1,720,976 research outputs found
Energy Consumption Profiling Of Appliances Inside Smart Buildings Based On k-means Clustering
No full textSmart Building Energy Management Systems schedule appliances when it is more convenient, e.g. during off-peak times or when power is produced by Renewable Energy Sources (RES). In order to do so, it is necessary for the system to know the power consumption profiles over time of the appliances present in the building. Nevertheless, obtaining this information with sufficient detail is not straightforward, since appliance consumption varies significantly over time as compared to the average consumption. Furthermore, considering appliances with different working cycles, i.e., different power consumption profiles, such as washing machines and dishwashers, the system does not know which cycle will be selected in advance. This paper proposes an appliance power profiling system that analyses the power consumption data collected by smart meters, identifies which features are most relevant for the specific appliance and, using the k-means algorithm, extracts the set of power consumption profiles that are associated with each appliance. Preliminary simulation results show that each profile can be approximated with a single reference consumption cycle representative for the entire cluster with errors that are always lower than 10%, considering both total energy consumption and power values time interval by time interval
Smart building energy and comfort management based on sensor activity recognition and prediction
No full texthanks to Building Energy and Comfort Managements (BECM) systems, it is possible monitor and control
buildings with the aim to ease appliance management and at the same time ensuring efficient use of them from the energetic point of view. To develop such kind of systems, it is necessary to monitor users’ habits, learning their preferences and predicting their sequences of performed activities and appliance usage during the
day. To this aim, in this paper a system capable of controlling home appliances according to user preferences and trying to reduce energy consumption is proposed. The main objective of the system is to learn users’ daily behaviour and to be able to predict their future activities basing on statistical data about the activities they usually perform. The system can then execute a scheduling algorithm of the appliances based on the expected energy consumption and user annoyance related with shifting the appliance starting time from their preferred one. Experimental results demonstrate that thanks to the scheduling algorithm energy cost can be reduced of 50.43% and 49.2%
depending on different tariffs, just by shifting the use of the appliance to time periods of non-peak hours. Scheduling based on probability evaluation of preferred time of usage of the appliances allows to still obtain evident energy savings even considering the errors on predicted activities
Application Task Allocation in Cognitive IoT: A Reward-Driven Game Theoretical Approach
Full text linkIn this study we consider the scenario of sensors belonging to different platforms and owned by different owners that join the efforts in an opportunistic way to improve the overall sensing capabilities in a given geographical area by forming clusters of nodes. The considered nodes have cognitive radio and exploit device-to-device communications. A solution is proposed which relies on a Cluster Head (CH) that guides the whole task allocation strategy. The addressed challenges are the following: i) collaborative spectrum sensing for effective communications within the cluster; ii) assignment of each request of sensing tasks to a single node in the cluster. The first challenge is addressed by proposing a collaborative sensing procedure where each node communicates to the CH the received signal energy of licensed users so that the latter makes a decision on the availability of the band by fusing the received information towards a minimisation of the uncertainty in detecting the free spectrum. The second challenge is addressed by proposing a non-cooperative Game theory based approach in which cluster nodes make effort to selfishly increase utility by winning the task. Each node takes part to the competition by considering two elements: the gain that is won for its contribution to sensing and for the execution of the task (in case it wins the competition); the cost in terms of energy to be consumed in case the task is executed. A Nash Equilibrium Point (NEP) is found for the aforementioned game in which each object has no incentive to deviate uni-laterally from the NEP. Extensive simulations are performed to evaluate the impact of probability of false alarm, utility function weighting factors and presence of licensed users on the cumulative system utility
Task Allocation among Connected Devices: Requirements, Approaches and Challenges
Full text linkTask allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to the best devices, according to the context and task requirements. The main purpose of this paper is to study the different connotations of the concept of TA efficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. The paper first analyzes the domains of connected devices where TA has an important role, which brings to this classification: Internet of Things (IoT), Sensor and Actuator Networks (SAN), Multi-Robot Systems (MRS), Mobile Crowdsensing (MCS), and Unmanned Aerial Vehicles (UAV). The paper then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-of-the-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. The paper also discusses the issues that need to be addressed by future research activities, i.e.: allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; extending the list of tasks performed by objects; designing TA strategies where network service providers have a role in TA functionalities’ provisioning
Sensor-Based Activity Recognition Inside Smart Building Energy and Comfort Management Systems
Full text linkThe challenge of Smart Building Energy and Comfort Management (BECM) systems is to schedule home appliances according to users' comfort requirements, while contributing to an efficient and sustainable use of the available energy sources, supplied by either the electricity grid or Renewable Energy Sources (RES). To this aim, BECM systems have to monitor users' habits and learn their preferences, so that their actions can be predicted and appliances can be scheduled accordingly.This paper stems from the observation that actions are usually performed in sequences that repeat according to a pattern. Therefore, activities can be recognized and predicted as soon as a pattern of actions is detected. The framework proposed in this paper aims to predict activities by analyzing the sequences of actions detected by sensors deployed in a Smart Building. Furthermore, a correlation between subsequent activities is found so that sequences of activities can be predicted. Simulation results show that activities can be predicted with an accuracy of 74.78%
