Manhattan Grid Research Articles

Traffic coordination by reducing jamming attackers in VANET using probabilistic Manhattan Grid Topology for automobile applications

In recent years Intelligent Transportation System (ITS) has been growing interest in the development of vehicular communication technology. The traffic in India shows considerable fluctuations owing to the static and dynamic characteristics of road vehicles in VANET (Vehicular Adhoc Network). These vehicles take up a convenient side lane position on the road, disregarding lane discipline. They utilize the opposing lane to overtake slower-moving vehicles, even when there are oncoming vehicles approaching. The primary objective of this study is to minimize injuries resulting from vehicle interactions in mixed traffic conditions on undivided roads. This is achieved through the implementation of the Modified Manhattan grid topology, which primarily serves to guide drivers in the correct path when navigating undivided roads. Furthermore, the Fuzzy C-Means algorithm (FCM) is applied to detect potential jamming attackers, while the Modified Fisheye State Routing (MFSR) Algorithm is employed to minimize the amount of information exchanged among vehicles. Subsequently, the Particle Swarm Optimization (PSO) algorithm is developed to enhance the accuracy of determining the coordinates of jamming attackers within individual clusters. The effectiveness of the outcomes is affirmed through the utilization of the Fuzzy C-Means algorithm, showcasing a notable 30% reduction in the number of attackers, along with the attainment of a 70% accuracy rate in this research endeavor.

Analysis of Quality of Experience Evaluation Framework for RPL Protocol in Mobile IoT Environments under Manhattan Grid Mobility

This research paper introduces a novel and advanced framework for assessing the Quality of Experience (QoE) implications associated with the utilization of the Routing protocol for low power and lossy networks (RPL) in the context of the Internet of Things (IoT). The study undertakes a thorough investigation into RPL's performance under the influence of Manhattan grid (MG) mobility scenarios, addressing a notable gap in current research. By meticulously incorporating essential Quality of Service (QoS) metrics like average packet delivery ratio (PDR), average power consumption (Avg_P), and average inter-packet time (Avg_IPT), the framework enables an in-depth evaluation of QoE. The uniqueness of this work lies in its incorporation of a comprehensive dependency matrix (DM) and the subsequent application of a dependency function (DF) that comprehensively captures the multifaceted aspects of the system's perceived quality.
 Beyond its methodological innovation, this research enhances our comprehension of RPL's adaptability through the shift from static to dynamic environments. Furthermore, the study systematically explores various scalability levels, contributing novel insights into how RPL performs across diverse IoT scenarios.Based on the Quality of Experience (QoE) analysis, it can be deduced that the network effectively maintains the performance of a static model even under conditions of MG scenarios. In the static model, the RPL's performance was measured at 67.73%. However, when exposed to the MG mobility model, its performance decreased to 42.93%. Given that RPL is primarily optimized for static models and considering its static model performance as a reference benchmark, it manages to retain approximately 63.38% of the static model's performance when subjected to the MG mobility model.

Energy and Mobility Models based Performance Evaluation in MANET

Mobile ad hoc networks are constituted with randomly moving nodes and movement of these nodes is depended upon moving model used in the network. Performance of the network directly depends on the movements and energy consumed in a specific time period by the nodes. Also performance of the protocol used for communication depends on the type of mobility model used by that specific protocol. In this paper, performance of AODV (Ad hoc On demand Distance Vector) routing protocol have been evaluated in respect of five mobility models Random Way Point Mobility Model, Manhattan Grid Mobility Model, Gauss Markov Mobility Model, Random Direction Mobility Model, RPGM (Reference Point Group Mobility)). Performance metrics are considered as: average energy consumption and average residual energy. By varying the network connections, speed of the nodes, and node densities, in different scenarios, routing protocol has been simulated in network simulator 2. Simulation results show that reference point group mobility model is best suitable model as compared to other mobility models for AODV protocol in terms of energy consumption.

Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular Networks

The communication between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless resources and limited system capacity, and the rapid channel changes in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In addition, road and traffic safety have very strict requirements for low-latency, high-reliability communication, and information transmission is only valuable within a certain range, and requires timeliness. For the Internet of Vehicles (IoV), it is a fundamental challenge to achieve low latency and high reliability communication for real-time data interaction over short distances in a complex wireless propagation environment, as well as to attenuate and avoid inter-vehicle interference through a reasonable spectrum allocation. To solve the above problems, this paper proposes a resource allocation (RA) method using dueling double deep-Q network reinforcement learning (RL) with low-latitude fingerprints and soft-update architecture (D3QN-LS) while constructing a multi-agent model based on a Manhattan grid layout urban virtual environment, with communication links between V2V links acting as agents to reuse vehicle-to-infrastructure (V2I) spectrum resources. These agents work in concert, interact with the environment, receive appropriate observations, rewards, and ultimately learn to improve power and spectrum allocation to provide a better entertainment experience and safer driving environment for users. We also address the shortcomings in existing literature studies. Firstly, the low upper limit of transmitted data volume; Secondly, the artificial assumption that the spectrum is consistent with the number of V2V links, which is insufficient for future applications in spectrum shortage sections. We accordingly extend the amount of transmitted data while adding a scenario where the spectrum resources are relatively short, i.e., the number of V2V links is significantly larger than the amount of spectrum. Experimental results show that with proper training mechanism and reward function construction, multiple intelligence can cooperate effectively. Therefore, the total link capacity of the V2I link and the success rate of periodic security information transmission are further improved.

Dispatching point selection for a drone-based delivery system operating in a mixed Euclidean–Manhattan grid

AbstractIn this paper, we present a drone-based delivery system that assumes to deal with a mixed-area, i.e., two areas, one rural and one urban, placed side-by-side. In the mixed-areas, called EM-grids, the distances are measured with two different metrics, and the shortest path between two destinations concatenates the Euclidean and Manhattan metrics. Due to payload constraints, the drone serves a single customer at a time returning back to the dispatching point (DP) after each delivery to load a new parcel for the next customer. In this paper, we present the $$1$$ 1 -Median Euclidean–Manhattan grid Problem (MEMP) for EM-grids, whose goal is to determine the drone’s DP position that minimizes the sum of the distances between all the locations to be served and the point itself. We study the MEMP on two different scenarios, i.e., one in which all the customers in the area need to be served (full-grid) and another one where only a subset of these must be served (partial-grid). For the full-grid scenario we devise optimal and approximation algorithms, while for the partial-grid scenario we devise an optimal algorithm.

Performance of Indoor Small-Cell Networks Under Interior Wall Penetration Losses

The performance of indoor small-cell networks (SCNs) is affected by the indoor environment, such as walls, blockages, etc. In this paper, we investigate the effect of interior wall attenuation on the performance of an indoor SCN. Specifically, the spatial distribution of interior walls is modelled based on the random shape theory and the indoor base stations (BSs) are distributed following a homogeneous Poisson point process. The channel model includes the path loss, Rayleigh fading, and wall attenuation. We analytically derive the downlink coverage probability under the strongest received signal user association strategy, which is validated by Monte Carlo simulations for three typical interior wall layouts (i.e., random layout, binary orientation layout, and Manhattan grid). The analytical results show that for a given density of interior walls and signal strength attenuation per wall, there is an optimal BS density that maximises the coverage probability, and the optimal BS density increases as the wall attenuation increases.

