Water distribution systems are one of the critical infrastructures and major assets of the water utility in a nation. The infrastructure of the distribution systems consists of resources, treatment plants, reservoirs, distribution lines, and consumers. A sustainable water distribution network management has to take care of accessibility, quality, quantity, and reliability of water. As water is becoming a depleting resource for the coming decades, the regulation and accounting of water in terms of the above four parameters is a critical task. There have been many efforts towards the establishment of a monitoring and controlling framework, capable of automating various stages of the water distribution processes. The current trending technologies such as Information and Communication Technology (ICT), Internet of Things (IoT), and Artificial Intelligence (AI) have the potential to track this spatially varying network to collect, process, and analyze the water distribution network attributes and events. In this work, we investigate the role and scope of the IoT technologies in different stages of the water distribution systems. Our survey covers the state-of-the-art monitoring and control systems for the water distribution networks, and the status of IoT architectures for water distribution networks. We explore the existing water distribution systems, providing the necessary background information on the current status. This work also presents an IoT Architecture for Intelligent Water Networks - IoTA4IWNet, for real-time monitoring and control of water distribution networks. We believe that, these components need to be designed and implemented effectively to build a robust water distribution network.

Power outages can severely affect individuals, businesses, and communities, leading to disruptions, economic losses, and safety risks. The existing power recovery strategies often fail to adequately address the challenges associated with such outages. These challenges encompass a range of complexities, including resource allocation disparities, efficient prosumer integration, energy demand variability, and isolated generators. This paper presents a microgrid-centric power recovery strategy that leverages IoT, blockchain, smart contracts, and optimisation techniques for peer-to-peer energy sharing within the microgrid. The proposed strategy comprehensively addresses the challenges associated with the existing power recovery strategies. The paper outlines the system architecture for IoT and blockchain-enabled microgrids, discusses the mathematical modelling for energy sharing, and explores cost-optimal power restoration strategies. An incentive mechanism motivates prosumers to support restoration strategies during outages. Furthermore, the paper describes a blockchain smart contract facilitating peer-to-peer energy exchange in regions affected by power outages. This approach can mitigate the disruptive impact of power outages by providing reliable and community-centric power recovery solutions. Through validation with real-world data from our university’s distribution grid test bed, Mean Time To Recover (MTTR) analysis and performance evaluations using the Hyperledger Caliper benchmark tool, this paper demonstrates its feasibility and effectiveness, paving the way for enhanced power recovery strategies and increased resilience in the face of energy disruptions.

Electricity losses in India during transmission and distribution are extremely high and vary between 30 to 45%. Wireless network based architecture is proposed in this paper, for monitoring and optimizing the electric transmission and distribution system in India. The system consists of multiple smart wireless transformer sensor node, smart controlling station, smart transmission line sensor node, and smart wireless consumer sensor node. The proposed software module also incorporates different data aggregation algorithms needed for the different pathways of the electricity distribution system. This design incorporates effective solutions for multiple problems faced by India's electricity distribution system such as varying voltage levels experienced due to the varying electrical consumption, power theft, manual billing system, and transmission line fault. The proposed architecture is designed for single phase electricity distribution system, and this design can be implemented for three phase system of electricity distribution with minor modifications. The implementation of this system will save large amount of electricity, and thereby electricity will be available for more number of consumers than earlier, in a highly populated country such as India. The proposed smart grid architecture developed for Indian scenario, delivers continuous real-time monitoring of energy utilization, efficient energy consumption, minimum energy loss, power theft detection, line fault detection, and automated billing.

