Routing in a many-to-one communication scenario in a realistic VDTN

In this paper, we evaluate and compare the performance of different routing protocols in a many-to-one communication within a Vehicular Delay Tolerant Network (VDTN). Seven groups with three stationary sensor nodes sense the temperature, humidity and wind speed and send these data to a stationary destination node that collect them for statistical and data analysis purposes. Vehicles moving in Tirana city roads in Albania during the opportunistic contacts will exchange the sensed data to destination node. The simulations are conducted with the Opportunistic Network Environment (ONE) simulator. For the simulations we considered two different scenarios where the distance of the source nodes from the destination is short and long. For both scenarios the effect of node density, ttl and node movement model is evaluated. The performance is analyzed using delivery probability, overhead ratio, average latency, average number of hops and average buffer time metrics. The simulation results show that the increase of node density increases the delivery probability for all protocols and both scenarios, and better results are achieved when shortest-path map-based movement model is used. The increase of ttl slightly affects the performance of all protocols. By increasing the distance between source nodes and destination node, delivery probability is decreased almost 10% for all protocols, the overhead for sprayandwait protocol does not change, but for other protocols is slightly increased and the average number of hops and average latency is increased.Peer ReviewedPostprint (author's final draft

A Hybrid Intelligent Simulation System for Building IoT Networks: Performance Comparison of Different Router Replacement Methods for WMNs Considering Stadium Distribution of IoT Devices

As the Internet of Things (IoT) devices and applications proliferate, it becomes increasingly important to design robust networks that can continue to meet user demands at a high level. Wireless local area networks (WLANs) can be a good choice as IoT infrastructure when high throughput is required. On the other hand, wireless mesh networks (WMNs), which are WLANs with mesh topology following the IEEE802.11s standard, have many advantages compared to conventional WLANs. Nevertheless, there are some problems that need solutions. One of them is the node placement problem. In this work, we propose and implement a hybrid intelligent system that solves this problem by determining the position of mesh nodes by maximizing the mesh connectivity and the coverage of IoT devices. The system is based on particle swarm optimization (PSO), simulated annealing (SA), and distributed genetic algorithm (DGA). We compare the performance of three router replacement methods: constriction method (CM), random inertia weight method (RIWM), and rational decrement of Vmax method (RDVM). The simulation results show that RIWM achieves better performance compared to CM and RDVM because it achieves the highest connectivity while covering more clients than the other two methods

A Fuzzy-Based Approach for the Assessment of the Edge Layer Processing Capability in SDN-VANETs: A Comparation Study of Testbed and Simulation System Results

Vehicular Ad Hoc Networks (VANETs) have gained significant attention due to their potential to enhance road safety, traffic efficiency, and passenger comfort through vehicle-to-vehicle and vehicle-to-infrastructure communication. However, VANETs face resource management challenges due to the dynamic and resource constrained nature of vehicular environments. Integrating cloud-fog-edge computing and Software-Defined Networking (SDN) with VANETs can harness the computational capabilities and resources available at different tiers to efficiently process and manage vehicular data. In this work, we used this paradigm and proposed an intelligent approach based on Fuzzy Logic (FL) to evaluate the processing and storage capability of vehicles for helping other vehicles in need of additional resources. The effectiveness of the proposed system is evaluated through extensive simulations and a testbed. Performance analysis between the simulation results and the testbed offers a comprehensive understanding of the proposed system and its performance and feasibility

A Fuzzy Logic Approach for Determining Driver Impatience and Stress Leveraging Internet of Vehicles Infrastructure

Drivers are held responsible for the vast majority of traffic crashes. Although most of the errors causing these accidents are involuntary, a significant number of them are caused by irresponsible driving behaviors, which must be utterly preventable. Irresponsible driving, on the other hand, is often associated with driver stress and the impatience they show while driving. In this paper, we consider the factors that cause drivers to become impatient and experience stress and propose an integrated fuzzy logic system that determines the stress level in real time. Based on the stress level, the proposed system can take the appropriate action that improves the driving situation and consequently road safety. By using inputs, such as the unnecessary maneuvers that drivers make, the time pressure, and the number of times they are forced to stop, a fuzzy logic controller determines the driver’s impatience, which is then considered alongside other factors, such as the driving experience and history, the behavior of other drivers, and the traffic condition to determine the stress level. We show, through simulations, the feasibility of the proposed approach to accurately determine driver stress and demonstrate some actions that can be performed when stress exceeds certain levels