Performance Evaluation of Enhanced Manhattan Mobility Model Over GM, RWP, Manhattan Grid, SLAW, and TLW Mobility Models in MANETs

: The mobility model is the basis of simulation experiments in the Mobile Ad-hoc Network. A composite model for mobility for city scenarios which includes a realistic model of obstacle avoidance, and movement in the vertical direction, is proposed. The comparison of its performance with those of other available mobility models is encouraging. We believe that it can upgrade the routing performance. Here, we discuss the synthetic mobility models (Gauss-Markov, Random Waypoint, Manhattan Grid), and trace-based mobility models (Truncated Levy Walk, Self-Similar Least Action Walk). Then we propose a new mobility model by replacing a speed calculating formula using Bonnmotion-3.0.1 on simulator NS2. The proposed mobility model, named Enhanced Manhattan Mobility Model, is compared with the existing Manhattan Grid mobility model in a tabulated form. AODV, DSR, and DSDV are analysed for above-mentioned mobility models against the proposed one. Furthermore, the accuracy of the best protocol over the best mobility model is investigated through Packet Delivery Ratio (PDR), throughput, average end-to-end delay, packet overhead, and packet drop rate performance metrics. Due to the smooth movements created by the proposed model, it shows an improvement of 1 percent to 7 percent in throughput, 0.8 percent to 1.7 percent in packet overhead, 1 percent to 7 percent in PDR, and 1 percent in dropped packets.

Reinforcing Communication on the Internet of Aerial Vehicles

The increasing demand for green Unmanned Aerial Vehicles (UAVs) in civil and military applications has been noticed in the last few decades. As a result, Flying Ad-hoc Networks (FANETs) caught the attention of industries and academia. Due to its exceptional features, it has replaced other traditional networks deployed in all circumstances. However, UAVs have limited resources, and efficient use of these resources is critical for maximizing network lifetime and Quality of Experience (QoE). In this paper, Fisheye State Routing (FSR) protocol is exploited for communication between several UAVs in a topology changing network employing a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3x3</i> Manhattan grid mobility model. Several network parameters, i.e., packet delivery and drop ratios, end-to-end delay, channel utilization, dropped packet ratio behavior, throughput, jitter, are employed to validate FSR’s effectiveness in FANETs. Results analysis is carried out in two different network scenarios with twenty and thirty nodes, respectively. The proposed routing protocol has effectively utilized UAV resources with 46% and 53% packet delivery ratios with maximum channel utilization and throughput over time in both scenarios. Moreover, FSR has 0.010 and 0.253 seconds delay and minimum jitter in twenty and thirty nodes comparatively influential among other routing protocols in end-to-end delay. The proposed approach provides excellent results compared to several other topology-based routing protocols and efficiently utilizes onboard resources.

Security Improvements for Connected Vehicles Position-Based Routing

The constant growing on the number of vehicles is increasing the complexity of traffic in urban and highway environments. It is paramount to improve traffic management to guarantee better road usage and people’s safety. Through efficient communications, Vehicular Ad hoc Networks (VANETs) can provide enough information for traffic safety initiatives, daily traffic data processing, and entertainment information. However, VANETs are vulnerable to malicious nodes applying different types of net-work attacks, where an attacker can, for instance, forge its position to receive the data packet and drop the message. This can lead vehicles and authorities to make incorrect assumptions and decisions, which can result in dangerous situations. Therefore, any data dissemination protocol designed for VANET should consider security issues when selecting the next-hop forwarding node. In this paper, we propose a security scheme designed for position-based routing algorithms, which analyzes nodes position, transmission range, and hello packet interval. The scheme deals with malicious nodes performing network attacks, faking their positions forcing packets to be dropped. We used the Simulation of Urban MObility (SUMO) and Network Simulator-version 3 (NS-3) to compare our proposed scheme integrated with two well-known position-based algorithms. The results were collected in an urban Manhattan grid environment varying the number of nodes, the number of malicious nodes, as well as the number of source-destination pairs. The results show that the proposed security scheme can successfully improve the packet delivery ratio while maintaining low average end-to-end delay of the algorithms.

