Interoperation of IoT Platforms in Confined Smart Spaces: The SymbIoTe Smart Space Architecture

2018 ◽  
Author(s):  
G. Carrozzo ◽  
M. Pardi ◽  
P. Tedeschi ◽  
G. Piro ◽  
M. Dobski ◽  
...  
Keyword(s):  
Smart Space ◽  
Smart Spaces ◽  
Iot Platforms ◽  
Space Architecture

Individual Client Strategies for Active Control of Information-Driven Service Construction in IoT-enabled Smart Spaces

2019 ◽  
Vol 10 (2) ◽  
pp. 20-36 ◽  
Author(s):  
Olga Bogoiavlenskaia ◽  
Andrey Vdovenko ◽  
Dmitry G. Korzun ◽  
Alexey Kashevnik
Keyword(s):  
Active Control ◽  
Performance Metrics ◽  
The Internet ◽  
Service Needs ◽  
Smart Space ◽  
Smart Spaces ◽  
Individual Strategy ◽  
Individual Client ◽  
The Internet Of Things ◽  
Information Broker

Smart spaces provide a platform for cooperative service construction by many devices in the Internet of Things (IoT) environments. When a service is constructed the service needs delivering to appropriate clients, which is typically implemented using the subscription operation (i.e., information-driven service construction). The passive form of subscription is ineffective in the IoT settings since the centralized solution—smart space information broker—needs to control all service construction updates and to notify all interested clients. This article considers the problem of active control for information-driven service construction when each client can use its own (individual) strategy to (additionally) control ongoing updates in the subscribed information. Five strategies for active control are selected for this study. For some simplified assumptions, analytical estimates are provided. For close-to-real evaluation of the strategies a simulation model is developed, based on which several performance metrics are experimentally studied.

scholarly journals Surveying Human Habit Modeling and Mining Techniques in Smart Spaces

2019 ◽  
Vol 11 (1) ◽  
pp. 23 ◽  
Author(s):  
Francesco Leotta ◽  
Massimo Mecella ◽  
Daniele Sora ◽  
Tiziana Catarci
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Literature Review ◽  
Visual Analysis ◽  
Future Research ◽  
Specific Method ◽  
Human Intervention ◽  
Smart Space ◽  
Smart Spaces ◽  
Research Questions

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field.

scholarly journals Internet of Things for Smart Spaces: A University Campus Case Study

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3716
Author(s):  
Ekaterina Gilman ◽  
Satu Tamminen ◽  
Rumana Yasmin ◽  
Eemeli Ristimella ◽  
Ella Peltonen ◽  
...  
Keyword(s):  
Data Analysis ◽  
Internet Of Things ◽  
User Interfaces ◽  
Real World ◽  
Lessons Learned ◽  
University Campus ◽  
Smart Space ◽  
Smart Spaces

Advances in technology and data analysis provide rich opportunities for developing intelligent environments assisting their inhabitants, so-called smart environments or smart spaces. Enhanced with technology, sensors, user interfaces, and various applications, such smart spaces are capable of recognizing users and situations they are in, react accordingly, e.g., by providing certain services or changes to the environment itself. Therefore, smart space solutions are gradually coming to different application domains, each with corresponding specific characteristics. In this article, we discuss our experiences and explore the challenges of a long-term real-world Internet of Things (IoT) deployment at a University campus. We demonstrate the technical implementation and data quality issues. We conduct several studies, from data analysis to interaction with space, utilizing the developed infrastructure, and we also share our actions to open the data for education purposes and discuss their outcomes. With this article, we aim to share our experience and provide real-world lessons learned when building an open, multipurpose, publicly used smart space at a University campus.

scholarly journals Static and Dynamic Activity Detection with Ambient Sensors in Smart Spaces

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 804 ◽  
Author(s):  
Sagar Shelke ◽  
Baris Aksanli
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Real Time ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Connected Components ◽  
Activity Detection ◽  
Smart Space ◽  
Smart Spaces ◽  
Feed Forward

Convergence of Machine Learning, Internet of Things, and computationally powerful single-board computers has boosted research and implementation of smart spaces. Smart spaces make predictions based on historical data to enhance user experience. In this paper, we present a low-cost, low-energy smart space implementation to detect static and dynamic human activities that require simple motions. We use low-resolution (4 × 16) and non-intrusive thermal sensors to collect data. We train six machine learning algorithms, namely logistic regression, naive Bayes, support vector machine, decision tree, random forest and artificial neural network (vanilla feed-forward) on the dataset collected in our lab. Our experiments reveal a very high static activity detection rate with all algorithms, where the feed-forward neural network method gives the best accuracy of 99.96%. We also show how data collection methods and sensor placement plays an important role in the resulting accuracy of different machine learning algorithms. To detect dynamic activities in real time, we use cross-correlation and connected components of thermal images. Our smart space implementation, with its real-time properties, can be used in various domains and applications, such as conference room automation, elderly health-care, etc.

