The increasing complexity of JavaScript (JS) in modern mobile web pages has become a performance bottleneck for low-end mobile phone users, especially in developing regions. In this paper we propose SlimWeb, a novel approach that automatically derives lightweight versions of mobile web pages on-the-fly by eliminating non-essential JavaScript that does not impact the core page content and interactive functionality. SlimWeb consists of a JavaScript classification service powered by a supervised Machine Learning (ML) model that provides insights into each JavaScript element embedded in a web page. SlimWeb aims to improve the web browsing experience by predicting the class of each element, such that essential elements are preserved and non-essential elements are blocked by the browsers using the service. We motivate SlimWeb’s core design via a preference survey where 306 users overwhelmingly preferred having faster page load times over fetching various categories of non-essential JavaScript. We evaluate SlimWeb across 500 popular web pages in a developing region on real cellular networks, along with a user experience study with 20 real-world users and a usage willingness survey of 588 users. Evaluation results show that SlimWeb achieves 50% reduction in page load time compared to the original pages, and more than 30% reduction compared to competing solutions, while achieving high similarity scores to the original pages measured via a qualitative evaluation study with 62 users. SlimWeb improves the overall user experience metric (defined by Google Lighthouse combining first contentful paint, time to interactive, speed index) by more than 60% compared to the original pages, while maintaining 90-100% of the visual and functional components of most pages.

To quickly create interactive web pages, developers heavily rely on (large) general-purpose JavaScript libraries. This practice bloats web pages with complex unused functions dead code which are unnecessarily downloaded and processed by the browser. The identification and the elimination of these functions is an open problem, which this paper tackles with Muzeel, a black-box approach requiring neither knowledge of the code nor execution traces. While the state-of-the-art solutions stop analyzing JavaScript when the page loads, the core design principle of Muzeel is to address the challenge of dynamically analyzing JavaScript after the page is loaded, by emulating all possible user interactions with the page, such that the used functions (executed when interactivity events fire) are accurately identified, whereas unused functions are filtered out and eliminated. We run Muzeel against 15,000 popular web pages and show that half of the 300,000 JavaScript files used in these pages have at least 70% of unused functions, accounting for 55% of the files' sizes. To assess the impact of dead code elimination on Mobile Web performance, we serve 200 Muzeel-ed pages to several Android phones and browsers, under variable network conditions. Our evaluation shows that Muzeel can speed up page loads by 25-30% thanks to a combination of lower CPU and bandwidth usage. Most importantly, we show that such savings are achieved while maintaining the pages' visual appearance and interactive functionality.

The increasing complexity of the web has attracted a number of solutions to offer optimized versions of web pages that are lighter to process and faster to load. These solutions have been quantitatively evaluated to show significant speed-ups in load times and/or considerable savings in bandwidth/memory consumption. However, while these solutions often produce optimized versions from existing pages, they rarely evaluate the impact of their optimizations on the original content and functionality. Additionally, due to the lack of a unified metric to evaluate the similarity of the pages generated by these solutions in comparison to the original pages, it is not yet possible to fairly compare the results obtained from different user studies campaigns, unless recruiting the exact same users, which is extremely challenging. In this paper, we demonstrate the lack of qualitative evaluation metrics, and propose QLUE (QuaLitative Uniform Evaluation), a tool that automates the qualitative evaluation of web pages generated by web complexity solutions with respect to their original versions using computer vision. QLUE evaluates the content and the functionality of these pages separately using two metrics: QLUE’s Structural Similarity, to assess the former, and QLUE’s Functional Similarity to assess the latter—a task that is proven to be challenging for humans given the complex functional dependencies in modern pages. Our results show that QLUE computes comparable content and functional scores to those provided by humans. Specifically, 90% of a set of 100 pages were given a similarity score between 90% and 100% by human evaluators, while QLUE shows similar scores for more than 75% of the same pages. In terms of time complexity, QLUE shows that it is capable of evaluating an optimized web page in a few minutes.

