The current agriculture systems compete to take advantage of industry advanced technologies, including the internet of things (IoT), cloud/fog/edge computing, artificial intelligence, and agricultural robots to monitor, track, analyze and process various functions and services in real-time. Additionally, these technologies can make the agricultural processes smarter and more cost-efficient by using automated systems and eliminating any human interventions, hence enhancing agricultural production to meet future expectations. Although the current agriculture systems that adopt the traditional cloud-based architecture have provided powerful computing infrastructure to distributed IoT sensors. However, the cost of energy consumption associated with transferring heterogeneous data over the multiple network tiers to process, analyze and store the sensor's information in the cloud has created a huge load on information and communication infrastructure. Besides, the energy consumed by cloud data centers has an environmental impact associated with using non-clean fuels, which usually release carbon emissions (CO ₂ ) to produce electricity. Thus, to tackle these issues, we propose a new integrated edge-fog-cloud architectural paradigm that promises to enhance the energy-efficient of smart agriculture systems and corresponding carbon emissions. This architecture allows data collection from several sensors to process and analyze the agriculture data that require real-time operation (e.g., weather temperature, soil moisture, soil acidity, irrigation, etc.) in several layers (edge, fog, and cloud). Thus, the real-time processing could be held by the edge and fog layers to reduce the load on the cloud layer, which will help to enhance the overall energy consumption and process the agriculture applications/services efficiently. Mathematical modeling is conducted using mixed-integer linear programming (MILP) for a smart agriculture environment, where the proposed architecture is implemented, and results are analyzed and compared to the traditional implementation. According to the results of thousands of agriculture sensors, the proposed architecture outperforms the traditional cloud-based architecture in terms of reducing the overall energy consumption by 36% and the carbon emissions by 43%. In addition to these achievements, the results show that our proposed architecture can reduce network traffic by up to 86%, which can reduce network congestion. Finally, we develop a heuristic algorithm to validate and mimic the presented approach, and it shows comparable results to the MILP model.

Advancements in the agricultural sector are essential because the need for food is rising as the population of the world is expanding day by day. Traditional agricultural practices are not able to fulfill these needs. Furthermore, these practices are manual and are not optimized resulting in the wastage of resources, which is not suitable for resource-constrained agricultural environments. Besides this, the Internet of Things (IoT) network is playing an important role in the modern farming system. In this paper, we introduce an innovative IoT-enabled hybrid model for smart agriculture, integrating Machine Learning (ML) and Artificial Intelligence (AI) algorithms to provide a cost-effective and reliable decision-making system. Furthermore, we introduce a robust anomaly detection mechanism while applying the capabilities of Multilayer Perceptron (MLP), Naïve Bayes, and Support Vector Machine (SVM) on the dry beans’ dataset. Hybrid models, combining neural networks with Random Forest and SVM, were also explored for anomaly detection in the dataset. Furthermore, deep learning models known as MobileNetV2, VGG16, and InceptionV3 are used for the classification of soil type datasets. The hybrid deep learning models were also developed, incorporating InceptionV3 with Long Short-Term Memory (LSTM) and VGG16 with fully connected dense layers. Two types of data sets are used in this study, which are the dry beans dataset (2021) and soil type Dataset (2024). Both datasets contain images. The ML techniques are applied to these datasets for anomaly detection. The simulations results show that the classification performance of the MobileNetV2 model, it has an accuracy and recall of 0.97. It shows that the model can correctly identify the soil type around 97%. On the other hand, the hybrid model combining random forest and neural network achieved an accuracy of 92%, further validating the effectiveness of our approach. Furthermore, the SVM model achieves an impressive overall accuracy of 0.93. Additionally, this accuracy is further enhanced with the integration of SVM and neural networks. Similarly, the hybrid model combining inception V3 with the LSTM layer exhibits a notable accuracy of 0.91, highlighting its efficiency in accurately classifying various instances. Lastly, the hybrid model employing random forest and neural network architecture achieves a commendable accuracy of 92%.

The emergence of delay-sensitive and computationally-intensive mobile applications and services pose a significant challenge for Unmanned Aerial Vehicles (UAVs) devices due to the scarcity in their resources such as computational power and battery lifetime. Mobile cloud computing has been introduced as a promising solution to overcome these limitations through task offloading. However, high-latency and security issues are considered the main challenges of this paradigm. Subsequently, the edge-cloud computing paradigm has been introduced and widely used to help to mitigate these issues. Nevertheless, the current task offloading models permit UAVs to execute their intensive tasks at the connected edge server, which leads to excessive loads due to the large number of UAVs and thereby increases the delay. Therefore, in this paper, we propose a delay-optimal task offloading approach for multi-tier edge-cloud computing in a multi-user environment. The problem is formulated as an optimization model using Integer Linear Programming (ILP) techniques to minimize the total service time of UAVs. Simulation results demonstrate that the proposed approach not only saves the service time by 33.5% and 55% for edge and cloud execution policies respectively, but also scales well for a large number of UAVs.

