Search

Blackhole detection in the opportunistic networks offers an effective means to mitigate the routing performance degradation but faces many challenges from corrupted nodes due to their collusion behaviors. Most existing effort in the literature focuses on the blackhole feature extraction from the message exchange. However, the decay effect of features and the forged features from the corrupted node, which acts as the rational node in performing message exchange, degrade the performance of the detection. In this article, we investigate the evidence construction, i.e., the direct and indirect evidence with the statistical parameters in message exchange. Specifically, we construct behavior classifiers to distinguish the blackhole behaviors from rational ones and design the collusion filtering strategy to improve the detection accuracy by separating corrupted nodes from rational ones, laying a behavior identification foundation. The contact evidence-driven blackhole detection (CEBD) based on machine learning is proposed to improve the routing performance. The soundness of the proposed scheme is verified statistically and the detection accuracy is evaluated based on random waypoint model (RWP) trace and Shanghai taxi trace. Extensive simulations show that our scheme outperforms the benchmarks, including SDBG, Li, and MDS, in terms of the delivery ratio in various scenarios.

The current work investigates the outage probability of a vertical diffused line-of-sight underwater optical wireless communication link where a fixed transmitter at the sea surface communicates with a receiver floated below sea level, which may be still but randomly placed at a given location or moving following the random waypoint model. In both considered scenarios, the channel gain follows a random distribution making the problem mathematically challenging. Novel mathematical expressions are derived for the channel gain statistics for either random placement and mobility effects. Afterward, the signal-to-noise ratio statistics are extracted, assuming indirect modulation/direct detection at the receiver. Hence, the outage probability and the average bit-error-rate for on–off keying are readily calculated. Finally, some relevant numerical results are depicted in a series of figures to highlight the overall performance.

In mobile ad hoc network (MANET), the traffic load impacts energy utilization and throughput. It can be overcome by efficient congestion control and link discontinuity identifier techniques. To improve the overall network performance of MANET, the link discontinuity and optimal route data delivery (LDORDD) protocol has been implanted based on AOMDV protocol is proposed along with cross-layer design. In this approach, local route recovery and transmission queue management (TQM) activities are used to control the packet's drop and congestion. This protocol works on buffering the incoming packets at the TQM layer, which controls the packets drop by locally repairing the route. The simulation results of the LDORDD protocol is evaluated in terms of network throughput, packet delivery ratio, and average end to end delay. The numerical and graphical representation of the LDORDD proves that the network resource rudiments are enhanced when compared to the existing methods.

An In-band-full-duplex (FD) technology-empowered cognitive radio (CR) system with a non-time-slotted primary user activity is presented in this work. FD here means that the transmission and reception can be performed at the same frequency and time simultaneously. Moreover, to incorporate the mobility of the CR, the random waypoint model is used. The non-time-slotted primary user activity implies that the primary and the secondary network are not synchronized. Non-time-slotted means that the primary user can be active or inactive during the CR's sensing/transmission duration. In the conventional time-slotted model, the primary user changes its state during the initial time and not later in the CR sensing window. The performance of the considered FD-CR network is observed in a Rayleigh fading environment. Specifically, the expression for miss-detection probability is derived. It is observed that the CR mobility and non-time-slotted primary activity would very much affect the miss-detection probability. Also, the proposed system with an efficient self-interference suppression mechanism outperforms the conventional half-duplex CRN.

