Smart Water Quality Monitoring with IoT Wireless Sensor Networks.

Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a MAE¯ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a MAE¯ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll.

Automatic Dynamic Range Adjustment for Pedestrian Detection in Thermal (Infrared) Surveillance Videos.

This paper presents a novel candidate generation algorithm for pedestrian detection in infrared surveillance videos. The proposed method uses a combination of histogram specification and iterative histogram partitioning to progressively adjust the dynamic range and efficiently suppress the background of each video frame. This pairing eliminates the general-purpose nature associated with histogram partitioning where chosen thresholds, although reasonable, are usually not suitable for specific purposes. Moreover, as the initial threshold value chosen by histogram partitioning is sensitive to the shape of the histogram, specifying a uniformly distributed histogram before initial partitioning provides a stable histogram shape. This ensures that pedestrians are present in the image at the convergence point of the algorithm. The performance of the method is tested using four publicly available thermal datasets. Experiments were performed with images from four publicly available databases. The results show the improvement of the proposed method over thresholding with minimum-cross entropy, the robustness across images acquired under different conditions, and the comparable results with other methods in the literature.

Energy Efficient Medium Access Control Protocol for Clustered Wireless Sensor Networks with Adaptive Cross-Layer Scheduling.

This paper presents an Energy Efficient Medium Access Control (MAC) protocol for clustered wireless sensor networks that aims to improve energy efficiency and delay performance. The proposed protocol employs an adaptive cross-layer intra-cluster scheduling and an inter-cluster relay selection diversity. The scheduling is based on available data packets and remaining energy level of the source node (SN). This helps to minimize idle listening on nodes without data to transmit as well as reducing control packet overhead. The relay selection diversity is carried out between clusters, by the cluster head (CH), and the base station (BS). The diversity helps to improve network reliability and prolong the network lifetime. Relay selection is determined based on the communication distance, the remaining energy and the channel quality indicator (CQI) for the relay cluster head (RCH). An analytical framework for energy consumption and transmission delay for the proposed MAC protocol is presented in this work. The performance of the proposed MAC protocol is evaluated based on transmission delay, energy consumption, and network lifetime. The results obtained indicate that the proposed MAC protocol provides improved performance than traditional cluster based MAC protocols.

A semi-automatic motion-constrained Graph Cut algorithm for Pedestrian Detection in thermal surveillance videos.

This article presents a semi-automatic algorithm that can detect pedestrians from the background in thermal infrared images. The proposed method is based on the powerful Graph Cut optimisation algorithm which produces exact solutions for binary labelling problems. An additional term is incorporated into the energy formulation to bias the detection framework towards pedestrians. Therefore, the proposed method obtains reliable and robust results through user-selected seeds and the inclusion of motion constraints. An additional advantage is that it enables the algorithm to generalise well across different databases. The effectiveness of our method is demonstrated on four public databases and compared with several methods proposed in the literature and the state-of-the-art. The method obtained an average precision of 98.92% and an average recall of 99.25% across the four databases considered and outperformed methods which made use of the same databases.