Although Internet-of-Things (IoT) is revolutionizing the IT sector, it is not mature yet as several technologies are  still being offered to be candidates for supporting the backbone of this system. IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is one of those promising candidate technologies to be adopted by IoT and Industrial IoT (IIoT). Attacks against RPL have shown to be possible, as the attackers utilize the unauthorized parent selection system of the RLP protocol. In this work, we are proposing a methodology and architecture to detect intrusions against IIoT. Especially, we are targeting to detect attacks against RPL by using genetic programming. Our results indicate that the developed framework can successfully (with high accuracy, along with high true positive and low false positive rates) detect routing attacks in RPL-based Industrial IoT networks.

Accurate and real-time perception of the operating status of rolling bearings, which constitute a key component of rotating machinery, is of vital significance. However, most existing solutions not only require substantial expertise to conduct feature engineering, but also seldom consider the temporal correlation of sensor sequences, ultimately leading to complex modeling processes. Therefore, we present a novel model, named Attention-based Equitable Segmentation Gated Recurrent Unit Networks (AESGRU), to improve diagnostic accuracy and model-building efficiency. Specifically, our proposed AESGRU consists of two modules, an equitable segmentation approach and an improved deep model. We first transform the original dataset into time-series segments with temporal correlation, so that the model enables end-to-end learning from the strongly correlated data. Then, we deploy a single-layer bidirectional GRU network, which is enhanced by attention mechanism, to capture the long-term dependency of sensor segments and focus limited attention resources on those informative sampling points. Finally, our experimental results show that the proposed approach outperforms previous approaches in terms of the accuracy.

Localization has gained great attention in recent years, where different technologies have been utilized to achieve high positioning accuracy. Fingerprinting is a common technique for indoor positioning using short-range radio frequency (RF) technologies such as Bluetooth Low Energy (BLE). In this paper, we investigate the suitability of LoRa (Long Range) technology to implement a positioning system using received signal strength indicator (RSSI) fingerprinting. We test in real line-of-sight (LOS) and non-LOS (NLOS) environments to determine appropriate LoRa packet specifications for an accurate RSSI-to-distance mapping function. To further improve the positioning accuracy, we consider the environmental context. Extensive experiments are conducted to examine the performance of LoRa at different spreading factors. We analyze the path loss exponent and the standard deviation of shadowing in each environment

As a key component of rotating machinery, the health perception of hearings is essential to ensure the safe and reliable operation of industrial equipment. In recent years, research on equipment health perception based on data-driven methods has received extensive attention. Overall, most studies focus on several public datasets to verify the effectiveness of their algorithms. However, the scale of these datasets cannot completely satisfy the representation learning of deep models. Therefore, this paper proposes a novel method, called CarNet, to obtain a more robust model and ensure that the model is sufficiently trained on a limited dataset. Specifically, it is composed of a data augmentation method named equitable sliding stride segmentation (ESSS) and a hybrid-stacked deep model (HSDM). The ESSS not only amplifies the scale of the original dataset but also enables newly generated data with both spatial and temporal correlations. The HSDM can, therefore, extract shallow spatial features and deep temporal information from the strongly correlated 2-dimensional (2-D) sensor array using a CNN and a bi-GRU, respectively. Moreover, the integrated attention mechanism contributes to focusing limited resources on informative areas. The effectiveness of CarNet is evaluated on the CWRU dataset, and an optimal diagnostic accuracy of 99.92% is achieved.

The exponential growth in global mobile data traffic, especially with regards to the massive deployment of devices envisioned for the fifth generation (5G) mobile networks, has given impetus to exploring new spectrum opportunities to support the new traffic demands. The millimeter wave (mmWave) frequency band is considered as a potential candidate for alleviating the spectrum scarcity. Moreover, the concept of multi-tier networks has gained popularity, especially for dense network environments. In this article, we deviate from the conventional multi-tier networks and employ the concept of control-data separation architecture (CDSA), which comprises of a control base station (CBS) overlaying the data base station (DBS). We assume that the CBS operates on the sub-6 GHz single band, while the DBS possesses a dual-band mmWave capability, i.e., 26 GHz unlicensed band and 60 GHz licensed band. We formulate a multi-objective optimization (MOO) problem, which jointly optimizes conflicting objectives: the spectral efficiency (SE) and the energy efficiency (EE). The unique aspect of this work includes the analysis of a joint radio resource allocation algorithm based on Lagrangian Dual Decomposition (LDD) and we compare the proposed algorithm with the maximal-rate (maxRx), dynamic sub-carrier allocation (DSA) and joint power and rate adaptation (JPRA) algorithms to show the performance gains achieved by the proposed algorithm.

