Enabling entity discovery in indoor commercial environments without pre-deployed infrastructure

Finding entities of interest in indoor commercial places, such as the merchandise in supermarkets and shopping malls, is an essential issue for customers, especially when they are unfamiliar with an ad hoc indoor environment. This type of location-based indoor service requires comprehensive knowledge of indoor entities, including locations as well as their semantic information. However, the existing indoor localization approaches fail to directly localize these general entities without dedicated devices. This paper first focuses on the problem of discovering large-scale general entities of interest in indoor commercial spaces without pre-deployed infrastructure. We present a unique entity localization approach that leverages the localization results from multiple independent users to accurately determine the location of corresponding entities. Our key idea is to exploit the short-distance estimation with dead reckoning to guarantee the accuracy of entity localization. We develop a prototype system based on the crowdsourcing method, iScan, and test it in one of the biggest supermarkets in Changsha, China, to validate the performance of our design. Extensive experimental results show that our approach can achieve meter-level accuracy in a single day with 70 participants. Moreover, in a monthly evaluation with 500 effective participants, iScan discovered more than 200 entities and localized approximately 75% of them within 2 m.