# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### General Applications
#### 6.1.1 Localization  
In many emerging fields, location consciousness has become progressively necessary. In the context of environment, location information is studied and classified as outdoor and indoor localization. Several tools and techniques have been developed to estimate accurate location information. For outdoor localization, the global positioning system (GPS) has been widely and successfully used to accurately estimate location but fails to provide such accuracy in indoor location estimation due to its costly energy tags and weak penetration of its signal via a complex indoor environment [64; 65]. However, indoor location-based services are realized using various systems and techniques such as Wi-Fi, ultra-wide band (UWB), RFID, Bluetooth, light and magnetic field, FM radio, acoustic analysis, sensor network etc. Amidst the afore-named systems, Wi-Fi signal has been broadly utilized compared to other techniques due to its low cost, vast coverage features, and its ability to successfully allow signal accumulation and aggregation operation in indoor locations with no need for extra investment in infrastructure [65; 66; 67]. Intelligent localization that can scale with huge data without human perception is becoming increasingly important in next-generation internet of things (IoT) systems. Localization based on crowdsourcing has been shown to be beneficial for commercial location-based IoT applications. [PERSON] [68] proposed an enhanced localization technique based on crowdsourcing which avoids the intervention of users and tuning of parameters. Figure 26 shows the diagram of proposed crowdsourced-based localization method The proposed technique integrated three sensor (wireless, inertial and magnetic sensors) and utilized crowdsourced sensor data to update simultaneously both the magnetic and wireless databases throughout the localization process. Since in multi-sensor integration, it is complicated to set and tune the variability of fingerprinting solutions, the authors presented the fingerprinting accuracy indicator (FAI) to predict the accuracy of both wireless and magnetic fingerprinting fromsignal, geometry and database level. They further proposed an FAI-enhanced extended Kalman filter (EKF) to effectively increase the accuracy and reliability of localization. The investigation of the advantages and drawbacks of each FAI factor on magnetic and wireless fingerprinting using proposed crowdsourcing localization technique showed that the weighted DSF based FAI successfully predicted the location error and outliers. On the other hand, the geometry-based FAI proved effective in predicting short-term errors as well as outliers but was deficient in detecting long-term error. The signal strength FAI, on the contrary, could comparatively predict long-term location error effectively. [PERSON] and [PERSON] [69] proposed a survey reduction and signal map construction method called Surecose based on implicit crowdsourcing, and cooperation of low-cost collaborative sensors. Figure 27 shows the system flow of the proposed survey-reduction framework. First, the authors used a light-weight sparse collection of signal map to instantiate Surecose. After the background application is set up, naive users which act as implicit surveyors unintentionally upload crowdsourced signals. It is possible for phones to recognize other phones (mobile sensors) or beacons (fixed sensors). To label the location of the sensors, the proposed method calculates the proximity of the interacting IoT devices and can clearly locate the users through collaborative local labelling as well as sensor calibration in spite of the device used. Since estimating user locations (using radio signals) is usually sparse and irregular in the site, preprocessing is therefore necessary for complete scaling of the signal map for actual use. To address this, the authors modified the Gaussian Process (GP) to enable the construction of signal map anywhere.