Random Mobility Research Articles

Denial-of-service attack (BH and PDA) effect analysis on AODV Routing Protocol with Random Mobility

Mobile ad hoc networks (MANETs) has received significant attention in the research domain of routing protocol as they provide security to transmit the data at the destination node. Mobile ad hoc networks do not have a fixed infrastructure, and nodes have random mobility due to this network topology changing dynamically and it vulnerable to external threats. Block hole and packet dropping are well-known network layer Denial of service attacks. during the route, discovery phase malicious nodes work normally and it became the shortest path to route the data and after that, it will drop all the data instead of transferring the data to the destination and in case of the packet dropping attack node drop the packet due to the elfishness of a node, Lack of energy resources, Bandwidth consumed by the attacker node, Overflow of the transmission queue, Misbehavior of a malicious node, it will drop the packet in the network. In this paper, we analyze the impact of both attacks on the AODV routing protocol in the random mobility environments with the different simulation parameters. DOI: https://doi.org/10.52783/pst.922

Load-adaptive MAC protocol for frontier detection in Underwater Mobile Sensor Network

This work proposes a load-adaptive Medium Access Control (MAC) protocol for the frontier/boundary detection application of underwater phenomena using Underwater Mobile Sensor Network (UWMSN). A leader-follower architecture of a swarm of underwater vehicles is proposed here. Autonomous Underwater Vehicles (AUVs) traverse a random mobility pattern beneath one Autonomous Surface Vehicle (ASV) (leader) in the proposed network. ASV has to guide multiple-follower AUVs in the event of interest. The vehicular swarm aims to explore the frontiers in the event to build the map. Load-adaptive MAC protocol is therefore proposed and implemented in this hybrid multi-vehicular network to ensure seamless vehicular communications. The ASV has navigational capabilities to aid the AUVs in navigation and data collection. The proposed MAC protocol can adjust the dynamic mobility and load in the network. The protocol aims to provide dynamic Time Division Multiple Access (TDMA) slots for the AUVs wirelessly linked in the vicinity of the ASV. These slots are used for ranging/navigation and data transmission. Additional urgent data from any AUVs can be transmitted in open Carrier Sense Multiple Access (CSMA) protocol following the TDMA duration. Results have been generated by comparing protocols like CSMA, ALOHA, and TDMA with the proposed Load-Adaptive MAC protocol. The protocols have been compared to the throughput vs number of nodes and throughput vs simulation time. It has been observed that the proposed MAC can perform better than ALOHA and CSMA protocols. Nevertheless, it can produce comparable results for TDMA protocol while supporting the dynamic mobility and load in the network meantime supporting urgent data transmission for nodes in demand.

Accelerating and breaking adaptive nano-colloids (

Unlike conventional colloids showing random mobility because of Brownian motion, active colloids contain nanomotors that translate chemical or physical triggers into directed movement. Whereas the acceleration of such particles works well, it is difficult to decelerate them by request. Compared to the existing literature on microscaled swimmers/robots, the main question of the current paper is whether nanoscaled colloids (<100 nm) can also be actively controlled despite the stronger relevance of rotational diffusion at such dimensions. We developed nanoparticles comprising two independent mechanisms for propulsion: a chemical engine associated with a Janus-type modification of organosilica nanoparticles and physical locomotion because of a superparamagnetic core inside these particles. Both triggers can be used independently to initiate the particles' directed and anisotropic movement. The magnetic forces can be tuned, most importantly concerning the angle defining the chemical acceleration. Superposition and a boost state are adopted for a parallel alignment. However, when the magnetic field acting on the particles is turned to an antiparallel orientation, a rapid deceleration can be observed, and the colloids halt.