The amount of JavaScript used in web pages has substantially grown in the past decade, leading to large and complex pages that are computationally intensive for handheld mobile devices. Due to the increasing usage of these devices to access today’s web, and to accommodate the needs of a large number of mobile web users who solely rely on low-end devices, we propose “JSAnalyzer,” an easy-to-use tool that enables web developers to quickly optimize JavaScript usage in their pages and to generate simpler versions of these pages for mobile web users. JSAnalyzer is motivated by the widespread use of non-critical JavaScript elements, i.e., those that have negligible (if any) impact on the page’s visual content and interactive functionality. JSAnalyzer allows the developer to selectively enable or disable JavaScript elements in any given page while visually observing their impact on the page to (1) accurately identify any non-critical JavaScript elements and (2) create a simplified page with these elements removed. Our quantitative evaluation shows that, given a low-end mobile phone, JSAnalyzer achieves an increase of nearly 90% in Google’s lighthouse performance score while reducing the page load time by 30%. A qualitative study of 22 users shows that the lighter pages produced by JSAnalyzer maintain more than 90% visual similarity compared to the original pages. Moreover, JSAnalyzer was evaluated by 69 developers, showing that it scores nearly 90% in terms of usefulness and usability while retaining the page’s content and functionality. Finally, we show that JSAnalyzer outperforms state-of-the-art solutions in terms of timing speedups and resource savings.

In the above article[1], in the first-page footnote at the bottom, the corresponding author information should read: “(Corresponding authors: Moumena Chaqfeh; Hesham El-Sayed.)”

A significant fraction of the World Wide Web suffers from the excessive usage of JavaScript (JS). Based on an analysis of popular webpages, we observed that a considerable number of JS elements utilized by these pages are not essential for their visual and functional features. In this paper, we propose JSCleaner, a JavaScript de-cluttering engine that aims at simplifying webpages without compromising their content or functionality. JSCleaner relies on a rule-based classification algorithm that classifies JS into three main categories: non-critical, replaceable, and critical. JSCleaner removes non-critical JS from a webpage, translates replaceable JS elements with their HTML outcomes, and preserves critical JS. Our quantitative evaluation of 500 popular webpages shows that JSCleaner achieves around 30% reduction in page load times coupled with a 50% reduction in the number of requests and the page size. In addition, our qualitative user study of 103 evaluators shows that JSCleaner preserves 95% of the page content similarity, while maintaining nearly 88% of the page functionality (the remaining 12% did not have a major impact on the user browsing experience).

Vehicular communication systems play an important role in the context of smart cities. Different applications are being proposed and evaluated to improve our daily driving in terms of safety and convenience. These applications require efficient data dissemination that guarantees full coverage with minimum overhead and delay. Existing data dissemination protocols often rely on extra communication to gather knowledge about the neighborhood and set the dissemination criteria accordingly. This extra communication poses serious overhead issues that affect the scalability of data dissemination, which is an essential criterion under high density scenarios. In this paper, we propose and evaluate an Efficient multi-directional Data Dissemination Protocol (EDDP), which considers the requirements of an urban vehicular environment without requiring the extra communication overhead. We rely only on simple local data to indicate the road condition for better dissemination performance. In this paper, the design considerations of urban layout include message format, broadcast suppression mechanism, and delay control. The EDDP utilizes the properties of the received messages along with positioning information to make decisions on suppressing broadcasts, with the objective of improving coverage in different directions without unnecessary transmissions. Simulation results show that the EDDP can effectively disseminate traffic data with a high data delivery ratio and a minimized overhead.

