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As the Internet of Things (IoT) continues to evolve, the demand for efficient hardware solutions is crucial. The paper explores the significance of hardware multiplication in IoT devices and discusses the challenges and opportunities in implementing dedicated hardware accelerators for multiplication operations. It specifically focuses on a highly efficient NxN-bit folded pipeline multiplier designed using advanced 22 nm CMOS technology and a modified hierarchical design approach. This multiplier achieves remarkable performance enhancements by reducing critical path delay through pipelining, resulting in a three-fold increase in operational speed compared to conventional multipliers. Additionally, it consumes only 0.2 mW of power and reduces chip area consumption by over 28 %. The paper emphasizes the importance of minimizing computational latency and power consumption to enable IoT devices to execute complex algorithms with improved speed and resource utilization. It also addresses trade-offs, such as area overhead and design flexibility, associated with hardware multiplication. The insights provided contribute to ongoing efforts to optimize hardware components for IoT applications, fostering the development of more capable and responsive IoT ecosystems. The study presents a rapid and energy-efficient alternative for multiplication in digital hardware, addressing the need for high-performance and energy-efficient devices in the context of the IoT. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative simplified two-stage algorithm for precise and durable regulation of the buck power systems. A critically damped of response is captured and synthesized in the first stage via a second order model. In the second stage, the desired robust response of the system is enforced and stirred by a new parameterless controller. The main feature of this controller is simplicity, which is facilitated by its independence from tunable control parameters. Practical numerical simulations that take into account the impact of plant uncertainties and noise measurements with varying variances are used to thoroughly assess the feasibility of the proposed controller. To highlight the main contribution of this paper, the devised control algorithm is juxtaposed with established controllers, namely the PSO-tuned versions of the sliding mode, backstepping, and PID controllers, alongside other cut cutting-edge controllers, revealing its prowess performance superiority and heightened structural simplicity. Finally, the suggested structure is integrated in a standalone photovoltaic system for appraisal of PV emulation potency. It is found to preserved a superior performance in the context of PV emulation as compared to existing emulators, achieving a dynamic efficiency of 97.85 % in the specific case of MPPT. [ABSTRACT FROM AUTHOR]

In today's shopping platform, some enterprises can use all commercial means to obtain users' personal information. How to protect the privacy of consumers has become an important issue. This paper discusses the shortcomings of the traditional neural network algorithm, and puts forward some countermeasures to improve it. Based on the basic principle of standard neural network, a new improved algorithm is developed according to the needs of this paper. The basic principle of the algorithm is based on the added momentum method. This paper mainly uses data models to collect and organize user data through user behavior patterns, so as to prove that the record of target user data cannot predict the possibility of user transportation and consumption, so as to better promote the improvement of transactions and services through these data models. [ABSTRACT FROM AUTHOR]

The operation Economic efficiency of high-performance electric vehicles is relatively low. There are still certain limitations on the range of electric vehicles and the coverage of charging facilities, which affects the user experience and convenience. This paper aims to explore ways to improve the Economic efficiency of electric vehicle operation based on neural network algorithm. Based on the neural network algorithm, this paper establishes a prediction model for the operational Economic efficiency of high-performance electric vehicles. By analyzing and modeling the relevant data, we can predict the operational Economic efficiency of electric vehicles, and extract the influencing factors from it. In this paper, the operation Economic efficiency of high-performance electric vehicles is predicted and analyzed through neural network algorithm, and the methods to improve the operation Economic efficiency are proposed. These methods can provide certain reference and guidance for the development of the electric vehicle industry. [ABSTRACT FROM AUTHOR]

3D multi-object tracking (MOT) is pivotal for associating the trajectories of objects in autonomous driving. The integration of image and point cloud data in 3D MOT algorithms has emerged as a research hotspot, aiming to achieve both high precision and efficiency tracking. In this paper, we present Smart3DMOT, an novel method that leverages multi-modal detection data and incorporates a cascade matching strategy, intelligently utilizing motion and multi-modal appearance affinity for data association across frames. Our algorithm introduces two association models: the strong motion association model (SM-AM) and the multi-modal fusion feature association model (MMFF-AM), tailored to different tracking scenarios. The SM-AM robustly associates objects by enhancing motion similarity, while the MMFF-AM integrates multi-modal fusion features from images and point clouds to generate appearance similarity between objects and combine motion cues for joint reasoning. Finally, we propose a cascade tracking strategy with a "spatial tracking state confirmer" (STSC). The SM-AM and MMFF-AM models are performed staged matching facilitated by STSC. This intelligent implementation allows SM-AM or MMFF-AM to tackle instances with different tracking difficulties, maintaining the operational efficiency and tracking accuracy of trade-offs. Our approach demonstrates the effectiveness of the proposed modules through comprehensive ablation experiments and the results obtained in the KITTI and nuScenes benchmark show that our method attains state-of-the-art performance in 3D MOT. [ABSTRACT FROM AUTHOR]

Community detection is a key task in social network analysis, as it reveals the underlying structure and function of the network. Various global and local techniques exist for uncovering community structures in social networks wherein diffusion-based algorithms are proposed as novel methods for local community detection, particularly suited for large-scale networks. The efficacy of diffusion processes and initial detection is paramount in the successful identification of community structures within social networks. This effectiveness hinges significantly on the meticulous selection of the label diffuser core, which serves as the foundation for propagating labels through the network, and the precise labeling of boundary nodes. Addressing the constraints of current community detection algorithms, notably their time complexity and efficiency, this paper proposes a novel local community detection algorithm that combines core expansion with label diffusion, and deep embedding techniques. In the proposed method, a new centrality measure is introduced for appropriate core selection to facilitate precise label diffusion in the initial phase. Subsequently, a deep embedding technique is employed for updating labels of boundary and core nodes using the GraphSage embedding method. Finally, a rapid merging step is executed to amalgamate initially proximate communities into finalized community structures in large-scale social networks. We evaluate our algorithm on 14 real-world and 4 synthetic networks and show that it outperforms existing methods in terms of NMI, F-measure, ARI, and modularity. According to numerical results, the proposed method shows approximately 1.04 %, 1.03 %, and 1.12 % improvement in F-measure, NMI, and ARI measures respectively, compared to the second-best method, LBLD, in the networks with ground-truth. In addition, our method is able to accurately identify communities in large-scale networks such as Orkut, YouTube, and LiveJournal, where it ranks among the top-performing methods. Our approach exhibits the best performance in terms of ARI compared to other algorithms under comparison. [ABSTRACT FROM AUTHOR]

