Amr Alasaad

Innovative Schemes for Resource Allocation in the Cloud for Media Streaming Applications

Media streaming applications have recently attracted a large number of users in the Internet. With the advent of these bandwidth-intensive applications, it is economically inefficient to provide streaming distribution with guaranteed QoS relying only on central resources at a media content provider. Cloud computing offers an elastic infrastructure that media content providers (e.g., Video on Demand (VoD) providers) can use to obtain streaming resources that match the demand. Media content providers are charged for the amount of resources allocated (reserved) in the cloud. Most of the existing cloud providers employ a pricing model for the reserved resources that is based on non-linear time-discount tariffs (e.g., Amazon CloudFront and Amazon EC2). Such a pricing scheme offers discount rates depending non-linearly on the period of time during which the resources are reserved in the cloud. In this case, an open problem is to decide on both the right amount of resources reserved in the cloud, and their reservation time such that the financial cost on the media content provider is minimized. We propose a simple-easy to implement-algorithm for resource reservation that maximally exploits discounted rates offered in the tariffs, while ensuring that sufficient resources are reserved in the cloud. Based on the prediction of demand for streaming capacity, our algorithm is carefully designed to reduce the risk of making wrong resource allocation decisions. The results of our numerical evaluations and simulations show that the proposed algorithm significantly reduces the monetary cost of resource allocations in the cloud as compared to other conventional schemes.

Enhanced Network Coding to Maintain Privacy in Smart Grid Communication

Cyber-physical systems (CPS) are aimed at combining the physical system with the cyber ones to provide a better control and improve the management of physical systems around us. Recently, the CPS and its applications, e.g., health-care and smart grid, have gained attention of the research community. In this paper, we consider the privacy aspect of users in a CPS, particularly in smart grid system as our use-case, and provide a mechanism that utilizes the advances in network coding to maintain data privacy. We address privacy issues associated with gathering metering information of clients in a smart grid system. In smart grid systems, wireless multi-hop communications are mainly used to gather metering information through exchanging data and control messages between smart meters and the utility. We argue that any communication paradigm used in a smart grid should support all aspects of privacy such as anonymity, unlinkability, unobservablity, and undetectablity. We propose innovative schemes for traffic routing and encryption that benefit from the enhanced network coding technology. Our analysis shows that our schemes maintain privacy of users despite the possibility of detecting metering data by an adversary. In addition, our scheme has extra favorable features such as less computation complexity, reliable, and robust communication.

A hybrid approach for cost-effective media streaming based on prediction of demand in community networks

Media streaming applications have recently attracted large number of users in the Internet. This large demand creates a burden on existing infrastructure to sustain the QoS guarantees. The huge capacity resources and power consumption are the main aspects that hinder the evolution of media streaming applications in the Internet. This motivates us to explore new approaches in collaborative media streaming in community networks (e.g., college and office campuses) residing in a larger network of the access connection provider such as Internet Service Provider (ISP). The proposed approaches are aimed at exploiting the redundancy and abundantly available network “micro-resources” in a community network to create an aggregate virtual “macro-resource”. Specifically, the ISP can leverage its control over a large amount of under-utilized micro-resources in a community network to replicate into caches of those micro-resources files that are desired in that community. In this paper, we characterize the evolution of demand in a community network. By using this demand-awareness model (i.e., future prediction of the demand in a community network), micro-resources in the community network can be optimally utilized. We show analytically and using simulations that this approach mitigates the cost of media streaming on both the Content Delivery Networks (CDNs) and ISPs. By allocating micro-resources to manage the demand in a community network on a per-need basis, much of the network and computing capacities at the edge-servers can be alleviated. This reduces the cost on the CDN in terms of both purchase and maintenance. Moreover, ISPs can reduce the bandwidth and power consumption in their networks by pushing the media content closer to users in the community network.

Network Coding Based Encryption System for Advanced Metering Infrastructure

In a smart grid system, the metering data collected by smart meters (SMs) and transferred via an Advanced Metering Infrastructure to the utility for billing purposes, and to the demand-response system to achieve cost effective resource allocation. The collected data at the SMs are sent to aggregators (AGRs), which in turn forward these data to a higher layer data collection system using secured communications. However, metering data are typically transferred between SMs and AGRs over wireless multi-hop communication networks. Due to the broadcast nature of wireless transmissions, the communications between a SM and AGR are susceptible to many security attacks. We argue that advanced network coding (NC) technology can be utilized to address this problem. We propose a novel system that supports data collection security at AGR by encrypting metering data transmitted between SMs and AGR using NC technology. Our innovative scheme eliminates the use of previously specified public key encryption system between SMs and AGR, which consequently makes our system very efficient. Analyses show that our proposed scheme enhances robustness and throughput of data routing in the wireless multi-hop network between SMs and AGR while maintaining a strong security.

Heart rate variability estimation in photoplethysmography signals using Bayesian learning approach.

Heart rate variability (HRV) has become a marker for various health and disease conditions. Photoplethysmography (PPG) sensors integrated in wearable devices such as smart watches and phones are widely used to measure heart activities. HRV requires accurate estimation of time interval between consecutive peaks in the PPG signal. However, PPG signal is very sensitive to motion artefact which may lead to poor HRV estimation if false peaks are detected. In this Letter, the authors propose a probabilistic approach based on Bayesian learning to better estimate HRV from PPG signal recorded by wearable devices and enhance the performance of the automatic multi scale-based peak detection (AMPD) algorithm used for peak detection. The authors’ experiments show that their approach enhances the performance of the AMPD algorithm in terms of number of HRV related metrics such as sensitivity, positive predictive value, and average temporal resolution.

