Maher Ben Jemaa
Other affiliations: École Normale Supérieure
Bio: Maher Ben Jemaa is an academic researcher from University of Sfax. The author has contributed to research in topics: Image retrieval & Wireless sensor network. The author has an hindex of 11, co-authored 79 publications receiving 545 citations. Previous affiliations of Maher Ben Jemaa include École Normale Supérieure.
TL;DR: This work surveys the vehicular cloud paradigm, focusing on its features and architectures, and highlights the features of existingVehicular cloud architectures: a taxonomy of vehicles followed by classification criteria.
Abstract: Vehicular Cloud Computing is a promising solution to exploit the underutilized vehicular resources and to meet the requirements of VANET applications and services. Although modern vehicles have important capacities of computation and storage, there is an increasing need for resources, in particular, for safety applications which require the cooperation between vehicles. The vehicular cloud offers to users the opportunity to rent resources on-demand or to share them freely to run their applications or to carry out some tasks. Even though this paradigm is feasible, its implementation still faces problems. Many researchers have focused on the architectural design in order to overcome different challenges and consequently meet user requirements to provide him/her with reliable services. In this work, we survey the vehicular cloud paradigm. We focus on its features and architectures. We first present a brief overview of the motivation of vehicular cloud. Then, we explore challenges related to its design. Furthermore, we highlight the features of existing vehicular cloud architectures: we provide a taxonomy of vehicular cloud followed by our classification criteria. Finally, we discuss issues that can be considered as open research directions.
TL;DR: This paper describes existing DHT-based routing and data management protocols and includes a detailed classification of them and presents an analytical survey on applying DHT techniques in WSNs.
Abstract: Recent advances in Wireless Sensor Networks (WSN) have led to a great breakthrough in sensors design and features. These technological novelties have brought additional challenges to WSN. Sensornets are seeking for new approaches for efficient data routing and management. The last few years have witnessed the emergence of several approaches that build Distributed Hash Tables (DHTs) over WSN. DHTs are initially conceived for efficient data lookup in large-scale wired networks. The main objective of this combination is to manage location-independent data and nodes identification. DHT mapping over WSN brings however new challenges. This paper presents an analytical survey on applying DHT techniques in WSNs. It describes existing DHT-based routing and data management protocols and includes a detailed classification of them.
01 Aug 2019
TL;DR: A multi-access edge based vehicular fog computing architecture on the internet of vehicles where vehicles are the fog nodes is proposed and formulated as a multi-objective optimization problem, and solved using ant colony optimization.
Abstract: Fog computing (FC) and multi-access edge computing (MEC) are two promising technologies that have been emerged to solve problems related to the access to the cloud computing (CC), mainly high latency and high bandwidth consumption. These two paradigms consist in enabling the cloud closer to users at the edge of the network. The pool of vehicular resources provided by the vehicular cloud (VC) can be exploited to process and store end users data instead of accessing remote servers. The combination of these three concepts can considerably augment the edge resources. In this context, we propose a multi-access edge based vehicular fog computing architecture on the internet of vehicles where vehicles are the fog nodes. In this paper, we present a detailed description of our suggested architecture and its modules. Then, we focus on a particular module which is the gateways selection module. The role of this module is the election of suitable fog nodes (i.e. vehicles) to access the MEC servers and the conventional cloud in order to reduce communication costs (e.g. bandwidth use, delay). The proposed selection approach has two steps. The first step consists in selecting a set of candidate gateways based on fuzzy logic. The second step allows the optimization of the number of selected gateways. We formulate it as a multi-objective optimization problem, and we solve it using ant colony optimization. The obtained simulation results show the efficiency of our proposed approach in terms of the number of selected gateways and connected fog nodes. In both static and mobile scenarios, the number of selected gateways is reduced up to 82% and 92%, respectively, compared to the fuzzy step. The ratio of connected vehicles is more than 94% in the static scenario.
TL;DR: This paper aims to answer the question: Does the slight modification of temperature and humidity in indoor environment have a significant impact on RSSI value?
