Performance analysis to evaluate overtaking behavior on highways

Today, cities are facing new issues with the increase of the population and the massive urbanization. One of them is the mobility as the cities were not designed to support such increase. Improving mobility in smart cities, becomes an important challenge to avoid traffic jam and improving sustainability by reducing greenhouse effect. This contributes as well to having better life for citizen. Even with rich infrastructures, the vehicles behavior could decrease the traffic flow. This is why figure out how the mobility is done on the highways can give more details on how it can create traffic jams and then several solutions could be proposed to contribute to the improvements. This is the goal of this study. In this paper, a new stochastic model based on Markov chain is proposed, which represents the behaviors of overtaking on the highways. A full description of the model is given with numerical resolution to calculate several rewards such as delays and congestion. Intensive simulations were carried out to compare both simulations and analytical model results and to provide results for more complex configurations

Eco-Friendly Low Resource Security Surveillance Framework Toward Green AI Digital Twin

Most intelligent systems focused on how to improve performance including accuracy, processing speed with a massive number of data sets and those performance-biased intelligent systems, Red AI systems, have been applied to digital twin in smart cities. On the other hand, it is highly reasonable to consider Green AI features covering environmental, economic, social costs for advanced digital twin services. In this letter, we propose eco-friendly low resource security surveillance toward Green AI-enabled digital twin service, which provides eco-friendly security by the active participation of low resource devices. And, we formally define a problem whose objective is to maximize the participation of low source or reusable devices such that reusable surveillance borders are created within security district. Also, a dense sub-district with low resource devices priority completion scheme is proposed to resolve the problem. Then, the devised method is performed by expanded simulations and the achieved result is evaluated with demonstrated discussions

BeneWinD: An Adaptive Benefit Win–Win Platform with Distributed Virtual Emotion Foundation

In recent decades, online platforms that use Web 3.0 have tremendously expanded their goods, services, and values to numerous applications thanks to its inherent advantages of convenience, service speed, connectivity, etc. Although online commerce and other relevant platforms have clear merits, offline-based commerce and payments are indispensable and should be activated continuously, because offline systems have intrinsic value for people. With the theme of benefiting all humankind, we propose a new adaptive benefit platform, called BeneWinD, which is endowed with strengths of online and offline platforms. Furthermore, a new currency for integrated benefits, the win–win digital currency, is used in the proposed platform. Essentially, the proposed platform with a distributed virtual emotion foundation aims to provide a wide scope of benefits to both parties, the seller and consumer, in online and offline settings. We primarily introduce features, applicable scenarios, and services of the proposed platform. Different from previous systems and perspectives, BeneWinD can be combined with Web 3.0 because it deliberates based on the decentralized or distributed virtual emotion foundation, and the virtual emotion feature and the detected virtual emotion information with anonymity are open to everyone who wants to participate in the platform. It follows that the BeneWinD platform can be connected to the linked virtual emotion data block or win–win digital currency. Furthermore, crucial research challenges and issues are addressed in order to make great contributions to improve the development of the platform

Maximum Activation 3D Cube Transition System for Virtual Emotion Surveillance

The concept of barrier coverage has been utilized for with various applications of surveillance, object tracking in smart cities. In barrier coverage, it is desirable to have large number of active barriers to maximize lifetime of UAV-assisted application. Because existing studies primarily focused on the formation of barriers in two-dimensional area with limited applicability, it is indispensable to extend the barrier constructions in three-dimensional area. In this letter, a cube transition barrier system using smart UAVs is designed for three-dimensional space. Then, we formally define a problem whose goal is to maximize the number of cube transition barriers by applying a two-dimensional theory to a three-dimensional spaces. To solve this problem, we propose two algorithms to return the number of barriers and evaluate their performances based on numerical simulation results

