One of the pillars to run businesses is the telecommunications. Most of the institutions are migrating, if not already migrated, to Voice over Internet Protocol (VoIP) technology. However, VoIP still need some improvements, in terms of networks bandwidth exploitation and VoIP call quality, to meet the businesses expectations. Networks bandwidth exploitation, which is our concern in this paper, has been enhanced using different approaches and methods. This paper suggests a new method to enhance networks bandwidth exploitation Packet's payload shrinking (compression) approach. The suggested method works with the RTP protocol and called RTP Payload Shrinking (RPS) method. As the name implies, the RPS method will reduce the size of the RTP packet payload, through shrinking it based on specific algorithm, which enhances the networks bandwidth exploitation. The RPS method utilizes the RTP fields to store the values that are needed to apply the shrinking algorithm at the sender and receiver sides. The effectiveness of the proposed RPS method has been examined in comparison to conventional RTP protocol without shrinking. The deployment result showed that the saved bandwidth ratio has reached up to nearly 17% in the tested scenarios. Therefore, enhancing the network bandwidth exploitation.

The human body contains a near-infinite supply of energy in chemical, thermal, and mechanical forms. However, the majority of implantable and wearable devices are still operated by batteries, whose insufficient capacity and large size limit their lifespan and increase the risk of hazardous material leakage. Such energy can be used to exceed the battery power limits of implantable and wearable devices. Moreover, novel materials and fabrication methods can be used to create various medical therapies and life-enhancing technologies. This review paper focuses on energy-harvesting technologies used in medical and health applications, primarily power collectors from the human body. Current approaches to energy harvesting from the bodies of living subjects for self-powered electronics are summarized. Using the human body as an energy source encompasses numerous topics: thermoelectric generators, power harvesting by kinetic energy, cardiovascular energy harvesting, and blood pressure. The review considers various perspectives on future research, which can provide a new forum for advancing new technologies for the diagnosis, treatment, and prevention of diseases by integrating different energy harvesters with advanced electronics.

Applying machine learning techniques on Internet of Things (IoT) data streams will help achieve better understanding, predict future perceptions, and make crucial decisions based on those analytics. The collaboration between IoT, Big Data and machine learning can be found in different domains such as Health care, Smart cities, and Telecommunications. The aim of this paper is to develop a method for automated learning of electrocardiogram (ECG) streaming data to detect any heart beat anomalies. A promising solution is to use medical sensors that transfer vital signs to medical care computer systems, combined with machine learning, such that clinicians can get alerted about patient's critical condition and act accordingly. Since the probability of false alarms pose serious impact to the accuracy of cardiac arrhythmia detection, it is the most important factor to keep false alarms to the lowest level. The proposed method in this paper demonstrates an example of how machine learning can contribute to health technologies with in detecting heart disease through minimizing negative false alarms. Stages of heartbeat learning model are proposed and explained besides the stages heartbeat anomalies detection stages.

Infrastructure as a Service (IaaS) provides logical separation between data, network, applications and machines from the physical constrains of real machines. IaaS is one of the basis of cloud virtualization. Recently, security issues are also gradually emerging with virtualization of cloud computing. Different security aspects of cloud virtualization will be explored in this research paper, security recognizing potential threats or attacks that exploit these vulnerabilities, and what security measures are used to alleviate such threats. In addition, a discussion of general security requirements and the existing security schemes is also provided. As shown in this paper, different components of virtualization environment are targets to various attacks that in turn leads to security issues compromising the whole cloud infrastructure. In this paper an overview of various cloud security aspects is also provided. Different attack scenarios of virtualization environments and security solutions to cater these attacks have been discussed in the paper. We then proceed to discuss API security concerns, data security, hijacking of user account and other security concerns. The aforementioned discussions can be used in the future to propose assessment criteria, which could be useful in analyzing the efficiency of security solutions of virtualization environment in the face of various virtual environment attacks.

The purpose of this work is to increase the level of concealment of information from unauthorized access by preencrypting and hiding it in multimedia files such as images. A crypto-steganographic information protection algorithm with LSB-method was implemented. The algorithm hides AES preencrypted confidential information in the form of text or images into target containing image files. This method uses the concept of data concealing in the least significant pixel bits of the target image files. The proposed method relies on the use of Diffie-Hellman public key exchange protocol for securely exchanging the stego-key used for LSB as well as the public key used for encrypting the secret information. The algorithm ensures that the visual quality of the image remains unchanged, with no distortions perceived by the human eye. The algorithm also complicates the detection of concealed information embedded in the target image with use of PRNG as an enhancement for LSB. The proposed system scheme achieved competitive results. On an average, the system achieved a Peak Signal-to-Noise Ratio (PSNR) of 96.3 dB and a Mean Square Error (MSE) of 0.00408. The results obtained demonstrate that the proposed system offers high payload capabilities with immunity against visual degradation of resultant stego images.