In recent years, radio frequency identification (RFID) technology has been applied in various fields to efficiently identify objects. Considering that identification is usually performed continuously, the latest RFID approaches use previous identification results for subsequent identifications and can identify two tags per unit time. However, these approaches have not utilized bit-level collision information in ID transmissions, which can facilitate the identification. Moreover, the assumption of reliable communication is not always valid in real-world applications. In this article, we propose a new approach called the multiple resolution bit tracking (MRB) to further improve the identification performance. This approach dynamically computes a tag set that can be unambiguously identified irrespective of any missing subset. These tags are requested to transmit their IDs in the same timeslot. Strategies to handle unreliable communication have also been proposed and optimized for MRB. We performed extensive simulations to validate the performance of our proposed approach. The results show that MRB can achieve 3.7 tags per unit time, which is 1.85 times the number achieved using the existing approaches. Furthermore, MRB handles unreliable communications well and achieves a small given tag missing rate.

Large-scale urban pipeline systems (LSUPSs) are complex pipeline networks of flows (e.g., water, oil, or gas). Flow capacity intelligent monitoring, e.g., automatically monitor the sum of flows in an LSUPS, is an important task in smart cities. Recently, mobile sensors and static receiver nodes are used to perform the task. Mobile sensors are released into the network at selected entrances to collect data, and upload the data to receiver nodes deployed at selected locations of the network for further analysis. Due to cost constraints, the numbers of mobile sensors and receiver nodes are limited, which cause the problem that some pipelines may not be monitored. However, applications normally require that some Zones of Interest (ZoIs) in the network have to be monitored. Therefore, how to select optimal entrances and locations for given numbers of mobile sensors and receiver nodes, so that the capacity of monitored flow is maximized within a given time under the constraint that all ZoIs are also monitored with expected probabilities, is a challenging problem. First, we prove the problem is NP-complete. Then, we design two algorithms based on submodular set function optimization to solve it. The first algorithm can obtain an approximate optimal solution with high time complexity, while the second algorithm can obtain a suboptimal solution with much lower time complexity. Finally, we analyze time complexity and approximate ratio of the two algorithms. Theoretical analyses and simulation results show that the proposed algorithms outperform the state-of-the-art algorithms.

Automatic driving services have large volume, location-aware, and time-changing contents, which are suitable to be cached by the edge. However, the traffic on the edge will be extremely high especially in the area with high vehicle density, if the vehicles directly access the contents from the edge as they demand. A hybrid data dissemination model with both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) disseminations has been proposed to reduce the traffic on the edge, in which the edge (infrastructure) selectively injects data to the vehicles and leverages the vehicle network to disseminate the data. In this article, we study the hybrid data dissemination problem, i.e., to optimally determine when and which vehicle the data are injected into, and whether the vehicle acquires the demanded data directly from the edge or from nearby neighbors, with the aim of minimizing the traffic cost on the edge and meeting the deadlines of acquiring the data. Existing approach prioritizes the selection of V2I disseminations at first and then explores the V2V disseminations which have no conflict with the V2I disseminations. This approach cannot fully take advantage of V2V to reduce the traffic cost on the edge. We propose a new data dissemination algorithm, named the offline algorithm for hybrid data dissemination (OFDD), which seeks the most beneficial V2V broadcasts with priority, and then choose feasible V2I disseminations. Based on OFDD, we develop both the snapshot and prediction-based online algorithms. We follow with extensive simulations to validate the proposed algorithms. The results show that our algorithms significantly outperform the state-of-the-art approaches in terms of data acquisition rate and traffic cost.

In this article, with the rapid development of industrial Internet of Things, a large number of embedded devices, such as sensors and tag readers, have been widely deployed for gathering and sending data. These devices are commonly unreliable and vulnerable to many threats, because they are located in unattended areas which are vulnerable to device capture attacks. Such environments can be regarded as white-box attack contexts, in which the adversary has total visibility and full control of the implementations. White-box cryptography (WBC) aims to protect implementations of symmetric encryption algorithms in white-box attack contexts. Unfortunately, existing WBC schemes are vulnerable to various attacks, and most of them are insufficiently secure in strict white-box attack contexts. Based on the investigation of existing designs and the corresponding cryptanalysis, we propose a novel design approach for securing WBC schemes, which is named state-dependent selectable random substitutions (SDSRS). It uses SDSRSs to defeat various related white-box cryptanalytic approaches. With special considerations for IIoT systems, such as high performance for supporting real-time applications and small block size for fitting industrial protocols, a concrete WBC scheme designed with the proposed approach has been provided. Our theoretical analysis shows that the proposed scheme is secure. Additionally, experimental results indicate that the scheme performs well in practice, and it is significantly efficient in time and energy consumptions compared with existing secure white-box cryptographic schemes.

