Dhiah Al-Shammary

Cloud enabled fractal based ECG compression in wireless body sensor networks

E-health applications deal with a huge amount of biological signals such as ECG generated by body sensor networks (BSN). Moreover, many healthcare organizations require access to these records. Therefore, cloud is widely used in healthcare systems to serve as a central service repository. To minimize the traffic going to and coming from cloud ECG compression is one of the proposed solutions to overcome this problem. In this paper, a new fractal based ECG lossy compression technique is proposed. It is found that the ECG signal self-similarity characteristic can be used efficiently to achieve high compression ratios. The proposed technique is based on modifying the popular fractal model to be used in compression in conjunction with the iterated function system. The ECG signal is divided into equal blocks called range blocks. Subsequently, another down-sampled copy of the ECG signal is created which is called domain. For each range block the most similar block in the domain is found. As a result, fractal coefficients (i.e. parameters defining fractal compression model) are calculated and stored inside the compressed file for each ECG signal range block. In order to make our technique cloud friendly, the decompression operation is designed in such a way that allows the user to retrieve part of the file (i.e. ECG segment) without decompressing the whole file. Therefore, the clients do not need to download the full compressed file before they can view the result. The proposed algorithm has been implemented and compared with other existing lossy ECG compression techniques. It is found that the proposed technique can achieve a higher compression ratio of 40 with lower Percentage Residual Difference (PRD) Value less than 1%.

Embedding patients confidential data in ECG signal for healthcare information systems

In Wireless tele-cardiology applications, ECG signal is widely used to monitor cardiac activities of patients. Accordingly, in most e-health applications, ECG signals need to be combined with patient confidential information. Data hiding and watermarking techniques can play a crucial role in ECG wireless tele-monitoring systems by combining the confidential information with the ECG signal since digital ECG data is huge enough to act as host to carry tiny amount of additional secret data. In this paper, a new steganography technique is proposed that helps embed confidential information of patients into specific locations (called special range numbers) of digital ECG host signal that will cause minimal distortion to ECG, and at the same time, any secret information embedded is completely extractable. We show that there are 2.1475 × 109 possible special range numbers making it extremely difficult for intruders to identify locations of secret bits. Experiments show that percentage residual difference (PRD) of watermarked ECGs can be as low as 0.0247% and 0.0678% for normal and abnormal ECG segments (taken from MIT-BIH Arrhythmia database) respectively.

Dynamic Fractal Clustering Technique for SOAP Web Messages

The Simple Object Access Protocol (SOAP) is an XML based protocol that is widely used over the Internet as it supports interoperability by establishing access among Web servers and clients from the same or different platforms. However, SOAP Web services suffer the bottlenecks and congestions as a result of Web messages being bigger than the real payload in addition to the potentially increasing demand of the requested Web services. Aggregation of SOAP messages is an effective solution that has been developed to significantly reduce network traffic by aggregating SOAP messages at the server side and then multicast them to the Web clients. The major problem of the aggregation techniques is that they require efficient similarity criteria that can compute the similarity of SOAP messages as group-wise and not just pair-wise. In this paper, a new unsupervised auto class Fractal clustering technique is proposed for clustering SOAP messages into a dynamic number of clusters according to their Fractal similarities. The experimental results showed that the proposed Fractal clustering technique can improve the performance of Web services significantly better than other clustering standards such as the K-means and PCA combined with K-means by enabling the aggregation model to aggregate the most similar messages in one group resulting in better messages size reduction. Furthermore, the proposed Fractal clustering technique potentially reduces the required processing time in comparison with other standards.

An efficient method of biometric matching using interpolated ECG data

In this paper, a person identification method using electrocardiogram (ECG) is presented based on cubic spline interpolation method. Three different databases with two different sampling rates containing 36 ECG recordings were used for development and evaluation. Each ECG recording is divided into two segments: a segment for enrolment, and a segment for recognition. The ECG features are extracted from both the training dataset and the test dataset for model development and identification. Two ECG biometric algorithms which are Cross Correlation (CC) and Percent Root-Mean-Square Deviation (PRD) were used for performance evaluation. Results of experiments confirmed that the template matching using interpolation method achieved better accuracy (up to 4.46%) than the existing method without interpolation when using ECG data with lower sampling rate.

Compression-based aggregation model for medical Web Services

Many organizations such as hospitals have adopted Cloud Web services in applying their network services to avoid investing heavily computing infrastructure. SOAP (Simple Object Access Protocol) is the basic communication protocol of Cloud Web services that is XML based protocol. Generally,Web services often suffer congestions and bottlenecks as a result of the high network traffic that is caused by the large XML overhead size. At the same time, the massive load on Cloud Web services in terms of the large demand of client requests has resulted in the same problem. In this paper, two XML-aware aggregation techniques that are based on exploiting the compression concepts are proposed in order to aggregate the medical Web messages and achieve higher message size reduction.

