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Accurate energy consumption prediction in commercial buildings is a challenging research task. Energy prediction plays a crucial role in energy efficiency, management, planning, sustainability, risk management, diagnosis, and demand response. Although many studies have been conducted on building energy predictions, the impact of occupancy on energy prediction models for office-type commercial buildings remains insufficiently explored, despite its potential to improve energy efficiency by 20%. This study investigates energy prediction using a Long Short-Term Memory (LSTM) model that incorporates time-series power consumption data and considers occupancy. A real-world dataset containing the per-minute electricity consumption of various appliances in an office building in Houston, TX, USA, is utilized. The proposed machine learning models forecast future energy consumption based on hourly, 3-hourly, daily, and quarterly predictions for individual appliances and total energy usage. The model’s performance is evaluated using the following three metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results demonstrate the superiority of the proposed system.

It is widely acknowledged that traditional agricultural practices must effectively address the increasing global demand for food while facing water scarcity and climate change challenges. The imperative for environmentally sustainable agricultural approaches has never been more urgent. In response, IoT-based Smart Agriculture has emerged as a promising solution. Smart Agriculture can significantly bolster agricultural development by integrating renewable energy sources, particularly in arid regions with abundant sunlight. Real-time control systems utilizing big data acquisition and processing are pivotal in this advancement. This study introduces a cloud-based smart irrigation system to connect numerous small-scale smart farms and centralize pertinent data. The system optimizes irrigation water usage through comprehensive big data collection, storage, and analysis. Leveraging the insights from this data can facilitate informed decision-making regarding water management, thereby fostering conservation efforts, particularly in arid regions. Additionally, this research explores weather prediction services to enhance irrigation control, particularly during intermittent rainy periods, within a real-world testbed powered by solar energy. The testbed incorporates a sophisticated big data management system. It showcases a Smart Farm prototype leveraging the Internet of Things, embedded systems, low-cost Wireless Sensor Networks, NI CompactRIO controller, and Cloud Computing. Encouragingly, the results demonstrate tangible improvements in water conservation. Furthermore, the deployment methodology outlined in this study provides a clear roadmap that can be readily adapted for similar research endeavors.© 2023 Elsevier Inc. All rights reserved.

Cloud computing is a critical component in the success of 5G and 6G networks, particularly given the computation-intensive nature of emerging applications. Despite all it advantages, cloud computing faces limitations in meeting the strict latency and bandwidth requirements of applications such as eHealth and automotive systems. To overcome these limitations, edge computing has emerged as a novel paradigm that bring computation closer to the user. Moreover, intelligent tasks such as deep learning ones demand more memory and processing power than edge devices can handle. To address these challenges, methods like quantization, pruning, and distributed inference have been proposed. Similarly, this paper study a promising approach for running deep learning models at the edge: split neural networks (SNN). SNNs feature a neural network architecture with multiple early exit points, allowing the model to make confident decisions at earlier layers without processing the entire network. This not only reduces memory and computational demands but it also makes SNNs well-suited for edge computing applications. As the use of SNNs expands, ensuring their safety-particularly their robustness to perturbations-becomes crucial for deployment in safety-critical scenarios. This paper presents the first in-depth study on the robustness of split Edge Cloud neural networks. We review state-of-the-art robustness certification techniques and evaluate SNN robustness using the auto_LiRPA and Auto Attack libraries, comparing them to standard neural networks. Our results demonstrate that SNNs reduce average inference time by 75‘% and certify 4 to 10 times more images as robust, while improving overall robustness accuracy by 1% to 10%.

The global demand for button mushrooms has surged in recent years, driven by their health benefits, creating a significant challenge for the mushroom industry in meeting this increasing demand. The increasing reliance on human labor, which is becoming unsustainable due to labor shortages and rising wage costs, highlights the urgent need for automated harvesting solutions. This review examines the integration of automated systems in button mushroom harvesting, delving into the key components such as robots, mechanisms, machine elements, programming, and algorithms. It offers a thorough analysis of the performance, design, operational mechanisms, and advantages and limitations of robotic systems, comparing the different methods employed in automated harvesting. This paper compares the performance of all the mushroom harvesters, including the commercially available ones with manual harvesting, and identifies their potential and limitations. The commercial harvesters are shown to pick 2000 mushrooms per hour on average, which is similar to how much a skilled worker picks at the same time. However, commercial automation harvesting has a relatively low success rate, high initial cost, high operating cost, and energy consumption, identifying areas for future research and challenges. This paper serves as a valuable resource for researchers and industry professionals striving to advance automated harvesting technology and improve its efficiency in meeting the rising demand for button mushrooms.