Adaptive HELLO protocol for vehicular networks

In vehicular networks, the update of car Firmware Over The Air (FOTA) is becoming a challenging issue and it mainly relies on topology discovery of neighbouring nodes. Topology discovery in mobile wireless networks is usually done by using HELLO messages. Due to mobility, topology changes occur frequently and must be quickly discovered to avoid routing failures. Since the optimal HELLO frequency depends on parameters that are subject to changes (e.g., speed of nodes, density of nodes), it must be dynamically adjusted to obtain the best trade-off between the network load and the freshness of routing tables. Existing solutions assume random mobility, constant node density and average speed, which do not hold in vehicular networks because vehicles follow specific trajectory patterns (the roads) and density and speed evolve as a function of time (rush hour vs non-rush hour) and area (urban, rural, highway). In this paper, we first draw the specific features of a vehicular network at different times and spaces by analysing real datasets and then propose a dynamic neighbour discovery protocol, Vehicular Adaptive Neighbour discovery Protocol (VANP). VANP is a fully-distributed protocol that sends beacons at an optimal frequency without knowing it a priori. The objective is to reduce the frequency at which HELLO messages are sent to save bandwidth and energy while still preserving the quality of the neighbour discovery. Through extensive simulations run on real datasets, we show that the optimal HELLO frequency can be reached by maintaining a constant optimal turnover, independent from the speed of the nodes and by aiming at this turnover, nodes automatically use the optimal HELLO frequency. Results show that VANP allows the discovery of relevant neighbours by missing at most two neighbours over all scenarios and reducing the number of HELLO messages up to twice, hence saving bandwidth and energy.

Estimation of Random Mobility Models with Application to Unmanned Aerial Vehicles

Random mobility models (RMMs) capture the statistical movement characteristics of mobile agents and play an important role in the evaluation and design of mobile wireless networks. Particularly, RMMs are used to model the movement of unmanned aerial vehicles (UAVs) as the platforms for airborne communication networks. In many RMMs, the movement characteristics are captured as stochastic processes constructed using two types of independent random variables. The first type describes the movement characteristics for each maneuver and the second type describes how often the maneuvers are switched. We develop a generic method to estimate RMMs that are composed of these two types of random variables. Specifically, we formulate the dynamics of movement characteristics generated by the two types of random variables as a special Jump Markov System and develop an estimation method based on the Expectation–Maximization principle. Both off-line and on-line variants of the method are developed. We apply the estimation method to the Smooth–Turn RMM developed for fixed-wing UAVs. The simulation study validates the performance of the proposed estimation method. We further conduct a UAV experimental study and apply the estimation methods to real UAV trajectories.

Closed-Form Model for Performance Analysis of THz Joint Radar-Communication Systems

As an enabler technology in future network, terahertz (THz) joint radar-communication (T-JRC) has been envisioned as a solution to overcome the challenges in THz communications. Recent works on JRC mainly emphasize either performance tradeoffs or mutual benefits between radar and communication functionalities. Unable to characterize both benefit and tradeoff relationships, existing metrics and models are inapplicable to performance analysis on T-JRC systems. In this paper, a closed-form T-JRC performance model are proposed to explore the relationship between radar and communication metrics. To begin with, a novel T-JRC system model concerning random mobility and THz beam tracking is put forward. Next, the performance bounds of T-JRC performance in terms of communication rate and a proposed radar rate are acquired in three different integration cases. Furthermore, the expression of communication rate by radar rate in T-JRC is obtained theoretically, which is the first time to derive the closed-form relationship considering both benefits and tradeoffs to the best of our knowledge. Finally, numerical results validate the proposed model as well as closed-form expression, showing that the model succeeds in benefit and tradeoff relationship characterization in T-JRC systems and the fitting bounds are applicable in non-extreme scenarios.

Numerical analysis of irregular heat source/sink through a wall jet flow of spherical graphene oxide nanoparticle in the presence of thermophoretic particle deposition: The case of non-Newtonian Eyring–Powell fluid model

