The localization of sensor nodes is a significant issue in wireless sensor networks (WSNs) because many applications cannot provide services without geolocation data, especially during disaster management. In recent years, a promising unknown-nodes positioning method has been developed that localizes unknown nodes, employing a GPS-enabled mobile anchor node moving in the network, and broadcasting its location information periodically to assist localization. In contrast to most studies on path planning that assume infinite energy of the mobile anchor node, the anchor node in this study, consumes different amounts of energy during phases of startup, turning, and uniform motion considering the aftermath of disasters. To enable a trade-off between location accuracy and energy consumption, a path-planning algorithm combining a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) and SCAN algorithm (SLMAT) is proposed. SLMAT ensures that each unknown node is covered by a regular triangle formed by beacons. Furthermore, the number of corners along the planned path is reduced to save the energy of the mobile anchor node. In addition, a series of experiments have been conducted to evaluate the performance of the SLMAT algorithm. Simulation results indicate that SLMAT outperforms SCAN, LMAT, HILBERT, and Z-curve in terms of localization accuracy and energy consumption.

The Industrial Internet of Things (IIoT) is one of the main catalysts towards the realization of the Industry 4.0 paradigm, thus it is regarded as an essential element in future industrial systems - which can assist in reducing energy consumption and in enhancing product life-cycle management. In this study, an industrial multi-energy scheduling framework (IMSF) is proposed, with the aim of optimizing the usage of renewable energy and reducing the energy costs. The proposed method addresses the management of multi-energy flows in industrial integrated energy systems - incorporating multi-energy storage, renewable energy generation, energy conversion, and energy trading in a synchronous manner. The method considers the typical energy load of the industrial users, the energy price of the national grid and the trading platform, and the trade-off between investment costs and benefits from the various sub-systems. As this results in a complex system of systems, an artificial intelligence method is proposed to treat the problem, using reinforcement learning based differential evolution (RLDE), that can determine the optimal mutation strategy and associated parameters in an adaptive way. Case studies on real-world data evidence the effectiveness of the IMSF and the RLDE algorithm in reducing the energy costs in industrial environments.

Internet of Things (IoT) is an emerging networking paradigm that enhances smart device communications through internet-enabled systems. Due to massive IoT devices connectivity with economic and greenhouse emission effects, the energy efficiency poses critical concerns. Under imperfect channel state information (CSI), this paper investigates joint optimization of user selection, power allocation and the number of activated Base Station (BS) antennas of multiple IoT devices considering the transmit power and different quality of service (QoS) requirements in combinatorial mode to maximize energy efficiency. The optimization problem formulated is a non-convex mixed-integer nonlinear programming, which is NP-hard with no practical solution. The primal optimization problem is transformed into tractable convex optimization problem and separated into inner and outer loop subproblems. The paper proposed joint energy efficient iterative algorithm, which utilizes successive convex approximation technique and Lagrangian dual decomposition method to achieve near-optimal solutions with guaranteed convergence. Simulation results are provided to evaluate the proposed algorithm and its significant performance gain over the baseline algorithms in terms of energy efficiency maximization.

Specific emitter identification (SEI) is a technology that extracts subtle features from signals sent by emitters to identify different individuals. It can effectively improve the security of the Industrial Internet of Things (IIoT) by acting on the physical layer of the Internet. Recent research on SEI has focused on deep learning (DL) models that can automatically learn effective inherent emitter features from raw signals. Nevertheless, training popular DL models is computationally expensive because of the numerous hyperparameters and nonscalable structures. This limits the application of DL-based SEI models in certain practical IIoT scenarios. To address this concern, we propose an adaptive broad learning (ABL) method to build a lightweight SEI model. In the proposed model, the raw signal samples are mapped to feature nodes, and the emitters are denoted as the output nodes. The hidden nodes are directly connected to the output nodes by a broad network. Through this flat structure, the size and calculation amount of the model can be effectively reduced. To further economize the computational cost, we designed an adaptive node expansion strategy for rapidly obtaining the optimal hyperparameters of the models. The results of experiments on real-world data prove the superiority of ABL over popular state-of-the-art DL-based SEI models.

The Industrial Internet of Things (IIoT), which is based on Wireless Sensor Networks (WSNs) and several new technologies such as social and cognitive mobile computing, is expected to play a very important role in daily life. Social and cognitive mobile computing is also based on WSNs because it requires the sharing of the data recorded by the sensor nodes. However, these data are vulnerable to attacks, and it is of utmost importance to protect the privacy and ensure the security of the WSNs. In this study, we focused on the source location privacy (SLP) of WSNs, and propose a dynamic multipath privacy-preserving routing scheme based on multiple sinks (DMPPR) for protecting the privacy. A packet-slicing transmission scheme that generates a large number of dynamic routings based on multiple sink nodes is adopted for transmitting the packets. Local adversaries are considered, and to cope with these adversaries, a transmission loop constructed using real and fake packets is generated to confuse the adversaries during the source detection process. The aim is to break the sociality between the sensor nodes. Simulations performed in MATLAB show that the proposed method outperforms similar existing schemes in terms of the secure time, adversary's capture probability, and node utilization ratio. Moreover, the DMPPR also reduces energy consumption by letting more nodes in the non-hotspot areas participate in the packet transmission process.

As a result of the increasing deployment of Industrial-Internet-of-Things (IIoT) architectures, large volumes of multidimensional data are continuously generated. An important issue with these data is that higher dimensionality increases the degree of fragmentation. Furthermore, data sets collected by IIoT nodes often display outliers, which are usually caused by anomalous events or errors. These outliers contain considerable valuable information, which prevent the normal operation of the system. Thus, methodologies are able to quantify the obtained information to protect the high priority IIoT nodes, are crucial. This study aims at developing such a method driven by sixth-generation (6G) networks. The proposed algorithm uses a multidimensional data relationship diagram to characterize the spatiotemporal correlations among heterogeneous data. Then, an autoregressive exogenous model is used to eliminate the effects of noise on sensor data, and to help in detecting anomalies. Finally, the algorithm produces a Cumulative Coefficient of Value (CCoV), to identify high-value sensing devices and enable massive Internet of Things (IoT) with 6G-using the characteristic patterns hidden within the data. The experimental results demonstrate that the proposed method can effectively handle the effects of the ubiquitous interference noise in complex industrial environments. Moreover, the method yields effective anomaly detection and compensates for some of the shortcomings in traditional methods.