Button mushrooms (Agaricus bisporus) grow in multilayered Dutch shelves with limited space between two shelves. As an alternative to conventional hand-picking, automated harvesting in recent times has gained widespread popularity. However, automated harvesting of mushrooms faces critical challenges in the form of growing environment, limited spaces, picking forces, and efficiency. End effectors for picking button mushrooms are an integral part of the automated harvesting process. The end effectors developed so far are oversized, bulky, and slow and thus are unsuitable for commercial mushroom harvesting applications. This paper introduces a novel three-finger hybrid gripper with rigid and soft parts, specifically designed for harvesting button mushrooms in automated systems even on narrow shelves. It discusses the design, fabrication, force analysis, and picking performance of the gripper in detail for both individual and clustered mushrooms. The results indicate that the gripping force depends on mushroom density and size. The inclusion of textured soft pads on gripper fingertips performs better compared with plain soft pads by reducing force by up to 20% and improving picking time. The gripper achieved a 100% picking success rate for single-grown mushrooms and 64% for clusters, with reduced picking times compared with existing end effectors. However, harvesting clustered mushrooms led to increased damage, suggesting the need for future improvements.

With the increasing stringent QoS constraints (e.g., latency, bandwidth, jitter) imposed by novel applications (e.g., e-Health, autonomous vehicles, smart cities, etc.), as well as the rapidly increasing number of connected IoT (Internet of Things) devices, the core network is becoming increasingly congested. To cope with those constraints, Edge Computing (EC) is emerging as an innovative computing paradigm that leverages Cloud computing and brings it closer to the customer. “EC” refers to transferring computing power and intelligence from the central Cloud to the network’s Edge. With that, EC promotes the idea of processing and caching data at the Edge, thus reducing network congestion and latency. This paper presents a detailed, thorough, and well-structured assessment of Edge Computing and its enabling technologies. Initially, we start by defining EC from the ground up, outlining its architectures and evolution from Cloudlets to Multi-Access Edge Computing. Next, we survey recent studies on the main cornerstones of an EC system, including resource management, computation offloading, data management, network management, etc. Besides, we emphasized EC technology enablers, starting with Edge Intelligence, the branch of Artificial Intelligence (AI) that integrates AI models at resource-constrained edge nodes with significant heterogeneity and mobility. Then, moving on to 5G and its empowering technologies, we explored how EC and 5G complement each other. After that, we studied virtualization and containerization as promising hosting runtime for edge applications. Further to that, we delineated a variety of EC use-case scenarios, e.g., smart cities, e-Health, military applications, etc. Finally, we concluded our survey by highlighting the role of EC integration with future concerns regarding green energy and standardization.

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.

Living and working in comfort while a building’s energy consumption is kept under control requires monitoring a system’s consumption to optimize the energy performance. The way energy is generally used is often far from optimal, which requires the use of smart meters that can record the energy consumption and communicate the information to an energy manager who can analyze the consumption behavior, monitor, and optimize energy performance. Given that the heating, ventilation, and air conditioning (HVAC) systems are the largest electricity consumers in buildings, this paper discusses the importance of incorporating occupancy data in the energy efficiency analysis and unveils energy inefficiencies in the way the system operates. This paper uses 1-year data of a highly efficient certified office building located in the Houston area and shows the power of self-organizing maps and data analysis in identifying up to 4.6% possible savings in energy. The use of time series analysis and machine-learning techniques is conducive to helping energy managers discover more energy savings.

Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents' experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contain valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.

The authors wish to make the following correction to their paper [...]

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 that is based on single-board computers (SBCs) to process data stemming from SG. The use of SBCs is driven by the energy efficiency and cost effectiveness concepts that the SG is trying to apply. The platform in question is tested against a distributed job that averages random numbers using Hadoop’s MapReduce programming model. The SBC that we are using in this implementation is the NVIDIA Jetson Developer Kit. The results of this work show that a cluster of SBCs is low-cost, easy to maintain, and simple to deploy, which makes it a great candidate for providing edge computing. Although it revealed a performance that beat the one of the remote cloud servers, it could not outperform the single-computer edge platform.

