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Present-day demand and supply of connectivity necessitate the rapid production of Microwave (MW) filter units. The production of these filters is then followed by the step of utmost importance in the assembly line, viz., the 'tuning of the filter', as tuning is crucial to meeting the selectivity requirements of the band. Since the advent of filters, tuning has always been done manually, and hence it is considered a bottleneck by experts in the field. Thus, the need to automate the system is highly implied. The goal of the current work is to outline various MW filter tuning techniques that have been advocated by the community of researchers. The limitations of the said research works and their comparative analysis are also encapsulated in tabular form in the present paper. The paper ends with the implementation of an Expert-Based Hybrid Deep Learning Algorithm to fully automate the filter tuning process. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) technology enables mobile devices in communication network systems offloading tasks to edge servers, effectively reducing response time and energy consumption. However, task offloading decisions become a significant difficulty when the system's mobile and service device count rises. In this paper, the problem of response time and energy consumption of the system is modeled as a multi-objective optimization problem, and we design an improved evolutionary algorithm based on immune algorithm, which can effectively obtain a set of solutions between response time and energy consumption. The simulation results show that our scheme can meet the response time requirements and obtain a lower energy consumption strategy when compared to the offloading scheme in the existing literature. [ABSTRACT FROM AUTHOR]

Capacity analysis is a hot topic in wireless sensor network research. This paper proposes an improved algorithm for the upper bound transmission capacity. Firstly, we introduce the signal-to-interference and noise ratio (SINR) interference model based on the traffic rate. The closed-form expression of the upper-bound transmission capacity was derived based on the Weber model for wireless sensor network, where the node distribution follows a Poisson point process. The effects of parameters such as communication range, density, and SINR threshold were evaluated through sensitivity analysis to determine the upper-bounded transmission capacity. The numerical simulation indicates that the upper limit of transmission capacity can be achieved with an optimum node density though dichotomizing searching algorithm. The results of simulation show capacity increases initially and then decreases as the node density increases [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are networks consisting of many sensors, each of which acquires data and communicates with each other through wireless equipment in time. To make the data obtained by each sensor node meaningful, the precise localization technology of WSNs should be investigated. As an easy-to-implement localization algorithm, DV-Hop has been studied by many researchers. But its localization accuracy needs to be further improved. In this paper, an improved DV-Hop localization algorithm (2DHYP-GA DV-Hop) is proposed, which combines the 2D hyperbolic localization algorithm and an improved adaptive genetic algorithm (IAGA) to estimate the unknown node coordinates, and improves the localization accuracy. In addition, the radio irregularity model is considered in this paper to evaluate the proposed algorithm in anisotropic networks. Simulation results show that the accuracy of the proposed algorithm is 15.9%, 11.1%, and 7.6% than the GA DV-Hop, the PSO DV-Hop, and the IAGA DV-Hop, respectively. The stability of our proposed algorithm is 11.3%, 26.5%, and 16.6% higher than the GA DV-Hop, the PSO DV-Hop, and the IAGA DV-Hop, respectively, and the convergence speed is also the best. [ABSTRACT FROM AUTHOR]

Multiple input multiple output (MIMO) system with Millimeter Wave spectrum is currently used in most wireless applications and all cellular system to provides high data rates. with using large antenna array which is possible by decrease the wavelength to achieve high beamforming gain and improve the spectral efficiency. in this paper, used low complexity with hybrid precoding at the transmitting side and combining at the receiver side with limited feedback system, by using the concept of orthogonal matching pursuit (OMP) in single and multi-user cases, and compared the results with analog only beasmstring. The results of simulation showed that when used Minimum Mean Square Error (MMSE) precoders performed better than other hybrid precoding approaches, in addition the MMSE hybrid precoding /combining technique offers higher spectral efficiency compared with analog only beamstring. [ABSTRACT FROM AUTHOR]

