In modern society, power consumption plays a crucial role, since it is capable of influencing multiple sectors including residential, commercial, and industrial domains. It covers the electrical energy amount used by various devices, appliances, machinery, and systems within a specific time frame. The accurate prediction of power consumption is imperative for effective energy management, resource allocation, infrastructure planning, and cost optimization. In this study, we focused on predicting power consumption within DAEWOO Steel CO. Ltd, located in South Korea. We acquired, preprocessed, and analyzed a dataset containing information about the daily operations of the industrial facility, with data being sampled at 15-minute intervals. By leveraging machine learning techniques, we employed six tree-based algorithms and three ensemble learners to forecast the target variable. Our comparative analysis examined the performance of these regressors across three forecasting horizons: 15 minutes, 1 hour, and 1 day. We discovered that the efficacy of the regressors is complex and is linked to the forecasting horizon. Notably, the stacking ensemble learner outperformed others for the 15-minute horizon, achieving impressive metrics of 98.5% for D2, 99.9% for R2, 0.81 for MSE, and 0.37 for MAE. For 1-hour ahead predictions, XGBoost emerged as the most accurate model, attaining metrics of 96.3% for D2, 99.7% for R2, 53.07 for MSE, and 3.5 for MAE. Finally, for 1-day ahead forecasting, the Extra Tree regressor surpassed its counterparts, achieving metrics of 93.1% for D2, 99.3% for R2, 13944 for MSE, and 74.46 for MAE. These findings underscore the importance of tailoring predictive models to specific forecasting horizons and highlight the efficacy of ensemble learning techniques in enhancing power consumption predictions across varying time frames.

Solar panels, the primary components of solar photovoltaic systems, play a pivotal role in converting sunlight into electricity. However, the efficiency and performance of solar panels can be significantly influenced by environmental factors, notably the accumulation of dust and debris on their surfaces. This paper focuses on the investigation of deep learning image classification techniques to detect dust periodically, utilizing solar panel images collected by drones or robots. This approach aims to reduce the impact of dust on solar panels and help identify effective cleaning methods for each case. This work proposes the development of a deep learning binary image classifier model specifically designed to differentiate between “dusty” and “clean” solar panels. The proposed system is based on pre-trained deep learning models fine-tuned for dusty solar panel detection. The results demonstrate that fine-tuning the weights of the pre-trained model enhances performance, with the EfficientNetB7 model yielding the best outcome.

Recently, several security schemes for Random Linear Network Coding (RLNC) have been proposed to increase the immunity of the RLNC technology against security attacks. One of the presented security schemes aims at securing the Global Encoding Vectors (GEV) that use other vectors to maintain the proper encoding process of RLNC at intermediate nodes. However, this approach introduces overhead in terms of computational complexity (block cipher with multiple rounds and operations) and communication (2xn elements for each packet instead of n). To that end, this paper proposes a new scheme, $S E R S$, that overcomes the disadvantages and limitations of the existing security schemes by relying on a single GEV instead of two, which is the case of the original RLNC. The proposed scheme reduces the required computational complexity by eliminating AES encryption and by keeping the source RLNC encoding step as a secret. SERS is based on a dynamic key structure, and the introduced modifications result in a modern lightweight, and secure RLNC while achieving higher efficiency and minimizing the space of vulnerabilities. SERS exhibits minimal computational complexity and communication overhead, and it ensures message confidentiality and availability, in addition to source authentication when a homomorphic keyed hash function is employed. A second variant of the proposed scheme is also presented. The main advantages of the proposed scheme are that 1) it operates at the sub-generation level, 2) it can be implemented in parallel, and 3) it increases the security level by using different RLNC encoding matrices instead of just one.

Cybersecurity-enabled smart renewable energy system security requires a multifaceted strategy that includes proactive risk assessment, strong defenses, stakeholder cooperation, and the combination of cutting-edge technology such as Artificial Intelligence (AI) and blockchain. To safeguard vital infrastructure and guarantee a continuous supply of energy, defense-in-depth strategies such as encryption protocols, and access controls can be put into place in conjunction with cybersecurity awareness campaigns and regular security audits. In the face of changing cyber threats, these initiatives are crucial to protect data privacy and system resilience for the development of sustainable energy. This study aims to provide important insights into current practices, identify areas for improvement, and provide insight for further studies and regulations intended to improve the energy infrastructure’s security and resilience in the digital era.

