Maha Ismail Raheem

Energy management is a key challenge in wireless sensor networks (WSNs), primarily due to the limited battery resources of sensors, which shorten the network lifespan. Uneven energy consumption and high routing overhead significantly reduce network longevity. In this paper, we present an energy efficient clustering method called green hybrid-low energy adaptive clustering hierarchy (GH-LEACH). The proposed method selects balanced and energy-efficient cluster heads (CHs) during the setup phase by considering residual energy, node density, node centrality, and distance to the base station (BS). Additionally, an adaptive CH rotation mechanism and multi-hop communication among CHs are incorporated to reduce transmission energy and distribute the network load more effectively. Results demonstrate that GH-LEACH minimizes network overhead, extends the time until the first node dies (FND), improves overall network performance, and enhances throughput compared to existing protocols such as conventional LEACH, LEACH-C, TL-LEACH, and LEACH-F. This approach distributes the energy load among sensors more evenly, especially in denser networks.

In order to address current urban difficulties, this study investigates how artificial intelligence (AI) can improve smart city frameworks. This paper contends that integrating AI techniques—such as machine learning, predictive analytics, and data-driven decision-making—can greatly enhance urban performance and sustainability, despite the fact that traditional smart city models mostly rely on information and communication technology. The research employs an applied and inductive methodology. First, by combining AI-driven systems with smart city concepts, a thorough theoretical framework is created. Second, a set of AI-oriented urban indicators are used to assess the framework's efficacy in the city of Baghdad as a case study. In order to replicate AI-assisted decision-making, the analysis combines survey data with analytical interpretation. The results show that AI-enhanced smart city systems can boost resource management, government transparency, environmental sustainability, and service efficiency. However, sociocultural, technological, and economic obstacles continue to be major obstacles to implementation. The study's conclusion suggests strategic investments in digital infrastructure, data systems, and AI capabilities as well as an AI-based urban model that can be modified for growing cities.

Rapid urbanisation necessitates reconsidering conventional urban management strategies and embracing creative alternatives for sustainable development due to rapid urbanisation and growing environmental challenges, including climate change, air pollution, and resource scarcity, which are mounting environmental concerns. In this regard, the idea of "smart cities" has surfaced as a strategic framework for enhancing urban efficiency via data-driven governance, digital technology, and intelligent infrastructure systems. This study looks at how digital infrastructure, artificial intelligence, and governance frameworks can be integrated into existing cities to create smart and sustainable urban systems. A comparative case study analysis of Barcelona, Singapore, Dubai, and Baghdad is used in conjunction with a conceptual integration technique. Infrastructure, data systems, artificial intelligence, and governance are the four interconnected layers that make up the study's Smart City Transformation Framework. The results emphasise how crucial data-driven decision-making, adaptive governance, and legacy infrastructure integration are to attaining sustainable urban change. The report also highlights important issues, such as data ethics, energy demands, and sociopolitical limitations. By presenting a multi-layered, context-sensitive model for changing existing cities, the suggested framework advances urban theory.