Flexible mobility and random jitter are two unique features of UAV communication platforms. Although advantages of mobility have been extensively explored, the random jitter of UAV platforms, caused by airflow and body vibrations, has been rarely studied. This work aims to answer the two fundamental questions: i) how to analyze the secrecy performance when considering UAV jitter and ii) can this inherent characteristic of UAV be exploited to enhance secrecy? Detailed, we study the modeling and analysis of UAV jitter on the secrecy performance in an air-to-ground (A2G) wiretap system with multiple non-colluding eavesdroppers, where a UAV-mounted transmitter equipped with directional antennas illuminates ground terminals in a finite area. Random waypoint model is applied to characterize the mobility of eavesdroppers. To be specific, by modeling UAV jitter in both horizontal azimuth and vertical elevation, distortion and shift of UAV illumination area are analyzed. Further, beam-illumination probability of a randomly located ground terminal is obtained. Following which, a tractable framework for analyzing the secrecy coverage probability (SCP) and ergodic secrecy capacity (ESC) is developed. Expressions for SCP and ESC are derived with characterizations for the signal-to-noise ratio received at legitimate receiver and eavesdroppers. Finally, extensive simulations are provided to validate the theoretical analysis. This is the first work to find that UAV jitter can be exploited to enhanced secrecy performance of A2G wiretap system with appropriate UAV height and beamwidth.

Radio frequency energy harvesting can prolong the battery life and improve energy efficiency of device-to-device (D2D) communication. In this paper, we analyze the performance of a mobile D2D device powered by EH from the transmissions of underlying cellular base stations (BSs), whose locations are modeled as a homogeneous Poisson point process. We model the movements of D2D nodes via a modified random waypoint model. Log-distance path loss and Rayleigh fading are considered, and EH takes place solely within harvesting zones surrounding each BS and each D2D user harvests energy for a fixed number of charging time slots before attempting to transmit. We derive the probability of a D2D device being within an EH region surrounding BSs after multiple movements, and the probability of being within the fully charged state using a Markov-chain approach taking into account temporal effects. Moreover, the statistics of the harvested energy are characterized, and subsequently, the outage probability of a D2D transmission utilizing the harvested energy is derived. We show that the number of movements required to be within a harvesting region increases significantly when the harvesting threshold power increases, and that the number of harvesting time slots should be selected judiciously.

Physical-layer security (PLS) in Internet of Things (IoT) has attracted great attentions recently. Although mobility is an intrinsic property of IoT networks, most of the existing works only investigate the secure transmission design for static users. To fill this gap, this article specifically investigates the secrecy throughput maximization problems for the mobile IoT user under two typical mobility models: 1) random waypoint model (RWP) and 2) random direction model (RD). The insights about how the mobility patterns, and security–reliability requirements affect the mobile user’s secrecy throughput are revealed. First, in order to establish the relationship between security and reliability of the mobile user, a general analytic framework is provided to derive the closed-form expressions of transmit secrecy outage probability (TSOP) for the mobile user. Second, two transmission schemes are proposed to maximize the secrecy throughput of the mobile user by ensuring a certain level of transmit probability (TP) and TSOP requirements. The numerical and simulation results verify the validity and effectiveness of the proposed schemes, and indicate that by adopting appropriate mobility pattern, the user’s secrecy throughput can be improved, and the constraint on its moving region can be largely reduced. Those properties are lightweight and feasible to enhance security for many mobile IoT scenarios.

Congestion is the main issue for routing in the mobile system which overall degrading performance in the network due to inadequate accessibility of resources of the wireless network of the nature, node mobility and dynamic topology of the wireless network. Congestion causes packet loss, time and energy wastage by connection failure and by mobile node failure. Congestion could be recognized as a base of deployment, link capacity node and routing routes of the network. The congestion detected on node-link by a source node along with the node of the path, which distributes traffic flow over the alternative node paths by considering routing path availability of the network. This paper has proposed a congestion control load balancing adaptive routing protocol (CCLBARP) algorithms on the direction to reduce delay, system routing overhead, congestion and enhances the life of network in MANET. The proposed congestion control technique is compared with original existing routing protocols such as DYMO and DSR of MANET in terms of throughput, end-to-end delay, packet drops average jitter, packet delivery ratio and normalized routing overhead of the network and this CCLBARP get outperformed the DSR and DYMO.

