User Signal Research Articles

A Meta-Learning Approach for Fast Personalization of Modality Translation Models in Wearable Physiological Sensing.

Modality translation grants diagnostic value to wearable devices by translating signals collected from low-power sensors to their highly-interpretable counterparts that are more familiar to healthcare providers. For instance, bio-impedance (Bio-Z) is a conveniently collected modality for measuring physiological parameters but is not highly interpretable. Thus, translating it to a well-known modality such as electrocardiogram (ECG) improves the usability of Bio-Z in wearables. Deep learning solutions are well-suited for this task given complex relationships between modalities generated by distinct processes. However, current algorithms usually train a single model for all users that results in ignoring cross-user variations. Retraining for new users usually requires collecting abundant labeled data, which is challenging in healthcare applications. In this paper, we build a modality translation framework to translate Bio-Z to ECG by learning personalized user information without training several independent architectures. Furthermore, our framework is able to adapt to new users in testing using very few samples. We design a meta-learning framework that contains shared and user-specific parameters to account for user differences while learning from the similarity amongst user signals. In this model, a meta-learner approximated by a neural network learns how to learn user-specific parameters and can efficiently update them in testing. Our experiments show that the proposed model reduces the percentage root mean square difference (PRD) by 41% compared to training a single model for all users and by 36% compared to training independent models for each user. When adapting the model to new users, our model outperforms fine-tuning a pre-trained model through back-propagation by 40% using as few as two new samples in testing.

Optimization of Receivers' Field of Views in Multi-User VLC Networks: A Bio-Inspired Approach

Visible light communication is a well-known emerging technology that enables high data rate wireless access in indoor environments by making dual-us e of light-emitting diode luminaires for providing lighting and communication. Managing multiple access and addressing the user's mobility are among the current challenges of this technology. To manage multiple access interference arising from unintended received signals of other users, different approaches have been proposed so far, including zero-forcing precoding and broadcasting of the users' signals. However, by this approach, the network performance is sensitive to the users' locations, since they affect the correlation properties of the network channel matrix. A higher correlation (e.g., for the case of users getting closer together) results in a performance degradation. To mitigate this, we propose a novel approach of optimizing the receivers field-of-views based on the bio-inspired particle swarm optimization in order to adapt to the users' location, and hence, improve the network's robustness. The efficiency of the proposed method is demonstrated through numerical simulations.

Mitigation of spectrum sensing data falsification attack using multilayer perception in cognitive radio networks

<p>Cognitive radio network (CRN) is used to solve spectrum scarcity and low spectrum utilization problems in wireless communication systems. Spectrum sensing is a vital process in CRNs, which needs continuous measurement of energy. It enables the sensors to sense the primary signal. Cooperative Spectrum Sensing (CSS) has recommended to sense spectrum accurately and to enhance detection performance. However, Spectrum Sensing Data Falsification (SSDF) attack being launched by malicious users can lead to wrong global decision on the availability of spectrum. It is an extremely challenging task to alleviate impact of SSDF attack. Over the years, numerous strategies have been proposed to mitigate SSDF attack ranging from statistical to machine learning models. Energy measurement through statistical models is based on some predefined criteria. On the other hand, machine learning models have low sensing performance. Therefore, it is necessary to develop an efficient method to mitigate the negative impact of SSDF attack. This paper intends to propose a Multilayer Perceptron (MLP) classifier to identify falsified data in CSS to prevent SSDF attack. The statistical features of the received signals are measured and taken as feature vectors to be trained by MLP. In this manner, measurement of these statistical features using MLP becomes a key task in cognitive radio networks. Trained network is employed to differentiate malicious users signal from honest users’ signal. The network is trained with the Levenberg-Marquart algorithm and then employed for eliminating the effect of attacks due to the SSDF process. Once the simulated results are observed, it can be revealed that the proposed model could efficiently reduce the impact of malicious users in CRN.</p>

Spectrum Sensing using Energy Measurement in Wireless Telemetry Networks using Logarithmic Adaptive Learning

