The journal Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and communication and information systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like:

    Signal Processing
    Power Engineering (including renewable and green energies)
    Artificial Intelligence - methods and applications
    Security
    Privacy
    Communication

The journal regularly publishes special sections covering specific topics of interest. Proposals for special sections should be submitted to the Editor-in-Chief. The list of current special sections can be found at https://www.sciencedirect.com/journal/computers-and-electrical-engineering/special-issues.

Special Issue on Protection Technologies for Data Security and Compliance
Submission Date: 2026-02-01

The deep integration of emerging network technologies has given rise to a "cloud-edge-end" network service architecture, underscoring data's pivotal role as a next-generation production asset. While the integrated architecture offers essential support for diverse network services, its complex service processes and environments introduce heightened security risks, such as privacy breaches, data corruption, and data abuse, thus posing new challenges to traditional data privacy protection technologies. In addition, the increasingly-perfect data privacy regulations impose stricter security goals on data services, mandating the protection of privacy, integrity, and compliance in the data use process. Distinctly, the challenges of data security and compliance protection are critical determinants of the availability and practicability of next-generation network services, driving the rapid development and application of advanced data security and compliance technologies for the complex "cloud-edge-end" environment.

The field of data security and compliance is navigating a "spiral" development trajectory characterized by iterative exploration and validation of new security challenges, which requires the collective attention and rigorous investigation from academia and industry. These security challenges encompass a wide range of aspects, including but not limited to the privacy, integrity, and compliance of various data types (e.g., data set, data streaming, database) in the process of collection, transmission, storage, sharing, and application, as well as the integrated on-chain and off-chain security architecture within the "cloud-edge-end" network. To address these challenges, this special issue seeks insights from scholars and practitioners in data security to collaboratively construct a robust theoretical and technical framework for data security and compliance. To this end, this special issue endeavors to disseminate the latest theoretical advancements and cutting-edge technical achievements in data security and compliance protection.

Guest editors:

Xinyi Huang
Jinan University, Guangzhou, China

Yang Xiang
Swinburne University of Technology, Melbourne, Australia

Elisa Bertino
Purdue University, West Lafayette, Indiana, United States of America

Manuscript submission information:

Manuscript submission deadline: 1 February 2026

You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact the Guest Editors.

Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: Protection Technologies” when submitting your manuscript online at the journal’s submission platform Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on the Journal Homepage.

Keywords:

Advanced Data Encryption; Attacks and Defense Schemes; Access Control; Big Data Protection; Integrity and Privacy; Blockchain Security; Blockchain Data Regulation; Compliant Data Process; Controlled Data Sharing; Data Ownership Transfer

Special Issue on Privacy-Preserving and Secure Large Language Models for Unmanned Aerial Vehicles (UAVs)
Submission Date: 2026-03-16

The integration of large-scale language models (LLMs) in Unmanned Aerial Vehicles (UAVs) opens new opportunities to improve the intelligence, reliability and system security of UAVs. LLMs are significantly advancing UAV systems by enhancing autonomy, decision-making, human-machine interaction, and operational efficiency. However, this integration also brings challenges in terms of data privacy, security and robustness. For example, with privacy-preserving mechanisms based on homomorphic encryption, UAVs can collaborate with third-party organizations to develop LLMs models while protecting system privacy. In addition, the federated learning architecture allows multiple organizations to co-train knowledge graph models without sharing local data, which improves the efficiency of knowledge discovery in UAVs and promotes the integration and sharing of UAVs resources.

The application of LLMs in UAVs raises significant concerns about data security and system privacy because UAVs data involves system privacy and sensitive information. Especially when dealing with a large amount of UAVs data for LLMs training, ensuring data security becomes particularly important. In addition, the content generated by LLMs may contain biases or errors, and extra caution is needed when used for decision support. Issues such as countering attacks and model interpretability likewise challenge the application of LLMs in the field of UAVs.

This special issue focuses on how to protect the security and privacy when integrating LLMs with UAVs. Topics of interest include, but are not limited to, the following:

    Privacy-Preserving Training for LLMs in UAV​
    Deployment of Sensitive Data-Driven LLMs in UAV Systems
    Federated Learning Strategies for Collaborative LLM Model in UAV Networks
    Differential Privacy Methods for LLM-Driven Data Analysis in UAV Applications
    Secure Multi-Party Computation for LLMs in UAV Systems
    LLM-Supported Self-Sovereign Identity Management for UAVs
    Blockchain-Enabled Auditable and Secure LLM for UAV Data Integrity
    Adversarial Robustness Enhancement for LLM Applications in UAV Systems
    Privacy-Preserving UAV Electronic Records Using LLMs
    Regulatory Compliance and Data Governance for LLM-Driven UAV Operations
    Secure Multimodal LLMs for UAV Systems
    Secure Intelligent Scheduling in UAVs and LLMs
    Secure distributed LLM Model Training and Deployment for UAV Systems
    Homomorphic Encryption Techniques for UAV-Based LLM Applications

Guest editors:

Weizhi MengTechnical University of Denmark, Lyngby, Denmark

Yongjun Ren
Nanjing University of Information Science and Technology, Nanjing, China

Rishikesh Sahay
University of Illinois Springfield, Springfield, Illinois, United States of America

Manuscript submission information:

Manuscript submission open date: 15 September 2025Manuscript submission deadline: 16 March 2026

You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact the Guest Editors.

Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: PPS-LLM-UAV” when submitting your manuscript online at the journal’s submission platform Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on the Journal Homepage.

Keywords:

Large Language Model; Unmanned Aerial Vehicles; Security; Data Privacy; Data Governance

Special Issue on Security and Privacy in Low-Altitude Intelligent Transportation System
Submission Date: 2026-03-31

The special issue on Security and Privacy in Low-Altitude Intelligent Transportation Systems (LITS) addresses the unique challenges pertaining to security and privacy raised by the integration of drones, UAVs, and autonomous aerial vehicles in low-altitude landscapes. The manuscript collections, which feature advanced encryption methods, safe communication protocols, AI-based anomaly and intrusion detection, and innovative solutions that ensure privacy, have the potential to safeguard the Low-Altitude Intelligent Transportation System and address the limitations of aerial networks, such as their limited bandwidth and high mobility. This unique collection of papers contributes to framing the robust security frameworks and provides abundant privacy to pave the way for intelligent transportation technologies in low-altitude airspaces.

Low-altitude intelligent transportation systems (LITS), which incorporate emerging technologies like artificial intelligence (AI), real-time data analytics, and advanced communication networks, enhance the efficiency, safety, and sustainability of the system. The cutting-edge transportation system, such as drones and unmanned aerial vehicles (UAVs), promotes optimized traffic flow, faster goods delivery, surveillance, and emergency services; mitigates congestion and emissions; and stimulates the simple lives of people living in densely populated areas. Moreover, the leading global courier services have already started courier delivery, and eVTOL (electric vertical takeoff and landing) aircraft and air taxis will ease life by air-commuting. We anticipate the arrival of eVTOL and air taxis in New York and Dubai by 2025. Additionally, Garden View asserts that the global market for UAVs and drones will reach a value of USD 19.89 billion in 2022 and grow at a compound annual growth rate (CAGR) of 13.9 percent from 2023 to 2030.

Despite so many advantages and a huge market size, LITS may suffer from severe security and privacy concerns. The reliance on wireless communications technologies makes them susceptible to cyberattacks (hacking, jamming, spoofing, etc.), data breaches, and unauthorized access. Therefore, ensuring tailored encryption, secure communication protocols, and real-time threat detection algorithms is opening up research challenges for safe and secure air mobility. AI-generated attacks, such as adversarial attacks, data poisoning, and AI-driven cyber physical attacks, may also affect the efficiency of LITS. Therefore, developing comprehensive solutions to combat AI-generated attacks is a crucial concern. Privacy concerns are equally important because LITS contains sensitive information, such as personal identifiers and location data. Therefore, it is crucial to provide robust privacy mechanisms for LITS, such as anonymization, secure storage, and access control.

We encourage researchers, academicians, and practitioners who are involved in security and privacy concerns of multifaceted LITS to submit high-quality manuscripts to redefine the transportation system.

    Novel secure architecture for LITS
    Federated learning-based security and privacy solutions for LITS
    Intrusion detection in LITS
    Adversarial attack-resistant models for LITS
    Machine learning attack-resistant protocols for LITS
    Anomaly and malware detection in LITS
    Secure Data Analytics in LITS
    Light-weight security protocols for resource-constrained LITS devices
    Secure interoperability of LITS with IOT, metaverse, etc.
    Blockchain-based secure model for LITS
    GPS data spoofing and data jamming attacks for LITS
    Authentication, authorization, and access control for LITS
    Location Privacy of UAVs

Guest editors:

Saru Kumari
Chaudhary Charan Singh University, Meerut, India

Marko Hölbl
University of Maribor, Maribor, Slovenia

Jia Hu
University of Exeter, Exeter, United Kingdom

Manuscript submission information:

Manuscript submission open date: 10 August 2025
Manuscript submission deadline: 31 March 2026

You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact the Guest Editors.

Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: Security and Privacy in LITS” when submitting your manuscript online at the journal’s submission platform Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on the Journal Homepage.

Keywords:

Low-Altitude Intelligent Transportation System; Security and Privacy; Attack Resilience; Secure Architecture and Framework

Special Issue on Deep Learning for Real-Time Prediction and Optimization in Renewable Energy Systems
Submission Date: 2026-05-16

Renewable energy system improvement is an infrastructure for energy strategy that assists clients achieve their energy efficiency and saving money objectives by providing simultaneous, multi-technology integration and evaluation options. Electricity production, water and heating and cooling of spaces, and transportation can all be accomplished with energy from renewable sources. In contrast, energy that is not renewable is derived from limited resources like coal, oil, and natural gas.

