A miniaturized conductor-backed coplanar waveguide high-pass filter for C-band satellite applications is presented in this article. It consists of high impedance line and hexagonal resonator with interdigital coupling implemented in coplanar by etching square ring metallic pattern at the ground plane of the substrate. The necessary transmission zero is obtained by the shunt connected square ring pattern. The hexagonal resonator arrangement is used to produce band pass characteristics and the back side strip introduces capacitance effect thereby improving the rejection level. Hence, the filter behaves as high-pass filter. Design, simulation, and fabrication of Satellite C-band high-pass filter characteristics are investigated. The proposed filter yields excellent band width starts from 2 to 8 GHz at 10 dB. Hence, the proposed filter will be best suitable for Satellite C-band applications. The overall size of filter is achieved to be 39 mm × 8 mm × 1.6 mm on accounting all the features. The fractional band width of the filter is calculated from bandwidth to center frequency ratio and it is found 120%. S-parameter results of high-pass filter are return loss (RL) S 11 is about −30 dB and insertion loss (IL) S 21 is −0.98 dB are obtained in simulation. Whereas in the measurement, the RL is nearly −25 dB and IL is −2 dB.

With rapid deployment of Internet-of-Things (IoT) devices, security issues related to data transmitted between the devices increases. Thus the integrity of perceptual layer devices is of utmost importance to secure the information being transmitted between the devices. In a secured information system, digital signature generation and verification processes are entirely different from data encryption and decryption processes. Digital signatures are rapidly emerging due to the problems related to data integrity thus playing a crucial role in the authentication process by enabling the sender to attach a signature to the encrypted message. Based on the devices it is beneficial to select an algorithm showing favorable behavior, therefore Keccak-f [1600] algorithm is best suited for devices having area and cost constraints. In this paper, implementation of the original Elliptic Curve Digital Signature Algorithm and its variants are considered and evaluated in terms of the security level and computational cost. Here the modified ECDSA scheme concepts related to signature generation and verification are similar to the original ECDSA scheme. The computational cost of the Modified ECDSA is reduced by removing inverse operation in key generation and signing phase, also problems related to signature being forged are resolved using hidden generator point concept. Hence the Modified ECDSA is more secure with less computational cost when implemented on FPGA using Verilog HDL. Therefore, this algorithm can be applied for the devices being connected in perceptual layer of the IoT.

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A novel type of wireless network that is rising in popularity with a wide assortment of civilian along with military applications is called WSN. Owing to several factors like nodes with resources that are constrained and packages that are resistant to tamper, WSNs are tremendously vulnerable to internal attacks and mainly external attacks also. Attacks on such critical schemes comprise penetrations into their network along with the installation of malicious tools or programs, which can reveal sensitive data or alter the specific physical equipment's behavior. For their action, the wireless networks are utilized by the threats like spoofing, injection, denial of services, and numerous attacks. Thus, for protecting devices from intruder attacks, security solutions are necessary. An intrusion detection system (IDS), which is wielded for detecting attacks against a system or a network by evaluating their activities along with events, is a tool. In this article, an efficient attack detection technique grounded on exponential polynomial kernel-centered deep neural networks (EPK-DNN) is proposed since intrusion detection is crucial in securing the data. Intrusion detection in WSN is extremely intricate for tasks like fault diagnosis, and real-time monitoring applications, owing to the WSN's dynamicity. To find diverse attacks along with to safeguard WSNs from security risks, numerous detection methodologies are created, because intrusion detection is decisive for protecting the data in WSNs. However, owing to the restricted resources and energy of WSN nodes, widespread computation and so forth, they are inefficient. In this article, an efficient attack detection methodology centered on EPK-DNN is proposed to lessen these problems. The attack detection system's training is the foremost step in the EPK-DNN technique. In step one, the input data are preprocessed; and then, in the training process, the preprocessed data are exploited for attribute extraction. In step two, by utilizing the linear scaling based BAT optimization (LS-BAT), the major attributes are chosen. Then, to detect attacks in WSN, the chosen features are trained by the EPK-DNN. In step three, by utilizing the Damerau-Levenshtein-based K-means algorithm (DL-K-Means), the WSN network is initialized along with the sensor nodes are clustered. To amass the sensor data, the cluster heads are selected by utilizing the swap, displacement, and reversion-centered rock hyraxes swarm optimization algorithm. After that, for

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