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Due to the widespread use of mobile and IoT devices, coupled with their continually expanding processing capabilities, dew computing environments have become a significant focus for researchers. These environments enable resource-constrained devices to contribute computing power to a local network. One major challenge within these environments revolves around task scheduling, specifically determining the optimal distribution of jobs across the available devices in the network. This challenge becomes particularly pronounced in dynamic environments where network conditions constantly change. This work proposes integrating the "reliability" concept into cutting-edge human-design job distribution heuristics named ReleSEAS and RelBPA as a means of adapting to dynamic and ever-changing network conditions caused by nodes' mobility. Additionally, we introduce a reinforcement learning (RL) approach, embedding both the notion of reliability and real-time network status into the RL agent. Our research rigorously contrasts our proposed algorithms' throughput and job completion rates with their predecessors. Simulated results reveal a marked improvement in overall throughput, with our algorithms potentially boosting the environment's performance. They also show a significant enhancement in job completion within dynamic environments compared to baseline findings. Moreover, when RL is applied, it surpasses the job completion rate of human-designed heuristics. Our study emphasizes the advantages of embedding inherent network characteristics into job distribution algorithms for dew computing. Such incorporation gives them a profound understanding of the network's diverse resources. Consequently, this insight enables the algorithms to manage resources more adeptly and effectively. [ABSTRACT FROM AUTHOR]

In scenarios of military operations and catastro- phes -- even when there is no infrastructure available or left -- there is a need for communication. Due to the specific context the communication systems used in these tactical scenarios need to be as reliable as possible. Thus, the performance of these systems has to be evaluated. Beside field-tests, computer simulations are an interesting alternative concerning costs, scalability, etc. Results of simulative performance evaluation strongly depend on the models used. Since tactical networks consist of, or, at least, contain mobile devices, the mobility model used has a decisive impact. However, in common performance evaluations mainly simple random-based models are used. In the paper we will provide classification and survey of existing mobility models. Furthermore, we will review these models concerning the requirements for tactical scenarios. [ABSTRACT FROM AUTHOR]

One promising paradigm for content-centric communication is Named Data Networking (NDN), which revolutionizes data delivery and retrieval. A crucial component of NDN, producer mobility, presents new difficulties and opportunities for network optimization. This article reviews simulation strategies designed to improve NDN producer mobility. Producer mobility strategies have developed due to NDN data access needs, and these methods optimize data retrieval in dynamic networks. However, assessing their performance in different situations is difficult. Moreover, simulation approaches offer a cost-effective and controlled setting for experimentation, making them useful for testing these technologies. This review analyzes cutting-edge simulation methodologies for NDN producer mobility evaluation. These methodologies fall into three categories: simulation frameworks, mobility models, and performance metrics. Popular simulation platforms, including ns-3, OMNeT++, and ndnSIM, and mobility models that simulate producer movement are discussed. We also examine producer mobility performance indicators, such as handover data latency, signaling cost, and total packet loss. In conclusion, this comprehensive evaluation will help researchers, network engineers, and practitioners understand NDN producer mobility modeling approaches. By knowing these methodologies' strengths and weaknesses, network stakeholders may make informed NDN solution development and deployment decisions, improving content-centric communication in dynamic network environments. [ABSTRACT FROM AUTHOR]

Summary: In recent years, artificial intelligence techniques, such as software‐defined networks (SDNs), machine learning classification (ML classification), and mobility models (MMs), have become vital in developing networks. Furthermore, communication methodologies, such as handover, directly affect network performance. In this paper, we propose a new system named SSHS, SDN Seamless Handover System, that combines SDN with an ML classifier to administer the network connection of mobile nodes. Through the SSHS system, the SDN will centralize the control to enable comprehensive management over the network, coupled with a decision tree (DT) classifier in the RYU controller to bring intelligence to the SDN application by enabling data analysis and prediction among mobile nodes generated by the RSSGM model. We present the SSHS model's effectiveness in providing a seamless communication handover among multiple access points (APs). The results of this study revealed that the SSHS provided a seamless handover among APs by improving the throughput by 26%, and decreasing the delay of arriving packets by 73% to standard SDN handover system. [ABSTRACT FROM AUTHOR]

