ONVisor: Towards a scalable and flexible SDN‐based network virtualization platform on ONOS

Network virtualization (NV) technologies have attracted a lot of attention as an essential solution for future networking infrastructure. The NV enables multiple tenants to share the same physical infrastructure and to create independent virtual networks (VNs) by decoupling the physical network in terms of topology, address, and control functions. One feasible way to realize full NV involves considering solutions based on the software‐defined networking (SDN) paradigm using its programmability. The SDN contributes many benefits to both network operations and management including programmability, agility, elasticity, and flexibility. There are several SDN‐based NV solutions; however, they suffered from a lack of scalability, high availability. Also, they have high latency between control and data plane because of proxy‐based architecture. In this thesis, we introduce a new NV platform, named Open Network Hypervisor (ONVisor). The design objectives include, among the features, (1) multitenancy, (2) scalability, (3) flexibility, (4) isolated VNs, and (5) VN federation. ONVisor was designed and implemented by extending Open Network Operating System, an open‐source SDN controller. The main features of ONVisor are (1) isolated control and data plane per VN, (2) support of distributed operations, (3) extensible translators, (4) on‐platform VN application development and execution, and (5) support of heterogenous SDN data‐plane implementations. Several experiments are conducted on various test scenarios in different test environments in terms of control and data plane performance compared to nonvirtualized SDN network. The results show that ONVisor can provide VNs a little bit lower control plane performance and similar data plane performance.     

Detection of algal blooms over optically complex waters of the Arabian Gulf and Sea of Oman using MODIS fluorescence data

The Arabian Gulf and the Sea of Oman are two of the most complex and turbid ecosystems in the world where algal blooms frequently occur. The conventional blue/green band ratio shows low performance to detect these algal batches in this region due to the effect of the non-algal parameters, shallow water depth, and atmospheric aerosols. Thus, an attempt to use MODIS (Moderate Resolution Imaging Spectroradiometer) fluorescence for the detection of algal blooms in this region have been undertaken using in situ measurements (Chlorophyll a: Chl-a, coloured dissolved organic matters: CDOM, Secchi disk depth: SDD, and radiometric) collected in 2006, 2013, and 2014, and MODIS satellite images. MODIS fluorescence line height (FLH in W m^?2 µm^?1 sr^?1) data showed low correlation (coefficient of determination: R² ~0.46) with near-concurrent in situ Chl-a (mg m^?3). This disparity is caused by the effect of the suspended sediments (SDD), CDOM (<2 mg m^?3 or >2 mg m^?3), and bottom reflectance (water depth: WD) parameters, where an increase of 1% in their magnitudes can cause a respective change of 13.4%, ?0.8% or 6%, and 1.4% in the FLH. In this work, the positions of the FLH bands have been relocated to include 645 nm to reduce the effect of these parameters on Chl-a, which has improved the performance to R² of 0.76. This modified FLH (MFLH) model was found to perform well in the Arabian Gulf where the estimated bias, root-mean-square error (RMSE), and coefficient of determination are, respectively, 0.03, 1.06, and 0.76. High values of MFLH are indicating the areas of the algal blooms, while no overestimation was observed in the mixed pixel coastal areas. This result is explained by less sensitivity of this model to the non-algal particles, shallow water, and aerosols.     

Multiple Views: different meanings and collocated words

We report on an in‐depth corpus linguistic study on ‘multiple views’ terminology and word collocation. We take a broad interpretation of these terms, and explore the meaning and diversity of their use in visualisation literature. First we explore senses of the term ‘multiple views’ (e.g., ‘multiple views’ can mean juxtaposition, many viewport projections or several alternative opinions). Second, we investigate term popularity and frequency of occurrences, investigating usage of ‘multiple’ and ‘view’ (e.g., multiple views, multiple visualisations, multiple sets). Third, we investigate word collocations and terms that have a similar sense (e.g., multiple views, side‐by‐side, small multiples). We built and used several corpora, including a 6‐million‐word corpus of all IEEE Visualisation conference articles published in IEEE Transactions on Visualisation and Computer Graphics 2012 to 2017. We draw on our substantial experience from early work in coordinated and multiple views, and with collocation analysis develop several lists of terms. This research provides insight into term use, a reference for novice and expert authors in visualisation, and contributes a taxonomy of ‘multiple view’ terms.     