Due to the volume of multimedia sensed data, a network of Multimedia Internet of Things (MIoT) devices faces various challenging constraints, most notably in terms of communication overhead, power consumption, and memory usage. A set of these MIoT devices is unable to overcome the large data-size challenge via the use of the Lossy Multimedia Compression (LMC) such as JPEG and BPG since they are limited in memory and computation. Instead, in this paper, we propose to down-scale images at MIoT devices with a factor of 2, 3 or ≥ 4, which reduces the memory consumption, computation, and communicated data size and consequently the latency and energy consumption. To recuperate high-quality images, we apply a Deep Learning (DL) denoising/super-resolution model at the server-side. On the other hand, as MIoT devices use a wireless connection, there is a higher risk of transmission packets loss compared to a wired connection. Almost, packets loss are managed through costly data re-transmissions or data redundancy. However, these solutions with intrinsically voluminous data such as the multimedia one are costly, especially for limited MIoT devices. To overcome this challenge, the denoising/super-resolution model did also undergo a training model to retrieve high-quality images from down-scaled erroneous ones. The obtained results show how effective this proposed solution is, especially when it comes to the enhancement of visual quality of down-scaled and erroneous images with minimum communication, latency, and consequently resource overhead.

In this work, we propose three efficient variants of a message authentication encryption (MAE) algorithm, which is based on the dynamic key-dependent concept and dynamic operation mode to reach a high level of security. These variants consist of a single pass and a single round, in addition to the use of common operations for the encryption and authentication processes to reduce the required execution time and resources. Accordingly, the proposed scheme outperforms the existing solutions that are based on the static approach with multiple rounds. Furthermore, to reduce the overhead associated with the regeneration of the dynamic key and the corresponding cryptographic primitives, we propose a simple, yet effective update process. In such a scheme, even when the same plaintext is processed, it will be encrypted and authenticated using different cryptographic primitives (substitution and permutation tables in addition to round keys), which guards against the existing cryptanalysis techniques. The experimental results show that the proposed MAE variants are more efficient than the counter with cipher block chaining message authentication code (CCM), Galois message authentication code (GMAC), offset codebook mode (OCB), and the Chacha20-poly1305. The best performance is achieved with the third MAE variant that presents a high throughput with an enhancement of at least 373% compared to CCM, 90% compared to GCM, 23% compared to OCB, and 22% compared to Chacha20-poly1305.

High number of rounds is needed for the existing message authentication algorithms, such as keyed hash functions like Hash-based Message Authentication Code (HMAC) or block cipher based functions like Cipher-based Message Authentication Code (CMAC) and Galois Message Authentication Code (GMAC). Moreover, the employed compression functions consist of several operations to achieve two main properties: confusion and diffusion. This large number of rounds introduces high overhead for resource-limited systems like Internet of Things (IoT) or delay-sensitive systems that have real-time requirements like Intelligent Transparent Systems. In this paper, a new lightweight message authentication algorithm is proposed to reduce the number of rounds to one. The proposed compression function is based on the RC4 stream cipher to reduce the required overhead in terms of latency and resources. Finally, the security and performance analysis shows that the proposed keyed hash function is resistant towards existing security attacks with low resources overhead.

Security and privacy are among the most pressing concerns that have evolved with the Internet. As networks expanded and became more open, security practices shifted to ensure protection of the ever growing Internet, its users, and data. Today, the Internet of Things (IoT) is emerging as a new type of network that connects everything to everyone, everywhere. Consequently, the margin of tolerance for security and privacy becomes narrower because a breach may lead to large-scale irreversible damage. One feature that helps alleviate the security concerns is authentication. While different authentication schemes are used in vertical network silos, a common identity and authentication scheme is needed to address the heterogeneity in IoT and to integrate the different protocols present in IoT. We propose in this paper an identity-based authentication scheme for heterogeneous IoT. The correctness of the proposed scheme is tested with the AVISPA tool and results showed that our scheme is immune to masquerade, man-in-the-middle, and replay attacks.

Internet of Things (IoT) is considered to be the Internet of the future. Thus, a lot of effort is being invested in finding the best design for a global IoT architecture. Recently, Software-Defined Networking (SDN) surfaced as a new networking paradigm that aims to centralize the network control and to separate the control and the data planes. Thus, one can benefit from SDN to abstract the major management complexities residing in this ubiquitous network of networks. Therefore, dealing with the huge amount of generated data in such network will be a major challenge; so, adopting advanced data technologies (cloud and fog computing) will be essential for the new architecture. In this paper, we review work done concerning the application of SDN to the IoT. Also, we propose and analyze a new SDN-based IoT architecture characterized by the centralization of the network control and the decentralization of the data management.