Reference Point Group Mobility Model Research Articles

An Energy-Efficient Bio-Inspired Mobility-Aware Cluster p-WOA Algorithm for Intelligent Whale Optimization and Fuzzy-Logic-Based Zonal Clustering Algorithm in FANET

The newest research topic is flight ad hoc network (FANET). The primary obstacles faced by unmanned aerial vehicles (UAVs) are their limited flight duration and inefficient routes resulting from their great mobility and low battery power. Compared to MANETs or VANETs, FANETS routing is thought to be more difficult because of these topological restrictions. Artificial intelligence (AI)-based clustering techniques can be applied to resolve intricate routing issues in situations when both static and dynamic routing are ineffective. To overcome these path difficulties, clustering techniques based on evolutionary algorithms, including intelligent, probabilistic, bio-inspired whale optimization algorithms (p-WOAs), we suggest fuzzy-logic-based zonal clustering-based routing algorithms in this study to be used in FANET to build clusters. In addition to requiring fewer cluster heads (CHs) for routing, p-WOA offers good coverage and low energy consumption. The stochastic whale optimization technique, which draws inspiration from nature, is utilized in this paper to build networks and deploy nodes. The next step is to choose cluster heads using a region clustering technique based on fuzzy logic. By selecting the right cluster head, you can decrease routing traffic and increase cluster longevity. Routing overhead is also decreased. The data are then sent to the best path using a reference point group mobility model. The proposed p-WOA was used to test fuzzy integral and fuzzy logic ant optimization, fuzzy integral and neural network interference system, fuzzy integral and whale optimization algorithm (ANFIS-WOA), and fuzzy integral and FL-ALO. An array of indicators, such as cluster count, longevity, cluster configuration time, cluster head consistency, and energy usage, are employed to assess the effectiveness of the suggested methodology. The suggested algorithm works better than the most advanced techniques available today, as demonstrated by the experimental findings presented in this paper.

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.

Adaptive Decoding Mechanisms for UAV-Enabled Double-Uplink Coordinated NOMA

In this paper, we propose a novel adaptive decoding mechanism (ADM) for the unmanned aerial vehicle (UAV)-enabled uplink (UL) non-orthogonal multiple access (NOMA) communications. Specifically, considering a harsh UAV environment, where ground-to-ground links are regularly unavailable, the proposed ADM overcomes the challenging problem of conventional UL-NOMA systems whose performance is sensitive to the transmitter's statistical channel state information and the receiver's decoding order. To evaluate the performance of the ADM, we derive closed-form expressions for the system outage probability (OP) and system throughput. In the performance analysis section, we provide novel expressions for practical air-to-ground and ground-to-air channels, while taking into account the practical implementation of imperfect successive interference cancellation (SIC) in UL-NOMA. Moreover, the obtained expression can be adopted to characterize the OP of various systems under a Mixture of Gamma (MG) distribution-based fading channels. Next, we propose a sub-optimal Gradient Descent-based algorithm to obtain the power allocation coefficients that result in maximum throughput with respect to each location on UAV's trajectory. To determine the significance of the proposed ADM in nonstationary environments, we consider the ground users and the UAV to move according to the Random Waypoint Mobility (RWM) and Reference Point Group Mobility (RPGM) models, respectively. Accurate formulas for the distance distributions are also provided. Numerical solutions demonstrate that the ADM-enhanced NOMA not only outperforms Orthogonal Multiple Access (OMA), but also improves the performance of UAV-enabled UL-NOMA even in mobile environments.