The effectiveness of wireless sensor network (WSN) in Internet of Thing (IoT) based large scale application depends on the deployment method along with the routing protocol. The sensor nodes are an important component of WSN-assisted IoT network running on limited and non-rechargeable energy resource. The performance of WSN-assisted IoT is decreased, when network is deployed at large area. So, developing robust and energy-efficient routing protocol is a challenging task to prolong the network lifetime. In contrast to the state-of-the-art techniques this paper introduces Scalable and energy efficient routing protocol (SEEP). SEEP leverages the multi-hop hierarchical routing scheme to minimize the energy consumption. To achieve scalable and energy efficient network, SEEP employs a multi-tier based clustering framework. The network area in SEEP is divided into various zones with the help of proposed subarea division algorithm. The number of zones in the network are increased as the network size increases to avoid long-distance communication. Every zone is divided into certain number of clusters (sub-zones) and the number of clusters are increased towards the base station, whereas the zone width is decreased. In every cluster, some of the optimal nodes are promoted as a Relay Node (RN) and Cluster Head (CH). Normal nodes send their sensed data to the base station via local RN and CH in a multi-hop way. Furthermore, propose protocol provides a trade-off between distance and energy to prolong the network lifetime. In the proposed framework, static and mobile scenarios have been considered by applying Random walk and Random waypoint model for node mobility in simulation to make it more realistic as the various application of WSN-assisted IoT. The effectiveness of SEEP is examined against LEACH, M-LEACH, EA-CRP, TDEEC, DEEC, SEP, and MIEEPB, and result indicates that SEEP performs better for different network metrics: network lifetime, scalability, and energy efficiency.

The present paper features results on global survival and extinction of an infection in a multi-layer network of mobile agents. Expanding on a model first presented in [CHJW22], we consider an urban environment, represented by line-segments in the plane, in which agents move according to a random waypoint model based on a Poisson point process. Whenever two agents are at sufficiently close proximity for a sufficiently long time the infection can be transmitted and then propagates into the system according to the same rule starting from a typical device. Inspired by wireless network architectures, the network is additionally equipped with a second class of agents that is able to transmit a patch to neighboring infected agents that in turn can further distribute the patch, leading to a chase-escape dynamics. We give conditions for parameter configurations that guarantee existence and absence of global survival as well as an in-and-out of the survival regime, depending on the speed of the devices. We also provide complementary results for the setting in which the chase-escape dynamics is defined as an independent process on the connectivity graph. The proofs mainly rest on percolation arguments via discretization and multiscale analysis., Comment: 20 pages, 5 figures

In this article we setup a dynamic device-to-device communication system where devices, given as a Poisson point process, move in an environment, given by a street system of random planar-tessellation type, via a random-waypoint model. Every device independently picks a target location on the street system using a general waypoint kernel, and travels to the target along the shortest path on the streets with an individual velocity. Then, any pair of devices becomes connected whenever they are on the same street in sufficiently close proximity, for a sufficiently long time. After presenting some general properties of the multi-parameter system, we focus on an analysis of the clustering behavior of the random connectivity graph. In our main results we isolate regimes for the almost-sure absence of percolation if, for example, the device intensity is too small, or the connectivity time is too large. On the other hand, we exhibit parameter regimes of sufficiently large intensities of devices, under favorable choices of the other parameters, such that percolation is possible with positive probability. Most interestingly, we also show an in-and-out of percolation as the velocity increases. The rigorous analysis of the system mainly rests on comparison arguments with simplified models via spatial coarse graining and thinning approaches. Here we also make contact to geostatistical percolation models with infinite-range dependencies., Comment: 24 pages, 8 figures

In this letter, we emulate real-world statistics for mobility patterns on road systems. We then propose modifications to the assumptions of the random waypoint (RWP) model to better represent high-mobility profiles. We call the model under our new framework as RWP+. Specifically, we show that the lengths of the transitions which constitute a trip, are best represented by a lognormal distribution, and that the velocities are best described by a linear combination of normal distributions with different mean values. Compared to the assumptions used in the literature for mobile cellular networks, our modeling provides mobility metrics, such as handoff rates, that better characterize actual emulated trips from the collected statistics., To appear in IEEE Communications Letters