With the constant increase of the number of autonomous vehicles and connected objects, tools to understand and reproduce their mobility models are required. We focus on chaotic dynamics and review their applications in the design of mobility models. We also provide a review of the nonlinear tools used to characterize mobility models, as it can be found in the literature. Finally, we propose a method to generate traces for a given scenario involving moving people, using tools from the nonlinear analysis domain usually dedicated to topological analysis of chaotic attractors. [ABSTRACT FROM AUTHOR]

Due to the widespread use of mobile and IoT devices, coupled with their continually expanding processing capabilities, dew computing environments have become a significant focus for researchers. These environments enable resource-constrained devices to contribute computing power to a local network. One major challenge within these environments revolves around task scheduling, specifically determining the optimal distribution of jobs across the available devices in the network. This challenge becomes particularly pronounced in dynamic environments where network conditions constantly change. This work proposes integrating the “reliability” concept into cutting-edge human-design job distribution heuristics named ReleSEAS and RelBPA as a means of adapting to dynamic and ever-changing network conditions caused by nodes’ mobility. Additionally, we introduce a reinforcement learning (RL) approach, embedding both the notion of reliability and real-time network status into the RL agent. Our research rigorously contrasts our proposed algorithms’ throughput and job completion rates with their predecessors. Simulated results reveal a marked improvement in overall throughput, with our algorithms potentially boosting the environment’s performance. They also show a significant enhancement in job completion within dynamic environments compared to baseline findings. Moreover, when RL is applied, it surpasses the job completion rate of human-designed heuristics. Our study emphasizes the advantages of embedding inherent network characteristics into job distribution algorithms for dew computing. Such incorporation gives them a profound understanding of the network’s diverse resources. Consequently, this insight enables the algorithms to manage resources more adeptly and effectively.

Nowadays, Vehicle Ad Hoc Networks (VANETs) have become a new trend and one of the most attractive areas of research. It has a wide range of applications such as smart cities and the Internet of Things (IoT). The main issue of the VANET networks is the limitation in network resources such as power, and memory. Moreover, since VANET nodes are dynamic and move over time, the connectivity of the networks is also considered an important issue. This paper designs experiments that reflect a variety of scenarios in VANET networks. Many issues and factors in VANET networks have also been investigated in this paper. There are many factors that affect the whole performance of VANET networks (i.e., mobility models, routing protocols, and things distribution) in terms of network resources. Therefore, this paper aims at testing different mobility models such as the Human Mobility model, Cauchy Flight Mobility model, and Correlated Directions Mobility model, and investigate their impact on the consumption of network resources under a particular routing protocol such as Spray and Wait routing protocol, Probabilistic Flooding routing protocol, and Epidemic routing protocol. In addition, testing different distributions such as Uniform distribution, Gaussian (Normal) distribution, and Power-Law distribution). The resources we plan to investigate are energy sources and the amount of data exchanged. In the designed experiments, each simulation includes a combination of a mobility model, nodes distribution, and a routing protocol. The findings showed that each mobility model, routing protocol, or distribution is effective in a particular application. As a result, it was found that determining the application of the VANET network is a crucial step before performing a simulation or designing a network. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and Flying Ad Hoc Networks (FANETs) have become hot topics among researchers because of the increased availability of Unmanned Aerial Vehicles (UAVs) and the electronic components required to control and connect them (e.g., microcontrollers, single board computers, and radios). LoRa is a wireless technology, intended for the IoT, that requires low power and provides long-range communications, which can be useful for ground and aerial applications. This paper explores the role that LoRa plays in FANET design by presenting a technical overview of both, and by performing a systematic literature review based on a breakdown of the communications, mobility and energy topics involved in a FANET implementation. Furthermore, open issues in protocol design are discussed, as well as other challenges associated with the use of LoRa in the deployment of FANETs. [ABSTRACT FROM AUTHOR]

In scenarios of military operations and catastrophes – even when there is no infrastructure available or left – there is a need for communication. Due to the specific context the communication systems used in these tactical scenarios need to be as reliable as possible. Thus, the performance of these systems has to be evaluated. Beside field-tests, computer simulations are an interesting alternative concerning costs, scalability, etc. Results of simulative performance evaluation strongly depend on the models used. Since tactical networks consist of, or, at least, contain mobile devices, the mobility model used has a decisive impact. However, in common performance evaluations mainly simple random-based models are used. In the paper we will provide classification and survey of existing mobility models. Furthermore, we will review these models concerning the requirements for tactical scenarios.

