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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]

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]

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]

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]

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]

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]

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]

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]

The dead reckoning (DR) is widely used for indoor positioning because it does not rely on external information, but it has the disadvantage of not being able to give an initial position and the problem of error accumulation. When the number of anchors is greater than two, the ranging-based multilateral positioning algorithm can provide the positioning position without accumulated error. However, in the practical application of indoor positioning, it is very costly to guarantee coverage with at least three anchor points at each location. In response to the shortcomings of the two indoor positioning methods mentioned above, this paper discusses for the first time how to effectively integrate single anchor and DR for indoor positioning, namely SADR. By combining the range information of a single anchor with the distance information of DR, SADR can not only provide the absolute position of the target to be located but also avoid the accumulation of positioning error and the need for more anchor coverage. The contribution of SADR mainly has two aspects: on the one hand, two specific initial positions and position refinement mathematical models are provided for the positioning problem proposed in this article; On the other hand, the existing least squares method and gradient descent method are used respectively to solve the above mathematical problems. The simulation results show that the average positioning error of SADR is about 1.4 m, the standard deviation of ranging and DR is 0.6 m, and 80% of the poslitioning error is less than 2.5 m. [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]

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]

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]

Aiming at the problem that the DV-HOP and MDS-MAP localization algorithms have large positioning errors when applied in the concave region, this paper proposes a fusion algorithm DV-MDS-SA localization algorithm. Firstly, the estimated distance from non-ranging unknown node to anchor node is obtained by DV hop algorithm. Then, the DV-Hop localization algorithm is used to multiply the shortest number of nodes by the single hop correction value to obtain the shortest distance between nodes. The shortest distance is applied to the MDS-MAP algorithm to find the estimated position of a group of non-ranging unknown nodes. Finally, in order to obtain more accurate positioning results in the concave area, the simulated annealing algorithm is used to optimize the estimated position of the unknown node obtained in the previous step, so as to further reduce the positioning error. The simulation results show that the DV-MDS-SA positioning algorithm proposed in this paper can obtain more accurate positioning results under the same network environment and has high application value. [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]

Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for resource allocation improvement in the (5th) generation of cellular networks. Compared to orthogonal multiple access techniques, NOMA offers extra benefits, including greater spectrum efficiency which is provided through multiplexing users in the transmission power domain while using the same spectrum resources non-orthogonally. Even though NOMA uses Successive Interference Cancellation to repeal the interference among users, user grouping has shown to have a substantial impact on its performance. This performance improvement can appear in different parameters such as system capacity, data rate, or power consumption. In this paper, we propose a novel user grouping scheme for sum-rate maximization which increases the sum rate by approximately 12–25% in comparison with random user grouping and two other authenticated recent works. In addition to being matrix-based and having a polynomial time complexity, the proposed method is also able to cope with users experiencing different channel gains and powers in different sub-bands. Moreover, the proposed scheme is scalable and can be used for any number of users and sub-bands. [ABSTRACT FROM AUTHOR]

Intrusion detection is a crucial technology in the communication network security field. In this paper, a dynamic evolutionary sparse neural network (DESNN) is proposed for intrusion detection, named as DESNN algorithm. Firstly, an ensemble neural network model is constructed, which is processed by a dynamic pruning rule and further divided into advantage subnetworks and disadvantage subnetworks. The dynamic pruning rule can effectively reduce the subnetworks weight parameters, thereby increasing the speed of the subnetworks intrusion detection. Then considering the subnetworks performance loss caused by the dynamic pruning rule, a novel evolutionary mechanism is proposed to optimize the training process of the disadvantage subnetworks. The weight of the disadvantage subnetworks approach the weight of the advantage subnetworks by the evolutionary mechanism, such that the performance of the ensemble neural network can be improved. Finally, an optimal subnetwork is selected from the ensemble neural network, which is used to detect multiple types of intrusion. Experiments show that the proposed DESNN algorithm improves intrusion detection speed without causing significant performance loss compare with other fully-connected neural network models. [ABSTRACT FROM AUTHOR]