Security Improvements for Connected Vehicles Position-Based Routing

The constant growing on the number of vehicles is increasing the complexity of traffic in urban and highway environments. It is paramount to improve traffic management to guarantee better road usage and people’s safety. Through efficient communications, Vehicular Ad hoc Networks (VANETs) can provide enough information for traffic safety initiatives, daily traffic data processing, and entertainment information. However, VANETs are vulnerable to malicious nodes applying different types of net-work attacks, where an attacker can, for instance, forge its position to receive the data packet and drop the message. This can lead vehicles and authorities to make incorrect assumptions and decisions, which can result in dangerous situations. Therefore, any data dissemination protocol designed for VANET should consider security issues when selecting the next-hop forwarding node. In this paper, we propose a security scheme designed for position-based routing algorithms, which analyzes nodes position, transmission range, and hello packet interval. The scheme deals with malicious nodes performing network attacks, faking their positions forcing packets to be dropped. We used the Simulation of Urban MObility (SUMO) and Network Simulator-version 3 (NS-3) to compare our proposed scheme integrated with two well-known position-based algorithms. The results were collected in an urban Manhattan grid environment varying the number of nodes, the number of malicious nodes, as well as the number of source-destination pairs. The results show that the proposed security scheme can successfully improve the packet delivery ratio while maintaining low average end-to-end delay of the algorithms.

Simulation-Based Optimisation for Autonomous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation

Abstract This work presents a novel approach to the simulation-based optimisation for Autonomous Transportation Systems (ATS) with the use of the proposed parallel genetic algorithm. The system being developed uses GPUs for the implementation of a massive agent-based model of Autonomous Vehicle (AV) behaviour in an Artificial Multi-Connected Road Network (AMСRN) consisting of the “Manhattan Grid” and the “Circular Motion Area” that are crossed. A new parallel Real-Coded Genetic Algorithm with a Scalable Nonuniform Mutation (RCGA-SNUM) is developed. The proposed algorithm (RCGA-SNUM) has been examined with the use of known test instances and compared with parallel RCGAs used with other mutation operators (e.g., standard mutation, Power Mutation (PM), mutation with Dynamic Rates (DMR), Scalable Uniform Mutation (SUM), etc.). As a result, RCGA-SNUM demonstrates superiority in solving large-scale optimisation problems when decision variables have wide feasible ranges and multiple local extrema are observed. Following this, RCGA-SNUM is applied to minimising the number of potential traffic accidents in the AMСRN.

Implementation of GPSR protocol with various mobility models in VANET Scenario

In Vehicular Ad hoc Networks (VANET), topology varies very frequently because vehicles move in high speed. VANET is deployed on road. In GPSR Protocol, as the data packets are forwarded depending upon on the beacon information sent by neighboring nodes, which contain the location of neighbors. Hence nodes move in mobile nature as they change their positions. Mobile pattern of node is reflected by Mobility Models, as they characterize the movement of users who are mobile on the road, with respect to their direction, velocity and location over a period of time. Mobility models are practiced in implementation of protocols and the pattern by which the mobility models reflect real world practices of vehicles on the roads. Using simple pattern randomly, graph constrained mobility models are common practices while doing research. These models donot describe mobility of vehicles in realistic manner. For instance while accelerating and decelerating in the presence of nearby vehicles, these situations greatly affect the Network performance. Selecting the mobility model which is realistic one is the main focus of this paper. To address the challenges such as high mobility of vehicle nodes on the road and random topology, VANET needs a suitable mobility model to obtain improved Packet Delivery Ratio, Throughput, End to End Delay etc. This paper first implements the GPSR Protocol and then GPSR is analyzed by applying different mobility models such as Random Way Point, Gauss Markov, Manhattan Grid, Reference Point Group and Random direction. Results are analyzed by taking the following parameters: Routing overhead, Throughput, PDR and End to End Delivery. The implementation is carried out using NS—2.35 and Bonmotion is used to create mobility models.

