Energy is an important factor that must be considered by multi-hop wireless mesh routing protocols because most sensors are powered by batteries with a limited capacity. We focus on the industry-standard RPL (Routing Protocol over Low-power and lossy networks) routing protocol that must find energy-efficient paths in low-power and lossy networks. However, the existing RPL objective functions route based on hop-count and ETX (expected transmission count) metrics alone, ignoring the energy cost of data transmission and reception. We address this issue in two ways. First, we design an objective function for RPL that finds paths that require, in expectation, the minimum amount of energy. Second, we design a probing mechanism which configures the transmission power of sensors to minimize energy consumption. The proposed approach is implemented and evaluated using simulations as well as on a small testbed with two Zolertial Z1 motes.

By 2040, global CO2 emissions and energy consumption are expected to increase by 40%. In the US, buildings account for 40% of national CO2 emissions and energy consumption, of which 75% is met by fossil fuels. Reducing this impact on the environment requires both improved building energy efficiency and increased renewable utilization. To this end, this dissertation presents a demand-supplystorage- based decision framework to enable strategic energy management in smart buildings. This framework includes important but largely unaddressed aspects pertaining to building demand and supply such as occupant plugloads and the integration of weather forecast-based solar prediction, respectively. We devote the first part of our work to study occupant plugloads, which account for up to 50% of demand in high performance buildings. We investigate the impact of plugload control mechanisms based on the analysis of real-world data from experiments we conducted at NASA Ames sustainability base and Carnegie Mellon University (SV campus). Our main contribution is in extending existing demand response approaches to an occupant-in-the-loop paradigm. In the second part of this work, we describe methods to develop weather forecastbased solar prediction models using both local sensor measurements and global weather forecast data from the National Ocean and Atmospheric Administration (NOAA).We contribute to the state-of-the-art solar prediction models by proposing the incorporation of both local and global weather characteristics into their predictions. This weather forecast-based solar model plus the plugload-integrated demand model, along with an energy storage model constitutes the weather-driven plugloadintegrated decision-making framework for energy management. To demonstrate the utility of this framework, we apply it to solve an optimal decision problem with the objective of minimizing the energy-related operating costs associated with a smart building. The findings indicate that the optimal decisions can result in savings of up to 74% in the expected operational costs. This framework enables inclusive energy management in smart buildings by accounting for occupants-in-the-loop. Results are presented and discussed in the context of commercial office buildings.

