Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh 11653, Saudi Arabia.
Sensors (Basel). 2022 Aug 29;22(17):6513. doi: 10.3390/s22176513.
The correlations between smartphone sensors, algorithms, and relevant techniques are major components facilitating indoor localization and tracking in the absence of communication and localization standards. A major research gap can be noted in terms of explaining the connections between these components to clarify the impacts and issues of models meant for indoor localization and tracking. In this paper, we comprehensively study the smartphone sensors, algorithms, and techniques that can support indoor localization and tracking without the need for any additional hardware or specific infrastructure. Reviews and comparisons detail the strengths and limitations of each component, following which we propose a handheld-device-based indoor localization with zero infrastructure (HDIZI) approach to connect the abovementioned components in a balanced manner. The sensors are the input source, while the algorithms are used as engines in an optimal manner, in order to produce a robust localizing and tracking model without requiring any further infrastructure. The proposed framework makes indoor and outdoor navigation more user-friendly, and is cost-effective for researchers working with embedded sensors in handheld devices, enabling technologies for Industry 4.0 and beyond. We conducted experiments using data collected from two different sites with five smartphones as an initial work. The data were sampled at 10 Hz for a duration of five seconds at fixed locations; furthermore, data were also collected while moving, allowing for analysis based on user stepping behavior and speed across multiple paths. We leveraged the capabilities of smartphones, through efficient implementation and the optimal integration of algorithms, in order to overcome the inherent limitations. Hence, the proposed HDIZI is expected to outperform approaches proposed in previous studies, helping researchers to deal with sensors for the purposes of indoor navigation-in terms of either positioning or tracking-for use in various fields, such as healthcare, transportation, environmental monitoring, or disaster situations.
智能手机传感器、算法和相关技术的相关性是促进室内定位和跟踪的主要组成部分，因为这些技术在缺乏通信和定位标准的情况下仍然可以使用。可以注意到，在解释这些组件之间的联系以阐明用于室内定位和跟踪的模型的影响和问题方面存在一个主要的研究差距。在本文中，我们全面研究了智能手机传感器、算法和技术，这些技术可以在不需要任何额外硬件或特定基础设施的情况下支持室内定位和跟踪。综述和比较详细说明了每个组件的优缺点，然后我们提出了一种基于手持设备的零基础设施室内定位（HDIZI）方法，以平衡地连接上述组件。传感器是输入源，而算法则以最佳方式用作引擎，以在不需要任何进一步基础设施的情况下生成强大的定位和跟踪模型。所提出的框架使室内和室外导航更加用户友好，并且对于在手持设备中使用嵌入式传感器的研究人员具有成本效益，为工业 4.0 及以后的技术提供了支持。我们使用从两个不同地点的五部智能手机收集的数据进行了实验，作为初始工作。数据以 10 Hz 的频率在固定位置采集五秒钟；此外，还在移动时采集数据，以便根据用户的步行动作和在多条路径上的速度进行分析。我们利用智能手机的功能，通过高效的实现和算法的最佳集成，克服了固有局限性。因此，所提出的 HDIZI 有望优于以前研究中提出的方法，帮助研究人员处理用于室内导航（无论是定位还是跟踪）的传感器，以便在医疗保健、交通、环境监测或灾难等各种领域中使用。
