Papers by Driss Benhaddou
Measuring Human Comfort for Smart Building Application
Proceedings of the 2nd International Conference on Smart Digital Environment
Realizing the importance of the effect of thermal comfort on the health and productivity of peopl... more Realizing the importance of the effect of thermal comfort on the health and productivity of people, many researches have been done in this area at the beginning of last century. These works are carried out in climatic chambers or in situ, or models or with human beings. They aim to identify the conditions of comfort and acceptability of the thermal environment without trying to understand the mechanisms involved. Following this work, several indices of thermal comfort have been developed based on models of thermal comfort. The developed models are different; there are the physical models are often measuring instruments whose physical responses to the thermal environment are similar to those of the human body. Besides, one of the key objectives in studying the behavior of people in certain environmental and personal conditions is to predict the degree of satisfaction/dissatisfaction with the thermal environment. The objective is to propose a predictive model of the thermal sensation of the users of indoor environments using subjective variables.
Energies, Apr 27, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Cornell University - arXiv, Jun 22, 2020
Objective. Different factors such as thermal comfort, humidity, air quality, and noise have signi... more Objective. Different factors such as thermal comfort, humidity, air quality, and noise have significant combined effects on the acceptability and quality of the activities performed by the buildings' occupants who spend most of their times indoors. Among the factors cited, thermal comfort, which contributes to the human well-being because of its connection with the thermoregulation of the human body. Therefore, the creation of thermally comfortable and energy efficient environments is of great importance in the design of the buildings and hence the heating, ventilation and airconditioning systems. In fact, among the strategies to improve thermal comfort while minimizing energy consumption is the use of control systems. Recent works have been directed towards more advanced control strategies, based mainly on artificial intelligence which has the ability to imitate human behavior. This systematic literature review aims to provide an overview of the intelligent control strategies inside building and to investigate their ability to balance thermal comfort and energy efficiency optimization in indoor environments. Methods. A systematic literature review examined the peerreviewed research works using ACM Digital Library, Scopus, Google Scholar, IEEE Xplore (IEOL), Web of Science, and Science Direct (SDOL), besides other sources from manual search. With the following string terms: thermal comfort, comfort temperature, preferred temperature, intelligent control, advanced control, artificial intelligence, computational intelligence, building, indoors, and built environment. Inclusion criteria were: English, studies monitoring, mainly, human thermal comfort in buildings and energy efficiency simultaneously based on control strategies using the intelligent approaches. Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines were used. Results. Initially, 1,077 articles were yielded, and 120 ultimately met inclusion criteria and were reviewed. Conclusions. From the systematic literature review, it was possible to identify the control methods used by the researchers, the most popular and efficient optimization strategies of thermal comfort and hence energy use in the built environments.
Agent Based for Comfort Control in Smart Building
2017 International Renewable and Sustainable Energy Conference (IRSEC)
Optimizing the energy efficiency of buildings and facilities has actually become essential for in... more Optimizing the energy efficiency of buildings and facilities has actually become essential for interconnecting them through a neighborhood or a metropolis, or more broadly across a territory. From this perspective, it has been mainly developed the information and communications technology (ICT) to assure the building service and communicative/intelligent grids as well. As shown in the present work, the use of ambient intelligence or technologies based on intelligent systems to model and control the behavior of a system involved in building automation contributes to significantly optimize their execution in terms of comfort, safety, and energy savings. In this paper, we suggest an agent-based architecture for smart building control, the prototype implemented in this work is composed of agents developed using JADE (Java Agent Development) framework.
Virtualization is strongly emerging back as a fundamental Cloud Computing (CC) technology enabler... more Virtualization is strongly emerging back as a fundamental Cloud Computing (CC) technology enabler whereby CC services are mainly provided via the instantiation of Virtual Machines (VMs). These instantiations follow a stochastic pattern, which is mainly dictated by the nature of the CC services requests and Cloud "elasticity". Consequently, a load-balancer emerges as indispensable to intervene in situations where VMs need to be dynamically migrated from a data center site to another in order to sustain optimal CC operation. In this paper, we briefly survey available VM migration techniques, delineate their pros and cons, and shed further light into the novel aspects to consider when approaching, these VM migration techniques, from a CC perspective, e.g., considering Mobile Cloud Computing (MCC) and Network Function Virtualization (NFV). In addition, we propose a novel VM migration scheme (soft-migration) inspired from mobile communication.