<p>To identify primary user signals in cognitive radios spectrum sensing method is used. Due to statistical variances in received signal, noise is present in primary user signals, this noise powers are varied due to random nature of noise signals and leads to noise uncertainty problem in the performance of energy detection. The task of energy measurement and further detecting the unused frequency spectrum is a key task in cognitive radio applications. For avoiding these problems, least logarithmic absolute difference (LLAD) algorithm is proposed in which noise powers are adjusted at sensing point of licensed users. With help of proposed method, estimated noise signals are eliminated. Sign regressor version of LLAD algorithm is considered due to it reduces computational complexity and convergence rate is improved. Further probability of detection (<em>P</em><sub>od</sub>), probability of false alarm (<em>P</em><sub>ofa</sub>) is estimated to know threshold value. From results, it is clear that good performance in terms of <em>P</em><sub>ofa</sub> versus <em>P</em><sub>od</sub> in range of low signal to noise ratio in multiple nodes. Therefore, the proposed energy measurement-based spectrum sensing method is useful in remote health care monitoring, medical telemetry applications by sharing the un-used spectrum.</p>

Blockchain based security framework for sharing digital images using reversible data hiding and encryption

Security is an important issue in current and next-generation networks. Blockchain will be an appropriate technology for securely sharing information in next-generation networks. Digital images are the prime medium attacked by cyber attackers. In this paper, a blockchain based security framework is proposed for sharing digital images in a multi user environment. The proposed framework uses reversible data hiding and encryption as component techniques. A novel high capacity reversible data hiding scheme is also proposed to protect digital images. Reversible data hiding in combination with encryption protects the confidentiality, integrity and authentication of digital images. In the proposed technique, the digital image is compressed first to create room for data hiding, then the user signature is embedded; afterwards the whole image is encrypted. For compression, JPEG lossy compression is used to create high capacity. For encryption, any symmetric block cipher or stream cipher can be used. Experimental results show that the proposed blockchain based framework provides high security and the proposed reversible data hiding scheme provides high capacity and image quality.

Grant-Free Communications With Adaptive Period for IIoT: Sparsity and Correlation-Based Joint Channel Estimation and Signal Detection

In this article, we investigate the grant-free communications with adaptive period for Industrial Internet of Things, where only a fraction of devices is active at a time. To the best of our knowledge, this is the first work to exploit the noncontinuous temporal correlation of the received signal for joint user activity detection (UAD), channel estimation, and signal detection, while all the previous work requires continuous transmission. Two schemes are proposed toward this purpose, namely, periodic block orthogonal matching pursuit (PBOMP) and periodic block sparse Bayesian learning (PBSBL), which outperform the previous schemes in terms of the success rate of UAD, bit error rate, and accuracy in period estimation and channel estimation. The Cramér–Rao lower bounds (CRLBs) of channel estimation by PBOMP and PBSBL are derived. It is shown that the two proposed approaches have close CRLBs and normalized mean-square error at high SNR.

All‐day wearable health monitoring system

AbstractWearable devices are widely used in the smart healthcare monitoring system to detect changes in user parameters through applications such as wristwatches, bands, and clothing electronic skin. In addition, multimode devices enable monitoring of vital signs, helping diagnose and prevent diseases. A wearable device detects the user's biological signals such as body temperature, movement, heartbeat, and humidity level, transmits the information to the mobile phone, and sends the information to an emergency center/family/clinician through cloud computing or wireless communication systems. This all‐day monitoring system enables the user's status information to be monitored 24 h a day to ensure appropriate treatment, thereby facilitating highly personalized care due to its human‐centricity. When integrated with higher‐level infrastructure, it is expected to be useful in healthcare scenarios, providing benefits to multiple stakeholders. In addition, it will help protect people exposed to potentially life‐threatening environments such as military personnel, first responders, and deep‐sea and space explorers. In this review, the components for implementing an all‐day monitoring system are described, including the electrode design strategy for realizing a skin attachable e‐skin device. Issues related to flexible storage devices and recent research results are also discussed.image

Secure collaborative cognitive radio based on chaotic modulation and compressive sensing

