Location-aware Mobile Services Research Articles

Semantic Crowdsourcing of Soundscapes Heritage: A Mojo Model for Data-Driven Storytelling

The current paper focuses on the development of an enhanced Mobile Journalism (MoJo) model for soundscape heritage crowdsourcing, data-driven storytelling, and management in the era of big data and the semantic web. Soundscapes and environmental sound semantics have a great impact on cultural heritage, also affecting the quality of human life, from multiple perspectives. In this view, context- and location-aware mobile services can be combined with state-of-the-art machine and deep learning approaches to offer multilevel semantic analysis monitoring of sound-related heritage. The targeted utilities can offer new insights toward sustainable growth of both urban and rural areas. Much emphasis is also put on the multimodal preservation and auralization of special soundscape areas and open ancient theaters with remarkable acoustic behavior, representing important cultural artifacts. For this purpose, a pervasive computing architecture is deployed and investigated, utilizing both client- and cloud-wise semantic analysis services, to implement and evaluate the envisioned MoJo methodology. Elaborating on previous/baseline MoJo tools, research hypotheses and questions are stated and put to test as part of the human-centered application design and development process. In this setting, primary algorithmic backend services on sound semantics are implemented and thoroughly validated, providing a convincing proof of concept of the proposed model.

From Reputation Perspective: A Hybrid Matrix Factorization for QoS Prediction in Location-Aware Mobile Service Recommendation System

With the great development of mobile services, the Quality of Services (QoS) becomes an essential factor to meet end users’ personalized requirement on the nonfunctional performance of mobile services. However, most of the QoS values in real cases are unattainable because a service user would only invoke some specific mobile services. Therefore, how to predict the missing QoS values and recommend high-quality services to end users becomes a significant challenge in mobile service recommendation research. Previous QoS prediction researches demonstrate that the nonfunctional performance of mobile services is closely related to users’ location information. However, most location-aware QoS prediction methods ignore the premise that the obtainable QoS values observed by different users in same location region would probably be untrustworthy, which will lead to inaccurate and unreliable prediction results. To make credible location-aware QoS prediction, we propose a hybrid matrix factorization method integrated location and reputation information (LRMF) to predict the unattainable QoS values. Our approach firstly cluster users into different locational region based on their geographical distribution, and then we compute users’ reputation to identify untrustworthy users in every locational region. Finally, the unknown QoS values can be predicted by integrating locational cluster information and users’ reputation into a hybrid matrix factorization model. Comprehensive experiments are conducted on a public QoS dataset which contains sufficient real-world service invocation records. The evaluation results indicate that our LRMF method can effectively reduce the impact of unreliable users on QoS prediction and make credible mobile service recommendation.

Integrated location aware medical care services

The advent of Mobile technologies has defined ways for providing better flexibility in communication, collaboration and information sharing through wireless medium using portable mobile devices. Technologies allow users to have anytime, anywhere access to information and applications. Specifically, location-aware mobile service has become the most essential component in dealing with mobile based applications. Mobile health care is one such promising area where mobile technologies are highly useful to provide services to the affected people. Worldwide the investment towards mobile health care applications is tremendously increasing. The significance of SMS, sensors, agent’s technologies is widely seen in most of the health care projects. This paper focuses on a system called LAMECS (Location Aware MEdical Care Services) which helps the needy people at the right time. The major focus of LAMECS is to provide timely facilities to people in the mobile based networked environment. Especially, the key objectives are to reduce the time taken to inform the service providers about the situation and to initiate the health care services at an early stage.

Location-aware Mobile Services for a Smart City: Desing, Implementation and Deployment

A smart city is a high-performance urban context, where citizens are more aware of, and more integrated into the city life, thanks to an intelligent city information system. In this paper we design, implement and deploy a smart application that retrieves and conveys to the user relevant information on the user's surroundings. This case study application let us discuss the challenges involved in creating a location-aware mobile service based on live information coming from the city IT infrastructure. The service, that is currently being deployed in the Italian city of Cesena, has been designed with the goal of being a general model for future applications. In particular, we discuss location-aware and mobile development, cloud and cluster based geographical data storage, and spatial data computation. For each of these topics we provide implementation and deployment solutions based on currently available technology. In particular we propose an architecture based on a complex On-Line Transaction Processing (OLTP) infrastructure. Furthermore, this paper represents the first comprehensive, scientific study on the subject.

