Detecting pollution timely and locating the pollution source is of great importance in environmental protection. Considering advantages of the sensor network technology, sensor networks have been adopted in pollution monitoring works. In this paper, a survey on researches of pollution monitoring using sensor networks in environment protection is given. Firstly, sensors and pollution monitoring network systems are studied. Secondly, different pollution detection methods are analyzed and compared. Thirdly, an overview of state-of-art technologies on pollution source localization is given. Finally, challenges on pollution monitoring using sensor networks are presented.

Water pollution detection is of great importance in water conservation. In this paper, the water pollution detection problems of the network and of the node in sensor networks are discussed. The detection problems in both cases of the distribution of the monitoring noise being normal and nonnormal are considered. The pollution detection problems are analyzed based on hypothesis testing theory firstly; then, the specific detection algorithms are given. Finally, two implementation examples are given to illustrate how the proposed detection methods are used in the water pollution detection in sensor networks and prove the effectiveness of the proposed detection methods.

This paper focuses on millimeter wave (mmWave) channel prediction by machine learning (ML) methods. Previous ML-based mmWave channel predictors have limitations on requirements of the amount of training data, model generalization ability, robustness to noise, etc. In this paper, we propose a CNN model with a novel feature selection strategy for mmWave channel prediction. Automatic hyperparameter tuning (AHT) algorithms are embedded in the training process to iteratively optimize the predictive performance of the proposed CNN. The diversification strategy is leveraged to enhance the robustness of the AHT procedure against different communication scenarios. To improve the generalization ability of the prediction model, the input features are designed to capture the correlation between the physical environment and the channel characteristics. In parallel, the Cartesian coordinates of the transmitter (Tx) and receiver (Rx) are transformed into polar ones to reduce the model's sensitivity to coordinate noise. Numerical results demonstrate the effectiveness of the proposed CNN model in predicting mmWave channel characteristics in various communication scenarios.

Aiming at the problem of uneven clustering and the unreasonable energy consumption of LEACH protocol in the perception layer of IoT-based microgrids of static nodes; in this paper, we propose a stationary-node energy-based routing protocol (SERP). First, we select a dynamic cluster radius for clustering to meet the actual needs of the network during clustering. Then, to solve the problem that the number of cluster heads is difficult to determine, a dynamic optimal cluster head ratio is adopted. The dynamic optimal cluster head ratio can be obtained by minimizing the total energy consumption of cluster formation and the stable transmission phase, which can improve the efficiency of network transmission. Finally, by setting the residual energy factor and distance factor to improve the calculation of the cluster head election threshold, the energy load of the network is more uniform, and the location of the cluster head is more reasonable. Compared with the LEACH protocol and the HEED protocol, the simulation results show that the SERP protocol can effectively prolong the lifetime of the whole network.