Variable Number Of Bits Research Articles

Predictability of Internet of Things Traffic at the Medium Access Control Layer Against Information-Theoretic Bounds

Most of the existing Medium Access Control (MAC) layer protocols for the Internet of Things (IoT) model the traffic generated by each IoT device via random arrivals such as those in a Poisson process. Under this model, since it is implied that IoT device traffic cannot be predicted, only reactive MAC-layer protocols in which the network responds to the current traffic are viable. In contrast, recent work has demonstrated that the traffic generated by an individual IoT device can be predictable, thus enabling predictive network protocols at the MAC layer. In this paper, we investigate information-theoretic bounds on the predictability of IoT traffic of individual devices. To this end, first, we compare the performance achieved by the following state-of-the-art forecasters on individual IoT device traffic: Logistic Regression, Multi-Layer Perceptron (MLP), 1-Dimensional Convolutional Neural Network (1D CNN), and Long Short Term Memory (LSTM) as well as MLP under feature selection based on Analysis of Variance (ANOVA) and Auto-Correlation Function (ACF). Second, we quantify the gap between the performance of these forecasters against information-theoretic bounds as follows: For IoT devices that generate a fixed number of bits at each generation instance, we measure the gap between the forecasting accuracy and the information-theoretic bound established by Fano’s inequality on the probability of correct prediction. Our empirical results show that existing forecasting schemes perform close to the information-theoretic bound in this case. For IoT devices that generate a variable number of bits, we measure the gap between the Mean Square Error (MSE) and the estimation-theoretic counterpart to Fano’s inequality. Our empirical results show that the performance of existing forecasting schemes is far from the information-theoretic bound in this case. This work thus motivates the machine learning community to develop forecasting schemes that approach information-theoretic bounds. Furthermore, this work is expected to impact the development of predictive MAC-layer protocols that exploit these bounds.

A Lower-Bound for Variable-Length Source Coding in Linear-Quadratic-Gaussian Control With Shared Randomness

In this letter, we consider a Linear Quadratic Gaussian (LQG) control system where feedback occurs over a noiseless binary channel and derive lower bounds on the minimum communication cost (quantified via the channel bitrate) required to attain a given control performance. We assume that at every time step an encoder can convey a packet containing a variable number of bits over the channel to a decoder at the controller. Our system model provides for the possibility that the encoder and decoder have shared randomness, as is the case in systems using dithered quantizers. We define two extremal prefix-free requirements that may be imposed on the message packets; such constraints are useful in that they allow the decoder, and potentially other agents to uniquely identify the end of a transmission in an online fashion. We then derive a lower bound on the rate of prefix-free coding in terms of directed information; in particular we show that a previously known bound still holds in the case with shared randomness. We generalize the bound for when prefix constraints are relaxed, and conclude with a rate-distortion formulation.

Trusted Data Transmission Using Data Scrambling Security Method with Asymmetric Key Algorithm for Synchronization

Security is a major concern of the internet world because the development of the Internet requires the security of data transmission. The security method helps us to store valuable information and send it over an insecure network so that it can not be read by anyone except the intended recipient. Security algorithm uses data randomization method. This method of data information randomization has a low computation time with a large number of bits when compared to other encryption algorithms. In general, the encryption algorithm is used to encrypt data information, but in this research the encryption algorithm is used for synchronization between the sender and the intended recipient. Number of bits on asymmetric key algorithm for synchronization are the 64-bits, 512-bits and 1024-bits. We will prove that security methods can secure data sent with low computational time with large number of bits. In the result will be shown the value of computing time with variable number of bits sent. When data are sent by 50 bytes, encryption time required 2 ms using 1024 bits for synchronization technique asymmetric key algorithm.Â

RETRACTED ARTICLE: Adaptive computing-based biometric security for intelligent medical applications

