Cooperation as a Service in VANET: Implementation and Simulation Results

The past decade has witnessed the emergence of Vehicular Ad-hoc Networks (VANET), specializing from the well-known Mobile Ad Hoc Networks (MANET) to Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communications. While the original motivation for Vehicular Networks was to promote traffic safety, recently it has become increasingly obvious that Vehicular Networks open new vistas for Internet access, providing weather or road condition, parking availability, distributed gaming, and advertisement. In previous papers [27,28], we introduced Cooperation as a Service (CaaS); a new service-oriented solution which enables improved and new services for the road users and an optimized use of the road network through vehicle\u27s cooperation and vehicle-to-vehicle communications. The current paper is an extension of the first ones; it describes an improved version of CaaS and provides its full implementation details and simulation results. CaaS structures the network into clusters, and uses Content Based Routing (CBR) for intra-cluster communications and DTN (Delay and disruption-Tolerant Network) routing for inter-cluster communications. To show the feasibility of our approach, we implemented and tested CaaS using Opnet modeler software package. Simulation results prove the correctness of our protocol and indicate that CaaS achieves higher performance as compared to an Epidemic approach

Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug Design

In recent years, deep learning has demonstrated promising results in de novo drug design. However, the proposed techniques still lack an efficient exploration of the large chemical space. Most of these methods explore a small fragment of the chemical space of known drugs, if the desired molecules were not found, the process ends. In this work, we introduce a curiosity-driven method to force the model to navigate many parts of the chemical space, therefore, achieving higher desirability and diversity as well. At first, we train a recurrent neural network-based general molecular generator (G), then we fine-tune G to maximize curiosity and desirability. We define curiosity as the Tanimoto similarity between two generated molecules, a first molecule generated by G, and a second one generated by a copy of G (Gcopy). We only backpropagate the loss through G while keeping Gcopy unchanged. We benchmarked our approach against two desirable chemical properties related to drug-likeness and showed that the discovered chemical space can be significantly expanded, thus, discovering a higher number of desirable molecules with more diversity and potentially easier to synthesize. All Code and data used in this paper are available at https://github.com/amine179/Curiosity-RL-for-Drug-Design

Cooperation as a Service in VANET: Implementation and Simulation Results

The past decade has witnessed the emergence of Vehicular Ad-hoc Networks (VANET), specializing from the well-known Mobile Ad Hoc Networks (MANET) to Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communications. While the original motivation for Vehicular Networks was to promote traffic safety, recently it has become increasingly obvious that Vehicular Networks open new vistas for Internet access, providing weather or road condition, parking availability, distributed gaming, and advertisement. In previous papers [27,28], we introduced Cooperation as a Service (CaaS); a new service-oriented solution which enables improved and new services for the road users and an optimized use of the road network through vehicle's cooperation and vehicle-to-vehicle communications. The current paper is an extension of the first ones; it describes an improved version of CaaS and provides its full implementation details and simulation results. CaaS structures the network into clusters, and uses Content Based Routing (CBR) for intra-cluster communications and DTN (Delay–and disruption-Tolerant Network) routing for inter-cluster communications. To show the feasibility of our approach, we implemented and tested CaaS using Opnet modeler software package. Simulation results prove the correctness of our protocol and indicate that CaaS achieves higher performance as compared to an Epidemic approach

Are distributed ledger technologies ready for intelligent transportation systems?

none3noThe aim of this paper is to understand whether Distributed Ledger Technologies (DLTs) are ready to support complex services, such as those related to Intelligent Transportation Systems (ITS). In smart transportation services, a huge amount of sensed data is generated by a multitude of vehicles. While DLTs provide very interesting features, such as immutability, traceability and verifiability of data, some doubts on the scalability and responsiveness of these technologies appear to be well-founded. We propose an architecture for ITS that resorts to DLT features. Moreover, we provide experimental results of a real test-bed over IOTA, a promising DLT for IoT. Results clearly show that, while the viability of the proposal cannot be rejected, further work is needed on the responsiveness of DLT infrastructures.openZichichi, Mirko; Ferretti, Stefano; D'Angelo, GabrieleZichichi, Mirko; Ferretti, Stefano; D'Angelo, Gabriel