Due to the development and growth of Internet platforms and web services as communication resources, the competition for the network and its limited resources is increasing day by day. Distribution and use of distributed services is one of the effective solutions in this field. Therefore, one of the most effective ways to implement distributed services is to use content distribution networks. Content Distribution Network (CDN) An extensive network of distributed servers located in different data centers in different geographical locations. One of the most important issues in content distribution networks is the algorithm for selecting a suitable alternative server to serve the user's request. A common solution is to refer the user request to the nearest alternative server. The most important concern when choosing an alternative server is that the nearest alternative server is not always the best choice, but a set of parameters such as server-user distance, server speed, available bandwidth, server processing load and service price. Etc. should be considered in the selection. In this paper, the aim is to provide an algorithm for selecting the most suitable alternative server using the qualitative and quantitative characteristics of different sources. In this regard, the issue is defined as a multi-feature decision problem and our decision method we use PROMETHEE as one of the most widely used methods to solve this type of problem. The simulation results show that the proposed method in terms of response time averaged 63.2051 and 68.756% and in terms of service price with 59.8303 and 58.4607% respectively had better performance than the method. RR and MCT. [ABSTRACT FROM AUTHOR]

Multiple-input-multiple-output (MIMO) can provide superior performances such as system capacity, linkage, etc. But also it will bring high RF costs and system complexity, especially in large scale MIMO systems. Antenna selection (AS) is proved to be a trade-off between good performances and complexity. Specifically, from the perspective of both transmit and receive antennas, the joint transmit and receive antenna selection (JTRAS) is employed in MIMO systems. Up to now, some algorithms of JTRAS have been studied in MIMO systems. However, most of them are mainly focused on just one aspect about accuracy or complexity. Especially, compared to numerical analysis, the implementation of swarm intelligence algorithm in JTRAS needs to be studied extensively. In the paper, three intelligent algorithms, i.e. genetic algorithm, cat swarm algorithm and particle swarm algorithm are studied and compared in terms of accuracy, cost, and complexity. In addition, fractional coding is proposed in the algorithms instead of binary integer coding. The simulation results demonstrate that all three algorithms can efficiently accomplish the antenna selection. PSO has the best accuracy and stability, but the complexity of PSO is also highest. If we take overall performances in consideration, CSO is the best choice especially in practical implementation. Moreover, fractional coding will provide better performance than binary integer coding. [ABSTRACT FROM AUTHOR]

The Dynamic Bandwidth Allocation (DBA) algorithm is required for efficient bandwidth utilization in XG-PON. The majority of existing DBA algorithms do not make use of the unused bandwidth of a queue with other service classes queue. In the update operation, the efficient bandwidth utilization (EBU) method employs a Borrow Refund (BR) method to utilize unused bandwidth between traffic class queues. This paper presents an extra efficient bandwidth utilization (EEBU) algorithm, which overcomes the limitations mentioned above with proper polling and scheduling mechanism. The theoretical and simulation results show that EEBU improvements for T-CONT 2 delay are 1% and 10%, and for T-CONT 3 delay are 8% and 22%, and for T-CONT 4 delay by 6% and 4.5% compared to the EBU and Giga PON access network (GIANT) respectively. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is proved as technologically competent connecting many devices via the internet. Now in networks transmitting a large quantity of data in a secure manner is of vital concern as communication is on the increase. Hence an efficient cryptographic methodology is essential to transmit securely. However, conventional encryption algorithms are having high computational complexity, more power consumption and high memory occupation. In this paper, low latency, area and optimal power Hybrid Lightweight Cryptography Authentication Scheme which is utilizing the 8-bit manipulation principle (HLCAS) is proposed and implemented. For such a method verification is done and validated for speech signal utilizing MATLAB. The correlation and histogram attributes have been computed to validate the security level. The complexity of the hardware is validated utilizing devices of FPGA of Spartan3E XC3S500E. From the implementation result, it has been found that the proposed HLCAS method has 5.4 ns latency, 0.9 K bytes RAM and consumes 202 mW power. From the comparison with a few reported methods it has been observed that the proposed HLCAS method outperform other methods. [ABSTRACT FROM AUTHOR]

