The escalating global energy crisis and the increasing${\text{CO}_{2}}$emissions have necessitated the optimization of energy efficiency. The proliferation of Internet of Things (IoTs) devices, expected to reach 100 billion by 2030, contributed to this energy crisis and subsequently to the global${\text{CO}_{2}}$emissions increase. Concomitantly, climate and energy targets have paved the way for an escalating adoption of solar photovoltaic power generation in residences. The IoT integration into home energy management systems holds the potential to yield energy and peak demand savings. Optimizing device planning to mitigate${\text{CO}_{2}}$emissions poses significant challenges due to the complexity of user-defined preferences and consumption patterns. In this article, we propose an innovative IoT data platform, coinedSustainable Energy Management Framework (SEMF), which aims to balance the trade-off between the imported energy from the grid, users’ comfort, and${\text{CO}_{2}}$emissions.SEMFincorporates a Green Planning evolutionary algorithm, coinedGreenCap$^+$+, to facilitate load shifting of IoT-enabled devices, taking into consideration the integration of renewable energy sources, multiple constraints, peak-demand times, and dynamic pricing. Based on our experimental evaluation utilizing real-world data, our prototype system has outperformed the state-of-the-art approach by up to$\approx$29% reduction in imported energy,$\approx$35% increase in self-consumption of renewable energy, and$\approx$34% decrease in${\text{CO}_{2}}$emissions, while maintaining a high level of user comfort$\approx$94%-99%.

In this paper, we present an innovative framework whose objective is to allow drivers to recharge their Electric Vehicles (EVs) from the most environmentally friendly chargers using an intelligent hoarding approach. These chargers maximize renewable (e.g., solar) self-consumption, minimizing this way CO2 production and also the need for expensive stationary batteries on the electricity grid to store renewable energy that cannot be used otherwise. We model our problem as a Continuous k-Nearest Neighbor query, where the distance function is computed using Estimated Components (ECs), i.e., a query we term CkNN-EC. An EC defines a function that can have a fuzzy value based on some estimates. Specific ECs used in this work are: (i) the (available clean) power at the charger, which depends on the estimated weather; (ii) the charger availability, which depends on the estimated busy timetables that show when the charger is crowded; and (iii) the derouting cost, which is the time to reach the charger depending on estimated traffic. We devise the EcoCharge framework that combines these multiple non-conflicting objectives into an optimization task providing user-defined ranking means through an intuitive mobile GIS application. Particularly, our core algorithm uses lower and upper values derived from the ECs to recommend the top ranked EV chargers and present them through an intuitive map user interface to users. Our experimental evaluation with extensive synthetic and real traces from Germany, China, and USA along with EV charger data from Plugshare shows that EcoCharge meets the objective functions in an efficient manner, allowing continuous recomputation on the edge devices (e.g., Android Automotive OS, Android Auto or Apple Carplay).

In this demonstration paper, we present an innovative framework for sustainable Electric Vehicles (EVs) charging, dubbed EcoCharge, which utilizes an intelligent energy hoarding approach. Particularly, EcoCharge employs a Continuous k-Nearest Neighbor query, where the distance function is computed using Estimated Components (ECs) (i.e., a query we term CkNN-EC). An EC defines a function that can have a fuzzy value based on some estimates. Specific ECs used in this work are: (i) the (available clean) power at the charger, which depends on the estimated weather; (ii) the charger availability, which depends on the estimated busy timetables that show when the charger is crowded; and (iii) the derouting cost, which is the time to reach the charger depending on estimated traffic. Our framework combines these multiple non-conflicting objectives into an optimization task providing user-defined ranking means through an intuitive spatial application. The algorithm utilizes lower and upper interval values derived from ECs to recommend the top ranked EV chargers and present them through a map interface to users. We demonstrate EcoCharge using a complete prototype system developed using the Leaflet - OpenStreetMap library. In our demonstration scenario, attendees will have the opportunity to observe through mobile devices the benefits of EcoCharge by simulating its execution over various scheduled trips with real data retrieved from API requests (i.e., ECs).

Energy efficiency has become a primary optimization objective due to the global energy crisis and high levels of CO2emissions. Climate and energy targets have been leading to a growing utilization of solar photovoltaic power generation in residential buildings. As the number of IoT devices drastically increases, their automation through an intelligent home energy management system can provide energy and peak demand savings. The planning optimization of devices can be very challenging due to the unsophisticated user-defined preference rules. Existing solutions face convergence difficulties due to the management of multiple IoT devices tackling multiobjective problems. In this paper, we propose an innovative IoT data system, coined GreenCap, which utilizes a Green Planning evolutionary algorithm for load shifting of IoT-enabled devices, considering the integration of renewable energy sources, multiple constraints, peak-demand times, and dynamic pricing. We have implemented a complete prototype system available on Raspberry Pi and linked with openHAB framework. Our experimental evaluation with extensive real traces shows that the GreenCap prototype system efficiently generates a sustainable plan obtaining high levels of user comfort 92-99% along with ≈52% of self-consumption, while reducing ≈35% of the imported energy from the grid and ≈40% of CO2emissions.