A compact size ultra wideband MIMO antenna with simple decoupling structure

Designing a MIMO antenna for UWB communication has many challenges including the mutual coupling and the correlation between the neighboring elements of the antenna system. In this paper, we propose a compact size four elements MIMO antenna with simple cross shaped decoupling structure (DS) that is suitable for wide range of UWB applications. Our simulation results show that the proposed antenna provides good performance for wide range of UWB bandwidth from 4.5 to 10.6 GHz. Furthermore, many important parameters of the MIMO antennas such as radiation characteristics envelop correlation coefficient, and diversity gain are analyzed to evaluate the performance.

Towards efficient heart rate variability estimation in artifact-induced Photoplethysmography signals

Heart Rate Variability (HRV) has become a marker for various health and disease conditions. Photoplethysmography (PPG) sensors integrated in wearable/portable devices such as smart watches and phones are widely used to measure heart activities. HRV requires accurate estimation of time interval between consecutive peaks in the PPG signal. Artifacts often degrade the quality of the PPG signal, which could lead to wrong HRV estimation. In this paper, we present an adaptive real-time approach that employs Linear Prediction analysis (LPC) and Wavelet transformation techniques for estimating HRV from PPG signal recorded by wearable devices. Our algorithm outperforms two other related algorithms, especially for low PPG signal to noise ratio. By comparing the proposed algorithm to the ground truth recorded simultaneously from ECG, an average temporal resolution of 8.7 ms was achieved with a sensitivity of 82.9% and a positive predictive value of 82.7%.

Design and analysis of compact size dual polarised ultra wideband MIMO antennas with simple decoupling structure

In this paper, a quad elements, differentially fed, dual polarized Ultra-Wideband Multiple-Input Multiple-Output (UWB-MIMO) antenna system is presented. The unit UWB antenna element is of compact size with the dimensions of 22×24 mm2. The proposed MIMO antenna structure consists of two pairs of dual polarized rectangular shape UWB monopole antennas. A simple decoupling structure (DS) comprising of rectangular stubs is optimized with several full wave simulations to improve the isolation among the antennas. The proposed MIMO system exhibits reflection coefficient whose value is less than -10 dB from 4.5 GHz - 11.5 GHz. Different important parameters such as radiation characters, time domain received signals from each antenna element, electric field distribution, envelop correlation coefficient and diversity gain are calculated, analyzed and compared. The diversity antenna system with decoupling structure has shown a significant better performance compared to the system without decoupling structure.

Demonstration of Millimeter Wave 5G Setup Employing High-Gain Vivaldi Array

We present a 4 × 4 slot-coupled Vivaldi antenna (SCVA) array unit cell, which offers wide bandwidth and high gain (~23 dBi) at the millimeter wave (mmW) frequencies of 28 GHz and 38 GHz. A single SCVA element is first presented, which has a bandwidth of 25–40 GHz with an average gain of ~13 dBi at the frequencies of interest. This antenna element is then used to design a 1 × 4 linear SCVA array matched to a 50 Ω impedance via a modified Wilkinson power divider (WPD). Next, the 1 × 4 linear array is used to construct a 4 × 4 antenna array unit cell. The proposed 4 × 4 antenna array unit cell is fabricated, and the characteristics of its elements (i.e., the single SCVA, 1 × 4 linear array, and WPD) are thoroughly investigated. Further, the 4 × 4 array is tested for signal reception of various digital modulation formats at lab environment using high-speed digital signal oscilloscope. In particular, a 2.5 Gbps data rate is successfully transmitted achieving receiver sensitivity of −50 dBm at 2 × 10−3 bit error rate (BER) for 32 quadrature amplitude modulation (QAM) with a system baud rate of 500 MHz. The wide bandwidth and high gain along with the excellent performance of the proposed 4 × 4 antenna array unit cell makes it an excellent candidate for future 5G wireless communication applications.

Bayesian inference approach for establishing efficient routes in wireless ad-hoc networks

Mobile ad hoc networks (MANETs) are dynamic wireless networks that have no fixed infrastructures and do not require predefined configurations. In this infrastructure-less paradigm, nodes in addition of being hosts, they also act as relays and forward data packets for other nodes in the network. Due to limited resources in MANETs such as bandwidth and power, the performance of the routing protocol plays a significant role. A routing protocol in MATET should not introduce excessive control messages to the network in order to save network bandwidth and nodes power. In this paper, we propose a probabilistic approach based on Bayesian inference to enable efficient routing in MANETs. Nodes in the proposed approach utilize the broadcast nature of the wireless channel to observe the network topology by overhearing wireless transmissions at neighboring nodes in a distributed manner, and learn from these observations when taking packet forwarding decision on the IP network layer. Our simulation results show that our routing approach reduces the number of control message (routing overhead) by a ratio up to 20 % when the network size is 60 nodes, while maintaining similar average route establishment delay as compared to the ad-hoc on demand routing protocol.