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Über dieses Buch

This book contains extended versions of the best papers presented at the First International Workshop on Distributed Computing for Emerging Smart Networks, DiCES-N 2019, held in Hammamet, Tunisia, in October 2019.
The 9 revised full papers included in this volume were carefully reviewed and selected from 24 initial submissions. The papers are organized in the following topical sections: ​intelligent transportation systems; distributed computing for networking and communication; articial intelligence applied to cyber physical systems.

Inhaltsverzeichnis

Frontmatter

Intelligent Transportation Systems

Frontmatter

VANETs Routing Protocols Survey: Classifications, Optimization Methods and New Trends

Abstract
The specific characteristics of vehicular ad-hoc networks, such as high-speed nodes, frequent topology changes and predefined vehicle movement paths, make mobile ad-hoc networks routing protocols not convenient to disseminate data in the vehicular environment. In addition, the new vision towards Internet of Vehicles concept along with the advent of autonomous cars contribute to the proliferation of new innovative applications with different QoS requirements, rising new challenging issues. In this paper, we survey the different taxonomies for vehicular routing protocols, while exposing several optimization techniques used to enhance routing protocols. Moreover, in order to foster the deployment of robust Internet of Vehicles routing protocols at large scale, we give some directions for future research work.
Chahrazed Ksouri, Imen Jemili, Mohamed Mosbah, Abdelfettah Belghith

A Systematic Literature Review of Studies on Road Congestion Modelling

Abstract
Congestion was one of the most serious global problems which create highly problematic social, economic and environmental conditions. In this regard, we have elaborated a systematic literature review study on the magnitudes of congestion that will attempt to answer this problem by presenting successively the causes of traffic congestion, the economic, societal and environmental issues, the solutions proposed to reduce road congestion and finally the actions to be taken for this purpose. In our pursuit of research, we have found that microscopic modeling has been used effectively to solve the most serious problems of road congestion through urban transportation system applications and road pricing policy. To the contrary, the macroscopic modeling applications are generally geared toward the achievement of long-term goals to alleviate road congestion through road traffic management and improved public transport.
Ahmed Derbel, Younes Boujelbene

A Comparative Study of Vehicle Detection Methods in a Video Sequence

Abstract
Vehicle detection plays a significant role in traffic monitoring. Vehicle detection approaches can be used for vehicle tracking, vehicle classification and traffic analysis. However, numerous attributes like shape, intensity, size, pose, illumination, shadows, occlusion, velocity of vehicles and environmental conditions, provide different challenges for the detection step. With an appropriate vehicle detection technique, we are able to extract valuable knowledge from video sequences, regardless these diverse factors. Since the vehicle detection method choice has a deep impact on this step and the whole traffic monitoring system performances, our objective in this study is to investigate different methods for vehicle detection. Comparison is made on the basis of different metrics such as recall, precision and detection accuracy. These approaches have been tested under different weather conditions (rainy, sunny) and various traffic conditions (light, medium, heavy).
Ameni Chetouane, Sabra Mabrouk, Imen Jemili, Mohamed Mosbah

Distributed Computing for Networking and Communication

Frontmatter

Energy Efficient Handshake Algorithm for Wireless Sensor Networks

Abstract
A Wireless Sensor Network (WSN) is composed of sensors that communicate together in a distributed way to supervise the environment. The energy consumption is an important performance measure for a WSN that spurs the development of energy-efficient distributed algorithms for WSNs. In this field, we focus on a specific type of distributed algorithms called handshake. A handshake algorithm allows making two sensors communicate safely by ensuring that they communicate together in an exclusive mode. In this paper, we propose a new energy-efficient WSN Handshake algorithm (WSN-HS). We present an evaluation of our algorithm compared to another similar one. The simulation results show that when using our WSN-HS, we can save the energy of the sensors and minimise the total number of exchanged messages. Alongside with its energy efficiency, our algorithm is fault-tolerant. Hence, we make the disappearance of some sensors caused by their energy depletion not blocking for other sensors.
Emna Taktak, Mohamed Tounsi, Mohamed Mosbah, Ahmed Hadj Kacem

