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Precision agriculture encompasses automation and application of a wide range of information technology devices to improve farm output. In this environment, smart devices collect and exchange a massive number of messages with other devices and servers over public channels. Consequently, smart farming is exposed to diverse attacks, which can have serious consequences since the sensed data are normally processed to help determine the agricultural field status and facilitate decision-making. Although a myriad of security schemes has been presented in the literature to curb these challenges, they either have poor performance or are susceptible to attacks. In this paper, an elliptic curve cryptography-based scheme is presented, which is shown to be formally secure under the Burrows–Abadi–Needham (BAN) logic. In addition, it is semantically demonstrated to offer user privacy, anonymity, unlinkability, untraceability, robust authentication, session key agreement, and key secrecy and does not require the deployment of verifier tables. In addition, it can withstand side-channeling, physical capture, eavesdropping, password guessing, spoofing, forgery, replay, session hijacking, impersonation, de-synchronization, man-in-the-middle, privileged insider, denial of service, stolen smart device, and known session-specific temporary information attacks. In terms of performance, the proposed protocol results in 14.67% and 18% reductions in computation and communication costs, respectively, and a 35.29% improvement in supported security features.

Improving the accuracy of PV power prediction is conducive to PV participation in economic dispatch and power market transactions in the distribution network, as well as safe dispatch and operation of the grid. Considering that the selection of highly correlated historical data can improve the accuracy of PV power prediction, this study proposes an integrated PV power prediction method based on a multi-resolution similarity consideration that considers both trend similarity and detail similarity. Firstly, using irradiance as the similarity variable, similar-days were selected using grey correlation analysis to form a set of similar data to control the similarity, with the overall trend of the day to be predicted at a macro level. Using irradiance to calculate the similarity at each specific point in time via Euclidean distance, similar-times were identified to form another set of similar data to consider the degree of similarity in detail. The above approach enables the selection of similarity data for both resolutions. Then, a 1DCNN-LSTM prediction model that considers the feature correlation of different variables and the temporal dependence of a single variable was proposed. Three important features were selected by a random forest model as inputs to the prediction model, and two similar data training models with different resolutions were used to generate a photovoltaic power prediction model based on similar-days and similar-times. Ultimately, the learning of the two predictions integrated with LightGBM compensate for each other, generating highly accurate predictions that combine the advantages of multi-resolution similarity considerations. Actual operation data of a PV power station was used for verification. The simulation results show that the prediction effect of ensemble learning was better than that of the single 1DCNN-LSTM model. The proposed method was compared with other commonly used PV power prediction models. In the data case of this study, it was found that the proposed method reduced the prediction error rate by 1.48%, 11.4%, and 6.45%, compared to the LSTM, CNN, and BP, respectively. Experiments show that model prediction results considering the selection of similar data at multiple resolutions can provide more extensive information to an ensemble learner and reduce the deviation in model predictions. Therefore, the proposed method can provide a reference for PV integration into the grid and participation in market-based electricity trading, which is of great significance.

Battery energy storage system (BESS) is an important component of future energy infrastructure with significant renewable energy penetration. Lead‐carbon battery is an evolution of the traditional lead‐acid technology with the advantage of lower life cycle cost and it is regarded as a promising candidate for grid‐side BESS deployment. However, inconsistency among lead‐carbon batteries in a BESS is a major concern which has to be carefully considered in practical operation. One of the available approaches to relieve this issue is to develop a sophisticated power allocation strategy (PAS) among multiple converters inside the BESS. This work conducts a comprehensive case study on the impact of PAS in a grid‐side 12 MW/48 MWh BESS recently constructed in Zhejiang, China (Zhicheng energy storage station, the first grid‐side lead‐carbon BESS in China). Three different PASs (i.e. averaged, state weighted and state prioritized) are investigated and benchmarked with two typical BESS application scenarios of peak shaving and frequency regulation, from real‐world experiments using the aforementioned BESS. The performance of the strategies is quantitatively evaluated by three indices defined in this work. The state prioritized strategy is found to be the most effective for this lead‐carbon BESS.

Hollow Cu2O/Cu/Au is synthesized using hollow Cu2O/Cu derived from hollow Cu-MOF-74 as a self-sacrificial template with a uniform dispersion of Au particles. It integrates high sensitivity and wide detection range for H2O2 non-enzymatic sensing.

The advancement of systems with the capacity to compress audio signals and simultaneously secure is a highly attractive research subject. This is because of the need to enhance storage usage and speed up the transmission of data, as well as securing the transmission of sensitive signals over limited and insecure communication channels. Thus, many researchers have studied and produced different systems, either to compress or encrypt audio data using different algorithms and methods, all of which suffer from certain issues including high time consumption or complex calculations. This paper proposes a compressing sensing-based system that compresses audio signals and simultaneously provides an encryption system. The audio signal is segmented into small matrices of samples and then multiplied by a non-square sensing matrix generated by a Gaussian random generator. The reconstruction process is carried out by solving a linear system using the pseudoinverse of Moore-Penrose. The statistical analysis results obtaining from implementing different types and sizes of audio signals prove that the proposed system succeeds in compressing the audio signals with a ratio reaching 28% of real size and reconstructing the signal with a correlation metric between 0.98 and 0.99. It also scores very good results in the normalized mean square error (MSE), peak signal-to-noise ratio metrics (PSNR), and the structural similarity index (SSIM), as well as giving the signal a high level of security.

