Over the last decade, the Internet of Things (IoT) technology has advanced significantly in a variety of fields. As a pivotal application of IoT, intelligent transportation systems (ITS) have harvested great attention from the research community. Radio frequency identification (RFID) which is an essential technology in IoT plays a key role in ITS to identify tagged vehicles. Unknown tag identification which aims at identifying the existing unknown tags is crucial to monitor the newly entering vehicles in the RFID-assisted intelligent transportation systems. However, the COTS (commercial-off-the-shelf) RFID tags that harvest energy from the reader can not support the hash function in reality, which hinders the widespread deployment of hash-enabled unknown tag identification protocols. To conquer this tough issue, we propose two approaches to efficiently identify unknown COTS RFID tags. We first propose a Single-Point Selective unknown tag identification approach called SPS, where an analog hash pattern using the EPC (Electronic Product Code) segments is deployed to exclusively identify unknown tags. An unknown tag will be identified when it selects a singleton slot to reply. To improve the time efficiency of SPS, we further propose a Multi-Point Selective unknown tag identification approach called MPS. In MPS, two techniques of batch identification and batch division are developed to reduce the number of empty slots and avoid tag collisions, respectively. Then the parameters are theoretically analyzed to maximize the identification efficiency. The effectiveness of the proposed approaches is validated via both the simulations and COTS RFID device based experiments.

The significance of RFID-based information collection is becoming increasingly visible as more and more sensor-augmented RFID systems are deployed. Tag information collection aims at efficiently and accurately collecting valuable information from target objects attached with RFID tags. Polling-based information collection can effectively avoid response collisions between RFID tags, and it is widely adopted to accurately inventory tags. However, in the traditional polling mode, a polling vector can only be used to query a tag at a time, which is inefficient. In this paper, we design a double polling mode to improve the utilization of polling vectors, which can simultaneously interrogate a pair of tags. Afterwards, several techniques are developed to reduce the polling vector length. Firstly, the Basic Double Polling-based protocol (BDP) employs double indexes to collect information, which greatly reduces the number of polling vectors. Secondly, the Segmented Double Polling-based protocol (SDP) divides the double indexes into several segments to cut the polling vector length down. Thirdly, the Partial Double Polling-based protocol (PDP) replaces the double index with the size of the empty segment between two adjacent non-zero indexes to further reduce the average polling vector length. Finally, the Differential Double Polling-based protocol (DDP) utilizes the size of the empty segment between two double indexes to improve the utilization of polling vectors. After that, extensive theoretical analyses and simulations are conducted, which demonstrate the feasibility and effectiveness of the proposed protocols.

With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoTs), an increasing number of computation intensive or delay sensitive biomedical data processing and analysis tasks are produced in vehicles, bringing more and more challenges to the biometric monitoring of drivers. Edge computing is a new paradigm to solve these challenges by offloading tasks from the resource-limited vehicles to Edge Servers (ESs) in Road Side Units (RSUs). However, most of the traditional offloading schedules for vehicular networks concentrate on the edge, while some tasks may be too complex for ESs to process. To this end, we consider a collaborative vehicular network in which the cloud, edge and terminal can cooperate with each other to accomplish the tasks. The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge. We further construct the virtual resource pool which can integrate the resource of multiple ESs since some regions may be covered by multiple RSUs. In this paper, we propose a Multi-Scenario offloading schedule for biomedical data processing and analysis in Cloud-Edge-Terminal collaborative vehicular networks called MSCET. The parameters of the proposed MSCET are optimized to maximize the system utility. We also conduct extensive simulations to evaluate the proposed MSCET and the results illustrate that MSCET outperforms other existing schedules.

