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.

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 Designing Smart Home (IOT) Using Cisco Packet Tracer 7.2 Simulator

2020 ◽  
Vol 6 (12) ◽  
pp. 208-216
Author(s):  
Tanishka and Prof. Shikha Gupta
Keyword(s):  
Internet Of Things ◽  
Smart Home ◽  
Data Exchange ◽  
Real Life ◽  
The Internet ◽  
Different Types ◽  
Life Issues ◽  
Packet Tracer ◽  
Iot Devices ◽  
The Internet Of Things

The internet of things, or IoT, is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Internet of Things (IoT) is rapidly gaining momentum in the scenario of telecommunications. Conventional networks allow for interactivity and data exchange, but these networks have not been designed for the new features and functions of IoT devices. In this paper, an algorithm is proposed to share common recourse among Things, that is, between different types of smart appliances. . Purpose is to analyze deeper the cases separating the network and IoT layout, giving a deeper explanation of the purpose of the simulations, presenting all the information needed to utilize the exercises but also giving suggestion how to expand the exercises further. This implementation can be implemented effectively using package tracking software that includes IoT functions to control and simulate a smart home. IoT technology can be applied to many real life issues, such as: homework, treatment, campus, office, etc.

Security Aspects of the Internet of Things

2021 ◽  
pp. 207-233
Author(s):  
Dominik Hromada ◽  
Rogério Luís de C. Costa ◽  
Leonel Santos ◽  
Carlos Rabadão
Keyword(s):  
Internet Of Things ◽  
New Technologies ◽  
Smart Cities ◽  
The Internet ◽  
Detection Systems ◽  
Private Data ◽  
Promising Solution ◽  
Iot Devices ◽  
Smart Industry ◽  
The Internet Of Things

The Internet of Things (IoT) comprises the interconnection of a wide range of different devices, from Smart Bluetooth speakers to humidity sensors. The great variety of devices enables applications in several contexts, including Smart Cities and Smart Industry. IoT devices collect and process a large amount of data on machines and the environment and even monitor people's activities. Due to their characteristics and architecture, IoT devices and networks are potential targets for cyberattacks. Indeed, cyberattacks can lead to malfunctions of the IoT environment and access and misuse of private data. This chapter addresses security concerns in the IoT ecosystem. It identifies common threats for each of IoT layers and presents advantages, challenges, and limitations of promising countermeasures based on new technologies and strategies, like Blockchain and Machine Learning. It also contains a more in-depth discussion on Intrusion Detection Systems (IDS) for IoT, a promising solution for cybersecurity in IoT ecosystems.

scholarly journals Blockchain Technology for Intelligent Environments

2019 ◽  
Vol 11 (10) ◽  
pp. 213 ◽  
Author(s):  
Spyros Voulgaris ◽  
Nikos Fotiou ◽  
Vasilios A. Siris ◽  
George C. Polyzos ◽  
Mikael Jaatinen ◽  
...  
Keyword(s):  
State Of The Art ◽  
Third Party ◽  
Intelligent Environments ◽  
Digital World ◽  
Blockchain Technology ◽  
Physical Environments ◽  
Fast Pace ◽  
Iot Devices ◽  
First Time ◽  
The Internet Of Things

In the last few years, we have been witnessing the convergence of the physical with the digital world. The Internet of Things (IoT) is progressing at a fast pace, and IoT devices are becoming pervasive in our physical environments, bringing the vision of Intelligent Environments closer to reality. At the same time, the newly-introduced blockchain technology is offering for the first time ever cryptographically proven trust based on a set of mutually untrusted nodes. Blockchain technology thus has the potential to become a key component of many IoT systems, offering them an unprecedented level of accountability, transparency, and reliability. This paper first lays out the principles on which blockchain systems are operating, along with descriptions of the most noteworthy blockchain implementations. It then presents a number of systems through which blockchains may interact with external systems and third-party data sources. Finally, it provides a survey of the state-of-the-art blockchain-based systems targeting IoT applications.

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 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.

Ambulatory Assessment

2009 ◽  
Vol 14 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Ulrich W. Ebner-Priemer ◽  
Timothy J. Trull
Keyword(s):  
Real Time ◽  
Clinical Psychology ◽  
Real Life ◽  
Ambulatory Assessment ◽  
Computer Assisted ◽  
Affective Valence ◽  
Time Data ◽  
Physiological Processes ◽  
Context Specific ◽  
Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

Agricultural Application of Web of Things Architecture

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 61-63 ◽  
Author(s):  
Akihiro Fujii
Keyword(s):  
Internet Of Things ◽  
Agricultural Sector ◽  
Electronic Device ◽  
Specific Work ◽  
Science And Engineering ◽  
Unique Identifier ◽  
Agricultural Application ◽  
Iot Devices ◽  
The Internet Of Things ◽  
Type Pressure

The Internet of Things (IoT) is a term that describes a system of computing devices, digital machines, objects, animals or people that are interrelated. Each of the interrelated 'things' are given a unique identifier and the ability to transfer data over a network that does not require human-to-human or human-to-computer interaction. Examples of IoT in practice include a human with a heart monitor implant, an animal with a biochip transponder (an electronic device inserted under the skin that gives the animal a unique identification number) and a car that has built-in sensors which can alert the driver about any problems, such as when the type pressure is low. The concept of a network of devices was established as early as 1982, although the term 'Internet of Things' was almost certainly first coined by Kevin Ashton in 1999. Since then, IoT devices have become ubiquitous, certainly in some parts of the world. Although there have been significant developments in the technology associated with IoT, the concept is far from being fully realised. Indeed, the potential for the reach of IoT extends to areas which some would find surprising. Researchers at the Faculty of Science and Engineering, Hosei University in Japan, are exploring using IoT in the agricultural sector, with some specific work on the production of melons. For the advancement of IoT in agriculture, difficult and important issues are implementation of subtle activities into computers procedure. The researchers challenges are going on.

scholarly journals Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
E. Bertino ◽  
M. R. Jahanshahi ◽  
A. Singla ◽  
R.-T. Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
The Times ◽  
Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

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