This letter investigates the minimum-delay data aggregation scheduling problem for wireless powered Internet of Things (WPIoT). Unlike traditional Internet of Things (IoT), WPIoT devices should harvest enough energy before data transmission or reception. Hence, many existing solutions for traditional IoT cannot be directly applied to WPIoT. In this letter, we propose a delay-efficient algorithm by fully considering the energy features and cumulative interference effects of WPIoT. This algorithm constructs the data aggregation tree based on the fewest-children-first rule and implements a novel energy-aware concurrent link scheduling method leveraging the hypergraph theory. This novel link scheduling method can satisfy both the residual energy constraint and signal interference constraint. The performance of our algorithm is evaluated by extensive experiments based on a real-world dataset under non-linear energy harvesting models with both perfect and imperfect channel state information. The results confirm the superiority of our algorithm over the baseline algorithms.

Caching is a promising technique in the next generation cellular network, reducing backhaul traffic while improving network efficiency. In this letter, we introduce a cache-enabled small base station cooperative transmission scheme while considering the macro base station (MBS) silence and mutual information accumulation (MIA). A tractable analytical framework for characterizing the successful transmission probability (STP) of the introduced scheme is described. The STP is maximized by optimizing the caching placement policy based on a constrained cross entropy optimization algorithm. Simulation results demonstrate that the proposed SBS cooperative transmission with MBS silence and MIA achieves higher STP than other compared schemes.

The current study aims to explore the potential of the combination of hyperspectral imaging and chemometrics in the rapid identification of four chicken breeds. The hyperspectral data of four chicken breeds were collected in the range of 400–900 nm. Five pretreatment methods were used to pretreat the original spectra. The important characteristic wavelength variables were extracted by random frog (RF), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS) algorithms. The classification models were established by using support vector machine (SVM), k-nearest neighbor (KNN), and partial least squares-discriminant analysis (PLS-DA). The results showed that the mean normalization pretreatment method was preferable, and overall classification accuracy of SVM-based models was higher than that of KNN-based and PLS-DA-based models. The correct classification rate (CCR) of the full-spectrum SVM model (Full-SVM) could reach 96.25%. The SPA method extracted 13 important wavelengths, and the SVM model based on SPA (SPA-SVM) achieved 90% CCR. This study can provide a theoretical reference for the discriminant analysis of chicken breeds.

Freshness and quality are two important metrics in real-time video applications in surveillance networks. However, these two metrics conflict with each other in many cases. To enhance the performance of real-time video services, it is very important, but also challenging, to find a good tradeoff between freshness and quality. In this article, we focus on studying the tradeoff between freshness and quality, where video packets are encoded with scalable video coding (SVC) and adaptive random network coding (ARNC). Moreover, the Age of Information (AoI) is applied to model video freshness, while the video quality is modeled by the number of layers received by users. A utility function is defined to capture the tradeoff between video freshness and video quality. By maximizing the defined utilities, an excellent tradeoff between freshness and quality could be obtained. To solve the formulated optimization problem, the maximization of the utility function is characterized as a Markov decision process (MDP) problem, which is effectively solved by a heuristic algorithm and a deep $Q$ network (DQN)-based algorithm. Simulation results indicate that the applied ARNC technique can achieve higher utility performance than other benchmark transmission techniques, and the DQN-based algorithm outperforms the heuristic algorithm in most cases.

To alleviate the impact of output uncertainty of renewable energy sources (RESs) in unbalanced active distribution networks (ADNs), this paper proposed a two-time scale robust optimization method for the multi-terminal soft open point (SOP). In the long-term scale, the operation points of the SOP were optimized by semidefinite programming (SDP) model to minimize system loss and mitigate voltage unbalance. An improved iterative cutting plane (ICP) method was proposed to strengthen the exactness of the SDP rank-one relaxation. In the short-term scale, V 2- P and V 2- Q droop control mode was implemented in the SOP to better respond to the output uncertainty of RESs. Then a slope robust optimization model was established based on long-term operation points and short-term forecast data to improve system robust security. To coordinate these two time scales, a co-optimization re-dispatch strategy was proposed in the short-term model so that the robust safety margin could be further expanded in extreme conditions. Case studies show that the proposed method can fully utilize the flexible power flow regulation ability of the multi-terminal SOP to reduce system loss, mitigate voltage unbalance and guarantee system robust security in the presence of RES output uncertainty.

Operational bottlenecks are commonly observed in power systems and lead to severe system security issues, which may be caused by the fluctuating and uncertain nature of renewable energy. This paper presents an approach to define, identify and eliminate such bottlenecks in the scope of system balance for renewable energy integrated bulk power systems, so as to quantify the requirement of energy storage. A mixed-integer linear programming (MILP) formulation for system operational bottleneck identification is proposed given renewable generation profile, in order to obtain operational restriction indices to assess the adequacy of power adjustment margin and power ramp rate. Cosine similarity based density-based spatial clustering of applications with noise (DBSCAN) method is used to cluster a large number of scenarios by operational restriction indices, then scenarios with bottlenecks are attributed to corresponding clusters. Finally, various bottleneck elimination options, including energy storage with different technologies, are compared for each cluster. Case studies of an eight-bus test system and a practical Chinese power system are presented to verify the proposed approach, the numerical results indicate energy storage is the most effective option to eliminate bottlenecks identified in power downward adjustment margin and ramp rate dominated clusters aforementioned.

The direct photocurrent detection capability of light orbital angular momentum (OAM) has recently been realized with topological Weyl semimetals, but it is limited to the near-infrared wavelength range. The extension of the direct OAM detection capability to the mid-infrared band, which is a wave band that plays an important role in a vast range of applications, has not yet been realized. This is because the photocurrent responses of most photodetectors are neither sensitive to the phase information nor efficient in the mid-infrared region. In this study, a photodetector based on the type-II Weyl semimetal tantalum iridium telluride (TaIrTe