangun dan dijalankan dengan perintah yang disediakan Docker. Dalam hal resource usage (cpu) MQTT broker yang berjalan diatas docker dengan basis image belenalib/raspberry-pi2 mampu lebih baik dari native dengan 0,53% sampai 10,38% penggunaan cpu lebih rendah. Namun dalam hal resource usage(memory) kedua MQTT broker yang berjalan diatas docker masih lebih tinggi dibandingkan dengan MQTT broker yang dijalankan secara native, dengan perbedaan lebih dari 50%. Pada pengujian latency kedua MQTT broker yang berjalan diatas docker masih juga lebih tinggi lebih dari 50% dibandingkan dengan MQTT broker yang dijalankan secara native. Pada pengujian packet loss, MQTT broker dengan basis image belenalib/raspberry-pi2 memiliki kinerja lebih baik dengan hanya mengalami packet loss sebesar 28.60% pada klien MQTT berjumlah 1000 dan QoS 0

Integration of LXD System Containers with OpenStack, CHEF and its Application on a 3-Tier IoT Architecture

Internet of Things has moved from being a 2-tier server-client into a 3-tier server-gateway-client architecture. The gateway plays a vital role in this 3-tier architecture with intelligence being built into it. With no proper standardization and with more vendors having proprietary apps, which are shared in this multi-tenant gateway, it demands sandboxing and isolation of apps at the gateway. My thesis explores light weight LXD System containers and state of the art configuration management tools like Chef, to build an architecture, leveraging Infrastructure as a Code, creating an app delivery pipeline to deploy apps in jailed environments at an IoT Gateway while maintaining a minimal overhead. The framework also provides ways to automate tests for deployment validation

Flexible network management in software defined wireless sensor networks for monitoring application systems

Wireless Sensor Networks (WSNs) are the commonly applied information technologies of modern networking and computing platforms for application-specific systems. Today’s network computing applications are faced with high demand of reliable and powerful network functionalities. Hence, efficient network performance is central to the entire ecosystem, more especially where human life is a concern. However, effective management of WSNs remains a challenge due to problems supplemental to them. As a result, WSNs application systems such as in monitored environments, surveillance, aeronautics, medicine, processing and control, tend to suffer in terms of capacity to support compute intensive services due to limitations experienced on them. A recent technology shift proposes Software Defined Networking (SDN) for improving computing networks as well as enhancing network resource management, especially for life guarding systems. As an optimization strategy, a software-oriented approach for WSNs, known as Software Defined Wireless Sensor Network (SDWSN) is implemented to evolve, enhance and provide computing capacity to these resource constrained technologies. Software developmental strategies are applied with the focus to ensure efficient network management, introduce network flexibility and advance network innovation towards the maximum operation potential for WSNs application systems. The need to develop WSNs application systems which are powerful and scalable has grown tremendously due to their simplicity in implementation and application. Their nature of design serves as a potential direction for the much anticipated and resource abundant IoT networks. Information systems such as data analytics, shared computing resources, control systems, big data support, visualizations, system audits, artificial intelligence (AI), etc. are a necessity to everyday life of consumers. Such systems can greatly benefit from the SDN programmability strategy, in terms of improving how data is mined, analysed and committed to other parts of the system for greater functionality. This work proposes and implements SDN strategies for enhancing WSNs application systems especially for life critical systems. It also highlights implementation considerations for designing powerful WSNs application systems by focusing on system critical aspects that should not be disregarded when planning to improve core network functionalities. Due to their inherent challenges, WSN application systems lack robustness, reliability and scalability to support high computing demands. Anticipated systems must have greater capabilities to ubiquitously support many applications with flexible resources that can be easily accessed. To achieve this, such systems must incorporate powerful strategies for efficient data aggregation, query computations, communication and information presentation. The notion of applying machine learning methods to WSN systems is fairly new, though carries the potential to enhance WSN application technologies. This technological direction seeks to bring intelligent functionalities to WSN systems given the characteristics of wireless sensor nodes in terms of cooperative data transmission. With these technological aspects, a technical study is therefore conducted with a focus on WSN application systems as to how SDN strategies coupled with machine learning methods, can contribute with viable solutions on monitoring application systems to support and provide various applications and services with greater performance. To realize this, this work further proposes and implements machine learning (ML) methods coupled with SDN strategies to; enhance sensor data aggregation, introduce network flexibility, improve resource management, query processing and sensor information presentation. Hence, this work directly contributes to SDWSN strategies for monitoring application systems.Thesis (PhD)--University of Pretoria, 2018.National Research Foundation (NRF)Telkom Centre of ExcellenceElectrical, Electronic and Computer EngineeringPhDUnrestricte

MAPiS 2019 - First MAP-i Seminar: proceedings

This book contains a selection of Informatics papers accepted for presentation and discussion at “MAPiS 2019 - First MAP-i Seminar”, held in Aveiro, Portugal, January 31, 2019. MAPiS is the first conference organized by the MAP-i first year students, in the context of the Seminar course. The MAP-i Doctoral Programme in Computer Science is a joint Doctoral Programme in Computer Science of the University of Minho, the University of Aveiro and the University of Porto. This programme aims to form highly-qualified professionals, fostering their capacity and knowledge to the research area. This Conference was organized by the first grade students attending the Seminar Course. The aim of the course was to introduce concepts which are complementary to scientific and technological education, but fundamental to both completing a PhD successfully and entailing a career on scientific research. The students had contact with the typical procedures and difficulties of organizing and participate in such a complex event. These students were in charge of the organization and management of all the aspects of the event, such as the accommodation of participants or revision of the papers. The works presented in the Conference and the papers submitted were also developed by these students, fomenting their enthusiasm regarding the investigation in the Informatics area. (...)publishe

Quem ou o que pensa? Uma busca de aportes para questões filosóficas suscitadas pela revolução informática atual

Taking as an assumption the existence of an informatics revolution nowadays and that the examination of studies and debates related to it may allow the identification of questions of a philosophical nature, the present study aims to identify and formulate some of these questions, as well as to investigate whether the historical controversy about monopsychism, which occurred at the University of Paris in 1270, can be considered a theoretical framework capable of providing contributions to these philosophical questions. The answer to this research problem may be positive, insofar as the aforementioned theoretical framework allows to identify contributions to the solution of the above mentioned questions. Or negative, otherwise. Or even in terms, to the extent that such subsidies only partially meet their objectives or raise, in turn, new questions