Residential buildings in the United States are responsible for the consumption of 38% of electricity, and for much of the fluctuations in the power demands on the electric grid, particularly in hot climates. Residential buildings are also where occupants spend nearly 69% of their time. As “smart” technologies, including electric grid-connected devices and home energy management systems are increasingly available and installed in buildings, this research focuses on the use of these technologies combined with available energy use data in accomplishing three main objectives. The research aims to: (a) better understand how residential buildings currently use electricity, (b) evaluate the use of these smart technologies and data to reduce buildings’ electricity use and their contribution to peak loads, and (c) develop a methodology to assess the impacts of these operational changes on occupant thermal comfort. Specifically this study focuses on two of the most significant electricity consumers in residential buildings: large appliances, including refrigerators, clothes washers, clothes dryers and dishwashers, and heating, ventilation and air conditioning (HVAC) systems. First, to develop an improved understanding of current electricity use patterns of large appliances and residential HVAC systems, this research analyzes a large set of field-collected data. This dataset includes highly granular electricity consumption information for residential buildings located in a hot and humid climate. The results show that refrigerators have the most reliable and consistent use, while the three user-dependent appliances varied more greatly among houses and by time-of-day. In addition, the daily use patterns of appliances vary in shape depending on a number of factors, particularly whether or not the occupants work from home, which contrasts with common residential building energy modeling assumptions. For the all-air central HVAC systems studied, the average annual HVAC duty cycle was found to be approximately 20%, and varied significantly depending on the season, time of day, and type of residential building. Duty cycle was also correlated to monthly energy use. This information provides an improvement to previously assumed values in indoor air modeling studies. Overall, the work presented here enhances the knowledge of how the largest consumers of residential buildings, large appliances and HVAC, operate and use energy, and identifies influential factors that affect these use patterns. The methodologies developed can be applied to determine use patterns for other energy consuming devices and types of buildings, to further expand the body of knowledge in this area. Expanding on this knowledge of current energy use, smart large appliances and residential HVAC systems are investigated for use in reducing peak electric grid loads, and building energy use, respectively. This includes a combination of laboratory testing, field-collected data, and modeling. For appliance peak load reduction, refrigerators are found to have a good demand response potential, in part due to the nearly 100% of residential buildings that have one or more of these appliances, and the predictability of their energy consumption behavior. Dryers provide less consistent energy use across all homes, but have a higher individual peak power demand during afternoon and evening peak use times. These characteristics also make dryers also a good candidate for demand response. The study of continuous commissioning of HVAC systems using energy data found that both runtime and energy use are increased, and cooling capacity and efficiency are reduced due to the presence of faults or inefficiencies. The correction of these faults have an estimated 1.4% to 5.7% annual impact on a residential building’s electricity use in a cooling-dominated climate such as the one studied. Overall, appliance peak load reduction results are useful for utility companies and policy makers in identifying what smart appliance may provide the most peak energy reduction potential through demand response programs. The results of the HVAC study provides a methodology that can be used with energy use data, to determine if an HVAC system has the characteristics implying an inefficiency may be present, and to quantify the annual savings resulting from its correction. The final aspect of this research focuses on the development of a tool to enable an assessment the effect of operational changes of a building associated with energy and peak load reduction on occupant comfort. This is accomplished by developing a methodology that uses the response surface methodology (RSM), combined with building performance data as input, and uncertainly analysis. A second-order RSM model constructed using a full-factorial design was generally found to provide strong agreement to in and out-of-sample building simulation data when evaluating the Average Percent of People Dissatisfied (PPD[subscript avg]). This 5-step methodology was applied to assess occupant thermal comfort in a residential building due to a 1-hour demand response event and a time-of-use pricing rate schedule for a variety of residential building characteristics. This methodology provides a model that can quickly assess, over a continuous range of values for each of the studied design variables, the effect on occupant comfort. This may be useful for building designers and operators who wish to quickly assess the effect of a change in building operations on occupants. / text

Στην παρούσα µεταπτυχιακή διπλωµατική εργασία παρουσιάζεται ο σχεδιασµός, η ανάπτυξη, η εγκατάσταση και λειτουργία ενός ολοκληρωµένου συστήματος παρακολούθησης και διαχείρισης έξυπνων κτιρίων με χρήση ετε- ρογενών ασυρµάτων δικτύων αισθητήρων. Οι συγκεκριµένες συσκευές µπο- ρούν να λειτουργήσουν είτε ως απλοί αισθητήρες είτε ως ελεγκτές συσκευών, με χρήση ασύρµατης επικοινωνίας και state-of-the-art τεχνολογιών του Δια- δικτύου των Συσκευών που διευκολύνουν την αλληλεπίδραση με αυτές αλλά και την εννοποίησή τους με διαδυκτιακές εφαρμογές. Στα πλαίσια της συ- γκεκριµένης διπλωµατικής, εστιάζουµε αρχικά στην υλοποίηση του πρωτο- κόλλου CoAP που προσφέρη τις δυνατότητες ελένχου και αίσθησης μέσω της ασύρµατη επικοινωνία των συσκευών με ένα δομημένο και κοινά κατανοητό τρόπο. Προτείνουµε επίσης, µια ένα συγκεκριµένου συστήµατος το οποίο προ- σφέρει κεντρικά τις υπηρησίες των ασύρματων αισθητήρων για την διευκό- ληνση της αναζήτησης συσκευών και χαρακτηριστικών όπως και την δυνα- τότητα κεντρικής διαχείρισης των συσκευών. Το συγκεκριµένο σύστηµα εί- ναι ανεξάρτητο από τις συσκευές που χρησιµοποιούνται (platform & hardware independent) καθώς οι λειτουργίες και τα δεδοµένα παρέχονται µε δοµηµένο τρόπο με την χρήση RESTful Web Services. Για την µελέτη της συµπεριφο- ράς του συστήµατος αναπτύχθηκαν ολοκληρωµένες εφαρµογές οι οποίες απο- δικνύουν την ευκολία χρήσης των δεδομένων που προέρχονται από τις συ- σκευές και αλληλεπίδρασης με αυτές. Κάθε µία από αυτές υλοποιήθηκε με χρήση διαφορετικών τεχνολογιών όπως HTML5, Android, Microsoft Windows 8 και On{x}, αλλά και διαφορετικές συσκευές αισθητήρων και συγκεκριµένα iSense, Arduino, TelosB και XBee. / Within the scope of this MSc dissertation, we present the design and implementation of pervasive applications on top of heterogeneous wireless sensor network environment. The wireless communication between heterogeneous devices is an inherently difficult research problem due to fundamental differences in system architecture, properties and capabilities of the these devices. Initially, our research focused on the identification of the problems related to the intercommunication among the devices of a heterogeneous wireless sensor network. As a solution, we propose a new abstract system that provides the key qualities needed for a successful pervasive system; expandability, scalability and performance. The new architecture achieves interoperability among the devices by introducing abstraction in the communication protocols (MAC, Transport and Application Layers). In order to demonstrate the applicability of our system we include various representative use case scenarios, that illustrate the usage of our infrastructure. Each scenario focuses on different properties of the system and uses a combination of devices such as iSense, Arduino, SunSPOT, TelosB and XBee.