Simulation of Motion of an Ensemble of Unmanned Ground Vehicles Using FLAME GPU

A new approach to modeling the spatial dynamics of unmanned ground vehicles (UV) and conventional vehicles (CV) using the FLAME GPU supercomputer agent-based simulation platform is presented. A new simulation model of an artificial road network (ARN) of the "Manhattan Grid" type is proposed, within the framework of which the spatial dynamics of the UV and CV ensemble is studied under various scenario conditions. The effects of "turbulence" and "crush" (traffic congestion) resulting from intensive and dense traffic of vehicles are investigated.

Approximate Gradient Descent Convergence Dynamics for Adaptive Control on Heterogeneous Networks

Adaptive control is a classical control method for complex cyber-physical systems, including transportation networks. In this work, we analyze the convergence properties of such methods on exemplar graphs, both theoretically and numerically. We first illustrate a limitation of the standard backpressure algorithm for scheduling optimization, and prove that a re-scaling of the model state can lead to an improvement in the overall system optimality by a factor of at most O(k) depending on the network parameters, where k characterizes the network heterogeneity. We exhaustively describe the associated transient and steady-state regimes, and derive convergence properties within this generalized class of backpressure algorithms. Extensive simulations are conducted on both a synthetic network and on a more realistic large-scale network modeled on the Manhattan grid on which theoretical results are verified.

Urban Traffic Optimization Based on Modeling Analysis of Sector-Based Time Variable: The Case of Simulated Ningbo, China

This paper studies the important factor of sector-based time variable, which is critical to urban mobility patterns in an urban environment. In particular, this study analyzes urban traffic optimization based on modeling analysis of a simulated urban environment. In doing so, we develop and assess the urban traffic model based on three key components of agents, urban map, and mobility pattern. We develop an urban sample based on a medium-to-large city in China, which is represented by Manhattan grid pattern layout. By developing a homogeneous urban layout, we distribute the urban blocks of various sectors across the sample model. Through simulation studies, we model urban traffic based on “conventional operation hours” and “proposed operation hours” of all sectors. This urban traffic model is used to study the impact of the proposed approach on urban traffic based on two measured metrics of end-to-end delay (ETE) and Agent queue count (AQC). By suggesting a new sector-based time variable, we then evaluate the urban traffic model based on multiple active agent ratio. The findings from this simulation uncover the importance of sector-based time variable in optimizing urban traffic.

Performance Evaluation of Mobility Models over UDP Traffic Pattern for MANET Using NS-2

The current study presents the simulative study and evaluation of MANET mobility models over UDP traffic pattern to determine the effects of this traffic pattern on mobility models in MANET which is implemented in NS-2.35 according to various performance metri (Throughput, AED (Average End-2-end Delay), drop packets, NRL (Normalize Routing Load) and PDF (Packet Delivery Fraction)) with various parameters such as different velocities, different environment areas, different number of nodes, different traffic rates, different traffic sources, different pause times and different simulation times . A routing protocol.…was exploited AODV(Adhoc On demand Distance Vector) and RWP (Random Waypoint), GMM (Gauss Markov Model), RPGM (Reference Point Group Model) and MGM (Manhattan Grid Model) mobility models above CBR traffic sources. The results of Reference Point Group Model simulation illuminate that routing protocol performance is best with RPG mobility model than other models.

Black Rock City versus Manhattan: An economist's view.

Urban street networks take many forms, from the circular streets in Black Rock City (which is built and removed every year as part of the Burning Man Festival) to the streets and avenues in the Manhattan grid. This paper compares the traits of cities with different street networks using the tools of urban economics. When both cities have commuting arteries of the same length, cities with circular streets have higher population densities unless access to these arteries is expensive and the number of arteries is large. Cities with arteries set at a 45o angle to the grid have smaller population densities under all circumstances.

