scholarly journals SBIoT: Scalable Broker Design for Real Time Streaming Big Data in the Internet of Things Environment

2021 ◽  
Vol 13 (4) ◽  
pp. 47-52
Author(s):  
Halil ARSLAN ◽  
◽  
Mustafa YALCIN ◽  
Yasin ŞAHAN
Keyword(s):  
Real Time ◽  
Business Processes ◽  
Streaming Data ◽  
Crucial Importance ◽  
Internet Of Things Environment ◽  
Container Management ◽  
Iot Devices ◽  
Streaming Big Data ◽  
Data Transferring ◽  
The Internet Of Things

Thanks to the recent development in the technology number of IoT devices increased dramatically. Therefore,industries have been started to use IoT devices for their business processes. Many systems can be done automatically thanks to them. For this purpose, there is a server to process sensors data. Transferring these data to the server without any loss has crucial importance for the accuracy of IoT applications. Therefore, in this thesis a scalable broker for real time streaming data is proposed. Open source technologies, which are NoSql and in-memory databases, queueing, fulltext index search, virtualization and container management orchestration algorithms, are used to increase efficiency of the broker. Firstly, it is planned to be used for the biggest airport in Turkey to determine the staff location. Considering the experiment analysis, proposed system is good enough to transfer data produced by devices in that airport. In addition to this, the system can adapt to device increase, which means if number of devices increasing in time, number of nodes can be increased to capture more data.

Applying Security to a Big Stream Cloud Architecture for the Internet of Things

2020 ◽  
pp. 1260-1284
Author(s):  
Laura Belli ◽  
Simone Cirani ◽  
Luca Davoli ◽  
Gianluigi Ferrari ◽  
Lorenzo Melegari ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Large Data ◽  
The Internet ◽  
Low Latency ◽  
Stream Data ◽  
Memory Processing ◽  
Cloud Architecture ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) is expected to interconnect billions (around 50 by 2020) of heterogeneous sensor/actuator-equipped devices denoted as “Smart Objects” (SOs), characterized by constrained resources in terms of memory, processing, and communication reliability. Several IoT applications have real-time and low-latency requirements and must rely on architectures specifically designed to manage gigantic streams of information (in terms of number of data sources and transmission data rate). We refer to “Big Stream” as the paradigm which best fits the selected IoT scenario, in contrast to the traditional “Big Data” concept, which does not consider real-time constraints. Moreover, there are many security concerns related to IoT devices and to the Cloud. In this paper, we analyze security aspects in a novel Cloud architecture for Big Stream applications, which efficiently handles Big Stream data through a Graph-based platform and delivers processed data to consumers, with low latency. The authors detail each module defined in the system architecture, describing all refinements required to make the platform able to secure large data streams. An experimentation is also conducted in order to evaluate the performance of the proposed architecture when integrating security mechanisms.

scholarly journals Detecting security attacks in cyber-physical systems: a comparison of Mule and WSO2 intelligent IoT architectures

2021 ◽  
Vol 7 ◽  
pp. e787
Author(s):  
José Roldán-Gómez ◽  
Juan Boubeta-Puig ◽  
Gabriela Pachacama-Castillo ◽  
Guadalupe Ortiz ◽  
Jose Luis Martínez
Keyword(s):  
Real Time ◽  
Smart Cities ◽  
Cyber Physical Systems ◽  
Security Attacks ◽  
Physical Systems ◽  
The Real ◽  
Real Time Detection ◽  
Iot Devices ◽  
Definition Of ◽  
The Internet Of Things