This work addresses the problem of localizing an impact in a dispersive medium (waveguide) using a network of vibration sensors (accelerometers), distributed at various locations in the waveguide, measuring (and detecting the arrival of) the impact-generated seismic wave. In particular, the last part of this document focuses on the problem of localizing footsteps using underfloor accelerometers. The author believes the outcomes of this work pave the way for realizing real-time indoor occupant tracking using underfloor accelerometers; a system that is tamper-proof and non-intrusive compared to occupant tracking systems that rely on video image processing. A dispersive waveguide (e.g., a floor) causes the impact-generated wave to distort with the traveled distance and renders conventional time of flight localization methods inaccurate. Therefore, this work focuses on laying the foundation of a new alternative approach to impact localization in dispersive waveguides. In this document, localization algorithms, including wave-signal detection and signal processing, are developed utilizing the fact that the generated wave's energy is attenuated with the traveled distance. The proposed localization algorithms were evaluated using simulations and experiments of hammer impacts, in addition to occupant tracking experiments. The experiments were carried out on an instrumented floor section inside a smart building. As will be explained in this document, energy-based localization will turn out to be computationally cheap and more accurate than conventional time of flight techniques. / PHD

With the rise of the Internet of Things (IoT) and smart buildings, new algorithms are being developed to understand how occupants are interacting with buildings via structural vibration measurements. These vibration-based occupant inference algorithms (VBOI) have been developed to localize footsteps within a building, to classify occupants, and to monitor occupant health. This dissertation will present a three-stage journey proposing a path forward for VBOI research based on physically informed data-driven models of structural dynamical systems. The first part of this dissertation presents a method for extracting temporal gait parameters via underfloor accelerometers. The time between an occupant's consecutive steps can be measured with only structural vibration measurements with a similar accuracy to current gait analysis tools such as force plates and in-shoe pressure sensors. The benefit of this, and other VBOI gait analysis algorithms, is in their ease of use. Gait analysis is currently limited to a clinical setting with specialized measurement systems, however VBOI gait analysis provides the ability to bring gait analysis to any building. VBOI algorithms often make some simplifying assumptions about the dynamics of the building in which they operate. Through a calibration procedure, many VBOI algorithms can learn some system parameters. However, as demonstrated in the second part of this dissertation, some commonly made assumptions oversimplify phenomena present in civil structures such as: attenuation, reflections, and dispersion. A series of experimental and theoretical investigations show that three common assumptions made in VBOI algorithms are unable to account for at least one of these phenomena, leading to algorithms which are more accurate under certain conditions. The final part of this dissertation introduces a physically informed data-driven modelling technique which could be used in VBOI to create a more complete model of a building. Continuous residue interpolation (CRI) takes FRF measurements at a discrete number of testing locations, and creates a predictive model with continuous spatial resolution. The fitted CRI model can be used to simulate the response at any location to an input at any other location. An example of using CRI for VBOI localization is shown. / Doctor of Philosophy / Vibration-based occupant inference (VBOI) algorithms are an emerging area of research in smart buildings instrumented with vibration sensors. These algorithms use vibration measurements of the building's structure to learn something about the occupants inside the building. For example the vibration of a floor in response to a person's footstep could be used to estimate where that person is without the need for any line-of-sight sensors like cameras or motion sensors. The storyline of this dissertation will make three stops: The first is the demonstration of a VBOI algorithm for monitoring occupant health. The second is an investigation of some assumptions commonly made while developing VBOI algorithms, seeking to shed light on when they lead to accurate results and when they should be used with caution. The third, and final, is the development of a data-driven modelling method which uses knowledge about how systems vibrate to build as detailed a