International Journal of Wireless and Ad Hoc Communication

Wireless networks are considered a hot topic in dealing with data without need to routers or other infrastructures. Each node has a part of routing responsibility. This result to a huge of data in forwarding to other nodes and will need high speed to process.  Photonic crystal applications come to solve the necessity for such speed with small circuitry area. One of the main factors that affect their operation is the structure topology. Ring resonator, cavity based structures, self-collimation, and waveguide approaches are some of these topologies.  OR gate is proposed in this paper to be simulated and evaluated as one of the basic element block. This design is built on a square lattice- photonic crystal construction on a ring resonator basis. Rotation of 90, 180, and 270 degrees are applied in clockwise direction. Sensitivity analysis, and carefully rod locations are considered to obtain remarkable performance. Minimum size and highly data rate are two characteristics that discriminates this design. The minimum size of 51.48 μm2 is obtained. The bit rates of 1.35, 6.35, 3.2, and 2.53 Tb/s are calculated with the 0, 90, 180, and 270 degrees, respectively. Comparison table is well organized for the recently published photonic crystal OR-gate that based on ring resonator. Finite difference time domain and Plan wave expansion method are used to analyze the proposed structure at 1.55μm wavelength to verify OR- gate operation.    

As computational power has increased rapidly in recent years, deep learning techniques have found widespread use in wireless internet of things (IoT) networks, where they have shown remarkable results. In order to make the most of the data contained in graphs and their surrounding contexts, graph intelligence has seen extensive use in a wide variety of tailored wireless applications. However, the sensitive nature of client data poses serious challenges to conventional customization approaches, which depend on centralized graph learning on globe graphs. In this work, we introduce federated graph learning, dubbed FGL, that is capable of producing accurate personalization while still protecting clients' anonymity. To train graph intelligence models jointly based on distributed graphs inferred from local data, we employ a trustworthy model updating technique. In order to make use of graph knowledge beyond the scope of dynamic interplay, we present a trustworthy graph extension mechanism for incorporating high-level knowledge while yet maintaining confidentiality. Six customization datasets were used to show that with excellent trustworthy protection, FGL achieves 2.0% to 5.0% lower errors than the state-of-the-art federated customization approaches. For ethical and insightful personalization, FGL offers a potential path forward for mining distributed graph data.

The critical dependence of industrial smart grid systems on cutting-edge Internet of Things (IoT) technologies has made these systems more susceptible to a diverse array of assaults. This consequently puts at risk the integrity of energy data as well as the safety of energy management activities that depend on those data. This study offers a generative federated learning framework for semi-supervised threat detection in an IoT-assisted smart grid system. We refer to this framework as FSEI-Net. A unique semi-supervised edge intelligence network (SEI-Net) is presented in the FSEI-Net to enable semi-supervised training using labeled and unlabeled data in the edge tier. The design of SEI-Net is based on with bidirectional generative convolutional network that can intelligently capture the patterns of threat data from partially labeled smart grid data.  We present federated training to enable remote edge servers to work together on training a semi-supervised detector without disclosing their own private local data. This is accomplished through cooperative training. To facilitate communication between cloud and edge layers that is both secure and respectful of users' privacy, a reputation-based block chain is introduced in the FSEI-Net. The outcomes from the practical applications demonstrate that the effectiveness of the proposed FSEI-Net over the most recent cutting-edge detection approaches are valid

Large numbers of devices with varying hardware capabilities and data traffic patterns make up what we call the Internet of Things (IoT). Furthermore, various IoT services, like knowledge economy, e-health, e-business, parking management, etc., display dynamically varying QoS (Quality of Service) needs inside the IoT network. As a consequence of the inconsistency in service delivery, it is difficult to attain spectrum efficiency in the Internet of Things (IoT). There will be a shortage of spectrum for critical IoT services as a result. In this study, we suggest using a Multi-Criteria Decision Making (MCDM) technique to coordinate spectrum sharing across IoT devices. To ensure that the capacity and quality-of-service requirements of IoT devices are met, this framework prioritizes the accessible spectrum bands based on their numerous spectral properties. When all relevant information for reaching a choice is supplied by decision-makers, as is the case in both the trapezoidal and bipolar neutrosophic environments, this research presents a novel, effective approach to tackling these challenges. Conceptually related, the bipolar trapezoidal neutrosophic set's governing principles and rules of operation are laid forth. We cover several important accumulation operations for accumulating bipolar trapezoidal neutrosophic data. The ARAS technique is combined with the bipolar trapezoidal neutrosophic sets to compute the weights of principles and rank the substitutions.

There is annual financial loss, mental pain, bodily injury, and loss of life due to natural and artificial disasters. Unfortunately, natural disasters are becoming much deadlier due to climate change. Consequently, IoT-based catastrophe detection and response systems have been developed to improve the handling of catastrophic disasters and other times of extreme urgency. As a consequence, information gathered from Internet-connected devices is utilized to aid in the categorization of several types of disasters, both natural and artificial. A determination of the nature of the crisis and notification of the relevant command center is accomplished using preexisting methods. We have shown how to modify an existing system into a particular early warning system for natural disasters using two Internet of Things (IoT) devices: the Arduino Uno and the Nodemcu. Using this data, we can pinpoint the exact position of every person whose phone is within range of the disaster and send them warnings before the situation worsens. The botmasters have shifted their paradigm away from IRC and toward an HTTP-based C&C server due to the widespread use of HTTP services. Like HTTP bots, IRC bots have a single point of failure. HTTP bots, however, are harder to stop. It is also challenging to detect HTTP botnets while keeping the false positive rate low since every service on the Internet utilizes the HTTP protocol. This chapter provides a host-based HTTP botnet detection approach that uses Hidden semi-Markov Model (HsMM) variables and the Simple Network Management Protocol-Management Information Base (SNMP-MIB). The device operates following the specifications established by the LoRa network. In this project, we used a device called Nodemcu, which was made to be configured explicitly on the receiving end to identify the users at the place where the catastrophe was detected. At that point, everyone connected to the gadget would receive a geolocation-based alert. MQTT is used to notify the right people when an issue arises. We saw better and more beneficial results from the IoT project after including LoRa.

Although there are many networks security tools, both wire and wireless connected networks are still suffering from many types of attacks. YouTube's meteoric rise to prominence as a social platform speaks for itself. The sheer volume of comments on YouTube has made it an ideal medium for spammers to spread their malicious software. Phishing attacks, in which anyone who clicks on a bad link might be a victim, have contributed to this problem. Classification systems may be used to examine spam for its unique characteristics and identify it. This is why it is suggested that YouTube already has built-in mechanisms for identifying spam. A YouTube Spam detection framework was designed with the five stages of data collection, pre-processing, features extraction, classification, and detection, allowing for the execution of the tests. To analyze and validate each stage of the YouTube detection methodology presented in this study, two metaheuristic optimization methods are employed to optimize the parameters of a new voting ensemble classifier. These methods are the particle swarm optimization (PSO) and the Genetic Algorithm (GA). The ensemble model is based on three classifiers: neural. Results indicate that the proposed approach is accurate. In addition, statistical analysis is performed to emphasize the superiority and effectiveness of the proposed methodology.