This paper presents an Enhanced Cost and Sub-Epoch based Stable Energy-Efficient Clustering (ECSSEEC) for Heterogeneous Wireless Sensor Networks. In ECSSEEC protocol, modeled cost function selects optimum number of cluster heads (CHs) and sub-epoch rotates previously selected CHs again as normal sensing nodes for future rounds as selected in CSSEEC. It also provides generalized radio model expression for different fading channels of HWSNs. Simulation results show that ECSSEEC achieves effective performance in network for stability period, usable period, and weak sensing period than previously existing hierarchical protocols (i.e., SEARCH, DEEC, SEP and LEACH) in Rayleigh fading environment for two level HWSNs. [ABSTRACT FROM AUTHOR]

The multi agent path planning strategy for unmanned aerial vehicles (UAVs) might play a crucial role in seeking the most feasible path in 3D environment owning to power limitations and other environmental factors. UAVs path planning is a high precision task that is needed for wide range of commercial, military, and rescue operations. However, the path planning for multi-UAVs is a tedious task due to needing an optimal path between source and destination points. There are numerous algorithms available in literature for providing solutions to path planning problems; however, they do not provide an efficient solution, especially in the face of three-dimensional (3-D) aviation workspace. Hence, in this paper, novel reinforcement learning based enhanced variable weight grey wolf optimization algorithm named RLV-GWO is proposed to address this issue. In the proposed algorithm, two prominent variants of GWO named modified grey wolf optimizer (MGWO) and variable weight grey wolf optimizer (VM-GWO) are integrated to advance the performance of baseline model GWO through mitigating the convergence rate and escalating the algorithm speed by assigning variable weights to participating candidates during exploration and hunting process. Furthermore, reinforcement learning is introduced to control the switching operations of individual candidates adaptively based on their accumulated performance. The simulation results demonstrate that the proposed RLV-GWO algorithm can acquire feasible and effective path planning solution for UAVs in three dimensional environments. [ABSTRACT FROM AUTHOR]

Network densification is a key means which is able to solve the rapid growth of mobile communication traffic. Using millimeter wave with rich spectrum resource to accomplish dense coverage of network has the characteristics of small co-frequency interference and large network capacity. However, it also increases the complexity of resource allocation algorithm and system maintenance becomes inconvenient. In view of this, we propose a clustering algorithm for dense millimeter wave heterogeneous cellular network, which takes the signal-to-interference-plus-noise ratio (SINR) in number form as optimization goal, and completes network clustering according to reference base stations and the maximum number of users within cluster. For the integer programming problem which is difficult to solve after modeling, this paper adopts the extended penalty factor and difference of two convex functions programming to obtain solution. The simulation results show that compared with the clustering method which takes SINR in decibel form as optimization goal, the proposed algorithm has higher stability and practicability because it avoids nonlinear operation. And the algorithm has better network performance than K-means clustering method. [ABSTRACT FROM AUTHOR]

IoT smart devices are a confluence of microprocessors, sensors, power source and transceiver modules to effectively sense, communicate and transfer data. Energy efficiency is a key governing value of the network performance of smart devices in distributed IoT networks. Low and discrete power and limited amount of memory and finite number of resources form some major bottlenecks in the workflow. Dynamic load balancing, reliability and flexibility are heavily relied upon by cloud computing for its accessibility. Resources are dynamically provided to the end client in an as-come on-demand fashion with the global network that is the Internet. Proportionally the need for services is increasing at a rate that is astonishing compared to any other forms of development. Load balancing seems a major challenge faced due to the architecture and the modular nature of our cloud environment. Loads need to be distributed dynamically to all the nodes. In this paper, we have introduced a technique that combines fuzzy logic with various nature inspired algorithms—grey wolf algorithm and firefly algorithm to effectively balance the load in a network of IoT devices. The performances of various nature inspired algorithms are compared with a brute force approach based on energy efficiency, network lifetime maximization, node failure rate and packet delivery ratio. [ABSTRACT FROM AUTHOR]

