Multi-representation Based Data Processing Architecture for IoT Applications

Due to decentralization of Internet of Things(IoT) applications and anything, anytime, anywhere connectivity has increased burden of data processing and decision making at IoT end devices. This overhead initiated new bugs and vulnerabilities thus security threats are emerging and presenting new challenges on these end devices. IoT End Devices rely on Trusted Execution Environments (TEEs) by implementing Root of trust (RoT) as soon as power is on thus forming Chain of trust (CoT) to ensure authenticity, integrity and confidentiality of every bit and byte of Trusted Computing Base (TCB) but due to un-trusted external world connectivity and security flaws such as Spectre and meltdown vulnerabilities present in the TCB of TEE has made CoT unstable and whole TEE are being misutilized. This paper suggests remedial solutions for the threats arising due to bugs and vulnerabilities present in the different components of TCB so as to ensure the stable CoT resulting into robust TEE.

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Research on Data Processing Architecture of NQI Service Platform in Intelligent Measurement Domain

Journal of Physics Conference Series ◽

10.1088/1742-6596/1607/1/012128 ◽

2020 ◽

Vol 1607 ◽

pp. 012128

Author(s):

Xu Fang ◽

Yongguang Liu ◽

Yong Wang

Keyword(s):

Data Processing ◽

Service Platform ◽

Intelligent Measurement ◽

Measurement Domain ◽

Processing Architecture

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Distributed real time data processing architecture for the TJ-II data acquisition system

Review of Scientific Instruments ◽

10.1063/1.1789614 ◽

2004 ◽

Vol 75 (10) ◽

pp. 4261-4264 ◽

Cited By ~ 15

Author(s):

M. Ruiz ◽

E. Barrera ◽

S. López ◽

D. Machón ◽

J. Vega ◽

...

Keyword(s):

Data Processing ◽

Data Acquisition ◽

Real Time ◽

Data Acquisition System ◽

Acquisition System ◽

Time Data ◽

Real Time Data ◽

Real Time Data Processing ◽

Processing Architecture

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In-Network Data Processing in Software-Defined IoT with a Programmable Data Plane

Mobile Information Systems ◽

10.1155/2018/8618267 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Ki-Wook Kim ◽

Sung-Gi Min ◽

Youn-Hee Han

Keyword(s):

Data Processing ◽

Data Aggregation ◽

Sensor Nodes ◽

Network Data ◽

Packet Forwarding ◽

Processing Scheme ◽

Sensing Data ◽

Iot Applications ◽

Proposed Model ◽

Data Plane

Making an SDN data plane flexible enough to satisfy the various requirements of heterogeneous IoT applications is very desirable in terms of software-defined IoT (SD-IoT) networking. Network devices with a programmable data plane provide an ability to dynamically add new packet- and data-processing procedures to IoT applications. The previously proposed solutions for the addition of the programmability feature to the SDN data plane provide extensibility for the packet-forwarding operations of new protocols, but IoT applications need a more flexible programmability for in-network data-processing operations (e.g., the sensing-data aggregation from thousands of sensor nodes). Moreover, some IoT models such as OMG DDS, oneM2M, and Eclipse SCADA use the publish-subscribe model that is difficult to represent using the operations of the existing message-centric data-plane models. We introduce a new in-network data-processing scheme for the SD-IoT data plane that defines an event-driven data-processing model that can express a variety of in-network data-processing cases in the SD-IoT environment. Also, the proposed model comprises a language for the programming of the data-processing procedures, while a flexible data-plane structure that can install and execute the programs at runtime is additionally presented. We demonstrate the flexibility of the proposed scheme by using sample programs in a number of example SD-IoT cases.

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IoTEF: A Federated Edge-Cloud Architecture for Fault-Tolerant IoT Applications

Journal of Grid Computing ◽

10.1007/s10723-019-09498-8 ◽

2020 ◽

Vol 18 (1) ◽

pp. 57-80 ◽

Cited By ~ 5

Author(s):

Asad Javed ◽

Jérémy Robert ◽

Keijo Heljanko ◽

Kary Främling

Keyword(s):

Internet Of Things ◽

Data Processing ◽

Fault Tolerant ◽

Network Connectivity ◽

Functional Requirements ◽

Long Distance ◽

Network Bandwidth ◽

Iot Applications ◽

Layered Design ◽

Hostile Environments

AbstractThe evolution of Internet of Things (IoT) technology has led to an increased emphasis on edge computing for Cyber-Physical Systems (CPS), in which applications rely on processing data closer to the data sources, and sharing the results across heterogeneous clusters. This has simplified the data exchanges between IoT/CPS systems, the cloud, and the edge for managing low latency, minimal bandwidth, and fault-tolerant applications. Nonetheless, many of these applications administer data collection on the edge and offer data analytic and storage capabilities in the cloud. This raises the problem of separate software stacks between the edge and the cloud with no unified fault-tolerant management, hindering dynamic relocation of data processing. In such systems, the data must also be preserved from being corrupted or duplicated in the case of intermittent long-distance network connectivity issues, malicious harming of edge devices, or other hostile environments. Within this context, the contributions of this paper are threefold: (i) to propose a new Internet of Things Edge-Cloud Federation (IoTEF) architecture for multi-cluster IoT applications by adapting our earlier Cloud and Edge Fault-Tolerant IoT (CEFIoT) layered design. We address the fault tolerance issue by employing the Apache Kafka publish/subscribe platform as the unified data replication solution. We also deploy Kubernetes for fault-tolerant management, combined with the federated scheme, offering a single management interface and allowing automatic reconfiguration of the data processing pipeline, (ii) to formulate functional and non-functional requirements of our proposed solution by comparing several IoT architectures, and (iii) to implement a smart buildings use case of the ongoing Otaniemi3D project as proof-of-concept for assessing IoTEF capabilities. The experimental results conclude that the architecture minimizes latency, saves network bandwidth, and handles both hardware and network connectivity based failures.

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