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• An unequal grouping algorithm is introduced in this work to address the energy-hole problem in Internet-of-things (IoT) and wireless sensor networks (WSNs). • The work introduced an energy level-based group head selection model that considers multi-objective parameters such as hop count, energy, and packet loss ratio. • An energy and delay aware routing optimization mechanism is introduced to reduce delay and minimize energy consumption. • Finally, a multipath-based route is established, and effective load balancing is done for transmitting packets with QoS (quality of service) and without QoS. • The existing methods' performance is generally studied considering total node death; however, the EDAR model is studied considering different scenarios such as first node death and total node death. • The simulation results show the EDAR is very efficient in reducing communication overhead, delay, enhanced lifetime, and improved throughput in comparison with the recent state-of-art routing mechanisms such as low energy adaptive clustering hierarchy (LEACH), unequal clustering algorithm (UCA), and reliable and energy efficient routing (REER). The adoption of modern technology into farming has seen significant expansion to enhance the quality and quantity of crop production. In this research work, wireless sensor networks (WSNs) and the Internet of Things (IoTs) are integrated to provide a real-time crop monitoring system to farmers. Usually, battery-powered IoT devices equipped with sensors like temperature, humidity, motion sensors, etc. are placed across agriculture areas for real-time monitoring of crops and cattle. In improving energy efficiency and reducing delay clustering multi-hop routing has been used with good effect; However, the existing methods failed to enhance network lifetime and target coverage due to failure in addressing load-balancing and hotspot problems. In addressing research problems this paper presents an Energy and Delay Aware Routing (EDAR) scheme for heterogenous WSN-IoTs leveraging unequal grouping of networks and adoption of multipath routing for provisioning IoT applications. The simulation outcome shows the EDAR model achieves very good performance in terms of throughput and lifetime performance with minimal delay and communication overhead. Graphical abstract of energy and delay aware routing design. [Display omitted] [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) are pivotal for real-time monitoring in diverse sectors through the Internet of Things (IoT). However, current wireless communication protocols face challenges in integrating Precision Agriculture (PA) management systems with Artificial Intelligence (AI). PA optimizes nutrient supply and pesticide use, enhancing crop yield and product quality. Despite these advantages, structural changes in WSNs, including deployment, coverage, scalability, and energy consumption, hinder their potential. This paper conducts a comparative study of WSNs in agriculture, focusing on wireless protocols, energy harvesting, and efficiency procedures. The research delves into challenges faced by WSNs in agriculture, emphasizing the need for power-saving agricultural management strategies to ensure sustained, effective long-term monitoring. The study addresses integration issues, explores agricultural benefits, and proposes solutions to enhance WSN performance in dynamic field environments. [ABSTRACT FROM AUTHOR]

Smart agriculture has a broad prospect with the fast development of information technologies, such as Internet of Things, big data, edge computing and artificial intelligence. As more intelligent devices are deployed for agricultural scenarios, such as quality monitoring and information aggregation, the communication channels among these devices will be crowded and the performance can be greatly affected. Cognitive Radio Networks (CRNs) are promising in promoting better spectrum utilization, where unlicensed users can opportunistically use the vacant spectrum assigned to licensed users. On the basis of CRNs, many agricultural scenarios can be empowered with efficient communication capabilities. However, the broadcasting problem, which handles the information dissemination, has not been thoroughly studied in CRNs. Existing works are either centralized solutions performing broadcast on single/multiple channels, or distributed algorithms without performance guarantee for general networks. In this paper, we propose efficient distributed algorithms with short broadcast delay and high success ratio. The difficulties lie in the non-uniform channel availability, which entails broadcasting on multiple channels even for single-hop neighbors, and the distributed behaviors, where the users are only aware of the local information. Our contributions are threefold. First, we propose an efficient distributed rendezvous algorithm which spurs the neighbors to find the common channels in a short time. Second, we handle the single-hop broadcast by presenting two distributed algorithms and they guarantee a successful broadcast in O (Δ 2 log log N) and Δ time slots respectively, where N is the number of channels and Δ is the maximum number of neighbors. Finally, we extend these algorithms to multi-hop broadcast and the broadcast delay is only up to D times the delay of single-hop, where D is the network diameter. We also conduct extensive simulations and the results corroborate our theoretical analyses. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technology can be used to enhance traditional approaches by combining advanced technologies with sophisticated methodologies aiming to boost agricultural production quality and quantity. The global population is rapidly growing, and the demand for food is rising accordingly. Thus, traditional agriculture will be unable to fulfill the demands of the crops. This paper aims to review the state-of-the-art contributions of smart IoT-based agriculture system design including IoT technology which presents the core framework of present and future agricultural development. Also, the basic structure of the IoT agricultural system is highlighted including the hardware and the software along with the data processing components. The potential issues systems are also investigated. The results of previous works show that the developed agricultural based-IoT systems can provide higher accuracy for making the best decision to structure a healthy farm environment. Consequently, crop production can be increased many folds. [Display omitted] [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has been a major influence in Agriculture since its application to the sector. This paper provides an extensive review of the use of smart technologies in agriculture and elaborates the state-of-the-art technologies for smart agriculture including, Internet of Things, cloud computing, machine learning, and artificial intelligence. The application of smart farming to crop and animal production and post-harvesting is discussed. The impact of climate change on agriculture is also considered. This paper contributes to knowledge by iterating the challenges of smart technology to agriculture while highlighting the issues identified from existing framework of smart agriculture. The authors identify many gaps in existing research affecting the application of IoT in smart farming, and suggest further research to improve the current food production globally, to provide better food management and sustainability measures across the globe. [ABSTRACT FROM AUTHOR]

• The proposed study investigates the consequences of smallholder economic growth during the agricultural crisis. • The proposed model combines the satellite data and artificial intelligence to analyze the agricultural promotion of inclusive finance during the disaster. • Based on satellite data and artificial intelligence, we build an application system for inclusive financing of small farmers in agricultural disasters. • The proposed study validates the impact of an application system for small farmer inclusive finance based on satellite data and artificial intelligence in agricultural catastrophes. Investigating the consequences of smallholder economic growth amid the agricultural emergency is essential. In this paper, we evaluate the agricultural promotion of inclusive financing during the disaster using satellite data. We use inclusive finance to improve the income of small farmers during agricultural disasters, improve satellite remote sensing data analysis algorithms through neural networks, and combine satellite data and Artificial Intelligence (AI) to process data during agricultural disasters. Additionally, we construct an application system for inclusive finance of small farmers in agricultural disasters based on satellite data and AI. The proposed study verifies the effect of the application system for inclusive finance of small farmers based on satellite data and AI in agricultural disasters. The study's findings indicate that an inclusive finance system for small farmers in agricultural disasters based on satellite data and AI has a role in boosting smallholder economic development during the crisis. [ABSTRACT FROM AUTHOR]