Random mobility and thermomigration of nanoparticles in the regular fluid are the requisite posited two key characteristics of nanofluids (NFs) that have the potential to influence not only the transport processes but also mass and heat transfer within the boundary layer and across the domain. Therefore, this paper inspects the wall jet Eyring–Powell fluid flow of heat and mass transfer conveying spherical graphene oxide (GO) nanoparticles with substantial influence of erratic heat generation/absorption and thermophoretic particle deposition. The governing system of equations is reformed into requisite posited ODEs via incorporating the similarity technique. The approach (bvp4c) based on finite difference scheme is applied to determine numerical solutions to these nonlinear ODE (similarity) equations. The impacts of several distinguished constraints on the dimensionless temperature distribution, concentration, and velocity fields are discussed. Also, the heat transfer rate (HTR), shear stress (SS), and Sherwood number were elaborated with the aid of tables. The results expose that the velocity grows as one raises the material parameter, but the temperature falls. Also, the temperature uplifts because of the factor of absorption and declines due to the internal generation factor. Moreover, the profile of velocity reduces with greater impacts of NF whilst the temperature distribution increases. Additionally, the existence of NF causes an upsurge in SS, HTR and mass transfer rate by about 0.82%, 0.72%, and 0.13%, respectively.

Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks.

The involvement of wireless sensor networks in large-scale real-time applications is exponentially growing. These applications can range from hazardous area supervision to military applications. In such critical contexts, the simultaneous improvement of the quality of service and the network lifetime represents a big challenge. To meet these requirements, using multiple mobile sinks can be a key solution to accommodate the variations that may affect the network. Recent studies were based on predefined mobility models for sinks and relied on multi-hop routing techniques. Besides, most of these studies focused only on improving energy consumption without considering QoS metrics. In this paper, multiple mobile sinks with random mobile models are used to establish a tradeoff between power consumption and the quality of service. The simulation results show that using hierarchical data routing with random mobile sinks represents an efficient method to balance the distribution of the energy levels of nodes and to reduce the overall power consumption. Moreover, it is proven that the proposed routing methods allow for minimizing the latency of the transmitted data, increasing the reliability, and improving the throughput of the received data compared to recent works, which are based on predefined trajectories of mobile sinks and multi-hop architectures.

A novel framework to classify opinion dynamics of mobile agents under the bounded confidence model

The formation and evolution of public opinion have been widely studied to understand how consensus forms due to atomic interactions between individuals. While many studies have paid attention to modelling influence and interaction, most of the literature assumes static agents, ignoring the frequent changes in physical locations expected in real life. This feature naturally allows humans to interact with diverse people and avoid disagreement, which heavily impacts the co-evolution of opinions, communities or isolation in human societies. Our previous work proposed an extension of the bounded confidence model inspired by the theories of homophily and cognitive dissonance, which concern humans’ natural behaviours of attraction and disagreement. Although this demonstrated a marked difference to a static opinion model and purely random mobility, the limited experiments gave little insight into the causes or the resulting structures of consensus. This article addresses these shortcomings through a thorough investigation of the impact of mobility modelled by different mechanisms. Through extensive simulation, we observe a transition from multiple stable opinion clusters to complete consensus and a shift from a geographically based organisation to isolated structure-less agents. Lastly, we propose a novel classification of the different outcomes of self-organisation in opinion models, highlighting the patterns of emerging behaviours across the spectrum of interaction range and influence parameters.

Mobile Sink Node with Discerning Motility Approach for Energy Efficient Delay Sensitive Data Communication over Wireless Sensor Body Area Networks

The sensors nearby the static sink drains their energy resources rapidly, since they continuously involve to build routes in Wireless sensor networks, which are between data sources and static sink. Hence, the sensors nearby the sink having limited lifespan, which axing the network lifetime.The mobile-sink strategy that allows the sink to move around the network area to distribute the transmission overhead to multiple sensor nodes. However, the mobile-sink strategy is often tall ordered practice due to the continuous need of establishing routes between source nodes and the mobile sink (MS) at new position occurred due to its random mobility. In regard to above stated argument, this manuscript proposed a novel energy data transmission strategy which is effective for WSN with mobile sink. Unlike the traditional contributions, which relies on mobile sink with random mobility strategies, the proposal defines a discerning path for mobile sink routing between sectioned clusters of the WSN. The proposal of the manuscript titled “Mobile Sink Node with Discerning Motility Approach (MSDMA) for Energy Efficient Data Communication over WBAN”. The method defined in proposed model sections the target network in to multiple geographical clusters and prioritize these clusters by the delay sensitivity of the data transmitted by the sensor nodes of the corresponding clusters. Further, discriminating these clusters by their delay sensitive priority to define mobile sink route. For estimation of the delay sensitive priority of the clusters, set of metrics are proposed. The experimental study carried on simulation to assess the significance of the suggested method. The performance improvement of the suggested method is ascended through comparative analysis performed against benchmark model under divergent metrics.

