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Vehicular social networks (VSNs) have attracted the research community due to its diverse applications ranging from safety to entertainment. Social vehicles standing for private cars and floating cars standing for taxis are two important components of VSN. However, the lack of social vehicles data causes some factors are neglected including social aspects and macroscopic features, which blocks researching social attributes of vehicles in VSN. Generating a realistic mobility dataset for VSN validation has been a great challenge. In this paper, we present the detailed procedure to generate social vehicular mobility dataset from the view of floating car data, which has the advantage of wide universality. First, through the deep analysis and modeling of the dataset of floating cars and combining with the official data, we predict the origin–destination (OD) matrix of social vehicles with the gravity model, and then calibrate the OD matrix with the average growth factor method. Second, we construct network description after editing the road network. Third, we use simulation of urban mobility to reproduce the scenario in view of microsimulation by generating the mobility dataset of social vehicles based on floating car data and urban functional areas. At last, we prove the effectiveness of our method by comparing with real traffic situation in Beijing. The generated mobility model may not accurately represent the mobility of social vehicles in few spots, such as train station or airport, however, exploiting figures and facts of transportation in the city have been considered in the study to calibrate the model up to maximum possible realization. [ABSTRACT FROM PUBLISHER]

Reliable social connectivity and transmission of data for popular nodes is vital in multihop ad-hoc social networks (ASNETs). In this networking paradigm, transmission unreliability could be caused by multiple social applications running on a single node. This leads to contentions among nodes and connection paths. In addition, congestion can be the result of multiple senders transmitting data to a single receiver and every sender waiting for a positive acknowledgment to move on. Therefore, the traditional Transmission Control Protocol (TCP) performs poorly in ASNETs because the available bandwidth is shared among nodes using round-trip time, and the acknowledgment is provided individually to every data packet. To solve these issues, we propose a technique called Overhead Control with Reliable Transmission of Popular Packets in Ad-Hoc Social Networks (RTPS), which improves transmission reliability by assigning bandwidth to users based on their popularity levels: Extra bandwidth is assigned to the nodes with higher popularity, and their acknowledgments are sent with higher priority. In addition, RTPS further reduces contentions and packet losses by delaying acknowledgment packet transmissions. Our detailed investigations demonstrate the excellent performance of RTPS in terms of throughput latency and overhead with different hop distances and different numbers of concurrent TCP flows. [ABSTRACT FROM PUBLISHER]

Socially aware networking (SAN) provides a promising paradigm for routing and forwarding data packets by exploiting social properties of involved entities, for example, in vehicular social networks (VSNs). The mobility of individuals often features some regularity in location and time, particularly in vehicular environments. However, individuals' learning capability and awareness to the dynamic environments have not been well explored in the literature. Inspired by the artificial bee colony, we present BEEINFO, which is a set of interest-based forwarding schemes for SAN, which consists of BEEINFO-D, BEEINFO-S, and BEEINFO-D&S. BEEINFO adopts the food foraging behavior of bees to detect the environment information and to optimize the forwarding procedure. BEEINFO takes advantage of individuals' perceiving and learning capability to gather information of density and social ties. BEEINFO-D, BEEINFO-S, and BEEINFO-D&S are distinct from each other according to different utilization of density and social ties. This enhances the adaptability to dynamic environments. Additionally, BEEINFO performs message scheduling and buffer management to improve the forwarding performance. Extensive simulations have been conducted to compare BEEINFO with two representative protocols, i.e., PRoPHET and Epidemic. The results illustrate that BEEINFO outperforms PRoPHET and Epidemic with higher message delivery ratio, less overhead, and fewer hop counts. [ABSTRACT FROM PUBLISHER]

Socially aware networking (SAN) is a new communication paradigm, in which the social characteristics of mobile nodes are exploited to improve the performance of data distribution. In SAN, mobile carriers may exhibit selfish behaviors and refuse to relay messages for others for various reasons, such as limited resources (e.g., buffer, energy, and bandwidth) or social relationships. Several incentive schemes have recently been investigated to stimulate selfish users for cooperation in data forwarding. However, a majority of the existing methods have not fully studied nodes' social relationships in their selfish behaviors. In this paper, we propose a copy adjustable incentive scheme (CAIS), which adopts the virtual credit concept to stimulate selfish nodes to cooperate in data forwarding. In CAIS, we consider a network in which the nodes are divided into certain communities based on their social relationships. Then, we apply two types of credits, i.e., social credit and nonsocial credit, to reward the nodes when they relay data for other nodes inside their community or outsiders, respectively. Based on our mechanism, the number of messages a node can replicate to other nodes is adjusted according to its cooperation level and earned credits. To further improve the performance of CAIS, a single-copy data replication policy is employed, which manages the credit distribution of each node according to its available resources. The results of our extensive experiments using both synthetic and trace-driven simulations illustrate that CAIS copes well with node selfishness in community-based networks and outperforms other benchmark protocols with high data delivery ratio, low communication overhead, and short data delivery latency. [ABSTRACT FROM PUBLISHER]

As an important branch of the Internet of Things (IoT), the Internet of Vehicles (IoV) has attracted extensive attention in the research field. To deeply study the IoV and build a vehicle spatiotemporal interaction network, it is necessary to use the trajectory data of private cars. However, due to privacy and security protection policies and other reasons, the data set of private cars cannot be obtained, which hinders the research on the social attributes of vehicles in the IoV. Most of the previous work generated the same type of data, and how to generate private car data sets from various existing data sets is a huge challenge. In this paper, we propose a tri-layer framework to solve this problem. First, we propose a novel region division scheme that considers detailed inter-region relations connected by traffic flux. Second, a new spatial-temporal interaction model is developed to estimate the traffic flow between two regions. Third, we devise an evaluation pipeline to validate generation results from microscopic and macroscopic perspectives. Qualitative and quantitative results demonstrate that the data generated in heavy density scenarios can provide strong data support for downstream IoV and mobility research tasks. [ABSTRACT FROM AUTHOR]

In many radio-frequency identification (RFID) applications, one of the essential systematic functionalities is to quickly detect missing-tag event in case of misplacement or other incorrect operations. In this paper, we focus on probabilistically detecting the missing tags for the anonymous multicategory RFID systems without revealing the tag privacy. The main objective is to minimize the detection time while satisfying the required detection reliability of each category. First, we propose to use a multihash technique to sequentially detect the missing tags category-by-category, called segmented sequential detection approach, in which the frame segmentation is adopted to reduce the detection time. Then, we propose an enhanced segmented sequential detection approach to further improve the detection efficiency by deactivating the identified existing tags. We conduct extensive simulations to illustrate the effectiveness of our proposed two missing tag detection approaches. [ABSTRACT FROM AUTHOR]