Abstract: Many Wireless Sensor Networks (WSN) protocols such as lot of localization protocols rely on the accuracy of the Received Signal Strength Indicator (RSSI) This RSSI metric is sensitive to different factors which lead to its disturbance Other works have already proved that temperature and humidity have important impact on RSSI in outdoor environment It is evident that temperature and humidity vary more importantly on outdoor than on indoor In this paper, we aim to answer at this question: Does the slight modification of temperature and humidity in indoor environment have a significant impact on RSSI value? We have conducted various experiments to study this impact
TL;DR: The existing resilience techniques are discussed and a solution to design antifragile systems in cloud computing environments is proposed.
Abstract: Cloud computing systems are rapidly growing in scale and complexity. They are also changing dynamically as a result of dynamic addition and removal of system components, different execution environments, common updates and upgrades, runtime repairs, mobility of devices and more. Such large-scale, complex and dynamic cloud environments are prone to failures and per- formance anomalies. Thus, dependability and resilience in cloud computing are of paramount importance to guarantee availability and reliability of services and application execution, even in the presence of large number of faulty components. Antifragility is the key to such techniques. It proposes that some systems could be strengthened by changes and faults instead of be weakened by them. In contrast to classical resilience methods, antifragile techniques aim to build systems that handle unpredictable and irregular events, while growing and getting stronger. Most of the classical resilience techniques are not sufficient to build highly available cloud infrastructures. In fact, they just resist shocks and stay the same. They should be complemented by some other aspects like learning from failure to built more elastic and stronger cloud infrastructures. This may represent the idea of building antfragile cloud systems. In this paper, we discuss the existing resilience techniques and propose a solution to design antifragile systems in cloud computing environments.
TL;DR: The basics of WSN virtualization are introduced and motivate its pertinence with carefully selected scenarios and existing works are presented in detail and critically evaluated using a set of requirements derived from the scenarios.
Abstract: Wireless Sensor Networks (WSNs) are the key components of the emerging Internet-of-Things (IoT) paradigm. They are now ubiquitous and used in a plurality of application domains. WSNs are still domain specific and usually deployed to support a specific application. However, as WSNs' nodes are becoming more and more powerful, it is getting more and more pertinent to research how multiple applications could share a very same WSN infrastructure. Virtualization is a technology that can potentially enable this sharing. This paper is a survey on WSN virtualization. It provides a comprehensive review of the state-of-the-art and an in-depth discussion of the research issues. We introduce the basics of WSN virtualization and motivate its pertinence with carefully selected scenarios. Existing works are presented in detail and critically evaluated using a set of requirements derived from the scenarios. The pertinent research projects are also reviewed. Several research issues are also discussed with hints on how they could be tackled.
••01 Feb 2020
TL;DR: Key design issues, methodologies, and hardware platforms are introduced, including edge-assisted perception, mapping, and localization for intelligent IoV, and typical use cases for intelligent vehicles are illustrated.
Abstract: The Internet of Vehicles (IoV) is an emerging paradigm that is driven by recent advancements in vehicular communications and networking. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications that will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend on communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and cloud computing will not be sufficient due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. EIS will provide not only low-latency content delivery and computation services but also localized data acquisition, aggregation, and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies, and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open-research problems are identified.
TL;DR: In this paper, the research was support by National Natural ScienceFoundation of P. R. China ( Grant No.61170065,61373017,61171053,61103195 and 61203217), Peak of Six Major Talent in Jiangsu Province (Grant No.2010DZXX026), the Natural Science Foundation of Jiangsu province(Grant NoBK2012436 and BK20130882), Scientific Research & Industry Promotion Project for Higher Education InstitutionsJHB2012-7),Scientific & Technological Support Project of Jiang
Abstract: The research is support by National Natural ScienceFoundation of P. R. China ( Grant No.61170065,61373017,61171053,61103195 and 61203217), Peak of Six Major Talent in Jiangsu Province (Grant No.2010DZXX026), the Natural Science Foundation of Jiangsu Province(Grant No.BK2012436 and BK20130882),Scientific & Technological Support Project of Jiangsu ProvinceGrant No. BE2012183 and BE2012755), Natural Science Key Fund for Colleges and Universities in Jiangsu Province (Grant No. 11KJA520001 and 12KJA520002), Scientific Research & Industry Promotion Project for Higher Education InstitutionsJHB2012-7) .