Inter-slice Mobility Management in the Context of SDN/NFV Networks

Abstract
Software Defined Networking (SDN), Network Function Virtualization (NFV) and Network Slicing technologies present promising solutions to enhance vehicular networks. Using these technologies, a dedicated slice will be deployed whenever a new service is requested. However, in this context, mobility management should be considered in order to support seamless roaming among different network slices. The roaming of users requires inter-slices interactions. In this paper, we propose a network slicing architecture for vehicular network application. We are interested especially in the management process. The challenge is to respect the required Quality of Service (QoS) for users during their movement from one slice to another. For this purpose, we propose an algorithm for the control of users’ mobility between different network slices. Mininet emulator and Ryu controller were considered to validate our proposed algorithm.
Amal Kammoun, Nabil Tabbane, Gladys Diaz, Nadjib Achir, Abdulhalim Dandoush

On a New Quantization Algorithm for Secondary User Scheduling in 5G Network

Abstract
Opportunistic beamforming (OB) has been investigated in the 5th generation (5G) network to jointly maximize the sum rate of the secondary network and minimize the interference induced to the primary users. In this paper, we consider the cognitive radio context and we investigate the problem of SINR (signal to interference plus noise ratio) quantization in secondary network. Based on OB, we propose a suitable quantization scheme that minimizes the secondary system throughput loss due to the quantization. Via theoretical analysis and Matlab simulations, we demonstrate that our proposed algorithm attains maximum sum rate and outperforms others schemes proposed in the literature.
Ayman Massaoudi, Noura Sellami, Mohamed Siala

An Efficient Fault-Tolerant Scheduling Approach with Energy Minimization for Hard Real-Time Embedded Systems

Abstract
In this paper, we focus on two major problems in hard real-time embedded systems fault-tolerance and energy minimization. Fault-tolerance is achieved via both checkpointing technique and active replication strategy to tolerate multiple transient faults, whereas energy minimization is achieved by adapting Dynamic Voltage Frequency Scaling (DVFS) technique. First, we introduce an original fault-tolerance approach for hard real-time systems on multiprocessor platforms. Based on this approach, we then propose DVFS_FTS algorithm for energy-efficient fault-tolerant scheduling of precedence-constrained applications. DVFS_FTS is based on a list scheduling heuristics, it satisfies real-time constraints and minimizes energy consumption even in the presence of faults by exploring the multiprocessor architecture. The experimental results reveal that the proposed algorithm can reduce a considerable amount of energy while ensuring the required fault-tolerance of the system and outperforms other related approaches.
Barkahoum Kada, Hamoudi Kalla

Artificial Intelligence Applied to Cyber Physical Systems

Frontmatter

Using Dynamic Bayesian Networks to Solve Road Traffic Congestion in the Sfax City

Abstract
The development of a road traffic management system is used to model traffic movements in the city and to detect road axes that are often congested. In this case, it will be important to measure the demand for travel and to simulate its contribution to urban congestion in Sfax city. With a view to improving traffic management, our contribution focuses on the adaptation of a method capable of both identifying the different relevant variables of road traffic, modeling the probabilistic dependence structure on a road segment and to analyze the probabilities of urban congestion. The results produced by the diagnosis and analysis provided elements of response to the questioning of road traffic management. We found that the city of Sfax has shown a failure in intra-urban transport services and the transport system in this region is not able to handle the expected increase in the volume of road traffic. We have demonstrated that the integration of public transport services contributes will improve traffic fluidity, and it is still able to make the public and urban space less polluting, more fluid and more attractive.
Ahmed Derbel, Younes Boujelbene

Energy Efficient Target Coverage in Wireless Sensor Networks Using Adaptive Learning

Abstract
Over the past few years, innovation in the development of Wireless Sensor Networks (WSNs) has evolved rapidly. WSNs are being used in many application fields such as target coverage, battlefield surveillance, home security, health care supervision, and many more. However, power usage in WSNs remains a challenging issue due to the low capacity of batteries and the difficulty of replacing or charging them, especially in harsh environments. Therefore, this has led to the development of various architectures and algorithms to deal with optimizing the energy usage of WSNs. In particular, extending the lifetime of the WSN in the context of target coverage problems by resorting to intelligent scheduling has received a lot of research attention. In this paper, we propose a scheduling technique for WSN based on a novel concept within the theory of Learning Automata (LA) called pursuit LA. Each sensor node in the WSN is equipped with an LA so that it can autonomously select its proper state, i.e., either sleep or active with the aim to cover all targets with the lowest energy cost. Through comprehensive experimental testing, we verify the efficiency of our algorithm and its ability to yield a near-optimal solution. The results are promising, given the low computational footprint of the algorithm.
Ashish Rauniyar, Jeevan Kunwar, Hårek Haugerud, Anis Yazidi, Paal Engelstad

Backmatter

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