Nowadays, Radio Frequency IDentification (RFID) technology is profoundly affecting all walks of life. Unknown tag identification, as an important service for RFID-enabled applications, aims to exactly collect all EPCs (Electronic Product Code) of unknown tags that are not recorded by the back-end server in the RFID systems. Efficient unknown tag identification is significant to accurately discover the unregistered or newly entering tags in many scenarios, such as warehouse management and retail industry. However, the replies of known tags and the unpredictable behaviors of unknown tags bring serious challenges for accurate and efficient identification of unknown tags. To handle these tough issues, we propose a Compact Unknown Tag identification protocol (CUT) to collect unknown tag EPCs in large-scale RFID systems. Firstly, we introduce a compact indicator vector to simultaneously label unknown tags and deactivate known tags. Then the unknown tags are instructed to reply their EPCs via another compact reply based indicator vector. In each indicator vector, the amount of expected empty and singleton slots is increased to greatly improve the labeling, deactivation and collection efficiency. After that, we validate the effectiveness of proposed CUT protocol by extensive theoretical analyses and simulations. The simulation results demonstrate that CUT protocol outperforms the state-of-the-art one.

Radio frequency identification (RFID) systems have achieved wide applications in various scenarios, such as warehouse management, logistic tracking, smart transportation, etc. Despite the enormous benefits from the RFID systems, the security issues are still of great concern, such as the cloning attacks. In this article, we focus on the detection of probabilistic cloning attacks for the anonymous RFID systems, in which each cloned genuine tag suffers attacks from its clone tags with a certain probability. We propose an efficient detection protocol against the probabilistic cloning attacks in anonymous RFID systems named DAP, which can detect the probabilistic cloning attacks with the required detection reliability$\alpha$if at least one tag is attacked with the probability no less than the threshold$P_T$. The proposed DAP protocol fully utilizes the inconsistency and unreconcilable collision caused by the probabilistic cloning attacks to effectively detect the probabilistic cloning attacks.The parameters are theoretically analyzed to maximize the detection efficiency. The extensive simulations are conducted and the results demonstrate the effectiveness of the proposed DAP protocol.

Mobile crowdsensing (MCS) is an emerging paradigm that leverages pervasive smart terminals equipped with various embedded sensors to collect sensory data for wide applications. As the sensing scale increases in MCS, the design of efficient task allocation becomes crucial. However, many prior task allocation schemes, which ignore the time for task-performing, are not applicable to the scenario where mobile users with limited time budgets are able to undertake multiple sensing tasks. In this article, we focus on the task allocation in time dependent crowdsensing systems and formulate the time dependent task allocation problem, in which both the sensing duration and the user's sensing capacity are considered. We prove that the task allocation problem is NP-hard and propose an efficient task allocation algorithm called optimized allocation scheme of time-dependent tasks (OPAT), which can maximize the sensing capacity of each mobile user. The extensive simulations are conducted to demonstrate the effectiveness of the proposed OPAT scheme.

Radio frequency identification (RFID) technology has been deployed in various scenarios in recent years. In some practical RFID applications, the items attached with tags can be divided into multiple groups. Thus, the efficient and accurate missing tag detection of each group is critical. Accordingly, this article concentrates on the problem of missing tag detection in the multiple-group RFID systems, after which three distinctive protocols are proposed. First, we propose an aptitudinal multiple-group missing tag detection protocol, which makes full use of the expected singleton slots. Then, an enhanced multiple-group missing tag detection protocol is proposed, which can achieve significant broadcast and response savings. Finally, an accurate and expeditious multiple-group missing tag detection protocol is designed, the detection reliability of which can approximate 100%. The theoretical analysis and extensive simulations are conducted and the results verify that the proposed protocols in this article outperform the other ones.

Recently recommender systems become more and more significant in the daily life such as event recommendation, content recommendation and commodity recommendation, and so forth. Although the recommender systems based on the generative adversarial network (GAN) are competent, the user trust information is seldom taken into consideration to improve the recommendation accuracy. In this paper, we propose a Trust-Aware GAN with recurrent neural network (RNN) for RECommender systems named TagRec, which makes use of the user trust information for top-N recommendation. In the framework, the discriminative model is a multilayer perceptron to distinguish whether a sample is from the real data or fake data generated by the generative model. The discriminator helps to guide the training of the generative model to make it fit the data distribution of the user trust information. The generative model is a RNN with long short-term memory cells, aiming to confuse the discriminative model by generating samples as similar as possible to the real data. Through the adversarial training between the discriminative and generative models, the user trust information can be fully used to improve the recommendation performance. We conduct extensive experiments on real-word data sets to validate the effectiveness of the TagRec by comparing it with the benchmarks.