Learning basic concept of computer programming with path-finding task in ar and its properties

This paper designs and develops a computer programming learning system for total beginners and those who have no programming experience. The traditional computer programming learning systems require prior knowledge about variables, their types, operators for arithmetic calculations and relational calculations etc., for adopting a wide range of representation of program codes, leading to the burden to them. The proposed system focuses on learning the three basic flows of control: sequential, conditional and iterative controls, for not requiring the prior knowledge, and exploits a path-finding task for confining representation of program codes to a specific task, which is a navigation task where students are asked to organize a sequence of orders for an avatar to move from a given start to a given end on a Manhattan grid map. The previous work employs the path-finding task to foster understanding of the sequence of orders by encouraging students to perform self-tracing process while our proposed system employs it to include that property of understanding for the three basic flows of control which are not covered in the previous work. To add high interactivity and tangibility for student motivation and engagement to learning, the proposed system also employs AR capability. An experiment on feasibility of our system for education is conducted and the results show that our system has the potential for improving students’ understanding of how to make program codes by decrease of the ratio of program codes’ length made by beginners to experts, which is from 2.186 to 1.267, regardless of AR capability such that the ratio decreased from 2.481 to 1.346 without AR capability and it was from 1.974 to 1.206 with it. The results also show that our system significantly leverages their motivation of learning by increase of beginners’ score from 5.00 without AR capability to 6.67 with it for interest and it is from 5.67 to 7.00 for amusement, and even if sacrificing a wide range of representation of program codes that the traditional systems take, a certain stress is significantly given on spending time thinking and building up a program code in proportion to its difficulty and complexity.

Evaluation of Routing Protocols and Mobility in Flying Ad-hoc Network

The ability of dynamic reconfigurability, quick response and ease of deployment has made Unmanned Aerial Vehicles (UAVs), a paramount solution in several areas such as military applications. Flying ad-hoc network (FANET) is a net-work of UAVs connected wirelessly and configured continuously without infrastructures. Routing on its own is not significant, but the mobility sequence of a UAV in FANETs is a more significant factor and an interesting research topic. The routing protocols gives us a certain and better perception of routing structure for FANETs. In this paper, routing protocols such as Ad-hoc On-Demand Vector (AODV), Dynamic Source Routing (DSR), Temporally Ordered Routing Algorithm (TORA), Geographic Routing Protocol (GRP) and Optimized Link State Routing (OLSR) are compared using performance parameters such as number-of-hops, packet loss ratio, throughput, end-to-end delay and throughput. The mobility models like Pursue Mobility Model (PRS), Semi-Circular Random Movement (SCRM), Manhattan Grid Mobility Model (MGM) and Random Waypoint Mobility (RWPM). The evaluation is carried out with three scenarios including one sender node and one receiver node, all senders one receiver and all senders all receivers are considered for above protocols and mobility models. For all evaluation scenarios, the performance of OLSR is the most efficient among the five routing protocols under four different performance parameters due to its proactive nature which makes the routing information up to date with the help of MPR (Multi Point Relay) in the network, resulting in the reduction of routing overhead in the network.

Modeling parking search behavior in the city center: A game-based approach

Drivers cruising for scarce on-street parking in city centers create negative externalities, including congestion and pollution. We apply a serious game – PARKGAME – to understand and model drivers' two intertwined instantaneous parking choices: when to quit cruising and where to cruise. Forty-nine participants took part in a lab experiment in which they had to arrive on time to a fictional appointment or face monetary penalties, and had to choose between uncertain but cheap on-street parking or a certain but costly parking lot. Scenarios diverged on the time to appointment and distance between the meeting place and parking lot locations. Participants played a series of 8 or 16 computer games on a Manhattan grid road network with high on-street parking occupancy and a nearby parking lot of unlimited capacity. Players’ parking choices were analyzed with accelerated-failure time (AFT) and multinomial logistic regression models. Results show that drivers are mostly myopic and risk-averse, and quit their on-street parking search long before the optimal moment. Spatially, drivers are attracted by the lot-destination axis, and their turn choices at junctions comply with a second-order biased random walk. The implications of game-based methods for simulation model development and sustainable parking policy are further discussed.