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This paper develops a mobile sensing system, the first system used in adaptive resolution urban air quality monitoring. In this system, we employ several taxis as sensor carries to collect original PM2.5 data and collect a variety of datasets, including meteorological data, traffic status data, and geographical data in the city. This paper also presents a novel method AG-PCEM (Adaptive Grid-Probabilistic Concentration Estimation Method) to infer the PM2.5 concentration for undetected grids using dynamic adaptive grids. We gradually collect the measurements throughout a year using a prototype system in Xiasha District of Hangzhou City, China. Experimental data has verified that the proposed system can achieve good performance in terms of computational cost and accuracy. The computational cost of AG-PCEM is reduced by about 40.2% compared with a static grid method PCEM under the condition of reaching the close accuracy, and the accuracy of AG-PCEM is far superior as widely used artificial neural network (ANN) and Gaussian process (GP), enhanced by 38.8% and 14.6%, respectively. The system can be expanded to wide-range air quality monitor by adjusting the initial grid resolution, and our findings can tell citizens actual air quality and help official management find pollution sources. [ABSTRACT FROM AUTHOR]

Location information acquisition is crucial for many wireless sensor network (WSN) applications. While existing localization approaches mainly focus on 2D plane, the emerging 3D localization brings WSNs closer to reality with much enhanced accuracy. Two types of 3D localization algorithms are mainly used in localization application: the range-based localization and the range-free localization. The range-based localization algorithm has strict requirements on hardware and therefore is costly to implement in practice. The range-free localization algorithm reduces the hardware cost but at the expense of low localization accuracy. On addressing the shortage of both algorithms, in this paper, we develop a novel hybrid localization scheme, which utilizes the range-based attribute RSSI and the range-free attribute hopsize, to achieve accurate yet low-cost 3D localization. As anchor node deployment strategy plays an important role in improving the localization accuracy, an anchor node configuration scheme is also developed in this work by utilizing the MIS (maximal independent set) of a network. With proper anchor node configuration and propagation model selection, using simulations, we show that our proposed algorithm improves the localization accuracy by 38.9% compared with 3D DV-HOP and 52.7% compared with 3D centroid. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed localization algorithm which can be applied to an irregular three-dimensional wireless sensor network, considering the algorithm accuracy and complexity. The algorithm uses clusters to eliminate the multihop distance errors. An anchor node position optimization scheme is proposed to maximize the uniformity of each subnetwork. The proposed algorithm also employs a new three-dimensional coordinate transformation algorithm, which helps to reduce the errors introduced by coordinate integration between clusters and improves the localization precision. The simulation and performance analysis results show that the localization accuracy of the D3D-MDS algorithm increases by 49.1% compared with 3D-DV-HOP and 38.6% compared with 3D-MDS-MAP. This distributed localization scheme also demonstrates a low computational complexity compared with other centralized localization algorithms. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce a transmission power control scheme based on the remaining energy level and the energy-harvesting status of individual sensor nodes to extend the overall lifetime of wireless sensor networks (WSNs) and balance the energy usage. Ambient energy harvesting has been introduced as a promising technique to solve the energy constraint problem of WSNs. However, considering the tiny equipment and the inherent low and unbalanced harvesting capability due to environmental issues, there is still a long distance from perfectly solving the problem. In this paper, a wind and solar power joint-harvested WSN system has been demonstrated, which uses ultracapacitor as energy storage. By analyzing the power recharging, leakage, and energy consumption rate, a novel energy-level-based transmission power control scheme (EL-TPC) is produced. In EL-TPC scheme, the transmission power is classified into various levels according to the remaining energy level. By adapting the nodes' operation pattern, hierarchical network architecture can be formed, which prioritizes the use of high energy level, fast charging nodes to save the energy of uncharged nodes. The simulation and demonstration results show that EL-TPC scheme can significantly balance the energy consumption and extend the entire network lifetime. [ABSTRACT FROM AUTHOR]