NoSQL databases

Internet of Things (IoT) systems deployments are becoming both ubiquitous and business critical in numerous business verticals, both for process automation and data-driven decision-making based on distributed sensors networks. Beneath the simplicity offered by these solutions, we usually find complex, multi-layer architectures—from hardware sensors up to data analytics systems. These rely heavily on software running on the on-location gateway devices designed to bridge the communication between the sensors and the cloud. This will generally require updates and improvements—raising deployment and maintenance challenges. Especially for large scale commercial solutions, a secure and fail-safe updating system becomes crucial for a successful IoT deployment. This paper explores the specific challenges for infrastructures dedicated to remote application deployment and management, addresses the management challenges related to IoT sensors systems, and proposes a mathematical model and a methodology for tackling this. To test the model’s efficiency, we implemented it as a software infrastructure system for complete commercial IoT products. As proof, we present the deployment of 100 smart soda dispensing machines in three locations. Each machine relies on sensors monitoring its status and on gateways controlling its behaviour, each receiving 133 different remote software updates through our solution. In addition, 80% of the machines ran non-interrupted for 250 days, with 20% failing due to external factors; out of the 80%, 30% experienced temporary update failures due to reduced hardware capabilities and the system successfully performed automatic rollback of the system, thus recovering in 100% of the temporary failures

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“…A MongoDB [67] distributed database has been used for persistent data storage. We have used MongoDB Atlas [68] and Azure DocumentDB [69].…”

Section: Cloud Infrastructurementioning

confidence: 99%

A Model for the Remote Deployment, Update, and Safe Recovery for Commercial Sensor-Based IoT Systems

Radovici

Culic

Rosner

et al. 2020

Sensors

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“…Here, Cassandra was chosen over MongoDB after comparing the scalability of the two systems. In fact, MongoDB is more efficient with small amounts of data, while Cassandra outperforms it when large datasets are involved [55]. Since handling large amounts of data is vital to the system, Cassandra was clearly the best option.…”

Section: Persistent Storagementioning

confidence: 99%

Cyber-physical manufacturing systems: An architecture for sensor integration, production line simulation and cloud services

Prist¹,

Monteriù²,

Pallotta³

et al. 2020

ACTA IMEKO

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The pillars of Industry 4.0 require the integration of a modern smart factory, data storage in the Cloud, access to the Cloud for data analytics, and information sharing at the software level for simulation and hardware-in-the-loop (HIL) capabilities. The resulting cyber-physical system (CPS) is often termed the cyber-physical manufacturing system, and it has become crucial to cope with this increased system complexity and to attain the desired performances. However, since a great number of old production systems are based on monolithic architectures with limited external communication ports and reduced local computational capabilities, it is difficult to ensure such production lines are compliant with the Industry 4.0 pillars. A wireless sensor network is one solution for the smart connection of a production line to a CPS elaborating data through cloud computing. The scope of this research work lies in developing a modular software architecture based on the open service gateway initiative framework, which is able to seamlessly integrate both hardware and software wireless sensors, send data into the Cloud for further data analysis and enable both HIL and cloud computing capabilities. The CPS architecture was initially tested using HIL tools before it was deployed within a real manufacturing line for data collection and analysis over a period of two months.

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“…Schema can be built any time, as Cassandra provides flexibility. Clustering, partitioning, and replication while taking care of fault tolerance, reduced latency, and scalability are the prominent features of Cassandra (Lakshman and Malik, 2010;Abramova and Bernardino, 2013). It is a reasonable choice for continuously growing systems such as Facebook.…”

Section: Contemporary Big Data Storage Technologiesmentioning

confidence: 99%

Big data storage technologies: a survey

Siddiqa

Karim

Gani

2017

Frontiers Inf Technol Electronic Eng

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Abstract:There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed 'big data'. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mechanism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the existing approaches using Brewer's CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system.

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NoSQL databases

Cited by 142 publications

References 12 publications

A Model for the Remote Deployment, Update, and Safe Recovery for Commercial Sensor-Based IoT Systems

A Model for the Remote Deployment, Update, and Safe Recovery for Commercial Sensor-Based IoT Systems

Cyber-physical manufacturing systems: An architecture for sensor integration, production line simulation and cloud services

Big data storage technologies: a survey

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