The development of Indoor Positioning Systems (IPS) is being catalyzed by the growing influence of the Internet of Things (IoT). In the field of indoor positioning, balancing low cost and high precision has always been a challenging task. Ultra-wideband (UWB) technology stands out for high precision but comes with higher costs, while Bluetooth Low Energy (BLE), on the other hand, is cost-effective but faces challenges in signal stability. Therefore, this study explores the integration of BLE and UWB, presenting an innovative approach aimed at enhancing the accuracy of indoor positioning while maintaining cost-effectiveness. The proposed approach leverages BLE trilateration for initial solutions and optimizes coordinates through network adjustment using UWB distance observations between neighboring users, achieving more stable and accurate indoor positioning. The experiments conducted with varying user counts demonstrate that UWB adjustments outperform Bluetooth adjustments and trilateration, significantly improving positioning accuracy by up to 65% of trilateration. The results highlight the potential of this integrated approach to overcome challenges in indoor positioning systems, offering a more robust and precise solution.

Finding a parking space is usually challenging in urban areas. The literature shows that 30% of traffic congestion is caused by searching for parking spaces, which results in unnecessary energy consumption and environmental pollution. With the development of sensor technologies, smart parking guidance systems provide users with a variety of real-time parking space information. However, users cannot know whether the target parking space remains available upon arrival. Moreover, parking resources may be under competition when multiple users target the same open parking space. In this research, we develop a new framework named prediction-based parking allocation (PPA) that provides smart parking services to users. In PPA, we first construct a prediction model of parking occupancy and predict the subsequent parking availabilities. Then, we design a matching-based allocation strategy to assign users to selected parking spaces. To the best of our knowledge, this is the first study that combines occupancy prediction and space allocation simultaneously to address smart parking issues. Finally, we collect a real dataset from the SFPark on-street parking system for performance evaluation. According to experimental results, PPA can effectively increase the parking success rate and reduce costs, fuel consumption, and carbon emissions.

As people get increasingly busy, more and more consumers prefer online shopping that leads to a large number of goods need to be transported. The researches on logistic route planning have attracted extensive attentions. Although a number of routing problems have been proposed, most of them may not be directly applied to this problem because real logistic constraints are not considered such as vehicle capacity, logistic requirements, etc. In this paper, we propose a novel approach named Partition-Routing-Insertion Navigator (PRI-Navigator) to efficiently find high quality logistic routes meeting logistic constraints by considering both of pick-up and delivery requirements. In PRI-Navigator, we design various strategies and algorithms for goods partition, route planning and goods insertion. To the best of our knowledge, this is the first work on logistic rout planning that considers the pick-up and delivery requirements together and keeps high efficiency of route planning, simultaneously. Through extensive experimental evaluations and comparisons on real logistic datasets, PRI-Navigator were shown to deliver excellent performance.

With the advances of mobile communication techniques in recent years, numerous kinds of Location-Based Services (LBSs) have been developed and one popular application of LBSs is trip recommendation. Although there exist already a number of studies on this topic in literatures, most of them focused on combining a set of point-of-interests (POIs, or say attractions) as a trip based on user-specific constraints. In another way, some few works discussed making recommendation in terms of travel packages, which have the benefits of lower cost and higher convenience. However, no prior work explores to integrate attractions and travel packages simultaneously for trip recommendation. In fact, such a hybrid-style recommender can provide higher benefits for users although there exist critical challenges here like the efficiency issue in such kind of real-time applications. In this paper, we propose a novel framework named Package-Attraction-based Trip Recommender (PATR) to efficiently recommend the personalized trips satisfying multiple constraints by effectively combining attractions and packages. In PATR, a Score Inference Model is proposed to infer the scores of attractions and packages by taking user-based preference and temporal-based properties into account. Then, the Hybrid Trip-Mine algorithm is proposed to efficiently discover the optimal trip which satisfies the multiple user-specific constraints with both of attractions and packages considered simultaneously. Furthermore, we propose two pruning strategies based on Hybrid Trip-Mine, named Score Estimation (SE) and Score Bound Tightening (SBT), to further improve the execution efficiency and memory utilization. To the best of our knowledge, this is the first work on travel recommendation that considers attractions and packages simultaneously. Through extensive experimental evaluations, our proposed approaches were shown to deliver excellent performance.

