The increasingly wide utilization of Wireless Sensor Networks (WSNs) in industrial applications outstands the significance of the Delay Constrained Relay Node Placement (DCRNP) problem. The unreliable and unpredictable wireless links in WSNs may lead existing algorithms to fail in practice. Therefore, we first conduct extensive real-world deployments under the guidance of existing algorithms to evaluate their performance and to gain some insights for designing practical deployment algorithms. Based on the experiences learned from practical experiments, we first devise a Set-Covering-based Algorithm (SCA) which figures out the DCRNP problem while trying to make the quality of each link better than a given threshold. As our experiments also show that the fault-tolerant topology can significantly improve network reliability, we then design a $k$ -Set-Covering-based Algorithm ( $k$ SCA) to build fault-tolerant WSNs based on the methodology of SCA. Furthermore, the elaborate analysis proves that both SCA and $k$ SCA are polynomial-time algorithms, and their approximation ratios are $\text{O}(\ln n)$ and $\text{O}(\lg n)$ , respectively, where $n$ is the number of sensor nodes. Finally, extensive experiments are performed under the guidance of SCA and $k$ SCA to demonstrate the effectivenesses of these two algorithms.

There is a temperature difference in the abnormal seepage area of a dam body. Infrared thermal imaging technology can effectively obtain temperature information and use infrared images to analyze the area of a dam seepage anomaly. Three indoor experiments were conducted to study the effects of the temperature of the abnormal seepage location, shooting distance, and shooting angle on the acquisition of infrared images. On this basis, the target area is obtained adopting a temperature threshold and image segmentation, and the calculation results are then compared with the actual area. Experimental results show that within a certain range, the temperature of the target area and the shooting distance and angle do not appreciably affect the calculation of the target area in the infrared image, and the temperature threshold and image segmentation methods can effectively obtain the abnormal seepage area in practical engineering applications.

Due to the advantages of being low cost and low power consumption, pyroelectric infrared sensors are widely employed in many applications, such as target location and tracking. In such applications, reference structure which is used to modulate views of binary sensors can help improve spatial resolutions by segmenting monitoring spaces into many cells which are identified by states (called signatures). A spatial resolution determines localization or tracking accuracy, which depends on the sizes, the shapes, and the signatures of the cells, and is drastically impacted by deployment of sensors and reference structure. However, in order to obtain a better deployment formation and a better spatial resolution, researchers have to spend lots of time to deploy and measure the deployment results of cells (such as their locations, sizes, shapes, and signatures), i.e., a huge amount of measurements which cost time and money in practice. Hence, in this paper, we propose a tool to visually and efficiently represent and analyze the deployment formation and spatial resolution. Generally, the location of a cell is represented by a list of vertices scattered in a cartesian coordinate system and can help researchers get its size, shape, and signature. A cell reconstruction algorithm is proposed to reconstruct cells generated by the deployment formation in which lists of vertices are generated by regarding cells as shortest path rings. We further provide some theoretical analysis and computational complexity analysis of the proposed algorithm. We conduct experiments using our tool to study sensor efficiency and spatial resolution easily and effectively.

A paramount factor limiting the applications of binary sensors is these senors’ on-off property outputting binary digits of “0” or “1”. To overcome this limitation, modulators or obscurants are added to enhance the sensing ability of binary sensors and render them usable in applications such as multi-target tracking and human activity recognition. Obscurants segment the field of interest into subregions and distinguish each subregion by a list of sensor states called signatures. This paper studies two placement scenarios in a two-dimensional planar graph. In the first scenario, we prove upper and lower bounds on the maximum number of achievable signatures. In the second scenario, starting from the placement of sensors and obscurants in which the maximum number of signatures is achievable, we propose a novel mathematical model based on four main metrics: the object space size, the sensor space size, the obscurant space size, and the sizes of individual obscurants. We find the minimum and maximum radiuses that bound the object detection area given the number and sizes of sensors and obscurants. We derive the obscurant space size as a function of the object space size and the size of individual obscurants. We also provide a linear relationship formula between the obscurant space radius, the sensor space radius, and the obscurants’ radiuses and conduct modeling experiments to study the relationship between these metrics. Finally, we deduct an explicit formula for the maximum obscurant space size for the sensor space and the individual obscurant sizes.

Due to the low cost and good privacy protection of binary sensors, there are many applications of binary sensors. To enhance the spatial awareness of binary sensors, researchers utilize modulators to modulate views of sensors into visible and invisible regions so that the monitoring space is segmented into small cells identified by signatures. When a warm object moves in these cells, its location or moving trajectory can be acquired more accurately with modulators than without modulators. Accordingly, the maximum number of cells (MNC) in a deployment determines partially the maximal spatial awareness of the binary sensor system. In this paper, we provide a theoretical study of the MNC, given the number of sensors and the number of modulators. We also find the sufficient and necessary conditions to achieve the MNC so that we provide the reasons why deployment cannot obtain the MNC. These conditions can guide researchers to design the MNC deployments. Furthermore, we provide a method to calculate the number of cells when a deployment sometime cannot obtain the MNC. Our experiments provide deep insights into the influences of those conditions on the MNC and the MNC on the spatial awareness of binary sensor systems.