2019 IEEE 5th World Forum on Internet of Things (WF-IoT), 2019
extending the battery lifetime of Internet of Things (IoT) devices is still a challenging researc... more extending the battery lifetime of Internet of Things (IoT) devices is still a challenging research question. A lot of work has been done to optimize IoT wireless sensors in terms of hardware architecture, operating system, along with the usage of low power data acquisition techniques and energy aware routing protocols. In Smart Energy Efficient Building (SEEB), Energy Management System (EMS) uses WSN for data acquisition to monitor energy consumption and to track user behaviour. For EMS, context recognition is a key element for HVAC (Heating, Ventilation and Air Conditioning system) control. Therefore, the more the context is precise, the more the decision that will be taken, by the EMS, is accurate. In most SEEB, sensor nodes are configured to send data periodically. Thus, unnecessarily increasing battery-energy consumption as sensor nodes keep sending redundant data (e.g., when context did not change). To solve this issue, we propose an Energy Aware Context Recognition Algorithm (EACRA) that dynamically configures sensors to send specific data under specific conditions and at a specific time, thus avoiding redundant data transmissions. This algorithm uses SEEB declared knowledge, and forcing the sensor node to send data only when context changes. The experiment results shows the difference between the periodic sampling and sampling using EACRA in terms of energy consumption.
Smart Agriculture as a Cyber Physical System: A Real-World Deployment
2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), 2020
Water is becoming a scarce resource. With the steadily increasing demand on food, the optimizatio... more Water is becoming a scarce resource. With the steadily increasing demand on food, the optimization of resources usage proves indispensable for a sustained agriculture. In this context, Smart Agriculture (SA) is emerging as a promising field leveraging on the introduction of ICT (Information & Communication Technology) with the ultimate goal of both optimal usage of resources (e.g., groundwater, fertilizers, and electrical energy) and better crop yields. The smartness of SA stems from the real-time data acquisition, processing, and dissemination. SA is a Cyber Physical System (CPS). It continuously monitors and acts upon physical entities (e.g., soil, weather, pumps, etc.). Wireless sensors/actuators, along with the control unit, constitute, the main ICT components in SA. In this paper, we shed further light onto the ICT components of SA and present the details of a real-world testbed deployment. We present the system architecture, the wireless communication infrastructure, and the processing behind the control unit. The latter is an intelligent system using Fuzzy logic. Furthermore, we present venues for renewable energy integration into SA and advocate the use of batteries for optimal energy usage.
IEEE Access, 2021
Water is becoming scarcer. The unmonitored control and the extensive use of fossil fuel in water-... more Water is becoming scarcer. The unmonitored control and the extensive use of fossil fuel in water-table pumping for irrigation exacerbate global warming and harm the environment. Along with the rapid population growth and the concomitant increase in the demand for food, optimal usage of water-table and energy is becoming a must and indispensable for sustainable agriculture. In this context, Smart Agriculture (SA) is emerging as a promising field that leverages ICT (Information and Communication Technology) to optimize resources' usage while enhancing crops' yields. In this paper, we present an integral SA solution that leverages cost-effectiveness. Commercial solutions are costly and thus become impossible to adopt by small and medium farmers. Our solution revolves around three main axes: 1. Smart Water Metering promotes optimal usage and conservation of water-table (a.k.a., groundwater) via real-time data collection and monitoring using a Cloud-based IoT (Internet of Things) system; 2. Renewable-Energy integration promotes energy-efficient agriculture by reducing reliance on fossil fuels in water-table pumping, and 3. Smart Irrigation to promote good crops quality and quantity without harming the soil and the water-table ecosystems. Our solution has been deployed and tested in a real-world Smart Farm testbed. The results have shown that the adoption of our SA system reduces the amount of water consumption (with a traditional irrigation system) up to 71.8%. Finally, our solution is open-source and can be easily adopted and adapted by other researchers to promote the setting of a dedicated Cloud-based platform for water-table usage, especially in arid and sub-Saharan countries. INDEX TERMS Smart agriculture (SA), wireless sensors networks (WSN), Internet of Things (IoT), fuzzy logic control, information communication technology (ICT).
Towards Green Data Centers
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2020
Green Computing has been the trend among computer scientists for its eco-friendliness. It serves ... more Green Computing has been the trend among computer scientists for its eco-friendliness. It serves as a great solution to be integrated with Smart Grids (SG). Data stemming from SGs falls under the realm of Big Data as it is voluminous, various, and has a great velocity. Hence, these data need processing and storage. For this, High-Performance Computing, through clustering a set of computers, proves necessary. Nowadays, with the hardware advances that the world is witnessing, the Raspberry Pi (RP) creates a number of opportunities to deploy cost-effective and energy-efficient clusters, which respect the concepts of Green Computing. In this paper, we are presenting the work done within a USAID sponsored project which aims at developing a SG testbed at Al Akhawayn University in Ifrane, Morocco. We are presenting the deployment of a 5-node cluster based on RPs. The cluster has Hadoop installed and runs the TestDFSIO and Terasort benchmarks for the performance analysis in addition to an energy efficiency analysis.