model of the system as possible. Current VBOI algorithms have demonstrated the ability to accurately infer a range of information about occupants through vibration measurements. This is shown with a varied literature of localization algorithms, as well as a growing number of algorithms for performing gait analysis. Gait analysis is the study of how people walk, and its correlation to their health. The vibration-based gait analysis procedure in this work demonstrates extracting distributions of temporal gait parameters, like the time between steps. However, many current VBOI algorithms make significant simplifying assumptions about the dynamics of civil structures. Experimental and theoretical investigations of some of these assumptions show that while all assumptions are accurate in certain situations, the dynamics of civil structures are too complex to be completely captured by these simplified models. The proposed path forward for VBOI algorithms is to employ more sophisticated data-drive modelling techniques. Data-driven models use measurements from the system to build a model of how the system would respond to new inputs. The final part of this dissertation is the development of a novel data-driven modelling technique that could be useful for VBOI. The new method, continuous residue interpolation (CRI) uses knowledge of how systems vibrate to build a model of a vibrating system, not only at the locations which were measured, but over the whole system. This allows a relatively small amount of testing to be used to create a model of the entire system, which can in turn be used for VBOI algorithms.

This work demonstrates an application of Vibration-Based Occupant Inference (VBOI) in frailty analysis. The rise of both Internet-of-Things (IoT) and VBOI provide new techniques to perform gait analysis via footstep-induced vibration which can be analyzed for early detection of human frailty. Thus, this work provides an application of VBOI to passively track gait parameters (e.g., gait speed) using floor-mounted accelerometers as opposed to using a manual chronometer as it is commonly performed by healthcare professionals. The first part of this thesis describes the techniques used for footstep detection by measuring the power of the footstep-generated vibration waves. The extraction of temporal gait parameters from consecutive footsteps can then be used to estimate temporal features such as cadence and stride time variation. VBOI provides many algorithms to accurately detect when a human-induced vibration event happened, however, spatial information is also needed for many gait parameters used in frailty diagnosis. Detecting where an event happened is a complicated problem because footsteps waves travel and decay in different ways according to the medium (floor system), the number of people walking, and even the walking speed. Therefore, the second part of this work will utilize an energy-based approach of footstep localization in which it is assumed that footstep waves decay exponentially as they travel across the medium. The results from this approach are then used to calculate spatial and tempo-spatial parameters. The main goal of this study is to understand the applicability of VBOI algorithms in gait analysis for frailty detection in a healthcare setting. / Master of Science / Human frailty is responsible for one of the highest healthcare costs and the death of many people every year. Although anyone suffering from frailty has a higher chance of death, it is particularly dangerous for the elderly population and for those suffering from other comorbidities. Diagnosing frailty is hard because it usually happens slowly over time. However, it has been shown that changes in some walking parameters (such as gait speed) can be an early indication of frailty. Many technologies have been created in order to track gait parameters, many of which either require expensive equipment (e.g., force plates) or the use of wearable devices, which can introduce privacy concerns. It has been proposed in the literature that Vibration-Based Occupant Inference (VBOI) techniques could be used in healthcare applications. Such algorithms measure footstep-induced vibration waves in order to detect and track footsteps. This system can provide several advantages in frailty analysis because of its affordability, ease of use, and little impact on patients' privacy. Therefore, the aim of this study is to understand the applicability of VBOI algorithms in gait analysis for frailty detection to be used in a healthcare setting. This thesis will proceed as follows: 1- The demonstration of an energy-based footstep detection and localization algorithm in VBOI. 2 - The application of such algorithms for gait parameters extraction with simulated frail walkers. 3 - Finally, an analysis of the proposed VBOI techniques for deployment in a real hospital setting.

