The emergence of smartphones has accelerated the development of cutting-edge positioning-based systems since they are contained to have more processing, memory, and battery power.
Position information represents a core element in the human-centred activities, assisting in visualising complex environments effectively and providing a representational coordinate for localisation, tracking, and navigation purposes. The recent developments in mobile positioning technologies and the increasing demands of ubiquitous computing have significantly contributed to sophisticated positioning applications and services. Finally, the practical results have significance in designing a cloudlet-based cloud computing system enabling Wi-Fi indoor positioning and navigation. A core cloud manages items that have the same position (i.e., a global position) defined as the corresponding Wi-Fi location. The system was tested in a real environment with the following results: (1) our system autonomously performed actions, such as turning right or left or going straight according to a movement decision algorithm and determined the position within a stable range of Wi-Fi coverage (2) the cloudlet and core cloud can track navigation for an indoor self-driving cart (3) the global and local positions designed for reference access points and a specific position can navigate the self-driving cart to a particular position accurately (4) the moving edge clouds play a role in deciding three action movements (go straight, turn left, and turn right), as well as managing the local position of the items and (5) a core cloud is deployed to store all information for the items, such as their positions and corresponding Wi-Fi locations. Our cloudlet-based cloud computing system provides the reference point data and real-time interactive response for a self-driving indoor cart. In this paper, we propose a cloudlet-based cloud computing system enabling Wi-Fi indoor positioning and navigation through a Wi-Fi located on a one-hop wireless network. Most Wi-Fi-based indoor positioning techniques using wireless received signal strength (RSS)-based methods are affected by the indoor environment and depend on the respective signals from at least three reference access points. Wi-Fi-based indoor positioning for determining accurate wireless indoor location information has become crucial in meeting increasing demands for location-based services by leveraging the Internet of Things (IoT) and ubiquitous connectivity. The article concludes with a brief insight into future directions in indoor positioning and navigation systems. Various evaluation criteria for indoor navigation systems are proposed in this work. Moreover, this article investigates and contrasts the different navigation systems in each category.
#Radbeacon dot update frequency Bluetooth#
Navigation and positioning systems that utilize pedestrian dead reckoning (PDR) methods and various communication technologies, such as Wi-Fi, Radio Frequency Identification (RFID) visible light, Bluetooth and ultra-wide band (UWB), are detailed as well. In particular, the paper reviews different computer vision-based indoor navigation and positioning systems along with indoor scene recognition methods that can aid the indoor navigation. This article provides a comprehensive summary of evolution in indoor navigation and indoor positioning technologies.
Radio frequency (RF) signals, computer vision, and sensor-based solutions are more suitable for tracking the users in indoor environments.
In indoor environments, lack of Global Positioning System (GPS) signals and line of sight with orbiting satellites makes navigation more challenging compared to outdoor environments. Current technological advancements enable users to encapsulate these systems in handheld devices, which effectively increases the popularity of navigation systems and the number of users. Navigation systems help users access unfamiliar environments. All of these techniques present a number of issues, including low precision, high computational complexity, and unreliability due to wireless channel impairments such as multipath effects caused by non line of sight (NLOS) propagation in indoor environments, while most positioning devices lack sufficient computing power. Yet in the literature, in addition to the range-free techniques such as Centroid method and distance vector hop (DV-Hop) technique, typical ranging techniques based on channel state information (CSI), angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), and radio signal strength indicator (RSSI) using various wireless technologies such as radio frequency identification (RFID), ultra-wide bandwidth (UWB), WiFi, and Bluetooth have been proposed for indoor positioning. All these transformational applications drive the need for accurate localization systems which require lots of resources due to the massive deployment of IoT devices.
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