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Scheduling algorithms are important components in the provision of guaranteed quality of service parameters. The design of scheduling algorithms for mobile ad hoc networks is challenging one because of highly variable link error rates and dynamic nature of the network. This paper provides a survey of scheduling techniques in wireless Ad hoc networks. Desirable features and classifications of schedulers for the different scheduling algorithms are discussed.

In this article, a dual‐port ring‐shaped ceramic antenna is proposed and examined. The antenna ports are orthogonally placed for achieving the high isolation (more than 20 dB). Ceramic material is fed through circular aperture, which excites the HEM11δ+2 mode inside it. Metasurface module is suspended over the proposed antenna for converting the LP‐to‐CP waves. Fabricated antenna confirms its operation from 5.43 to 5.82 GHz with a fractional bandwidth of 6.93%. After applying the metasurface, 3‐dB axial ratio is achieved from 5.45 to 5.65 GHz. Stable radiation properties and good value of diversity parameter makes it suitable for WLAN (5.5 GHz) application.

The healthcare industry is being transformed by the Internet of Things (IoT), as it provides wide connectivity among physicians, medical devices, clinical and nursing staff, and patients to simplify the task of real-time monitoring. As the network is vast and heterogeneous, opportunities and challenges are presented in gathering and sharing information. Focusing on patient information such as health status, medical devices used by such patients must be protected to ensure safety and privacy. Healthcare information is confidentially shared among experts for analyzing healthcare and to provide treatment on time for patients. Cryptographic and biometric systems are widely used, including deep-learning (DL) techniques to authenticate and detect anomalies, andprovide security for medical systems. As sensors in the network are energy-restricted devices, security and efficiency must be balanced, which is the most important concept to be considered while deploying a security system based on deep-learning approaches. Hence, in this work, an innovative framework, the deep Q-learning-based neural network with privacy preservation method (DQ-NNPP), was designed to protect data transmission from external threats with less encryption and decryption time. This method is used to process patient data, which reduces network traffic. This process also reduces the cost and error of communication. Comparatively, the proposed model outperformed some standard approaches, such as thesecure and anonymous biometric based user authentication scheme (SAB-UAS), MSCryptoNet, and privacy-preserving disease prediction (PPDP). Specifically, the proposed method achieved accuracy of 93.74%, sensitivity of 92%, specificity of 92.1%, communication overhead of 67.08%, 58.72 ms encryption time, and 62.72 ms decryption time.

With advent of the Internet of things (IoT), body area networks (BAN) has reached its different new dimension in terms of implementation and smart monitoring applications. Integration of the Internet of things along with the body area network leads to design of pervasive smart and wearable devices. Even though these devices are omnipresence, several design challenges need its solutions for an effective implementation. Energy and performance are the most important challenge which still remains on the darker side of the research. Hence, we proposed energy efficient architecture for IoT-enabled body area networks. The proposed architecture has been designed and developed with the integration of fog nodes along with the extreme learning machine for achieving the low power network and high performance. The proposed BAN architecture has been tested with the different transceivers such as WIFI and ZigBee interfaced with the versions of embedded architectures. Also, the proposed architecture has been compared with existing algorithm such as DARE and M-ATTEMPT in which the energy consumption is reduced to 40% and high performance has been obtained.

Nowadays, there is a growing demand for information security and security rules all across the world. Intrusion detection (ID) is a critical technique for detecting dangers in a network during data transmission. Artificial Intelligence (AI) methods support the Internet of Things (IoT) and smart cities by creating gadgets replicating intelligent behavior and enabling decision making with little or no human intervention. This research proposes novel technique for secure data transmission and detecting an intruder in a biometric authentication system by feature extraction with classification. Here, an intruder is detected by collecting the biometric database of the smart building based on the IoT. These biometric data are processed for noise removal, smoothening, and normalization. The processed data features are extracted using the kernel-based principal component analysis (KPCA). Then, the processed features are classified using the convolutional VGG−16 Net architecture. Then, the entire network is secured using a deterministic trust transfer protocol (DTTP). The suggested technique’s performance was calculated utilizing several measures, such as the accuracy, f-score, precision, recall, and RMSE. The simulation results revealed that the proposed method provides better intrusion detection outcomes.

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