Along with many other advantages including enhancing air quality, safeguarding the environment, and enhancing energy security, optimising energy use can result in significant cost savings. By modifying the power injection to be perpendicular to the load current, energy optimization increases ride-through capability without compromising energy storage capacity. Emissions of greenhouse gases are greatly decreased by improving energy consumption across multiple industries, which encourages a sustainable future. The concept of renewable energy application emphasises the use of dual fuels under a variety of circumstances to maximise the potential of renewable energy sources, such as LPG, natural gas, and hydrogen, particularly in low load situations to efficiently handle the unpredictability of these sources. To precisely forecast wind and solar energy production, renewable system operators combine weather data, satellite data, statistical techniques, and numerical models for weather prediction. When historical and real-time data are accessible, forecasting methods become more accurate. A variety of fields and viewpoints are welcome to contribute, including but not limited to: Deep Learning for Real-Time Prediction and Optimization in Renewable Energy Systems.

The following topic of interest can be included but not restricted:

    Deep Learning Networks for Real-Time Solar Power Prediction.​
    Forecasting Wind Power with Real-Time Deep Learning Techniques.
    Deep Learning-Based Energy Storage System Optimization in Renewable Energy Networks.
    Deep Learning-Based Fault Detection and Predictive Maintenance for Solar and Wind Farms.
    Deep Neural Networks for Short-Term Load Forecasting in Renewable Energy-Powered Smart Grids.
    Combining Solar and Wind Power Prediction with Hybrid Deep Learning Models.
    Optimising Energy Demand Response in Renewable-Integrated Systems using Deep Reinforcement Learning.
    Autoencoders and GANs for the Identification of Anomalies in Renewable Energy Systems.
    Energy Pricing Optimization for Renewable Power Markets Using Deep Learning.
    Using Transfer Learning to Forecast Solar Power Generation under Cloudy Circumstances.
    Optimising Smart Grids in Renewable Networks using Deep Learning-Driven Control Mechanisms.
    Optimisation of Renewable Energy-Powered Electric Vehicle Charging Stations in Real Time.
    Deep Learning-Based Power Quality Improvement in Renewable Energy Systems approach.

Guest editors:

Thomas O Olwal
Tshwane University of Technology, Pretoria, South Africa

Karim Djouani
University Paris-Est Créteil Val de Marne, Créteil, France

Olumide Alamu
University of Lagos, Yaba, Nigeria

Manuscript submission information:

Manuscript submission deadline: 16 May 2025

You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact the Guest Editors.

Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: DL for Real-Time Prediction and Opt in RES” when submitting your manuscript online at the journal’s submission platform Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on the Journal Homepage.

Keywords:

Deep Learning; Machine Learning; Artificial Intelligence; Real-Time Prediction; Optimisation; Renewable Energy Systems; Smart Grids; Predictive Maintenance

Special Issue on Recent Advances in Multimedia and Multimodal Data Security
Submission Date: 2026-05-31

With the development of Internet technologies, large volumes of multimedia and more generally, multimodal data, can easily be shared among different users or entities for the purpose of diagnosis, analysis, search and recommendation, prediction etc. in a variety of critical application contexts with no hard-copy backups being kept. Due to new development in science and technology, different methods are used to copy, recreate, distribute and store these contents easily for many real-world applications. However, various criminal offences such as identity theft, copyright violation, misuse of personal and medical information, authenticity and confidentiality is being made every day in our daily life and greatly impact on financial loss. Malicious actors can extract sensitive information or manipulate outcomes. These challenges are particularly critical in multimedia and multimodal environments, where the convergence of different data types—such as images, audio, and video—presents unique opportunities and risks. Applications span diverse domains, including smart healthcare, intelligent transportation, industrial automation, robotics, media and entertainment, legal proceedings, news, insurance, and business sectors. Therefore, addressing these challenges has been an interesting problem for researchers in the field. Also, by using techniques such as data augmentation and generative adversarial networks, we can automatically generate large-scale multimedia and multimodal data to train the AI models. However, new attacks are still being developed and the protection of the multimedia systems from these attacks will continue to be crucial. Motivated by these facts, this special issue targets the researchers from both academia and industrial to explore and share new ideas, approaches, theories and practices with focus on multimedia and multimodal data security and privacy solutions for real-world applications.

Guest editors:

Amit Kumar Singh
National Institute of Technology Patna, Patna, India

Stefano Berretti
University of Florence, Florence, Italy

Manuscript submission information:

Manuscript submission open date: December 31, 2025
Manuscript submission deadline: May 31, 2026

You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact the Guest Editors.

Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: Multimodal Data Security” when submitting your manuscript online at the journal’s submission platform Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on the Journal Homepage.

Keywords:

Multimedia; Multimodal; Data Hiding; Encryption; Security; Privacy; Blockchain; Cybersecurity; Social Media; Biometrics; Attacks; Forensics; Deepfake