This paper presents a low overhead multimedia data compression method for Solar Insecticidal Lamps Internet of Things (SIL-IoTs), achieving efficient audio data transmission. First, the audio and video data generated by working solar insecticidal lamps are collected to form an original dataset in the solar insecticidal lamps applications. Then, we propose SILGAN, a generative adversarial network to compress multimedia audio data in the SIL-IoTs. Specifically, a depth-wise separable convolution is adopted to reduce the computational resources for training networks and running programs. Moreover, the network parameters are optimized so that obtaining a better neural network model with stable operation on the nodes of the SIL-IoTs. Finally, experimental evaluation is conducted, showing the effectiveness of the proposed SILGAN approach.

As the birthplace of soybeans, China has become the world's largest importer and consumer of soybeans. The supply and demand imbalance of soybeans has become one of the significant food and oil security concerns in China.To solve this problem, this paper aims to study a soybean germination rate detection method based on image processing. By replacing manual detection of soybean germination rate and measuring soybean root length with machine-based approaches, basic image processing techniques including grayscale conversion, denoising, binarization, dilation, and erosion are applied to preprocess the images of soybean germination. An improved object detection algorithm is used to extract phenotypic features during soybean germination. The main focus of the study is twofold: (1) Measuring the seed area and the minimum bounding rectangle area of the image regions to determine the germination of seeds by comparing their ratio, thus obtaining the germination rate; (2) Using skeleton extraction and embryo-root separation methods to measure the changes in seed root length during germination, thereby selecting seeds with high vitality and improving the quality of soybean seeds.

Solar Insecticidal Lamp Internet of Things (SIL-IoT) nodes are susceptible to failures due to the harsh environment, burn-in, theft, and vandalism in the agricultural setting. Most state-of-the-art research mainly focuses on fault detection without considering hardware and communication performance in terms of potential fault modes, energy consumption, and network loads. This study presents a completely decentralized solution, namely a SIL-oriented binary sliding window-based fault self-detection scheme (SILOS), which can be performed on each SIL-IoT node. The problem we are trying to answer is how to detect faults as accurately as possible while keeping the communication overhead, memory, and computational costs low. Specifically, we develop a fault dictionary concept to 1) model the faults of SIL-IoT nodes; 2) construct the fault dictionary according to the characteristics of measurements; and 3) detect faults via the fault dictionary. In addition, a binary-based sliding window (BSW) fault self-detection approach is proposed to save detection costs and reduce the false alarm rate (with only 92 B system caches). A series of experiments are performed to evaluate the performance of the proposed method. The result demonstrates that the BSW method can detect faults with an average accuracy of 99.14% with less than 1% energy consumption. By only sending the fault code, 71-B data (i.e., data transmitting and forwarding) can be reduced, saving energy consumption, and decreasing network congestion.

Solar insecticidal lamps (SILs) play a vital role in green prevention and control of pests. By embedding SILs in wireless sensor networks (WSNs), we establish a novel agricultural Internet of Things (IoT), referred to as the SIL-IoTs. In practice, the deployment of SIL nodes is determined by the geographical characteristics of an actual farmland, the constraints on the locations of SIL nodes, and the radio-wave propagation in a complex agricultural environment. In this article, we mainly focus on the constrained SIL deployment problem (cSILDP) in a mixed-crop farmland, where the locations used to deploy SIL nodes are a limited set of candidates located on the ridges. We formulate the cSILDP in this Scenario as a connected set cover (CSC) problem and propose a hole-aware node deployment method (HANDM) based on the greedy algorithm to solve the constrained optimization problem. The HANDM is a two-phase method. In the first phase, a novel deployment strategy is utilized to guarantee only a single coverage hole in each iteration, based on which a set of suboptimal locations is found for the deployment of SIL nodes. In the second phase, according to the operations of deletion and fusion, the optimal locations are obtained to meet the requirements on complete coverage and connectivity. Experimental results show that our proposed method achieves better performance than the peer algorithms, specifically in terms of deployment cost.

Solar insecticidal lamp (SIL) is a green prevention and control technology for pests. With the development of wireless sensor networks (WSNs), the combination of SILs and WSNs forms a novel agricultural Internet of Things-SIL Internet of Things (SIL-IoTs). However, the complex geographical characteristic of actual farmland has a great impact on SIL deployment. In this article, we study the SIL deployment problem (SILDP) with characteristics of full coverage, penetrable obstacles, irregular boundary, and partition structure. According to the 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 the partition structure as the quadratic assignment problem. After that, we propose two deployment methods based on the genetic algorithm to address the SILDP. These two methods are the same in optimization objectives, but different in deployment sequence. The experimental results show that the proposed deployment methods equips better performance in terms of deployment cost compared with the other six peer algorithms.

As the compensation and extension of static wireless sensor nodes, wearable helmets can build regional early warning network of personnel security. In this paper, a wearable helmet is presented towards early warning of leaking toxic gas in large-scale petrochemical plants for protecting the lives and safety of workers better.

This paper focuses on using wearable equipment to make workers participate in constructing monitoring maps in large-scale petrochemical plants, which collaborates with static sensor nodes in given areas. Furthermore, this study provides a sensing pattern with less cost and higher flexibility, which effectively contribute to collective effort with static sensor nodes. Several open research issues are discussed.