The increasing diversity of Internet-of-Things (IoT) application scenarios and explosive growth of access devices have brought more frequent exchanges of resources between different administrative domains. Cross-domain authentication has become a key to safeguard communication and resource interaction among domains. Traditional centralized authentication schemes present heavy management overhead and trust challenges in cross-domain scenarios. Most of existing studies are incapable of establishing trust relationships between domains deployed with different authentication schemes, rendering such high-cost schemes difficult to be generalized. Further, the cross-domain scenario of IoT also raises additional requirements for device privacy and system overhead. In order to tackle these issues, this paper proposes a complete cross-domain authentication and privacy protection scheme, called CCAP, for the IoT based on consortium blockchain. CCAP achieves cross-domain authentication among the IoT domains which may have different configurations from each other. Further, CCAP can be cost-effectively deployed in resource-limited IoT domains and can offer privacy protection and efficient and secure communication for IoT devices. We demonstrate the effectiveness and efficiency of the scheme through experiments in virtual and physical experiment environments as well as comparing and analyzing CCAP with state-of-the-art work.

Narrow-band Internet of Things (NB-IoT) is one of the most promising technologies for low-power, wide-area, and low-traffic applications. In NB-IoT, random access is implemented in Media Access Control (MAC) layer to resolve the channel contention conflict among multiple User Equipments (UEs), and is crucial to the throughput performance of NB-IoT. Previous results in Long-Term Evolution (LTE) can not be directly applied due to specification differences. In this paper, we take the first attempt to systematically analyze the performance of random access in NB-IoT. First, after extensively studying the backoff mechanism, we characterize the probability that a UE initiates random access, the probability that a packet is transmitted successfully and the probability that a channel is busy. Then, we define each UE’s buffer as a First-In-First-Out (FIFO) queue. We employ Markov chain to model retransmission number caused by collisions and the length of the queue simultaneously. By exploiting the characteristic of the steady-state distribution of the Markov chain, the above three probabilities in steady state can be obtained explicitly. Based on these probabilities, we calculate the system throughput in terms of UE number, packet generation rate, retransmission number and the length of the queue. Finally, we investigate the system throughput and conduct extensive simulations under various parameters, which validate our analysis.

In this letter, we propose a novel communication scheme that combines intelligent reflecting surface (IRS) with generalized space shift keying (GSSK), where the IRS adjusts the phase shift according to the incoming bits at any specific time slot and multiple antennas are selected at the receiver to convey the index signals. Besides, a practical pre-greedy aided maximum likelihood (PGML) detector is proposed to supplement this IRS-aided received GSSK (IRS-RGSSK) system with lower complexity compared with the maximum likelihood (ML) detector and better error performance compared with the greedy detector (GD). By comparing with the IRS-aided received spatial modulation (IRS-RSM), the theoretical and simulation results show that the proposed system has better bit error rate (BER) performance.

Device-to-device (D2D) communications in cellular networks are promising technologies for improving network throughput, spectrum efficiency, and transmission delay. In this paper, we first introduce the concept of guard distance to explore a proper system model for enabling multiple concurrent D2D pairs in the same cell. Considering the Signal to Interference Ratio (SIR) requirements for both macro-cell and D2D communications, a geometrical method is proposed to obtain the guard distances from a D2D user equipment (DUE) to the base station (BS), to the transmitting cellular user equipment (CUE), and to other communicating D2D pairs, respectively, when the uplink resource is reused. By utilizing the guard distances, we then derive the bounds of the maximum throughput improvement provided by D2D communications in a cell. Extensive simulations are conducted to demonstrate the impact of different parameters on the optimal maximum throughput. We believe that the obtained results can provide useful guidelines for the deployment of future cellular networks with underlaying D2D communications.

Recently intrusion detection technology has been deployed in the Industrial Internet of Things (IIoT), which is an efficacious approach to enhancing security. However, identifying previously unseen and unknown attacks, referred to as the open-set problem, has become increasingly difficult due to the openness of IoT architecture and the continuous evolution of attack patterns. Moreover, existing open-set intrusion detection solutions are challenging to be applied directly to IIoT because of their unique characteristics, such as limited computational and storage capabilities, long detection times, and the inability to continuously learn. In this paper, we propose an efficient, lightweight, and dynamic open-set intrusion detection scheme for IIoT. It consists of three stages: the known attack classification stage focuses on extracting features from known data to efficiently classify normal data and known attacks; the unknown attack recognition stage analyzes the distribution of reconstruction errors to effectively distinguish between known data and unknown attacks; and the dynamic update detection stage introduces a lightweight detection architecture for unknown attacks detection, significantly reducing the computational overhead and storage requirements of IIoT devices. Simultaneously, it learns from and updates with newly detected unknown attacks to further optimize detection capabilities. We conduct experiments on four widely used datasets to evaluate the performance of open-set intrusion detection for IIoT. The experimental results delineate the superiority of our proposed method over four state-of-the-art approaches in open-set intrusion detection. Meanwhile, our proposed lightweight model updating method significantly reduces detection time by over 65% and memory overhead by over 80% compared to retraining methods, while achieving an average detection accuracy of 96%.

Due to the advantages including low cost, low power, massive connections, and wide coverage, and with the assistance of fog computing to achieve low latency, location awareness, etc., Narrow-Band Internet of Things (NB-IoT) can be widely applied in industry. Many NB-IoT industrial applications need a positioning feature for tracking, fault localization and fast repair, etc. When dedicated positioning systems, e.g., Global Position System, are unavailable for a low-power, low-cost NBIoT device, it is necessary to rely on terrestrial cellular network for localization. Existing performance studies in NB-IoT cellular network-based positioning have been mostly carried out by targeting deterministic scenarios. However, it is hard to provide general insights with changing system design parameters and propagation effects, due to the random variations in device location, network coverage, channel condition, etc. This paper develops a general analytical model to study the NB-IoT positioning performance. The location randomness of the device to be localized is considered through the distance distributions between the device and its surrounding Evolved Node Bs (eNBs). This makes it possible for using the powerful probabilistic distancebased tools from geometric probability to derive the probability that at least a fixed number of participating eNBs can be heard by the device. The model considers both the interference and the NB-IoT system-related parameter settings. It also includes modeling the eNB transmission coordination and traffic load. The obtained results reveal how participating eNBs impact the NB-IoT positioning performance, with the effects of a set of parameter settings investigated.