A New Objective Function for RPL Based on Combined Metrics in Mobile IoT

To satisfy the quality of service requirements for some applications in the Internet of Things (IoT) network with mobile nodes, routing protocols must be adaptable to make better routing decisions. Routing Protocol for Lowpower and Lossy Networks (RPL) is one of the routing protocols that support metrics and constraints while building a Destination Oriented Directed Acyclic Graphs (DODAG) using a specific Objective Function (OF). However, RPL metrics that use standard OFs do not guarantee the performance criteria for a specific mobile IoT in terms of received and lost packets, overhead, throughput and power consumption. In this paper, we propose a new version of mrhof based on a combination of metrics such as total energy, the number of neighbors, and expected transmission count (ETX) to evaluate RPL performances in a mobile context. Then, we compare the assessment of this function to existing OFs based on various mobility models (Random Waypoint (RWP), Reference Point Group Mobility (RPGM), Nomadic) and node densities. The results show that this new function is more efficient than the standards OF in terms of packets received and packets lost with Random Way Point and RPGM models. Regardless of the number of nodes, it also reduces ETX and traffic overhead for all mobility models, as well as convergence time in the Random Waypoint environment. Furthermore, this new function contributes to the improvement of Rtmetric, the enhancement of throughput at low density only in favor of RWP and RPGM models, and an effective power consumption reduction through all densities and mobility models.

Evaluation of Traffic Models under Multiple Black hole Attack in Wireless Mesh Network

Wireless networks are the networks in which data communication is done through the wireless channels and wireless nodes. In such type of the networks, cooperation of the all nodes is required. Due to which, chances of security attacks is possible and network performance and security is badly affected. Black hole attack is a security attack comes under the active, internal as well as external attacks. In black hole attack, malicious node presents a suitable, shortest and low cost path. If the above path is selected for data transmission, malicious node drops the received packets and as a result performance of the network is degraded. In this paper, impact of Random Direction (RD) and Reference Point Group Mobility (RPGM) models have been analyzed for wireless mesh network in the presence of multiple black hole attacks. By varying the number of network connections and node density, (Ad hoc on demand distance vector) AODV routing protocol is evaluated. Normalized routing load, average end to end delay, average throughput, and total number of packets received are considered as performance metrics. Bonn-motion tool has been used to create the scenario of mobility models. In the presence of black hole attack, and under RPGM mobility model, AODV routing protocol presents better results in respect of average throughput, normalized routing load. Simulation work has been carried out on network simulator version NS-2.35.

Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization

FANET (flying ad-hoc networks) is currently a trending research topic. Unmanned aerial vehicles (UAVs) have two significant challenges: short flight times and inefficient routing due to low battery power and high mobility. Due to these topological restrictions, FANETS routing is considered more complicated than MANETs or VANETs. Clustering approaches based on artificial intelligence (AI) approaches can be used to solve complex routing issues when static and dynamic routings fail. Evolutionary algorithm-based clustering techniques, such as moth flame optimization, and ant colony optimization, can be used to solve these kinds of problems with routes. Moth flame optimization gives excellent coverage while consuming little energy and requiring a minimum number of cluster heads (CHs) for routing. This paper employs a moth flame optimization algorithm for network building and node deployment. Then, we employ a variation of the K-Means Density clustering approach to choosing the cluster head. Choosing the right cluster heads increases the cluster’s lifespan and reduces routing traffic. Moreover, it lowers the number of routing overheads. This step is followed by MRCQ image-based compression techniques to reduce the amount of data that must be transmitted. Finally, the reference point group mobility model is used to send data by the most optimal path. Particle swarm optimization (PSO), ant colony optimization (ACO), and grey wolf optimization (GWO) were put to the test against our proposed EECP-MFO. Several metrics are used to gauge the efficiency of our proposed method, including the number of clusters, cluster construction time, cluster lifespan, consistency of cluster heads, and energy consumption. This paper demonstrates that our proposed algorithm performance is superior to the current state-of-the-art approaches using experimental results.