One promising paradigm for content-centric communication is Named Data Networking (NDN), which revolutionizes data delivery and retrieval. A crucial component of NDN, producer mobility, presents new difficulties and opportunities for network optimization. This article reviews simulation strategies designed to improve NDN producer mobility. Producer mobility strategies have developed due to NDN data access needs, and these methods optimize data retrieval in dynamic networks. However, assessing their performance in different situations is difficult. Moreover, simulation approaches offer a cost-effective and controlled setting for experimentation, making them useful for testing these technologies. This review analyzes cutting-edge simulation methodologies for NDN producer mobility evaluation. These methodologies fall into three categories: simulation frameworks, mobility models, and performance metrics. Popular simulation platforms, including ns-3, OMNeT++, and ndnSIM, and mobility models that simulate producer movement are discussed. We also examine producer mobility performance indicators, such as handover data latency, signaling cost, and total packet loss. In conclusion, this comprehensive evaluation will help researchers, network engineers, and practitioners understand NDN producer mobility modeling approaches. By knowing these methodologies’ strengths and weaknesses, network stakeholders may make informed NDN solution development and deployment decisions, improving content-centric communication in dynamic network environments.

Vehicle ad hoc networks are considered mobile networks where the nodes are mobile objects and can change their positions within an environment over time. These objects can be connected at any time according to a predefined strategy. Simulating this kind of network needs high attention to many details. Moreover, the literature lacks works that describe the requirements of simulating such networks. Therefore, this work tries to describe the requirements of simulating vehicle networks (VANETs). Moreover, the goal is to determine what is needed to simulate vehicle networks in terms of the distribution of vehicles, the movement patterns, and the routing protocols used. The simulation results show interesting facts about the VANET networks and the best strategies to minimize the consumption of network resources. Finally, this work considers two communication technologies among network nodes; Wi-Fi 5 and Wi-Fi 6.

In the past decade, a steady increase has been noticed in the number of mobile users and the use of mobile communication. In wireless communication links are stable. Duration of the link is the most important design by which the performance of the mobile Ad Hoc network (MANET) can be determined. In this research article, a derivative metric of graph theory attained from mobility model technique in Ad Hoc network of casually moving object, link duration of probability density function (PDF) been examined by using three mobility models namely random waypoint, random direction, and freeway model. The performance is based on the node speed, transmission range, the density of node, area of simulation, and computational complexity with time. By using Monte Carlo simulations, the reliability and robustness of models have been analyzed. The main advantage of the proposed scheme is implemented is an easy hardware platform because of the concept in mobility models and the easiness of code. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recent advancement in processing power, mobile wireless networks enabled technology are leading to more researches and studies, investigating mobility prediction in wireless network environments. User mobility behavior knowledge is a key element to understand the network traffic behavior in today mobile applications, including context-aware advertising and city wide sensing applications. We extend our previous work that was dedicated to mobility prediction schemes, in order to cover a correlated research topic: study of the user mobility behavior. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of testing military systems and applications over different communication scenarios with both network conditions and user data flows changing independently. We assume that systems developed to handle ever-changing communication scenarios are more likely to be reliable and robust during real military operations. Therefore, we propose the Tactical Network Test (TNT) platform to automate the evaluation of military systems and applications over real military radios using a reproducible test methodology. TNT has four main goals (i) the creation of QoS-constrained data flows; (ii) the execution of models to change network conditions; (iii) the automation of experiments to quantify the performance of military systems over ever-changing communication scenarios; and (iv) the monitoring of quantitative metrics and performing data analysis. Our platform was used to execute experiments in a VHF network by sending uniformly distributed data flows during seven different communication scenarios, either generated by a stochastic model or mobility models. The experimental results are used to discuss the military system’s performance by quantitative analysis using network metrics such as packet loss, delay, jitter, and data rate, and the test scenario characterization using mobility metrics such as speed, distance, and acceleration.