The Domain Name System (DNS) is a prime target for cyber attacks, necessitating the monitoring and analysis of DNS activities to detect malicious behaviors. This paper presents an innovative DNS behavioral profiling approach that addresses challenges posed by the dynamic landscape of cyber threats, encompassing issues like evasion tactics, content variability, discerning malicious intent, navigating URL obfuscation, low and slow tactics, and maintaining accuracy in the face of diverse normal behaviors, contributing to the advancement of robust threat detection. The framework leverages unique feature behaviors and correlations, incorporating a novel feature selection algorithm, pattern extraction methodology, and a robust neural network architecture for accurate profile construction. The research also includes the development of ALFlowLyzer, a custom application layer network flow analyzer, and introduces the BCCC-CIC-Bell-DNS-2024 dataset, addressing limitations in widely used public DNS datasets. Experimental results demonstrate the effectiveness of the proposed model in profiling various DNS activities. [Display omitted] • Introduction of a new graph-based feature selection algorithm. • Introduction of a behavior similarity calculation algorithm. • Introduction of DNS Flow as a vital component in DNS analysis. • Creation of a new and sophisticated behavioral profiling system through pattern extraction and neural network architecture. • Implementation of ALFlowLyzer, an application layer network flow analyzer with more than 120 DNS features. • Introduction of a new malicious DNS dataset, BCCC-CIC-Bell-DNS-2024, with a DNS flow-based approach. [ABSTRACT FROM AUTHOR]

The recent surge in electric vehicle (EV) adoption has presented various challenges, notably in the charging and discharging processes of EV batteries, each characterized by unique traits. While conventional charging stations remain popular, battery swap stations (BSS) offer a compelling alternative, addressing issues like prolonged waiting times and potential battery degradation from fast charging. BSS, with its extensive array of battery systems, ensures efficient services for EVs. However, meticulous planning for the charging and discharging operations is imperative for both BSS and the overall grid to guarantee optimal functionality. This paper proposes an efficient approach to enhance the efficiency of battery swapping and charging mechanisms (BSCM) for electric vehicles, leveraging the bat algorithm. The BSCM is conceived as a system that incorporates both the battery swapping mechanism (BSM) and the battery charging mechanism (BCM). The key contribution lies in designing an effective BSCM where the BSM functions as a manager, handling battery swapping requests from EV users, while the BCM acts as a supporter, interfacing with the grid to regulate battery charging and discharging power. To efficiently address the mixed-integer nonlinear program (MINLP) inherent in this system, a Bat algorithm is developed. The results clearly demonstrate the effectiveness of the proposed algorithm in efficiently addressing large-scale problems, producing solutions that closely approach optimality. It promptly achieves a substantial reduction in battery swapping energy by 30% and 24%, respectively, and significantly enhances charging station utilization by 25% and 21% compared to the LSTM-Based Rolling Horizon Approach and Bilevel Optimization Approach. Additionally, the algorithm showcases remarkable improvements in battery swapping performance, boasting a 25% and 19% enhancement, and noteworthy increases in charging station utilization by 20% and 17% compared to the aforementioned approaches. This enhancement in the energy exchange with grid and regulation contributes to the overall efficiency and sustainability of electric vehicle operations. [ABSTRACT FROM AUTHOR]

The feature subset obtained by traditional feature selection algorithms usually contains many irrelevant features and redundant features, which increases the size of the feature set and reduces classification accuracy. More importantly, these irrelevant features and redundant features may lead to local optimum. How to efficiently select the global optimal feature subset from high-dimensional data is a challenging task. To solve the problem, an adaptive dual-strategy constrained optimization-based coevolutionary optimizer for high-dimensional feature selection (ADSCO) is proposed in this paper. The contributions of this algorithm are as follows. Firstly, an adaptive dual strategy constrained optimization mechanism (ADSC) is developed. In the ADSC, the adaptive linear constraint strategy and the random constraint strategy are designed to search for the feature subsets that satisfy the constraints. This can reduce the search space of candidate solutions. Secondly, the dual population-constrained coevolutionary optimizer (CO) is developed to help the algorithm jump out of the local optimum and search for better optimal solutions to further improve the classification accuracy of ADSCO. Finally, the ablation experiments are executed to validate the effectiveness of the proposed ADSC and CO. The ADSCO algorithm is tested on 8 high-dimensional datasets and 8 low-dimensional datasets, and compared with seven typical population feature selection algorithms. Experimental results show that the proposed ADSCO algorithm is superior to the 7 comparison algorithms in terms of classification accuracy and feature subset size, demonstrating its strong competitiveness in tackling high-dimensional data feature selection. [ABSTRACT FROM AUTHOR]

It is well known that multi-UAV cooperative dynamic target path planning is a challenging field. In this field, multi-UAV cooperative dynamic target path planning is very important to achieve efficient task completion. However, the existing algorithms have some limitations in solving the problems of insufficient search range, premature convergence, local optimization and insufficient population diversity. In order to solve these problems and improve the efficiency and accuracy of path planning, this paper proposes an innovative method. Firstly, we use A* algorithm to obtain the initial assignment result of UAV target, and then use Hungarian algorithm and genetic algorithm to optimize the assignment result and expand the target assignment range. Secondly, in the path planning stage, the bat algorithm is improved, and sine function, dynamic expansion factor and nonlinear function are introduced to solve the problems of insufficient search range, premature convergence and local optimization. At the same time, genetic algorithm is used to solve the problem of insufficient population. By optimizing target assignment and path planning, the performance of multi-UAV cooperative system is improved, so as to better adapt to the task requirements in dynamic environment and provide more reliable solutions for UAV applications. The experimental results show that the tracking and path planning are more effective than BA, CCWOA and OUMPOA under the condition of good stability. Compared with BA, the fitness function and convergence speed are improved by 34.64% and 30.29% respectively. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive system for detecting and defending against distributed denial-of-service (DDoS) and distributed reflective denial-of-service (DRDoS) flood attacks in software-defined networking (SDN) environments using Programming Protocol-Independent Packet Processors (P4). The proposed system introduces a hybrid intrusion statistical threshold algorithm (HISTA) that analyzes intrusion data statistics to identify potential malicious attacks. Upon detection, the system utilises P4 programmability to implement a custom defence mechanism on the P4 switch. A novel Uruk protocol header collaborates with HISTA for enhanced cross-layer attack detection and categorisation. The HISTA-Uruk mechanism selectively discards malicious packets while ensuring uninterrupted communication for legitimate packets, effectively preventing DDoS/DRDoS attacks. Extensive experiments validate the system's ability to efficiently detect and thwart various DDoS, DRDoS, and internal attacks, demonstrating its effectiveness in identifying threats and restoring impacted network connections across diverse attack scenarios. [Display omitted] • Proposed HISTA accurately detects network attacks using intrusion statistics. • Introducing Uruk protocol header, collaborating with HISTA in P4 SDNs. • Extensive experimentation validates HISTA Uruk mechanism efficacy. [ABSTRACT FROM AUTHOR]