Broadcasting forms the basis of all types of communications in ad hoc networks. In Vehicular Ad hoc Networks (VANETs), broadcasting is usually appropriate for data dissemination, since traffic data is of public interest and usually benefits a group of users rather than a specific individual. However, broadcasting in high density networks may easily lead to the broadcast storm problem due to high data redundancy and collisions. In VANETs, it is still challenging for data dissemination methods to address scalability in broadcasting under high densities, such that data redundancy is reduced while the delivery ratio is maintained. In this paper, we develop a beacon-free approach for data dissemination in multi-hop VAENT that relies on traffic regime estimation adaptively to provide scalable broadcast, without extra communication overhead. Simulation results show the efficiency of our approach in achieving low broadcasting overhead while maintaining a high delivery ratio.

The rapid evolution of wireless communication capabilities and vehicular technology would allow traffic data to be collected and disseminated by travelling vehicles in the near future. Vehicular Ad hoc Networks (VANETs) are self-organizing networks that can support different types of traffic information systems without requiring fixed infrastructure or centralized administration. Since traffic data is of public interest and usually benefits a group of users rather than a specific individual, it is more appropriate to rely on a broadcasting scheme for data dissemination in VANETs. However, broadcasting in dense networks suffers from a high percentage of redundant data that wastes the limited radio channel bandwidth. Moreover, packet collisions may lead to the broadcast storm problem since a large number of vehicles in the same vicinity may rebroadcast the same data nearly simultaneously. A common employed solution to deal with such scalability issues is reducing the percentage of redundant data. This is typically done by selecting only some of the vehicles to relay data packet as opposed to letting every single vehicle rebroadcast it. In this work, we propose involving the high mobility feature of VANETs to improve scalability in data dissemination, by relying on the speed of travelling vehicles to determine the rebroadcasting probability.

Since vehicular transportation is the preferred travel mode, people are spending more time on the road due to traffic problems, that are increasing with the growing levels of motorization worldwide. A significant percentage of this time is spent during traffic congestion and at road junctions. With the advances in wireless technologies and the emergence of Inter-Vehicular Communication (IVC) techniques, new research studies are carried out to develop new applications and services over vehicular ad hoc networks, for improving the efficiency and safety on the road, with the advantage of bypassing the need for expensive infrastructure. However, despite the considerable research effort that is concentrating on data dissemination and routing protocols, the support for applications other than the access to the internet remains limited. In this paper, we propose a vehicular communication application for providing travelling vehicles with timely information to find the shortest-time route to their destinations according to the road condition. The proposed application employs a Geocasting protocol for information sharing among vehicles within a certain geographic area. We show, through simulation results, that the proposed application can significantly decrease the average travel time under different traffic patterns.

Vehicular Ad hoc Networks (VANETs) have become a cornerstone component of Intelligent Transportation Systems (ITS). VANET applications present a huge potential for improving road safety and travel comfort, hence the growing interest of both academia and industry. The main advantage of VANETs is its ad hoc nature which does not require fixed infrastructure or centralized administration. However, designing scalable information dissemination techniques for VANET applications remains a challenging task due to the inherent nature of such highly dynamic environments. Existing dissemination techniques often resort to simulation for performance evaluation and there are only few studies that offer mathematical modeling. In this paper we provide a comparative study of existing performance modeling approaches for data dissemination techniques designed for different VANET applications.

Thanks to the advances in wireless technologies, mobile vehicular networks are likely to become the most relevant form of mobile ad hoc networks (MANET). Vehicular communication which facilitates the exchange of information between vehicles is the prerequisite not only for extending the access to the Internet while on the road, but also to cater for special applications such as of road traffic and travel management. However despite the increasing number of studies on vehicular routing protocols, the support for applications other than the access to the internet remains limited. In this paper we propose a novel vehicular communication system for road congestion detection and avoidance by disseminating and exploiting road information. The system is an integrated solution for sharing congestion information using a simple geocast protocol and dynamic Dijkstra algorithm for planning and computing least congested travel itineraries. In this paper, we show, through simulation results, that the proposed solution not only allows vehicles to detect road congestion ahead of time, but also contributes to reducing the congestion level on the roads by allowing vehicles to avoid congestion points.