Non-orthogonal multipulse modulation (NMM) has been proven to be with high efficiency in supplying diversity compared with conventional direct sequence spreading system, and the multiuser system constructed by NMM is capable of exploiting both capacity and diversity. However, as conventional code division multiplexing access (CDMA) systems, multi-access interference (MAI) also appears in the NMM-directed multiuser systems, so to improve the system performance, MAI has to be mitigated. Aiming at the MAI in the NMM multiuser systems, QR decomposition-based noncoherent multiuser receiver has been regarded as an effective method for the non-orthogonal multipulse modulation systems. Based on that, we in this paper put forward an iterative decision feedback scheme to pursue the diversity, where two different kinds of interference cancellation algorithms are put forward alternately according to the upper and lower triangular matrices obtained by QR decompositions, respectively. To optimize the detecting property, the criterion of Maximum Rule and the Average Rule are demonstrated and compared by numerical simulations. Finally, a parallel implementation structure is further proposed, which can reduce half of the processing delay for the overall algorithm, meanwhile, the approximate spectral efficiency of the proposed algorithm is presented. Computer simulations are employed to testify the proposed schemes, and the results show that the SNR gains of 1 dB and 2 dB can be obtained by our iterative decision feedback schemes with Maximum Rule and Average Rule, respectively. [ABSTRACT FROM AUTHOR]

A web service is a software interface that describes a collection of operations that can be accessed over the Internet using standard protocols. Though web services have significant features, centralized UDDI architecture is one of the most challenging issues which attract researchers for an efficient solution. In this paper, a cooperative distributed UDDI (dUDDI) architecture for P2P service networks has been proposed. dUDDI system decentralizes the traditional UDDI using a collection of minimum traffic components which maintains the service provider discovery start list. Service providers act cooperatively on the service discovery operation by linking to other providers who offer similar services. A comprehensive description of the various elements in the dUDDI architecture and their internal component is presented. We also presented an effective algorithm for service publish and discovery operations using dUDDI architecture. The proposed model improves the efficiency of service resource retrieval and also applies different security measures. The proposed dUDDI model is evaluated with the best-in-class working decentralized UDDI models by considering different conditions like the registry size, QoS factors and discovery of the relevant services based on user request. A testbed has been generated consisting of 1000 web services of various domains and services are manually divided into 21 domains with different QoS requirement combinations. The experimentation results justify that the proposed model outperforms the existing decentralized UDDI models in terms of precision, recall and f-measure factors. [ABSTRACT FROM AUTHOR]

This paper uses learning-based color illumination techniques for the classification and localization of passive image geometric attack forgery classification and localization. Copy move and splicing forgery are classified using CNN. The classification accuracy obtained during validation for CASIAv1.0 is 97.35, CASIAv2.0 is 97.93, and DVMM is 97.86. The large data set is created to classify geometrical attacks by combining all the data sets with rotation and scale artifacts. The classification accuracy between rotation and scale during validation is 99.29. Machine learning-based color illumination technique is used for localization of forgery. An experiment was conducted on the CoMoFoD data set to detect passive image and geometric attack forgery. There are 48 images in the dataset with various geometric attacks such as scale and rotation. The results for identifying simple CMF attacks show an F1 score of 98.53%, a precision rate of 97.25%, and a recall rate of 100%. In the case of detecting CMF attacks on a larger scale, the F1 score is 79.1%, the precision rate is 85.2%, and the recall rate is 74.8%. For CMF attack rotation, the F1 score is 86.16%, the precision rate is 87.83%, and the recall rate is 76.33%. The proposed method demonstrates improved accuracy in detecting forgeries compared to existing approaches. [ABSTRACT FROM AUTHOR]