Universal Geographic Information System Productivity beneath optimum Underwater Sensor Networks Routing

Study Framework: The system of geographical information (GIS) is a computerized method for collecting, storing, disseminating, querying, updating, processing and analyzing geographic data. (GIS) enables the collection of diverse spatial data from various sources and transforms it into an information board for multiple users to boost productivity. Intelligent techniques, remote sensing, and underwater Wireless sensor networks are applied to improve GIS dramatically. Remote sensing data sources for GIS come from underwater sensor networks, The implementation of effective sensing devices, intelligent mobile nodes, and a multi-hop routing system can facilitate the efficient and optimal routing of traffic towards the basin or base station. They have been using GIS, which has revolutionized response scenarios with its ability to collect, analyze and manipulate spatial points. The objective of the study: The aim of this research is to differentiate the significant routing protocol issues that are relevant to underwater sensor networks. These networks serve as key transceivers that have established mechanisms for seamless and efficient data transmission. The mechanisms: The article presented a performance evaluation simulation based on a comparison of five different routing protocols that perform to promote GIS execution. A rigorous comparison of the outcomes varied by five routing protocols, where it evaluated with random mobility UnderWater sensor model based on the varied network parameters, was performed to validate this work. The continued to improve results: The suggested study in this manuscript contains spatial-information services universal to routing protocols to aid a group of navigators in harsh circumstances. According to the findings of the study, the CTP-ABS-SEEC routing protocol demonstrated superior performance compared to the other four routing protocols (CTP-SEEC, SEEC, EEDBR, and DBR) in the specified network configuration.

Performance Analysis of Wirelessly Powered Cognitive Radio Network with Statistical CSI and Random Mobility.

The relentless expansion of communications services and applications in 5G networks and their further projected growth bring the challenge of necessary spectrum scarcity, a challenge which might be overcome using the concept of cognitive radio. Furthermore, an extremely high number of low-power devices are introduced by the concept of the Internet of Things (IoT), which also requires efficient energy usage and practically applicable device powering. Motivated by these facts, in this paper, we analyze a wirelessly powered underlay cognitive system based on a realistic case in which statistical channel state information (CSI) is available. In the system considered, the primary and the cognitive networks share the same spectrum band under the constraint of an interference threshold and a maximal tolerable outage permitted by the primary user. To adopt the system model in realistic IoT application scenarios in which network nodes are mobile, we consider the randomly moving cognitive user receiver. For the analyzed system, we derive the closed-form expressions for the outage probability, the outage capacity, and the ergodic capacity. The obtained analytical results are corroborated by an independent simulation method.

Mobile Localization Techniques for Wireless Sensor Networks: Survey and Recommendations

This article provides a comprehensive survey of pioneer and state-of-the-art localization algorithms based on the mobility of the network. The basic concepts of the localization task in a wireless sensor network are revisited and the most common techniques suitable for random mobility are reviewed. This survey compiles and discusses the most relevant algorithms regarding localization in mobile networks, focusing on scenarios where nodes have no control over their mobility and hardware restrictions are imposed, including recent advances in learning-based solutions. It focuses on presenting techniques that do not rely on human intervention or a specialized field configuration. This unpredictability brings challenges that are not present in a static network nor in a mobile network built upon robotic entities. The basis for theoretical concepts of localization algorithms is gathered and organized in a comprehensive way, so researchers may quickly get started in this field. Significant aspects addressed throughout the article, such as mobility pattern, range scheme, and computational complexity are organized and discussed as well as performance data for quantitative analysis alongside related ponderations. This survey concludes by pointing out current and future trends.