Highlights • This paper model and discuss the performance of a hybrid energy distributed power system (HEDPS) coordinated with reheat thermal power plant. • Dragonfly algorithm optimized 3DOF-PID controller is proposed to improve the frequency and power profiles of the modeled HEDPS. • Results obtained using Dragonfly algorithm is compared with classical and some recently published evolutionary algorithms. • Time varying step load perturbation, random wind perturbation, and parameter variations are considered to confirm feasibility and superiority of the designed controller. • Probability density function and statistical results are shown to validate the robustness of the proposed method. Abstract The objective of this paper is to study the dynamic stability of a hybrid energy distributed power system (HEDPS) subject to load and wind power variations. A three degree-of-freedom (3-DOF) proportional-integral-derivative (PID) controller is designed and implemented in the HEDPS to stabilize frequency and power fluctuations after the perturbation. For enhancing system dynamics, the parameters of the 3-DOF PID controller are optimized by using dragonfly algorithm (DA). The results are compared with the results obtained by Zeigler–Nichols tuning and some other well-known meta-heuristic algorithms. The efficacy of proposed DA over different reported algorithms is established in terms of convergence rate, minimum fitness value and dynamic performance of the system. The robustness of the 3-DOF PID-controller is ascertained with time-varying step load perturbation, random wind power perturbation, and under system parameter variation. The robust performance of proposed DA has also been established by performing statistical analysis. [ABSTRACT FROM AUTHOR]

Abstract Salient object detection has always been a valuable tool in the field of image processing. However, there are many challenges associated with the salient object detection algorithms. A new conceptually clear and robust algorithm for salient object detection has been proposed in this paper that has reconsidered some design varieties of the existing methods. Recent developments in the state-of-the-art have exploited either background information or estimation of foreground information. Therefore, an algorithm that covers both the boundary connectivity from background cues and foreground connectivity has been proposed in this paper. This proposed algorithm handles the limitations of existing object detection algorithms effectively and accurately by working on the edges of the objects or images. A thorough analysis has been carried out on the segmentation of brain Magnetic Resonance (MR) Images. Experimental results reveal that the proposed approach, Beaming Edge SALient (BE-SAL) has rendered the promising direction in the field of image analysis. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes methods for super resolving single noisy low resolution images. Even if single image super resolution has been a topic of research for last few decades, super resolution of noisy low resolution images is still a challenging problem. Most of the state of the art super resolution algorithms will fail to perform if significant amount of noise is present in the observed image. In this paper, we propose a denoised patch dictionary based single image super resolution algorithm. To enhance the robustness to noise performance, this method is further modified by using a wavelet based fusion algorithm which combines the result of proposed method with direct super resolved image, and super resolved image after denoising to preserve the finer details of the super resolved image. The proposed methods are applied on the commonly used test images. The results validate that the proposed methods show improvement over the existing techniques. [ABSTRACT FROM AUTHOR]

This paper investigates the conventional algorithms for addressing the issue of tag collision in radio-frequency identification (RFID) systems. To improve the efficiency of RFID reader inquiry, this paper proposes an approach based on dynamic grouping binary trees, named DGBT, for RFID-based robots. Through DGBT, the collision probability and query times of RFID readers are lowered by adaptive anti-collision prefixes, the efficiency of RFID identification is improved via the group-based approach, and the binary-tree-based queries within groups are ensured to be stable, flexible, effective and practicable. Analysis and simulation verify that the query times and throughput rate are significantly improved by DGBT. [ABSTRACT FROM AUTHOR]

Evolutionary multi-objective optimization (EMO) algorithms are actively used for answering optimization problems with multiple contradictory objectives and scheming interpretable and precise real-time applications. A majority of existing EMO algorithms performs better on two or three objectives non-dominated problems; however, they meet complications in managing and maintaining a set of optimal solutions to multi-objective optimization problems. This paper proposes a search space-based multi-objective evolutionary algorithm (SSMOEA) for multi-objective optimization problems. To accomplish the potential of the search space-based method for solving multi-objective optimization problems and to reinforce the selection procedure toward the ideal direction while sustaining an extensive and uniform distribution of solutions is our key objective. To the best of our knowledge, this paper is the first attempt to propose a search space-based multi-objective evolutionary algorithm for multi-objective optimization. The experimental setup used showed that the proposed algorithm is good and competitive in comparison to the existing EMO algorithms from the viewpoint of finding a scattered and estimated solution set in multi-objective optimization problems. SSMOEA can achieve a good trade-off between search space convergence and search space diversity in the appropriate experimental setup. [ABSTRACT FROM AUTHOR]

This paper brings an idea of establishing a downstream bit channel based on reusing of bit sequences and bit compression technique in point-to-multipoint passive optical networks for increasing their summary throughput. The downstream communication of these networks is based on broadcasting of multi-frames towards all active end-point nodes, therefore these frames cold be potentially reused multiple times to carry additional data in a form of segmented bit sequences spread within the entire bitstream. In order to achieve maximum effectiveness of a proposed method, bit addressing technique using relative sequence positions is proposed. Moreover, the presented bit compression technique might further increase the resulting summary throughput, while the application of existing bit compression algorithms cannot result in better performance. The illustrate the potential of a proposed algorithm together with the bit compression technique, the simulations and evaluations were performed and are presented within this paper as well. [ABSTRACT FROM AUTHOR]

This paper proposes three algorithms for multiple-choice hardware-software partitioning with the objectives of minimizing execution time and power consumption, while meeting area constraint. Firstly, a heuristic algorithm is proposed to rapidly generate an approximate solution. In the second algorithm we refined the approximate solution using a customized tabu search algorithm. Finally, a dynamic programming algorithm is proposed to calculate the exact solution. Simulation results show that the approximate solution is very close to the exact solution. This can be further refined by tabu search to achieve a solution with less than 1.5% error for all cases considered in this paper. [ABSTRACT FROM AUTHOR]