The emergence of infectious disease COVID-19 has challenged and changed the world in an unprecedented manner. The integration of wireless networks with edge computing (namely wireless edge networks) brings opportunities to address this crisis. In this paper, we aim to investigate the prediction of the infectious probability and propose precautionary measures against COVID-19 with the assistance of wireless edge networks. Due to the availability of the recorded detention time and the density of individuals within a wireless edge network, we propose a stochastic geometry-based method to analyze the infectious probability of individuals. The proposed method can well keep the privacy of individuals in the system since it does not require to know the location or trajectory of each individual. Moreover, we also consider three types of mobility models and the static model of individuals. Numerical results show that analytical results well match with simulation results, thereby validating the accuracy of the proposed model. Moreover, numerical results also offer many insightful implications. Thereafter, we also offer a number of countermeasures against the spread of COVID-19 based on wireless edge networks. This study lays the foundation toward predicting the infectious risk in realistic environment and points out directions in mitigating the spread of infectious diseases with the aid of wireless edge networks. [ABSTRACT FROM AUTHOR]

A Greedy Perimeter Coordinator Routing and Mobility Awareness (GPCR-MA) vehicular routing is a widely accepted routing protocol for VANET (Vehicular Ad hoc Network). The insufficiency of security measures in the operating design of GPCR-MA gives possible exposure to a Sybil attack. During a Sybil attack, the attacker (usually a vehicle) collects data packets by replicating multiple forged identities of numerous vehicles. The collected data packets are dropped instead of being forwarded. This paper presented a novel strategy to reduce Sybil attacks effect in the network through reduced storage and routing with computational overhead. The process integrates the phony route request to target or destination vehicles, the sequence number of destination vehicles and then further hop information to improve the restrictions of prevailing methods. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles framework largely relies on Vehicular Ad-hoc Network (VANET) to achieve the vision of connected, smart cars with mobility as a core component. Since deploying and testing a VANET in real-time is expensive, simulations are an essential tool as part of research on vehicular communication. Moreover, including an appropriate mobility model for a successful simulation of VANET is quite challenging because of its less realistic nature. This paper presents a survey on vehicular mobility models, emphasizing the realistic nature of vehicular movements and their corresponding challenges. [ABSTRACT FROM AUTHOR]

Data traffic is the most important data transmission between users inside every network. This data traffic can be videos, files, voice, pictures, and many more. It is divided into two types, real-time and non-real-time traffic. Most real-time traffic data have a low tolerance for delays during transmission, as they need to be quickly received between communication devices. In this paper, a comprehensive analysis was made to evaluate the two types of data transmitted through FANET drones, with different mobility models and two types of IEEE 802.11 2.4 GHz and 5 GHz using the optimized link state routing protocol. Metrics such as delay and throughput were measured. This paper gives an important overview of how real-time and non-real-time traffic will be handled during data transmission in FANET networks. [ABSTRACT FROM AUTHOR]

Mobile wireless sensor network (MWSN) has eventual applications in various areas such as health care monitoring, flood and fire detection, wildlife monitoring etc. MWSNs have fascinated much attentiveness from patrons in recent years due to their applications in various fields. MWSNs are resource restraints and demand performance investigation by numerous node mobility patterns. Generally, in MWSN, the routing algorithms have been investigated for predefined mobility. But for real-time applications, it is essential to develop an effective routing algorithm and study the effects of various mobility patterns on routing strategies to give effective outcomes. Therefore, keeping in view of the above issue, we proposed an Energy Constrained Mobile Routing (ECMR) in this paper. Simulations have been performed in MATLAB on diverse parameters to check the efficiency of ECMR and other existing routing protocols. Simulation results show that ECMR gives better performance than the Position-Based Routing (PBR) protocol, which comprises Mobility Aware Routing (MAR) and Geographic Robust Clustering (GRC). ECMR has also shown better performance than Non-Position Based Routing (N-PBR) protocols comprising Distributed Efficient Clustering Approach (DECA) and Distributed Efficient Multi-hop Clustering (DEMC). ECMR reduces the percentage of packet loss 10–12%, increases packet delivery ratio 11–13%, minimizes average end-to-end delay 13–15%, enhances throughput 12–14%, reduces overhead 11–12%, minimizes energy consumption 16–18%, and prolongs network lifetime 15–17% on the mobility of sensor nodes. ECMR performs better with Random Waypoint Mobility (RWPM) model than Pathway mobility (PM) and Random Walk Mobility (RWM) model. [ABSTRACT FROM AUTHOR]

