Billions of Internet of Thing (IoT) devices are deployed in edge network. They are used to monitor specific event, process and to collect huge data to control center with smart decision based on the collected data. However, some malicious IoT devices may interrupt and interfere with normal nodes in data collection, causing damage to edge network. Due to the open character of the edge network, how to identify the credibility of these nodes, thereby identifying malicious IoT devices, and ensure reliable data collection in the edge network is a great challenge. In this paper, an Active and Verifiable Trust Evaluation (AVTE) approach is proposed to identify the credibility of IoT devices, so to ensure reliable data collection for Edge Computing with low cost. The main innovations of the AVTE approach compared with the existing work are as follows: (1) In AVTE approach, the trust of the device is obtained by an actively initiated trusted detection routing method. It is fast, accurate and targeted. (2) The acquisition of trust in the AVTE approach is based on a verifiable method and it ensures that the trust degree has higher reliability. (3) The trust acquisition method proposed in this paper is low-cost. An encoding returned verification method is applied to obtain verification messages at a very low cost. This paper proposes an encoding returned verification method, which can obtain verification messages at a very low cost. In addition, the strategy of this paper adopts initiation and verification of adaptive active trust detection according to the different energy consumption of IoT devices, so as to reliably obtain the trust of device under the premise of ensuring network lifetime. Theoretical analysis shows that AVTE approach can improve the data collection rate by 0.5 ~ 23.16% while ensuring long network lifetime compared with the existing scheme.

Data aggregation can reduce the data transmission between the nodes, and thus save the energy and extend the life of the network. Many related researches on in-network data aggregation take the generalized maximum (GM) functions. For the cases that the original packets of N nodes aggregated into M (1 < M < N) packets, it is a challenge to improve the energy efficiency and reduce the transmission delay under the transmission reliability guarantee. In this paper, a novel ring based in-network data aggregation scheme is proposed to this problem. The network is partitioned into rings and the data aggregation is executed ring by ring from outside to inside. To ensure transmission reliability, the source or intermediate aggregating node unicasts multiple aggregated packet copies to its next hop node in the inner ring with the maximum residual energy. The reliability is higher with the more unicasting packet copies. However, more sending packets copies will lead to more additional energy cost. Besides, nodes close to the sink tend to relay more size of data packets and the energy is depleted more quickly than nodes far to the sink. Meanwhile, the nodes close to the sink need to relay the aggregated packets, which contain more information. If the number of packet copies is too small, the packets loss will greatly worse the transmission reliability. Based on this, the number of unicasting packet copies is adaptively adjusted through fuzzy logic. The proposed scheme adaptively unicasts variable number of aggregated packets copies continuously in a window according to the request transmission reliability and the imbalance of nodes energy cost. Our analysis and simulation results show the effectiveness of the proposed scheme.

The 5th-generation wireless networks (5G) technologies and mobile edge computing (MEC) provide great promises of enabling new capabilities for the industrial Internet of Things. However, the solutions enabled by the 5G ultra-reliable 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 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, and propose a new uplink-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 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.

In this paper, a dual-band pixel terahertz (THz) metamaterial absorber sensor has been designed based on an Improved Binary Ant Colony Optimization (IBACO) algorithm incorporating competitive learning and teaching learning mechanisms, which enables transcend the constraints of metamaterial absorber sensor methods that rely on a priori knowledge and design experience, leading to significant consumption of time and computational resources. Equivalent circuit model theory and impedance matching theory, as well as further discussion of current and field distributions, are undertaken to elucidate the underlying physical mechanisms. Notably, the sensor demonstrates exceptional frequency selectivity and polarization independence, achieving absorption rates exceeding 97% at 0.6598 THz and 0.9443 THz, and sensitivities of 69.6 GHz/RIU and 146.4 GHz/RIU, respectively, corresponding to figures of merit (FOM) of 8.81 and 31.15, which positions the sensor as a viable candidate for applications in RF stealth and detection sensing. Compared to conventional design approaches, the proposed method facilitates the rapid development of sensors tailored to specific design objectives, thereby heralding new possibilities for designing high-performance terahertz and other electromagnetic devices.