The Internet of Things and cyber physical systems are emerging networks that enable several additional layers of services to improve various facets of human life. The risk of network intrusions also rises as a result of these additional connected vulnerabilities. One method for detecting attacks and anomalies in the network is the intrusion detection system (IDS). But an efficient IDS is defined by two characteristics i.e., computational efficiency and classification efficiency with less false alarm rates, which can be achieved by preprocessing network traffic and identification of essential features. A k-nearest neighbor-(KNN) algorithm was used prominently in the development of network IDS due to its better detection rates. But it is very challenging to pick up an appropriate K-value for KNN and especially, when the data classes are imbalanced. Additionally, KNN is a lazy classifier since it does not learn a discriminative function from the training samples instead it memorizes them. This paper focuses on improving existing KNN classifier to achieve classification efficiency and speed in the execution of intrusion detection process. An improvement in shallow KNN is proposed by arranging the attributes of the data in a way that the sample data that is pertinent to distance computation, followed by quantification, and indexing nearest neighbors of the data block. The design and development of the proposed modified KNN driven IDS is carried out using python programming language executed on Anaconda distribution. The validation and effectiveness of the proposed work is done against benchmarked NSL-KDD dataset. The results shows that the proposed KNN++ are higher than classical KNN by 5.33%, LR by 28.17%, GNB by 72.67%, and SVM by 20.21%, in terms of F1 score. [ABSTRACT FROM AUTHOR]

The technology advances from the micron level to the Nanometer level. This striking change in the technology with so many factors might influence the embedded device design and its performance. In the fast-growing technology, it is very difficult to find suitable algorithms to test embedded SRAM. It is noticed that while going to deep sub-nano technologies, the existing test methods may not fully satisfy the test results due to the increased number of faults and defects. Scale-down technologies have an impact on the parasitic effects, creating an additional source of faulty behavior, and making the existing test techniques less effective in detecting them. In this paper we propose a new method, taking the parasitic effect into the consideration, which gives the fault information along with its location. In the proposed method we have considered node-to-node open and short defects for different technologies (45 nm, 32 nm, and 7 nm). It is observed that the proposed test method gives 100% fault coverage which is independent of technology variation. [ABSTRACT FROM AUTHOR]

Cloud-edge computing is an emerging computing model based on Service Oriented Architecture that provides reliable and available cloud services as scalable resources by collaborating fog nodes on Internet of Things (IoT) environments. One of the important issues on service discovery is energy efficiency and security for existing cloud providers and fog nodes. An optimal service discovery and selection approach as an NP-Hard problem can effective on decreasing time and cost in cloud providers to achieve through maximum capacity of Quality of Service (QoS) factors. To address of the above challenges, this paper focuses on above-mentioned outcomes and presents a QoS-aware cloud-edge service discovery and selection model in IoT environment. This model is evaluated based on a hybrid multi-objective meta-heuristic algorithm based on a Grey Wolf Optimizer and a Genetic Algorithm (GWO-GA) for evaluating QoS factors as non-functional properties. The proposed model is meant to guarantee QoS factors such as the response time, energy consumption and cost factors for the service discovery and selection problem in the IoT environment. Experimental showed that the proposed method performs 30% better than the other algorithms for decreasing cost factor. [ABSTRACT FROM AUTHOR]

In a multi cell scenario, co channel and adjacent channel interferences occur and degrade the performance of cellular networks particularly of the edge users. In this paper, a fusion algorithm is proposed for cancellation of Inter cell Interference and resource allocation. This algorithm is based on Energy Efficiency and capacity. The main objective of this algorithm is considering the edge user equal to center user. Due to transmission through relay node, energy efficiency will be high for edge users. Energy efficient algorithm is proposed for users and it provides better cell edge efficiency and fairness. In this method, inter cell interference (ICI) is cancelled by modified frequency reuse (MFR) technique. After cancellation of ICI, the resources are allocated to the user equipment based on scheduling metric and transmitted energy per bit. The proposed method is compared with the previous techniques like maximum carrier to interference ratio (MCI) and round robin technique (RR). Performance metrics like spectral efficiency, SINR, cell energy efficiency, capacity and fairness are analyzed. [ABSTRACT FROM AUTHOR]