In this research paper, the highlights include the development of a comprehensive trust-based security framework for enhancing the security and reliability of dynamic RPL (Routing protocol for low-power and lossy Networks) networks in the context of the Internet of Things (IoT). The research explores and addresses various challenges and attacks specific to RPL-based networks, including rank attacks, sybil attacks, and wormhole attacks. Here are the key highlights of this research: • Trust-Based Security Model for Attack Detection and Isolation : The research introduces a robust trust-based security model that integrates trust management techniques, including node behavior, reputation, and past performance, to calculate trust values for individual nodes within the network. These trust values serve as indicators of node trustworthiness. The developed model excels in identifying and isolating various types of attacks such as rank attacks, sybil attacks, and wormhole attacks. By assessing trust values, nodes exhibiting suspicious behavior are marked as potential attackers, facilitating the timely detection and mitigation of threats. • Mobility Impact Analysis : The research conducts a detailed analysis of the impact of mobility on IoT routing efficiency. It highlights the significance of considering dynamic network challenges in the evaluation of routing performance parameters, including throughput, packet delivery Ratio, control traffic overhead, and energy consumption. • Effective Trust Calculation using Lightweight and Resource-Efficient Design : The research emphasizes the importance of efficient trust calculation algorithms and mechanisms for accurate and proactive security. Trust values are continuously updated based on node behavior and interactions, ensuring that trustworthiness assessments remain reliable. A notable feature of this research is its focus on designing a lightweight system suitable for resource-constrained IoT environments. By distributing trust management tasks among multiple sink nodes and leveraging a centralized border router, the system optimizes energy consumption and reduces the burden on individual nodes. • Comprehensive performance Evaluation : The research provides extensive simulation results that showcase the model's effectiveness in securing IoT communications in dynamic environments. Performance metrics include packet loss, energy consumption, and control overhead, which help assess the overall efficiency of the system. • Contributions to IoT Security : This research makes significant contributions to enhancing IoT network security by introducing a proactive defense mechanism based on trust management. It aids network operators in identifying vulnerabilities and threats, promoting a robust and resilient network infrastructure. The model utilizes machine learning techniques for real-time attack detection, enabling timely responses to potential threats and enhancing network security. This study presents an innovative IoT routing security model that integrates trust management to bolster network reliability, improve resilience against routing attacks, and isolate malicious activities. The model, emphasizing node behavior, reputation, and past performance, offers a nuanced approach to network security. Through comprehensive comparisons between dynamic and static models in IoT routing, the impact on crucial performance parameters, including throughput, packet delivery ratio, control traffic overhead, and energy consumption, is quantified. Simulations showcase the model's effectiveness in securing IoT communication, achieving an impressive 98 % accuracy in detecting and mitigating attacks. Comparative analysis against prior studies underscores its exceptional performance, particularly in identifying and classifying attack types such as wormhole, Sybil, and rank, alongside normal traffic. This trust-based IoT routing security model represents a substantial advancement in securing dynamic IoT environments, standing out as a valuable contribution. Noteworthy is its low average power consumption, contributing to its exceptional lightweight nature. [ABSTRACT FROM AUTHOR]

Nodes in wireless networks can be static or dynamic. Static nodes are stationary and their positions are fixed and their distribution may follow a particular topology. On the other hand, dynamic nodes are considered mobile and their positions changed over time due to following a specific pattern of movement, which also lead to the concept of the topology being irrelated. Simulating such networks needs a lot of attention and many requirements should be held. For instance, nodes in dynamic wireless networks are distributed according to a distribution that reflects the nature of the environment (e.g., Gaussian, Power-Law, Uniform distribution, etc.). Moreover, describing the movement patterns of nodes in dynamic wireless networks need to follow a particular mobility model that describes the movements of nodes in terms of speed and direction (e.g., Individual mobility model, Levy Flight Model, Cauchy Model). In addition to the aforementioned requirements, it is needed to incorporate a routing protocol that governs data spread within the simulation environment. This work designed a variety of experiments that combine the mentioned requirements and measure the performance of dynamic wireless networks under colorful settings and configurations. The metrics used in this approach are the amount of data exchanged, coverage area, and power and memory consumption. Mobility models, node deployment strategy (distributions), and routing protocols are the three factors that will be involved in the experiments. The experiments deal with four mobility models Random Way Point Mobility Model (RWPM), Street Random Way Point Mobility Model (SRWPM), Manhattan Mobility Model (MM), and Levy Flight Mobility Model (LFM). Three distribution models will be used in the experiments Power-Law Distribution, Chi-Squared Distribution, and Normal Distribution. The third factor that will be involved in the routing protocol, is Probabilistic Flooding Routing Protocol. Different experiments are benchmarked using these metrics. The results of this work will reveal facts about dynamic network simulations and provide some recommendations to network developers and architects. [ABSTRACT FROM AUTHOR]