There have been significant changes and dynamic growth in power grids since the inception of the smart grid. To ensure a self-sustainable grid, the power grid should facilitate the real-time sharing of the dynamic attributes of the power grid between the spatially distributed power grid elements. This demands a flawless design of the overlay communication network for a smart grid. However, instrumenting the entire grid with intelligent communication devices would be prohibitively expensive. A cost-optimal model is necessary to select the actual cardinality of intelligent communication devices required for the data transmission by ensuring its Quality of Service (QoS) metrics. This research work presents a smart distribution grid architecture with a network of microgrids and details the need for hybrid communication for intra-microgrid communication. A cost-optimal model is proposed to offer an optimal combination of technologies for intelligent devices in the hybrid communication overlay network. The formulation considers QoS metrics such as data packet latency, bandwidth requirement, link reliability, packet drops, and communication range of the technology to derive a cost-optimal solution. Based on this model, a Recursive Algorithm for Cost Optimal Combination of Communication Technology (RACOCCT) for a power grid topology is presented in this work. The simulation study performed on selected power grid topology based on the Standard IEEE 33 bus network reveals the cost-optimal combination of hybrid wireless communication technologies for the varying probability of link unreliability.

Electrical energy plays a pivotal role in modern society by powering homes, industries, and transportation systems. However, the production of electricity is associated with significant carbon emissions, primarily from fossil fuel-based power generation, and there is 1.1% rise in carbon emissions by 2023 compared to 2022. Mitigating carbon emissions from electrical energy is a critical global challenge that requires a multifaceted approach. Transitioning to cleaner energy sources and improving energy efficiency are essential steps to reduce the environmental impact of electricity generation. Energy management is crucial to reduce energy consumption effectively. So this study proposes a Multi-Model Energy Management System (MEnMS) integrated with a Fractal Internet of Things (IoT) architecture to address enhanced energy management by reducing energy usage, and carbon footprint. The study conducts a detailed energy consumption analysis across distinct cases. From the analysis, it can be seen that an average of 25% of energy can be saved with MEnMS without IoT energy overhead. Key observations include, EnMS with IoT devices and automation offers smartness, they do not lead to a significant reduction in energy consumption. Moreover, these IoT devices and centralized learning consume more energy. However, integrating IoT devices with distributed learning and multiple models significantly reduces energy consumption as well as the carbon footprint. The analysis reveals that the MEnMS system outperforms alternative approaches, particularly at higher occupancy levels, establishing itself as the most efficient energy management solution. At an occupancy level of 25 users, it achieves an impressive 8% reduction in energy consumption compared to the Traditional System, showcasing its unique capability to scale energy savings as occupancy increases. This innovative system combines advanced local processing with EQC optimization, providing a cutting-edge approach to sustainable energy management in high-occupancy scenarios. Furthermore, the algorithms driving occupant-centric automation and the indoor localization method demonstrate remarkable performance, achieving an efficiency of 92% and an accuracy of 90%, respectively. Therefore, the MEnMs framework can be used to monitor energy usage thereby reducing energy consumption, which results in a low-carbon footprint. By tracking the activity, the occupants get a clear understanding of their carbon footprint and they can make adjustment to reduce carbon emissions.

Addressing the inadequacy of medical facilities in rural communities and the high number of patients affected by ailments that need to be treated immediately is of prime importance for all countries. The various recent healthcare emergency situations bring out the importance of telemedicine and demand rapid transportation of patients to nearby hospitals with available resources to provide the required medical care. Many current healthcare facilities and ambulances are not equipped to provide real-time risk assessment for each patient and dynamically provide the required medical interventions. This work proposes an IoT-based mobile medical edge (IM²E) node to be integrated with wearable and portable devices for the continuous monitoring of emergency patients transported via ambulances and it delves deeper into the existing challenges, such as (a) a lack of a simplified patient risk scoring system, (b) the need for architecture that enables seamless communication for dynamically varying QoS requirements, and (c)the need for context-aware knowledge regarding the effect of end-to-end delay and the packet loss ratio (PLR) on the real-time monitoring of health risks in emergency patients. The proposed work builds a data path selection model to identify the most effective path through which to route the data packets in an effective manner. The signal-to-noise interference ratio and the fading in the path are chosen to analyze the suitable path for data transmission.