Shivendra Tiwari

A survey on LBS: system architecture, trends and broad research areas

The Location Based Services (LBS) seem to be the next revolution on small computing handheld devices in terms of location aware advertising, security alerts, news updates, disaster management, geo-fencing, buddy-findings, gaming, criminal investigations, turn-by-turn navigation and so on. In todays scenario there is an explosion of technologies to communicate with mobile, connected devices and sensors. In this paper we are presenting a literature survey of LBS that includes the architecture of the LBS ecosystem, the key market players, and the latest trends in LBS development. Finally, the broad research areas such as location determination techniques, geo-sensor networks, and location based natural queries, location privacy and authorization, geo-social networks, LBS QoS, and Location Based Recommender Systems (LBRS) have been studied and presented briefly.

Extracting Region of Interest (ROI) Details Using LBS Infrastructure and Web-Databases

The geographical areas that are considered to be popular and interesting are called Region of Interest (ROI). There are multiple sources that can be used for erecting the ROIs such as user trajectory, POI databases, internet news etc. A tourist spot, historical region, monuments, a forest reserve, city, state or countrys administrative boundaries are considered as the ROI objects. The interesting facts of the regions can be used for on-the-spot infotainment (information + entertainment) while user is walking, driving or just sitting idle on the flight. One may want the information in different level of details based on the interests, travel direction, and speed. The best way to get the recent information is going through the internet news, discussion forums, and encyclopedias on the web. The challenge is that there is no universal database available that contains the region information along with the location based search capabilities. The maintaining the granularity of the information based on the size or the details of the region adds more challenges. The broader idea of our work is to use the existing LBS infrastructure to track the user and achieve other navigation objectives in the system. However, the freely available up-to-date internet infrastructure is used as the information source. Initially, the user location is determined by the conventional methods. In order to relate the location with the web content, we use the semantic labels associated to the underlying location. The semantic labels are fetched by using reverse geocoding that returns the local attractions, street city, state and country names etc. Then these labels are used to search the associated detailed content in the web. The region data is maintained in two forms i.e. locally populated database and the web databases. The locally populated database can be updated on the preconfigured time interval in order to avoid data staleness problem. We have used Wikipedia as the internet data source in our prototype. The further research is in progress extracting the ROI information from the POI databases with their spatial and non-spatial attributes.

Reducing Dependency on Middleware for Pull Based Active Services in LBS Systems

The middleware is the most commonly used solution to address the location privacy. But it becomes a bottleneck in terms of system performance and availability as the entire client’s service transactions are routed through the middleware to the actual Location Based Service Providers (LSP). The proposed architecture mainly targets a variety of applications where the availability of the services is probably more important than the location security. In the new flexible middleware based architecture the client and the LSPs can communicate directly. Autonomy on the client-server communication increases the possibility of communication even in the scenarios where the middleware is not available. But it also introduces authentication and security challenges to be addressed. The trusted middleware is used to generate the authentication certificates containing the Proxy Identity (also called Pseudonyms) to fulfill the authentication requirements at the LSP servers. The rest of transactions among the clients and the LSPs are accomplished independently. Further, the level of anonymity can be tuned by altering pseudonyms generation techniques i.e. “One-to-One”, “One-to-Many” and “Many-to-One” depending on the type of the service and security requirements. It also attempts to maintain almost the same level of security for the targeted services.

Using middleware as a certifying authority in LBS applications

The trusted middleware is the most commonly used solution to address the location privacy in location based services as generally such service providers are un-trusted entities that can be adversary attack sensitive points. The authors proposed an alternative solution which helps in avoiding a bottleneck in the existing system in terms of performance and availability as the entire clients service transactions are routed through the middleware to the actual Location Based Service Providers (LSP). In the proposed solution, the client and the LSPs can directly communicate with the same level of location security, privacy and anonymity. The trusted middleware is used as certifying authority that generates authentication certificates which contains the Proxy Identity (also called Pseudonyms), and the services subscribed with validity period. The encrypted certificate fulfills the authentication requirements at the LSP servers. In this paper we are reporting the implementation of the proposed system as a proof of concept using Struts Technology of Java. While evaluating the system features such as response time, delay, drop rate etc., the Google Maps location services and the internet browser have been considered as a service provider and client respectively. Performance analysis of our solution and that of prevalent architecture is done using Packmime model for http traffic generation of NS2 (Network Simulator 2) tool. The comparative graphs of the simulation results show that the proposed solution is better in terms of throughput, response time, drop rate and scalability in comparison to the existing middleware architectures in which the request response is every time routed through middleware, thus increasing the overheads.