This paper addresses congestion avoidance using enhanced blue algorithm (EBA) for data transferring in a network. The congestion of data always affects the data transmission on the internet for various applications. For developing data transmission performance, the congestion of data is a challenging task. Although, different approaches have been used to avoid data congestion, yet we have considered a data transmission framework for better performance compare to existing approaches. Thus, we considered the advanced Blue Algorithm which is used to determine the node's capacity with middle path and it prevents congestion by monitoring of data during data transmission. The role of gateway is considered to supervise status of congestion for both data sending and receiving based on positive or negative acknowledgment as well as data size. The gateway is also used for a congestion notification system to alleviate congestion and enhance throughput. During experimental analysis, we have taken comparative performance between existing and our proposed model. For example, in Enhanced Ad hoc On-demand Distance Vector (EAODV), during the packet size of 10, the average end-to-end delay is 32.63 ms whereas in proposed advanced Blue algorithm, the average delay is only 19.11 ms. Thus, the proposed model using Blue algorithm is performed better than existing method. [ABSTRACT FROM AUTHOR]

Delay Tolerant Network offers communication architecture for scenarios where attaining uninterrupted connectivity between source and destination is challenging due to dynamic topology, short transmission range, and mobility of nodes. The messages are delivered by a store, carry, and forward mechanism in which the node store the messages in its buffer, and carries them while moving and forward to its opportunistically connected peers. The buffer space is limited, and the stored messages are dropped to overcome congestion. The existing buffer management policies have utilized the message header data fields to compute heuristic metrics, or probability measures and have not focused on incorporating community characteristics in designing algorithms. In this paper, we have proposed a Social Community Buffer Management Policy for Delay Tolerant Networks in which the community metric consisting of Global Rank Value (GRV) and Local Rank Value (LRV) has been introduced. The GRV is the ability of any individual node to encounter the community in which the message destination resides. Similarly, LRV is the ability of a node to directly connect to the message destination. The congested node does not drop messages for which it has higher GRV and LRV values than predefined thresholds. Furthermore, we have integrated a message-locking mechanism in which the combination of lock variables such as Source Drop Lock, Must Accommodate Lock, Local Rank Lock, and Global Rank Lock are introduced within the message header. We have performed the simulation of existing BMSS, DOA, LIFO, MOFO, Ndrop, SHLI, and DLA with SCBM under real-time mobility scenarios. The simulation result proves the proposed SCBM performs better in terms of increasing delivery rate, buffer time average, and reducing overhead, message transmissions, and message drop. [ABSTRACT FROM AUTHOR]

The classical least mean square (LMS) algorithm is a widely studied method for adaptive beamforming. It is well known for its lower computational complexity. However, fast and robust beamforming is not possible with the classical LMS method since it uses a constant step size. This nature hinders its applications in many advanced communication systems. Furthermore, this method degrades when the signal-to-noise ratio is rapidly changing. To circumvent these issues posed by the classical LMS method, two modified LMS beamformers are presented in this paper. We name these methods as M-LMS-1 and M-LMS-2. We present two new complex array weights to accelerate the rate of convergence. Computer simulations show that both methods present fast and robust beamforming. That is these algorithms have convergence improvement of about 37.5 % and 50 % over the standard LMS algorithm. [ABSTRACT FROM AUTHOR]

Recent development of cognitive computing driven evolutionary techniques improve the overall quality of service and user experience in wireless communication network. This Paper consists of a feature selection method based on improvement of Crow Search Algorithm which has been used in Intrusion Detection System to limit the size of the dataset with which the system is working with and getting better results. Since IDS deals with a large data, the crucial task of IDS is to keep efficient features which represents the whole data and there is no duplicity and irrelevancy. The previous model that was proposed used the crow search algorithm in the intrusion detection system (CSA-IDS) as a model to find the optimal feature's subset and random forest as a judgement on features that are produced by the CSA-IDS. The KDD and UNSW datasets are used to evaluate the earlier proposed model. The proposed model achieved an accuracy of 99.84% for attack detection using UNSW datasets. Similarly, R2L and U2R attacks have detected accuracy of 99.97% for NSL-KDD dataset. The development of proposed model improve the overall communication services and feature selection in wireless communication network. The outcome proves that the subset of features that are obtained by using CSA-IDS fetches higher accuracy rate using a smaller number of features. [ABSTRACT FROM AUTHOR]