Reinforcement Learning Based Decision Support Tool For Epidemic Control

Rationale: Covid-19 Is Certainly One Of The Worst Pandemics Ever. In The Absence Of A Vaccine, Classical Epidemiological Measures Such As Testing In Order To Isolate The Infected People, Quarantine And Social Distancing Are Ways To Reduce The Growing Speed Of New Infections As Much As Possible And As Soon As Possible, But With A Cost To Economic And Social Disruption. It Is Therefore A Challenge To Implement Timely And Appropriate Public Health Interventions. Objective: This Study Investigates A Reinforcement Learning Based Approach To Incrementally Learn How Much Intensity Of Each Public Health Intervention Should Be Applied At Each Period In A Given Region. Methods: First We Define The Basic Components Of A Reinforcement Learning (Rl) Set Up (I.E., States, Reward, Actions, And Transition Function), This Represents The Learning Environment For The Agent (I.E., An Ai-Model). Then We Train Our Agent Using Rl In An Online Fashion, Using A Reinforcement Learning Algorithm Known As Reinforce. Finally, A Developed Flow Network, Serving As An Epidemiological Model Is Used To Visualize The Results Of The Decisions Taken By The Agent Given Different Epidemic And Demographic State Scenarios. Main Results: After A Relatively Short Period Of Training, The Agent Starts Taking Reasonable Actions Allowing A Balance Between The Public Health And Economic Considerations. In Order To Test The Developed Tool, We Ran The Rl-Agent On Different Regions (Demographic Scale) And Recorded The Output Policy Which Was Still Consistent With The Training Performance. The Flow Network Used To Visualize The Results Of The Simulation Is Considerably Useful Since It Shows A High Correlation Between The Simulated Results And The Real Case Scenarios. Conclusion: This Work Shows That Reinforcement Learning Paradigm Can Be Used To Learn Public Health Policies In Complex Epidemiological Models. Moreover, Through This Experiment, We Demonstrate That The Developed Model Can Be Very Useful If Fed In With Real Data. Future Work: When Treating Trade-Off Problems (Balance Between Two Goals) Like Here, Engineering A Good Reward (That Encapsulates All Goals) Can Be Difficult, Therefore Future Work Might Tackle This Problem By Investigating Other Techniques Such As Inverse Reinforcement Learning And Human-In-The-Loop. Also, Regarding The Developed Epidemiological Model, We Aim To Gather Proper Real Data That Can Be Used To Make The Training Environment More Realistic, As Well As To Apply It For Network Of Regions Instead Of A Single Region

A Reinforcement Learning Based Decision Support Tool for Epidemic Control: Validation Study for COVID-19

Epidemics such as COVID-19 present a substantial menace to public health and global economies. While the problem of epidemic forecasting has been thoroughly investigated in the literature, there is limited work studying the problem of optimal epidemic control. In the present paper, we introduce a novel epidemiological model (EM) that is inherently suitable for analyzing different control policies. We validated the potential of the developed EM in modeling the evolution of COVID-19 infections with a mean Pearson correlation of 0.609 CI 0.525–0.690 and P-value < 0.001. To automate the process of analyzing control policies and finding the optimal one, we adapted the developed EM to the reinforcement learning (RL) setting and ran several experiments. The results of this work show that the problem of optimal epidemic control can be significantly difficult for governments and policymakers, especially if faced with several constraints at once, hence, the need for such machine learning-based decision support tools. Moreover, it demonstrated the potential of deep RL in addressing such real-world problems

Cooperation as a Service in VANETs

Vehicular Networks, including Vehicular Adhoc Networks (VANETs) and Vehicular Sensor Networks (VSNs), stimulate a brand new variety of services, ranging from driver safety services, traffic information and warnings regarding traffic jams and accidents, to providing weather or road condition, parking availability, and advertisement. 3G networks and sophisticated Intelligent Transportation Systems (ITS), including deploying costly roadside base stations, can indeed be used to offer such services, but these come with a cost, both at network and hardware levels. In this paper we introduce Cooperation as a service (CaaS): A novel architecture that will allow providing a set of services for free and without any additional infrastructure, by taking advantage of Vehicle-to-Vehicle communications. CaaS uses a hybrid publish/subscribe mechanism where the driver (or subscriber) expresses his interests regarding a service (or a set of services) and where cars having subscribed to the same service will cooperate to provide the subscriber with the necessary information regarding the service he subscribed to, by publishing this information in the network. CaaS structures the network into clusters, and uses Content Based Routing (CBR) for intra-cluster communications and geographic routing for inter-cluster communications