Virtual user in the IoT: definition, technologies and experiments
Full text linkVirtualization technologies are characterizing major advancements in the Internet of Things (IoT) arena, as they allow for achieving a cyber-physical world where everything can be found, activated, probed, interconnected, and updated at both the virtual and the physical levels. We believe these technologies should apply to human users other than things, bringing us the concept of the Virtual User (VU). This should represent the virtual counterpart of the IoT users with the ultimate goal of: (i) avoiding the user from having the burden of following the tedious processes of setting, configuring and updating IoT services the user is involved in; (ii) acting on behalf of the user when basic operations are required; (iii) exploiting to the best of its ability the IoT potentialities, always taking always account the user profile and interests. Accordingly, the VU is a complex representation of the user and acts as a proxy in between the virtual objects and IoT services and application; to this, it includes the following major functionalities: user profiling, authorization management, quality of experience modeling and management, social networking and context management. In this respect, the major contributions of this paper are to: provide the definition of VU, present the major functionalities, discuss the legal issues related to its introduction, provide some implementation details, and analyze key performance aspects in terms of the capability of the VU to correctly identify the user profile and context
How to exploit the Social Internet of Things: Query Generation Model and Device Profiles’ Dataset
Full text linkThe future Internet of Things (IoT) will be characterized by an increasing number of object-to-object interactions for the implementation of distributed applications running in smart environments. The Social IoT (SIoT) is one of the possible paradigms that is proposed to make the objects’ interactions easier by facilitating the search of services and the management of objects’ trustworthiness. In this scenario, we address the issue of modeling the queries that are generated by the objects when fulfilling applications’ requests that could be provided by any of the peers in the SIoT. To this, the defined model takes into account the objects’ major features in terms of typology and associated functionalities, and the characteristics of the applications. We have then generated a dataset, by extracting objects’ information and positions from the city of Santander in Spain. We have classified all the available devices according to the FIWARE Data Models, so as to enable the portability of the dataset among different platforms. The dataset and the proposed query generation model are made available to the research community to study the navigability of the SIoT network, with an application also to other IoT networks. Experimental analyses have also been conducted, which give some key insights on the impact of the query model parameters on the average number of hops needed for each search
An online energy management tool for sizing integrated PV-BESS systems for residential prosumers
Full text linkThis paper presents an online energy management tool that suggests the most suitable size of a hybrid photovoltaic-battery energy storage system (PV-BESS) to residential prosumers based on their self-sufficiency expectations. An offline analysis of electricity generation and consumption expected from 128 residential prosumers has been carried out at first in order to find out their self-sufficiency map with different sizes of PV and BESS; this is carried out by the genetic algorithm based energy management (GA) presented in a previous work. Subsequently, a number of clusters have been defined, each of which groups prosumers that share similar self-efficiency maps; particularly, clustering has been carried out and refined by identifying the most significant features of prosumers belonging to the same cluster, as well as those that differentiate prosumers belonging to different clusters. As a result, it has been revealed that the habit of usage of some appliances, such as Heat Ventilation Air Conditioning system (HVAC) and water heater, and peak electricity consumption represent the most important features influencing clustering. Based on these outcomes, the proposed online energy management tool is able to assign a prosumer to the most suitable cluster just based on the answers to a few simple questions related to energy consumption habits, providing the corresponding self-efficiency map almost immediately. The results achieved by the proposed tool, which is currently running online, are promising and show that significant self-sufficiency increases can be obtained, allowing the proper choice of PV-BESS depending on specific prosumer's needs and expectations
Trustworthy task allocation in IoT: a cognitive game-theoretical use case
No full textThis Chapter analyzes how malicious attacks affect the performance of a heterogeneous Internet of Things (IoT) system where cognitive devices collaborate to negotiate task assignments. In the reference scenario, the involved devices create clusters, each managed by a Cluster Head (CH). Whenever a task is required, the CH triggers spectrum sensing to detect spectrum holes that can be opportunistically exploited by the nodes of the cluster for task allocation. In this scenario, not all the nodes are Honest Nodes (HN). Indeed, Malicious Nodes (MNs) may hinder the process and try to disrupt it by providing tampered data, which would lead to a higher likelihood that the spectrum sensing is not performed correctly. When the spectrum is considered free, the cluster nodes negotiate to execute the required task by means of an auction-based game theory approach. The negotiation takes into account two factors: the reward gained from contributing to the execution of the task, which is provided to the node that wins the competition, and the energy cost to perform the task. Specifically, the Chapter investigates how MNs affect the reward aspect when they try to gain maximum control over the task and potentially launch a Denial of Service (DoS) attack. Extensive simulations are run to assess the effect of the key system parameters on the overall performance and provide recommendations for future research
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