Efficient data delivery accompanied with low end-to-end delay is important in vehicular ad-hoc networks and optimal route selection is vital to improve these performance metrics. Different routing algorithms are proposed in VANETs. This paper reviews and analyzes these algorithms briefly and based on the analysis, a new opportunistic-based routing algorithm (OSTD) is proposed for urban scenarios. The proposed algorithm severely considers the type of vehicular distribution in the calculation of utility function. This utility function is used to evaluate the routes in the network. The reason of such a severe consideration is expressed and evaluated in the paper. Vehicle's driving path predictability is also used in the algorithm to forward the packet to a more suitable next hop, as vehicular mobility is the reflection of human social activity. Simulation results show that OSTD achieves a higher delivery ratio and lower end-to-end delay and packet loss compared to the other well-known protocols. [ABSTRACT FROM AUTHOR]

• The proposed method improves the production efficiency of the machine. • The reliability and superiority of the proposed method are verified by simulation. • The proposed method in this paper is able to achieve the optimal solution. In an automated stereoscopic warehouse, the efficiency of the Rail Guided Vehicle (RGV) is the bottleneck. This paper proposes a foresight stepping model to optimize the intelligent RGV scheduling scheme. We incorporate the chaotic particle swarm optimization algorithm into the model and design the mechanism of multi-step processing. The machine optimization is used to compare the optimal alignment effect of the Back Propagation (BP) network algorithm and GradientBoostingDecisionTree (GBDT) algorithm. The real-life system test is performed by simulation. The simulation results show that the GBDT-foresight stepping model is superior to the traditional models in terms of complexity, reliability and accuracy. Image, graphical abstract The problem addressed in this paper is the work optimization problem in a workshop. [ABSTRACT FROM AUTHOR]

This paper considers the high-dimensional table compression problem on balanced k -partite graph. The objective is to choose half of the vertices from each of the k -partite to maximize the total weight of edges connecting the chosen vertices. Our main contribution is a 0.8785-approximation algorithm for the high-dimensional table compression problem by introducing the α - i n d e p e n d e n t solutions through Lasserre semidefinite programming. This new algorithm improved two previous low-dimensional results, namely the 0.8731-approximation algorithm of Wu et al. for the one-dimensional case and the 0.6708-approximation of Xu and Du for the two-dimensional case. [Display omitted] • Present a 0.8785-approximation algorithm for high-dimensional table compression problem. • Obtain a group of independent optimal solutions through Lasserre hierarchy semidefinite programming. • The integer solutions to the original problem are obtained through deformed random hyperplane recognition. [ABSTRACT FROM AUTHOR]

Wireless communication technology is increasingly gaining popularity, with aerial wireless sensor networks being highly regarded for their high mobility, ability to handle special environments, and wide coverage. To enhance the performance of aerial wireless sensor networks, the study of area coverage algorithms is crucial. However, most existing coverage algorithms primarily focus on the coverage area without considering coverage quality. To address this limitation, this paper proposes a coverage algorithm based on coverage quality for regional coverage by unmanned aerial vehicles (UAVs). The algorithm incorporates the importance and priority of the coverage area, utilizes an adaptive spatial distribution function for weight distribution, and employs horizontal and vertical control strategies to strike a balance between maximum coverage and optimization of coverage quality. Simulation experiments are conducted to validate the effectiveness of the proposed algorithm. This research contributes to the field by emphasizing the significance and impact of considering coverage quality in aerial wireless sensor networks, thereby enhancing their overall performance. [ABSTRACT FROM AUTHOR]

Abstract: A class of voltage-driven robot manipulators is addressed in this paper. Such a class includes those manipulators actuated by direct current (DC) motors with servo-amplifiers in current mode. For those robots, it is assumed that the control inputs to the manipulator are the servo-amplifier input voltages, instead of the applied torques. And even though it is common to consider that the applied torque in each joint is proportional to the servo-amplifier input voltage, in this paper it is assumed that the voltage-to-torque conversion constant is unknown. Notwithstanding, the robot model parameters may still be computed using standard identification methods. An important fact of the class of robots considered in the present study is that the robot dynamics is not passive from the servo-amplifier input voltage to the joint velocity. The paper shows how this drawback can be overcome by using a model-based trajectory tracking controller, which makes the closed-loop system passive from an auxiliary control input to a filtered error signal. To confirm the theoretical conclusions, real-time experiments are carried out in a two-degree-of-freedom direct-drive manipulator. Two known control strategies are compared with respect to the proposed controller. The success of the implementations is based on real-time computing, which ensures that the access to I/O ports and the computation of the control algorithms meet timing requirements accurately. [Copyright &y& Elsevier]

Abstract: This paper deals with robust blind linear minimum mean square error (LMMSE) detection using the particle swarm optimization (PSO) algorithm in the presence of code mismatch. The paper shows that the PSO algorithm incorporating the linear system of the LMMSE detector, which is termed as LPSO, can significantly improve the bit error rate (BER) and the system capacity. As the code mismatch occurs, the output BER performance is vulnerable to degradation for LPSO. To remedy this problem, a blind LMMSE scheme is proposed and combined with PSO to form a robust blind LPSO (BLPSO) detector under code mismatch scenarios. Several computer simulations are provided to demonstrate the effectiveness of the proposed scheme. [Copyright &y& Elsevier]

Abstract: This paper introduces a nonlinear function into the frequency spectrum that improves the detection of vowels, diphthongs, and semivowels within the speech signal. The lower efficiency of consonant detection was solved by implementing the hangover and hangbefore criteria. This paper presents a procedure for faster definition of those optimal constants used by hangover and hangbefore criteria. A nonlinearly changed frequency spectrum is used in the proposed GMM (Gaussian Mixture Model) based VAD (Voice Activity Detection) algorithm. Comparative tests between the proposed VAD algorithm and seven other VAD algorithms were made on the Aurora 2 database. The experiments were based on frame error detection and on speech recognition performance for two types of acoustic training modes (multi-condition and clean only). The lowest average percentage of frame errors was obtained by the proposed VAD algorithm, which also achieved positive improvement in the speech recognition performance for both types of acoustic training modes. [Copyright &y& Elsevier]