Smart Buildings For Smart Grids: A Real-world Testbed
2018 6th International Renewable and Sustainable Energy Conference (IRSEC), 2018
Smart Grids (SG) are emerging as a very promising technology meant to cope with the stringent wor... more Smart Grids (SG) are emerging as a very promising technology meant to cope with the stringent worldwide demand on energy and on relevant ecologic measures. At the heart of the Smart Grids lie Smart Buildings (SB). SB are the building blocks of SG. Either residential or industrial, SBs are consuming most of the produced electrical energy. However, and in the context of SG, SBs are meant to produce energy as well and contribute to stabilizing the Demand/Response (DR) variance whereby produced energy is injected back into the SG. This occurs mainly in case of energy shortage in the main SG or in case of excess in energy production. To leverage “smartness” in buildings, continuous data monitoring (e.g., energy production/consumption levels) using wireless sensors, and real-time dissemination/processing of this data, is essential. To further boost buildings’ smartness, deploying context-awareness, whereby electrical appliances can be switched On/Off depending on context (e.g., presence and ambient temperature), would bring considerable added-value. In this paper, a blueprint for deploying a real-world SB testbed is presented.
ArXiv, 2016
The electricity grid is crucial to our lives. House- holds and institutions count on it. In recen... more The electricity grid is crucial to our lives. House- holds and institutions count on it. In recent years, the sources of energy have become less and less available and they are driving the price of electricity higher and higher. It has been estimated that 40% of power is spent in residential and institutional buildings. Most of this power is absorbed by space cooling and heating. In modern buildings, the HVAC (heating, ventilation, and air conditioning) system is centralised and operated by a department usually called the central plant. The central plant produces chilled water and steam that is then consumed by the building AHUs (Air Handling Units) to maintain the buildings at a comfortable temperature. However, the heating and cooling model does not take into account human occupancy. The AHU within the building distributes air according to the design parameters of the building ignoring the occupancy. As a matter of fact, there is a potential for optimization lowering consumption t...
Wind and Photovoltaic Energy Availability and Its Cost Estimation for Tangier Region
Advances in Intelligent Systems and Computing, 2019
This work presents methodology and results used to estimate both wind and solar energies availabi... more This work presents methodology and results used to estimate both wind and solar energies availability and their cost. The objective is to analyze the potential of these two major sources of alternative energy in Tangier region and to show their ability to replace fossil fuel energy sources especially for remote areas and agriculture applications. Thus, energy availability and its cost for these technologies are determined based on meteorological data, and their engineering and technical characteristics. The obtained results provide the hourly average energy production (in kWh) and its cost (in USD/kWh) for a maximum designed power output of 5 kW for each technology and for each month in the year. Thus, these results show that energy production cost is ranging between 0.01 and 0.3 USD/kWh for solar energy using photovoltaic panels and it is ranging between 0.05 and 0.35 USD/kWh for wind energy using wind turbines with rated power equals to 1 kW. Results of this work could be used as ...
Journal of Sensor and Actuator Networks, 2021
The design of Wireless Sensor Networks (WSN) requires the fulfillment of several design requireme... more The design of Wireless Sensor Networks (WSN) requires the fulfillment of several design requirements. The most important one is optimizing the battery’s lifetime, which is tightly coupled to the sensor lifetime. End-users usually avoid replacing sensors’ batteries, especially in massive deployment scenarios like smart agriculture and smart buildings. To optimize battery lifetime, wireless sensor designers need to delineate and optimize active components at different levels of the sensor’s layered architecture, mainly, (1) the number of data sets being generated and processed at the application layer, (2) the size and the architecture of the operating systems (OS), (3) the networking layers’ protocols, and (4) the architecture of electronic components and duty cycling techniques. This paper reviews the different relevant technologies and investigates how they optimize energy consumption at each layer of the sensor’s architecture, e.g., hardware, operating system, application, and net...
Applied Sciences, 2021
Increases in power demand and consumption are very noticeable. This increase presents a number of... more Increases in power demand and consumption are very noticeable. This increase presents a number of challenges to the traditional grid systems. Thus, there is the need to come up with a new solution that copes with the stringent demand on energy and provides better power quality, which gives a better experience to the end users. This is how the concept of smart grids (SG) came to light. SGs have been introduced to better monitor and control the power produced and consumed. In addition to this, SGs help with reducing the electricity bill through the integration of renewable energy sources. The underlying smartness of the SGs resides in the flow of information in addition to the flow of energy. Information/data flowing implies the use of smart sensors and smart meters that sense and send data about the power produced and consumed, and the data about the environment where they are deployed. This makes SGs a direct application of IoT. In this paper, we are implementing an edge platform th...
Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments, 2017
Non-intrusive occupant identi cation enables numerous applications in Smart Buildings such as per... more Non-intrusive occupant identi cation enables numerous applications in Smart Buildings such as personalization of climate and lighting. Current non-intrusive identi cation techniques do not scale beyond 20 people whereas commercial buildings can have 100 or more people. is paper proposes a new method to identify
A Novel Mobile CrowdSensing Architecture for Road Safety
Innovations in Smart Cities Applications Volume 4, 2021
Deploying Smart Micro Grids for Researchers: a Practical Approach
2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), 2021
Smart Grids (SG) are emerging as a very promising technology to cope with the increasing stochast... more Smart Grids (SG) are emerging as a very promising technology to cope with the increasing stochastic demand on energy, the rapid introduction of distributed renewables, and the expected large-scale adoption of electrical vehicles (EVs). Micro Grids (MG) constitute the building blocks of SG. Spanning small geographic areas, MGs are leveraging modularity and thus reducing the complexity of SG. The main challenge in SG is the real-time tracking and dissemination of electricity consumption/production data. This data falls within the realm of Big Data and needs to be processed in real-time in order to generate appropriate control actions and to monitor the stochastic Demand Response (DR) variance. To do so, we need to call upon a mixture of ICTs (Information and Communication Technologies), e.g., Networking, HPC, Big Data processing and analytics, Machine Learning, Control theory, Context-Awareness, etc. In this paper, and based on a real-world testbed deployment, we present the practical rudiments of deploying a real-world MG in a university campus. We mainly address ICT related aspects. As a first milestone, we integrated Renewable energy into a Smart Building and set the appropriate ICTs towards a full MG implementation.
Context-Aware Wireless Sensors for IoT-Centeric Energy-Efficient Campuses
2017 IEEE International Conference on Smart Computing (SMARTCOMP), 2017
Energy Efficiency is becoming a world-wide concern and attracting increasing interest in both ind... more Energy Efficiency is becoming a world-wide concern and attracting increasing interest in both industry and academia. A smart building is at the cornerstone of energy-efficiency as it represents the main constituent in a smart micro-grid. To promote energy-efficiency in buildings, an Energy Management System (EMSs) that controls HVAC appliances is indispensable. Based on the Plan-Do- Check-Act (PDCA) cycle, an EMS needs to handle data acquisition, data analysis, and acting. In this paper, we present a real-world EMS deployed in a real-world building. We used wireless sensors, along with microcontrollers, to implement the networking component that connects data readers, actuators, and the control plane where the database lies. To test our platform, we sense rooms' temperature and react upon corresponding heaters. The presented solution is promoted for a wide deployment covering a whole university campus.
Energies, 2021
The climate of Houston, classified as a humid subtropical climate with tropical influences, makes... more The climate of Houston, classified as a humid subtropical climate with tropical influences, makes the heating, ventilation, and air conditioning (HVAC) systems the largest electricity consumers in buildings. HVAC systems in commercial buildings are usually operated by a centralized control system and/or an energy management system based on a fixed schedule and scheduled control of a zone setpoint, which is not appropriate for many buildings with changing occupancy rates. Lately, as part of energy efficiency analysis, attention has focused on collecting and analyzing smart meters and building-related data, as well as applying supervised learning techniques, to propose new strategies to operate HVAC systems and reduce energy consumption. On the other hand, unsupervised learning techniques have been used to study the consumption information and profile characterization of different buildings after cluster analysis is performed. This paper adopts a different approach by revealing the po...
2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2017
Knowing how many people occupy a building, and where they are located, is a key component of smar... more Knowing how many people occupy a building, and where they are located, is a key component of smart building services. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy. However, relatively simple sensor technology and control algorithms limit the effectiveness of smart building services. In this paper we propose to replace sensor technology with time series models that can predict the number of occupants at a given location and time. We use Wi-Fi datasets readily available in abundance for smart building services and train Auto Regression Integrating Moving Average (ARIMA) models and Long Short-Term Memory (LSTM) time series models. As a use case scenario of smart building services, these models allow forecasting of the number of people at a given time and location in 15, 30 and 60 minutes time intervals at building as well as Access Point (AP) level. For LSTM, we build our models in two ways: a separate model for every time scale, and a combined model for the three time scales. Our experiments show that LSTM combined model reduced the computational resources with respect to the number of neurons by 74.48 % for the AP level, and by 67.13 % for the building level. Further, the root mean square error (RMSE) was reduced by 88.2%-93.4% for LSTM in comparison to ARIMA for the building levels models and by 80.9 %-87% for the AP level models.
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Papers by Driss Benhaddou