Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms

Copyright © 2018 John Wiley & Sons, Ltd. Recent technological advances led to the rapid and uncontrolled proliferation of intelligent surveillance systems (ISSs), serving to supervise urban areas. Driven by pressing public safety and security requirements, modern cities are being transformed into tangled cyber-physical environments, consisting of numerous heterogeneous ISSs under different administrative domains with low or no capabilities for reuse and interaction. This isolated pattern renders itself unsustainable in city-wide scenarios that typically require to aggregate, manage, and process multiple video streams continuously generated by distributed ISS sources. A coordinated approach is therefore required to enable an interoperable ISS for metropolitan areas, facilitating technological sustainability to prevent network bandwidth saturation. To meet these requirements, this paper combines several approaches and technologies, namely the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a unified approach to implementing an ISS at an urban scale, thus paving the way for the metropolitan intelligent surveillance system (MISS). The proposed solution aims to push data management and processing tasks as close to data sources as possible, thus increasing performance and security levels that are usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of this approach, the paper presents a case study based on a distributed ISS scenario in a crowded urban area, implemented on clustered edge devices that are able to off-load tasks in a “horizontal” manner in the context of the developed MISS framework. As demonstrated by the initial experiments, the MISS prototype is able to obtain face recognition results 8 times faster compared with the traditional off-loading pattern, where processing tasks are pushed “vertically” to the cloud

Data agility through clustered edge computing and stream processing

© 2018 John Wiley & Sons, Ltd. The Internet of Things is underpinned by the global penetration of network-connected smart devices continuously generating extreme amounts of raw data to be processed in a timely manner. Supported by Cloud and Fog/Edge infrastructures – on the one hand, and Big Data processing techniques – on the other, existing approaches, however, primarily adopt a vertical offloading model that is heavily dependent on the underlying network bandwidth. That is, (constrained) network communication remains the main limitation to achieve truly agile IoT data management and processing. This paper aims to bridge this gap by defining Clustered Edge Computing – a new approach to enable rapid data processing at the very edge of the IoT network by clustering edge devices into fully functional decentralized ensembles, capable of workload distribution and balancing to accomplish relatively complex computational tasks. This paper also proposes ECStream Processing that implements Clustered Edge Computing using Stream Processing techniques to enable dynamic in-memory computation close to the data source. By spreading the workload among a cluster of collocated edge devices to process data in parallel, the proposed approach aims to improve performance, thereby supporting agile data management. The experimental results confirm that such a distributed in-memory approach to data processing at the very edge of an IoT network can outperform currently adopted Cloud-enabled architectures, and has the potential to address a wide range of IoT-related data-intensive time-critical scenarios

Staying at the Edge of Privacy: Edge Computing and Impersonal Extraction

From self-driving cars to smart city sensors, billions of devices will be connected to networks in the next few years. These devices will collect vast amounts of data which needs to be processed in real-time, overwhelming centralized cloud architectures. To address this need, the industry seeks to process data closer to the source, driving a major shift from the cloud to the ‘edge.’ This article critically investigates the privacy implications of edge computing. It outlines the abilities introduced by the edge by drawing on two recently published scenarios, an automated license plate reader and an ethnic facial detection model. Based on these affordances, three key questions arise: what kind of data will be collected, how will this data be processed at the edge, and how will this data be ‘completed’ in the cloud? As a site of intermediation between user and cloud, the edge allows data to be extracted from individuals, acted on in real-time, and then abstracted or sterilized, removing identifying information before being stored in conventional data centers. The article thus argues that edge affordances establish a fundamental new ‘privacy condition’ while sidestepping the safeguards associated with the ‘privacy proper’ of personal data use. Responding effectively to these challenges will mean rethinking person-based approaches to privacy at both regulatory and citizen-led levels

Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms

Copyright © 2018 John Wiley & Sons, Ltd. Recent technological advances led to the rapid and uncontrolled proliferation of intelligent surveillance systems (ISSs), serving to supervise urban areas. Driven by pressing public safety and security requirements, modern cities are being transformed into tangled cyber-physical environments, consisting of numerous heterogeneous ISSs under different administrative domains with low or no capabilities for reuse and interaction. This isolated pattern renders itself unsustainable in city-wide scenarios that typically require to aggregate, manage, and process multiple video streams continuously generated by distributed ISS sources. A coordinated approach is therefore required to enable an interoperable ISS for metropolitan areas, facilitating technological sustainability to prevent network bandwidth saturation. To meet these requirements, this paper combines several approaches and technologies, namely the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a unified approach to implementing an ISS at an urban scale, thus paving the way for the metropolitan intelligent surveillance system (MISS). The proposed solution aims to push data management and processing tasks as close to data sources as possible, thus increasing performance and security levels that are usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of this approach, the paper presents a case study based on a distributed ISS scenario in a crowded urban area, implemented on clustered edge devices that are able to off-load tasks in a “horizontal” manner in the context of the developed MISS framework. As demonstrated by the initial experiments, the MISS prototype is able to obtain face recognition results 8 times faster compared with the traditional off-loading pattern, where processing tasks are pushed “vertically” to the cloud

Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities

[EN] Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. Smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). Smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2-4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices.This research was funded by the Spanish Science and Innovation Ministry grant number MICINN: CICYT project PRECON-I4: "Predictable and dependable computer systems for Industry 4.0" TIN2017-86520-C3-1-R.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Blanes Noguera, F. (2020). Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities. Sensors. 20(1):1-18. https://doi.org/10.3390/s20010112S118201Munera, E., Poza-Lujan, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E., & Noguera, J. (2015). Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems. 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IT governance enablers for an efficient IoT implementation

IoT is considered to be one of the focal points for the 4.0 industry revolution because of the way it is changing the business models of each organization. IT governance is now an increasingly important tool for organizations to align their IT infrastructure with the organization's business objectives. IT governance has been used to help implement new technologies using the best practices such as COBIT, which defines a number of enablers that facilitate the implementation, identification and management of IT. This research aims to explore and define the most suitable enablers for an IoT implementation. These objectives will be achieved through the Design Science Research methodology, which incorporates two literature reviews, a Delphi method and, finally, a semi-structured interview. With a first systematic review of the literature, it was possible to identify the main enablers to implement IoT. Next, the list was improved using the Delphi method, gathering expert opinion. In the Delphi method, the level of agreement was verified to create exclusion criteria and a level of efficiency in each recommendation. Finally, a specialist was interviewed to demonstrate the applicability and validation of the proposed artifact in the various IoT projects implemented by his organization. At the end, a final list of enablers for IoT implementation is provided. The results indicate that data privacy, data protection, and data analysis are currently the best recommendations to be considered in an IoT implementation because they increase the efficiency of the solution and increase the credibility of the data obtained. Future work and limitations are detailed in the end.A IoT é considerada como um dos pontos fulcrais para a revolução da indústria 4.0, devido à maneira como está a alterar os modelos de negócio das organizações. A governação das TI é atualmente uma ferramenta cada vez mais importante para as organizações alinharem a sua infraestrutura tecnológica com os objetivos de negócio da organização. A governação de TI tem sido utilizada para ajudar na implementação de novas tecnologias recorrendo à utilização de boas práticas como por exemplo o COBIT, que define vários enablers que facilitam a implementação, identificação e gestão das TI. Esta investigação visa explorar e definir os enablers mais adequados para uma implementação de IoT. Estes objetivos vão ser alcançados através da metodologia Design Science Research, que incorpora duas revisões de literatura, um método Delphi e por fim uma entrevista semiestruturada. Com uma primeira revisão sistemática da literatura, foi possível identificar os principais enablers para implementar IoT. De seguida, a lista foi melhorada utilizando o método Delphi, recolhendo a opinião de especialistas. No método Delphi, verificou-se o nível de concordância para criar critérios de exclusão e um nível de eficiência em cada recomendação. Finalmente, um especialista foi entrevistado para demonstrar a aplicabilidade e validar o artefacto proposto nos diversos projetos de IoT implementados pela sua organização. No final a lista de enablers para implementar IoT é fornecida. Os resultados indicam que atualmente, a privacidade de dados, a proteção de dados e a análise de dados são as melhores recomendações a serem consideradas numa implementação de IoT, porque aumentam a eficiência da solução e aumentam a credibilidade dos dados obtidos. Trabalho futuro e limitações são detalhadas no final