Living IoT: A Flying Wireless Platform on Live Insects

Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time is limited by the energy density of current battery technology. We explore an alternative, biology-based solution: integrate sensing, computing and communication functionalities onto live flying insects to create a mobile IoT platform. Such an approach takes advantage of these tiny, highly efficient biological insects which are ubiquitous in many outdoor ecosystems, to essentially provide mobility for free. Doing so however requires addressing key technical challenges of power, size, weight and self-localization in order for the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.Comment: Co-primary authors: Vikram Iyer, Rajalakshmi Nandakumar, Anran Wang, In Proceedings of Mobicom. ACM, New York, NY, USA, 15 pages, 201

Developing Flexible, Networked Lighting Control Systems That Reliably Save Energy in California Buildings

An important strategy to meet California's ambitious energy efficiency goals is to use innovative wireless communications, embedded sensors, data analytics and controls to significantly reduce lighting energy use in commercial buildings. This project developed a suite of networked lighting solutions to further this goal. The technologies include a platform for low-cost sensing, distributed intelligence and communications, the “PermaMote,” which is a self-powered sensor and controller for lighting applications. The project team also developed a task ambient daylighting system that integrates sensors with data-driven daylighting control using an open communication interface, called the “Readings-At-Desk” (RAD) system. To address the problem of building occupants being confused about how to operate traditional lighting control systems, the research team created content that could be the basis for a user interface standard for lighting controls. Finally, to address the difficulty of ensuring that advanced lighting control systems actually deliver their promised energy savings, the project team developed a new method for evaluating and specifying lighting systems’ performance. The research team validated these technologies in the laboratory, showing significant lighting energy savings, up to 73% for the PermaMote sensor system from occupancy control and daylight dimming features, compared to the same light source (LED replacement lamps) operated via simple on/off scheduling. The project team also developed a proposed standard lighting data model and user interface elements, which were contributed to the ANSI Lighting Systems Committee (C137) for standardization. Existing data models are incomplete and inconsistent, whereas the lighting-specific data model developed here is clear and comprehensive, to serve as a starting point for creating common, universally agreed upon semantic definitions of key lighting parameters, to promote interoperability. For the task on verifiable performance of lighting systems, the project team developed a more effective metric for capturing the actual energy impact of a lighting system over time — the energy usage intensity (kWh/ft2/year). Three commercial lighting systems were tested in FLEXLAB® using this new metric, and the tests show a wide range in the accuracy of the self-reported energy-use metric, from 0.5% to 28% error compared to direct measurement of lighting energy using dedicated submeters. Overall, the project team estimates that these advanced technologies can reduce California office lighting energy use by 20% (above and beyond normal advanced lighting controls mandated by Title 24), resulting in about 1,600 GWh/year in savings

Intelligent Lighting System: An Education Kit to Increase the Awareness of Energy-Saving for Primary School Children.

High energy consumption per capita reported in our country has shown a significant issue pertaining to the overconsumption of electricity. This issue has become a problem when the negative effects of climate change are felt by citizens. Thus, public must be taught to be energy-saving conscious, especially from their young age. However, there is a lack of such education material to teach the young generations. Thus, this report presents an education kit, which consists of an education website, a prototype and a case study, which aims to increase the awareness of energy-saving in the young minds of the primary school students. The website acts as an interactive tool for students to learn about energy-saving and how the prototype works. The prototype consists of an intelligent lighting device that uses a combination of sensors, or dual-technology to reduce unnecessary electricity consumption of lighting in a target building. The case study focuses on academic building Academic Building 2 in Univerisiti Teknologi PETRONAS (UTP), where the lighting consumption pattern is studied, and how the intelligent lighting system can help to reduce the unnecessary electricity wastage within lecture room in the campus. The methodology used is Rapid Application Development (RAD) cycle and prototyping, where the website and the prototype are developed and tested on a regular basis. Findings have shown that UTP is consuming a very high amount of electricity, and the intelligent lighting system is estimated to reduce up to 36.58% of electricity spent on lighting in Academic Building 2. In short, the whole package is interactive and educative to the primary school students to learn about energy-saving in an unprecedented way

Adaptalight: An inexpensive PAR sensor system for daylight harvesting in a Micro Indoor Smart Hydroponic System