For intelligent medical systems, clinical information security is one of critical requirements. Recently, random binary sequences (RBSs) based on interpulse intervals (IPIs) were applied as secret keys to secure medical information in medical applications. Most of the existing RBS generation methods acquire a uniform quantity of bits per IPI, thereby requiring more processing time. However, the functional capacity of humans influences their heart rate variability, thereby resulting in the extraction of adaptive entropic bits per IPI across individuals. Therefore, adaptive computing-based RBS generation method is proposed to extract a variable number of bits on the basis of the heart rate (HR) bands of individuals to provide a balance between processing time and security in WBSNs. The proposed method is evaluated by using ECG recordings of 126 subjects with dynamic scenarios. Our experimental results that 128-bit RBSs generated by applying the proposed method can be used as secret keys for entity identifiers or patient’s data encryption for securing intelligent medical applications. In this study, the hamming distance metric is used to measure the uniqueness of the generated RBSs, and randomness of RBSs is computed by means of statistical tests, for different HR bands. Furthermore, the processing time of the proposed method for RBS generation shows improvement compared with the conventional techniques. The proposed approach is approximately three times faster for 55 ≤ HR < 80 and approximately two times faster for 80 ≤ HR < 105 and 80 ≤ HR < 105 than the existing IPI-based RBS generation techniques. Therefore, this study has got real-time significance for smart healthcare applications.

Non-uniform Quantized Data Fusion Rule for Data Rate Saving and Reducing Control Channel Overhead for Cooperative Spectrum Sensing in Cognitive Radio Networks

In this paper a pretty new concept of non-uniform quantized data fusion (N-QDF) rule reducing control channel data overhead has been proposed for energy detection based cooperative spectrum sensing scheme in cognitive radio networks. To strike a balance between efficient detection performance and less complexity, the network has to allow soften hard or quantized data fusion (QDF) technique though this technique incurs few bit overhead on the control channel from each user. Again lower bit QDF causes loss of more information, where as higher bit QDF increases detection probability at the cost of some extra bits per user. Here lies the beauty of NQDF scheme which uses variable number of bits: more number of bits for lower energy region—thus increases detection probability for a given false alarm, and less number of bits for higher energy region—thus data rate gets saved which in turn alleviates control channel overhead. A holistic simulation study has been done in this very paper where the performance of variable bit NQDF scheme is compared with different uniform bit i.e. 2, 3, 4, 5 QDF with respect to different parameters to validate our proposed scheme.

Adaptive Cross-Packet HARQ

In this paper, we investigate a coding strategy devised to increase the throughput in hybrid ARQ (HARQ) transmission over a block fading channel. In our approach, the transmitter jointly encodes a variable number of bits for the each round of HARQ. The parameters (rates) of this joint coding can vary and may be based on the negative acknowledgment provided by the receiver or, on the past (outdated) information about the channel states. These new degrees of freedom allow us to improve the match between the codebook and the channel states experienced by the receiver. The results indicate that gains obtained using the proposed cross-packet coding strategy are particularly notable for the large values of the throughput. In this region, the conventional HARQ fails to offer throughput improvement even if the number of transmission rounds is increased. We implement the proposed cross-packet HARQ using turbo codes, where we show that the theoretically predicted throughput gains materialize in practice; the implementation challenges are also discussed.

Quality optimized medical image information hiding algorithm that employs edge detection and data coding

ObjectivesThe present work has the goal of developing a secure medical imaging information system based on a combined steganography and cryptography technique. It attempts to securely embed patient's confidential information into his/her medical images. MethodsThe proposed information security scheme conceals coded Electronic Patient Records (EPRs) into medical images in order to protect the EPRs’ confidentiality without affecting the image quality and particularly the Region of Interest (ROI), which is essential for diagnosis. The secret EPR data is converted into ciphertext using private symmetric encryption method. Since the Human Visual System (HVS) is less sensitive to alterations in sharp regions compared to uniform regions, a simple edge detection method has been introduced to identify and embed in edge pixels, which will lead to an improved stego image quality. In order to increase the embedding capacity, the algorithm embeds variable number of bits (up to 3) in edge pixels based on the strength of edges. Moreover, to increase the efficiency, two message coding mechanisms have been utilized to enhance the ±1 steganography. The first one, which is based on Hamming code, is simple and fast, while the other which is known as the Syndrome Trellis Code (STC), is more sophisticated as it attempts to find a stego image that is close to the cover image through minimizing the embedding impact. The proposed steganography algorithm embeds the secret data bits into the Region of Non Interest (RONI), where due to its importance; the ROI is preserved from modifications. ResultsThe experimental results demonstrate that the proposed method can embed large amount of secret data without leaving a noticeable distortion in the output image. The effectiveness of the proposed algorithm is also proven using one of the efficient steganalysis techniques. ConclusionThe proposed medical imaging information system proved to be capable of concealing EPR data and producing imperceptible stego images with minimal embedding distortions compared to other existing methods. In order to refrain from introducing any modifications to the ROI, the proposed system only utilizes the Region of Non Interest (RONI) in embedding the EPR data.