Energy-Efficient Data Dissemination Schemes for Nearest Neighbor Query Processing

Indexing provides for selective tuning but suffers from the drawback that, in order to conserve battery power, the client has to wait for and tune to the index segment. In location-aware mobile services (LAMSs), it is important to reduce the query response time since a late query response may contain out-of-date information. In this paper, we present a broadcast-based spatial query processing scheme designed to support nearest neighbor (NN) query processing. With the proposed schemes, broadcast data items are sorted sequentially based on their locations and the clients can selectively tune to the desired data item without the need for an index segment. For the purpose of selective tuning, we present the exponential sequence scheme (ESS) and cluster-based Fibonacci sequence scheme (CFS) schemes. The ESS and CFS schemes attempt to conserve battery power. The performance of our schemes is investigated in relation to various environmental variables such as the distributions of data objects, the average speed of the clients, and the size of the service area. The resulting latency and tuning time are close to the optimum values, as our analysis and simulation results indicate

Efficient Processing of Continual Range Queries for Location-Aware Mobile Services

Efficient processing of continual range queries is important in providing location-aware mobile services. In this paper, we study a new main memory-based approach to indexing continual range queries to support location-aware mobile services. The query index is used to quickly answer the following question continually: "Which moving objects are currently located inside the boundaries of individual queries?" We present a covering tile-based (COVET) query index. A set of virtual tiles are predefined, each with a unique ID. One or more of the virtual tiles are used to strictly cover the region defined by an individual range query. The query ID is inserted into the ID lists associated with the covering tiles. These covering tiles touch each other only at the edges. A COVET index maintains a mapping between a covering tile and all the queries that contain that tile. For any object position, search is conducted indirectly via the covering tiles. More importantly, a COVET-based query index allows query evaluation to take advantage of incremental changes in object locations. Computation can be saved for those objects that have not moved outside the boundaries of covering tiles. Simulations are conducted to evaluate the effectiveness of the COVET index and compare virtual tiles of different shapes and sizes.

Adaptive data dissemination schemes for location-aware mobile services

Broadcasting is the natural method of propagating information in wireless links, which guarantees scalability in the case of bulk data transfers. It is particularly attractive for resource limited mobile clients in asymmetric communications. To facilitate power saving via wireless data broadcast, index information is typically broadcast along with the data. By first accessing the broadcast index, the mobile client is able to predict the arrival time of the desired data. However, it suffers from the drawback that the client has to wait and tune for an index segment, in order to conserve battery power consumption. In location-aware mobile services (LAMSs), it is important to reduce the query response time, since a late query response may contain out-of-date information. This paper proposes a new broadcast-based spatial query processing method, called BBS designed to support NN query processing. In the BBS, broadcasted data objects are sorted sequentially based on their locations, and the server broadcasts the location dependent data along with an index segment. In this method, since the data objects broadcasted by the server are sequentially ordered based on their location, it is not necessary for the client to wait for an index segment, if it has already identified the desired data items before the associated index segment has arrived. The performance of this scheme is investigated in relation to various environmental variables, such as the distributions of the data objects, the average speed of the clients and the size of the service area.

Performance Analysis of Location-Aware Mobile Service Proxies for Reducing Network Cost in Personal Communication Systems

We propose and analyze mobile service management schemes based on location-aware proxies with the objective to reduce the network signaling and communication cost in future personal communication systems (PCS). Under these schemes, a mobile user uses personal proxies as intelligent client-side agents to communication with services engaged by the mobile user. A personal proxy cooperates with the underlying location management system so that it is location-aware and can optimally decide when and how often it should move with the roaming user. We show that, when given a set of model parameters characterizing the network and workload conditions, there exists an optimal proxy service area size for service handoffs such that the overall network signaling and communication cost for servicing location and service operations is minimized. We demonstrate via Petri net models that our proposed proxy-based mobile service management schemes outperform non-proxy-based schemes over a wide range of identified conditions. Further, when the mobile user is concurrently engaged in multiple services, the per-service proxy scheme that uses a separate proxy for each service outperforms the aggregate proxy scheme that uses a single proxy to interface with multiple services taking their aggregate service characteristics into consideration.

User needs for location-aware mobile services

Mobile contexts of use vary a lot, and may even be continuously changing during use. The context is much more than location, but its other elements are still difficult to identify or measure. Location information is becoming an integral part of different mobile devices. Current mobile services can be enhanced with location-aware features, thus providing the user with a smooth transition towards context-aware services. Potential application fields can be found in areas such as travel information, shopping, entertainment, event information and different mobile professions. This paper studies location-aware mobile services from the user's point of view. The paper draws conclusions about key issues related to user needs, based on user interviews, laboratory and field evaluations with users, and expert evaluations of location-aware services. The user needs are presented under five main themes: topical and comprehensive contents, smooth user interaction, personal and user-generated contents, seamless service entities and privacy issues.