SOAP Web Services Compression Using Variable and Fixed Length Coding

SOAP Web services create high network traffic because of its generated large XML messages resulting in poor network performance. Therefore, enhancing the performance of Web services by compressing SOAP messages is considered to be an important issue. Compression ratios achieved by most of the existing techniques and tools are not high enough, and even a tiny improvement could save tremendous amount of network bandwidth in emerging cloud and mobile scenarios. In this paper, we try to achieve this objective by proposing two innovative techniques capable of reducing small as well as very large messages. Instead of encoding the characters of XML message individually, Fixed-length encoding and Huffman encoding as a variable-length technique are developed to deal with XML tags as individual input items. XML tree and binary tree are constructed that support the encoding algorithm by removing the closing tags. A high Compression Ratio has been achieved that is up to 7.8 and around 13.5 for large and very large messages respectively.

A distributed aggregation and fast fractal clustering approach for SOAP traffic

Abstract The adoption of Web Services has increased tremendously by many network organizations. Although Web services as XML-based network applications provide significant advantages over other communication technologies in terms of interoperability and dynamic scalability, they suffer from congestion and bottlenecks as a result of the large demand and large XML messages. Aggregation of SOAP messages is one of the potential solutions to reduce network traffic by aggregating large numbers of messages (responses), minimizing their volume over the Internet. However, existing aggregation models only support this function at one node (Web server) and therefore they do not allow aggregation of SOAP messages at different servers as one collaborative technique. In this paper, a new distributed aggregation model is proposed to support aggregation of messages from several nodes that share the path of SOAP responses over the Internet. Furthermore, a fast fractal similarity-based clustering technique is proposed speeding up the computation of similar messages that can be aggregated together in order to achieve a higher reduction. The proposed models show significant results, with distributed aggregation outperforming regular aggregation, resulting in a 100% higher compression ratio. The fast fractal clustering has reduced the required processing time by 85% when compared to the classical fractal clustering technique.

Redundancy-aware SOAP messages compression and aggregation for enhanced performance

Many organizations around the world have started to adopt Web services as well as server farms and clouds hosted by large enterprise and data centers for various applications. Web Services offer several advantages over other communication technologies. However, they have high latency and often suffer from congestion and bottlenecks due to the massive load generated by web service requests from large numbers of end users. SOAP (Simple Object Access Protocol) is the basic XML-based communication protocol of Web services. XML is a verbose encoding language in comparison with other technologies such CORBA and RMI. In this paper, two new redundancy-aware SOAP Web message aggregation models - Two-bit and One-bit XML status tree - are proposed to enable the Web servers to aggregate SOAP responses and send them back as one compact aggregated message in order to reduce the required bandwidth, latency, and improve the overall performance of Web services. XML message compressibility, the Jaccard based clustering technique, and the vector space model are three similarity measurements that are proposed to cluster SOAP messages as groups based on their similarity degree. The clustering based similarity measurements enable the aggregation techniques to potentially reduce the required network traffic by minimizing the overall size of the messages. The experiments show significant performance for both aggregation techniques achieving compression ratios as high as 25 for aggregated SOAP messages.

Clustering SOAP Web Services on Internet Computing Using Fast Fractals

The interoperability of Web services has resulted in its adoption for recently-emerging cloud platforms. SOAP (Simple Object Access Protocol) is considered as the main platform independent communication tool for the Cloud Web service. Generally, Cloud Web services suffer performance bottlenecks and congestions that are mainly caused by the encoding of XML messages as they are bigger than the real payloads. In this paper, Fractal clustering model is proposed to compute the Fractal clustering similarity of SOAP messages in order to cluster them and enable the aggregation of SOAP messages to significantly reduce the size of the aggregated SOAP messages. Furthermore, as Fractal is a well-known as a time-consuming technique especially for large dataset, two fast Fractal clustering models have been proposed that are aiming to reduce the required clustering time. The proposed fast Fractal models have tremendously outperformed the classical Fractal model in terms of the processing time and have outperformed both K-means and PCA combined with K-means models in terms of both the processing time and SOAP messages size reduction.

A new XML-aware compression technique for improving performance of healthcare information systems over hospital networks

Most organizations exchange, collect, store and process data over the Internet. Many hospital networks deploy Web services to send and receive patient information. SOAP (Simple Object Access Protocol) is the most usable communication protocol for Web services. XML is the standard encoding language of SOAP messages. However, the major drawback of XML messages is the high network traffic caused by large overheads. In this paper, two XML-aware compressors are suggested to compress patient messages stemming from any data transactions between Web clients and servers. The proposed compression techniques are based on the XML structure concepts and use both fixed-length and Huffman encoding methods for translating the XML message tree. Experiments show that they outperform all the conventional compression methods and can save tremendous amount of network bandwidth.