Cognitive radio (CR) is a wireless technology that is used to overcome the spectrum scarcity problem. CR includes several stages, spectrum sensing is the first stage in the CR cycle. Traditional spectrum sensing (SS) techniques have many challenges in the wideband spectrum. CR security is an important problem, since when an attacker from outside the network access the sensing information this produces an increase in sensing time and reduces the opportunities for exploiting vacant band. Compressive sensing (CS) is proposed to capture all the wideband spectrum at the same time to solve the challenges and improve the performance in the traditional techniques and then one of the traditional SS techniques are applied to the reconstructed signal for detection purpose. The sensing matrix is the core of CS must be designed in a way that produces a low reconstruction error with high compression. There are many types of sensing matrices, the chaotic matrix is the best type in terms of security, memory storage, and system performance. Few works in the literature use the chaotic matrix in CS based CR and these works have many challenges: they used sample distance in the chaotic map to generate a chaotic sequence which consumes high resources, they did not take into consideration the security in reporting channel, and they did not measure their works using real primary user (PU) signal of a practical application under fading channel and low SNR values. In this paper, we propose a chaotic CS based collaborative scenario to solve all challenges that have been presented. We proposed a chaotic matrix based on the Henon map and use the differential chaotic shift keying (DCSK) modulation to transmit the measurement vector through the reporting channel to increase the security and improve the performance under fading channel. The simulation results are tested based on a recorded real-TV signal as PU and Compressive Sampling Matching Pursuit (CoSaMP) recovery algorithm under AWGN and TDL-C fading channels in collaborative and non-collaborative scenarios. The performance of the proposed system has been measured using recovery error, mean square error (MSE), derived probability of detection (Pdrec), and sensitivity to initial values. To measure the improvement introduced by the proposed system, it is evaluated in comparison with selected chaotic and random matrices. The results show that the proposed system provides low recovery error, MSE, with high Pdrec, security, and compression under SNR equal to −30 dB in AWGN and TDL-C fading channels as compared to other matrices in the literature.

Throughput performance of a non‐linear energy‐harvesting cognitive radio‐enabled device‐to‐device network

SummaryIn cellular networks, device‐to‐device (D2D) communication is a new paradigm. In this paper, the performance of a non‐linear energy‐harvesting D2D communication network underlaying a cellular network is studied under a channel quality constraint. To make the study more realistic, spectrum sensing is incorporated with the D2D communication. Thus, the user equipments (UEs), that is, D2D devices, are enabled with cognitive radio (CR) technology. Since the UEs are battery powered, there exists an energy constraint. Thus, each UE is also equipped with an energy‐harvesting circuit and harvest energy from radio frequency (RF) signal of power transfer units (PTUs) and cellular users (CUs). Each UE uses its harvested energy for transmission. A D2D transmitter uses sensing information to decide its mode of transmission over a CU channel and is shared in hybrid mode. An algorithm is proposed based on threshold‐based censoring to maximize the throughput. In the proposed scheme, D2D users associated with the bad quality of channels are eliminated from the transmission process. Analytical expressions of throughput and improvement in spectrum utilization (ISU) are developed. The impact of the interference threshold to CU, the censoring threshold and the saturation level of the energy harvester on throughput is studied. The network performance is also investigated for several types of channels. It is found that the amount of fading has a significant impact on throughput performance. It is observed that the proposed scheme outperforms the conventional scheme.

On Selection of Parameters for Cooperative Spectrum Sensing Schemes over κ–μ Fading Channels

The current paper studies comprehensive analysis of various cooperative spectrum sensing (CSS) schemes in generalized κ − μ fading environment. More precisely, the optimization of several network parameters as well as throughput performance analysis of various CSS schemes are studied. In each CSS scheme, every cognitive radio user (CRU) first senses and receives the primary user (PU) signal using sensing channels, performs an energy detection (ED) operation. Depending upon the type of the scheme, CRU makes and forwards its local binary decision or simply forwards its data to the fusion center (FC) with the help of reporting channels. Then FC implements any one of the CSS schemes for making a final decision about busy or free status on PU. The novel expressions for missed and false detection probabilities subject to CSS scheme under fading are developed. The performance of several decision and data CSS schemes through total error rate, considering different network and κ − μ fading parameters is investigated. The impact of erroneous channel conditions on each CSS scheme is investigated. The expressions for optimal system parameters of decision-based CSS schemes under fading are developed and optimal values for different network conditions are estimated. Further, the throughput performance of decision-based CSS schemes for different networks and κ − μ fading parameters values is studied.

Low-Complexity Self-Interference Cancellation for Multiple Access Full Duplex Systems.

