Hassan H. Halawa

Bio: Hassan H. Halawa is an academic researcher from University of British Columbia. The author has contributed to research in topics: Fault tolerance & Network packet. The author has an hindex of 9, co-authored 56 publications receiving 296 citations. Previous affiliations of Hassan H. Halawa include American University in Cairo.

A layered IoT architecture for greenhouse monitoring and remote control

Abstract: Wireless Sensor Networks have been often used in the context of Greenhouse architectures. In this paper, an architecture is proposed for two Greenhouses based on Networked Control Systems. This architecture is IoT-based and built on top of switched Ethernet and Wi-Fi. Some sensors in the proposed architecture require a one-second real-time deadline. Riverbed simulations prove that there is zero packet loss and no over-delayed packets. An important contribution of this work is the design of a channel allocation scheme that prevents interference in this relatively large Greenhouse system. Another contribution of this work is the introduction of fault tolerance at the controller level. If one controller fails in one of the Greenhouses, the other controller automatically takes over the entire operation of the two-Greenhouse system. Riverbed simulations again show that this fault-tolerant system does not suffer any packet loss or over-delayed packets. Continuous Time Markov Chains are then developed to calculate the reliability as well as the steady state availability of the two-Greenhouse system. The Coverage parameter is taken into account. Finally, a case study is presented to quantitatively assess the advantage of fault tolerance in terms of downtime reduction; this is expected to be attractive especially in developing countries.

NVIDIA Jetson Platform Characterization

Abstract: This study characterizes the NVIDIA Jetson TK1 and TX1 Platforms, both built on a NVIDIA Tegra System on Chip and combining a quad-core ARM CPU and an NVIDIA GPU. Their heterogeneous nature, as well as their wide operating frequency range, make it hard for application developers to reason about performance and determine which optimizations are worth pursuing. This paper attempts to inform developers’ choices by characterizing the platforms’ performance using Roofline models obtained through an empirical measurement-based approach as well as through a case study of a heterogeneous application (matrix multiplication). Our results highlight a difference of more than an order of magnitude in compute performance between the CPU and GPU on both platforms. Given that the CPU and GPU share the same memory bus, their Roofline models’ balance points are also more than an order of magnitude apart. We also explore the impact of frequency scaling: build CPU and GPU Roofline profiles and characterize both platforms’ balance point variation, power consumption, and performance per watt as frequency is scaled.

On the effect of interference on Wi-Fi-based Wireless Networked Control Systems

Abstract: This study investigates a Wi-Fi-based Wireless Networked Control System. This system is first modified in order to enhance resistance to interference. Then, it is subjected to different types of interference. The system consists of a workcell containing 30 sensors, 30 actuators and 1 controller. All forms of interference modeled focus on the operating frequency band used by the control nodes. All simulations are conducted on OPNET Network Modeler and system performance is measured in terms of packet end-to-end delay and correct packet transmission/reception. All results are subjected to a 95% confidence analysis. The results show that intentional jamming is more damaging than medium congestion, which in turn is more damaging than network congestion.

Analysis of parallel redundant WLAN with timing diversity

Abstract: Redundancy techniques based on the combination of multiple diverse communication channels are an established countermeasure to improve performance characteristics of wireless communication systems. Besides parallel redundancy in the space and frequency domain, serial redundancy in the time domain can be utilized. It is known that the parallel approaches can significantly improve performance characteristics like jitter and reliability, when applied for wireless packet-based data transmission. In this work, an investigation on the performance impact of additional time diversity on top of the parallel redundancy approaches is performed. An OPNET simulation model is created and analysed for its performance characteristics.

A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming

Abstract: Internet of things (IoT) is a promising technology which provides efficient and reliable solutions towards the modernization of several domains. IoT based solutions are being developed to automatically maintain and monitor agricultural farms with minimal human involvement. The article presents many aspects of technologies involved in the domain of IoT in agriculture. It explains the major components of IoT based smart farming. A rigorous discussion on network technologies used in IoT based agriculture has been presented, that involves network architecture and layers, network topologies used, and protocols. Furthermore, the connection of IoT based agriculture systems with relevant technologies including cloud computing, big data storage and analytics has also been presented. In addition, security issues in IoT agriculture have been highlighted. A list of smart phone based and sensor based applications developed for different aspects of farm management has also been presented. Lastly, the regulations and policies made by several countries to standardize IoT based agriculture have been presented along with few available success stories. In the end, some open research issues and challenges in IoT agriculture field have been presented.

V2X Access Technologies: Regulation, Research, and Remaining Challenges

Abstract: As we edge closer to the broad implementation of intelligent transportation systems, the need to extend the perceptual bounds of sensor-equipped vehicles beyond the individual vehicle is more pressing than ever. Research and standardization efforts toward vehicle to everything (V2X), technology is intended to enable the communication of individual vehicles with both one another and supporting road infrastructure. The topic has drawn interest from a large number of stakeholders, from governmental authorities to automotive manufacturers and mobile network operators. With interest sourced from many disparate parties and a wealth of research on a large number of topics, trying to grasp the bigger picture of V2X development can be a daunting task. In this tutorial survey, to the best of our knowledge, we collate research across a number of topics in V2X, from historical developments to standardization activities and a high-level view of research in a number of important fields. In so doing, we hope to provide a useful reference for the state of V2X research and development for newcomers and veterans alike.

Graph Pattern Matching.

Abstract: Graph pattern matching is typically defined in terms of subgraph isomorphism, which makes it an np-complete problem. Moreover, it requires bijective functions, which are often too restrictive to characterize patterns in emerging applications. We propose a class of graph patterns, in which an edge denotes the connectivity in a data graph within a predefined number of hops. In addition, we define matching based on a notion of bounded simulation, an extension of graph simulation. We show that with this revision, graph pattern matching can be performed in cubic-time, by providing such an algorithm. We also develop algorithms for incrementally finding matches when data graphs are updated, with performance guarantees for dag patterns. We experimentally verify that these algorithms scale well, and that the revised notion of graph pattern matching allows us to identify communities commonly found in real-world networks.

SpotFake: A Multi-modal Framework for Fake News Detection

Abstract: A rapid growth in the amount of fake news on social media is a very serious concern in our society. It is usually created by manipulating images, text, audio, and videos. This indicates that there is a need of multimodal system for fake news detection. Though, there are multimodal fake news detection systems but they tend to solve the problem of fake news by considering an additional sub-task like event discriminator and finding correlations across the modalities. The results of fake news detection are heavily dependent on the subtask and in absence of subtask training, the performance of fake news detection degrade by 10% on an average. To solve this issue, we introduce SpotFake-a multi-modal framework for fake news detection. Our proposed solution detects fake news without taking into account any other subtasks. It exploits both the textual and visual features of an article. Specifically, we made use of language models (like BERT) to learn text features, and image features are learned from VGG-19 pre-trained on ImageNet dataset. All the experiments are performed on two publicly available datasets, i.e., Twitter and Weibo. The proposed model performs better than the current state-of-the-art on Twitter and Weibo datasets by 3.27% and 6.83%, respectively.

Resolving Ethical Dilemmas: A Guide for Clinicians

Abstract: The book begins with a concise discussion of clinical ethics that provides the background information essential to understanding key ethical issues. Readers then explore a wide range of real-world ethical dilemmas, each accompanied by expert guidance on salient issues and how to approach them. The book’s two-color design improves retention of material for visual learners. An accompanying website lets readers access the full text, along with features designed to reinforce understanding and test knowledge.