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 networking layer. This paper aims to make the researcher aware of the various optimization opportunities when designing WSN nodes. To our knowledge, there is no other work in the literature that reviews energy optimization of WSN in the context of Smart Energy-Efficient Buildings (SEEB) and from the formerly four listed perspectives to help in the design and implementation of optimal WSN for SEEB.

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 power of unsupervised learning to cluster data and unveiling hidden patterns. In this study, we also identify energy inefficiencies after exploring the cluster results of a single building’s HVAC consumption data and building usage data as part of the energy efficiency analysis. Time series analysis and the K-means clustering algorithm are successfully applied to identify new energy-saving opportunities in a highly efficient office building located in the Houston area (TX, USA). The paper uses 1-year data from a highly efficient Leadership in Energy and Environment Design (LEED)-, Energy Star-, and Net Zero-certified building, showing a potential energy savings of 6% using the K-means algorithm. The results show that clustering is instrumental in helping building managers identify potential additional energy savings.

Energy Management Systems (EMS) are indispensable for Smart Energy-Efficient Buildings (SEEB). This paper proposes a Wireless Sensor Network (WSN)-based EMS deployed and tested in a real-world smart building on a university campus. The at-scale implementation enabled the deployment of a WSN mesh topology to evaluate performance in terms of routing capabilities, data collection, and throughput. The proposed EMS uses the Context-Based Reasoning (CBR) Model to represent different types of buildings and offices. We implemented a new energy-efficient policy for electrical heaters control based on a Finite State Machine (FSM) leveraging on context-related events. This demonstrated significant effectiveness in minimizing the processing load, especially when adopting multithreading in data acquisition and control. To optimize sensors’ battery lifetime, we deployed a new Energy Aware Context Recognition Algorithm (EACRA) that dynamically configures sensors to send data under specific conditions and at particular times to avoid redundant data transmissions. EACRA increases the sensors’ battery lifetime by optimizing the number of samples, used modules, and transmissions. Our proposed EMS design can be used as a model to retrofit other kinds of buildings, such as residential and industrial, and thus converting them to SEEBs.

As education constitutes an essential development standard for individuals and societies, researchers have been exploring the use of artificial intelligence (AI) in this domain and have embedded the technology within it through a myriad of applications. In order to provide a detailed overview of the efforts, this article pays particular attention to these developments by highlighting key application areas of data-driven AI in education; it also analyzes existing tools, research trends, as well as limitations of the role data-driven AI can play in education. In particular, this article reviews various applications of AI in education including student grading and assessments, student retention and drop-out predictions, sentiment analysis, intelligent tutoring, classroom monitoring, and recommender systems. This article also provides a detailed bibliometric analysis to highlight the salient research trends in AI in education over nine years (2014-2022) and further provides a detailed description of the tools and platforms developed as the outcome of research and development efforts in AI in education. For the bibliometric analysis, articles from several top venues are analyzed to explore research trends in the domain. The analysis shows sufficient contribution in the domain from different parts of the world with a clear lead for the United States. Moreover, students' grading and evaluation have been observed as the most widely explored application. Despite the significant success, we observed several aspects of education where AI alone has not contributed much. We believe such detailed analysis is expected to provide a baseline for future research in the domain.

Nowadays, mobile ad hoc networks (MANETs) have to support new applications including VoIP (voice over IP) that impose stringent QoS (quality of service) constraints and requirements. However, VoIP applications make a very inefficient use of the MANET resources. Our work represents a first step toward improving aspects at the network layer by addressing issues from the standpoint of adaptation, claiming that effective adaptation of routing parameters can enhance VoIP quality. The most important contribution is the adaptive OLSR-VA algorithm which provides an integrated environment where VoIP activity is constantly detected and routing parameters are adapted in order to meet the application requirements. To investigate the performance advantage achieved by such algorithm, a number of realistic simulations (MANET scenarios) are performed under different conditions. The most important observation is that performance is satisfactory in terms of the perceived voice quality.