Abstract: This paper proposes two algorithms for multi-video-source in a new mobile P2P architecture for streaming media systems. One is serial, and the other is parallel. When the service peer set providing expected QoS is not empty, the former is called, otherwise the latter is called. The former triggers the video source change event, re-selects a video source, and synchronizes the multi-video-source by the time model of the streaming sequence when QoS is degraded. The latter allows the multiple video sources to concurrently send the data to the receiving peer according to the assigned transmission. Compared to the existing papers, the contribution of this paper is fourfold: (1) the serial and parallel scheduling algorithms, correctly switched according to the actual situation, are firstly proposed; (2) the mobile feature of peers are firstly considered and validated; (3) the client nodes have better and smoother video quality; (4) our algorithms have shorter run time, which is a crucial factor for an on-line system. [Copyright &y& Elsevier]

Deep learning technology has been widely used in object detection. Although the deep learning technology greatly improves the accuracy of object detection, we also have the challenge of a high computational time. You Only Look Once (YOLO) is a network for object detection in images. In this paper, we propose a real-time object detection algorithm for videos based on the YOLO network. We eliminate the influence of the image background by image preprocessing, and then we train the Fast YOLO model for object detection to obtain the object information. Based on the Google Inception Net (GoogLeNet) architecture, we improve the YOLO network by using a small convolution operation to replace the original convolution operation, which can reduce the number of parameters and greatly shorten the time for object detection. Our Fast YOLO algorithm can be applied to real-time object detection in video. [ABSTRACT FROM AUTHOR]

• The presented exposition introduces mobile resource Mutual exclusion (MRME) problem in FANET. • A novel token-based algorithm to solve the MRME problem has also been proposed. • The proposed algorithm is fault-tolerant as it handles token-loss. • The paper proves the correctness properties of the problem. • The static and dynamic performance analysis of the procedure has also been provided. Mutual exclusion (ME) is a highly researched problem in distributed computing systems. In the mutual exclusion problem, no two nodes can use critical resource simultaneously. Numerous protocols have been proposed for various types of static as well as mobile distributed systems, namely, Mobile Ad Hoc Networks (MANET), Vehicular Ad Hoc Networks (VANET) and cellular networks. The flying ad hoc networks (FANET) is an interesting variant of distributed systems and, to the best of our knowledge, no protocol exists in the literature for mutual exclusion in FANETs. In FANETs, the critical resource is mounted on an unmanned aerial vehicle (UAV) and user nodes are assumed in the transmission range of the UAV. Ours is the first algorithm to ensure ME in FANETs. The algorithm is token-based and we name it Mobile Resource Mutual Exclusion (MRME) algorithm. Unlike other ad hoc networks, due to swift mobility of nodes as well as resource, the FANETs topology is highly dynamic and fault prone. The MRME algorithm handles it successfully. Further, we present the correctness proof, complexity analysis and simulation results. The worst-case complexity of MRME is O(n) and synchronization delay is T, where T is message propagation delay. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a fast algorithm to solve the well known total variation (TV) inpainting model. Classically, the Euler–Lagrange equation deduced from TV inpainting model is solved by the gradient descent method and discretized by an explicit scheme, which produces a slow inpainting process. Sometimes an implicit scheme is also used to tackle the problem. Although the implicit scheme is several times faster than the explicit one, it is still too slow in many practical applications. In this paper, we propose to use an operator splitting method by adding new variables in the Euler–Lagrange equation of TV inpainting model such that the equation is split into a few very simple subproblems. Then we solve these subproblems by an alternate iteration. Numerically, the proposed algorithm is very easy to implement. In the numerical experiments, we mainly compare our algorithm with the existing implicit TV inpainting algorithms. It is shown that our algorithm is about ten to twenty times faster than the implicit TV inpainting algorithms with similar inpainting quality. The comparison of our algorithm with harmonic inpainting algorithm also shows some advantages and disadvantages of the TV inpainting model. [Copyright &y& Elsevier]

Abstract: The widespread usage of image fusion causes an increase in the importance of assessing the performance of different fusion algorithms. The problem of introducing a suitable quality measure for image fusion lies in the difficulty of defining an ideal fused image. In this paper, we propose a non-reference objective image fusion metric based on mutual information which calculates the amount of information conducted from the source images to the fused image. The considered information is represented by image features like gradients or edges, which are often in the form of two-dimensional signals. In this paper, a method of estimating the joint probability distribution from marginal distributions is also presented which is employed in calculation of mutual information. The proposed method is compared with the most popular existing algorithms. Various experiments, performed on several databases, certify the efficiency of our proposed method which is more consistent with the subjective criteria. [Copyright &y& Elsevier]

Abstract: Keystream reuse, also known as the two time pad problem, is a well known weakness in stream ciphers. The implementers of the cryptographic algorithms are still underestimating this threat. The keystream reuse exploitation techniques presented so far assume the underlying plaintext to be textual data and all the heuristics presented previously are based on the language characteristics of the underlying text based data, which fail when compression is applied on the plaintext before encryption. This paper presents exploitation techniques for two time pads in case of stream ciphered digitized and compressed speech signals. In this paper we show that how an adversary can automatically recover the digitized speech signals encrypted under the same keystream provided the language (e.g. English) and digital encoding/compression scheme details of the underlying speech signals are known. Our technique of cryptanalysis is based on the modeling of the speech parameters by exploiting the inter frame correlations between each components of the speech vectors in different frames and then using these models to decode the two speech signals in the keystream reuse scenario. The technique is flexible enough to incorporate all modern speech coding schemes based on parameter or hybrid encoding and compression techniques. The simulation experiments have showed promising results for most of the present day speech digitization and compression techniques. [Copyright &y& Elsevier]

Abstract: Compressed sensing (CS) is considered as a promising signal processing technique, and successful applications of the CS theory depend mainly on the accuracy and speed of the reconstruction algorithms. In this paper, a generalized objective functional, which has been developed using the combinational estimation and an extended stabilizing functional, is proposed. An efficient iterative scheme, which integrates the beneficial advantages of the homotopy method, the shuffled frog-leaping (SFL) algorithm and the harmony search (HS) algorithm, is designed for searching a possible global optimal solution. Numerical simulations are implemented to evaluate the numerical performances and effectiveness of the proposed algorithm. Excellent numerical performances and encouraging results are observed. For the cases considered in this paper, a dramatic improvement in the reconstruction accuracy is achieved, which indicates that the proposed algorithm is a promising candidate for solving CS inverse problem. [Copyright &y& Elsevier]

