This work proposes a novel one-port 3D acoustic resonator based on the lithium niobate thin film on conductive silicon carbide substrate (LiNbO3-on-SiC, LNCSiC). The fabricated resonator shows coupled frequency responses of the shear-horizontal surface acoustic wave (SH-SAW), the longitudinal leaky SAW (LL-SAW), and the high-overtone bulk acoustic waves (HBAWs). The HBAWs propagating in the thickness direction of LNCSiC show a wide frequency response span exceeding 4 GHz and an excellent maximum quality factor ( ) of 7980. The GHz SH-SAW propagating in the surface of LNCSiC show a large electromechanical coupling coefficient ( ) of 25.95\%, while the LL-SAW shows an extremely high velocity of \textasciitilde6900 m/s. Such hybrid resonators could potentially open up new applications in radio frequency communications, 3D imaging, and sensing.

The practical Internet of Things at the current stage still persists in handling an energy minimized network. For a proper network communication an energy consumption of 80\% is indulged only on the communication setup. 6LoWSD (6LoWPAN Software Defined) is an SDN based IoT network protocol developed to minimized the IoT constraints. The SDN’s feature of decoupling the controller plane from the data plane enhances the network efficiency. These target conducts towards data rate, traffic, throughput and duty cycling management. Besides these it also provides a sense of flexibility towards program-ability for the current IoT networks. Efficient power system is a highly Important domain which needed for handling the stability for the whole SDN-IoT system. An effort towards enveloping state transition schedulers for energy optimization has been experimented in this paper.

The Routing Protocol for Low power and Lossy networks (RPL) has been developed by the Internet Engineering Task Force (IETF) standardization body to serve as a part of the 6LoWPAN (IPv6 over Low-Power Wireless Personal Area Networks) standard, a core communication technology for the Internet of Things (IoT) networks. RPL organizes its network in the form of a tree-like structure where a node is configured as the root of the tree while others integrate themselves into that structure based on their relative distance. A value called the Rank is used to define each node’s relative position and it is used by other nodes to take their routing decisions. A malicious node can illegitimately claim a closer position to the root by advertising a lower rank value trapping other nodes to forward their traffic through that malicious node. In this study, we show how this behavior can have a detrimental side effect on the network via extensive simulations and propose a new secure objective function to prevent such an attack.

IoT will be capable to openly provide entry to selected data groups to enable the building of diverse digitized programs while also clearly and fluidly integrating a large range of different and unsuitable end devices. It is a highly challenging task to develop a common design for IoT due to the large variety of devices, connection layer technologies, and applications that could be incorporated in such a system. Urban Iot applications, while still a sizable segment, are the focus of this investigation. The target application domain of these algorithms sets them apart. Urban IoTs are actually created to support the idea of the "Urban Development," which aims to use the most modern networking technology to allow additional offerings for both the municipal government and the citizens. Thus, this article provides a full survey of technology options, rules and regulations, and building design for simply an urban IoT. This Padova initiative, that serves as a convincing example of an IoT offshore rollout conducted out in cooperation with the municipal administration inside the Italian province of Padova, will be covered in detail along with the methodological techniques and finest standards employed there.

Autonomous and Supported Lifestyle (AAL) has been highlighted as a requirement in today s environment in a number of theories, techniques, and different uses for the Internet of Things. (IoT). Technologies standardization initiatives like Wireless V4.x (Wireless smart), for example, have sparked a meteoric rise in creative relatively brief wireless devices that can provide a variety of services to AAL. Additionally, new potential for major carrier is created by enabling equipment (Sq.m) connectivity between all of these technologies. To support M2M exchanges, telecommunications companies, especially telecom companies, might have to build new infrastructure and rethink their corporate objectives. Simple Square meters or IoT products often need another suitable tool, like a telephone, to serve as a doorway to the World wide web in order to function to their fullest capacity. The unique Concept of Iot examined in this study enables any nearby Innertubes device to serve as an M2M entry point for Internet of things. As a result, the user of a Sensor node no longer has to own a smartphone or other Innertubes equipment in order to access capabilities like internet - based. In this research, an unique IoT architectural prototype system for short-range signal repeaters is described. The test bed s installation, benefits and drawbacks, and sampling analysis using data acquired from a real-world event are discussed, and the findings are positive.

In this research, a power consumption analysis of wireless devices for Internet of Things applications is described. The research analyzes and contrasts a variety of tiny wireless communication techniques and their modules, including ZigBee, Energy Saver Wi-Fi, Six-Low-PAN, and LPWA, all of which aim to conserve energy and lengthen the lifespan of the devices that make up an IoT network. This focuses on the significance of employing small wireless techniques and components in IoT applications. The study s methodology is defined by the individual module used to implement the protocol. According to the degree of communication between sensor nodes, the proposed protocols are categorized. ZigBee, 6LoWPAN, and low power Wi-Fi are the candidate protocols for connectivity over short distances. The LoRaWAN protocol is a possibility for long-distance connectivity. Given the wide variation in power consumption between modules and protocols, the results of this study demonstrate how carefully selecting units for every protocol can greatly affect the duration of its use. Accordingly, protocols are compared with one another in various ways based on the module in question.