Dealing with complex routing requirements using an MCDM based approach

The last decade has witnessed an ever-growing user demand for a better QoS (Quality Of Service) and the fast growth of connected devices still put high pressure on the legacy network infrastructures. To improve network performances, better manage the resources and have a greater control over traffic transmission, intelligent routing procedures are increasingly demanded. Modern applications in the dynamic context of new emerging networks have their own routing requirements, in terms of set of metrics to consider, their importance and thresholds to respect. The objective of this work is to design an approach based on MCDM (Multi-Criteria Decision Making) to decide complex routing problems when assuming threshold constraints on metrics. We give the mathematical framework to capture such requirements and to decide the routing. A case study is presented to advocate the benefit of using our approach

Dealing with complex routing requirements using an MCDM based approach

The last decade has witnessed an ever-growing user demand for a better QoS (Quality Of Service) and the fast growth of connected devices still put high pressure on the legacy network infrastructures. To improve network performances, better manage the resources and have a greater control over traffic transmission, intelligent routing procedures are increasingly demanded. Modern applications in the dynamic context of new emerging networks have their own routing requirements, in terms of set of metrics to consider, their importance and thresholds to respect. The objective of this work is to design an approach based on MCDM (Multi-Criteria Decision Making) to decide complex routing problems when assuming threshold constraints on metrics. We give the mathematical framework to capture such requirements and to decide the routing. A case study is presented to advocate the benefit of using our approach

Congestion control protocols in wireless sensor networks: A survey

The performance of wireless sensor networks (WSN) is affected by the lossy communication medium, application diversity, dense deployment, limited processing power and storage capacity, frequent topology change. All these limitations provide significant and unique design challenges to data transport control in wireless sensor networks. An effective transport protocol should consider reliable message delivery, energy-efficiency, quality of service and congestion control. The latter is vital for achieving a high throughput and a long network lifetime. Despite the huge number of protocols proposed in the literature, congestion control in WSN remains challenging. A review and taxonomy of the state-of-the-art protocols from the literature up to 2013 is provided in this paper. First, depending on the control policy, the protocols are divided into resource control vs. traffic control. Traffic control protocols are either reactive or preventive (avoiding). Reactive solutions are classified following the reaction scale, while preventive solutions are split up into buffer limitation vs. interference control. Resource control protocols are classified according to the type of resource to be tuned. © 2014 IEEE

Intelligent Aerial-Ground Surveillance and Epidemic Prevention with Discriminative Public and Private Services

Since complete surveillance is essential to provide safe daily life to citizen in smart cities, the issue of how to achieve secure surveillance has been driven by various research communities. Also, due to recent epidemic spread such as COVID-19, it is obvious that we should focus on how to manage a cooperative framework for possible future pandemic fights and allied medical services continuously. To support those purposes, it is anticipated that we can utilize AI-assisted communications and technologies using a variety of devices and equipment, including UAVs, mobile robots, and smart devices on the aerial and ground sides. In this article, an aerial-ground cooperative infrastructure is designed to study surveillance and epidemic prevention with managing energy recharge and AI-supported communications through collected or pre-knowledge information for public and private areas. Also, in the proposed architecture, we specify system settings, promising scenarios, and strategies in order to satisfy several objectives and tasks. Then possible research challenges and issues are addressed for successful realization and management of intelligent surveillance and efficient epidemic prevention

LoRaLOFT-A Local Outlier Factor-based Malicious Nodes detection Method on MAC Layer for LoRaWAN

LoRaWAN is one of the network technologies that provide a long-range wireless network at low energy consumption. However, the pure Aloha MAC protocol and the duty-cycle limitation at both end devices and gateway make LoRaWAN very sensitive to malicious behaviors in the MAC layer. Moreover, this kind of sensitivity makes the false-positives problem challenging for malicious behavior detection with simple threshold methods. This study investigates two malicious behaviors - greedy and attack on the MAC layer. Furthermore, by combining the threshold method with a Local Outlier Factor (LOF) model in machine learning, LoRaLOFT is proposed. It is a centralized malicious node detection method. Analytical results show that the proposed method gives high detection accuracy while significantly reducing the false-positive rate in both behaviors