The heterogeneous environment of next-generation Consumer Internet of Things (CIoT) demands efficient resource utilization and reliable network services. On the contrary, the proliferation in the diverse nature of smart consumer IoT devices is causing spectrum scarcity and uneven utilization of available resources. Cognitive Radios (CRs) provide the most suitable solution for spectrum scarcity through dynamic spectrum access. To achieve spectral efficiency and provide consumer-centric network services we propose a novel Cognitive Radio based Autonomous Network Management framework called (CR-ANM). The framework combines the benefits of cognitive radios, Network Function Virtualization (NFV), and Software Defined Networking (SDN), to decouple the control plane from the data plane and is divided into two further operations called Dynamic Priority Determination (DPD) and Efficient Channel Allocation (ECA). DPD is responsible for determining the SUs priority using a fuzzy logic-based decision controller. Whereas ECA optimizes the channel allocation process and allocates the best available channel to SU. Which increases the channel availability by 77% and reduces the service drop rate by 81.8%. Both schemes run as Virtual Utility Functions (VUFs) on dedicated virtual machines assigned by the SDN controller. This approach increases energy efficiency for low-power consumer IoT devices and improves network reliability.

A modification to an existing iterative method for computing zeros with unknown multiplicities of nonlinear equations or a system of nonlinear equations is presented. We introduce preconditioners to nonlinear equations or a system of nonlinear equations and their corresponding Jacobians. The inclusion of preconditioners provides numerical stability and accuracy. The different selection of preconditioner offers a family of iterative methods. We modified an existing method in a way that we do not alter its inherited quadratic convergence. Numerical simulations confirm the quadratic convergence of the preconditioned iterative method. The influence of preconditioners is clearly reflected in the numerically achieved accuracy of computed solutions.

World Wide Web (WWW) is playing a vital role for sharing dynamic knowledge in every field of life. The information on web comprises a huge amount of data in different forms such as structured, semi structured, or few is totally in unstructured format. Due to huge size of information, searching from larger textual data about the specific topic or getting precise information is a challenging task. All this leads to the problem of word sense ambiguity (WSA). Urdu language-based information retrieval system using different techniques related to Web Semantic Search Engine architecture is proposed to efficiently retrieve the relevant information and solve the problem of WSA. The proposed system has average precision ratio 96% as compared to average precision ratio of 74% and 75% average precision Google for single word query. For the long text queries, our system outperforms the existing famous search engines with 92% accuracy such as Bing and Google having 16.50% and 16% accuracy, respectively. Similarly, the proposed system for single word query, the recall ratio is 32.25% as compared to 25% and 25% of Bing and Google. The results of recall ratio for long text query are improved as well, showing 6.38% as compared to 6.20% and 4.8% of Bing and Google, respectively. The results showed that the proposed system gives better and efficient results as compared to the existing systems for Urdu language.

The Content-Centric Network (CCN) presents an alternative to the conventional TCP/IP network, where IP is fundamental for communication between the source and destination. Instead of relying on IP addresses, CCN emphasizes content to enable efficient data distribution through caching and delivery. The increasing demand of graphic-intensive applications requires minimal response time and optimized resource utilization. Therefore, the CCN plays a vital role due to its efficient architecture and content management approach. To reduce data retrieval delays in CCNs, traditional methods improve caching mechanisms through clustering. However, these methods do not address the optimal use of resources, including CPU, memory, storage, and available links, along with the incorporation of social awareness. This study proposes SARAC4N, a socially and resource-aware caching framework for clustered Content-Centric Networks that integrates dual-head clustering and popularity-driven content placement. It enhances caching efficiency, reduces retrieval delays, and improves resource utilization across heterogeneous network topologies. This approach will help resolve congestion issues while enhancing social awareness, lowering error rates, and ensuring efficient content delivery. The proposed Socially and Resource-Aware Caching in Clustered Content-Centric Network (SARAC4N) enhances caching effectiveness by optimally utilizing resources and positioning them with social awareness within the cluster. Furthermore, it enhances metrics such as data retrieval time, reduces computation and memory usage, minimizes data redundancy, optimizes network usage, and lowers storage requirements, all while maintaining a very low error rate.

Passive localization is an important technology within Internet of Things (IoT) applications that allows for tracking and monitoring of people without needing active transmitters. This feature proves useful in privacy‐conscious areas like security and healthcare, where active tracking or wearable devices are not practicable. Traditional radio tomographic imaging (RTI) methods suffer limitations due to vulnerability to environmental noise and rough spatial resolution. To overcome these issues, we suggest a new framework that combines a wireless mesh of 14 ESP32‐transceiver nodes, multipath‐sensitive RTI modeling, and a deep convolutional neural network (CNN) for accurate localization. The system records bidirectional received signal strength indicator (RSSI) measures to produce RTIs, which are then optimized by CNN to provide submeter accuracy. Experimental tests illustrate a precision of 92.81%, outperforming traditional RTI methods and other machine learning baselines. The suggested solution successfully alleviates line‐of‐sight (LOS) and nonline‐of‐sight (NLOS) problems, ensuring scalability and cost‐effectiveness for real‐world IoT applications.