Self-interference occurs when there is electromagnetic coupling between the transmission and reception of the same node; thus, degrading the RX sensitivity to incoming signals. In this paper we present a low-complexity technique for self-interference cancellation in multiple carrier multiple access systems employing whole band direct to digital sampling. In this scenario, multiple users are simultaneously received and transmitted by the system at overlapping arbitrary bandwidths and powers. Traditional algorithms for self-interference mitigation based on recursive least squares (RLS) or least mean squares (LMS), fail to provide sufficient rejection, since the incoming signal is far from being spectrally flat, which is critical for their performance. The proposed algorithm mitigates the interference by modeling the incoming multiple user signal as an autoregressive (AR) process and jointly estimates the AR parameters and self-interference. The resulting algorithm can be implemented using a low-complexity architecture comprised of only two RLS modules. The novel algorithm further satisfies low latency constraints and is adaptive, supporting time varying channel conditions. We compare this to many self-interference cancellation algorithms, mostly adopted from the acoustic echo cancellation literature, and show significant performance gain.

Next-Generation Passive Optical Network Based on Sparse Code Multiple Access and Graph Neural Networks

Sparse code multiple access (SCMA) is a promising technology to provide high throughput and overall improved system performance at affordable cost for next-generation passive optical networks (NG-PONs). Message passing algorithm (MPA) based on a factor graph is usually used for low-complexity multi-user detection in SCMA. However, MPA requires accurate and effective channel estimation due to the interference between the user's signals on the same resource block and suffers uncertain convergence caused by the cycles in the factor graph. In this paper, a graph neural networks (GNN)-based detection method is proposed for SCMA-PON, which performs channel impairment compensation and signal detection in a joint manner. 25.5 Gb/s SCMA-OFDM system over 20/60 km single mode fiber link is simulated to demonstrate the feasibility with 8.5-G class optical devices. The simulation results show that GNN-based detection method outperforms MPA and is more robust to the nonlinear distortion for the same level of computational complexity.

M-ARY CDMA SCHEME WITH CCSK MODULATION AND PARALLEL INTERFERENCE CANCELLATION TECHNIQUE

In this paper, we tackle the problem of implementation a parallel interference cancellation scheme for multi-user detection (MUD) in M-ary CCSK-modulated CDMA system. Complexity remains the main obstacle to the practical implementation of MUD and a factor limiting, if not the very possibility, then the feasibility of using high order M-ary modulation formats for applications such as low-probability-of-intercept systems with a large spreading factor or, myriads of small self-powered IoT devices. The design of MUD algorithms for M-ary systems leads to a new round of growth in the receiver complexity comparing with the binary modulation. Demodulator has a major impact on the complexity of CDMA system. Cyclic code-shift keying (CCSK) is a modulation technique which is designed to reduce the complexity of M-ary signaling. In this, each symbol is a circularly shifted version of a single code sequence. Assuming synchronization, the receiver cyclically correlates the input signal plus noise with the base sequence and estimates the position of the correlation peak. The preliminary stage of the proposed scheme is a conventional multi-channel CDMA receiver. The preliminary estimates of the data is multiplied by user codes and amplitude estimates in the spreader, and then fed to the adder to generate the MAI estimates. After the latter are subtracted from the group signal, K-dimensional vector of cleared user signals is passed to the matched filter bank to form the refined estimates of the data. At each subsequent stage, the estimates of the output data of the previous stage are used as input data. The complexity gain is the ratio of the number of computations for finding cyclic convolution and, the amount of computations for finding M linear convolutions, i.e., M/log2M. Since the performance of the algorithm is sensitive to the reliability of the preliminary decisions, there are strict criteria for the selection of spreading codes: the length which must be an integer power of 2, the large family size, good periodic autocorrelation and good cross-correlation properties. To examine the proposed algorithm using computer simulation, we selected minimax periodic and odd-periodic complementary codes, the properties of which are close to the properties of the codes used in the CCSK scheme implemented in Link-16 protocol tactical data networks JTIDS and MIDS. Our study shows that the gain over the conventional receiver increases as the SNR increases, achieving 15 dB for BER 10-5. The system with complementary codes outperform system based on minimax PN-codes, achieving a target bit error rate at a lower SNR.

Analysis of the Impact of Detection Threshold Adjustments and Noise Uncertainty on Energy Detection Performance in MIMO-OFDM Cognitive Radio Systems.

