Sensor networks in IoT play a crucial role in harsh and complex environments, such as pipeline monitoring and irregular terrains. Traditional geographic OR (GOR) schemes that rely on Euclidean distance for packet forwarding are often unsuitable for irregular network topologies, frequently resulting in incorrect forwarding directions and routing holes. To address the limitations, this paper proposes a Manifold Learning-based Geographic Opportunistic Routing (MLGOR) scheme for 3D strip sensor networks. Inspired by the Isomap algorithm, MLGOR first maps the irregular 3D network onto a regular 2D strip network, enabling the construction of effective forwarding candidates using Euclidean distance and thus mitigating topological issues. A novel forwarding node selection scheme is then introduced that combines both node and network-based metrics. This hybrid approach calculates forwarding priorities and leverages connectivity to minimize duplicate transmissions, supplemented by a time-based coordination mechanism. The applicability of MLGOR is discussed, and simulation results demonstrate its energy efficiency in low-reliability and irregular 3D network environments, thereby extending network lifetime.

A large number of Internet of Things (IoT) devices such as sensor nodes are deployed in various urban infrastructures to monitor surrounding information. However, it is still a challenging issue to collect data in a low‐cost, high‐quality, and reliable manner through IoT technique. Although the recruitment of mobile vehicles (MVs) to collect urban data has proved to be an effective method, most existing data collection systems lack a trust detection mechanism for malicious terminal nodes and malicious vehicles, which should lead to security vulnerabilities in practice. This paper proposes a novel data collection strategy based on a layered trust mechanism (DC‐LTM). The strategy recruits MVs as data collectors of the sensor nodes based on the data value in the city, evaluates the trustworthiness of the data reported by the nodes, and records the results to the cloud data center. Furthermore, in order to make the data collection system more efficient and trust mechanism more reliable, we introduce unmanned aerial vehicles (UAVs) dispatched by data centers to actively verify the core sensor node data and use the core sensor data as baseline data to evaluate the credibility of the vehicles and the trust value of the whole network sensor nodes. Different from the previous strategies, UAVs adopts the DC‐LTM method to obtain the node data while actively obtaining the trust value of MVs and nodes, which effectively improves the quality of data acquisition. Simulation results show that the mechanism effectively distinguishes malicious vehicles that provide false data in exchange for payment and reduces the total cost of system recruitment payments. At the same time, the proposed incentive mechanism encourages vehicle to complete the evaluation task and improves the accuracy of node trust evaluation. The recognition rates of false data attacks and flooding attacks as well as the recognition error rate of normal nodes are 100%, 98.9%, and 3.9%, respectively, which improves the quality of system data collection as a whole.

The fifth-generation (5G) wireless network technologies and mobile-edge computing (MEC) provide great promises of enabling new capabilities for the industrial Internet of Things (IoT). However, the solutions enabled by the 5G ultrareliable low-latency communication (URLLC) paradigm come with challenges, where URLLC alone does not necessarily guarantee the efficient execution of time-critical fault detection and diagnostics (FDD) applications. Based on the Tennessee Eastman (TE) process model, we propose the concept of the communication-edge-computing (CEC) loop and a system model for evaluating the efficiency of FDD applications. We then formulate an optimization problem for achieving the defined CEC efficiency and discuss some typical solutions to the generic CEC-based FDD services (FDDS) and propose a new uplink (UL)-based communication protocol called “ReFlexUp.” From the performance analysis and numerical results, the proposed ReFlexUp protocol shows its effectiveness compared to the typical protocols, such as Selective Repeat automatic repeat request (ARQ), hybrid ARQ (HARQ), and “Occupy CoW” in terms of the key metrics, such as latency, reliability, and efficiency. These results are further convinced from the mmWave-based simulations in a typical 5G MEC-based implementation.

This paper estimates safety benefits of crash with precrash braking maneuvers under different pretensioning control factors. A sled test was conducted at 40 kmph with a lap-shoulder-belted Hybrid III 50thpercentile male dummy to simulate a frontal crash. A multi-body model of the sled test was developed based on the actual situation and verified by test data including the seat belt loads, head and chest injury responses of the test dummy and motion postures of the dummy. An impact pulse with a 0.2-second-long constant deceleration of 0.8 g ahead of crash was loaded on the verification model to simulate the real deceleration and the crash process with precrash braking maneuvers. The protection performance of a 3-point seat belt, a seat belt with pyrotechnic pretensioner and a motorized seat belt was compared, and the motorized seat belt had the best protection effect. Different pretensioning control factors, such as pretensioning time and pretensioning force, had remarkable effects on injury responses of the dummy. This method could also be used to develop advanced occupant restraint systems coupled with precrash systems to integrate vehicle active safety and passive safety.