Environmental changes and the reduction in arable land have led to food security concerns around the world, particularly in urban settings. Hydroponic soilless growing methods deliver plant nutrients using water, conserving resources and can be constructed nearly anywhere. Hydroponic systems have several complex attributes that need to be managed, and this can be daunting for the layperson. Micro Indoor Smart Hydroponics (MISH) leverage Internet of Things (IoT) technology to manage the complexities of hydroponic techniques, for growing food at home for everyday citizens. Two prohibitive costs in the advancement of MISH systems are power consumption and equipment expense. Reducing cost through harvesting ambient light can potentially reduce power consumption but must be done accurately to sustain sufficient plant yields. Photosynthetic Active Radiation (PAR) meters are commercially used to measure only the light spectrum that plants use, but are expensive. This study presents Adaptalight, a MISH system that harvests ambient light using an inexpensive AS7265x IoT sensor to measure PAR. The system is built on commonly found IoT technology and a well-established architecture for MISH systems. Adpatalight was deployed in a real-world application in the living space of an apartment and experiments were carried out accordingly. A two-phase experiment was conducted over three months, each phase lasting 21 days. Phase one measured the IoT sensor’s capability to accurately measure PAR. Phase two measured the ability of the system to harvest ambient PAR light and produce sufficient yields, using the calibrated IoT sensor from phase one. The results showed that the Adaptalight system was successful in saving a significant amount of power, harvesting ambient PAR light and producing yields with no significant differences from the control. The amount of power savings would be potentially greater in a location with more ambient light. Additionally, the findings show that, when calibrated, the AS7265x sensor is well suited to accurately measure PAR light in MISH systems

Wireless sensors and IoT platform for intelligent HVAC control

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.QREN SIDT [38798]; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

Proper maintenance way for the multifunctional windows

Recent developments have helped create windows that can fulfill their contrary functions effectively in addition to generating energy, which are known as multifunctional windows. Permanent maintenance is required for windows to achieve their functions properly, but the current window cleaning methods can harm and are not appropriate for the recently developed multifunctional windows. The author presents a suggested multifunctional window and sheds light on the disadvantages that could be accomplished when using the current methods to clean it. Using analytical and logical methods, this paper shows the proper way of the multifunctional window maintenance. This way depends on the self-cleaning features. The author proposes a solution for the disadvantages that could accompany that features. The main result is the validity of a successful multifunctional window that can be maintained with minimum disadvantages and maximum efficiency. Therefore, this study contributes to the window industry by presenting the proper way of maintaining multifunctional windows. Thus, future maintenance research should be redirected properly to conserve and benefit the efforts spent in impropriate directions and technologie

Literature review - Energy saving potential of user-centered integrated lighting solutions

Measures for the reduction of electric energy loads for lighting have predominantly focussed on increasing the efficiency of lighting systems. This efficiency has now reached levels unthinkable a few decades ago. However, a focus on mere efficiency is physically limiting, and does not necessarily ensure that the anticipated energy savings actually materialize. There are technical and non-technical reasons because of which effective integration of lighting solutions and their controls, and thus a reduction in energy use, does not happen. This literature review aims to assess the energy saving potential of integrated daylight and electric lighting design and controls, especially with respect to user preferences and behaviour. It does so by collecting available scientific knowledge and experience on daylighting, electric lighting, and related control systems, as well as on effective strategies for their integration. Based on this knowledge, the review suggests design processes, innovative design strategies and design solutions which – if implemented appropriately – could improve user comfort, health, well-being and productivity, while saving energy as well as the operation and maintenance of lighting systems. The review highlights also regulatory, technical, and design challenges hindering energy savings. Potential energy savings are reported from the retrieved studies. However, these savings derived from separate studies are dependent on their specific contexts, which lowers the ecological validity of the findings. Studies on strategies based on behavioural interventions, like information, feedback, and social norms, did not report energy saving performance. This is an interesting conclusion, since the papers indicate high potentials that deserve further exploration. Quantifying potential savings is fundamental to fostering large scale adoption of user-driven strategies, since this would allow at least a rough estimation of returns for the investors. However, such quantification requires that studies are designed with an inter-disciplinary approach. The literature also shows that strategies, where there is more communication between façade and lighting designers, are more successful in integrated design, which calls for more communication between stakeholders in future building processes

Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning

With the advent of the Internet of Things (IoT), an increasing number of energy harvesting methods are being used to supplement or supplant battery based sensors. Energy harvesting sensors need to be configured according to the application, hardware, and environmental conditions to maximize their usefulness. As of today, the configuration of sensors is either manual or heuristics based, requiring valuable domain expertise. Reinforcement learning (RL) is a promising approach to automate configuration and efficiently scale IoT deployments, but it is not yet adopted in practice. We propose solutions to bridge this gap: reduce the training phase of RL so that nodes are operational within a short time after deployment and reduce the computational requirements to scale to large deployments. We focus on configuration of the sampling rate of indoor solar panel based energy harvesting sensors. We created a simulator based on 3 months of data collected from 5 sensor nodes subject to different lighting conditions. Our simulation results show that RL can effectively learn energy availability patterns and configure the sampling rate of the sensor nodes to maximize the sensing data while ensuring that energy storage is not depleted. The nodes can be operational within the first day by using our methods. We show that it is possible to reduce the number of RL policies by using a single policy for nodes that share similar lighting conditions.Comment: 7 pages, 5 figure