In recent years, research into the mining of user check-in behavior for point-of-interest (POI) recommendations has attracted a lot of attention. Existing studies on this topic mainly treat such recommendations in a traditional manner—that is, they treat POIs as items and check-ins as ratings. However, users usually visit a place for reasons other than to simply say that they have visited. In this article, we propose an approach referred to as Urban POI-Walk (UPOI-Walk), which takes into account a user's social-triggered intentions (SI), preference-triggered intentions (PreI), and popularity-triggered intentions (PopI), to estimate the probability of a user checking-in to a POI. The core idea of UPOI-Walk involves building a HITS-based random walk on the normalized check-in network, thus supporting the prediction of POI properties related to each user's preferences. To achieve this goal, we define several user--POI graphs to capture the key properties of the check-in behavior motivated by user intentions. In our UPOI-Walk approach, we propose a new kind of random walk model—Dynamic HITS-based Random Walk—which comprehensively considers the relevance between POIs and users from different aspects. On the basis of similitude, we make an online recommendation as to the POI the user intends to visit. To the best of our knowledge, this is the first work on urban POI recommendations that considers user check-in behavior motivated by SI, PreI, and PopI in location-based social network data. Through comprehensive experimental evaluations on two real datasets, the proposed UPOI-Walk is shown to deliver excellent performance.

In recent years, with the rapid developments of wireless technologies, researches on Location-Based Services (LBSs) have attracted extensive attentions and one active topic among them is constraint-based route planning on a Point-Of-Interest (POI) network. Although a number of studies on this topic have been proposed in literatures, most of them primarily consider the geographic properties of the POIs in planning a route. In fact, the motivation of a user to visit a POI is frequently due to that the POI can provide some services meeting the user's needs. Hence, user requests should be considered in route planning, especially in an urban area where a POI may provide various kinds of services. Besides, the efficiency of route planning is critical in such kind of real-time LBS applications. In this paper, we address a novel route planning problem named Multi-Requests Route Planning (MRRP) and propose four approaches, namely kNN-MS, kMD-MS, EMB and kRA-MS to efficiently plan a time-saving route based on the user-specific requests. Furthermore, we propose two refinement mechanisms, three pruning strategies and two caching techniques to further enhance the route quality and planning efficiency for MRRP, respectively. To the best of our knowledge, this is the first work on route planning that considers multiple services provided by a POI and multiple requests specified by a user, simultaneously. Through extensive experimental evaluations, our approaches were shown to deliver excellent performance.

In recent years, researches on travel recommendation have attracted extensive attentions due to the wide applications. Among them, one of the active topics is constraint-based trip recommendation for meeting user's personal requirements. Although a number of studies on this topic have been proposed in literatures, most of them only regard the user-specific constraints as some filtering conditions for planning the trip. In fact, immersing the constraints into travel recommendation systems to provide a personalized trip is desired for users. Furthermore, time complexity of trip planning from a set of attractions is sensitive to the scalability of travel regions. Hence, how to reduce the computational cost by parallel cloud computing techniques is also a critical issue. In this paper, we propose a novel framework named Personalized Trip Recommendation (PTR) to efficiently recommend the personalized trips meeting multiple constraints of users by mining user's check-in behaviors. In PTR, a mining-based module is first proposed to estimate the scores of attractions by considering both of user-based preferences and temporal-based properties. Then, a trip planning algorithm named Parallel Trip-Mine+ is proposed to efficiently plan the trip that satisfies multiple user-specific constraints. To our best knowledge, this is the first work on travel recommendation that considers the issues of multiple constraints, social relationship, temporal property and parallel computing simultaneously. Through comprehensive experimental evaluations on a real check-in dataset obtained from Gowalla, PTR is shown to deliver excellent performance.