Smart-M3 Techniques

2017 ◽  
pp. 82-104
Keyword(s):  
Information Management ◽  
Semantic Information ◽  
Design Principles ◽  
Context Aware ◽  
Smart Space ◽  
Smart Spaces ◽  
Personalized Service ◽  
Heterogeneous Devices ◽  
Modeling Techniques ◽  
Smart Service

The previous chapters elaborated the design principles that guide the development of smart spaces-based applications using the Smart-M3 platform. The principles aim at such properties for applications as (i) interoperability for a multitude of participated heterogeneous devices, services, and users localized in the physical surrounding and (ii) context-aware, situational, and personalized service construction and delivery. In this chapter, we present selected ontology-oriented modeling techniques for applying the principles. The aspect of shared semantic information management becomes essential for service construction. We describe techniques how implement this management in a smart space. A question of what is a smart service compared with regular service is still debatable. We describe techniques how implement various intelligence attributes in services constructed and delivered in M3 spaces.

scholarly journals Processing models for conflicting user requests in ubiquitous corporate smart spaces

2018 ◽  
Vol 161 ◽  
pp. 03006 ◽  
Author(s):  
Dmitriy Levonevskiy ◽  
Irina Vatamaniuk ◽  
Anton Saveliev
Keyword(s):  
Quality Of Service ◽  
Human Computer Interaction ◽  
Simulation Model ◽  
User Experience ◽  
Management Strategies ◽  
Task Management ◽  
Smart Space ◽  
Smart Spaces ◽  
Digital Signage

This paper considers processing of conflicting user requests in ubiquitous corporate smart spaces. The formulated problem consists in the contradiction between the limitation of available smart space resources to perform the conflicting user requests and necessity to provide the proper quality of service in corporate smart spaces. The principles of constructing the simulation model are described. The experiments were carried out basing on a model of the SPIIRAS digital signage service. Several task management strategies are discussed, an assessment of their effectiveness is given. The research is aimed at improving the quality of service and user experience in human-computer interaction within the corporate smart spaces.

scholarly journals Enabling Semantic Technology Empowered Smart Spaces

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Jussi Kiljander ◽  
Arto Ylisaukko-oja ◽  
Janne Takalo-Mattila ◽  
Matti Eteläperä ◽  
Juha-Pekka Soininen
Keyword(s):  
Interaction Model ◽  
Context Aware ◽  
Semantic Technologies ◽  
Smart Space ◽  
Smart Spaces ◽  
Semantic Web Technologies ◽  
Space Applications ◽  
Semantic Technology ◽  
Context Aware Computing ◽  
Semantic Data

It has been proposed that Semantic Web technologies would be key enablers in achieving context-aware computing in our everyday environments. In our vision of semantic technology empowered smart spaces, the whole interaction model is based on the sharing of semantic data via common blackboards. This approach allows smart space applications to take full advantage of semantic technologies. Because of its novelty, there is, however, a lack of solutions and methods for developing semantic smart space applications according to this vision. In this paper, we present solutions to the most relevant challenges we have faced when developing context-aware computing in smart spaces. In particular the paper describes (1) methods for utilizing semantic technologies with resource restricted-devices, (2) a solution for identifying real world objects in semantic technology empowered smart spaces, (3) a method for users to modify the behavior of context-aware smart space applications, and (4) an approach for content sharing between autonomous smart space agents. The proposed solutions include ontologies, system models, and guidelines for building smart spaces with the M3 semantic information sharing platform. To validate and demonstrate the approaches in practice, we have implemented various prototype smart space applications and tools.

IoTranx: Transactions for Safer Smart Spaces

2022 ◽  
Vol 6 (1) ◽  
pp. 1-26
Author(s):  
Chao Chen ◽  
Abdelsalam (Sumi) Helal ◽  
Zhi Jin ◽  
Mingyue Zhang ◽  
Choonhwa Lee
Keyword(s):  
Physical Nature ◽  
Safety Properties ◽  
Smart Space ◽  
Smart Spaces ◽  
Physical Content ◽  
Daily Lives ◽  
Digital Services ◽  
Full Control ◽  
Computer Crash ◽  
The Impact

Smart spaces such as smart homes deliver digital services to optimize space use and enhance user experience. They are composed of an Internet of Things (IoT), people, and physical content. They differ from traditional computer systems in that their cyber-physical nature ties intimately with the users and the built environment. The impact of ill-programmed applications in such spaces goes beyond loss of data or a computer crash, risking potentially physical harm to the space and its users. Ensuring smart space safety is therefore critically important to successfully deliver intimate and convenient services surrounding our daily lives. By modeling smart space as a highly dynamic database, we present IoT Transactions, an analogy to database transactions, as an abstraction for programming and executing the services as the handling of the devices in smart space. Unlike traditional database management systems that take a “clear room approach,” smart spaces take a “dirty room approach” where imperfection and unattainability of full control and guarantees are the new normal. We identify Atomicity, Isolation, Integrity and Durability (AI 2 D) as the set of properties necessary to define the safe runtime behavior for IoT transactions for maintaining “permissible device settings” of execution and to avoid or detect and resolve “impermissible settings.” Furthermore, we introduce a lock protocol, utilizing variations of lock concepts, that enforces AI 2 D safety properties during transaction processing. We show a brief proof of the protocol correctness and a detailed analytical model to evaluate its performance.

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