A New Deep Q-Network Design for QoS Multicast Routing in Cognitive Radio MANETs

In this paper, we propose a new deep Q-network (DQN) design for quality-of-service (QoS) multicast routing (DQMR) protocol to establish efficient QoS multicast (EQM) trees in cognitive radio mobile ad hoc networks (CR-MANETs). An EQM tree is a shortest-path multicast tree with minimum end-to-end (E2E) cost (a combination of queuing size ratio and link stability) subject to QoS constraints such as queuing size ratio, link stability, number of hops, number of time slots and avoiding the licensed channel of primary users. Particularly, we propose an NP-complete optimization problem such that its feasible solution is an EQM tree. To address this problem, we design a new DQN model and a new game-based model to form EQM trees in real-time by offline training instead of online training as done in previous papers. Moreover, the DQMR protocol is also guaranteed to have high stability, low routing delay, low control overhead, and high packet delivery ratio (PDR). Furthermore, one more new contribution of the paper is that exact closed-form expressions for the E2E queuing delay of a multicast routing tree are also derived assuming random waypoint mobility and the reference point group mobility models to compare with simulation results of routing delay. Simulation results show that the DQMR protocol outperforms multicast ad hoc on-demand distance vector routing protocol in terms of routing delay, control overhead, and PDR.

AMIGM: Animal Migration Inspired Group Mobility Model for Mobile Ad hoc Networks

The most widespread notion of mobility model is the representation of mobile node’s movement pattern in the wireless ad hoc networks which has a significant impact on the performance of the network protocols. In this paper, we have proposed an Animal Migration Inspired Group Mobility (AMIGM) model for mobile ad hoc networks based on the migration behavior of animals like, insects, flock of birds, schools of fishes, reptiles, amphibians, etc. The propound model tries to overcome the limitations of the existing mobility models, such as temporal dependencies, spatial dependencies, geographical restrictions and migration of nodes between the group of nodes so that it can realistically model the real world application scenarios. The proposed AMIGM model is based on Animal Migration Optimization (AMO) algorithm, in which each group of nodes has two phases namely, Migration phase and Population updating phase. In the first phase, the model simulates the movement of nodes in the group from one position to another by obeying the swarming laws. In the second phase, the model simulates joining and leaving of the nodes in the group during migration. The protocol dependent and independent performance metrics of the proposed model are compared with Random Waypoint Mobility model (RWP) and Reference Point Group Mobility model (RPGM) through ns-2 simulator.

TARCS: A Topology Change Aware-Based Routing Protocol Choosing Scheme of FANETs

The rapid change of topology is one of the most important factors affecting the performance of the routing protocols of flying ad hoc networks (FANETs). A routing scheme suitable for highly dynamic mobile ad hoc networks is proposed for the rapid change of topology in complex scenarios. In the scheme moving nodes sense changes of the surrounding network topology periodically, and the current mobile scenario is confirmed according to the perceived result. Furthermore, a suitable routing protocol is selected for maintaining network performances at a high level. The concerned performance metrics are packet delivery ratio, network throughput, average end-to-end delay and average jitter. The experiments combine the random waypoint model, the reference point group mobility model and the pursue model to a chain scenario, and simulate the large changes of the network topology. Results show that an appropriate routing scheme can adapt to rapid changes in network topology and effectively improve network performance.

Manhattan & RPGM Parameters Based Mobility Aware GPCR-MA Routing Protocol For Highway Scenario in VANET

A high experiment scenario can be seen in Vehicular Ad-hoc Network (VANET) systems due to rhapsodic exchange of information and coherent system relation. The ace transmitting device which is appropriate and operative is improved by the successful configuration of VANET network. A realistic node motion model is used in the current scenario, which is based on the vehicle motion for the real road network. We compare the current model of mobility of Manhattan and Reference Point Group Mobility Model (RPGM).Results analysis of both models of mobility based on two different scenarios 1) rapidly changes network simulation time. 2) Change network traffic density. Both models of mobility have different parameter of simulation, which is mentioned in the methodology. The model of mobility of Manhattan and RPGM with their own mobility parameter obtained the QOS metric. RPGM model of mobility is well suited for realistic vehicular ad-hoc network in both scenarios, which shows that average delay, energy consumption, the packet delivery ratio (PDR) performance value are higher as compare to the Manhattan model of mobility. RPGM deliver data packets in a serial queue with any dropped or delay for motion of vehicles. For all the conditions and both the scenarios RPGM model of mobility having better performance as compared to Manhattan, which is clearly indicated in the result analysis section. These all parameters of model of mobility are simulated on a network simulator (NS-2), which makes easy for researchers and student for future work.

