Iyad F. Jafar

Performance evaluation of selective and adaptive heads clustering algorithms over wireless sensor networks

Target tracking in wireless sensor networks can be considered as a milestone of a wide range of applications to permanently report, through network sensors, the positions of a mobile target to the base station during its move across a certain path. While tracking a mobile target, a lot of open challenges arise and need to be investigated and maintained which mainly include energy efficiency and tracking accuracy. In this paper, we propose three algorithms for tracking a mobile target in wireless sensor network utilizing cluster-based architecture, namely adaptive head, static head, and selective static head. Our goal is to achieve a promising tracking accuracy and energy efficiency by choosing the candidate sensor nodes nearby the target to participate in the tracking process while preserving the others in sleep state. Through Matlab simulation, we investigate the performance of the proposed algorithms in terms of energy consumption, tracking error, sensor density, as well as target speed. The results show that the adaptive head is the most efficient algorithm in terms of energy consumption while static and selective static heads algorithms are preferred as far as the tracking error is concerned especially when the target moves rapidly. Furthermore, the effectiveness of our proposed algorithms is verified through comparing their results with those obtained from previous algorithms.

Efficient Improvements on the BDND Filtering Algorithm for the Removal of High-Density Impulse Noise

Switching median filters are known to outperform standard median filters in the removal of impulse noise due to their capability of filtering candidate noisy pixels and leaving other pixels intact. The boundary discriminative noise detection (BDND) is one powerful example in this class of filters. However, there are some issues related to the filtering step in the BDND algorithm that may degrade its performance. In this paper, we propose two modifications to the filtering step of the BDND algorithm to address these issues. Experimental evaluation shows the effectiveness of the proposed modifications in producing sharper images than the BDND algorithm.

Incorporating automatic repeat request and thresholds with variable complexity decoding algorithms over wireless networks: queuing analysis

Wireless networks are spreading very fast compared to wired-based networks because of its ease in installation, lower cost, reduced dependence on infrastructure and support for emerging mobile and sensing applications. Unfortunately, wireless channels are less efficient in carrying much of data. Moreover, they are characterised by having lower signal-to-noise ratio, resulting in more corrupted packets. Consequently, the performance of such networks may be severely affected due to invoking transmission control protocol (TCP) congestion algorithms whenever a packet is lost. The authors propose a novel queuing model that utilises Fano decoding and automatic repeat request (ARQ) to reduce the drawbacks of TCP in wireless networks. The proposed model describes how received packets are corrected based on Fano decoding mechanism and how the retransmission of corrupted packets is performed. The major aim of proposing this queuing model, when ARQ is totally incorporated, is to find a generic form expression for the average system capacity. The proposed model is validated through simulation in which the results show perfect agreement with those of the analytical model. The authors do not stop to this extent, but rather verify the correctness of the results through comparing them with those obtained in the previous work, where the mechanism of ARQ was completely neglected.

An improved queuing model for packet retransmission policy and variable latency decoders

The convolutional coding is a very popular channel coding technique for the major reason of mitigating the probability of having many retransmissions because of difficult (noisy) communication channels. Sequential decoding is a type of convolutional codes that becomes of interest in wireless communication since it affords a decoding time that can be adaptive to channel state. In this study, the authors propose an improved queuing model, using discrete-time semi-Markov chain, which represents a modified packet retransmission policy over previously proposed queuing model. The authors queuing model mainly describes the behaviour of the buffer, which belongs to intermediate hops, when sequential decoding is implemented and concerns also about packets being transmitted over erroneous channels. The authors aim after conducting queuing analysis to find a real mathematical form for the average buffer occupancy as a network performance metric. Although the improved queuing model when incorporating the new policy for retransmission is very complicated, we are finally able to derive an expression for that performance metric considering practical assumptions. They further illustrate how the modified queuing model has a better impact on the end-to-end delay of messages, being transmitted without any extra buffering requirements needed, than the other relevant proposed queuing model. They conduct a simulation study using computer programming with the same assumptions used for queuing analysis to validate their analytical explanations and results. Furthermore, they validate the correctness of their closed-form expression through comparing its results with those obtained from expression related to a queuing model which discarded employing any retransmission policy.

An improved image least significant bit replacement method

With the development of internet technologies and communication services, message transmissions over the internet still have to face all kinds of security problems. Hence, how to protect secret messages during transmission becomes a challenging issue for most of the researchers. It is worth mentioning that many applications in computer science and other related fields rely on steganography and watermarking techniques to ensure information safety during communication. In this paper, we propose a new steganographic method to embed the secret data inside a cover image based on least-significant-bit (LSB) replacement method. The embedding process predominantly concentrates on distributing the secret message inside one share of a color image to appear like a 3D geometric shape. The dimensions of the geometric shape are variable pursuant to the size of secret message. Data distribution process makes our method to be of a great interest as of being so difficult for the hackers or intruders to reconstruct the shape from stego-images, thereby the security is improved. Furthermore, we compare the performance of our approach with two other relevant approaches in terms of peak signal-to-noise ratio (PSNR). The contribution of our approach was immensely impressive.