Abstract: Processor Allocator (PA) is a crucial factor in modern Chip MultiProcessors (CMPs). A modern CMP uses Network on Chip (NoC) as communication technique between cores. Thus, the topology of the implemented NoC has also significant impact on the CMP’s performance. A good processor allocation technique needs to be fast and ensure the highest possible system utilization. In this paper, we propose a processor allocation technique for such an efficient and fast PA. The PA is driven by a Bit Map Allocation for Torus (BMAT) algorithm, which is a technique designed for k-ary 2-cube topology. The proposed BMAT scheme is presented and described along with a new Busy List Allocation for Torus (BLAT), Sorting Allocation for Torus (SAT) and Stack Based Allocation for Torus (SBAT) algorithms. The presented techniques are compared with previously known important schemes for k-ary 2-mesh topology. The research ideas have been verified using experiments that have also been described in the paper. The presented simulation results reveal that the proposed processor allocation algorithm for k-ary 2-cube, as a technique for PA, achieves better allocation time than all other existing algorithms while the CMP with such a PA is characterized by very high system utilization. [ABSTRACT FROM AUTHOR]

Abstract: The challenges confronting in mobile grid systems are: limited CPU power, limited memory, small screen, short battery life, and intermittent disconnection. Considering all these limitations, this paper is targeted to control energy consumption without compromising system’s performance in mobile grid. In this paper, we focus on using the mobile devices on the mobile grid environment. Mobile devices can serve two important functions in mobile grid environment either as service consumer or as valuable service providers. The proposed approach is not only to reduce energy consumption, but also to improve system performance in mobile grid environment. Utility functions are used to express grid users’ requirements, resource providers’ benefit function and system’s objectives. Dynamic programming is used to optimize the total utility function of mobile grid. A distributed controlling energy algorithm in mobile grid environment is proposed which decomposes mobile grid system optimization problem into sub-problems. In order to verify the efficiency of the proposed algorithm, in the experiment, the performance evaluation of controlling energy algorithm is conducted. [Copyright &y& Elsevier]

Abstract: Access control lists are core features of today’s internetwork routers. They serve several purposes, most notably in filtering network traffic and securing critical networked resources. However, the addition of access control lists increases packet latency due to the overhead of extra computations involved. This paper presents simple techniques and algorithms for optimizing access control lists that can reduce significantly expected packet latencies without sacrificing security requirements. The emphasis throughout the paper is in providing a modular approach that can be implemented either fully or partially, both online and offline, based on the amount of overhead allowed. It also shows empirically and analytically where and why the greatest potential for optimization lies. [Copyright &y& Elsevier]

This paper considers the sliding mode control issue for the complex neural network coupled with multi-interconnected neurons. Firstly, the stability of the neural network is realized by designing a multi-event triggered mechanism and a multi-sliding surface. In addition, the corresponding stability criterion is proposed with solvable matrix constraints. Secondly, a multi-sliding mode control law is developed based on a multi-event triggered mechanism, and then sliding mode control synthesis and reachability issues are formulated for the corresponding closed-loop neural network. Based on these discussions, an innovative sliding mode control algorithm is designed to achieve the sliding mode gain and control law. To conclude the paper, an example is provided to illustrate the feasibility and validity for the proposed methods and criteria. • The stability analysis of the neural network system is realized by designing multi-event triggered mechanism and multi-sliding surface. • Multi-sliding mode control law is developed based on the multi-event triggered mechanism. • The sliding mode control synthesis and reachability are formulated for the corresponding closed loop neural network. [Display omitted] [ABSTRACT FROM AUTHOR]

The Open Circuit (OC) faults occurring in switches of Multilevel Converters (MLC) may lead to undesirable operation of the converter. Therefore, fault detection and its localization in minimum time are necessary. This paper focuses on the fast fault detection algorithm based on the two samples technique and the fault localization algorithm using the Entropy of Wavelet Packets (EWP) as a feature. The EWP feature is used to classify and localize the OC faults in Insulated Gate Bipolar Transistors (IGBTs) of three-phase, three-level inverter using Support Vector Machine (SVM) based fault classification algorithm. The proposed technique can detect the fault in single IGBT and multiple IGBTs in a lesser time range of microseconds to 0.33 ms. It gives better performance and accuracy (99.70%) than previously proposed SVM algorithms, as the EWP-based feature extraction process used in this paper is simple and accurate with a less computational burden. [Display omitted] • An observer-based algorithm is proposed using two samples-based technique. • The Entropy of Wavelet Packets (EWP-SVM) technique is used for fault diagnosis. • The proposed algorithm can detect the faults in a single IGBT and multiple IGBTs. • The technique is faster and accurate than techniques available in the literature. • The comparison of detection time of different techniques is also discussed. [ABSTRACT FROM AUTHOR]

Abstract: A robust multi bit and high quality audio watermarking technique in time domain is proposed in this paper. Watermarking is a technique used to label digital media by hiding copyright or other information into the underlying data. The watermark must be imperceptible and undetectable by the user and should be robust to various types of distortion. To enhance the robustness and taper-resistance of the embedded watermark, in this paper a multi bit technique is employed. Instead of embedding one bit into an audio frame, multiple bits can be embedded into each audio sub-frame. For attackers, since they do not know the parameters, this significantly reduces the possibility of unauthorized bit detection and removal of watermark. In real-time watermarking applications, robustness is not the only factor that plays an important role. The other factor that plays a very an important role is computational complexity. In general, audio file is transmitted in compressed form. Real-time watermark embedding must take into account this compressed form, because first decompressing the data, adding a watermark and then recompressing the data is computationally too demanding. In this paper, we propose robust and low complexity audio watermarking algorithm. To evaluate the performance of the proposed audio watermarking algorithm, subjective and objective quality tests including bit error rate (BER) and signal-to-noise ratio (SNR) were conducted. Compared to traditional one bit embedding algorithm, the proposed algorithm gives highly recovery rate after attack by commonly used audio data manipulations, such as low pass filtering, requantization, resampling, and MP3 compression. [Copyright &y& Elsevier]