Due to overlapping coverage areas of nodes, traditional data encryption methods for sensor networks fail to consider the logical correlation between common nodes and transmission paths, resulting in low data capture efficiency, low data communication after encryption, and poor data sensitivity extraction effect. Therefore, this paper proposes an automatic encryption method for sensor network capture data based on symmetric algorithm. Based on the analysis of WSN architecture and sensor node structure, symmetric algorithm encryption flow is designed; The network attack model is established, and the multi-path transmission security is analyzed according to the symmetric algorithm, the probability of successful eavesdropping is calculated, and the sensor network capture data is encrypted accordingly. In order to verify the performance of network capture data encryption, a comparative experiment is designed. The results show that the data recall rate is as high as 99.0%, the data communication volume can reach 6.97 × 104byte, and the extraction accuracy of sensitive information can reach 97.9%. This method can effectively solve the problems existing in traditional methods and lay a foundation for the development of sensor network data encryption. [ABSTRACT FROM AUTHOR]

The problem of assignment and sequencing of tasks is a very complex problem, which is related to whether the computer system can effectively exert the overall efficiency. Solving this problem can make the lowest cost and obtain the greatest benefit. However, the current algorithms for coordinating job assignment and sorting are not completely suitable for heterogeneous computing systems. In order to rationally arrange the problem of computer assignment and sorting, this paper proposes a mathematical programming algorithm to effectively solve the inadaptability of assignment and sorting to heterogeneous computing systems. This paper adopts the control variable method and the comparative analysis method, selects the mathematical programming algorithm and the genetic algorithm, the simulated annealing algorithm these two algorithms, selects the relevant performance indicators, designs the experiments to perform calculations and collects the data. Through the comparison of different algorithms in heterogeneous computing systems, it can be seen that in terms of performance, the average response time and node utilization of the three algorithms are not much different, but the availability of the mathematical programming algorithm is significantly higher than that of the other two. When the rate is 1.0, it still has an availability of 0.59. With the increase in the number of tasks and CPU utilization, the advantages of the mathematical programming algorithm are gradually becoming obvious. Although the receiving capabilities of the three algorithms are decreasing with the increase of these two indicators, when the number of tasks reaches 140, the mathematical programming algorithm can receive tasks remains at 78%, indicating that the algorithm is stable. By applying heterogeneous computing systems on different platforms, GPU and FPGA each have their own advantages. The purpose of coordinating assignments and sequencing is to better allocate resources in the future and maximize benefits. Through the study of mathematical programming algorithms, the time required to execute programs in heterogeneous computing systems can be better reduced, thereby improving the overall system effectiveness. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) enables intelligent and heterogeneous things to access the Internet and subsequently interact and share info. A service management methodology is required by growing IoT applications and the number of services supplied by various objects. Nevertheless, making decisions, finding, and choosing a service is complex. Therefore, numerous techniques are explored in this regard. This paper employed Flower Pollination Algorithm (FPA) for service discovery and selection in IoT. The FPA is a nature-inspired algorithm that mimics flowering plant pollination behavior. Through a hand-over probability, it is possible to adjust the balance between local and global search properly. The survival of the fittest and the optimal reproducing plants regarding numbers are parts of an optimum plant reproduction strategy. These elements are optimization-oriented and constitute the FPA's basics. The suggested methodology has an excellent performance in minimizing data access time, energy usage and optimizing cost according to simulation findings. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks is identified to revolutionize the environmental research and science by deploying the sensor nodes over the area where monitoring and constant access through manpower is difficult. WSNs typically depends on the mean energy utilization of sensor nodes as it directly impacts the network lifespan. In this paper, Hybrid Tasmanian Devil, and Improved Simulated Annealing-based Clustering Algorithm (TDIOKTSACA) is proposed for constructing improved amount of clusters with efficient CH selection to sustain energy and prolong network lifetime. It specifically used TDOA for achieving potential CH selection based on evaluation of fitness factors that include energy and distance into account. It is proposed to improve QoS and optimize routing through selection of optimal CHs in network. Simulation results of TDIOKTSACA confirmed better network throughput of 22.28%, sustained residual energy of 25.62%, minimized packet delay of 20.98%, compared to competitive clustering algorithms used for investigation. [ABSTRACT FROM AUTHOR]