The aim of this study survey is to analyze energy-efficient smart home load forecasting techniques and determine the usage of energy or power with high spectrum allocation in future smart home with the help of clustering in data mining. The study work starts presenting an overview of the smart home energy sector and the challenges it is facing; it is observed a change on the energy policies promoting the energy efficiency, encouraging an active role of the consumer, instructing them about the importance of the consumer behavior and protecting consumer rights. Electricity is gaining room as energy source; its share will keep increasing constantly in the following decades. In this close future, smart homes and smart meters' deployment will benefit both the utility and the consumer. In this environment, new services and new business appear, focusing on the energy management field and tools, they require specialization in fields such as, computer science, software development and data science. This study work has segmented the smart home according to the similarities of their electrical load profiles, using the proportion of energy usage per hour (%) as a common framework with analysis done in this proposed research. The objective behind this energy consumption segmentation is to be able to provide personalized recommendations to each group to reduce their energy consumption and the associated costs, fostering energy efficiency measures and improving the consumer engagement for future smart homes.

The Healthcare Internet of Things (H-IoT) has made significant strides in transforming remote diagnostics, real-time monitoring, and data collection. However, achieving low latency remains a challenge, even for systems that critically depend on it, such as emergency response systems and remote surgeries. Fog computing (FC) emerges as a promising solution to mitigate latency issues by bringing computational resources closer to edge devices. This review provides a comprehensive analysis of latency optimization approaches within the H-IoT, framed within the context of FC. The study focuses on architectural enhancements, data communication methods, resource management strategies, and load distribution techniques aimed at reducing latency. Additionally, it evaluates existing challenges related to energy efficiency and trade-offs, security considerations, and scalability potential. Based on an analysis of recent advancements in the field, this study outlines potential development trends and future research directions for building adaptive, fast, and secure H-IoT systems. The study reaffirms the pivotal role of FC in shaping the future of healthcare services, particularly those with stringent low-latency requirements.

Mobile Ad Hoc Networks (MANETs) are decentralized, infrastructure-less wireless systems that rely heavily on efficient routing protocols due to their dynamic and resource-constrained nature. Dynamic Source Routing (DSR) is widely adopted for such environments; however, it suffers from notable limitations, including cache saturation, memory inefficiencies, and lack of link quality awareness. These weaknesses reduce performance in high-mobility or large-scale scenarios and limit the reliability of DSR in real-world applications like military or disaster recovery networks. Motivated by these challenges, this paper proposes a comprehensive enhancement to DSR through intelligent caching strategies to improve scalability, energy efficiency, and route reliability. The proposed model introduces a dynamic route compression technique that reduces route size by eliminating redundant intermediate nodes. It also incorporates a hierarchical caching scheme where clusters managed by energy-aware Cluster Heads (CHs) distribute cache responsibilities, thereby improving scalability and reducing overhead. In addition, link quality metrics such as residual energy and link duration are integrated into route selection, enabling more stable and energy-efficient communication paths. Simulations using NS-2 across varying network sizes (50, 75, 100 nodes) revealed that the enhanced DSR significantly improves key performance metrics such as Packet Delivery Ratio (PDR), routing overhead, cache hit rate, and energy consumption. Route compression alone yielded a 14.3 PDR improvement and 21% reduction in overhead, while the hierarchical approach further optimized network performance. These results confirm the feasibility and effectiveness of the proposed enhancements, addressing longstanding gaps in traditional DSR implementations and laying a foundation for scalable and intelligent MANET routing protocols.

The proliferation of IoT deployments has exposed critical vulnerabilities in MQTT-based systems, particularly ciphertext-repetition leakage in telemetry encryption, where identical plaintext blocks under a fixed key—common for static sensor readings—collapse ciphertext entropy and expose traffic patterns. We present a parallel multibroker MQTT framework with two encryption variants: a hybrid AES-ChaCha20 cipher (NeoCipher) and an 8D chaotic system-enhanced variant (Chaos-NeoCipher). While AES-128 and base NeoCipher exhibit leakage (entropy falling to 3.9–4.0 bits/byte), Chaos-NeoCipher mitigates it entirely (7.996 bits/byte) through chaotic key schedule advancement on each invocation. Across 100+ nodes, 10 brokers, and five attack scenarios, consensus-based detection achieves near-ceiling performance governed by protocol logic rather than cipher choice. Chaotic tuning via Lyapunov sweep ($\mathrm{\lambda }$ ≈ 0.54 bits/iteration) and batched key-schedule optimization reduce overhead from +39.9 to +17.6%. Our results satisfy key IoT requirements: sub-millisecond cryptographic processing latency (measured as encryption/decryption time only, not including network transmission, broker queuing, or TCP overhead), near-complete detection across five attack classes under controlled simulation conditions, and mitigated single-point-of-failure vulnerability.

The advanced encryption standard-lightweight encryption algorithm (AESLEA) hybrid algorithm (ALESA) addresses a critical gap in cryptographic systems by solving the inherent trade-off between high security and computational efficiency. While the AES offers robust security, its complex operations result in high latency and energy costs, making it less suitable for resource-constrained environments. Conversely, lightweight alternatives like the LEA provide high speed but potentially weaker diffusion properties. This paper proposes a novel hybrid encryption model that strategically integrates AES and LEA by replacing AES’s computationally intensive MixColumns transformation with a streamlined LEA-based operation. This solution delivers the best of both paradigms: the security strength of AES and the operational efficiency of LEA, while also demonstrating superior statistical security by passing all NIST tests with higher p-values and maintaining near-optimal entropy. The hybrid ALESA algorithm thus presents an ideal, balanced solution for applications requiring both strong security guarantees and high performance, particularly in IoT and large-scale data encryption scenarios.

American scientific publishing group