Industrial Wireless Sensor Networks (IWSNs) are emerged as flexible and cost-efficient alternatives to the traditional wired networks in various monitoring and control applications within the industrial domain. Low delay is a key feature of delay-sensitive applications as the data is typically valid for a short interval of time. If data arrives too late it is of limited use which may lead to performance drops or even system outages which can create significant economical losses. In this paper, we propose a decentralized optimization algorithm to minimize the End-to-End (E2E) delay of multi-hop IWSNs. Firstly, we formulate the optimization problem by considering the objective function as the network delay where the constraint is the stability criteria based on the total arrival rate and the total service rate. The objective function is proved to be strictly convex for the entire network, then a Decentralized Primal-Dual (DeP-D) algorithm is proposed based on the sub-gradient method to solve the formulated optimization problem. The performance of the proposed DeP-D is evaluated through simulations and compared with WirelessHART network and the results show that the proposed DeP-D can achieve at least 40% reduction in the average E2E delay.

Internet of Things (IoT) technology has succeed ingetting a great attention in the industry where it has been ableand still can solve many industrial intractable issues. The emergingtechnology is suffering from severe security shortcomings.Authentication is a cornerstone of IoT security as it presents the measures of checking the legitimacy of communication’s entities.The Industrial IoT (IIoT) technology has special conditions, resulting from a lack of resources and a shortage of security skills. As far as we can tell, from the literature, IIoT user authentication has not been studied extensively. In 2017 Tai et al. presented an authenticated key agreement for IoT networks. Here we prove that Tai et al. is susceptible to sever security weaknesses, such as;i. stolen smart card attack, ii. unknown key share attack, iii. node capturing attack. In this research article, we offer an innovative IIoT user authentication scheme that can achieve secure remote user authentication without timestamping that requires precise synchronization, it only needs Hashing and Xor-ing. We examine the efficiency of our presented scheme using Tmote Sky node over an MSP430 microcontroller using COOJA simulator as well aswe show its correctness using Scyther verification tool.

With recent adoption of Wireless Sensor-Actuator Networks (WSANs) in industrial automation, wireless control systems have emerged as a frontier of industrial networks. Hence, it has been shown that existing standards and researches concentrate on the reliability and real-time performance of WSANs. The multipath retransmission scheme with multiple channels is a key approach to guarantee the deterministic wireless communication. However, the efficiency of resource scheduling is seldom considered in applications with diverse data sampling rates. In this paper, we propose an efficient resources scheduling algorithm for multipath retransmission in WSANs. The objective of our algorithm is to improve efficiency and schedulability for the use of slot and channel resources. In detail, the proposed algorithm uses the approaches of CCA (clear channel assessment)-Embedded slot and Multiple sinks with Rate Monotonic scheme (CEM-RM) to decrease the number of collisions. We have simulated and implemented our algorithm in hardware and verified its performance in a real industrial environment. The achieved results show that the proposed algorithm significantly improves the schedulability without trading off reliability and real-time performance.

In this paper, we study cross-layer optimization of low-power wireless links for reliability-aware applications while considering both the constraints and the non-ideal characteristics of the hardware in Internet-of-things (IoT) devices. Specifically, we define an energy consumption (EC) model that captures the energy cost—of transceiver circuitry, power amplifier, packet error statistics, packet overhead, etc.—in delivering a useful data bit. We derive the EC models for an ideal and two realistic non-linear power amplifier models. To incorporate packet error statistics, we develop a simple, in the form of elementary functions, and accurate closed-form packet error rate (PER) approximation in Rayleigh block-fading. Using the EC models, we derive energy optimal yet reliability and hardware compliant conditions for limiting unconstrained optimal signal-to-noise ratio (SNR), and payload size. Together with these conditions, we develop a semi-analytic algorithm for resource-constrained IoT devices to jointly optimize parameters on physical (modulation size, SNR) and medium access control (payload size and the number of retransmissions) layers in relation to link distance. Our results show that despite reliability constraints, the common notion—higher-order M-ary modulations (MQAM) are energy optimal for short-range communication—prevails, and can provide up to 180% lifetime extension as compared to often used OQPSK modulation in IoT devices. However, the reliability constraints reduce both their range and the energy efficiency, while non-ideal traditional PA reduces the range further by 50% and diminishes the energy gains unless a better PA is used.