Light fidelity (LiFi) is an emerging communication technology, which utilizes the light-emitting diodes (LEDs) for high-speed wireless communications. Due to its huge unlicensed bandwidth, LiFi is capable of supporting high data rates. The quality of the LiFi channel fluctuates across the room due to interference, reflection from walls or blockage. On the other hand, WiFi is another wireless communication technology that is capable of providing moderate data rates with ubiquitous coverage. As the electromagnetic spectrum of LiFi does not overlap with WiFi, both of them can coexist to form a hybrid LiFi and WiFi network for seamless and high-throughput connectivity. The performance of a hybrid system significantly depends upon the access point (AP) assignment and resource allocation strategies. In this paper, a downlink hybrid system with one WiFi AP and four LiFi APs is considered, and a reinforcement learning (RL) algorithm is implemented in order to determine an optimal AP assignment strategy, which maximizes the long-term system throughput while ensuring the required users fairness and satisfaction. Furthermore, two different scenarios based on the random waypoint model with uniform and non-uniform distribution of users have been studied. The performance of the proposed system is compared against state-of-the-art benchmark approaches e.g., signal strength strategy (SSS), exhaustive search, and an iterative optimization method. The results are reported in terms of the average system throughput, user satisfaction, fairness, and capacity outage probability. It is shown that the proposed RL method performs closer to the exhaustive search scheme at fairly low complexity. The RL method also outperforms the SSS scheme and the iterative algorithm in most scenarios.

5G cellular mobile networks aspire to accommodate large numbers of users and devices most of which are expected to be mobile. They will also provide much higher data rates than previous generations’ networks. Analyzing how a cell’s performance is impacted by user mobility becomes paramount in an effort to provide the best possible quality of services. In this paper we propose a model of a generic cell where each user is moving based on the random waypoint model. We then adapt this model to a 5G cell to extract meaningful quality of service metrics. To this end and to provide a better perspective of how the model can be adapted to different bandwidth allocation policies, we extend the Engset multirate loss model to obtain recursive but efficient formulas for various performance measures, including call blocking and time congestion probabilities for both the complete sharing and bandwidth reservation policies.

This paper studies four rooting protocols in mobile social networks, which are the Epidemic protocol, the SimBet protocol, the Prophet protocol and the Popularity-Aided protocol. The theories of the protocols are studied and the experiments are done. With the random waypoint model, the simulation is implemented and the performance is analyzed. Through the comparison of the four protocols, the advantages and disadvantages are analyzed, which provides a reference for MSNs.

A Mobile ad-hoc Network (MANET) protocol must be configured correctly to ensure efficient data transfer. To achieve this aim a suitable routing protocol must be selected. Therefore, selecting the correct routing protocol is a critical condition, and it presents a classic problem in MANET. Also, using the proper values of the parameter in routing protocols plays a crucial role in MANET. MANET comprises several node devices run by battery as a power source. The primary function of MANET nodes is transmitting data based on routing protocols; thus, routing protocols play an essential role in MANET. Simultaneously, all routing protocols serve the same function in the network, but they differ in their performance. The current paper investigates four routing protocols performance by using the network simulator (NS-2) with various nodes speed, time simulations, network load, and network size. The current project evaluated the protocol performance based on metrics parameters such as throughput, end-to-end delay and packet delivery ratio. The simulation results showed that the ad-hoc On-demand Distance Vector (AODV) protocol was the best in all previous metrics parameters. In contrast, Zone Routing Protocol (ZRP) has the lowest performance. More details of the parameters have been presented in the current paper.