Proposed system, pollution monitoring, the automobile industry, and sports are just a few of the application areas that have grown as a result of ubiquitous sensing and the distinctive features (Sensor systems). As the underlying significantly expanded the number of linked things with realtime communication and data computation, WSNs have grown in importance in recent years. However, owing to the scale and accessibility of IoT, building a complex challenge, and past methodologies established for Iot technologies cannot be implemented directly. In this paper, pairwise clusters models for Iot networks in the Iot paradigm are proposed: I a resource grouping model and (ii) a business clusters model where responsibilities are allocated to individual sensor nodes depending on how well they provide services. The end-to-end latency, and communication bandwidth balancing.

The resource-constrained IPV6-based low power and lossy network (6LowPAN) is connected through the routing protocol for low power and lossy networks (RPL). This protocol is subject to a routing protocol attack called a rank attack (RA). This paper presents a performance evaluation where leveraging model-free reinforcement-learning (RL) algorithms helps the software-defined network (SDN) controller achieve a cost-efficient solution to prevent the harmful effects of RA. Experimental results demonstrate that the state action reward state action (SARSA) algorithm is more effective than the Q-learning (QL) algorithm, facilitating the implementation of intrusion prevention systems (IPSs) in software-defined 6LowPANs.

Scientific and technological advancements, particularly in IoT, have greatly enhanced the quality of life in society. Nevertheless, resource constrained IoT devices are now connected to the Internet through IPv6 and 6LoWPAN networks, which are often unreliable and untrusted. Securing these devices with robust security measures poses a significant challenge. Despite implementing encryption and authentication, these devices remain vulnerable to wireless attacks from within the 6LoWPAN network and from the Internet. Researchers have developed various methods to prevent attacks on the RPL protocol within the 6LoWPAN network. However, each method can only detect a limited number of attack types, and there are still several drawbacks that require improvement. This study aims to implement several attack prevention methods, such as Lightweight Heartbeat Protocol, SVELTE, and Contiki IDS. The study will provide an overview of these methods theories and simulate them on Contiki OS using Cooja software to assess their performance. The study s results demonstrate a correlation between the simulated data and the proposed theories. Furthermore, the study identifies and evaluates the strengths and weaknesses of these methods, highlighting areas that can be improved upon.

IoT technology establishes a platform for automating services by connecting diverse objects through the Internet backbone. However, the integration of IoT networks also introduces security challenges, rendering IoT infrastructure susceptible to cyber-attacks. Notably, Distributed Denial of Service (DDoS) attacks breach the authorization conditions and these attacks have the potential to disrupt the physical functioning of the IoT infrastructure, leading to significant financial losses and even endangering human lives. Yet, maintaining availability even when networking elements malfunction has not received much attention. This research paper introduces a novel Twin eye Architecture, which includes dual gateway connecting every IoT access network to provide reliability even with the failure or inaccessibility of connected gateway. It includes the module called DDoS Manager that is molded into the gateway to recognize the dangling of the gateway. The effectiveness of the proposed model is evaluated using dataset simulated in NS3 environment. The results highlight the outstanding performance of the proposed model, achieving high accuracy rates. These findings demonstrate the proposed network architecture continues to provide critical authentication services even upon the failure of assigned gateway.

The growing Internet of Things (IoT) has led to an increasing number of interconnected devices across diverse locations. To enable efficient data transmission in resourceconstrained IoT networks, selecting the right communication protocols is crucial. This study compares the performance of 6LoWPAN-CoAP and RPL-CoAP in LoRaWAN networks under limited settings, focusing on Packet Delivery Ratio (PDR) and latency. Tests with simulated LoRaWAN settings were conducted at various scales to evaluate both protocols’ scalability and dependability. The findings demonstrate that RPL-CoAP outperforms 6LoWPAN-CoAP in constrained LoRaWAN scenarios, consistently showing higher PDR and reduced latency. The RPL routing algorithm’s inherent characteristics contribute to this improved performance, effectively constructing routes while considering energy usage and link quality. Additionally, the study highlights LoRaWAN networks’ inherent PDR benefits over conventional networks, making the RPL-CoAP and LoRaWAN combination a powerful option for IoT applications in limited settings. These insights can guide the design of reliable and effective IoT applications in resource-limited environments, maximizing the IoT ecosystem’s potential.

