RSSI-Based Indoor Localization With the Internet of Things
ABSTRACT
In the era of smart cities, there are a plethora of applications where the localization of indoor environments is important, from monitoring and tracking in smart buildings to proximity marketing and advertising in shopping malls. The success of these applications is based on the development of a cost effcient and robust real-time system capable of accurately localizing objects. In most outdoor localization systems, global positioning system (GPS) is used due to its ease of implementation and accuracy up to five meters. However, due to the limited space that comes with performing localization of indoor environments and the large number of obstacles found indoors, GPS is not a suitable option. Hence, accurately and , effciently locating objects is a major challenge in indoor environments. Recent advancements in the Internet of Things (IoT) along with novel wireless technologies can alleviate the problem. Small-size and cost-effcient IoT devices which use wireless protocols can provide an attractive solution. In this paper, we compare four wireless technologies for indoor localization: Wi-Fi (IEEE 802.11n-2009 at the 2.4 GHz band), Bluetooth low energy, Zigbee, and long-range wide-area network. These technologies are compared in terms of localization accuracy and power consumption when IoT devices are used. The received signal strength indicator (RSSI) values from each modality were used and trilateration was performed for localization. The RSSI data set is available online. The experimental results can be used as an indicator in the selection of a wireless technology for an indoor localization system following application requirements.
EXISTING SYSTEM :
A novel smart phone -based indoor localization system that integrates an infrastructure-based acoustic localization system with inertial sensor-based dead reckoning. A fuzzy inference system is developed to achieve a short-term high accuracy tracking by using inertial sensors. The acoustic positioning and the inertial sensor-based dead reckoning are then fused by a Kalman filter with a carefully designed decision making algorithm. Hence, long-term stable and precise indoor localization, which is also robust against short-term measurement noise of the acoustic system, can be achieved. The experimental results show that the proposed system is able to accurately follow the true trajectory meanwhile to maintain the robustness and stability even if the position data of acoustic localization system are missing or erroneous.
PROPOSED SYSTEM:
In this paper, through extensive experimentation, a comparison between the accuracy and power consumption of WiFi , BLE , Zigbee , and LoRa WAN is performed. The wireless technologies were chosen based on factors such as popularity, public availability, and use in the IoT . Zigbee is a popular low power technology, often used in IoT applications. BLE and WiFi are both heavily present in society. Most devices are able to connect with at least one or both of these, allowing a network of devices to be created. LoRa WAN is a novel technology that is not as prevalent as the previous technologies mentioned. Transmitting at 915MHz and sacrificing high data rates, LoRa WAN nodes can reach distances of 15000 meters, which can greatly limit the number of nodes required in order to cover an environment. All tests were performed using a trilateration technique where the RSSI values were utilized in determining the approximate distances between the transmitting nodes and the receiver. Two different environments were used for experimentation in order to compare results accross multiple scenarios. we expand on these works and additionally compare the wireless communication technologies WiFi (2.4GHz band), BLE, Zigbee, and LoRaWAN. In addition, prototypes are used that are capable of testing each of the different technologies. Comparisons between the different technologies include the accuracy and the power consumption of the devices used.
CONCLUSION :
In this work, we compared WiFi, BLE, Zigbee, and LoRaWAN for use in an indoor localization system. By using three transmitting nodes broadcasting information, along with a single receiver, trilateration could be performed to determine an approximate receiver location. Through experimentation, WiFi proved to be the most accurate, deviating off the actual receiver position by 0.664 meters on average. WiFi was followed by BLE, which produced an error of 0.753 meters. BLEwas also found to use the lowest amount of power, consuming 0.367mWon average. For transmission range, LoRaWAN, the technology that was designed to transmit at a lower frequency of 915MHz, had the furthest transmission range when running at its maximum transmission power. The experimental results can be used as an indicator for the selection of a proper indoor localization system in smart buildings. All the data collected through experimentation are publicly available online