The Internet of Things (IoT) paradigm keeps growing, and many different IoT devices, such as smartphones and smart appliances, are extensively used in smart industries and smart cities. The benefits of this paradigm are obvious, but these IoT environments have brought with them new challenges, such as detecting and combating cybersecurity attacks against cyber-physical systems. This paper addresses the real-time detection of security attacks in these IoT systems through the combined used of Machine Learning (ML) techniques and Complex Event Processing (CEP). In this regard, in the past we proposed an intelligent architecture that integrates ML with CEP, and which permits the definition of event patterns for the real-time detection of not only specific IoT security attacks, but also novel attacks that have not previously been defined. Our current concern, and the main objective of this paper, is to ensure that the architecture is not necessarily linked to specific vendor technologies and that it can be implemented with other vendor technologies while maintaining its correct functionality. We also set out to evaluate and compare the performance and benefits of alternative implementations. This is why the proposed architecture has been implemented by using technologies from different vendors: firstly, the Mule Enterprise Service Bus (ESB) together with the Esper CEP engine; and secondly, the WSO2 ESB with the Siddhi CEP engine. Both implementations have been tested in terms of performance and stress, and they are compared and discussed in this paper. The results obtained demonstrate that both implementations are suitable and effective, but also that there are notable differences between them: the Mule-based architecture is faster when the architecture makes use of two message broker topics and compares different types of events, while the WSO2-based one is faster when there is a single topic and one event type, and the system has a heavy workload.

scholarly journals A case study of the benefits of the IoT in the Qatari retail industry

2021 ◽  
Vol 24 (1) ◽  
pp. 86-107
Author(s):  
Nayef Al-Nabet
Keyword(s):  
Internet Of Things ◽  
Business Processes ◽  
Driving Forces ◽  
Environmental Benefits ◽  
Retail Industry ◽  
The Internet ◽  
Structured Interviews ◽  
Main Research ◽  
Iot Devices ◽  
The Internet Of Things

Technological advancements have led to the creation of new paradigms like the Internet of Things (IoT). These technologies are moving the digital space into a period where they will power billions of devices leading to the transformations of products and services provided by businesses. Therefore, the main purpose of this study is to explore the benefits of deployment of the Internet of Things (IoT) in businesses using the case of the Qatari retail businesses. The study assumed an interpretivism research philosophical stance and an addictive approach to theory utilising the Technology, Organisation, Environment model to guide the research. The research data were gathered using a qualitative approach utilising semi-structured interviews as the method of data collection. Accordingly, the heads of information technology in the selected retail industry businesses in Qatar were interviewed to answer the main research questions. The findings uncovered that retailers in Qatar are using the IoT devices such as sensors, scanners, beacons, smart shelves, personalisation alerts, and FRIDs to enable their key business operations and processes. Even though the Qatar retail businesses are not concerned about the specific IoT devices utilised, the benefits of their deployment established from the study include automation of business processes (Technological benefits), efficiency and personalisation of customer requirements (Organisational benefits), and increased collaboration and supply chain optimisation within the entire Qatar retail industry (Environmental benefits). As such, the results of the study agree with the TOE model that technology, organisation, and environment are the driving forces behind technology adoption and utilisation. Finally, among practical implications, a collaboration between software developers and the retail industry project professionals will ensure that the IoT artefacts are designed with security mechanisms, thus enhancing the security and safety of the information gathered from the IoT devices. Further, the study offers guidance on the theoretical elements that contribute to the benefits of utilising the IoT in Qatar retail businesses.

Distributed Streaming Big Data Analytics for Internet of Things (IoT)

2018 ◽  
pp. 303-338 ◽  
Author(s):  
Sornalakshmi Krishnan ◽  
Kayalvizhi Jayavel
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cloud Services ◽  
Sensor Data ◽  
Efficient Processing ◽  
Streaming Big Data ◽  
The Internet Of Things

In this chapter, a discussion on the integration of distributed streaming Big Data Analytics with the Internet of Things is presented. The chapter begins with the introduction of these two technologies by discussing their features and characteristics. Discussion on how the integration of these two technologies benefit in efficient processing of IoT device generated sensor data follows next. Such data centric processing of IoT data powered by cloud, services and other enablers will be the architecture of most of the realtime systems involving sensors and real-time monitoring and actuation. The Volume, Variety and Velocity of sensor generated data make it a Big Data scenario. In addition, the data is real time and requires decisions or actuations immediately. This chapter discusses how IoT data can be processed using distributed, scalable stream processing systems. The chapter is concluded with future directions of such real time Big Data Analytics in IoT.