Wave filtering is one of the mandatory features of the state estimators in a dynamic position system. The optimization of statistical parameters of these state estimators can be done by covariance matching algorithms and appropriate objective (cost) functions. The proposed cost function has predictive behavior, based on some tuning parameters, which control the quality of wave filtering. These parameters assure convergence of the solution and consistent results in different adaptive algorithms based on the Kalman filter framework as AKF, AEKF, and AUKF.

An adaptive command filter control of nonlinear system with friction input is formulated in this paper. First, based on the obtained state space model, a command filter control method is proposed, which can address the “explosion of complexity” problem existed in traditional backstepping design and ensure the asymptotic convergence of the tracking errors. Moreover, to cope with the problem of filter error between filter output and virtual control signal, dynamic error compensation system is designed. Next, a HONN system is employed to simplify the calculation and approximate the uncertainties in the system. At last, in order to clarify the effectiveness of the above theory, simulation results are given.

To solve the problem that the filtering accuracy of the online calibration decreases or even diverges due to timevarying noise and outlier value interference, a SINS/CNS/GPS high-precision integrated navigation online calibration method based on the improved Sage-Husa adaptive filtering algorithm is designed. In the proposed method, a 21-dimensional state space model and 9-dimensional measurement model are established. Furthermore, on the basis of the simplified Sage-Husa adaptive filtering algorithm, a smoothing estimator and an adaptive robust factor are introduced to suppress the influence on the filtering accuracy due to the abnormal disturbances in the measurement information, which improving the online calibration accuracy of integrated navigation. The simulation results show that the online calibration method based on the improved Sage-Husa adaptive filtering algorithm can better calibrate the error parameters, especially the calibration of the lever arm error for the east and up directions.

This article presents a new concept of fully analogue adaptive filters. The adaptation is based on fully analogue neural networks. With the use of a filter bank, it can be used for high frequency and real-time adaptation. The properties of this concept are verified using electronic circuit simulations.

At the heart of most adaptive filtering techniques lies an iterative statistical optimisation process. These techniques typically depend on adaptation gains, which are scalar parameters that must reside within a region determined by the input signal statistics to achieve convergence. This manuscript revisits the paradigm of determining near-optimal adaptation gains in adaptive learning and filtering techniques. The adaptation gain is considered as a matrix that is learned from the relation between input signal and filtering error. The matrix formulation allows adequate degrees of freedom for near-optimal adaptation, while the learning procedure allows the adaption gain to be formulated even in cases where the statistics of the input signal are not precisely known.

This paper considers adaptive signal equalization in a channel with inter-symbol interference (ISI) distortion. In this process two adaptive FIR filters with different "forgetting factors" are used to update their filter coefficients. RLS algorithm is applied to optimize the filter coefficients. A comparison to a Genetic algorithm was done. The difference between the estimated mean square errors of both filters provides indication on how to change the "forgetting factors" to get closer to their optimal value. Finally, the computational efforts for filters with five different filter orders are compared. The obtained results prove the applicability of the presented approach.

Electrocardiography (ECG) is the most popular non-invasive method for generating an Electrocardiogram which contains some very interesting information about the electrical and myographic activities of the heart. It is a graph of voltage vs. time of the electrical activity of the heart using electrodes connected on the skin in various configurations. Due to the noninvasive nature of ECG and also due to capacitive or inductive coupling in this electrical circuit for ECG acquisition or electromyographic noises due to muscles adjacent to heart there is usually significant noises present in a typical ECG which makes it harder to analyze. There are many methods for denoising ECGs. In this paper an adaptive unscented Kalman filter, where the measurement noise covariance matrix is varied adaptively, is used for denoising acquired discrete ECG signals. The filtered output as well as the improvement of SNR is compared with other existing denoising frameworks like discrete wavelet transform and digital filters and extended Kalman filter, and unscented Kalman filter. The Adaptive Unscented Kalman Filter performed better than the aforementioned existing filtering algorithms in terms of maximum output SNR and MSE computed using Monte Carlo simulation.

This paper proposes improved combined step-size sign subband adaptive filter (ICSS-SSAF) algorithms with variable mixing factors robust to non-Gaussian noises such as impulsive noise. The CSS scheme is adopted to resolve a trade-off problem of step size in the SSAF, combining two adaptive filters with a large step size for a fast convergence rate and a small step size for low steady-state misalignment. Variable mixing factors (VMFs), whose values are changed at every iteration, are introduced to combine the two adaptive filters. To design the VMFs, a modified sigmoidal or arctangent function is employed. They are updated indirectly to minimize the power of approximated system output error, unlike the conventional algorithm using the 1 norm of the error vector composed of error signals divided by subbands. The recursive forms of VMFs are acquired by adopting the gradient method. The simulation results show that the proposed algorithms perform better than conventional algorithms in system identification scenarios.