A practical approach for provenance transmission in wireless sensor networks

Assessing the trustworthiness of sensor data and transmitters of this data is critical for quality assurance. Trust evaluation frameworks utilize data provenance along with the sensed data values to compute the trustworthiness of each data item. However, in a sizeable multi-hop sensor network, provenance information requires a large and variable number of bits in each packet, resulting in high energy dissipation due to the extended period of radio communication. In this paper, we design energy-efficient provenance encoding and construction schemes, which we refer to as Probabilistic Provenance Flow (PPF). Our work demonstrates the feasibility of adapting the Probabilistic Packet Marking (PPM) technique in IP traceback to wireless sensor networks. We design two bit-efficient provenance encoding schemes along with a complementary vanilla scheme. Depending on the network size and bit budget, we select the best method based on mathematical approximations and numerical analysis. We integrate PPF with provenance-based trust frameworks and investigate the trade-off between trustworthiness of data items and transmission overhead. We conduct TOSSIM simulations with realistic wireless links, and perform testbed experiments on 15–20TelosB motes to demonstrate the effectiveness of PPF. Our results show that the encoding schemes of PPF have identical performance with a low bit budget (∼32-bit), requiring 33% fewer packets and 30% less energy than PPM variants to construct provenance. With a twofold increase in bit budget, PPF with the selected encoding scheme reduces energy consumption by 46–60%.

A multi-standards HDTV video decoder for blu-ray disc standard

This paper presents an HDTV video decoder core that is able to decode MPEG-2, MPEG-4 AVC and VC-1 formats and is fully compatible with the Blu-ray Disc standard. The core has two major features which achieve low-cost hardware implementation for three different standards. First, a novel re-configurable architecture is adopted to realize reduced hardware for three different variable length coding tables. A hybrid architecture that consists of a cell array and coefficients memory is introduced. By considering the trade-off between performance and cost, the cell array can compare a variable number of bits in a flexible manner. Second, a data compression method suitable for all video decoding standards is applied to reduce memory data usage and access bandwidth. A compression syntax based on Run-Level encoding with Exp-Golomb tables is used to achieve both low compression workload and reasonable memory cost. The core is implemented by top-down HDL basis approach, and the circuit volume is 1.5 Mgates with 90 nm CMOS technology and operation clock frequency is 162 MHz for 1080 i at 30 fr/s.

Respiratory Sounds Compression

Recently, with the advances in digital signal processing, compression of biomedical signals has received great attention for telemedicine applications. In this paper, an adaptive transform coding-based method for compression of respiratory and swallowing sounds is proposed. Using special characteristics of respiratory sounds, the recorded signals are divided into stationary and nonstationary portions, and two different bit allocation methods (BAMs) are designed for each portion. The method was applied to the data of 12 subjects and its performance in terms of overall signal-to-noise ratio (SNR) values was calculated at different bit rates. The performance of different quantizers was also considered and the sensitivity of the quantizers to initial conditions has been alleviated. In addition, the fuzzy clustering method was examined for classifying the signal into different numbers of clusters and investigating the performance of the adaptive BAM with increasing the number of classes. Furthermore, the effects of assigning different numbers of bits for encoding stationary and nonstationary portions of the signal were studied. The adaptive BAM with variable number of bits was found to improve the SNR values of the fixed BAM by 5 dB. Last, the possibility of removing the training part for finding the parameters of adaptive BAMs for each individual was investigated. The results indicate that it is possible to use a predefined set of BAMs for all subjects and remove the training part completely. Moreover, the method is fast enough to be implemented for real-time application.

