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The function of logistics is to move objects from place A to place B Therefore it is an important task to determine the position of objects along the supply chain. Up to now, different technologies for indoor localization have been developed. During this technological evolution, two important aspects have been left behind, the communication between the systems and the central management of the data provided. Within the research project ‘Industrial Indoor Localization’, an open source software standard for environment modeling called ‘Reference Architecture Indoor Localization’ (RAIL) has been designed and tested. This protocol enables Location Based Services to allow querying location and object information quickly. Within the scope of the project, four services are being developed and exemplary described in this paper. At the beginning, a location-dependent order allocation algorithm for order picking is presented. This algorithm reduces the waiting time in narrow-aisle warehouses through forecasting. Secondly, an order orchestration service based on location data for picking control is evaluated. Furthermore, a functional area recognition to support picking and a service for finding points of interest is demonstrated, which includes navigation based on a routing algorithm. Using indoor localization, these services improve the intralogistical processes. These include increasing picking performance or reducing order throughput time by eliminating the need to scan the barcode. Finally, these services increase work safety, due to consideration of functional areas, and improve the transparency of the location of points of interest.

This paper presents a concept for a software system called RAIL representing industrial indoor environments in a dynamic spatial model, aimed at easing development and provision of location-based services. RAIL integrates data from different sensor modalities and additional contextual information through a unified interface. Approaches to environmental modelling from other domains are reviewed and analyzed for their suitability regarding the requirements for our target domains; intralogistics and production. Subsequently a novel way of modelling data representing indoor space, and an architecture for the software system are proposed.

Skyline query has been investigated extensively in many fields recently. One of the interesting ones is the spatial skyline query that retrieves those points of P not dominated by any other point in P with respect to their derived spatial attributes. However, being point-based, this kind of query method is not suitable for privacy protection. In this paper, we introduce the privacy-preserved spatial skyline query where the distances calculated between the query points and the objects change from ‘point to point’ to ‘region to point’. It is the first effort to process relative skyline queries based on a ‘region to point’ way. Accordingly, we proposed three approaches: BC, VRS2 and NVRS2. While BC is a straightforward method, VRS2 and NVRS2 manipulate the properties of Voronoi diagram and Network Voronoi diagram for the Euclidean space and road networks situations respectively. Furthermore, with respect to the changes of query conditions, another two algorithms DPJA and DPDA to dynamically update the results are proposed so that the heavy re-calculation could be avoided. Our empirical experiments show that our approaches have good performance in retrieving the skyline points of privacy-preserved spatial skyline query.

Privacy protection has become one of the important issues for location-based services (LBS) nowadays. In order to meet the requirements of humanization, security and quick response, this paper proposes an improved personalized k-anonymous location privacy protection algorithm with fake position generation mechanism. Compared to the normal personalized k-anonymity algorithm, our improved algorithm has higher success rate of anonymity. By generating fake queries for the source queries that expire, our algorithm guarantees that no source query will be dropped, namely all the source queries can get anonymized. The experimental results show that the algorithm proposed by this paper is able to achieve better performance in terms of success rate of anonymity.

In recent years, the location based service has been gradually integrated into people’s daily life. Many fields such as the public security, emergency rescue, warehousing logistics, and IOT (The Internet of things) applications present extensive application demand on indoor location. This paper discusses the cross correlation interference in the TC-OFDM (time divided CDMA-OFDM superimposed signal system) receiver which is based on ground mobile radio network. In the TC-OFDM system, because of the complexity of the indoor environment, multiple access interference is terrible, which greatly affects the performance of the receiver for signal acquisition and tracking. This paper studies the characteristics of the multiple access interference in TC-OFDM system in indoor environment. The cross correlation value between the strong and weak signal can be estimated by putting the characteristic information of strong signal into the weak signal tracking channel. While this information can be extracted from the strong signal’s tracking channel. Therefore, strong signal’s effection on the weak signal’s autocorrelation function can be reduced. So that the tracking loop can get more accurate phase value for tracking. This method can improve the TC-OFDM receiver’s ability of inhibiting cross correlation by 10–13 dB, and effectively enhance the capacity for sustained and stable tracking of weak signal. In addition, in the snapshot replay mode of the weak signal which cannot be tracked continuously, this method helps to estimate the correlation peak more accurate.

Localization has recently become increasingly important for ubiquitous computing. Most embedded devices require location-based services for monitoring and tracking purposes. Typically the location services provided by these devices can be achieved using global positioning system (GPS). Although GPS can indicate absolute position based on satellite system, several studies have demonstrated its inaccuracy when deployed indoor due to signal degradation from the obstacles and building structures. In this paper, we have developed a testbed that provided indoor positioning services based on multi-lateration technique. The empirical results have shown that appropriate indoor propagation model can improve the positioning accuracy, several propagation models have been studied and statistical models have been proposed for a site-specific environment.

