# Location Based Indoor and Outdoor Lightweight Activity Recognition System
## 2 Related Work  
Due to the high spread of services offered by innovative technologies, such as the one introduced by the Internet of Things (IoT) domain, during the last decade the research community has started to investigate and propose techniques, tools and solutions to predict users' behavior and recognize their current and future activities. Nowadays, the city and its facilities have been enhanced by services that allow users to live more fruitful days and enjoy their lives. The possibility of knowing the exact current position of the bus to reach the city center or avoiding the traffic while we are driving a car are only some examples of all the advantages for citizens brought thanks the new services introduced in the smart cities. Nonetheless, most of these duties base their functionalities on the monitoring of both the environment and the users themselves and sometimes fail simply because they cannot interpret the next actions of the user (e.g., a system could suggest to avoid a road that seems to be full but it is exactly the destination of all the cars that are occupying and so it could cause a delay that would have been avoided by predicting the destination of the occupants). These reasons motivate the research conducted to predict users' behavior and recognize their current and future activities [8; 9; 10; 11; 12]. As demonstrated by the various works presented in the literature, thanks to such predictions it is possible to enhance a multitude of existing services, such as improving logistics in different domains [13; 14; 15], reimagine and dynamically repurpose built environment or customize services [16; 17], prevent and manage dangerous situations [18; 19; 20; 21; 22], but also their provision. Related to this last aspect, the works proposed in [23; 24] demonstrate how the prediction of the user behavior and activities could help in predicting and consequently mitigate the resource demand in a particular moment to distribute the network bandwidth and the processor load dedicated to that service. Different techniques have already been proposed in the literature aimed to recognize the user behavior [25; 26; 27; 28] but only a few of them exploit innovative techniques based on POIs or semantic location as done in the proposed paper, and, to the best of our knowledge, none of them combine both techniques as part of our previous works that inspired the present one [7]. Therefore, the present paragraph will be divided into two main parts: the first one is dedicated to the techniques that exploit POI while the second one is focused on the one handling semantic location. In [29], the work proposed by authors is based on the Location-Based Social Network (LBSN) concept. A LBSN is a new social structure made up of individuals connected by the interdependency derived from their physical locations as well as their location-tagged media content, such as photos, video, and texts, that is created by adding a location to an existing social network so that people in the social structure can share location embedded information. The physical location in this context consists of an individual's instant location at a given timestamp and the location history that an individual has accumulated over a given period. Furthermore, interdependence encompasses not only the fact that two people co-occur in the same physical location or share similar location histories, but also knowledge, such as common interests, behavior, and activities, inferred from an individual's location (history) and location-tagged data. In particular, the authors propose an algorithm based on tree-based hierarchical graph (TBHG) and Hyperlink-Induced Topic Search (HITS) algorithm considering GPS trajectories model to infer users' travel experiences and interest of a location within a region. By analyzing the POI of the selected scene perception task, authors are capable to find that those users have similar POI and rank them by the location and experience using the proposed algorithm.