In the realm of sustainable IoT and AI applications for the well-being of elderly individuals living alone in their homes, falls can have severe consequences. These consequences include post-fall complications and extended periods of immobility on the floor. Researchers have been exploring various techniques for fall detection over the past decade, and this study introduces an innovative Elder Fall Detection system that harnesses IoT and AI technologies. In our IoT configuration, we integrate RFID tags into smart carpets along with RFID readers to identify falls among the elderly population. To simulate fall events, we conducted experiments with 13 participants. In these experiments, RFID tags embedded in the smart carpets transmit signals to RFID readers, effectively distinguishing signals from fall events and regular movements. When a fall is detected, the system activates a green signal, triggers an alarm, and sends notifications to alert caregivers or family members. To enhance the precision of fall detection, we employed various machine and deep learning classifiers, including Random Forest (RF), XGBoost, Gated Recurrent Units (GRUs), Logistic Regression (LGR), and K-Nearest Neighbors (KNN), to analyze the collected dataset. Results show that the Random Forest algorithm achieves a 43% accuracy rate, GRUs exhibit a 44% accuracy rate, and XGBoost achieves a 33% accuracy rate. Remarkably, KNN outperforms the others with an exceptional accuracy rate of 99%. This research aims to propose an efficient fall detection framework that significantly contributes to enhancing the safety and overall well-being of independently living elderly individuals. It aligns with the principles of sustainability in IoT and AI applications.

Fog computing is developed to complement cloud computing by extending the cloud services (computing, storage, networking, and management) to the edge of the network in order to reduce service latency. With the rapid growth of cloud users and the emergence of fog computing, the demand for cloud/fog resources has increased dramatically. Also, the incremental use of the cloud/fog resources and their applications has increased energy consumption and carbon emitted, which caused significant environmental challenges. Optimizing the placement of requested applications (e.g., in the form of virtual machines) is one of the main solutions, which has a primary effect in reducing the energy consumption of cloud/fog architecture and consequently their carbon emissions. However, due to the geographic distribution of cloud and fog data centers, there is a variety of carbon emission levels to consider, which makes optimizing the placement of applications in distributed cloud/fog more challenging than in centralized clouds in terms of carbon efficiency. In this paper, we propose a multi-level approach using a mixed-integer linear programming (MILP) model to minimize carbon emissions (CO2) by optimizing the usage of cloud/fog resources. These resources are managed by virtual machines (VMs), which can be optimally and virtually used. In our model, we used the British Telecom (BT) as a telecom network example with an aim to minimize the CO2 emission, considering several scenarios of traffic demand during different times of the day and year (seasons). The results show that the optimal location to host applications highly relies on the carbon intensity and traffic demands. The results also show there is a trade-off between CO2 emission reduced by shortening network journey, and CO2 increased by hosting more applications into the fog nodes. In addition, the results demonstrate that the proposed green fog-cloud architecture outperforms the central cloud and the distributed clouds in terms of reducing the total CO2 emission by up to 91% and 71%, respectively. Finally, we develop a heuristic algorithm to mimic and validate the presented work, and it shows comparable results to the MILP model.

Fog computing is an emerging paradigm that aims to improve the efficiency and QoS of cloud computing by extending the cloud to the edge of the network. This paper develops a comprehensive energy efficiency analysis framework based on mathematical modeling and heuristics to study the offloading of virtual machine (VM) services from the cloud to the fog. The analysis addresses the impact of different factors including the traffic between the VM and its users, the VM workload, the workload versus number of users profile and the proximity of fog nodes to users. Overall, the power consumption can be reduced if the VM users’ traffic is high and/or the VMs have a linear power profile. In such a linear profile case, the creation of multiple VM replicas does not increase the computing power consumption significantly (there may be a slight increase due to idle / baseline power consumption) if the number of users remains constant, however the VM replicas can be brought closer to the end users, thus reducing the transport network power consumption. In our scenario, the optimum placement of VMs over a cloud-fog architecture significantly decreased the total power consumption by 56% and 64% under high user data rates compared to optimized distributed clouds placement and placement in the existing AT&T network cloud locations, respectively.