Bi-group pre-handover authentication model solving signaling congestion of subway LiFi network

In the metro scenario, massive users will switch from the platform network to the subway network simultaneously. The resulting huge and burst authentication traffic could cause signaling congestion in the subway light fidelity (LiFi) network. In this paper, a bi-group pre-handover authentication model (BGPHA) is developed to solve this problem by analyzing the characteristics of LiFi networks and the mobility of metro users. Specifically, a bi-group authentication mechanism is created in the BGPHA based on the ultra-dense deployment characteristic of LiFi networks. Next, a re-authentication mechanism is created in the BGPHA based on the link vulnerability characteristic of LiFi networks. Besides, a quick rejoin mechanism is created in the BGPHA based on the random mobility of metro users. Finally, the effectiveness of BGPHA is demonstrated by numerical and simulation analysis. Numerical analysis indicates that BGPHA has a lower authentication overhead growth with the number of users compared to the conventional single-group authentication scheme. Simulation analysis demonstrates that BGPHA can significantly decrease the handover latency in the presence of light path blockages. Furthermore, the handover failure rate can be effectively reduced by up to 6 times with the BGPHA.

Markov Chain-based Mobility Prediction and Relay-node Selection for QoS Provisioned Routing in Opportunistic Wireless Network

In the fields of networking and communication, wireless sensor networks are a commonly used technology. Although Mobility in Wireless Networks or Mobile Ad hoc Networks (MANETs) were developed to solve most critical problems in a disaster scenario. However, data transmission in a disaster area is a difficult operation due to irregular connections between sensing devices and the sink. Opportunistic network-based MANETs are utilized to overcome this problem. There are many routing issues such as communication overhead ratio, packet loss, and packet delivery ratio at mobile nodes. There are several traditional approaches used in opportunistic networks for node localization and routing. The drawback of these traditional routing methods is their poor reliability. A Markov chain-based opportunistic routing protocol is proposed in this paper. A Markov chain model is used to predict the node mobility for the random mobility model, and a neighbor table-based relay node selection procedure is proposed. A Markov prediction model uses the history of a node’s movement without requiring additional information. By analyzing simulation metrics including delay, overhead rate, packet drop, and packet delivery rate, the proposed routing protocol is evaluated and verified based on the Packet Delivery Ratio (PDR) in an opportunistic network environment (ONE) simulator. In addition, existing routing protocols such as Spray and Wait (SNW), Epidemic, Prophet, and MaxPro differ from the proposed routing protocol in terms of buffer size, node count, and message generation rate. Corresponding result analysis demonstrates the superior performance of the proposed routing protocol over existing protocols.

Link Failure Approach to Maintain Quality of Service in Mobile Ad hoc Network

Abstract: Mobile Ad hoc Network is a network in which numerous are contained. These nodes are dynamic in nature and utilize multi-hop communication for communicating with one another. There isn’t any central controller included in such a network. These nodes have random mobility and they are allowed to move in any direction due to the infrastructure less quality of this network. DFCP is a routing algorithm using which a path can be established amid source and target. The link failure leads to mitigate the efficacy of this protocol whose enhancement is required for maintaining the QoS. This research work suggests the enhancement of DFCP algorithm to recover the path when the link is failed. NS2 is applied to deploy the suggested algorithm. The simulation outcomes exhibit that the suggested algorithm outperformed the traditional methods concerning higher throughput and lower packet loss.

Fedab: A Low-Latency Energy-Efficient Proactive Neighbor Discovery Protocol in MLDC-WSN

Mobile Low-Duty-Cycle Wireless Sensor Network (MLDC-WSN) is a new type of wireless multi-hop network composed of sensor nodes that have mobility and sleep for a long time in a wireless self-organizing manner. Neighbor discovery protocols are used for sensor nodes to discover neighbor nodes within their communication range and can provide neighbor information to other protocols. MLDC-WSN uses low duty cycle technology to reduce energy consumption, and captures dynamic environmental information through the movement of nodes. However, the random mobility and long dormancy of nodes lead to frequent changes in the network topology, and nodes need to consume a lot of energy and time to discover all their neighbors. The sensor nodes are often powered by energy-limited batteries, and battery replacement is difficult. How to implement efficient neighbor discovery with low-latency and low-energy is a challenge of current research. Different from the previous slot model that couples the listening and transmitting of beacons in one slot, this paper proposes a new discovery schedule based on the slot model that separates the listening and transmitting of beacons. The discovery schedule can guarantee unidirectional discovery. And the node proactively transmits the beacon to accelerate bidirectional discovery after unilaterally discovering its neighbor nodes. On this basis, a proactive asynchronous neighbor discovery protocol called Fedab is proposed in this paper. Ignoring beacon collisions and other realistic interference factors, Fedab can enable neighbor discovery within the theoretical worst-case discovery latency. Moreover, Fedab is extended to the scenario where nodes perform different duty cycles, i.e., asymmetric scenarios. After experimental simulations and numerical analysis, the Fedab protocol has better performance compared to other existing neighbor discovery protocols.

