iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs

A Consent Framework for the Internet of Things in the GDPR Era

The Internet of Things (IoT) is an environment of connected physical devices and objects that communicate amongst themselves over the internet. The IoT is based on the notion of always-connected customers, which allows businesses to collect large volumes of customer data to give them a competitive edge. Most of the data collected by these IoT devices include personal information, preferences, and behaviors. However, constant connectivity and sharing of data create security and privacy concerns. Laws and regulations like the General Data Protection Regulation (GDPR) of 2016 ensure that customers are protected by providing privacy and security guidelines to businesses. Data subjects (users) should be informed on what information is being collected about them and if they consent or not. This dissertation proposes a consent framework that consists of data collection, consent collection, consent management, consent enforcement, and consent auditing. In the framework, there are GDPR requirements embedded in different components of the framework. The consent framework can help organizations to be GDPR consent compliant. In our evaluation of the solution, the results show that our solution has coverage over GDPR consent based on our use case. Our main contributions are the consent framework, consent manager, and the consent auditing tool

A Survey on Systems Integration in the Energy Automation Domain through OPC Interface

[Abstract] The Object Linking and Embedding for Process Control (OPC) interface provides an effective means to exchange data between automation-related entities, both hardware and software. Since its creation, it has been profusely used not only for industrial scenarios but also for other spheres, among which energy automation is an important scope. In order to portray the relevance of such protocol, this paper presents a survey of applications of OPC communication to manage systems integration in the context of energy automationJunta de Extremadura; GR1815

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

A Privacy Impact Assessment Method for Organizations Implementing IoT for Occupational Health and Safety

Internet of Things (IoT) technologies are increasingly being integrated into occupational health and safety (OHS) practices; however, their adoption raises significant privacy concerns. The General Data Protection Regulation (GDPR) has established the requirement for organizations to conduct Privacy Impact Assessments (PIAs) prior to processing personal data, emphasizing the need for privacy safeguards in the workplace. Despite this, the GDPR provisions related to the IoT, particularly in the area of OHS, lack clarity and specificity. This research aims to bridge this gap by proposing a tailored method for conducting PIAs in the OHS context, with a particular focus on addressing the how to aspect of the assessment process. The proposed method integrates insights from domain experts, relevant literature sources, and GDPR regulations, ultimately leading to the development of an online PIA tool

Federated Embedded Systems – a review of the literature in related fields

This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways

The Post-Occupancy Digital Twin: a Quantitative Report on Data Standardisation and Dynamic Building Performance Evaluation

As a process, originally defined by the UK Government, Level 2 Building Information Modelling (BIM) involves the creation of digital project information, following industry standard guidelines. Through the application of Level 2 BIM, the construction industry can now develop digital representations of physical assets. By combining BIM with digital technologies such as the Internet of Things (IoT), an opportunity is created to link integrated building sensors to these digital representations via advanced Computer Aided Facility Management (CAFM) systems. Successfully combining physical elements to digital elements through a CAFM system results in the creation of Digital Twins (DT), providing an opportunity for dynamic data analysis throughout the capital delivery phase into the operation and maintenance (O&M) phase. A major aspect in the creation of DT involves the ongoing relationship between physical and digital versions of assets. To ensure that physical and digital elements remain aligned, bi-directional updating of data is required. This is achieved through the collection of real-time data via interlinked sensors, generating an opportunity to analyse the performance of the asset and it’s occupants. Level 2 BIM provides for delivery of clearly defined project data at intervals of maturity which are termed “data drops”. Where project outcomes are poorly defined, the process of digital information delivery often results in a return to traditional methods of data exchange, resulting in static data analysis. Traditional methods of information exchange include graphical and non-graphical data in the form of PDF and Construction Operations Building Information Exchange (COBie) data in Excel format. Static methods of delivering data do not present the DT with the dynamic data required to constantly adapt and reflect the physical version. The aim of this research paper was to determine if the replacement of existing information exchange deliverables with DT can improve building to operations information transfer, and contribute towards greater efficiencies in the post-occupancy operational phase of Level 2 BIM projects in Ireland