Wireless Coverage for Mobile Users in Dynamic Environments Using UAV

In this paper, the dynamic deployment of a single UAV as an aerial base station in providing wireless coverage for mobile outdoor and indoor users is studied. The problem of finding the efficient UAV trajectory is formulated with the objective to minimize the required UAV transmit power that satisfies the users’ minimum data rate. The proposed solution to the problem considers the users’ movement in a search and rescue (SAR) operation. More specifically, the outdoor rescue team members are considered to move in a group with the reference point group mobility (RPGM) model. Whilst, the indoor rescue team members are considered to move individually and in a group with random waypoint and RPGM models, respectively. The efficient UAV trajectory is developed using two approaches, namely, heuristic and optimal approaches. The employment of the heuristic approach, namely particle swarm optimization (PSO) and genetics algorithm (GA), to find the efficient UAV trajectory reduced the execution time by a factor of $\simeq ~1/60$ and $\simeq ~1/9$ compared to that when using the optimal approach of brute-force search space algorithm. Furthermore, the use of PSO algorithm reduced the execution time by a factor of $\simeq ~1/7$ compared to that when the GA algorithm is invoked.The performance of the dynamic UAV deployment also outperformed the static UAV deployment in terms of the required transmit power. More specifically, the dynamic UAV deployment required less total transmit power by a factor of about 1/2 compared to the static UAV deployment, in providing wireless coverage for rescue team to perform SAR operation within a rectangular sub-region.

A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network.

In mobile wireless sensor network (MWSN), the lifetime of the network largely depends on energy efficient routing protocol. In the literature, cluster leader (CL) is selected based on remaining energy of mobile sensor nodes to enhance sensor network lifetime. In this study, a novel connectivity-based Low-Energy Adaptive Clustering Hierarchy-Mobile Energy Efficient and Connected (LEACH-MEEC) routing protocol was proposed, where CL is selected based on connectivity among neighboring nodes and the remaining energy of mobile sensor nodes. Consequently, it improves data delivery, network lifetime and balances the energy consumption. We studied various performance metrics including the number of alive nodes (NAN), remaining energy (RE) and packet delivery ratio (PDR). Our proposed LEACH-MEEC outperforms all other algorithms due to the connectivity metric. Moreover, the performance of mobility models was investigated through graphical and statistically tabulated results. The results show that Reference Point Group Mobility model (RPGM) is better than other mobility models.

BIGM: A Biogeography Inspired Group Mobility Model for Mobile Ad Hoc Networks

This paper propounds a novel Biogeography Inspired Group Mobility model for Mobile Ad Hoc Networks (MANETs) based on the Biogeography Based Optimization (BBO) algorithm. BBO describes the migration behavior of species between the islands and how they become extinct and new species arise. Many mobility models present in the literature failed to realistically represent the movement of nodes within the group and migration of nodes from one group to another group. To address these issues, each group of nodes in the proposed mobility model follows the bird flocking rules; inspired from the movement of flock of birds. These nodes then migrate from one group to another group based on BBO approach, showing group mobility behavior among the group of nodes in MANETs which exhibit frequent group motion and network topology changes. The experimental results obtained through ns-2 simulator is compared with a Random Waypoint Mobility model and Reference Point Group Mobility model and evaluated their network performance under different routing protocols.