Scalable Method for k Optimal Meeting Points (k-OMP) Computation in the Road Network Databases

Given a set of points Q on a road network G = (V,E), an optimal meeting point (OMP) query offers a point on a road network with the smallest sum-of-distances (SoD) to all the points in Q. For example, a travel agency may issue OMP query to decide the location for a tourist bus to pick up the tourists thus minimizing the total travel cost for tourist. The OMP problem has been well studied in the Euclidean space. The currently available algorithms for solving this problem in the context of road networks are still not efficient for the practical applications and are in-memory algorithms which do not guarantee the scalability for the large road databases. Further, the most of the research work has been carried out around the single point OMP; however, the k-OMP problem on the road network setting is still unexplored. In this paper, we are proposing multiple variants of the scalable external-memory based algorithms for computing the optimal meeting point. There are mainly three variants of the proposed grid based algorithms i.e. Basic Grid based, Hierarchical Grid based and Greedy Centroid based OMP search. Later we used single point OMP as a start point to explore the k points OMP using breadth first search. The I/O optimized spatial grids are loaded from the secondary storage as and when required and hence the I/O complexity is reduced to O(N/B) as opposed to O(N) in the existing methods; where B is the average number of road vertices of the grid block. Extensive experiments are conducted on both real and synthetic datasets.

Location based recommender systems: Architecture, trends and research areas

The growths of Internet, Global Positioning System (GPS) and wireless telecommunication technologies have opened new avenues in potential area of mobile computing called Location Based Services (LBS). Recommendation of personalized information / services in location based services has become an attractive trend for success of businesses. A Recommender System attempts to solve the problem of information overload and provides product and service recommendation based on user profile and preferences. The location of the user is an important information item that can be associated to the existing user profile in order to provide efficient recommendations. Also, easy availability of GPS enabled devices brings a large amount of GPS trajectories representing users mobile logs. These GPS trajectories can be used to mine interesting patterns about users. We have studied the utility and application of information extracted from users GPS trajectory data in recommender systems. We conceive that recommendation has an intrinsic social component and therefore this work takes a perspective towards the social aspect in location based recommender systems. In this paper, we are presenting the state-of-the-art research trends, challenges and applications in the area of Location-Based Recommender Systems (LBRS). (8 pages)

Fusion of Navigation Technology and E-learning Systems for on-the-spot Learning

Huge information is inherently associated with certain places in the globe. The ancient cities have multiple locations with large historical, geographical, cultural and architectural specialties. E-Learning systems allow the learners to learn irrespective of their time and place. The location based learning (LBL) is another aspect where the site seeing, and touching the building walls are the means of real-time experience of the sites. This manifestation motivates us to extend the capabilities of conventional E-learning systems so that the information can be accessed at the real-time based on the users location. In this paper, the e-Learning system is used for the LBL with the help of navigation capabilities without modifying the existing systems. The learning content geocoding, reverse-geocoding, and real-time learning plan generation based on the user preferences and constraints have been proposed. The on-the-way learning is another aspect of the LBL that has been proposed with a prototype implementation. The implemented system is a flexible and visually appealing LBL platform with a possibility to get the real-time location specific information both from webdatabases and locally stored content. (6 pages)

Popularity estimation of interesting locations from visitor’s trajectories using fuzzy inference system

Abstract Identifying the interesting places through GPS trajectory mining has been well studied based on the visitor’s frequency. However, the places popularity estimation based on the trajectory analysis has not been explored yet. The limitation in the majority of the traditional popularity estimation and place user-rating based methods is that all the participants are given the same importance. In reality, it heavily depends on the visitor’s category, for example, international visitors make distinct impact on popularity. The proposed method maintains a registry to keep the information about the visited users, their stay time and the travel distance from their home location. Depending on the travel nature the visitors are labeled as native, regional and tourist for each place in question. It considers the fact that the higher stay in a place is an implicit measure of the greater likings. Theweighted frequency is eventually fuzzified and applied rule based fuzzy inference system (FIS) to compute popularity of the places in terms of the ratings ∈ [0, 5]. We have evaluated the proposed method using a large real road GPS trajectory of 182 users for identifying the ratings for the collected 26807 point of interests (POI) in Beijing (China).

Modeling On-the-Spot Learning: Storage, Landmarks Weighting Heuristic and Annotation Algorithm

Huge information is intrinsically associated with certain places in the globe such as historical, geographical, cultural and architectural specialties. The next generation systems require access of the site specific information where the user is roaming at the moment. The on-the-spot learning (OTSL) is a system that allows the users to learn about the location, landmarks, regions where he/she is walking through. In this paper, we have proposed an OTSL model that includes the storage, retrieval and the landmark weighting heuristic. Apart from learning about the individual landmarks, we have proposed two ways of storing the spatial learning objects. First, use the administrative hierarchy of the region to fetch the information. This can be easily done by the reverse-geocoding operation without actually storing the physical hierarchy. Second, spatial chunking, creates the region based on the groups of landmarks in order to define a learning region. A hybrid solution has also been considered to achieve the advantages of both the region based methods. We use a weighting model to select the correct landmarks in the basic model. We extend the core model to include other factors such as speed, direction, side of the road etc. A prototype has been implemented to show the feasibility of the proposed model.