The Internet of Things (IoT) has become deeply embedded in modern society, enabling applications across smart homes, healthcare, industrial automation, and environmental monitoring. However, as billions of interconnected devices continuously collect and exchange sensitive data, privacy and security concerns have escalated. This survey systematically reviews the state-of-the-art privacy-preserving techniques in IoT systems, emphasizing approaches that protect user data during collection, transmission, and storage. Peer-reviewed studies from 2016 to 2025 and technical reports were analyzed to examine applied mechanisms, datasets, and analytical models. Our analysis shows that blockchain and federated learning are the most prevalent decentralized privacy-preserving methods, while homomorphic encryption and differential privacy have recently gained traction for lightweight and edge-based IoT implementations. Despite these advancements, challenges persist, including computational overhead, limited scalability, and real-time performance constraints in resource-constrained devices. Furthermore, gaps remain in cross-domain interoperability, energy-efficient cryptographic designs, and privacy solutions for Unmanned Aerial Vehicle (UAV) and vehicular IoT systems. This survey offers a comprehensive overview of current research trends, identifies critical limitations, and outlines promising future directions to guide the design of secure and privacy-aware IoT architectures.

An integrated micro- and nano-grid with net-zero renewable energy is a sophisticated energy system framework aimed at attaining optimal efficiency and sustainability. This survey paper examines several contemporary research works in this domain. This document summarizes the latest papers selected for analysis to comprehend the current state-of-the-art, integration process, methodology, and research gaps. The objective of this review is to identify existing trends and ongoing transformations in this domain. At the conclusion of the study, emerging technologies for smart grid integration are offered, emphasizing Transactive Control, Blockchain Technology, and Quantum Cryptography, based on existing research gaps. Microgrids and nano-grids are localized energy systems capable of functioning alone or in tandem with larger power grids, offering resilience and adaptability. By incorporating renewable energy sources like solar, wind, and storage devices, these networks can produce and regulate energy locally, guaranteeing that the generated energy meets or surpasses the energy used. The incorporation of intelligent technology and control systems facilitates optimized energy distribution, real-time monitoring, and load balancing, advancing the objective of net-zero energy use. This strategy not only bolsters energy security but also markedly decreases carbon emissions, rendering it an essential element in the shift towards a sustainable and resilient energy future. The worldwide implementation of interconnected micro- and nano-grids utilizing net-zero renewable energy signifies a pivotal transition towards a sustainable and resilient energy future. These localized energy systems can function independently or in conjunction with conventional power grids, utilizing renewable energy sources like solar, wind, and advanced storage technology. Integrating these resources with intelligent control systems enables micro- and nano-grids to optimize energy production, distribution, and consumption at a detailed level, ensuring that communities and companies globally can attain net-zero energy usage. This method not only diminishes greenhouse gas emissions and reliance on fossil fuels but also improves energy security and grid stability in various places. These technologies, when implemented globally, provide a scalable answer to the issues of energy access, environmental sustainability, and climate change mitigation, facilitating a cleaner and more equal energy landscape worldwide.

We are living in the era of IoT systems where this technology supports our daily life in various aspects, whether it is our daily used Apple watches, our smart home systems, or our laptops. The goal is to make our everyday activities more convenient and more accessible by means of connectivity anytime, anywhere. The growth of IoT-based systems booms in the post-2020s with advanced network technologies like 5G and Edge Computing. The IoT-based systems are comparatively cost-effective and convenient to apply, especially in remote operations where manual intervention is impossible. Like other technologies, IoT applications also come with flaws and concerns. One of the main severe concerns nowadays is privacy and security issues related to IoT systems. Knowingly or unknowingly, we are being monitored by smart sensors or edge devices almost every moment, and our personal and professional sensitive information is being exposed to untrusted third parties like Google or Amazon. This is becoming a serious concern with the rapid expansion of IoT-based systems. In this survey paper, we have tried to categorically list and present the state-of-the-art techniques for privacy preservation in IoT-based systems in various application fields. Our work is a summary based on the 39 papers and two online reports that we chose to analyze to understand the current situation, privacy attacks, how to handle the sensitive data of the clients without breaching privacy and future directives in this fast-growing IoT-based systems.

This paper evaluates residential energy management systems (EMSs) that combine on-site renewable generation and battery energy storage in an all-electric house. This work compares four levels of control complexity: baseline operation, deterministic rule-based control, an optimization-based benchmark, and adaptive control using machine learning, predictive control, and a transactive framework. A calibrated gray-box house model based on the Archetype Sustainable House in Vaughan, Ontario, was used to test each strategy under the same operating assumptions. The comparison shows a clear trade-off between simplicity and performance. Deterministic load-shifting strategies are easy to implement but deliver the lowest savings. The optimized controller provides a practical upper bound on achievable performance. The machine-learning controller, trained from optimized historical operation, produced the strongest annual savings and outperformed deterministic control by a range of about 15–22%. Predictive control showed promise, but its demonstration was limited by forecast-data quality; more than 40% of collected forecast files were unusable, leaving only a 10-day continuous case study. A transactive energy management system delivered moderate direct savings, but its main value was flexibility, agent-based coordination, and future applicability to community-scale control. Experimental work further showed that 98% of an air-source heat pump peak-hour load could be shifted using battery control hardware. Despite these technical benefits, this study finds that battery-supported residential EMSs remain financially unattractive under the electricity prices and battery costs considered here. The results suggest that the most realistic path forward is not a one-size-fits-all controller, but a staged transition from simple battery logic to adaptive and transactive control as hardware prices fall, data quality improves, and homes become more connected.