Distributed Streaming Big Data Analytics for Internet of Things (IoT)

2022 ◽  
pp. 722-757
Author(s):  
Sornalakshmi Krishnan ◽  
Kayalvizhi Jayavel
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cloud Services ◽  
Sensor Data ◽  
Efficient Processing ◽  
Streaming Big Data ◽  
The Internet Of Things

In this chapter, a discussion on the integration of distributed streaming Big Data Analytics with the Internet of Things is presented. The chapter begins with the introduction of these two technologies by discussing their features and characteristics. Discussion on how the integration of these two technologies benefit in efficient processing of IoT device generated sensor data follows next. Such data centric processing of IoT data powered by cloud, services and other enablers will be the architecture of most of the realtime systems involving sensors and real-time monitoring and actuation. The Volume, Variety and Velocity of sensor generated data make it a Big Data scenario. In addition, the data is real time and requires decisions or actuations immediately. This chapter discusses how IoT data can be processed using distributed, scalable stream processing systems. The chapter is concluded with future directions of such real time Big Data Analytics in IoT.

Applying Security to a Big Stream Cloud Architecture for the Internet of Things

2016 ◽  
Vol 7 (1) ◽  
pp. 37-58 ◽  
Author(s):  
Laura Belli ◽  
Simone Cirani ◽  
Luca Davoli ◽  
Gianluigi Ferrari ◽  
Lorenzo Melegari ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Large Data ◽  
The Internet ◽  
Low Latency ◽  
Stream Data ◽  
Memory Processing ◽  
Cloud Architecture ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) is expected to interconnect billions (around 50 by 2020) of heterogeneous sensor/actuator-equipped devices denoted as “Smart Objects” (SOs), characterized by constrained resources in terms of memory, processing, and communication reliability. Several IoT applications have real-time and low-latency requirements and must rely on architectures specifically designed to manage gigantic streams of information (in terms of number of data sources and transmission data rate). We refer to “Big Stream” as the paradigm which best fits the selected IoT scenario, in contrast to the traditional “Big Data” concept, which does not consider real-time constraints. Moreover, there are many security concerns related to IoT devices and to the Cloud. In this paper, we analyze security aspects in a novel Cloud architecture for Big Stream applications, which efficiently handles Big Stream data through a Graph-based platform and delivers processed data to consumers, with low latency. The authors detail each module defined in the system architecture, describing all refinements required to make the platform able to secure large data streams. An experimentation is also conducted in order to evaluate the performance of the proposed architecture when integrating security mechanisms.

scholarly journals Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5457 ◽  
Author(s):  
Alireza Hassani ◽  
Alexey Medvedev ◽  
Arkady Zaslavsky ◽  
Pari Delir Haghighi ◽  
Prem Prakash Jayaraman ◽  
...  
Keyword(s):  
Real Time ◽  
Query Language ◽  
Real Life ◽  
Query Engine ◽  
Contextual Intelligence ◽  
Fast Pace ◽  
Promising Solution ◽  
Iot Devices ◽  
The Internet Of Things ◽  
Efficient Execution

As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actuators, smartphones and connected vehicles, to name but a few. Context management platforms (CMP) are emerging as a promising solution to deliver contextual intelligence for IoT. However, the development of a generic solution that allows IoT devices and services to publish, consume, monitor, and share context is still in its infancy. In this paper, we propose, validate and explain the details of a novel mechanism called Context Query Engine (CQE), which is an integral part of a pioneering CMP called Context-as-a-Service (CoaaS). CQE is responsible for efficient execution of context queries in near real-time. We present the architecture of CQE and illuminate its workflows. We also conduct extensive experimental performance and scalability evaluation of the proposed CQE. Results of experimental evaluation convincingly demonstrate that CoaaS outperforms its competitors in executing complex context queries. Moreover, the advanced functionality of the embedded query language makes CoaaS a decent candidate for real-life deployments.

Opinion Mining with Real Time Ontology Streaming Data

2019 ◽  
Vol 23 (1) ◽  
pp. 346-357
Author(s):  
Vithya G ◽  
Naren J ◽  
Varun V
Keyword(s):  
Real Time ◽  
Opinion Mining ◽  
Streaming Data
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