Spatial multiplexing with transmit antenna and constellation selection for correlated mimo fading channels

We consider spatial multiplexing systems in correlated multiple-input multiple-output (MIMO) fading channels with equal power allocated to each transmit antenna. Under this constraint, the number and subset of transmit antennas together with the transmit symbol constellations are determined assuming knowledge of the channel correlation matrices. We first consider a fixed data rate system and vary the number of transmit antennas and constellation such that the minimum margin in the signal-to-noise ratio (SNR) is maximized for linear and Vertical Bell Laboratories Layered Space-Time (V-BLAST) receivers. We also derive transmit antenna and constellation selection criteria for a successive interference cancellation receiver (SCR) with a fixed detection order and a variable number of bits transmitted on each substream. Compared with a system using all available antennas, performance results show significant gains using a subset of transmit antennas, even for independent fading channels. Finally, we select a subset of transmit antennas to maximize data rate given a minimum SNR margin. A lower bound on the maximum outage data rate is derived. The maximum outage data rate of the SCR receiver is seen to be close to the outage channel capacity.

On-chip error correcting techniques for new-generation flash memories

In new-generation flash memories, issues such as disturbs and data retention become more and more critical as a consequence of reduced cell size and decreased oxide thickness. Furthermore, the progressive increase in the cell count within a single die tends to decrease device reliability. In particular, reliability issues turn out to be more critical in multilevel (ML) flash memories, due to the reduced spacing between adjacent programmed levels. It is therefore deemed that the use of on-chip error correction codes (ECCs) will gain widespread acceptance in large-capacity flash memories. ECCs for flash memories must have very fast and compact encoding/decoding circuitry so as to have a minimum impact on memory access time. The area penalty due to check cells must also be minimized. Moreover, specific codes must be developed for ML storage. This paper presents error control coding techniques and schemes for new-generation flash memories, focusing on ML devices. The basic concepts of error control coding are reviewed, and the on-chip ECC design procedure is analyzed. Dedicated codes such as polyvalent ECCs, able to correct data stored in ML memories working at a variable number of bits per cell, and bit-layer organized ECCs are described.

Iterative estimation and decoding for FH-SS with slow Rayleigh fading

The performance of turbo codes is investigated in a frequency-hopped spread-spectrum (FH-SS) system with full-band thermal noise and Rayleigh fading. For cases where the data rate exceeds the hopping rate (i.e., there exists multiple bits per hop) and fading side information (SI) does not exist, our approach is to iteratively estimate the fading levels. Simulation is performed for coherent and noncoherent reception, variable number of bits per hop, and cases where fading SI is available or unavailable to the decoder. It is shown that iterative channel estimation performed in conjunction with iterative decoding can improve the overall decoding performance. Finally, the performance of an FH-SS system using standard fading assumptions is compared to the performance of a measured fading channel. Due to flaws in the assumptions, the measured fading channel yields several decibels of performance loss relative to the ideal fading channel.

Turbo codes for noncoherent FH-SS with partial band interference

Turbo codes are investigated in a slow frequency-hopped spread spectrum (FH-SS) system with partial band jamming. In addition, full-band thermal noise is present. The channel model is that of a partial-band jammer in which a fraction of the frequency band is jammed and the remaining fraction is unjammed. This paper focuses on the implementation and performance of a modified turbo decoder for this model. We refer to the knowledge that each transmitted bit is jammed as channel state information. We consider cases of known or unknown channel state and variable number of bits per hop. Our approach is to modify the calculation of branch transition probabilities inherent in the original turbo decoder. For the cases with no side information and multiple bits per hop, we iteratively calculate channel state estimates. Analytical bounds are derived and simulation is performed for noncoherent demodulation. The performance of turbo codes is compared with a Reed-Solomon and a concatenated code comprised of a convolutional inner code and Reed-Solomon outer code.

Fundamental properties of digital images.

The quality of a digital image is affected by matrix size, unsharpness in the underlying image, bit depth, and noise in the underlying image. The array of all of the pixels into which an image is divided is the image matrix. If the image matrix is small (ie, composed of a few large pixels), the resolution of the digital image is low. Image blur dominates when the pixel size is much smaller than the unsharpness of the underlying image. The density in a pixel is represented by a binary number with a variable number of bits (bit depth). The more noise present in the initial image, the fewer the bits that are needed to convey its representation. Insufficient numbers of bits in the digital representation of an image, whether from too few pixels or too small a bit depth in each pixel, result in inadequate image quality. Use of too many bits, relative to the quality of the underlying image, places an increasingly unwarranted demand on digital image handling and processing.