Due to the capability of the effective usage of the radio frequency spectrum, a concept known as cognitive radio has undergone a broad exploitation in real implementations. Spectrum sensing as a core function of the cognitive radio enables secondary users to monitor the frequency band of primary users and its exploitation in periods of availability. In this work, the efficiency of spectrum sensing performed with the energy detection method realized through the square-law combining of the received signals at secondary users has been analyzed. Performance evaluation of the energy detection method was done for the wireless system in which signal transmission is based on Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing. Although such transmission brings different advantages to wireless communication systems, the impact of noise variations known as noise uncertainty and the inability of selecting an optimal signal level threshold for deciding upon the presence of the primary user signal can compromise the sensing precision of the energy detection method. Since the energy detection may be enhanced by dynamic detection threshold adjustments, this manuscript analyses the influence of detection threshold adjustments and noise uncertainty on the performance of the energy detection spectrum sensing method in single-cell cognitive radio systems. For the evaluation of an energy detection method based on the square-law combining technique, the mathematical expressions of the main performance parameters used for the assessment of spectrum sensing efficiency have been derived. The developed expressions were further assessed by executing the algorithm that enabled the simulation of the energy detection method based on the square-law combining technique in Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing cognitive radio systems. The obtained simulation results provide insights into how different levels of detection threshold adjustments and noise uncertainty affect the probability of detection of primary user signals. It is shown that higher signal-to-noise-ratios, the transmitting powers of primary user, the number of primary user transmitting and the secondary user receiving antennas, the number of sampling points and the false alarm probabilities improve detection probability. The presented analyses establish the basis for understanding the energy detection operation through the possibility of exploiting the different combinations of operating parameters which can contribute to the improvement of spectrum sensing efficiency of the energy detection method.

Stress Level Estimation Based on Physiological Signals for Virtual Reality Applications

Virtual Reality (VR) video games have become popular in recent years with the emergence of a wide variety of enhanced VR hardware systems. Typically, current VR video games incorporate high-quality auditory and video feedback along with vibro-tactile cues to provide an immersive experience. However, the ongoing VR hardware and software do not consider the state of the user to determine whether the video game is generating an enjoyable experience, or if it has become a stressing or boring experience. This work provides an assessment of stress level estimation from features extracted from Electrocardiography (ECG), Electrodermal Activity (EDA), and Electromyography (EMG) signals of users while playing a VR video game with different difficulty levels. Statistical differences were found between the rest and gaming stages for several extracted features. Regardless of the fact that no significant statistical differences between the three levels of difficulty were found by analyzing the EDA and ECG features, an 83.1% accuracy was obtained for the classification between the three levels with a KNN model. For the EMG signal, the obtained accuracy ranged between 99% and 100% for the distinction between a difficult level 1 and rest stage for all models. When all features from the ECG, EDA and EMG signals were used, an accuracy of 99% was obtained for the differentiation between the three difficulty levels and a resting stage. The presented work results may serve as a reference for future work regarding feature extraction and game difficulty adaptation for user experience enhancement.

Secure Precoding in MIMO-NOMA: A Deep Learning Approach

A novel signaling design for secure transmission over two-user multiple-input multiple-output non-orthogonal multiple access channel using deep neural networks (DNNs) is proposed. The goal of the DNN is to form the covariance matrix of users' signals such that the message of each user is transmitted reliably while being confidential from its counterpart. The proposed DNN linearly precodes each user's signal before superimposing them and achieves near-optimal performance with significantly lower run time. Simulation results show that the proposed models reach about 98% of the secrecy capacity rates. The spectral efficiency of the DNN precoder is much higher than that of existing analytical linear precoders--e.g., generalized singular value decomposition--and its on-the-fly complexity is several times less than the existing iterative methods.