In this demonstration paper, we present an innovative IoT data platform, coined GreenCap, which utilizes a Green Planning evolutionary algorithm for load shifting of IoT-enabled devices in smart environments that feature renewable energy sources. Particularly, GreenCap deploys a hybrid genetic algorithm with domain-specific local search heuristics, which results in a Memetic Algorithm (MA) that offers users an energy efficient allocation plan of their IoT devices, based on their personal preference rules (e.g., operate AC from 10am - 1pm). Our system allocates operations in the daily time-slots considering devices’ energy bounds to minimize the imported energy from the grid, exploit self-consumption and maximize users’ comfort. We demonstrate GreenCap using a complete prototype system available on Raspberry Pi, developed in Laravel using MariaDB and linked to openHAB framework. In our demonstration scenario, attendees will be able to observe through mobile devices the benefits of GreenCap by simulating its execution with real data for one week, using pre-configured or custom rules.

This demo paper presents EnterCY, an integrated Virtual and Augmented Reality Tourism platform for Cyprus. The platform's web-based, spatio-temporal virtual exploration component allows potential visitors to explore the rich cultural heritage, variety of activities, and wealth of sightseeing locations in Cyprus before their visit. EnterCY also enhances tourists' experiences during their visit through its mobile component, which offers on-site visual and audio guidance, personalized recommendations, as well as entertaining and learning features (e.g., story-telling), based on mobile-friendly Augmented Reality, location-awareness and Machine Learning technologies. Through Immersive Reality technologies, the platform provides for an after visit experience by creating personalized 360 video mementos of tourists' tours and supports integrated features that allow for experience sharing in popular social media platforms.

The advancement of renewable energy infrastructure in smart buildings (e.g., photovoltaic) has highlighted the importance of energy self-consumption by energy-demanding IoT-enabled devices (e.g., heating/cooling, electromobility, and appliances), which refers to the process of intelligently consuming energy at the time it is available. This stabilizes the energy grid, minimizes energy dissipation on power lines but more importantly is good for the environment as energy from fossil sources with a high CO2 footprint is minimized. On the other hand, user comfort levels expressed in the form of Rule Automation Workflows (RAW) , are usually not aligned with renewable production patterns. In this work, we propose an innovative framework, coined IoT Meta-Control Firewall (IMCF + ) , which aims to bridge this gap and balance the trade-off between comfort, energy consumption, and CO2 emissions. The IMCF + framework incorporates an innovative Green Planner (GP) algorithm, which is an AI-inspired algorithm that schedules energy consumption with a variety of amortization strategies. We have implemented IMCF + and GP as part of a complete IoT ecosystem in openHAB and our extensive evaluation shows that we achieve a CO2 reduction of 45–59% to satisfy the comfort of a variety of user groups with only a moderate ≈ 3% in reducing their comfort levels.

Internet of Things (IoT) devices have penetrated massively into smart environments (e.g., smart-homes, smart-cars or more generally smart-anything). Besides data collection, many IoT devices also enable the execution of Rule Automation Workflows (RAW), which span from simple predicate statements to procedural workflows capturing a smart actuation pipeline. RAW aim to meet the convenience (comfort) level of users under specific conditions (e.g., raise room temperature to 22 C if cold), but unfortunately cannot express long-term objectives of users (e.g., consume less than 400 kWh in December). In this paper, we present an innovative system, coined IoT Meta-Control Firewall (IMCF), which internally deploys an AI-inspired Energy-Planner (EP) algorithm that exploits domain-specific operators to balance the trade-off between convenience and energy consumption in satisfying the RAW pipelines of users. IMCF filters the RAW pipelines in a way that these do not conflict with the long-term objectives of users (like a network firewall). Our experimental evaluation with extensive real traces from an apartment, a house, and campus dorms shows that IMCF achieves very high levels of user convenience while remaining within the target energy consumption budgets expressed by users.

The advancement of renewable energy infrastructure in smart buildings (such as photovoltaic) has highlighted the importance of self-consumption, which is the act of using local renewable energy to run IoT-enabled devices (e.g., HVAC, EV charging, white appliances) at the time the energy is produced. In countries with a high kg CO2per kWh factor, this effectively reduces CO2pollution but also contributes to the stabilization of the energy grid. User comfort preferences are often expressed in the form of Rule Automation Workflows (RAW), which are software rules as to when and how IoT appliances must operate to allow custodians reach their convenience levels, as opposed to self-consumption directives. The objective of this Ph.D. research is to extend the IoT Meta- Control Firewall's (IMCF) functionalities to bridge this gap and balance the trade-off between convenience, energy consumption and CO2emissions in satisfying the RAW pipelines of users.