In Wireless Mesh Networks (WMN), at the time of network consignment and bandwidth registration, the active network consignment method did not take into consideration the intrusion, congestion load and bandwidth necessities as a whole. The significance centred bandwidth registration methods result in famishment of slightest significance congestion. Hence in this paper, we propose a Joint Channel Assignment and Bandwidth Reservation using Improved FireFly Algorithm (JCABR-IFA) in WMN. Initially the priority of each node is determined based on the channel usage, future interference and link congestion probability metrics. The bandwidth is allocated proportional to the node priority and the total number of traffic flows served by the requested node. For channel assignment and path selection, the improved FireFly Algorithm (IFA) is used. The objective function of IFA is determined in terms of link capacity, interference and flow conservation constraints. Then the channels and the path which minimize the objective function are selected by applying IFA. By simulation results we show that the proposed technique minimizes the traffic and enhances the channel efficiency. [ABSTRACT FROM AUTHOR]

The rapid increase of vehicle population in many world cities implies the increase of road congestion and accidents. Fortunately, intelligent transport systems (ITS) with traffic signals come to solve or at lease minimize the road problems by guaranteeing for instance safe driving at road intersections. However, they can disturb and reduce the traffic fluency due to the queue delay at each traffic flow if environmental information is not taken into account. This paper aims to overcome the above concern by studying the use of distributed systems to implement intelligent transport systems through a wireless sensor network. The proposed solution is a combination of a detection unit and two communicating sensors deployed on the tracks that can react to the passage of a vehicle. This work focuses on a four-lane intersection and an algorithm allowing sensors to cooperate and manage traffic in real time according to traffic conditions is developed. The experiment area where data have been collected is Bujumbura city, the capital of Burundi. The results of data computation show that our algorithm allows a reduction of 10% in waiting time in the case of rush hours and 32% in the case of normal hours. It leads also to the maximization of the number of vehicles passing through each intersection, which allows an improvement of road safety by reducing road accidents. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the transceiver design schemes for the full-duplex multiple-input multiple-output relay-assisted K-user single-input multiple-output interference channels. Firstly, we propose an iterative optimized reference vector for IA (IORV-IA) algorithm in the perfect channel state information (CSI) scenario. The proposed IORV-IA algorithm not only achieves the alignment of interference signals at each receiver, but also iteratively optimizes the IA reference vector by orthogonalizing the directions of the interference signals and the desired signal. With the optimized IA reference vector, the relay processing matrix and the receiving filter vectors are designed to further improve the system performance. Considering that the relay cannot obtain perfect CSIs in practice due to many factors, and the performance of the IA scheme is very sensitive to this error. Furthermore, we propose a robust transceiver design scheme based on mean square error (MSE) in the imperfect CSI scenario, which minimizes the sum of MSEs in the worst case through iteration. The proposed algorithms are evaluated in terms of the average sum rate and bit error rate performance and the simulation results show the advantages of the proposed algorithms over existing centralized IA and centralized zero-forcing algorithms. [ABSTRACT FROM AUTHOR]

The performance of IEEE 802.15.6 standard for wireless body area network (WBAN) has been studied by many researchers and various techniques to improve its performance such as throughput have been proposed. For example, the throughput can be improved considerably by allowing the contention probability of each node to approach zero for each collision. But this approach leads to the situation where a single node monopolizes the channel while others are starving. The main model to analyze this phenomena and other performance issue is Markov chain, however previous efforts to use Markov chain were limited to fixed number of dimension because of the complexity of handling multi-dimensional case. This paper extends the Markov chain analysis to n-dimensional case and provides equations that computes the expected visit count at each node in this n-dimensional Markov chain. We have solved the equation numerically and have proved that indeed a single node monopolizes the channel. We also provide a simulation result that supports our conclusion for n-dimensional case. [ABSTRACT FROM AUTHOR]