An Intelligent Approach for Cloud-Fog-Edge Computing SDN-VANETs Based on Fuzzy Logic: Effect of Different Parameters on Coordination and Management of Resources

The integration of cloud-fog-edge computing in Software-Defined Vehicular Ad hoc Networks (SDN-VANETs) brings a new paradigm that provides the needed resources for supporting a myriad of emerging applications. While an abundance of resources may offer many benefits, it also causes management problems. In this work, we propose an intelligent approach to flexibly and efficiently manage resources in these networks. The proposed approach makes use of an integrated fuzzy logic system that determines the most appropriate resources that vehicles should use when set under various circumstances. These circumstances cover the quality of the network created between the vehicles, its size and longevity, the number of available resources, and the requirements of applications. We evaluated the proposed approach by computer simulations. The results demonstrate the feasibility of the proposed approach in coordinating and managing the available SDN-VANETs resources

A Fuzzy Logic Approach for Determining Driver Impatience and Stress Leveraging Internet of Vehicles Infrastructure

Drivers are held responsible for the vast majority of traffic crashes. Although most of the errors causing these accidents are involuntary, a significant number of them are caused by irresponsible driving behaviors, which must be utterly preventable. Irresponsible driving, on the other hand, is often associated with driver stress and the impatience they show while driving. In this paper, we consider the factors that cause drivers to become impatient and experience stress and propose an integrated fuzzy logic system that determines the stress level in real time. Based on the stress level, the proposed system can take the appropriate action that improves the driving situation and consequently road safety. By using inputs, such as the unnecessary maneuvers that drivers make, the time pressure, and the number of times they are forced to stop, a fuzzy logic controller determines the driver’s impatience, which is then considered alongside other factors, such as the driving experience and history, the behavior of other drivers, and the traffic condition to determine the stress level. We show, through simulations, the feasibility of the proposed approach to accurately determine driver stress and demonstrate some actions that can be performed when stress exceeds certain levels

Soluble Gamma-secretase Modulators Attenuate Alzheimer's β-amyloid Pathology and Induce Conformational Changes in Presenilin 1

A central pathogenic event of Alzheimer's disease (AD) is the accumulation of the Aβ42 peptide, which is generated from amyloid-β precursor protein (APP) via cleavages by β- and γ-secretase. We have developed a class of soluble 2-aminothiazole γ-secretase modulators (SGSMs) that preferentially decreases Aβ42 levels. However, the effects of SGSMs in AD animals and cells expressing familial AD mutations, as well as the mechanism of γ-secretase modulation remain largely unknown. Here, a representative of this SGSM scaffold, SGSM-36, was investigated using animals and cells expressing FAD mutations. SGSM-36 preferentially reduced Aβ42 levels without affecting either α- and β-secretase processing of APP nor Notch processing. Furthermore, an allosteric site was identified within the γ-secretase complex that allowed access of SGSM-36 using cell-based, fluorescence lifetime imaging microscopy analysis. Collectively, these studies provide mechanistic insights regarding SGSMs of this class and reinforce their therapeutic potential in AD

Soluble Gamma-secretase Modulators Attenuate Alzheimer's β-amyloid Pathology and Induce Conformational Changes in Presenilin 1.

A central pathogenic event of Alzheimer's disease (AD) is the accumulation of the Aβ42 peptide, which is generated from amyloid-β precursor protein (APP) via cleavages by β- and γ-secretase. We have developed a class of soluble 2-aminothiazole γ-secretase modulators (SGSMs) that preferentially decreases Aβ42 levels. However, the effects of SGSMs in AD animals and cells expressing familial AD mutations, as well as the mechanism of γ-secretase modulation remain largely unknown. Here, a representative of this SGSM scaffold, SGSM-36, was investigated using animals and cells expressing FAD mutations. SGSM-36 preferentially reduced Aβ42 levels without affecting either α- and β-secretase processing of APP nor Notch processing. Furthermore, an allosteric site was identified within the γ-secretase complex that allowed access of SGSM-36 using cell-based, fluorescence lifetime imaging microscopy analysis. Collectively, these studies provide mechanistic insights regarding SGSMs of this class and reinforce their therapeutic potential in AD