Improved clustering algorithms for target tracking in wireless sensor networks

In recent years, there has been a growing interest in wireless sensor networks because of their potential usage in a wide variety of applications such as remote environmental monitoring and target tracking. Target tracking is a typical and substantial application of wireless sensor networks. Generally, target tracking aims basically at estimating the location of the target while it is moving within an area of interest and consequently report it to the base station in a timely manner. However, achieving a high accuracy of tracking together with energy efficiency in target tracking algorithms is extremely challenging. In this article, we propose two algorithms to enhance the adaptive-head clustering algorithm, formerly lunched, namely, the improved adaptive-head and improved prediction-based adaptive head. Particularly, the first algorithm uses dynamic clustering to achieve impressive tracking quality and energy efficiency through optimally choosing the cluster head that participates in the tracking process. On the other hand, the second algorithm incorporates a prediction mechanism to the first proposed algorithm. Our proposed algorithms are simulated using Matlab considering various network conditions. Simulation results show that our proposed algorithms can accurately track a target, even when random moving speeds are considered and consume much less energy, when compared with the previous algorithm for target tracking, which in turn prolong the network lifetime much more.

An efficient reversible data hiding algorithm using two steganographic images

Reversible data hiding (RDH) algorithms are concerned with concealing data within images such that the original image can be fully recovered upon the extraction of hidden data. A substantial interest has grown recently in RDH algorithms that are based on using dual images in order to increase the embedding capacity. In this paper, we propose a RDH algorithm that is based on this concept. Effectively, embedding and extraction of data in the proposed algorithm is performed in three successive phases. In the first phase, four simple rules are used to embed about one bit in each pixel in the two images. On the other hand, the other two phases employ the concept of prediction for embedding secret data bits but without using any complex predictors. Specifically, these phases use one image as the prediction of the other image. Performance evaluation of the proposed algorithm showed its ability to embed around 1.23 bits per pixel with stego image quality above 48dB. Moreover, the proposed algorithm is of low computational complexity and requires no communication of overhead information. A novel dual-image reversible data hiding algorithm is proposed.Three consecutive stages that exploit the similarity between the two images to embed data are used.Significant increase in the embedding capacity with excellent stego image quality is achieved.

A generic buffer occupancy expression for stop-and-wait hybrid automatic repeat request protocol over unstable channels

Recently, there has been a rapid progress in the field of wireless networks and mobile communications which makes the constraints on the used links clearly unconcealed. Wireless links are characterized by limited bandwidth and high latencies. Moreover, the bit-error-rate (BER) is very high in such environments for various reasons out of which weather conditions, cross-link interference, and mobility. High BER causes corruption in the data being transmitted over these channels. Therefore, convolutional encoding has been originated to be a professional means of communication over noisy environments. Sequential decoding, a category of convolutional codes, represents an efficient error detection and correction mechanism which attracts the attention for most of current researchers as for having a complexity that is dependent to the channel condition. In this paper, we propose a new queuing study over networking systems that make use of sequential decoders. Hence, the adopted flow and error control refer to stop-and-wait hybrid automatic repeat request. However, our queuing study is a novel extension to our prior work in which the lowest decoding complexity was fixed and did not account for the channel state. In other words, our proposed closed-form expression of the average buffer occupancy is totally generic and parameterized by not only channel condition and packet incoming rate, but also those that are automatically adapted to the channel conditions which include lower and upper bound decoding limits.

An improved reversible data hiding algorithm based on modification of prediction errors

Reversible data hiding algorithms are concerned with the ability of hiding data and recovering the original digital image upon extraction. This issue is of interest in medical and military imaging applications. One particular class of such algorithms relies on the idea of histogram shifting of prediction errors. In this paper, we propose an improvement over one popular algorithm in this class. The improvement is achieved by employing a different predictor, the use of more bins in the prediction error histogram in addition to multilevel embedding. The proposed extension shows significant improvement over the original algorithm and its variations.

A Rule-Based Fuzzy Inference System for Adaptive Image Contrast Enhancement

Adaptive contrast enhancement (ACE) is a popular method for image contrast enhancement. In this method, enhancement is achieved by adding an amplified version of the high-frequency content of the image to its low-frequency content. The rationale behind that is supported by the fact that the human visual system is sensitive to discontinuities in images, which represent the high-frequency content of the image. Thus, emphasizing this content is expected to improve the perceived contrast. In this paper, a fuzzy ACE (FACE)-based enhancement method, FACE, is proposed. In this method, the contrast gain values are computed using a fuzzy inference system (FIS) whose parameters are entirely derived from the image local statistics. To the best of our knowledge, the computation of the ACE gain values using a FIS has never been addressed before. Experimental results have proved the capability of FACE in enhancing the image contrast with less noise amplification and overenhancement artifacts.