# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### General Applications
#### 6.1.1 Localization
Using the crowdsourced signals that have been labeled, the proposed method adaptively predicts signals at the structured grids of reference points (RPs) and developed a comprehensive signal database far off the sparse one. The modified GP takes into consideration the directions of the received signal in regard to APs, which in turn adjust to the complex and segregated indoor environment. The proposed method was experimented in different environments (campus hall, international airport, and shopping mall), and results showed that Surecose can adjust to different environments and construct a signal map with over 70% reduction in survey cost. Utilizing crowdsourced Wi-Fi fingerprinting together with Micro-Electro-Mechanical System (MEMS) sensors, [PERSON] [67] introduced an autonomous algorithm for 3D indoor localization. First, the authors designed an enhanced complementary filter (ACF) and used it to integrate data from multi-sensors to produce precise attitude information, constraining cumulative errors that emanate from the gyroscope update. The ACF variables could be adaptively adjusted via quasi-static magnetic field (QSMF) and external acceleration  
Figure 26: Diagram of proposed crowdsourced-based localization method [68]. detection. In addition, gradient decent algorithm was designed to enhance the forward pedestrian dead reckoning (PDR) crowdsourced trajectories to further reduce the cumulative error and enhance the robust generation of MEMS sensors database. Furthermore, a quality evaluation (QE) criterion was introduced to approximate the correctness of the crowdsourced trajectories and obtain a weighted fusion so as to achieve autonomous generation and updating of a highly accurate Wi-Fi fingerprinting database. Finally, the authors introduced an adaptive particle filter (APF) to obtain autonomous and accurate performance of crowdsourcing-based 3D indoor localization, using information supplied by the MEMS sensors and Wi-Fi fingerprinting. Experiments in real-world complex indoor environments proved that the proposed method was very effective. [PERSON] [70] Leveraged on smartphone crowdsourced Wi-Fi data and inertial measurement unit (IMU) data to construct a map-free and calibration-free indoor positioning system (IPS) named GraphIPS that dynamically obtains accurate radio maps. First, GraphIPS combines crowdsourced data to formulate a graph. The nodes in the developed graph depict the positions of devices or user steps in a time series. The edges are also approximated using the Wi-Fi RSS, IMU accelerometer, and AoA data. Second, the multidimensional scaling (MDS) algorithm is utilized to efficiently resolve the formulated graph and calculate the positions of the user's steps. Radio maps may be created without the need for calibration or map information by utilizing APs as anchors. The testing findings demonstrate that GraphIPS's accuracy is similar to that produced by calibration-based technique but better than that of the PDR or trilateration-based methods.