Physical layer key generation has attracted considerable attention in the past decade since it provides an alternative solution for the key establishment in wireless networks using channel reciprocity. In this paper we explore the possibility of physical layer key generation for emerging Low Power Wide Area Networks (LPWAN) such as LoRa (Long Range). However, due to the lower transmission rates of LPWANs compared to Wi-Fi and Zigbee, the channel reciprocity is relatively low, which makes timely key generation challenging. To address this problem, we propose a novel information-theoretic key generation scheme that can operate at all data rate settings, featuring a model-based key generation method. Furthermore, we derive an optimal window size to calculate the parameters of the channel model based on a random waypoint model to balance the channel reciprocity and entropy. Extensive evaluations on a campus testbed show that our method can achieve up to 13.8 bps key generation rate. Compared to state-of-the-art methods, the proposed method improves key generation rate by 3x to 5x. We also analyzed the security of the proposed approach and demonstrated it to be resilient to eavesdropping attacks.

A mobile ad hoc network is collection of self configuring and adaption of wireless link between communicating devices (mobile devices) to form an arbitrary topology and multihop wireless connectivity without the use of existing infrastructure. It requires efficient dynamic routing protocol to determine the routes subsequent to a set of rules that enables two or more devices to communicate with each others. This paper basically classifies and evaluates the mobility metrics into two categories- direct mobility metrics and derived mobility metrics. These two mobility metrics has been used to measure different mobility models, this paper considers some of mobility models i.e Random Waypoint Model, Reference Point Group Mobility Model, Random Direction Mobility Model, Random Walk Mobility Model, Probabilistic Random Walk, Gauss Markov, Column Mobility Model, Nomadic Community Mobility Model and Manhattan Grid Model.

With the growth of the worldwide number of IoT devices, finding the right placement and storage of the massive created data is a major challenge. In fact, most IoT applications are time-sensitive applications implemented in critical sectors where any delay can hinder their decision-making, operational execution and decrease their performance. Fog Computing is a better solution to reduce latency by storing and processing real-time data locally on the edge network. Efficient placement strategies are required to map data to distributed infrastructure nodes. Current solutions do not consider the mobility of IoT devices which exists in almost all the real systems. In this paper, we use the random waypoint model to shape the mobility of an IoT system. Furthermore, we formulate our data placement using multiple replicas as a multi-objective linear programming problem aimed at minimizing the overall system latency and the number of data replicas. Then, we propose a greedy heuristic algorithm for IoT devices mobility modeling and data assignments in a feasible time. It uses a refined method to filter possible solutions in order to find the final solution with minimum overall latency while fulfilling producer data storage resource requirements and taking into account data sharing between the distributed consumers in Fog infrastructure. We conducted the experiments with iFogSim simulator respecting two data consistency models for data replicas synchronization. The results show that the proposed data placement strategy improves significantly the overall system latency and the number of data replicas to use.

With expanding applications of the opportunistic Internet of things, there has been an ever-increasing enthusiasm in integrating blockchain with the IoT for enhancing privacy and security. However, due to high resource consumption by the blockchain, its implementation becomes a potential task for IoT networks. The main purpose of the paper is to develop a lightweight and secure communication protocol using blockchain architecture for decentralized IoT networks. Blockchain keeps a track of the messages, which are transmitted through the network. A traditional opportunistic IoT network sends multiple copies of messages for communicating information from source to destination, which gives rise to the risk of network security. Therefore, this paper proposes a secure routing protocol based on epidemic nature by integrating blockchain to ensure security and privacy. The main idea behind using blockchain is to keep a distributed ledger of all the messages (transactions), which are being transmitted through the network. For the simulation of our proposed network, we use the random waypoint model which we believe perfectly simulates the mobility of opportunistic devices.