This paper presents a low complexity pairwise layered tabu search based detection algorithm for a large-scale multiple-input multiple-output system. The proposed algorithm can compute two layers simultaneously and reduce the effective number of tabu searches. An efficient Gram matrix and matched filtered output update strategy is developed to reuse the computations from past visited layers. Also, a precomputation technique is adapted to reduce the redundancy in computation within tabu search iterations. Complexity analysis shows that the upper bound of initialization complexity in the proposed algorithm reduces from O (N t 4) to O (N t 3) . The detection performance of the proposed detector is almost the same as the conventional complex version of LTS for 64QAM and 16QAM modulations. However, the proposed detector outperforms the conventional system for 4QAM modulation, especially in 16 × 16 and 8 × 8 MIMO. Simulation results show that the percent of complexity reduction in the proposed method is approximately 75% for 64 × 64 , 64QAM and 85% for 64 × 64 16QAM systems to achieve a BER of 10 - 3 . Moreover, we have proposed three layer-wise iteration allocation strategies that can further reduce the upper bound of complexity with minor degradation in detection performance. [ABSTRACT FROM AUTHOR]

For two-dimensional (2-D) incoherently distributed sources, this paper presents an effective angular parameter estimation method based on shift invariant structure (SIS) of the beamspace array manifold (BAM), named as SIS-BAM algorithm. In the proposed method, a shift invariance structure of the observed vectors is established utilizing a generalized array manifold of a uniform linear orthogonal array. Then, based on Fourier basis vectors and the SIS, a beamspace transformation matrix can be obtained. It projects received signals into the corresponding beamspace, so as to carry out dimension reduction of observed signals in beamspace domain. Finally, according to the SIS of beamspace observed vectors, the closed form solutions of the nominal azimuth and elevation are derived. Compared with the previous works, the presented SIS-BAM method provides better estimation performance, for example: 1) the computational complexity is reduced due to dealing with low-dimension beamspace signals and avoiding spectral search; 2) it can not only improve the angular parameter estimation accuracy but also have excellent robustness to the change of signal-to-noise ratio (SNR) and snapshot number. The theoretical analysis and simulation results confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Now-a-days, with the augmenting accident statistics, the Vehicular Ad-hoc Networks (VANET) are turning out to be more popular, helping in prevention of accidents in addition to damage to the vehicles together with populace. In VANET, message can well be transmitted within a pre-stated region to attain system's safety and also improve its efficacy. Ensuring authenticity of messages' is a challenge in such dynamic environment. Though few researchers worked on this, security level is very less. Hence enhanced communication security on the VANET environment utilizing the American Standard Code for Information Interchange centred Elliptic Curve Cryptography (ASCII-ECC) is proposed in this paper. The network design is defined initially. Subsequently, the entire vehicles get registered to the Trusted Authority (TA); similarly, all vehicle users are registered with their On-Board Unit (OBU). This is followed by Median-centred K-Means (MKM) performs the cluster formation together with Cluster Head Selection (CHS). Next, TA takes care of the verification procedure. Modified Cockroach Swarm Optimization (MCSO) calculates the shortest path and the ASCII-ECC carries out the secure data communication if the vehicle is an authorized one. If not, TA sends the alert message for discarding the request. The system renders better performance when it was weighed against the top-notch methods. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed user grouping method and new user selection techniques in TDD Massive (MIMO) systems. Using K-means clustering algorithm we separate the users into different groups. Upon user groups are formed users are selected from different groups with Semi-Orthogonal (SO) and random user selection criteria. We use two user selection method to select users from different groups, those are-(i) Users are selected in intra-group those are SO with each other along with it also SO with other group's users, (ii)-Users are selected from inter-group those are SO with each other. For antenna scheduling we used maximum channel gain measure in both the user selection situation. Semi-Orthogonal user selection (SOU) and antenna scheduling with zero forcing precoding demonstrated maximum system sum-rate that is seen in results. In table-1 appraised the computational cost of our modified SOU algorithm. The proposed algorithm's efficiency is explored through the simulations. [ABSTRACT FROM AUTHOR]