Addressed Query Gossip Resource Discovery Protocol for Mobile P2P Networks and Its Performance in Diverse Mobility Models

This article describes how P2P resource discovery protocols perform poorly over mobile ad hoc networks mainly due to the frequent network dynamics. Peer-to-Peer search techniques including structured and unstructured can be employed over MANETs. Empirical studies indicate that searching in such resultant networks are not efficient and effective due to peer discovery, connectivity and mobility issues. The author proposes Addressed Query Gossip Resource Discovery algorithm, a lightweight resource discovery designed to suit the mobility requirements of ad hoc networks to optimize the search performance while at the same time minimize the extra usage of mobile and network resources. Mobility models represent the movements of mobile nodes. Such models are used to represent how the location, velocity and acceleration change over time. The author conducts performance analyses of the proposed protocol and widely used unstructured search techniques over MANET under 2 realistic mobility models, i.e., Random Waypoint and Reference Point Group Mobility models. The results show that the proposed protocol has the best performance in almost every measured metrics. In addition, the protocol consumes less energy, has less message overhead and good success ratio for random waypoint movement as compared to the reference group mobility model. After extensive evaluations, the simulation results clarify that the mobility models have a significant impact on the performance of peer-to-peer content discovery protocols over mobile ad hoc networks.

Application of RPGM Model in Wireless Ad-Hoc Network

The enchanting benefits of ad-hoc network have fascinated many researchers to developed new algorithm to provide efficient services in this arena. The mobility models are used to simulate the behavior of mobile nodes on different network scenarios before de facto accomplishing them. In antecedent research done on the ad-hoc network, it was observed that the mobility models are organized into two groups: Entity Mobility Model (EMM) and Group Mobility Model (GMM). The accomplishment of an ad-hoc network protocol can fluctuate effectively when the akin mobility model is applicable with variant characteristics. Reference Point Group Mobility (RPGM) Model is one of the ilks of GMM. This paper accompanying the mobility parameters of RPGM model and its applications. Further, it is reviewed to see that RPGM model can be easily adapted for existing applications.

Routing and the Impact of Group Mobility Model in VANETs

Group vehicular applications could play a vital role in our life, whether these applications are safety-oriented or convenience-oriented applications. The group concept has also been used in support of many solutions in VANETs. Reference Point Group Mobility model (RPGM) is a popular group mobility model and has been used heavily in the literature. In order to study the impact of this mobility model on routing in VANETs, two well-known protocols are considered, namely AODV and AOMDV, as representatives of single-path and multipath routing, respectively. These protocols have shown performance merits over other routing protocols and they have a vital role in other wireless networks including MANETs and Wireless Mesh Networks. This paper provides a thorough evaluation of these protocols, by examining them under various potential realistic scenarios, under the RPGM mobility model. To the best of knowledge, this is the first study that examines the behaviour of the two protocols under the RPGM mobility model. The results from extensive simulations have shown that both protocols have comparable performance merits when the number of communicating nodes is low. However, increasing the number of communicating nodes has demonstrated a performance advantage for AOMDV, in the majority of the performance metrics used in the paper.

Throughput and Delay Tradeoffs for Mobile Ad Hoc Networks With Reference Point Group Mobility

In this paper, we explore the throughput–delay tradeoff in a mobile ad hoc network (MANET) operating under the practical reference point group mobility model and also a general setting of node moving speed. In particular, we consider a MANET with unit area and $n$ nodes being divided evenly into $\Theta(n^{\alpha})$ groups, $\alpha\in[0,1] $ , where the center of each group moves according to a random direction model with speed of no more than $\upsilon\in[0,1] $ . We determine the regions of per-node throughput and average delay and their tradeoffs that can be achieved (in order sense) in such a network. For the regime of $\upsilon=0$ , we first prove that the per-node throughput capacity is $\Theta(n^{-\alpha/2} ) $ and then develop a routing scheme to achieve this capacity, resulting in an average delay of $\Theta(\max\{n^{1/2},n^{1-\alpha}\})$ for any $\alpha\in[0,1]$ . Regarding the regime of $\upsilon>0$ , we prove that the per-node throughput capacity can be improved to $\Theta(1) $ , which is achievable by adopting a new routing scheme with an average delay of $\Theta(\max\{n^{1-\alpha},n^{\alpha/2}/\upsilon\})$ for $\upsilon=o(1)$ and $\Theta(n)$ for $\upsilon=\Theta(1) $ . The results in this paper help us to have a deep understanding on the fundamental performance scaling laws and also enable an efficient throughput–delay tradeoff to be achieved in MANETs with correlated mobility.

