With large numbers of possible smart objects in Social Internet of Things (SIoT), a recommendation system is of great necessity to help users find smart objects they need. However, traditional recommendation techniques usually exploit user’s rating or feedback information, which are impractical as such kind of user preference information is difficult to collect in SIoT environment. In addition, temporal context plays an important role in smart object recommendation since most users tend to utilize different objects at different time slots in a day, e.g., making coffee at morning and playing game on weekends. In this work, we propose a time-aware smart object recommendation model by jointly considering user’s preference over time and smart object’s social similarity. We firstly learn user’s preference over time from his/her object usage events with a latent probabilistic model. Then, we estimate smart object’s social similarity by embedding their heterogeneous social relationships into a shared lower dimensional space. Finally, we generate the recommendation list with an item-based collaborative filtering. We conduct a comprehensive experimental study based on two real-world datasets, and the experimental results show our method outperforms all baselines significantly in terms of recommendation effectiveness.

To discover the condition of roads, a large number of detection algorithms have been proposed, most of which apply machine learning methods by time and frequency processing in acceleration and velocity data. However, few of them pay attention to the similarity of the data itself when the vehicle passes over the road anomalies. In this article, we propose a method to detect road anomalies by comparing the data windows with various length using Dynamic Time Warping(DTW) method. We propose a model to prove that the maximum acceleration of a vehicle passing through a road anomaly is linear with the height of the road barrier, and it's verified by an experiment. This finding suggests that it is reasonable to divide the window by threshold detection. We also apply a brief random forest filter to roughly distinguish normal windows from anomaly windows using the aforementioned theory, in order to reduce the time consumption. From our study, a system is proposed that utilizes a series of acceleration data to discover where might be anomalies on the road, named as Quick Filter Based Dynamic Time Warping (QFB-DTW). We show that our method performs clearly beyond some existing methods. To support this conclusion, experiments are conducted based on three data sets and the results are statistically analyzed. We expect to lay the first step to some new thoughts to the field of road anomalies detection in subsequent work.

While it is well understood that edge computing can significantly facilitate IoT-related applications by deploying edge servers close to IoT devices, it also faces many challenges with numerous IoT devices connected and interacted. One of the most important issues is how to efficiently deploy edge servers under a certain budget with the explosive growth of data scale and user base. Existing studies for edge server placement fail to consider user’s query preferences since individual users may be interested in events in particular regions and are keen to receive up-to-date data streams that originate in regions of interest. In this article, we present a preference-aware edge server placement approach that offers better workload distribution in terms of both minimizing query latency and balancing the load of edge servers. To achieve this, we formulate edge server placement with multiobjective optimization as a ${p}$ -center problem and design two progressive approaches. We first propose quadratic integer programming (QIP) for small-scale data sets. Since the ${p}$ -center problem is an NP-hard problem, we thus propose a heuristic algorithm named TAKG (TAbu search with $K$ -means and Genetic algorithm) for large-scale data sets. To evaluate the utility of the proposed models, we have conducted a comprehensive evaluation on a large data set that is collected by more than 1900 IoT devices during 30 days. Experimental results indicate our approaches outperform all baselines significantly in terms of both query latency and load balancing.

Road anomaly detection with crowdsourced sensor data has become an increasingly important field of research over the last few years. Traditional ways for road anomaly detection are either thresholdbased detection techniques or feature-based detection techniques. However, road anomaly patterns from crowdsourcing data are often shifted in time and exhibit local distortions/noise, thus existing methods rely on the original sensor data greatly limit the accuracy of road anomaly detection. In this paper, we present a road anomaly detection model by learning scale-invariant features from the differences of small local segments of road anomaly samples. Specifically, the proposed model consists of two phases: 1) Road anomaly segmentation. The phase is designed to roughly extract road anomaly subsequence using piecewise aggregate approximation representation of sensor series data; 2) Road anomaly detection. In this phase, we observe the differences among road anomaly classes are attributed to small local segments, then we learn scale-invariant features from these small local segments for road anomaly detection. To demonstrate the utility of our proposed model, we have performed a comprehensive experimental evaluation on two real-world datasets and one large-scale simulation dataset. Experimental results show our proposed model outperforms all baselines significantly in terms of road anomaly detection.

Indoor magnetic-based positioning has attracted tremendous interests in recent years due to its pervasiveness and independence from extra infrastructure. Existing methods for indoor magnetic-based positioning are either point-based fingerprint matching or sequence-based fingerprint matching using the raw magnetic field strength. However, the magnetometers in smartphones are vulnerable to a few factors such as user’s postures and walking speed, which causes the magnetic field strength corresponding to a location often shift in time or exhibit local distortions, thus greatly limits the positioning performance of existing methods rely on raw magnetic field strength. To this end, we observe the differences among magnetic field strength sequences are mainly attributed to small local segments, and design a new sequence-based fingerprint based on the differences among small local segments of raw MFS sequence to represent raw MFS sequence for indoor positioning. To demonstrate the utility of our proposed sequence-based fingerprint, we have performed a comprehensive experimental evaluation on two datasets, the results show that the proposed approach can significantly improve positioning performance compare with baseline methods.

Trajectory similarity computation is a fundamental problem for many intelligent applications such as trip trajectory mining to find the most popular routes and road anomaly detection. Existing similarity computation methods for vehicle trajectory, such as dynamic time warping (DTW) and Fréchet distance, are dynamic programming problems with a quadratic time complexity in all cases and need to handle local time shifts when computing the distance between trajectories, thus limiting the scalability of these methods. In addition, GPS-based vehicle trajectories usually contain errors, such as noise and outliers, due to poor satellite visibility in urban regions and nonuniform sampling rates. To this end, we propose an embedding-based method for trajectory similarity computation with linear time complexity, which encodes a trajectory as a vector via deep representation learning and learns the similarity between trajectories with an attention-based learning to rank model. We use an interpolation-based approach to reduce noise and outliers by considering vehicle trajectory is physically constrained to the road network. Experiments on a massive vehicle trajectory data set show that the proposed approach outperforms state-of-the-art baselines consistently and significantly.