Cognitive radio network (CRN) has been recognized by researchers to solve the spectrum shortage problem, where the unlicensed users opportunistically exploit the idle licensed channels for data transmission. Prior to the data transmission, the SUs should rendezvous on an available idle licensed channel to establish a link or to exchange control information without causing interference to the co-located licensed channels. However, the dynamic behavior of channels and its availability make the rendezvous more challenging. A blind rendezvous on the available channels without any centralized unit, or a common control channel is preferable to address issues like long-time blocking, control channel saturation, and scalability in a congested network. In this paper, a blind rendezvous for a specific CRN is proposed, where a Role-Based Channel Hopping is introduced to achieve guaranteed rendezvous in an asynchronous environment. Analytical study shows that for N available channels, the maximum time to rendezvous is N + ⌊ N 2 ⌋ , degree of rendezvous is N, maximum conditional time to rendezvous is N 2 and Channel loading is 1 N . [ABSTRACT FROM AUTHOR]

Everyday humans use cars to move faster, and the world is a chaotic place, and a little distraction or a mistake could be the reason for an accident and bring people great pain. An assistance system that can distinguish and detect signs on the roads and brings the driver's attention to road signs and make them aware of their meaning could be beneficial. The most important part of the Traffic Sign Recognition System is the algorithm. In this paper, a new way toward Traffic Sign Recognition algorithm taking the advantages of Color Segmentation, support vector machines, and histograms of oriented gradients on the GTSRB dataset is proposed. The unsupervised shuffled frog-leaping algorithm is employed for segmenting the images. The results show remarkable improvements by using meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is the future of next-generation's communication and computational technology. Now WSN is being used to fulfill various application requirements like medical, engineering, industries, agriculture, etc. It is a resource-constrained network. Additionally, mobility in WSN causes serious issues related to QoS (Quality of Service) requirements like energy efficiency. In order to deal with this issue, in this paper, an Energy Efficient Weighted Clustering Algorithm (EE-WCA) has been proposed. The main aim of EE-WCA is to create a clustered network, which minimizes energy consumption and creates efficient regional Cluster Heads (CH). For this, three phases in clustering are defined. First, evaluating QoS requirements (i.e., buffer length, node displacement, battery level, connectivity, and SNR (signal to noise ratio)). Second, minimize the computational overhead of nodes to save energy using the weighted computation-based technique. This technique helps to regulate the application's QoS requirements for the selection of CH. Finally, to distribute the resource consumption uniformly over the entire region of WSN, the CH updation process has been described. The experimental setup is prepared on the NS-2.35 simulator and the results are measured using 10 different sizes of network. The experimental observations on different performance factors, i.e. energy consumption, E2E(End to End) delay, throughput, packet delivery ratio, and packet drop ratio, confirm the enhanced performance of network with respect to a state-of-art WCA algorithm. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy and speed of traditional K-Nearest-Neighbor (KNN) algorithms and solve the problem of determining the most appropriate initial center and number of clusters of K-Means algorithm, an improved clustering dynamic threshold location algorithm (ICD-KNN) is proposed in this paper. The algorithm consists of two stages: clustering stage and position estimation stage. The clustering stage employs K-Means clustering algorithm based on Canopy. The Canopy is used as the preprocessing procedure to improve efficiency and clustering accuracy of K-Means algorithm. In the position estimation stage, threshold is set dynamically according to the dispersion of reference points to filter out singular reference points and improve positioning accuracy compared with the previous threshold algorithms. We compare proposed algorithm with several existing algorithms. The simulation results show that the positioning accuracy of ICD-KNN is improved by 10%, 38% and 39% respectively compared with dynamic threshold algorithms (DH-KNN), K-Means algorithms and KNN algorithms. [ABSTRACT FROM AUTHOR]