Prediction of postpartum depression using machine learning techniques from social media text

Early screening of mental disorders plays a crucial role in diagnosis and treatment. This study explores how data‐driven methods can leverage the information available on social media platforms to predict postpartum depression (PPD). A generalized approach is proposed where linguistic features are extracted from user‐generated textual posts on social media and categorized as general, depressive, and PPD representative using multiple machine learning techniques. We find that techniques used in our study exhibit strong predictive capabilities for PPD content. Holdout validation showed that multilayer perceptron outperformed other techniques such as support vector machine and logistic regression used in this study with 91.7% accuracy for depressive content identification and up to 86.9% accuracy for PPD content prediction. This work adopts a hierarchical approach to predict PPD. Therefore, the reported PPD accuracy represents the performance of the model to correctly classify PPD content from non‐PPD depressive content.     

New evolution in two-way relay networks based on physical layer network coding

Telecommunication Systems - In this paper, a physical-layer network coding (PNC) method is offered for a two-way relay network with spatial modulation (SM) for source node and relay node. For this...     

Overhead minimization of online codes

Telecommunication Systems - We introduce a novel method and two algorithms for designing efficient degree distributions with minimum overhead for online codes. The first algorithm is the general...     

A fault-tolerant and congestion-aware architecture for wireless networks-on-chip

Wireless Networks - The combination of traditional wired links for regular transmissions and express wireless paths for long distance communications is a promising solution to prevent multi-hop...     

Analysis of malware propagation behavior in Social Internet of Things

Social Internet of Things (SIoT) is an evolution of the Internet of Things, where objects interact socially with each other in the sense that they can independently establish new relationships, offer, or discover services, in order to accomplish their tasks with minimum involvement of the user. This additional convenience comes at the expense of higher risk of speeding up malware propagation through the dynamically created relationships. Because of the undesirable effects of malware (eg, disruption of device operation), it is essential to understand their spreading behavior in order to minimize their negative impacts. In this paper, we analyze malware propagation behavior in SIoT and investigate different parameters that influence spreading of malware. Toward that end, a simulator has been developed to simulate the spreading process of malware in SIoT. Many propagation scenarios were analyzed using the proposed simulator. Simulation results show that adding more relationships in the SIoT or increasing the number of owned objects per user has increased malware spreading rate. For example, the time to infect all objects is faster by 45% when objects communicate through four relationships compared with the case when objects communicate through only two relationships in SIoT. We also investigated ways to restrict the malware spreading. Results show that preventing objects from establishing dynamic social relationship slows down the infection by 40% compared with the next best scenario (ie, blocking co‐location relationships), which means more time for vendors to patch up their products.     

An online resource allocation algorithm to minimize system interference for inband underlay D2D communications

This paper addresses the research question of total system interference minimization while maintaining a target system sum rate gain in an inband underlay device‐to‐device (D2D) communication. To the best of our knowledge, most of the state of the art research works exploit offline resource allocation algorithms to address the research problem. However, in Long‐Term Evolution (LTE) and beyond systems (4G, 5G, or 5G+), offline resource allocation algorithms do not comply with the fast scheduling requirements because of the high data rate demand. In this paper, we propose a bi‐phase online resource allocation algorithm to minimize the total system interference for inband underlay D2D communication. Our proposed algorithm assumes D2D pairs as a set of variable elements whereas takes the cellular user equipment (UEs) as a set of constant elements. The novelty of our proposed online resource allocation algorithm is that it incurs a minimum number of changes in radio resource assignment between two successive allocations among the cellular UEs and the D2D pairs. Graphical representation of the simulation results suggests that our proposed algorithm outperforms the existing offline algorithm considering number of changes in successive allocation for a certain percentage of sum rate gain maintaining the total system interference and total system sum rate very similar.     

Enhanced fair earliest due date first scheduling strategy for multimedia applications in LTE downlink framework

Guaranteeing a certain delay threshold for delay‐sensitive applications in long term evolution (LTE) cellular communication system is a very challenging mission. By implementing an optimal scheduling strategy, this mission will be achieved. In this article, a novel scheduler is introduced in order to meet a predefined level of service quality by guaranteeing a specific delay threshold for delay‐sensitive applications in LTE cellular systems. The proposed scheduler assigns the available resource blocks (RBs) to active user equipments (UEs) tacking into consideration several attributes. The expiration date of each packet, the channel quality, the average data rate previously achieved by each UE, and the number of dropped packets for each UE compared with the average number of packets totally dropped are all considered in the proposed scheduler working mechanism. Consequently, the proposed scheduling strategy reduces the number of packets dropped for multimedia applications, and at the same time maximizes the overall throughput of the network. Simulation results are provided to study and evaluate the performance of the proposed scheduling strategy. A comparative study is presented between the proposed strategy and the most recent scheduling techniques. The obtained results prove that the proposed scheduling strategy has considerably acceptable and appreciated results compared with the results of the state‐of‐the‐art scheduling techniques.