With the developing of mobile applications based on indoor location based services (LBS), the higher accuracy of indoor positioning is required. The Location Fingerprinting and Dead Reckoning based hybrid indoor positioning (HIP) algorithm is proposed to calculate the current indoor location more precisely. During the whole process of indoor positioning, WiFi modules and inertial sensors, which are mounted in smart devices, are used to obtain essential sensing data to position. HIP algorithm calculates the initial location through the weighted fingerprinting K nearest neighbor (WFKNN) algorithm using RSSI signals of WiFi firstly, and then starts to update the current location through both the WFKNN algorithm and the dead reckoning technique. The experiments are implemented several smart phones with Android system, the results show the HIP algorithm performs much better than KNN and dead reckoning algorithm on positioning accuracy.

The city is facing the unprecedented pressure with the rapid development and the moving population. Some hidden knowledge can be found to service the social with human trajectory data. In this paper, we define a state-ofthe- art concept on fluctuant locations with PCA method and discover the same attribute of fluctuant locations called event with topic model. In the time slice, locations with the same attribute are called event region. Event regions aim to understand the relationship between spatial-temporal locations in the city and to early-warning analyze for the city planning, construction, intelligent navigation, route planning and location based service. We use GeoLife public data to experiment and verify this paper.

With the rapid development of Web technology and mobile devices, it is necessary to provide efficient models for service recommendation. In connection with the problems of existing systems and LBS (Location Based Service), we propose a model composed of three modules. In social network module, we detect community from the social network, classify users by computing the shortest length and filter users according to the destination. The location awareness module collects the history trace of users with the positioning algorithm based on mobile anchor node. In the module of LBS, we unify the description of user traces using locations obtained from extracting and clustering the key points. After that, we compute the relevance between two locations to realize the location recommendation service. At last, we provide some simulations to verify the validity and enforceability of the model we proposed.

An extreme dynamic rarely seen in any other sector can currently be observed in retailing, resulting in enormous changes to commercial structures. In particular, the digitalization of commerce and the development of mobile Internet are drivers of this trend. In this regard, it becomes clear that smartphone usage and location-based services (LBS) present great opportunities for brick-and-mortar retailing.

The online social network services have been growing rapidly over the past few years, and allows user to share location information by the GPS enabled mobile device. The information about the location can reflect social characteristic and record behaviors tracks of users. For location information has an impact on user behavior analysis of location-based social network, this paper proposed a new matrix factorization for user behavior analysis in location-based social network. The matrix model is based on the information of the user and location. Considering that large data sets and the problem of matrix sparsity would significantly increase the time and space complexity, matrix factorization was introduced to alleviate the effect of data problems, combining with the key information about the user in a social network. In order to evaluate the proposed method, we crawl live data from the Foursquare social network. The experimental results show that the proposed method is effective in solving the problem of matrix sparsity which has large data sets, and improving the accuracy of user behavior analysis.

Location Based Services (LBS) is a kind of real-time service with uncertain factors, and its modeling and validation is essential. In this paper, with the introduction of probability, we propose a Color Probability-TCPN (CP-TCPN) by using the tokens with specific colors as the research objects and redefining several relative parameters. We use CP-TCPN to realize modeling and functional verification of the dynamic services composition for LBS. Simulation result is presented to illustrate the application of CP-TCPN in the modeling and analyzing of the real-time system with uncertain factors.

The popularization of smartphones and advances in location-based technology have led to the creation of many applications and diverse mobile cloud technologies. Augmented reality (AR), which integrates virtual reality with the real world, is one of the mobile service technologies that have been receiving considerable attention in recent years. This study focuses on AR technology, which in conjunction with point of interest (POI) information

Recently, a wide range of dating applications has emerged for users of smart mobile devices. Besides allowing people to socialize with others who share the same interests, these applications use the location services of these devices to provide localized mapping of users. A user is given an approximation of his proximity to other users, making the application more attractive by increasing the chances of local interactions. While many applications provide an obfuscated location of the user, several others prefer to provide quantifiable results.

Unicast geo-routing protocols are reliant on a robust location service protocol to successfully seek the destination vehicle’s location. Furthermore successful V2X communication is reliant on the geo-routing protocol to successfully deliver the packet. In both cases successful packet delivery and robustness of the protocols is paramount, the failure of either renders communication a failure. In order to maximise packet delivery, this paper proposes a framework comprised of a location service, the Urban Vehicular Location Service (UVLS), and a geo-routing scheme, the Infrastructure Enhanced Geo-Routing Protocol (IEGRP), that exploits infrastructure where available to function in completely distributed, partially connected and fully infrastructure based networks. Unlike previous protocols that are typically fully distributed or centralised only, the proposed protocols are designed and evaluated to operate in a heterogeneous vehicular environment, specifically to maximise packet delivery and query resolution.