The Smart city provides infrastructure, urban services, and sustainability to the people residing in the same by utilizing information and communication technologies (ICT) and Internet of Things (IoT). The fast development and growth of urban and smart city population has forced more influence on the resources and infrastructure of smart cities to supply efficient sustainability and quality of life. While smart cities mostly depend on the data received from more Internet of Things (IoT) devices present in the smart city infrastructure. The core problem rests in getting the knowledgeable decision making through integration, processing, and visualization of the heterogeneous type of data obtained from these Internet of Things (IoT) devices present in the Smart City Infrastructure. This paper conducts a comprehensive survey on implementing smart city dashboard with the support of IoT integration frameworks available. We also analyzed a few of the case studies such as Rio de Janeiro's traffic management to Cascais's waste management. Our analysis shows that dashboards integrated with IoT can be used for the various scenarios ranging from environmental monitoring to traffic control, when implemented using robust technology stack or tools such as Apache Kafka, Apache Spark/Flink, and MongoDB/PostGIS.The robust technology stack and tools can transform raw data generated by IoT devices present in the smart city into real-time visualization using predictive analytics. It can also improve citizen engagement by delivering actionable intelligence to the Smart City user. This study concludes that integrated AI driven dashboards act as the core part of the smart city environment by significantly increasing operational efficiency, strategic planning, and transparent governance.

Face recognition has been a dominant biometric modality in surveillance and access control systems. The performance of biometric characteristics are usually impaired by the changes in illumination, occlusion, and expression. Conventional Local Binary Pattern (LBP) descriptors are computationally efficient but are vulnerable to noise and light. To overcome these problems, this paper provides a comparative analysis of Local Ternary Pattern (LTP) variants such as Rule-based LTP (RLTP), Directional LTP (DLTP) and Modified LTP (MLTP) to unconstrained face datasets. All the variants are tested based on their capability to encode strong local texture features and maintain discriminatory information in unfavorable circumstances. Extensive testing is done on standard data sets such as ORL, YALE and Extended Yale B. The experiments are performed using classifiers, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT). The findings indicate that all the LTP variants behave better than the traditional LBP during changes in illumination, but the combination of directional and ternary encoding provide better resistance to occlusions and changes in expressions. The paper identifies the swapping between accuracy and complexity of computation. This paper also gives the essential information of the lightweight face recognition systems in real time smart surveillance systems.

Rapid viral evolution and mutation pose continuous challenges to genomic surveillance and public health preparedness, while most existing computational approaches focus on analysing mutations only after they appear in real-world samples, limiting their ability to support early intervention. This paper presents the Quantum Bio-Mutation Foresight Simulator (Q-BMFS), a hybrid quantum-classical framework designed to explore and evaluate potential future mutation pathways before they naturally emerge. The proposed system combines quantum generative models with classical machine learning to simulate a large bio-mutation search space and assign probabilistic and risk-based interpretations to generated variants. Quantum Generative Adversarial Networks are employed to generate candidate mutation sequences, Quantum Circuit Born Machines are used to model mutation likelihood distributions, and Quantum Reservoir Computing captures temporal evolutionary behaviour, while a transformer-based classical model interprets long-range protein sequence dependencies. Experiments conducted using publicly available viral genome datasets and synthetically generated mutation samples demonstrate that Q-BMFS achieves improved predictive accuracy and more stable mutation likelihood modelling compared with classical baselines, and structural validation using RMSD analysis further indicates that the generated mutations preserve biologically plausible folding behaviour. These results suggest that quantum-enhanced foresight modelling can support proactive mutation analysis and may serve as a useful tool for future research in predictive virology and computational biology.

The Internet of Things (IoT) is a revolutionary paradigm that connects things and devices to the Internet, allowing for seamless data exchange and communication that boosts automation and efficiency across a variety of industries. The transformational promise of IoT rests in its capacity to collect, process, and act on massive volumes of real-time data from connected devices, enabling organizations and people to make wise decisions, streamline procedures, and enhance the quality of life in general. The widespread adoption of IoT has revolutionized industries and daily life, empowering businesses to streamline operations, optimize resource utilization, and enhance productivity through real-time data insights and intelligent automation. Businesses may optimize resource allocation, minimize downtime, and make choices based on real-time data by utilizing IoT's capabilities, while people can benefit from increased convenience, increased safety, and a more connected lifestyle. With the proliferation of IoT-enabled devices and applications, the seamless integration of the physical and digital realms has unlocked opportunities for innovative solutions, creating a smarter, more connected world that fosters efficiency, convenience, and personalized experiences for users. This chapter provides an overview of the IoT, the enabling technologies, and its applications. This chapter also reviews some of the existing research papers related to the application domain of IoT.

Securing edge networks against evolving cyber threats remains a critical challenge due to their distributed architecture and limited computational resources. This work introduces a hybrid framework that combines Quantum Neural Networks (QNNs) with Digital Twins (DTs) to enable proactive intrusion prevention. The DT continuously replicates the edge environment, allowing simulated attack scenarios and predictive threat analysis, while the QNN autoencoder captures complex traffic patterns in quantum feature space to detect subtle anomalies. Experimental results on the CICIDS2017 dataset indicate that the proposed system outperforms traditional machine learning models in accuracy, false-positive reduction, and real-time responsiveness. This study demonstrates the potential of integrating quantum computing with digital twin technology for adaptive, resilient edge cybersecurity.

Surveillance systems play a vital role in the safety and protection of people, although they are often faced with problems such as inadequate lighting, occlusions, and pose variations. Conventional face recognition methods typically fail in this kind of setting and deep learning algorithms require significant computational resources, which is incompatible with real time use on a compact IoT device. We present a lightweight texture-feature-based smart surveillance framework in this paper, combining Max-Regional Fuzzy Encoding (MRFE) and the Gray-Level Co-occurrence Matrix (GLCM) statistics and the variations of Local Ternary Pattern (LTP). The MRFE-GLCM component is complementary to enhance strength, as it combines fuzzy regional encoding with global texture encoding, whereas LTP variants are resilient to illumination and noise variations. These characteristics are merged into a small form and categorized with the help of SVM, KNN, and Decision Tree classifiers. It is experimented on benchmark datasets (ORL, YALE, Extended Yale B) that the proposed hybrid has better accuracy than traditional LBP variants and CNN-LBP hybrids, with low computational complexity. Based on a light weight structure, the framework can be used in real time smart surveillance applications, such as secure access control, smart monitoring, and IoT-based public safety systems. The proposed framework should be computationally lean and efficient. Extended performance analysis confirms the robustness and suitability of the model for real-time IoT-based surveillance.

Street light management system is one of the smart applications that requires huge energy expenses on developing a smart city infrastructure. A wise street light management method can definitely scale back the street lighting prices. Due to the increasing importance for saving power and proper maintenance results in the development of latest technologies, which allow significant power savings and largest respect for the environment and to reduce the road accidents. This project describes a brand new answer for street light system and major accidents. It consists of wireless technology which can be controlled by a base server by simply sending the information and works according to the weather. The primary motto of this research is to avoid the energy wastage and to save the lives of the individuals from accidents.