Aliaksei Andrushevich

Combinatorial evolution of ZigBee protocol

The article addresses combinatorial evolution of communication protocol for wireless sensor networks. Morphological tree structure (a version of and-or tree) is used as a hierarchical model for protocols. Three generations of protocol (ZigBee) are examined. A set of protocol change operations are generated and described. The change operations are used as items for forecasting based on combinatorial problems (e.g., clustering, knapsack problem, multiple choice knapsack problem). Two kinds of forecasts for the examined communication protocol are considered: (i) direct expert based forecast of the protocol, (ii) computed forecast that was based on expert judgment (i.e., assessment of change operations) and usage of multicriteria decision making and combinatorial optimization problems.

Interactions with recognized patients using smart glasses

Recently, different smart glasses solutions have been proposed on the market. The rapid development of this wearable technology has led to several research projects related to applications of smart glasses in healthcare. In this paper we propose a general architecture of the system enabling data integration for the recognized person. In the proposed system smart glasses integrates data obtained for the recognized patient from health care information systems, from devices connected to the patient and from the patient himself. The data integration is possible, if proper patient recognition procedure is used. Therefore, we evaluated three identification methods based on face recognition and using the recognition of graphical markers (i.e. QR-codes and proposed color-based codes). The results show that it is possible to obtain reliable and fast recognition results during the video acquisition by the smart glasses camera.

Interactions with recognized objects

Implicit interaction combined with object recognition techniques opens a new possibility for gathering data and analyzing user behavior for activity and context recognition. The electronic eyewear platform, eGlasses, is being developed, as an integrated and autonomous system to provide interactions with smart environment. In this paper we present a method for the interactions with the recognized objects that can be used for electronic eyewear. The design of the control node is presented. The control node is the extension of the power socket, equipped with a set of sensors and ZigBee communication interface. The node is used to control electronic objects through user interactions with a mobile device. The preliminary results are presented based on the Android mobile phone used to recognize controllable objects based on the captured image sequences.

Towards semantic buildings: Goal-driven approach for building automation service allocation and control

The idea of applying semantic web technologies to the area of smart homes (SH) and building automation has resulted in a number of research activities and initiatives that have been recently developed. This article starts with an overview of ongoing work towards embedding semantics into home automation services. We then highlight the problems not considered by previous solutions. The core value of the work presented here is contained in a novel goal-driven approach for building automation service allocation and control. A new concept of semantic homes (SeH) and its architectural vision is another significant contribution. The comparison between existing and suggested solutions is rounded off by a use case scenario from the area of ambient assisted living.

The data exchange between smart glasses and healthcare information systems using the HL7 FHIR standard

In this study we evaluated system architecture for the use of smart glasses as a viewer of information, as a source of medical data (vital sign measurements: temperature, pulse rate, and respiration rate), and as a filter of healthcare information. All activities were based on patient/device identification procedures using graphical markers or features based on visual appearance. The architecture and particular use cases were implemented and verified using smart glasses prototypes developed under the eGlasses project and using a reference Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) server. The results show that information about the identified patient can be quickly retrieved from FHIR servers and annotated using voice recognition services. Smart glasses can be used in the measurement of vital signs of the observed patient, providing values of body temperature, pulse rate, and respiration rate by means of non-contact measurements. Such measurements are sufficiently reliable for medical screening and for fast data exchange using HL7 FHIR actions.

Prometheus — Fuzzy information retrieval for semantic homes and environments

This paper introduces a novel vision for further enhanced Internet of Things services. Based on a variety of data — such as location data, ontology-backed search queries, in- and outdoor conditions — the Prometheus framework is intended to support users with helpful recommendations and information preceding a search for context-aware data. Adapted from artificial intelligence concepts, Prometheus proposes user-readjusted answers on umpteen conditions. A number of potential Prometheus framework applications are illustrated. Added value and possible future studies are discussed in the conclusion.

Improvement of building automation system

The paper addresses redesign/improvement of a building automation system (BAS). For the sake of simplicity, the field bus technology on KNX example and WSN technologies on IEEE.15.4/Zig.Bee basis are examined. The basic system example consists of four parts: (1) IP/KNX Gateway, (2) IP/WSN 6LoWPAN Gateway, (3) ZigBee Wireless Sensor Network, and (4) KNX Field Bus Infrastructure. A treelike system model (and/or morphological tree) is used. The following system improvement design schemes are examined: (i) upgrade of system components (strategy 1), (ii) extension by adding an additional part (strategy 2), and (iii) combined scheme (strategy 3). Three underlaying problems are used: (a) multicriteria ranking, (b) multicriteria multiple choice problem, and (c) combinatorial synthesis. Numerical examples illustrate the redesign processes.

Performance Analysis of Interaction between Smart Glasses and Smart Objects Using Image-Based Object Identification

We propose the use of smart glasses to collaborate with smart objects in the Internet of Things environment. Particularly we are focusing on new interaction methods and the analysis of acceptable reaction times in the process of object recognition using smart glasses. We evaluated the proposed method using user studies and experiments with three different smart glasses: Google Glass, Epson Moverio, and the developed eGlasses platform. We conclude that using the proposed method it is possible to recognize objects and process information allowing object detection below the average acceptance response times specified by almost all participants in the user study. Additionally, we showed that eye-tracking can be used for simple interaction between a user and a graphical user interface presented in the near-to-eye display.

Improving the Recognition Performance of NIALM Algorithms through Technical Labeling

A myriad of different electrical devices populate a typical household nowadays. Non-intrusive appliance load monitoring (NIALM) is an approach to find out how much energy each of them consumes in order to take measures to improve the overall energy efficiency. This article describes the ongoing research on improving electric loads recognition performed by NIALM algorithms within the context of smart homes and intelligent environments. The recognition performance can be significantly improved by decreasing the number of categories to be analyzed. The authors studied several labeling methods to categorize and group loads in order to increase the overall recognition rate. 31 different devices have been measured and labeled in different device states. Their input curves have been compared with 5 different machine learning algorithms. The best results could be reached by dividing all the loads into groups with small divergence in their normalized current curve. This approach has significantly increased the performance of NIALM recognition algorithms.

The autonomic computing paradigm in adaptive building / ambient intelligence systems

This work is devoted to the classification and adaptation of current ambient intelligence (AmI) research activities from the viewpoint of the autonomic computing paradigm. Special attention is given to the implementation of AmIs user-centric focus in autonomic computing.