Abstract: Hash functions are common and important cryptographic primitives, which are very critical for data integrity assurance and data origin authentication security services. Field programmable gate arrays (FPGAs) being reconfigurable, flexible and physically secure are a natural choice for implementation of hash functions in a broad range of applications with different area-performance requirements. In this paper, we explore alternative architectures for the implementation of hash algorithms of the secure hash standards SHA-256 and SHA-512 on FPGAs and study their area-performance trade-offs. As several 64-bit adders are needed in SHA-512 hash value computation, new architectures proposed in this paper implement modulo-64 addition as modulo-32, modulo-16 and modulo-8 additions with a view to reduce the chip area. Hash function SHA-512 is implemented in different FPGA families of ALTERA to compare their performance metrics such as area, memory, latency, clocking frequency and throughput to guide a designer to select the most suitable FPGA for an application. In addition, a common architecture is designed for implementing SHA-256 and SHA-512 algorithms. [Copyright &y& Elsevier]

Abstract: In this paper we have addressed the problem of finding a path through a maze of a given size. The traditional ways of finding a path through a maze employ recursive algorithms in which unwanted or non-paths are eliminated in a recursive manner. Neural networks with their parallel and distributed nature of processing seem to provide a natural solution to this problem. We present a biologically inspired solution using a two level hierarchical neural network for the mapping of the maze as also the generation of the path if it exists. For a maze of size S the amount of time it takes would be a function of S (O(S)) and a shortest path (if more than one path exists) could be found in around S cycles where each cycle involves all the neurons doing their processing in a parallel manner. The solution presented in this paper finds all valid paths and a simple technique for finding the shortest path amongst them is also given. The results are very encouraging and more applications of the network setup used in this report are currently being investigated. These include synthetic modeling of biological neural mechanisms, traversal of decision trees, modeling of associative neural networks (as in relating visual and auditory stimuli of a given phenomenon) and surgical micro-robot trajectory planning and execution. [Copyright &y& Elsevier]

This paper presents an effective compression method suitable for transmission the still images on public switching telephone networks (PSTN). Since compression algorithm reduce the number of pixels or the gray levels of a source picture, therefore this will lead to the reduction of the amount of memory needed to store the source information or the time necessary for transmitting by a channel with a limited bandwidth. First, we introduced some current standards and finally the lossy DCT-based JPEG compression method is chosen. According to our studies, this method is one of the suitable methods. However, it is not directly applicable for image transmission on usual telephone lines (PSTN). Therefore, it must be modified considerably to be suitable for our purposes. From Shannon’s Information Theory, we know that for a given information source like an image there is a coding technique which permits a source to be coded with an average code length as close as to the entropy of the source as desired. So, we have modified the Huffman coding technique and obtained a new optimized version of this coding, which has a high speed and is easily implemented. Then, we have applied the DCT1Discrete Cosine Transform.1 and the FDCT2Forward DCT.2 for compression of the data. We have analyzed and written the programs in C++ for image compression/decompression, which give a very high compression ratio (50:1 or more) with an excellent SNR.3Signal to Noise Ratio.3In this paper, we present the necessary modifications on Huffman coding algorithms and the results of simulations on typical images. [Copyright &y& Elsevier]

Recently, the residue number system (RNS) has been intensively studied. The Chinese remainder theorem (CRT) is a solution to the conversion problem of a number to RNS with a general moduli set. This paper introduces the generalized CRT (GCRT) with parallel algorithms used for the conversion. The GCRT differs from the CRT because it has the advantage of having more applications than does the CRT. The GCRT, however, has a disadvantage in computational performance. To remedy this shortcoming, this paper proposes algorithms that calculate concurrently for some non-related program fragments of GCRT computation. These proposed algorithms also allow the GCRT to compute more efficiently. [Copyright &y& Elsevier]

• GMM manual parallelization method on DSP outperform automatic method(OpenMP). • Parallelizing on a multi-core DSP platform can speed up algorithm processing. • OpenMP's dynamic scheduling is suitable for GMM parallelization on DSP. • For efficient use of DMA throughput (10 GB/s), data transfers should exceed 64KB. Scene segmentation is crucial in video processing applications, such as vehicle detection. The Gaussian Mixture Model (GMM) based background subtraction (BS) is a widely employed technique for scene segmentation. However, due to its high computational demands, an efficient parallel implementation is necessary. This paper proposes a parallelized implementation of the GMM algorithm on the C6678 digital signal processor (DSP), utilizing its eight cores. Three different parallelization approaches are presented: Open Multiprocessing (OpenMP), manually optimized method, and manually optimized method involving Direct Memory Access (DMA) engine. To evaluate their effectiveness, a metrics comparison is conducted using background truth images provided by the literature dataset references. The experimental results demonstrate that the proposed manually optimized method surpasses the current state-of-the-art. It achieves a remarkable parallel efficiency of approximately 99% for low and medium frame sizes and 83% and 79% for high-definition (HD) and full-HD resolution frames, respectively, when all eight cores are enabled. In contrast, the parallel efficiency obtained using OpenMP is only 45%. [Display omitted] [ABSTRACT FROM AUTHOR]

With the aim of minimizing task completion time and reducing resource consumption, this paper investigates the dynamic task allocation problem of a heterogeneous aircraft cooperative cluster with multiple fire spots in the forest. Firstly, we establish the fire point propagation model and task assignment model to enhance task assignment accuracy. Based on this, we propose the Chaotic Cosine Fusing T-Distribution Variation and Quasi-Reflection Learning Grey Wolf Optimization algorithm (CTQGWO), which integrates distributed mutation and quasi-reflective learning. By incorporating chaos population initialization of the Tent map, adaptive control parameter adjustment strategy, distributed mutation, and quasi-reflective learning strategy, we can enhance the convergence speed and accuracy of the algorithm. Finally, taking into account the complexity of the forest and the instability of the aircraft, we develop a task reassignment algorithm that can quickly allocate and reduce the number of communications. Through experiments, our algorithm enables rapid decision-making for forest fire emergency scheduling, under the condition of limited forest fire resources, effectively reducing the total rescue time and minimizing the consumption of rescue resources. [ABSTRACT FROM AUTHOR]

There are currently numerous automated fall detection devices available, but most require the deployment of additional specialized equipment, which can create inconvenience for elderly individuals in their daily lives. This paper proposes a solution that reuses existing smartphone and WiFi infrastructure, eliminating the need for extra equipment and greatly simplifying the implementation of fall event detection. Due to the lower quality of data collected by smartphones, we have developed an adaptive DBSCAN clustering algorithm to process and extract useful data. We then train a deep convolutional LSTM model to classify the collected data in real-time. When a fall is detected, the smartphone can transmit an alert via the internet to notify relatives or medical staff of the incident. Our experimental results demonstrate that utilizing smartphones for fall detection is both feasible and accurate. [Display omitted] • Reduce costs by reusing existing infrastructure without deploying additional equipment. • Improve signal quality by using clustering methods. • Selecting appropriate parameters for the clustering algorithm to improve the clustering effect. • Experimental results show the feasibility and accuracy of the scheme. [ABSTRACT FROM AUTHOR]

