This literature review examines the application of mobile crowd sensing (MCS) for assessing the performance of Internet of Things (IoT) infrastructure in smart cities. It analyzes several research papers focusing on various techniques and frameworks, including threshold techniques for road surface assessment, fog-based data analytics for resource provisioning, price-based data delivery for dynamic IoT networks, and machine learning for user profile verification. The review identifies the goals, components, processes, and impact factors of each solution, along with their advantages and limitations. It also explores potential improvements, missed analyses, and alternative techniques. Each study's conclusions are critically evaluated, and the tools used for assessment are identified. The review highlights the contribution of MCS in providing cost-efficient solutions, enabling real-time compatibility, and promoting sustainable development in smart cities, while also noting limitations related to data accuracy, power consumption, and network scalability. Desklib offers this and other solved assignments to help students.
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