This survey delves into cross-layer energy-efficient solutions and cutting-edge security measures for Internet of Things (IoT) networks. The conventional security techniques are considered inadequate, leading to the suggestion of AI-powered intrusion detection systems and novel strategies such as blockchain integration. This research aims to promote the development of smart cities by enhancing sustainability, security, and efficiency in the industrial and agricultural sectors through the use of IoT, blockchain, AI, and new communication technologies like 5G. In this paper, we provide a comprehensive review and analysis of secure and energy-efficient cross-layer IoT frameworks based on survey of more than 100 published research articles. We highlight the significance of developing IoT security for robust and sustainable connected systems. We discuss multi-layered security approaches and ways to enhance the energy efficiency of resource-constrained devices in IoT networks. Finally, we identify open research issues and future research directions in the emerging field of cross-layer design for secure and energy-efficient IoT networks. In order to improve cybersecurity and efficiency in smart cities, the research also focuses on developing a secure, energy-efficient IoT framework integrating blockchain, artificial intelligence, and quantum-safe cryptography.

This survey delves into cross layer energy-efficient solutions and cutting-edge security measures for Internet of Things (IoT) networks. The conventional security techniques are considered inadequate, leading to the suggestion of AI-powered intrusion detection systems and novel strategies such as blockchain integration. In this paper, we provide a comprehensive review and analysis of secure and energy efficient cross-layer framework for IoT.We highlight the significance of developing IoT security for robust and sustainable connected systems. We discuss multi-layered security approaches and ways to enhance energy efficiency of resource-constrained devices in IoT networks. Finally, we identify open research issues and future research directions in the emerging field ofcross-layer design for secure and energy efficient IoT networks. We expect this study will stimulate further research endeavors to build a secure and energy-efficient framework for next-generation IoT systems.

The widespread adoption of the Internet of Things (IoT) raises significant concerns regarding security and energy efficiency, particularly for low-resource devices. To address these IoT issues, we propose a cross-layer IoT architecture employing machine learning (ML) models and lightweight cryptography. Our proposed solution is based on role-based access control (RBAC), ensuring secure authentication in large-scale IoT deployments while preventing unauthorized access attempts. We integrate layer-specific ML models, such as long short-term memory networks for temporal anomaly detection and decision trees for application-layer validation, along with adaptive speck encryption for the dynamic adjustment of cryptographic overheads. We then introduce a granular RBAC system that incorporates energy-aware policies. The novelty of this work is the proposal of a cross-layer IoT architecture that harmonizes ML-driven security with energy-efficient operations. The performance of the proposed cross-layer system is evaluated by extensive simulations. The results obtained show that the proposed system can reduce false positives up to 32% and enhance system security by preventing unauthorized access up to 95%. We also achieve 30% reduction in power consumption using the proposed lightweight Speck encryption method compared to the traditional advanced encryption standard (AES). By leveraging convolutional neural networks and ML, our approach significantly enhances IoT security and energy efficiency in practical scenarios such as smart cities, homes, and schools.

A secure and energy-efficient network architecture is essential due to the rapid proliferation of Internet of Things (IoT) devices in critical sectors such as healthcare, smart cities, and industrial automation. In this paper, we propose a secure and energy-efficient cross-layer IoT architecture. The security features and energy-saving techniques across various open-system interconnected protocol layers are incorporated in the proposed architecture. The improved security and energy efficiency are achieved using the lightweight Speck and Present ciphers, as well as adaptive communication strategies. The system performance is evaluated by testbeds and extensive simulation experiments using Cooja (Contiki operating systems) and NS-3. The simulation results obtained show a 95% attack mitigation effectiveness, 30% reduction in energy usage, and 95% packet delivery ratio. In a 20-node network scenario, Speck uses 5.2% less radio power than the Advanced Encryption Standard (AES), making it the best tradeoff among the investigated encryption techniques. The NS-3 simulation results confirm that lightweight encryption, such as Present and Speck, uses much less power than the traditional AES, which makes them more appropriate for IoT contexts with limited energy. The scalability across various IoT contexts is ensured through a hybrid assessment approach that combines hardware testbeds and simulation for system validation. Our research findings highlight opportunities for advancing IoT systems toward secure and energy-efficient smart ecosystems.

IEEE 802.11be, commercially known as Wi-Fi 7, extends wireless local area network (WLAN) capability through wider channel bandwidths, higher-order modulation, and tri-band operation. However, realised indoor performance is still strongly affected by radio propagation conditions. This study presents a controlled empirical assessment of Wi-Fi 7 behaviour in a multi-storey university building by examining throughput and received signal strength (RSS) across the 2.4 GHz, 5 GHz, and 6 GHz bands using a single-link measurement setup. Six experimental scenarios were used to examine distance variation, wall penetration, line-of-sight (LOS) obstruction, floor separation, antenna orientation, and microwave interference. The measured RSS values were compared with the free-space, two-ray ground reflection, and log-distance shadowing models using mean absolute error (MAE). Six experimental scenarios were designed to isolate dominant indoor impairments, including distance variation, wall penetration, line-of-sight obstruction, floor separation, antenna orientation, and microwave interference. Measured RSS values were evaluated against free-space, two-ray, and log-distance shadowing models using mean absolute error as the comparison metric. Results show that 2.4 GHz retains greater penetration at lesser capacity, while 6 GHz offers the maximum short-range throughput under clear line-of-sight conditionsbut rapidly deteriorates with structural attenuation. Performance in all bands is greatly diminished by multi-wall blockage and line-of-sight loss. A single propagation model cannot adequately capture the divergence introduced by increasing distance and indoor attenuation, while short-range line-of-sight conditions more closely resemble deterministic predictions in terms of measured RSS alignment. Overall, the results highlight the trade-off between Wi-Fi 7’s capacity and coverage, and provide helpful advice for choosing frequencies, positioning access points, and organizing indoor coverage. The research findings provide insights into the practical deployment of next-generation Wi-Fi in multi-story buildings and residential houses.

As the number of natural and man-made catastrophes has increased in recent years, there has been an increasing need for quicker and more efficient disaster response. Information from traditional sources, such as radio, television, and websites, is sometimes incomplete or delayed. While mobile applications provide a means of enhancing real-time crisis communication, a secure mobile app-based solution has not been fully explored yet. In this paper, we propose a secure and scalable cross-layer disaster management system architecture. To validate the system performance, we developed a user-centred, scalable mobile application known as the disaster emergency events application (DEAPP) for real-time disaster reporting and visualization including disaster notifications and observing the affected areas on an interactive map. The solution connects a web-based backend, cloud database, and native Android mobile app via a cross-layer architecture. Role-based access control, HTTPS connection, and verified event publication all contribute to security. Moreover, Redis caching is employed to expedite data access in emergency situations. The need to verify publicly filed reports to prevent false alarms, safeguard real-time data transfer without slowing down the system, and create an intuitive user interface for individuals in high-stress circumstances are some of the issues that the project attempts to solve. The results obtained show that a mobile system that is secure, scalable, and easy to use can enhance catastrophe awareness and facilitate quicker emergency responses. For developers, researchers, and emergency organisations looking to leverage mobile technology for disaster preparedness, the findings provide helpful insights.