To ensure safety in confined environments such as mines or subway tunnels, a (wireless) sensor network can be deployed to monitor various environmental conditions. One of its most important applications is to track personnel, mobile equipment, and vehicles. However, the state-of-the-art algorithms assume that the positions of the sensors are perfectly known, which is not necessarily true due to imprecise placement and or dropping of sensors. Therefore, we propose an automatic approach for simultaneous refinement of sensors' positions and target tracking. We divide the considered area in a finite number of cells, define dynamic and measurement models, and apply a discrete variant of belief propagation that can efficiently solve this high-dimensional problem and handle all non-Gaussian uncertainties expected in this kind of environment. Finally, we use ray-tracing simulation to generate an artificial mine-like environment and generate synthetic measurement data. According to our extensive simulation study, the proposed approach performs significantly better than standard Bayesian target tracking and localization algorithms and provides robustness against outliers.
In @cite_18 , a centroid algorithm was proposed, in which the miner's location was found by averaging the coordinates of the detected anchors. The algorithm is a part of a structure-aware self-adapting (SASA) WSN, which is capable of detecting structure variations caused by mine collapses. The main problem of this approach is that it requires a high density of uniformly deployed sensor nodes.
@cite_18
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Environment monitoring in coal mines is an important application of wireless sensor networks (WSNs) that has commercial potential. We discuss the design of a structure-aware self-adaptive WSN system, SASA. By regulating the mesh sensor network deployment and formulating a collaborative mechanism based on a regular beacon strategy, SASA is able to rapidly detect structure variations caused by underground collapses. A prototype is deployed with 27 Mica2 motes. We present our implementation experiences as well as the experimental results. To better evaluate the scalability and reliability of SASA, we also conduct a large-scale trace-driven simulation based on real data collected from the experiments. Environment monitoring in coal mines is an important application of wireless sensor networks (WSNs) that has commercial potential. We discuss the design of a Structure-Aware Self-Adaptive WSN system, SASA. By regulating the mesh sensor network deployment and formulating a collaborative mechanism based on a regular beacon strategy, SASA is able to rapidly detect structure variations caused by underground collapses. We further develop a sound and robust mechanism for efficiently handling queries under instable circumstances. A prototype is deployed with 27 mica2 motes in a real coal mine. We present our implementation experiences as well as the experimental results. To better evaluate the scalability and reliability of SASA, we also conduct a large-scale trace-driven simulation based on real data collected from the experiments. This paper addresses the problem of detecting people and vehicles on a surface mine by presenting an architecture that combines the complementary strengths of deep convolutional networks DCN with cluster-based analysis. We highlight that using a DCN in a naive black box approach results in a significantly high rate of errors due to the lack of mining-specific training data and the unique landscape in a mine site. In this work, we propose a background model that exploits the abundance of background-only images to discover the natural clusters in visual appearance using features extracted from the DCN. Both a simple nearest cluster-based background model and an extended model with cosine features are investigated for their ability to identify and suppress potential false positives made by the DCN. Furthermore, localization of objects of interest is enabled through region proposals, which have been tuned to increase recall within the constraints of a computational budget. Finally, a soft fusion framework is presented to combine the estimates of both the DCN and background model to improve the accuracy of the detection. Our system is tested on over 11 km of real mine site data in both day and night conditions where we were able to detect both light and heavy vehicles along with mining personnel. We show that the introduction of our background model improves the detection performance. In particular, soft fusion of the background model and the DCN output produces a relative improvement in the F1 score of 46 and 28 compared to a baseline pretrained DCN and a DCN retrained with mining images, respectively. A suitable clustering algorithm for grouping sensor nodes can increase the energy efficiency of WSNs. However, clustering requires additional overhead, such as cluster head selection and assignment, and cluster construction. This paper proposes a new regional energy aware clustering method using isolated nodes for WSNs, called Regional Energy Aware Clustering with Isolated Nodes (REAC-IN). In REAC-IN, CHs are selected based on weight. Weight is determined according to the residual energy of each sensor and the regional average energy of all sensors in each cluster. Improperly designed distributed clustering algorithms can cause nodes to become isolated from CHs. Such isolated nodes communicate with the sink by consuming excess amount of energy. To prolong network lifetime, the regional average energy and the distance between sensors and the sink are used to determine whether the isolated node sends its data to a CH node in the previous round or to the sink. The simulation results of the current study revealed that REAC-IN outperforms other clustering algorithms.