Path length prediction in MANET under AODV routing: Comparative analysis of ARIMA and MLP model

Mobile Ad-hoc network (MANET) is infra-structure less collection of mobile nodes which can communicate with each other through single hop or multi-hop technique. The hop count also known as path length plays a crucial role in packet delivery, routing load, delay, etc. The path length between source destination pair nodes depends upon factors such as the mobility patterns of nodes, routing algorithm, transmission range, etc. In this article, we have tried to predict the path length between a source destination pair in MANET using Autoregressive Integrated Moving Average (ARIMA) and multilayer perceptron (MLP) models. The path length data are collected from MANETs using three different mobility models namely (i) Manhattan Grid Mobility Model (MHG), (ii) Random Way Point mobility model (RWP) and (iii) Reference Point Group Mobility Model (RPGM). This paper evaluates the predictive accuracy in forecasting the path length between source and destination nodes for Ad hoc On-Demand Distance Vector AODV routing in MANET using ARIMA model and MLP. It is found that neural networks can be effectively used in forecasting path length between mobile nodes better than statistical model and the MLP based neural network models are found to be better forecaster than ARIMA model.

The effect of Mobility Models on a Secured Enhanced and Reliable Ad Hoc Multicasting Protocol

Mobile Ad-hoc networks are characterized as networks without any physical connections. In these networks there is no fixed topology due to the mobility of nodes, interference, multi-path propagation and path loss. One particularly challenging environment for multicast is a mobile ad-hoc network (MANET), where the network topology can change randomly and rapidly, at unpredictable times. As a result, several specific multicast routing protocols for MANET have been proposed. [1]. The objective of this paper is to study the effects of mobility models on the new proposed secured and enhanced reliable Ad Hoc Multicasting Protocol (SERAMP). SERAMP is a new technique to be used for Multicasting in Ad-Hoc Networks and to solve the security problems associated with multicasting in Ah-Hoc Networks. The proposed protocol added two parameters to secure the network, the first parameter is the encryption of the message using random key for the selection of the encryption algorithm, and the second parameter is to use the same random key to calculate the authentication code of the message [2]. This paper applies the proposed secured protocol for the previous work and a comparative study has been made between the proposed secured enhanced and reliable Ad Hoc Multicasting Protocol under the two mobility models, Random Way Point Mobility Model and Reference Point Group Mobility Model.

Performance Evaluation and Statistical Analysis of MANET routing Protocols for RPGM and MG

Mobile Ad Hoc Network is a collection of mobile nodes forming temporary network. In MANET routing protocols are classified as Proactive, Reactive and Hybrid. The work presented here evaluates performance of three Reactive routing protocols such as AODV, DSR and TORA under six performance metrics such as packet delivery ratio, routing overhead, packet loss, normalized routing load, throughput and end to end delay The nodes follow two realistic mobility models such as Reference Point Group Mobility model (RPGM) and Manhattan Grid (MG) model. This work also presents the statistical analysis of AODV, DSR and TORA in Manhattan Grid (MG) model and Reference Point Group Mobility (RPGM) model in low load with low speed, average load with average speed, high load with high speed and very high load with high speed. Contribution in this work is beneficial in deciding which protocol to choose for better QoS.