Viktoriya Degeler

Optimizing Energy Costs for Offices Connected to the Smart Grid

In addition to providing for a more reliable distribution infrastructure, the smart grid promises to give the end users better pricing and usage information. It is thus interesting for them to be ready to take advantage of features such as dynamic energy pricing and real-time choice of operators. In this work, we propose a system to monitor and control an office environment and to couple it with the smart grid. The idea is to schedule the operation of devices according to policies defined by the users, in order to minimize the cost of operation while leaving unaffected user comfort and productivity. The implementation of the system and its testing in a living lab environment show interesting economic savings of an average of about 35% and in some cases even overall energy savings in the order of 10% for a building equipped with renewable generation plants, and economic and energy savings of 20% and 10%, respectively, for a building without local renewable installations.

Service-Oriented Architecture for Smart Environments (Short Paper)

The advances of pervasive technology offer new standards for user comfort by adding intelligence to ubiquitous home and office appliances. With intelligence being the core of some newly constructed buildings, it is important to design a scalable, robust, context-aware architecture, which not only has enough longevity and evolving capabilities to sustain itself over the buildings lifetime, but also provides enough potential for additional features to be added to the core Building Management Systems (BMS). Such features may include energy preservation system, or activity-recognition techniques. Service-Oriented Architecture (SOA) principles provide great tools that can be applied to the smart buildings design, however certain specifics of pervasive systems should be taken into account, such as high heterogeneity of available devices and capabilities. In this paper we propose an architecture for smart pervasive applications, which is based on SOA principles and is specifically designed for long-term applicability, scalability, and evolution capabilities of a BMS. We validate our proposal by implementing a smart office on the premises of the Technical University of Eindhoven and showing that it complies with the requirements of scalability and robustness, at the same time being a viable BMS.

Dynamic Constraint Reasoning in Smart Environments

Flexible and easily adjustable reasoning mechanisms are essential for rendering sensor and actuator rich indoor environments smart. Constraint-based solutions are a suitable approach for such systems. We propose an approach that allows users to specify the rules for a buildings behavior, and uses context information to represent the rules and environment as a dynamic constraint satisfaction problem. The dependency graph data structure allows to find efficiently only the affected parts of the environment, thus minimizing the computational efforts after every event. We evaluate the system on a building implementation as a living lab, and with performance experiments. The testing proves the high efficiency and applicability of the approach for dynamic control of smart environments.

Interpretation of inconsistencies via context consistency diagrams

Pervasive context-aware systems base their responses on information about the environment collected from ubiquitous sensors. The inevitable drawback of such systems is that raw data collected from sensors is often noisy, corrupted, and imprecise. Erroneous sensor readings create uncertainties and ambiguous interpretations. Thus creating an interpretation challenge for the context-aware system that needs to reason about possible states of only partially observable subjects. We propose a mechanism for pervasive context-aware systems to process the information gathered from sensors so to obtain knowledge about possible environment states. This includes both the ability to reason about a situation with incomplete knowledge and to cope with erroneous contexts. We present a probabilistic approach to reason about the likelihood of each particular situation, state of a variable, and variable interdependence. The evaluation shows that the proposed approach is applicable to real-time context inference problems.

Towards Context Consistency in a Rule-Based Activity Recognition Architecture

Accurate human activity recognition (AR) is crucial for intelligent pervasive environments, e.g., energy-saving buildings. In order to gain precise and fine-grained AR results, a system must overcome partial observability of the environment and noisy, imprecise, and corrupted sensor data. In this work, we propose a rule-based AR architecture that effectively handles multiple-user, multiple-area situations, recognizing real-time office activities. The proposed solution is based on an ontological approach, using low-cost, binary, wireless sensors. We employ context consistency diagrams (CCD) as a key component for fault correction. A CCD is a data structure that provides a mechanism for probabilistic reasoning about the current situation and determines the most probable current situation in the presence of inconsistencies, conflicts, and ambiguities in sensor readings. The implementation of the system and its evaluation in a living lab environment show that the CCD corrects up to 46.8% of sensor data faults, improving overall recognition accuracy by up to 11.1%, thus achieving reliable recognition results from unreliable sensor data.

Itemset-based mining of constraints for enacting smart environments

In order to automatically control the environment, smart systems should have sufficient rules, which describe expected systems behavior. While such rules may be added man-ually, usually this requires considerable efforts, often surpassing those that users are willing to spend to setup the system. In this paper, we propose a novel technique to mine such rules automatically, given a sensor log from the environment. In particular, we mine itemsets, but we consider abnormal drops in the frequency of variable state combinations w.r.t. the frequency of their subsets, which represent undesirability of these combinations. We evaluate the technique both on simulated and real datasets, showing that the approach is effective and promising for further extensions.

Dynamic Constraint Satisfaction with Space Reduction in Smart Environments

A scalable, reactive and easy to evolve reasoning mechanism is essential for the success of automated smart environments, augmented with a large number of sensors and actuators. While constraint satisfaction problem (CSP) model is applicable for modelling decision making in such environments, the straightforward representation of the model as a CSP leads to a great number of excessive calculations. In this paper, we propose a method of modelling the task as a Dynamic CSP in a way that avoids unnecessary recalculations with new events in the environment. We present a Dependency Graph data structure, which not only allows to reduce CSP search space for every consecutive sensor event by detecting only affected parts of the environment, but also allows to give enough information to users of the system to specify the exact reasons of systems decisions, even with a large number of constraints. We formally prove that partial recalculation of affected parts still keeps the full environment globally satisfied and globally optimal. The evaluation of the system in the living lab showed real-time responses for all events. Additional simulated performance experiments showed that the Dependency Graph approach consistently outperforms the straightforward CSP representation. The experiments also showed that the clusterization of the environment has a noticeable effect on the performance, with highly clusterized environments requiring less computations.

Cost-efficient context-aware rule maintenance

Energy and other costs reduction is important in the smart homes automation area. It is cumbersome and error-prone to create proper rules for saving costs manually, thus an automatic approach is desirable that continuously checks for the possibility to save costs. We propose an approach that unifies handling of user defined rules, and searches for a possibility to move each device to a more cost-efficient state when this does not violate any rules. With every event in the environment, our approach partially rechecks only those parts of the system that are affected by the change, thus saving computational resources.