Due to a wide range of potential applications, research on mobile commerce has received a lot of interests from both of the industry and academia. Among them, one of the active topic areas is the mining and prediction of users' mobile commerce behaviors such as their movements and purchase transactions. In this paper, we propose a novel framework, called Mobile Commerce Explorer (MCE), for mining and prediction of mobile users' movements and purchase transactions under the context of mobile commerce. The MCE framework consists of three major components: 1) Similarity Inference Model (SIM) for measuring the similarities among stores and items, which are two basic mobile commerce entities considered in this paper; 2) Personal Mobile Commerce Pattern Mine (PMCP-Mine) algorithm for efficient discovery of mobile users' Personal Mobile Commerce Patterns (PMCPs); and 3) Mobile Commerce Behavior Predictor (MCBP) for prediction of possible mobile user behaviors. To our best knowledge, this is the first work that facilitates mining and prediction of mobile users' commerce behaviors in order to recommend stores and items previously unknown to a user. We perform an extensive experimental evaluation by simulation and show that our proposals produce excellent results.

With the rapid development of wireless telecommunication technologies, a number of studies have been done on the Location-Based Services (LBSs) due to wide applications. Among them, one of the active topics is travel recommendation. Most of previous studies focused on recommendations of attractions or trips based on the user's location. However, such recommendation results may not satisfy the travel time constraints of users. Besides, the efficiency of trip planning is sensitive to the scalability of travel regions. In this paper, we propose a novel data mining-based approach, namely Trip-Mine, to efficiently find the optimal trip which satisfies the user's travel time constraint based on the user's location. Furthermore, we propose three optimization mechanisms based on Trip-Mine to further enhance the mining efficiency and memory storage requirement for optimal trip finding. To the best of our knowledge, this is the first work that takes efficient trip planning and travel time constraints into account simultaneously. Finally, we performed extensive experimental evaluations and show that our proposals deliver excellent results.

Researches on Location-Based Service (LBS) have been emerging in recent years due to a wide range of potential applications. One of the active topics is the mining and prediction of mobile movements and associated transactions. Most of existing studies focus on discovering mobile patterns from the whole logs. However, this kind of patterns may not be precise enough for predictions since the differentiated mobile behaviors among users and temporal periods are not considered. In this paper, we propose a novel algorithm, namely, Cluster-based Temporal Mobile Sequential Pattern Mine (CTMSP-Mine), to discover the Cluster-based Temporal Mobile Sequential Patterns (CTMSPs). Moreover, a prediction strategy is proposed to predict the subsequent mobile behaviors. In CTMSP-Mine, user clusters are constructed by a novel algorithm named Cluster-Object-based Smart Cluster Affinity Search Technique (CO-Smart-CAST) and similarities between users are evaluated by the proposed measure, Location-Based Service Alignment (LBS-Alignment). Meanwhile, a time segmentation approach is presented to find segmenting time intervals where similar mobile characteristics exist. To our best knowledge, this is the first work on mining and prediction of mobile behaviors with considerations of user relations and temporal property simultaneously. Through experimental evaluation under various simulated conditions, the proposed methods are shown to deliver excellent performance.

In recent years, a number of studies have been done on Location-Based Service (LBS) due to their wide range of potential applications. In this paper, we propose a novel data mining algorithm named Cluster-based Mobile Sequential Pattern Mine (CMSP-Mine) for efficiently discovering the Cluster-based Mobile Sequential Patterns (CMSPs) of users in LBS environments. In CMSP-Mine, we first propose a transaction similarity measurement named Location-Based Service Alignment (LBS-Alignment) to evaluate the similarity between two mobile transaction sequences. Then, we propose a transaction clustering algorithm named Cluster-Object based Smart Cluster Affinity Search Technique (CO-Smart-CAST) to form a user cluster model of the mobile transactions based on LBS-Alignment. Furthermore, we proposed the novel prediction strategy that utilizes the discovered CMSPs to precisely predict the next movement of mobile users. To our best knowledge, this is the first work on mining the mobile sequential patterns associated with moving path and user clusters in LBS environments. Finally, through a series of experiments, our proposed methods were shown to deliver excellent performance in terms of efficiency, accuracy and applicability under various system conditions.