This paper studies the problem of learning latent representations or embeddings for users in social networks, by leveraging relationship semantics associated with each link. User embeddings are low-dimensional vector-space representations designed to preserve structural proximity indicated by the pairwise relationships. In social networks, the closeness (or proximity) between pairs of users is very different w.r.t. multiple social relationships and thus cannot be represented accurately using a single embedding space. Furthermore, social networks pose a unique challenge of relationship label sparsity that precludes the application of knowledge-graph embedding techniques.In this paper, we associate each observed link with multiple relationship types through relationship weights and learn projection matrices for each relationship type to model the social distance (or proximity) between users specific to each relationship. We propose a novel two-step mutual enhancement framework to iteratively (a) learn user embeddings preserving relationship-specific proximity, and (b) link-relationship weights capturing the role of each link in multiple relationship types. The first step learns user embeddings optimizing relationship-specific proximity, while fixing the relationship weights (or roles) for each link. In the second step, the user embeddings and corresponding projection matrices are assumed to be fixed, while the link-relationship weights are learned. We demonstrate that the relationship-aware user embeddings learned through this mutual enhancement framework, are more effective in representing the users and outperform representative baseline techniques in multi-label classification and relationship prediction tasks.

In recent years, radio frequency identification (RFID) technology has been applied in various fields to identify objects efficiently. Anti-collision protocols are important for RFID tag identifications because it can overcome the problem of unsuccessful identification caused by simultaneous transmission of IDs from multiple tags. Considering that identification is usually performed multiple times, the latest anti-collision approach uses the previous identification results for later identifications, and can identify two tags per unit time. Existing anti-collision protocols ignore bit information, causing low performance problems. In this study, we propose a new approach called multiple resolution bit tracking protocol (MRB) to improve this performance further. This approach dynamically computes a tag set that can be unambiguously identified irrespective of any missing sub-set. The tags in the tag set are requested to transmit their IDs in the same time slot and terminate their identifications after proper processing. We perform extensive simulations to validate the performance of our proposed approach. The results show that MRB can achieve 3.7 tags per unit time, which is 1.85 times the number achieved using the existing approaches.

Texting-while-driving (T&D) is one of the top dangerous behaviors for drivers. Many interesting systems and mobile phone applications have been designed to help to detect or combat T&D. However, for a T&D detection system to be practical, a key property is its capability to distinguish driver's mobile phone from passengers'. Existing solutions to this problem generally rely on the user's manual input, or utilize specific localization devices to determine whether a mobile phone is at the driver's location. In this paper, we propose a method which is able to detect T&D automatically without using any extra devices. The idea is very simple: when a user is composing messages, the smartphone embedded sensors (i.e., gyroscopes, accelerometers, and GPS) collect the associated information such as touchstrokes, holding orientation and vehicle speed. This information will then be analyzed to see whether there exists some specific T&D patterns. Extensive experiments have been conducted by different persons and in different driving scenarios. The results show that our approach can achieve a good detection accuracy with low false positive rate. Besides being infrastructurefree and with high accuracy, the method does not access the content of messages and therefore is privacy-preserving.

Unknown tag identification is an important issue in large-scale RFID systems especially in automatic inventory control and asset tracking. Unknown tags refer to the tags, which have entered into the surveillance area, but not been identified by the reader yet. Many protocols have been proposed for unknown tag identification. However, they still suffer from a couple of drawbacks. Firstly, most of them leverage the classical EDFSA protocol in the identification phase, which is considerably time-consuming. Secondly, they do not filter known tags out so that known tags interfered with the unknown tag ID transmission process, especially when the density of known tags is high. To this end, we propose TIP, which is a time-efficient identification protocol for unknown RFID tags using Bloom filter. To avoid the interference of known tags to unknown tags, we employ the bloom filter to deactivate known tags. To make TIP protocol be time-efficient, we introduce an indicator vector for unknown tags so that each unknown tag transmits its ID only once during the holistic identification process. Extensive experimental results demonstrate that the proposed protocol achieves about 12% up to 74% time reduction during the identification process when the density of unknown tags ranges from 1% to 67%, compared with the existing protocols.

Traffic lights in urban area can significantly influence the efficiency and effectiveness of transportation. The real-time scheduling information of traffic lights is fundamentally important for many intelligent transportation applications, such as shortest-time navigation and green driving advisory. However, existing traffic light scheduling identification systems either entail dedicated infrastructures or depend on specialized traffic traces, which hinders the popularity and real world deployment. Differently, we propose to identify real-time traffic light scheduling by analyzing taxi traces that are widely accessible from taxi companies. The key idea is to exploit the periodicity in traffic patterns, which is directly affected by traffic lights. We also develop advanced algorithms to identify red/green lights duration and signal change time. We evaluate our solution using over one billion taxi records from Shenzhen, China. The evaluation results validate the effectiveness of our system.

