The connectivity and intelligence of Internet of Things (IoT) equipment offer improved services, but several technical challenges have emerged in recent years that hinder the widespread application of IoT, e.g., security and safety. Cyber-security and privacy countermeasures are widely used in IoT equipment, and many studies have been conducted. However, an important aspect that is often overlooked in security literature is IoT equipment’s physical security and safety, namely, preventing IoT equipment from vandalism and theft. Therefore, this article provides an overview of IoT equipment’s physical security and safety to draw attention to new research opportunities in this area. Afterward, we discuss, among other aspects, antitheft and antivandalism schemes along with circuit and system design, additional sensing devices, biometry and behavior analysis, and tracking methods. Besides, we summarize the artificial intelligence solutions for the physical security and safety of IoT equipment. Finally, we conclude with four future research opportunities.

Serious challenges for to drive agricultural sustainability combined with climate crisis issues have induced an urgent need to decarbonise agriculture. In this paper, we briefly introduce a novel concept of the Photovoltaic Agricultural Internet of Things (PAIoT). This system approach fuses agricultural production with renewable power generation and control via an IoT platform.We discuss PAIoT applications and potential to realize the next generation of smart farming. In addition, we provide a review of key issues on the feasibility of PAIoT and further propose novel techniques to mitigate these key issues.

Solar Insecticide Lamp (SIL) is a green prevention and control technology for pests. As the development of Wireless Sensor Networks (WSNs), the combination of SILs and WSNs forms a novel agricultural Internet of Things -Solar Insecticide Lamp Internet of Things (SIL-IoT). However, the complex geographical characteristic of actual farmland has a great impact on SIL deployment. In this paper, we study the SIL Deployment Problem (SILDP) with characteristics of full coverage, penetrable obstacles, irregular boundary and partition structure. According to partition structure caused by natural physiognomy feature, the actual farmland is divided into many subareas by ridges, and each subarea can be considered as a separate partition. Then, we formulate the SILDP in the scenario with partition structure as the quadratic assignment problem. After that, we propose two deployment methods based on genetic algorithm to address the SILDP. These two methods are the same in optimization objectives, but different in deployment sequence. Experimental results show that the proposed deployment methods equips better performance in terms of deployment cost compared with the other six peer algorithms.

Three-dimensional (3D) LiDAR is crucial for the autonomous navigation of orchard mobile robots, offering comprehensive and accurate environmental perception. However, the increased richness of information provided by 3D LiDAR also leads to a higher computational burden for point cloud data processing, posing challenges to real-time navigation. To address these issues, this paper proposes a 3D point cloud optimization method based on the octree data structure for autonomous navigation of orchard mobile robots. This approach includes two key components: 1) In terms of orchard mapping, the spatial indexing and segmentation features of the octree data structure are introduced. According to the sparsity and density of the point cloud, the 3D orchard map is adaptively divided and the key information of the orchard is retained. 2) In terms of path planning, by using octree nodes as the unit nodes for RRT* random tree expansion, an improved RRT* algorithm based on octree is proposed. Field experiments were conducted in a pear orchard based on this method. The experimental results show that: 1) The overall number of point cloud data points in the map was reduced by approximately 76.32%, while important features, including tree morphology, trellis structure, and road surface information, were fully preserved. 2) When different octree node resolutions were applied, the improved RRT* algorithm demonstrated significant improvements in path generation time, sampling point utilization, path length, and curvature. The lateral tracking error increased as the resolution of octree nodes decreased. At a resolution of 0.20 m, the maximum average lateral tracking error was 0.079 m, indicating strong path trackability. This method exhibits tremendous potential for processing large-scale 3D point cloud data and enhancing path planning efficiency, providing a valuable technical reference for the real-time autonomous navigation of mobile robots in complex orchard environments.

Orchard thinning can avoid biennial bearing and improve fruit quality, which is a necessary agronomic section in orchard management. The existing methods of artificial fruit thinning and chemical spraying are no longer suitable for the development of modern agriculture. With the continuous acceleration of the construction process of modern orchards, blossom thinning mechanization has become an inevitable trend in the development of the orchard flower and fruit management. Based on relevant reports in the past 20 years, the paper discusses the current level of development of mechanized blossom thinning technologies and equipment in orchards from three aspects: mechanism research, machine development, and intelligent upgrading. Firstly, for thinning mechanism research, three directions were investigated: the rope flexible hitting force, thinning agronomic requirements, and the fruit tree growth model between thinning and fruit yields. Secondly, for marketable machine developments, two types of machines were investigated: the hand-held thinner and tractor-mounted thinner. The hand-held thinner is mainly suitable for traditional old orchards with a messy canopy structure, especially in the interior and top of the canopy. The tractor-mounted thinner is mainly suitable for orchards with the same crown structure, such as the hedge type, trunk type, and V-type. Thirdly, for equipment intelligent upgrading, the research of the intelligent detection algorithm for inflorescence on the fruit tree was investigated, for species including the apple, pear, citrus, grape, litchi, mango, and apricot. Finally, combining the advantages and disadvantages of the research, the authors propose thoughts and prospects, which can provide a reference for the design and applications of orchard mechanized blossom thinning.

Federated Learning is a promising technique that facilitates cloud–edge–terminal collaboration in Artificial Intelligence of Things (AIoT). It will enable model training without centralizing data, addressing privacy, and security concerns. However, when applied to AIoT, this technique faces several challenges, such as low communication efficiency among terminal devices, edges, and cloud platforms. In this article, we propose a novel approach called asynchronous federated learning with quantization (QuAsyncFL), which combines asynchronous federated learning with an unbiased nonuniform quantizer to address the issue of low communication efficiency. Moreover, we provide a detailed theoretical analysis of convergence with quantized gradients proving that the model could converge to a certain bound. Our experiments demonstrate that QuAsyncFL outperforms the original approach, achieving significant improvements in terms of communication efficiency. The research results represent a further step toward developing cloud–edge–terminal collaboration enabled AIoT.