Human mobility plays an important role in multiple research areas, ranging from virus spread research to network performance analysis. As real-world campaigns tend to be rather time consuming and expensive, they are often used to extract statistical features characterizing human mobility, which are then used as basis to create mobility models. For the implementation of such models and the subsequent simulative generation of movement traces, various tools exist. One such tool, focusing on the mobility modeling aspect of network performance analysis, is BonnMotion (BM).In this paper, we introduce new features to BM which increase the overall usability of the framework, simplify the implementation of new models, and improve the trace generation performance by introducing parallel processing capabilities. In addition, the introduced features are used to implement two additional movement models: The Sedentary Random Waypoint model and the Working Day Model. Furthermore, we exemplarily evaluate both, the impact of our enhancements, and the newly added models.

In this paper, we consider a finite network of multiple mobile autonomous underwater vehicles (AUVs) collecting data in a given region and transferring them to a surface sink. Herein, we consider a serving AUV located at fixed sea depth communicating with the surface sink. Further, all AUVs other than the serving AUV are considered as interfering AUVs and assumed to be located a 2-dimensional circular area of a given radius. We further assume that all interfering AUVs in the network dynamically control their horizontal position over the time. The instantaneous horizontal position of the interfering AUVs is regarded as a random process. We apply a general random waypoint (RWP) mobility process to characterize the horizontal position movement process for the interfering AUVs. Considering the steady-state nonuniform distribution of the RWP mobility model, we first characterize the interference received at the surface sink, and then derive its successful transmission probability under frequency-selective fading channel. Simulation results are presented to verify the accuracy of our analysis.

For most of the current research on physical-layer security in indoor visible light communication (VLC) systems, a static communication environment was mainly considered, where secure communication about static users was investigated. However, much secure problems remain to be settled about mobile users. To improve the secrecy performance of mobile users, a two-dimensional circular optical atto-cell with security protected zone is considered. The proposed VLC systems include a LED transmitter Alice, a mobile user Bob and a passive eavesdropper Eve. A typical random waypoint model (RWP) being assumed, the secrecy outage probability (SOP) and secrecy throughput (ST) have been investigated for mobile users in VLC systems. The theoretical analysis results have been verified through Monte Carlo simulations. The simulation results show that the secrecy performance of mobile users in VLC can be improved by enlarging the radius of protected zone, and it also depends on the target secrecy rate and the LEDs’ configuration.

In the context of optical wireless communications (as well as millimeter wave and terahertz systems), it is important to consider the random orientation of the user equipment (UE). At such small wavelengths, the UE's random orientation affects the angle of arrival or incidence angle that leads to a change in channel gain as well as the received signal-to-noise ratio (SNR). Furthermore, it can result in frequent handover that may affect user's quality of experience. Hence, it is required to consider the random orientation of the UE in our analysis to obtain more realistic results. Therefore, a framework that combines the conventional mobility models with the random orientation model is required. In this study, we propose an extended orientation-based random waypoint mobility model, in which the random orientation of the UE is considered during both walking and pause time. The parameters of the model are set based on the experimental measurements of device orientation. The application of this model to an indoor light-fidelity (LiFi) network is studied as a use case by analyzing the handover rate.

In the existing works, the finite-area distribution of Unmanned Aerial Vehicle (UAV) and the effects of LoS and NLoS in air-to-ground channels have not been modeled when analyzing the temporal correlation in UAV networks, which makes the current analyses unsuitable for the practical deployment in hotspots. This paper analyzes the temporal correlation by deriving the expression of the interference correlation and joint coverage probability in mobile UAV networks, where all UAVs move independently in a finite area and the channel fading is calculated based on air-to-ground channel model with LoS and NLoS. Specifically, the temporal correlation is measured by incorporating the fluctuations caused by probability variation of LoS and the reliable network topology caused by finite mobility into the wireless channels. Furthermore, the non-uniform distribution caused by random mobility of UAVs is considered over different time slots. The results show that the interference correlation decreases as the moving distance of UAVs increases, and the decreasing interference correlation offsets part of the decrease in joint coverage probability caused by increasing moving distance of UAVs.