The imminent wireless technologies are demanding fusion of several networks with diverse technologies. A convenient user device for such an environment is the multihomed host, which is capable of making use of simultaneous multiple connections of heterogeneous networks and smartly distributing/receiving data among them. The aim of this work is to develop a mechanism for assessing the multihoming concept and to propose a system model for increasing its applicability. The proposed model provided a novel user-centric scheme for multihoming for multi-radio access technologies (multi-RAT). It is considered an evolution of traffic offloading for gaining more capacity, higher data rates, and real-time services. This work assumes data classifications, evaluates and ranks the available connections, and utilizes the application data in an unequal load balance scheme. This is achieved by analyzing the performance of three of the most widely used alternative-choice for multiple attributes decision making (MADM) methods. The analytical hierarchy process (AHP), along with a utility equation, is applied to the system model for unequal load balance solution. The proposed model is targeting an energy efficient mechanism which satisfies application needs. Moreover, it reveals an efficient transmission mechanism for a better quality of service, traffic management, and availability solutions. The simulation results show that the proposed system surpassed its former counterparts.

Smart agricultural systems are continually evolving to provide high-quality planting and defend against threats such as climate change, which necessitate improved adaptation and resource allocation. IoT technology offers a cost-effective approach to monitoring and managing system performance. However, this approach faces challenges, including connectivity issues and complex decision-making. While researchers have studied these problems individually, no fully automated solution has addressed them simultaneously. There is still a need for an offline solution that manages multiple processes and reduces human error. This paper introduces an AI-powered edge computing system that serves as an early-warning solution for climate impacts. This system enables autonomous management through an Agentic AI model that observes, predicts, decides, and adapts. It provides a low-cost AIoT platform for data forecasting, classification, and decision-making, converting sensor data into actionable insights. The system integrates forecast evaluation with real-time data comparisons to optimize scheduling, efficiency, sustainability, and yields. Moreover, this solution is totally autonomous and independent of internet connectivity. Demonstrating its superior performance, it reduced errors by 50% and achieved an R-squared value of 0.985.

Cureus Journals Smart agricultural systems are essential in increasing production rates and reducing losses, while poor connectivity hinders their capabilities. Ensuring robust connectivity among its components within a systematic agricultural field remains a significant challenge. Issues related to sensor allocation and wireless coverage further complicate this landscape. A practical solution lies in adopting an orchestrated wireless sensor network (WSN) design that addresses these research gaps in tandem. The orchestrated design provides a novel connectivity solution that improves connectivity development within two approaches. The first approach utilizes a hybrid method for sensor allocation. The second approach concerns maintaining more robust wireless coverage for those allocated sensors. This paper proposes an orchestrated design that employs a hybrid approach to incorporate area clustering using the rapidly exploring random trees method for efficient spatial sampling. Additionally, it introduces an improved allocation method for agricultural sensors based on a mathematical variable length distance technique combined with the Voronoi algorithm. To further enhance wireless coverage, the design targets the interoperability of wireless transmission via the Fuzzy Analytical Hierarchical Process method. This is complemented by the k-nearest neighbors method for planning wireless sensor communication pathways. Overall, the proposed orchestrated design demonstrates significant advancements. It provides economic implementation by allocating only 13 nodes, achieving minimal power consumption rates and optimal coverage. Furthermore, it has 86% WSN coverage and a performance score that surpasses Particle Swarm Optimization by 1.13%.

The innovative agriculture system is revolutionizing how we farm, making it one of the most critical innovations of our time! Yet it faces significant connectivity challenges, particularly with the sensors that power this technology. An efficient sensor deployment solution is still required to maximize the network's detection capabilities and efficiency while minimizing resource consumption and operational costs. This paper introduces an innovative sensor allocation optimization method that employs a Gradient-Based Iteration with Lagrange. The proposed method enhances coverage by utilizing a hybrid approach while minimizing the number of sensor nodes required under grid-based allocation. The proposed sensor distribution outperformed the classic deterministic deployment across coverage, number of sensors, cost, and power consumption. Furthermore, scalability is enhanced by extending sensing coverage to the remaining area via Bluetooth, which has a shorter communication range. Moreover, the proposed algorithm achieved 98.5% wireless sensor coverage, compared with 95% for the particle swarm distribution.

The smart agriculture system requires high efficiency to automatically maximize crop yields and minimize losses. Wireless sensor networks (WSNs) are essential for maintaining system sustainability through sensing and connectivity. However, they encounter challenges related to cost, interoperability, and reliability. Efforts have been made to expand sensing capabilities while managing costs and addressing variability in sensor communication and power consumption. Despite these efforts, a comprehensive solution—especially for orchard fields—remains undeveloped. This study introduces a coordinated WSN design to optimize sensing and connectivity in agricultural fields. We employ an integrated sensing and connectivity (ISAC) strategy to create a complete solution. Our hybrid approach combines graphical computation with distance-vector algorithms for reliable, cost-effective deployment. Additionally, resilient connectivity is achieved through effective channel modeling and adaptive beamforming. The proposed method, combined with quantitative heterogeneous network selection using MLR-AHP, addresses interoperability issues and improves network resilience. Results indicate improved sensor placement and wireless ranking, even with only 5 nodes. The solution extends sensor battery life, maintains 99% coverage, and empirical tests validate its effectiveness for designing and deploying WSNs in orchard fields.