Reliable wireless communication networks are a significant but challenging mission for post-disaster areas and hotspots in the era of information. However, with the maturity of unmanned aerial vehicle (UAV) technology, drone mobile networks have attracted considerable attention as a prominent solution for facilitating critical communications. This paper provides a system-level analysis for drone mobile networks on a finite three-dimensional (3D) space. Our aim is to explore the fundamental performance limits of drone mobile networks taking into account practical considerations. Most existing works on mobile drone networks use simplified mobility models (e.g., fixed height), but the movement of the drones in practice is significantly more complicated, which leads to difficulties in analyzing the performance of the drone mobile networks. Hence, to tackle this problem, we propose a stochastic geometry-based framework with a number of different mobility models including a random Brownian motion approach. The proposed framework allows to circumvent the extremely complex reality model and obtain upper and lower performance bounds for drone networks in practice. Also, we explicitly consider certain constraints, such as the small-scale fading characteristics relying on line-of-sight (LOS) and non line-of-sight (NLOS) propagation, and multi-antenna operations. The validity of the mathematical findings is verified via Monte-Carlo (MC) simulations for various network settings. In addition, the results reveal some design guidelines and important trends for the practical deployment of drone networks.

Within the opportunistic wireless network research community, the evaluation of a new network protocol depends almost entirely on simulation. Using simulation enables a researcher to test a wide variety of scenarios at a low cost compared to using a real system. In opportunistic networks, the mobility of users plays a key role in data delivery; therefore, the value of the validation of a data forwarding protocol is highly dependent on the mobility models used in the simulation. In this paper, popular mobility models are assessed. Based on this analysis, a novel mobility model “Realistic Human Mobility Model” (RHMM) is proposed. The performance of the RHMM is evaluated and compared with real traces. The results show that RHMM generates mobility patterns that present characteristics similar to real ones and it has the same statistical properties identified in the real human mobility patterns.

The Internet of Things (IoT) and Flying Ad Hoc Networks (FANETs) have become hot topics among researchers because of the increased availability of Unmanned Aerial Vehicles (UAVs) and the electronic components required to control and connect them (e.g., microcontrollers, single board computers, and radios). LoRa is a wireless technology, intended for the IoT, that requires low power and provides long-range communications, which can be useful for ground and aerial applications. This paper explores the role that LoRa plays in FANET design by presenting a technical overview of both, and by performing a systematic literature review based on a breakdown of the communications, mobility and energy topics involved in a FANET implementation. Furthermore, open issues in protocol design are discussed, as well as other challenges associated with the use of LoRa in the deployment of FANETs.

Different from mobile ad hoc networks (MANETs) and vehicular ad hoc networks (VANETs), a flying ad hoc network (FANET) is a very low-density network where node topology changes rapidly and irregularly. These characteristics, the density, mobility, and speed of flight nodes, affect the performance of FANET. Furthermore, application scenarios and environmental settings could affect the performance of FANETs. In this paper, we analyzed the representative FANET protocols, AODV, DSDV, and OLSR, according to mobility models, SRWP, MP, RDPZ, EGM, and DPR, under the multi-UAV-based reconnaissance scenario. We evaluated them in terms of the number of nodes, network connectivity, mobility model’s reconnaissance rate, speed of nodes, and ground control station (GCS) location. As a result, we found that AODV showed the highest PDR performance (81%) with SRWP in multiple UAV-based reconnaissance scenarios. As for a mobility model under the consideration of reconnaissance rate, SRWP was excellent at 76%, and RDPZ and EGM mobility models were reasonable at 62% and 60%, respectively. We also made several interesting observations such as how when the number of nodes increases, the connectivity of the network increases, but the performance of the routing protocol decreases, and how the GCS location affects the PDR performance of the combination of routing protocols and mobility models.

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.