Power Allocation in NOMA-CR for 5G Enabled IoT Networks

In the power domain, non-orthogonal multiple access (NOMA) supports multiple users on the same time-frequency resources, assigns different transmission powers to different users, and differentiates users by user channel gains. Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information, and multi-user detection algorithms, such as successive interference cancellation (SIC) is performed at the receiving end to demodulate the necessary user signals. In contrast to the orthogonal transmission method, the non-orthogonal method can achieve higher spectrum utilization. However, it will increase the receiver complexity. With the development of microelectronics technology, chip processing capabilities continue to increase, laying the foundation for the practical application of non-orthogonal transmission technology. In NOMA, different users are differentiated by different power levels. Therefore, the power allocation has a considerable impact on the NOMA system performance. To address this issue, the idea of splitting power into two portions, intra-subbands and inter-subbands, is proposed in this study as a useful algorithm. Then, such optimization problems are solved using proportional fair scheduling and water-filling algorithms. Finally, the error propagation was modeled and analyzed for the residual interference. The proposed technique effectively increased the system throughput and performance under various operating settings according to simulation findings. A comparison is performed with existing algorithms for performance evaluation.

A Recommender System for Predictive Control of Heating Systems in Economic Demand Response Programs

Flexibility from demand-side resources is increasingly required in modern power systems to maintain the dynamic balance between demand and supply. This flexibility comes from elastic users managing controllable loads. In this context, controlling Electric Space Heaters (ESHs) is of particular interest because it can leverage building inner thermal storage capacity to shift consumption while maintaining comfort conditions. Some economic Demand Response (DR) programs have considered exploiting EHSs flexibility potentials in recent years. However, these programs still struggle to engage customers due to the complexity of processing price signals for inexpert users. Therefore, it is necessary to develop automated tools for helping users to operate their loads. Accordingly, this paper presents a recommender system based on Gaussian processes to discover users' valuations of thermal comfort and perform the predictive control of their ESHs. The proposed method enables customers to participate in DR programs and impose their preferences through straightforward queries instead of directly changing control parameters. Validation results demonstrate that users maximize their utility by supplying noiseless and consistent data to the recommender system. Additionally, the suggested approach achieves a higher acceptance rate than other methods from the literature, such as persistency and support vector machines.

An Optimized Algorithm for CR-MIMO Wireless Networks

With the rapid development of wireless communication technology, the spectrum resources are increasingly strained which needs optimal solutions. Cognitive radio (CR) is one of the key technologies to solve this problem. Spectrum sensing not only includes the precise detection of the communication signal of the primary user (PU), but also the precise identification of its modulation type, which can then determine the a priori information such as the PU’ service category, so as to use this information to make the cognitive user (CU) aware to discover and use the idle spectrum more effectively, and improve the spectrum utilization. Spectrum sensing is the primary feature and core part of CR. Classical sensing algorithms includes energy detection, cyclostationary feature detection, matched filter detection, and so on. The energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal, but it is easily affected by noise and the threshold is not easy to determine. The combination of multiple-input multiple-output (MIMO) with CR improves the spectral efficiency and multi-path fading utilization. To best utilize the PU spectrum while minimizing the overall transmit power, an iterative technique based on semidefinite programming (SDP) and minimum mean squared error (MMSE) is proposed. Also, this article proposed a new method for max-min fairness beamforming. When compared to existing algorithms, the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-to-interference plus noise ratio (SINR). Furthermore, the proposed algorithm effectively improved the system performance in terms of number of iterations, interference temperature threshold and balance SINR level which makes it superior over the conventional schemes.

TAR-AFT: A Framework to Secure Shared Cloud Data with Group Management

In addition to replacing desktop-based methods, cloud computing is playing a significant role in several areas of data management. The health care industry, where so much data is needed to be handled correctly, is another arena in which artificial intelligence has a big role to play. The upshot of this innovation led to the creation of multiple healthcare clouds. The challenge of data privacy and confidentiality is the same for different clouds. Many existing works has provided security framework to ensure the security of data in clouds but still the drawback on revocation, resisting collusion attack along with privacy of data present a complex problem. For preserving the data privacy and confidentiality, a novel framework is proposed with two novel algorithms of Threat Aware Revocation (TAR) and Advance Flexi Twister Secret Block Encryption Standard (AFT-SBES) both are named as TAR-AFT. The TAR algorithm is mainly focus on generates the user signature and the AFT-SBES algorithm to generate the key. Both the signature and key are distributed to the users for enhancing the security to access cloud storage and files. The self-session shadow approach is used to monitor the activities of the cloud users. The revocation is carried out through removal of signature from the Enhanced Dynamic Hash Table (EDHT). From the performance analysis of TAR-AFT, it provides more effective to accessing of data stored in cloud and security such as data privacy and confidentiality.