ABS-SmartComAgri: An Agent-Based Simulator of Smart Communication Protocols in Wireless Sensor Networks for Debugging in Precision Agriculture

Advancements and Challenges in IoT Simulators: A Comprehensive Review

The Internet of Things (IoT) has emerged as an important concept, bridging the physical and digital worlds through interconnected devices. Although the idea of interconnected devices predates the term "Internet of Things", which was coined in 1999 by Kevin Ashton, the vision of a seamlessly integrated world of devices has been accelerated by advancements in wireless technologies, cost-effective computing, and the ubiquity of mobile devices. This study aims to provide an in-depth review of existing and emerging IoT simulators focusing on their capabilities and real-world applications, and discuss the current challenges and future trends in the IoT simulation area. Despite substantial research in the IoT simulation domain, many studies have a narrow focus, leaving a gap in comprehensive reviews that consider broader IoT development metrics, such as device mobility, energy models, Software-Defined Networking (SDN), and scalability. Notably, there is a lack of literature examining IoT simulators' capabilities in supporting renewable energy sources and their integration with Vehicular Ad-hoc Network (VANET) simulations. Our review seeks to address this gap, evaluating the ability of IoT simulators to simulate complex, large-scale IoT scenarios and meet specific developmental requirements, as well as examining the current challenges and future trends in the field of IoT simulation. Our systematic analysis has identified several significant gaps in the current literature. A primary concern is the lack of a generic simulator capable of effectively simulating various scenarios across different domains within the IoT environment. As a result, a comprehensive and versatile simulator is required to simulate the diverse scenarios occurring in IoT applications. Additionally, there is a notable gap in simulators that address specific security concerns, particularly battery depletion attacks, which are increasingly relevant in IoT systems. Furthermore, there is a need for further investigation and study regarding the integration of IoT simulators with traffic simulation for VANET environments. In addition, it is noteworthy that renewable energy sources are underrepresented in IoT simulations, despite an increasing global emphasis on environmental sustainability. As a result of these identified gaps, it is imperative to develop more advanced and adaptable IoT simulation tools that are designed to meet the multifaceted challenges and opportunities of the IoT domain.

A Systematic Review of IoT Solutions for Smart Farming

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.

CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture

Intelligence has been considered as the major challenge in promoting economic potential and production efficiency of precision agriculture. In order to apply advanced deep-learning technology to complete various agricultural tasks in online and offline ways, a large number of crop vision datasets with domain-specific annotation are urgently needed. To encourage further progress in challenging realistic agricultural conditions, we present the CropDeep species classification and detection dataset, consisting of 31,147 images with over 49,000 annotated instances from 31 different classes. In contrast to existing vision datasets, images were collected with different cameras and equipment in greenhouses, captured in a wide variety of situations. It features visually similar species and periodic changes with more representative annotations, which have supported a stronger benchmark for deep-learning-based classification and detection. To further verify the application prospect, we provide extensive baseline experiments using state-of-the-art deep-learning classification and detection models. Results show that current deep-learning-based methods achieve well performance in classification accuracy over 99%. While current deep-learning methods achieve only 92% detection accuracy, illustrating the difficulty of the dataset and improvement room of state-of-the-art deep-learning models when applied to crops production and management. Specifically, we suggest that the YOLOv3 network has good potential application in agricultural detection tasks.

A Mission Planning Approach for Precision Farming Systems Based on Multi-Objective Optimization

As the demand for food grows continuously, intelligent agriculture has drawn much attention due to its capability of producing great quantities of food efficiently. The main purpose of intelligent agriculture is to plan agricultural missions properly and use limited resources reasonably with minor human intervention. This paper proposes a Precision Farming System (PFS) as a Multi-Agent System (MAS). Components of PFS are treated as agents with different functionalities. These agents could form several coalitions to complete the complex agricultural missions cooperatively. In PFS, mission planning should consider several criteria, like expected benefit, energy consumption or equipment loss. Hence, mission planning could be treated as a Multi-objective Optimization Problem (MOP). In order to solve MOP, an improved algorithm, MP-PSOGA, is proposed, taking advantages of the Genetic Algorithms and Particle Swarm Optimization. A simulation, called precise pesticide spraying mission, is performed to verify the feasibility of the proposed approach. Simulation results illustrate that the proposed approach works properly. This approach enables the PFS to plan missions and allocate scarce resources efficiently. The theoretical analysis and simulation is a good foundation for the future study. Once the proposed approach is applied to a real scenario, it is expected to bring significant economic improvement.