Co-optimizing air temperature (AT) and CO2 concentration (CO2) for AIoT systems in controlled environment agriculture (CEA) has raised sustained attentions to balance energy efficiency and crop productivity. To tackle this issue, we propose a region-based co-optimization strategy that combines crop-environment interaction modeling with a multi-objective optimization algorithm to obtain the optimal ranges of AT and CO2. Experimental data are collected by measuring single-leaf photosynthetic rate (AL) in lettuce cultivated under diverse environmental conditions. AL is used as the primary crop growth indicator and modeled using support vector regression (SVR). Based on the prediction model, novel fitness functions and constraints are established considering biomass accumulation, energy consumption, and AIoT system actions. Subsequently, a hybrid multi-stage method integrating the non-dominated sorting genetic algorithm II (NSGAII) and differential evolutionary (DE) algorithm is employed to determine the non-dominated solution (NDS) sets for different conditions. Finally, the optimal solution within the NDS sets is calculated using the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method constrained by curvature feature. The rationalities of the proposed methods are verified by lettuce and cucumber samples. Experimental validation with lettuce cultivation across multiple environments demonstrates significant advantages over conventional threshold-based methods: 18.0% improvement in carbon use efficiency, 40.7% reduction in AIoT system operations, and 94.7% photosynthetic efficiency retention. This research achieves a groundbreaking integration of agronomic models, intelligent algorithms, and AIoT systems, establishing a novel approach for sustainable agricultural management that simultaneously optimizes production efficiency and resource utilization.

Traditional nitrogen management systems in plant factories, based on static nutrient formulations, cause 30 50% nitrogen wastage and environmental pollution. Fixed threshold parameters fail to meet the dynamic needs of crops, reducing yield and quality, this study proposes an Internet of Things (IoT) closed-loop control system based on a dynamic nitrogen regulation model. By integrating Maximum Information Coefficient (MIC), Analytic Hierarchy Process (AHP), U-chord curvature method, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a multi-parameter dynamic weighting nitrogen regulation interval model was developed. This model overcomes the limitations of traditional methods that rely on fixed formulas or single parameters. The experiments demonstrated that the model can accurately identify the optimal nitrogen concentrations during the seedling stage (5.0 8.0 mmol/L) and the maturity stage (11.0 14.0 mmol/L), reducing nitrogen fertilizer usage by 36.2% 49.7%, while increasing fresh weight by 12% and leaf potassium and phosphorus contents by 4.8% 19.5%. The system employs an edge-cloud collaborative architecture, with the Raspberry Pi 4B serving as the edge node for real-time regulation (response time. 0.5 seconds), supporting remote monitoring and model updates, forming a "perception-decision-execution" closed-loop. This research provides a nitrogen management paradigm for smart agriculture that combines dynamic precision with engineering practicality, which can be extended to vertical farms and urban agricultural scenarios.

Multivariate environmental prediction is essential for precisely regulating mushroom house Internet of Things system. Existing time-series prediction methods, such as long short-term memory (LSTM) and temporal convolutional network (TCN), consider the temporal features of multiple variables. However, the potential spatial relations between multiple variables cannot be effectively exploited. Especially, sudden environmental disturbances tend to increase the model’s predictive error. To address this challenge, we proposed a multi-input-multioutput (MIMO) prediction model employing a spatial–temporal fusion approach. The model combined TCN with graph sampling and aggregation network-based dynamic graph learning strategy (TCN-DGSA). It achieves the combined prediction of temperature and the humidity in the mushroom houses. First, TCN extracts temporal features from input data, which enhances the model’s ability to capture long-term temporal dependencies through dilation convolution. Additionally, a dynamic graph learning strategy was developed to learn spatial relationships of multiple variables. This strategy constructed implicit graph structures of input features without empirical knowledge. Then, the sampling and aggregation network effectively extracted the spatial pattern of the graph structure, and achieved the accurate multivariate prediction. Finally, the single-step and multihorizon prediction performance of the model was verified by ablation experiments. The TCN-DGSA model outperforms baseline models, achieving Mean Absolute Error (MAE), RMSE, and$R^{2}$of 0.21°C, 0.30°C, and 0.97 for temperature prediction, and 0.53%, 1.02%, and 0.98 for humidity. Further, after adding Gaussian, Poisson, and uniform noise to raw dataset, the model maintained similar MAE and RMSE across different output horizons. This result demonstrates that TCN-DGSA model has high stability and robustness in complex environments.

Indoor intrusion detection is a critical task for home security. Previous works in intrusion detection suffer from the problems such as blind spots in non-line-of-sight (NLOS) areas, restricted device locations, massive offline training required, and privacy concern. In this article, we design and implement an omnidirectional indoor intrusion detection system, named AudioGuard , using only a pair of speaker and microphone. AudioGuard is able to detect both line-of-sight (LOS) and NLOS intrusions. Our observation of acoustic signal propagation in an indoor environment shows that there exist abundant multipath reflections and human movement introduces Doppler shift in echo signals. We hence capture periodical Doppler shift caused by intruder's walking motion to detect intrusion. Specifically, we first extract the Doppler shift embedded in echo signals, and we then propose a periodicity polarization method to cancel out the impact of the change of radial angle and the distance on periodicity of Doppler shift. Finally, we detect intrusion by measuring periodicity of Doppler shift over time. Extensive experiments show that AudioGuard achieves a miss report rate of 0% and 1.75% for LOS and NLOS intrusion, respectively, and a false alarm rate of 4.17%.

In recent years, we have seen efforts made to monitor respiration for multiple users. Existing approaches capture chest movement relying on signals directly reflected from chest or separate breath waves based on breath rate difference between subjects. However, several limitations exist: 1) they may fail when subjects face away from the transceiver or are blocked by obstacles or other subjects; 2) they may fail to separate subjects' breath waves with the same or similar rates (i.e., breath rate differenceMultiResp, a multi-user respiration monitoring system using acoustic signal. By fully leveraging the abundant acoustic signals reflected indirectly from subjects' chest,MultiRespcan robustly capture chest movement even when they face away from the transceiver or are blocked. By extracting fine-grained breath rate and phase difference between different subjects,MultiRespcan separate the breath waves with the same or similar rates and adapt to dynamic change of subject number during monitoring. Extensive experiments show thatMultiRespis able to accurately monitor the respiration of multiple users with a median error of 0.3 bpm in various indoor scenarios, however, it fails when the sound pressure is lower than 55 dB or body movement is happening.

Contactless respiration monitoring using wireless signals has drawn much attention in recent years. Many approaches have been proposed, however, they may not work when there is a lack of signals directly reflected from target's chest, e.g., a target faces away from the transceiver or a target is blocked by furniture. In this paper, we design and implement a novel omnimonitoring system for human respiration,OmniRespMonitor, using a pair of speaker and microphone. Different from Radio Frequency (RF) signal, acoustic signals cannot penetrate through walls and furniture. The multipath reflection in an indoor environment will result in highly abundant acoustic signals. In this case, even though there are lack of acoustic signals directly reflected by a target's chest, indirectly-reflected acoustic signals can still be received by the microphone. We can therefore monitor the target's respiration by extracting this subtle variation of indirectly reflected signals. To achieve this, we model chest movement using truncated System Frequency Response (SFR). We then develop a global search method based on the autocorrelation function to extract minute chest movement from SFR sequences. Finally, we dynamically synthesize the chest movement information to recover the breathing wave in real time. We conduct extensive experiments with both humans and animals (goat), the results show thatOmniResMonitoris able to monitor single target's respiration within 5 meters in indoor environments in various challenging scenarios there are lack of directly-reflected acoustic signals.