Mobile ad-hoc networks (MANETs) play an important role in the future of the industrial internet-of-things communication, where smart devices will be connected in a completely distributed manner. In the digital properties owing to the digital data properties, there exist difficulties in directly applying the encryption schemes to the one-dimension data. Thus, it is necessary to develop secure lightweight key frame extraction technique for improving privacy in the e-healthcare. This paper plans to develop the robust and reliable security protocol in MANET IoT application. A chaotic cryptography-based privacy preservation model is proposed in this paper for the purpose of improving the security in the MANET IoT. The key generation process in the chaotic map is optimized by generating optimal key pairs through the newly developed SA-SFO algorithm. The key selected from the chaotic map is influenced by selecting the optimal parameters through the proposed Self Adaptive Sail fish Optimization (SA-SFO). Finally, the experimental analysis is conducted, where for the case of character length as 100; the proposed SA-SFO eventually surpassed the existing ones with the cost function 22% as higher than PSO, 20% higher than GWO, 19% higher than WOA, and 21% higher than SFO respectively. The comparative analysis over the conventional models ensures the efficient performance of the proposed model in terms of diverse analysis in MANET and IoT platform. [ABSTRACT FROM AUTHOR]

A vehicular Ad hoc NETwork -VANET has been emerged as a novel application for revolutionizing the driving experience and optimizing the traffic flow control networks. The development of high efficiency and optimized routing protocols for VANET remains a challenging task because of characteristics like rapid topology changes, large scale sizes and frequent link disconnections. Hence investigation of security and privacy problems is required to be emphasized. This paper presents a robust and novel method for improving the security in VANET environment and to evaluate the performance of the proposed ODMRP (On Demand Multi Cast Routing Protocol). Hence the study utilized RSA (Rivest-Shamir-Adleman) based key generation integrated with the asymmetric key generation with AES (Advanced Encryption Standard) algorithm.This proposed trust based integrated algorithm for key generation introduces a novel roadside unit message to the base station that enables Road side units- RSU responsible for the verification of the authentication of the encrypted messages received from the vehicles as well as for the notification of the results to the vehicles. The performance analysis represented that the proposed ODMRP with the use of RSA and AES algorithm possess a better adaptation capacity to VANET network in accordance to packet delivery ratio and throughput. The results obtained from the study have been compared with the existing system and proved to be more efficient. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSN), broadcast authentication is an important security service that provides secure communication. Several mobile users are allowed by this broadcast authentication service. There are some concerns in the sensor network, which depend on security based on maintaining consumer untracking and privacy for data transmission. To overcome the concern of security issues, an Identity (ID)-based cryptography method is introduced. This paper presented a Pairing-Free Identity-based Digital Signature (PF-IBDS) Algorithm based on Modified Elliptic Curve Cryptography (MECC) by using Battle Royal Optimization Algorithm. The main aim of this paper is to secure the data transmission for message authentication. The proposed protocol is to enhance the speed of authentication, reduce the signature size and speed up the signature verification. Moreover, this paper analyses the security by BAN (Burrows–Abadi–Needham) logic and comparing with the existing protocols. It is verified that the developed protocol is protected and well-organized for peer-to-peer communications. Therefore, the proposed method offers secure key management, fast authentication, and also minimizes the computation overhead. The proposed authentication process is simulated in the Java platform. The performance of this protocol has compared with other existing approaches in terms of key verification time, key generation time and computational time. [ABSTRACT FROM AUTHOR]

Devices in the Internet of Things (IoT) have resource constraints in terms of energy, computing power, and memory that make them vulnerable to some security attacks. Due to the increasing volume of multimedia content, lightweight encryption algorithms have been developed to allow IoT nodes to communicate securely with the least computational complexity and bandwidth usage. To adapt the low data rate of IoT devices, a lightweight data compression algorithm for image encryption is proposed in this paper which utilizes scan-based block compression and selective pixel encryption approach to encrypt the image data in only one round, resulting in low computational complexity and reduced data volume. The results of the implementing the proposed approach in IoT testbed show that, on average, the power consumption of the devices and packet rate is decreased by 15% and 26%, respectively, compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