With growing concerns on global energy demand and climate change, it is important to focus on efficient utilization of electricity in commercial buildings, which contribute significantly to the overall electricity consumption. Accordingly, there has been a number of Building Energy Management (BEM) software/hardware solutions to monitor energy consumption and other measurements of individual building loads. BEM software serves as a platform to implement smart control strategies and stores historical data. Although BEM software provides such lucrative benefits to building operators, in terms of energy savings and personalized control, these benefits are not harnessed by most small to mid-sized buildings due to a high cost of deployment and maintenance. A cloud-based BEM system can offer a low-cost solution to promote ease of use and support a maintenance-free installation. In a typical building, a conventional router has a public address and assigns private addresses to all devices connected to it. This led to a network topology, where the router is the only device in the Internet space with all other devices forming an isolated local area network behind the router. Due to this scenario, a cloud-based BEM software needs to pass through the router to access devices in a local area network. To address this issue, some devices, during operation, make an outbound connection to traverse through the router and provide an interface to itself on the Internet. Hence, based on their capability to traverse through the router, devices in a local area network can be distinguished as cloud and non-cloud devices. Cloud-based BEM software with sufficient authorization can access cloud devices. In order to access devices adhering to non-cloud protocols, cloud-based BEM software requires a device in the local area network which can perform traversal through the router on behalf of all non-cloud devices. Such a device acts as an IoT gateway, to securely interconnect devices in a local area network with cloud-based BEM software. This thesis focuses towards architecting, designing and prototyping an Internet-of-Things (IoT) gateway which can perform traversal on behalf of non-cloud devices. This IoT gateway enables cloud-based BEM software to have a comprehensive access to supported non-cloud devices. The IoT gateway has been designed to support BACnet, Modbus and HTTP RESTful, which are the three widely adopted communication protocols in the building automation and control domain. The developed software executes these three communication protocols concurrently to address requests from cloud-based BEM system. The performance of the designed architecture is independent of the number of devices supported by the IoT gateway software. / Master of Science

ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California / The system described in this paper can monitor utility usage in commercial and residential structures, and send an alert message over conventional cell phone networks when it detects an anomalous condition. Such a condition could indicate a utility outage, structure failure, HVAC system failure, water leak, etc. The microcontroller-based system can measure electrical current, carbon monoxide, methane, liquid propane, temperature, barometric pressure, and altitude using a wired and wireless sensor network. The microcontroller displays the measurements on local and external graphical user interface, and sends SMS alert messages when necessary. The system may be retrofitted into existing structures.

Existing Building Automation Systems (BASs) and Building Automation Networks (BANs) have been shown to have serious cybersecurity problems. Due to the safety-critical and interconnected nature of building subsystems, local and network access control needs to be finer grained, taking into consideration the varying criticality of applications running on heterogeneous devices. In this paper, we present a secure communication framework for BASs that 1) enforces rich access control policy for operating system services and objects, leveraging a microkernel-based architecture; 2) supports fine-grained network access control on a per-process basis; 3) unifies the security control of inter-device and intra-device communication using proxy processes; 4) tunnels legacy insecure communication protocols (e.g., BACnet) through a secure channel, such as SSL, in a manner transparent to legacy applications. We implemented the framework on seL4, a formally verified microkernel. We conducted extensive experiments and analysis to compare the performance and effectiveness of our communication systems against a traditional Linux-based implementation of the same control scenario. Our experiments show that the communication performance of our system is faster or comparable to the Linux-based architecture in embedded systems.