A software-defined network empowers the creation of a flexible network architecture by abstracting flow control from individual devices to the network level. In this paper, we address the challenges in applying SDN to develop high- performance vehicular networks. We present SDVN, a new SDN based vehicular network architecture. It organizes the topology of the vehicular networks and utilizes vehicle trajectory prediction to mitigate the overhead of the SDN control and data plane communication. Moreover, we propose a multicast protocol over SDVN, as multicast is the foundation of many vehicular network applications. The protocol exploits the network topology information provided by SDVN to make far more efficient multicast scheduling decision. The multicast scheduling problem is formulated to minimize the communication cost with bounded delay constraint. A polynomial time approximation algorithm is proposed. We conduct extensive experiments using traffic traces. The evaluation shows that the SDVN based multicast protocol outperforms existing decentralized approaches.

With the urban development and enlargement, various regions such as residential zones and administrative districts now appear as parts of cities. People exhibit different mobility patterns in each region, which is closely relevant to region-wide functions. In this article, we propose a scheme to discover region-wide functions using large-scale Shanghai taxicab trajectories that capture enormous traces for more than 13,000 taxicabs over a period of about 3 years. We investigate these taxicab trajectories and conduct an extensive preliminary study. Then, we divide the city into disjointed regions using Voronoi decomposition. By incorporating people's pick-up and drop-off information, we refine the Voronoi partitioning results to identify region-wide functional areas. Finally, we study people's movement frequency on weekdays and weekends for every kind of urban functional regions. We also look into human mobility within or across the identified urban functional regions. Experimental results show that human movement is bounded with the function of urban regions, and more than 90% of people visit neighboring (less than 20km travel distance) functional regions with high probability.

The potential of crowdsourcing for complex problem solving has been revealed by smartphones. Nowadays, vehicles have also been increasingly adopted as participants in crowd-sourcing applications. Different from smartphones, vehicles have the distinct advantage of predictable mobility, which brings new insight into improving the crowdsourcing quality. Unfortunately, utilizing the predictable mobility in participant recruitment poses a new challenge of considering not only current location but also the future trajectories of participants. Therefore, existing participant recruitment algorithms that only use the current location may not perform well. In this paper, based on the predicted trajectory, we present a new participant recruitment strategy for vehicle-based crowdsourcing. This strategy guarantees that the system can perform well using the currently recruited participants for a period of time in the future. The participant recruitment problem is proven to be NP-complete, and we propose two algorithms, a greedy approximation and a genetic algorithm, to find the solution for different application scenarios. The performance of our algorithms is demonstrated with traffic trace dataset. The results show that our algorithms outperform some existing approaches in terms of the crowdsourcing quality.

Due to the low cost and ease of deployment, wireless sensor networks (WSNs) are emerging as sensing paradigms that the structural engineering field has begun to consider as substitutes for traditional tethered structural health monitoring (SHM) systems. Different from other applications of WSNs such as environmental monitoring, SHM applications are much more data intensive and it is not feasible to stream the raw data back to the server due to the severe bandwidth and energy limitations of low-power sensor networks. In-network processing is a promising approach to address this problem but designing distributed versions for the sophisticated SHM algorithms is much more challenging because SHM algorithms are computationally intensive, and involve data-level collaboration of multiple sensors. In this paper, we select a classical SHM algorithm: the eigen-system realization algorithm (ERA), and propose a few distributed ERAs suitable for WSNs. In particular, we first design a method to incrementally calculate the ERA and then propose three schemes upon which the incremental ERA can be carried out along an Hamiltonian path, along a path in the minimum connected dominating set (MCDS) and along the shortest path tree (SPT). The efficacy of these schemes are demonstrated and compared through both simulation experiment. We believe the proposed schemes can also serve as a guideline when applying WSNs for other applications like SHM which are also data-intensive and involve sophisticated signal processing of collected information.

Vehicles can provide useful data to many urban computing applications. This paper addresses the issue of collecting data from multiple vehicles to a roadside base station using VANET. The impacts of real-time traffic condition have not been widely discussed in literature. In this paper, we study the data collection problem under different traffic conditions. The objective is to minimize the network communication overhead while satisfying the data collection time constraint. We formulate the problem as an scheduling optimization problem. A dynamic programming based solution and a genetic algorithm based solution are developed to solve the problem for different application scenarios. The solution can adaptively choose to carry or forward data based on current traffic information. Evaluation shows that the proposed solution outperforms some existing ones in terms of effectiveness and efficiency.

Wireless Sensor Networks (WSNs) are often deployed in hostile environments and an adversary can potentially capture sensor nodes. This is a typical white-box attack context, i.e., the adversary may have total visibility of the implementation of the build-in cryptosystem and full control over its execution platform - the sensor nodes. Existing encryption algorithms for white-box attack contexts require large memory footprint and hence are not applicable for wireless sensor networks scenarios. As a countermeasure against the threat in this context, a lightweight secure implementation of the symmetric encryption algorithm SMS4 is proposed. The basic idea of our solution is to merge several steps of the round function of SMS4 into table lookups, blended by randomly generated mixing bijections. Its security and efficiency are analyzed. Evaluation shows our solution satisfies the requirement of sensor nodes in terms of limited memory size and low computational costs.