The rapid growth of the technologies, high bandwidth applications, and cloud data centers consume heavy internet service. So, the consumer of the internet expects a high capacity medium for communication. The Elastic Optical Network (EON) provides a flexible and reliable transmission service for consumers. The spectrum fragmentation is a key challenge in EON. In simple terms, unaligned Frequency Slots (FSs) in the network are referred to as fragmented spectrum, while in defragmentation, the available FSs need to be rearranged to create room for the new connection requests. The problem in defragmentation occurs due to the lack of a continuous spectrum and it leads to depreciation in spectrum usage and simultaneously increasing the Blocking Probability (BP) which disrupts the majority of the existing connections in the network. Several techniques and approaches were suggested to reduce the possibility of disruption and reconfiguration in the network while defragmenting the frequency slots. This paper proposes a new algorithm to overcome the drawbacks and improvement in the quality of service of the network. The proposed algorithm holds the approach of proactive and reactive along with the meta-heuristic nature-inspired optimization technique called Jellyfish Search Optimization (JSO). The proposed combination, PR-DF-JFSO outperforms well in terms of spectrum utilization, network efficiency, and quality of service offered when compared to the state-of-the-art spectrum defragmentation algorithms according to the results of experiments done using standard quality metrics. The simulation results show the better utilization of spectrum, reduce the spectrum fragmentation complexity, better bandwidth fragmentation ratio, and overall connection blocking reduced. [ABSTRACT FROM AUTHOR]

Nowadays, the high number of devices and applications connected to the Internet has generated a great amount of data being which makes privacy and protection a more challenging task. In addition, new technologies, such as the Internet of Things, incorporate many resource-constrained devices in the network. Reliable cryptography algorithms have to be employed to deal with this problem, which also needs to be efficiently implemented in small devices. There are several algorithms for this purpose, among them, neural cryptography. In this context, this work proposes the implementation of an artificial neural network architecture called tree parity machine (TPM) to perform the exchange of keys through the mutual learning of these networks. This method is not based on number theory, which makes it less computationally costly, and can be an alternative for embedded systems, which generally have several limitations in the processing capacity and resources used. In the area of embedded systems, FPGAs have gained more space, thanks to their reconfiguration capacity. Thus, different methods for implementing a TPM in FPGA were tested and analyzed, in order to optimize the following performance parameters, the response time, the maximum frequency of operation, and the consumed area of the FPGA considering logical elements, embedded multipliers, and registers. In addition, software implementation based on a multi-core CPU was used for comparison purposes. Experimental results demonstrated that the implementation of parallelism in FPGA for different blocks of the TPM weight matrix reached the best performance results. Thus, our proposal intends to develop an economic component in terms of resource consumption, however, maintaining the characteristic of high processing capacity. Therefore, the methodologies presented in this paper intends to be a useful reference to optimize future implementations in FPGA for cryptography applications. [ABSTRACT FROM AUTHOR]

Cognitive Radio (CR) Network is a backbone for the 5G cellular Networks and Unmanned Aerial Vehicle (UAV) user identification at low power levels is a biggest task CR. Detection of UAV user is more difficult than the stable or fixed user. In the available literature various authors proposed their research with single detection algorithms low power levels as well as concatenation of two or three detection methods. To estimate the user presence the existing detection methods proposed with covariance based approach at static or predefined threshold power levels. In this paper, the authors proposed a novel Aninath computation detection algorithm to estimate the threshold dynamically with inverse covariance approach to improve the Probability of Detection (PD) and mitigate the Probability of false alarm (Pfa) and Probability of miss detection (Pmd) at low power levels. [ABSTRACT FROM AUTHOR]