Noise has become a significant concern in every domain. For instance, in image processing, we can see background noise when we take a snap, also in the field of communication, the information is corrupted by the noises present in the environment, and at the time of decryption, it is becoming challenging. Back then, in earlier days, discrete filters that had fixed frequency response were used to minimize the level of Noise in the information signals. But these filters were not effective as most noise sources have a flat wideband spectrum. After the availability of digital signal processors, to obliterate the wideband Noise, adaptive filters are frequently used in communication systems and digital signal processing systems to filter noisy signals. The Adaptive Noise Cancellation (ANC) approach helps to eliminate the Noise by altering its transient parameters dependent on the incoming signal. In this article, the performance of LMS, NLMS and RLS algorithms is studied for various types of ambient noises. A speech signal that is corrupted by engine noise, waterfall noise, and audio noise and with echo are applied to an ANC filter and the improvement in signal to noise ratio is evaluated with different adaptive filter algorithms.

Through the thorough exploration of the adaptive filter structure and the LMS adaptive filter algorithm, the filter performance of the adaptive filter algorithm can be clearly mastered.The solution formula of LMS algorithm is based on it, and DSP software programming and Matlab simulation programming methods are used to lay the foundation for the effective implementation of LMS algorithm.Therefore, based on the adaptive filtering algorithm, the embedded software simulation development system is analyzed to help the application of adaptive filtering theory.

Mobile Ad Hoc Networks (MANETs) are more susceptible to security threats due to the nature of openness and decentralized administration. Even though there exist several standard cryptography and encryption methods, they induce an additional computational and storage burden on the resource constrained mobile nodes in MANETs. To sort out this issue, this paper proposes a simple trust management mechanism called as Mobility and Trust Aware Adaptive Location Aided Routing (MTALAR). Initially, MTALAR founds the request zone whose sides are parallel to the line connecting the source and destination nodes. Next, the source node finds a trustworthy route through multi-hop nodes based on a new factor called as Mobile-Trust Factor (MTF). MTF is the combination of communication trust and mobility. Communication trust ensures a correct detection of malicious nodes and mobility ensures a proper protection for innocent nodes. After route discovery, the source node periodically measures the MTF of the multi-hop nodes through HELLO packets. Based on the obtained MTF values, the source node declares the corresponding node as malicious or not. Extensive simulations performed on the proposed method prove the superiority in the identification of malicious nodes.

The internal attack launched by compromised nodes is a serious security risk in distributed networks. It is due to the openness of wireless channel and the lack of trust relationships between nodes. The trust model is an interesting approach to detect and remove these compromised nodes from the trusted routing table and maintain trust relationships in distributed networks. However, how to construct the secure dominating node to aggregate and forward the information is an enormous challenge in distributed networks. In this paper, a distributed construction algorithm of multi-layer connected dominating set is proposed for secure routing with trust models. The probabilities of nodes being compromised, combined with the trust value of the trust model, are used to construct a trusted multi-layer connected dominating set. Moreover, the impact of a node being compromised on the distributed network is quantified as loss expectation. The simulation results show that the proposed algorithm can effectively reduce the impact of nodes being compromised on the distributed network, and enhance the security of the network.

Mobile Ad-hoc Network (MANET) is a collection of self-contained mobile nodes that communicate with each other over wireless links. The mobile units within the radio range can interact with one another directly, whilst other nodes need the assistance of nearby nodes in order to communicate, which is accomplished via the use of routing protocols. Several routing protocols make use of rebroadcasting strategies in order to decrease the amount of route overhead they incur. Zone-based scheduling heuristics are being explored to decrease redundant transmitting by on-demand simultaneous collision steered broadcasting, resulting in reduced energy consumption. While broadcast storms are common, they are caused by parallel collision directed transmission, which leads to increased power utilization. This article examined a unique technique for boosting the energy efficiency of zone-based scheduling strategies, which manage the network architecture by predicting the node die out rate and hence regulate the network configuration. In addition, a game theory framework integrated with an energy-efficient region routing protocol to enhance MANET QoS routing (QoS-IRBRP) was developed. Ultimately, the simulation results reveal the recommended algorithm s gives enlarged energy efficiency in terms of packet drop index and control overhead when compared to alternative routing strategies (RBRP, IRBRP).

Mobile ad hoc networks (MANETs) are selforganizing nodes that work together to create continuous nodes to reach its final destination. In this research, fractional hybrid optimization is proposed to attain the optimal path from the existing multipath during routing in MANET. The quality of service (QoS) is need to be optimization. Thus, the routing table is established to identify the performance of the node for further communication based on the trust factor, response time, request message, and packets sent, and received between the source as well as the destination. The multiobjective function considered for attaining the possible routes is energy, jitter, delay, throughput, distance, and latency using the shortest path algorithm. The fractional optimization attains the average residual energy as 0.077 J, delay as 0.512 sec, and 27 alive nodes for the analysis of 200 nodes.