Machine Learning for Millimeter Wave and Terahertz Beam Management: A Survey and Open Challenges

Next-generation wireless communication networks will benefit from beamforming gain to utilize higher bandwidths at millimeter wave (mmWave) and terahertz (THz) bands. For high directional gain, a beam management (BM) framework acquires and tracks optimal downlink and uplink beam pairs through exhaustive beam scan. However, for narrower beams at higher carrier frequencies this leads to a huge beam measurement overhead that negatively impacts the beam acquisition and tracking. Moreover, volatility of mmWave and THz channels, user random mobility patterns, and environmental changes further complicate the BM process. Consequently, machine learning (ML) algorithms that can identify and learn complex mobility patterns and track environmental dynamics have been identified as a remedy. In this article, we provide an overview of the existing ML-based mmWave/THz BM and beam tracking techniques. Especially, we highlight key characteristics of an optimal BM and tracking framework. By surveying the recent studies, we identify some open research challenges and provide our recommendations that can serve as a future direction for researchers in this area.

Mobility Prediction of Mobile Wireless Nodes

Artificial intelligence (AI) is a fundamental part of improving information technology systems. Essential AI techniques have revolutionized communication technology, such as mobility models and machine learning classification. Mobility models use a virtual testing methodology to evaluate new or updated products at a reasonable cost. Classifiers can be used with these models to achieve acceptable predictive accuracy. In this study, we analyzed the behavior of machine learning classification algorithms—more specifically decision tree (DT), logistic regression (LR), k-nearest neighbors (K-NN), latent Dirichlet allocation (LDA), Gaussian naive Bayes (GNB), and support vector machine (SVM)—when using different mobility models, such as random walk, random direction, Gauss–Markov, and recurrent self-similar Gauss–Markov (RSSGM). Subsequently, classifiers were applied in order to detect the most efficient mobility model over wireless nodes. Random mobility models (i.e., random direction and random walk) provided fluctuating accuracy values when machine learning classifiers were applied—resulting values ranged from 39% to 81%. The Gauss–Markov and RSSGM models achieved good prediction accuracy in scenarios using a different number of access points in a defined area. Gauss–Markov reached 89% with the LDA classifier, whereas RSSGM showed the greatest accuracy with all classifiers and through various samples (i.e., 2000, 5000, and 10,000 steps during the whole experiment). Finally, the decision tree classifier obtained better overall results, achieving 98% predictive accuracy for 5000 steps.

WIRELESS LAN FOR IOT 5G APPLICATIONS IN A CONGESTED INDOOR ENVIRONMENT: IMPROVED PRIMARY SIGNAL SENSING AT MILLIMETER-WAVE V-BAND [40 – 75 GHZ

Abstract. Presently, the IEEE 802.11ad wireless network standard supports the communication capability within the V band of millimeter-Waves (or mm-Waves) [40 – 75 GHz] for Internet of Things (or IoT) technology. This paper presents an algorithm to improve the Primary Signal 5G (or PS5G) sensing at 60 GHz in WLANIoT-5G (or Wireless Local Area Network for IoT-5G). To achieve that, we aim at investigating and assessing the sensing performances of the PS5G from the Access Point of WLANIoT-5G (or APWLANIoT-5G) by the Secondary User IoT-5G (or SUIoT-5G) in a congested environment using a simple energy detector (or SED) algorithm and a simple recursive estimator (or SRE). In addition, the SUIoT-5G is regarded as a mobile user in WLANIoT-5G by employing the Gauss-Markov Random Mobility Model (or GMRMM). Through various scenario simulations, the performances and the robustness of the proposed algorithm are proved.