One of the integral components in the architectural design and development of Internet of Things (IoT) is Microservice. Microservices are basically an architectural and organizational approach in the process of software development where the software is composed of small but independent services that would communicate over well-defined APIs (Application Programming Interfaces). It is quite challenging to ensure data integrity and data availability in the architecture design of microservices. As Blockchain technology has emerged as a panacea to many of the other domains, the distributed microservice architecture can also utilize it. We know that the consensus algorithms are used in the Blockchain technology to validate the transactions alongside providing extra level of security. Taking the advantage of consensus algorithms in blockchain-based architecture models, in this paper, we propose TCA (Trustworthy Consensus Algorithm), which is designed to solve the data integrity and availability challenges in microservice architectures. We have evaluated the proposed algorithm against the known alternatives and it shows good level of efficiency. [Display omitted] • One of the integral components of Internet of Things (IoT) is Microservice. • It is a challenge to ensure data integrity and data availability. • The consensus algorithms in blockchain provide extra level of security. • Trustworthy Consensus Algorithm enhances the security of microservice. [ABSTRACT FROM AUTHOR]

Edge computing can effectively provide a promising solution with ultra-low latency for a variety of user-oriented services in 5G and beyond networks. The existing studies, however, are unable to provide a flexible resource allocation strategy for various network slices with different user demands in edge computing. This paper proposes a new network slicing algorithm for cloud–edge collaboration hybrid computing (CECHC) in 5G and beyond networks. This algorithm designs a flexible and effective resource allocation strategy primarily for three network slices based on diversified requirements. Moreover, the CECHC model is designed with optimally distributed units (DUs) and centralized units (CUs) deployments to improve storage capability and computing power. This allows for more convenient function partitioning for different network slices. To validate the performance of the proposed algorithm, a series of agent-based simulations are conducted in various network models. The experimental results demonstrate that the proposed algorithm deployed in CECHC outperforms other network models, including fog computing (FC), multi-access edge computing (MEC), and the original CECHC models. It provides the lowest latency for various network slices and achieves all successful runs with different storage capabilities and computing power. [ABSTRACT FROM AUTHOR]

• Characterization of the network for the best optimized path using reliability, delay and capacity. • Tuning of the network parameters for the prediction/validation for the best optimized Path, according to a given data flow. • Best optimized path is useful to choose most appropriate path directly for mission-critical applications without using routing overhead after tuning rule formulations. In this paper, a novel model has been proposed for the computation of optimized path with link reliability, delay and capacity. Algorithms have been proposed for the optimized data path with a single link-weight and it constitutes two main parts: (i) characterization of the network for the best optimized path, and (ii) tuning of the network parameters for the prediction/validation for the Best Optimized Path, according to a given data flow. It also needs very little a priori information during the online computation as the computed optimized path requires less computational overhead as each data flow shall be assigned with a precomputed and optimized link weight. Now, with the help of this optimized link weight one can find the optimum path with respect to both risk and lag-time in an online approach simply by adopting the Dijkstra's algorithm. This approach has been illustrated here in this paper with the help of two different topologies for different values of data flow from single source to single destination. Algorithms have been proposed based on this mathematical model. It also can be observed that proposed algorithms are able to compute the optimized path with having complexity of Dijkstra's algorithm when computer together. This study can be claimed that the proposed algorithms are self-sufficient to provide the risk-aware quickest path routing, even without having any prior information on the path to be allocated. One can easily extend it for the case of many source nodes to the many destination nodes as well as for the software defined networking (SDN) in the future work. [Display omitted] [ABSTRACT FROM AUTHOR]

In modern era, the switched reluctance motors (SRM) are gaining attraction due to its inherent features such as robustness, low cost, simple, rugged structure, excellent fault tolerance and temperature withstanding capability. With these specialized advantages, the accurate and efficient energy analysis is still a challenging one since they operated at variable reluctance and oscillating excitation characteristics resulting in nonlinear characteristics. This paper focused on the energy improvement and performance analysis inclusive of smooth control in speed and minimized torque ripples of SRM by introducing the novel Bio- inspired methodology named Bacterial Foraging Optimization Algorithm (BFOA) for the selection of optimal parameters of the PID speed and current controllers. By minimizing the torque ripples, it increases the average torque which in turn increases the energy conversion. The performance of the SRM is measured in terms of speed, current, power and efficiency and BFOA efficiency has been verified with genetic algorithm (GA) and conventional PID controller using Euler forward approximation method. This paper discusses the modeling, controlling strategy using BFOA, GA and Euler forward approximation method and a comprehensive analysis of energy using optimized selection of controlling parameters. The performance evaluation objectives in this work, is to minimize Integral Square Error of both current and speed controller and also the reduction of torque ripples. The results obtained in this method are also compared with the Genetic Algorithm and Euler forward approximation-based controllers. The proposed algorithm employs individual and social intelligences, which in turn search the responses between the local optima along with global optimums of the problem adaptively. The outmost dynamic response increased average torque and minimized current ripple can be obtained when the parameters of PID controllers are optimized using BFOA. [ABSTRACT FROM AUTHOR]

• Providing efficient routing and content management for ICN. • It enables bandwidth and cost-based routing for mobile and social sensing applications. • Caching is an important feature of ICN. • Cache replacement policy proposed to improve ICN performance. Information-Centric Network (ICN) is an essential networking model which contains several future Internet research activities. This paper considers ICN architecture for mobile and social sensing data. Routing and caching are among the most critical features of ICN, which improves the performance of efficiency of network and content sharing by fulfilling user requirements with cached content. This paper introduces a new routing model for information-centric network based on least bandwidth and cost (LBC) with two cache replacement policies. The cache replacement policies find and replace the content in the free cache space. Time aware Least Recently Used (TLRU) and Least Frequent Recently Used (LFRU) are employed as a cache replacement policy. The proposed algorithm implemented and compared, with the previous algorithm in terms of the following metrics success rate, hop count and delay. The results of the experiments show that the proposed algorithm is more effective compared to other algorithms. [Display omitted] [ABSTRACT FROM AUTHOR]