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首页> 外文期刊>Multimedia, IEEE Transactions on >GPS/HPS-and Wi-Fi Fingerprint-Based Location Recognition for Check-In Applications Over Smartphones in Cloud-Based LBSs
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GPS/HPS-and Wi-Fi Fingerprint-Based Location Recognition for Check-In Applications Over Smartphones in Cloud-Based LBSs

机译：基于GPS / HPS和Wi-Fi指纹的位置识别，用于基于云的LBS中智能手机的签入应用

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This paper proposes a new location recognition algorithm for automatic check-in applications (LRACI), suited to be implemented within Smartphones, integrated in the Cloud platform and representing a service for Cloud end users. The algorithm, the performance of which is independent of the employed device, uses both global and hybrid positioning systems (GPS/HPS) and, in an opportunistic way, the presence of Wi-Fi access points (APs), through a new definition of Wi-Fi FingerPrint (FP), which is proposed in this paper. This FP definition considers the order relation among the received signal strength (RSS) rather than the absolute values. This is one of the main contributions of this paper. LRACI is designed to be employed where traditional approaches, usually based only on GPS/HPS, fail, and is aimed at finding user location, with a room-level resolution, in order to estimate the overall time spent in the location, called Permanence, instead of the simple presence. LRACI allows automatic check-in in a given location only if the users' Permanence is larger than a minimum amount of time, called Stay Length (SL), and may be exploited in the Cloud. For example, if many people check-in in a particular location (e.g., a supermarket or a post office), it means that the location is crowded. Using LRACI-based data, collected by smartphones in the Cloud and made available in the Cloud itself, end users can manage their daily activities (e.g., buying food or paying a bill) in a more efficient way. The proposal, practically implemented over Android operating system-based Smartphones, has been extensively tested. Experimental results have shown a location recognition accuracy of about 90%, opening the door to real LRACI employments. In this sense, a preliminary study of its application in the Cloud, obtained through simulation, has been provided to highlight the advantages of the LRACI features.
机译：本文针对自动签到应用程序（LRACI）提出了一种新的位置识别算法，该算法适合在智能手机中实现，并集成在Cloud平台中，并为Cloud最终用户提供服务。该算法的性能与所使用的设备无关，它使用全球定位系统和混合定位系统（GPS / HPS），并通过一种新的定义，以机会主义的方式使用Wi-Fi接入点（AP）。本文提出了Wi-Fi指纹（FP）。此FP定义考虑了接收信号强度（RSS）之间的顺序关系，而不是绝对值。这是本文的主要贡献之一。 LRACI旨在在通常仅基于GPS / HPS的传统方法失败的情况下使用，其目的是查找具有房间级别分辨率的用户位置，以便估算在该位置花费的总时间（称为“永久性”，而不是简单的存在。 LRACI仅在用户的永久时间大于最小停留时间（称为“停留时间”（SL））并且可以在云中利用时，才允许在给定位置自动签入。例如，如果许多人在特定位置（例如，超级市场或邮局）签到，则意味着该位置很拥挤。最终用户可以使用由智能手机在云中收集并在云本身中获得的基于LRACI的数据，以更有效的方式管理其日常活动（例如，购买食物或支付账单）。该提案实际上已在基于Android操作系统的Smartphones上实施，已经进行了广泛的测试。实验结果表明，位置识别精度约为90％，这为真正的LRACI就业打开了大门。从这个意义上讲，已经提供了通过模拟获得的对其在云中的应用的初步研究，以突出显示LRACI功能的优势。

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