Self-organizing Wireless Networks Research Articles

Symmetric Collaborative Fault-Tolerant Control of Multi-Intelligence under Long-Range Transmission in Air–Ground Integrated Wireless High-Mobility Self-Organizing Networks

With the continuous development and progress of wireless self-organizing network communication technology, how to carry out long-distance cooperative control of multiple intelligences under the framework of an air–ground integrated wireless high-mobility self-organizing network has become a hot and difficult topic that needs to be solved urgently. This paper takes the air–ground integrated wireless high-mobility self-organizing network system as the basic framework and focuses on solving the long-distance cooperative fault-tolerant control of multi-intelligent bodies and the topological stability of a wireless mobile self-organizing network. To solve the above problems, a direct neural network with a robust adaptive fault-tolerant controller is designed in this paper. By constructing a symmetric population neural network model and combining it with the Lyapunov stabilization criterion, the system feedback matrix K has the ability of autonomous adaptive learning, and symmetrically distorts, rotates, or scales the training data to instantly adjust the system’s fault-tolerant corrections and adaptive adjusting factors to resist the unknown disturbances and faults, to achieve the goals of multi-intelligent body stable control and the stable operation of a wireless high-mobility self-organizing network topology. Simulation results show that with the feedback adjustment of the multi-system under the designed controller, the multi-system as a whole has good fault-tolerant performance and autonomous learning approximation performance, and the tracking error asymptotically converges to zero. The experimental results show that the multi-flight subsystems fly stably, the air–ground integrated wireless high-mobility self-organizing network topology has good stability performance, and the maximum relative improvement of the topology stability performance is 50%.

An Effective Mechanism to Mitigate Packet Dropping Attack from MANETs using Chaotic Map based Authentication Technique

Background: MANET is a self-organized wireless network with no infrastructure. Especially data transfer from one system to another system needs to be done in a secure way. In order to provide data integrity, authentication plays an important role in data communication. RSA and ECC are widely used algorithms in the real world, but authentication using these algorithms is time-consuming. Towards this, various algorithms came into existence with different security primitives. However, it is important to design an effective key agreement process with reduced computational cost among these security mechanisms. We have designed an effective mechanism to mitigate packet dropping attacks to secure end-to-end communication in MANET. Objective: The proposed light weight authentication method is based on chaotic maps that uses Chebyshev polynomials as primitive operation. Methods: The methodology includes semi group property of Chebyshev polynomials and also the discrete logarithmic problem based chaotic maps that takes less time than these existing algorithms and evaluates with respect to attack Resilience, packet deliverya fraction, delay, throughput, and overhead. Results: Simulation produces the result of the proposed mechanism and give better performance in terms of packet delivery ratio, overhead and computational cost. Conclusion: Authentication based on developed mechanism consumes less time as shown in statistical analysis and mitigates packet dropping attacks effectively than that of traditional methods like RSA and ECC with respect to key generation mechanism.

A GAN-based security strategy for WSN networks based on honeypot algorithm

One of the essential elements of the cyber physical system is wireless sensor network (WSN), which is a multi-hop, self-organizing wireless network made up of numerousfixed or moving sensors. It jointly senses, gathers, analyses, and transfer the data ofdetected objects in the network's service area before sending this data to the network' sowner. Blackhole, Grayhole, Flooding, and Scheduling attacks are just a few of theusual WSN attacks that could quickly harm the system. Owing to constrained resourceof sensor nodes, a significant level of redundancy, network data's higher correlation,intrusion detection schemes for wireless sensor networks also have the drawbacks ofpoor identification rate, high computation overhead, and higher false alarm rate. Tooverwhelm the above problems, in this manuscript, Self-Attention-Based ProvisionalVariational Auto-Encoder Generative Adversarial Network (SAPVAGAN) optimized withHoney Badger Algorithms proposed for enhancing Cyber security in Wireless sensornetwork(ECS-WSN-SAPVAGANHBA). Based on the optimum features, the intruders in WSN data are categorizedinto normal and anomalous data (Black hole attack, Gray hole attack, Flooding attackand Scheduling attacks) with the help of SAPVAGAN.In general, SAPVAGANnotexpose any adaption of optimization modes for determining better parameters toassure accurate detection of WSN intrusion. Hence, Honey Badger Algorithm (HBA) isproposed in this work to optimize the SAPVAGAN, which precisely detects the WSNintrusion.The proposed ECS-WSN-SAPVAGANHBA method is performed in Pythonutilizing the WSN-DS dataset. Here, the performance metrics like recall, precision, f-measure,specificity, accuracy, RoC and computation time is evaluated. The proposedmethod provides 23.56%, 12.64%, and 15.63% higher accuracy, 23.14%, 16.78% and20.04% lower computational time analyzed to the existing models,such as IntrusionDetection System in Wireless Sensor Network Using Light GBM method(ECS-WSNSLGBM),Intrusion Detection Scheme in Wireless Sensor Network utilizing RecurrentNeural Network(ECS-WSN-RNN) and Intrusion Detection Scheme for Wireless SensorNetworks utilizing Whale Optimized Gate Recurrent Unit (ECS-WSN-WOGRU)respectively.

Enhancing cyber security in WSN using optimized self-attention-based provisional variational auto-encoder generative adversarial network

Wireless sensor network (WSN) is a multi-hop and self-organizing wireless network consists of fixed or moving sensors, this is one of the key components of the cyber physical system. It jointly senses, gathers, analyses, and transfer the data of detected objects in the network's service area before sending this data to the network's owner. The attacks like, Black hole, Gray hole, Flooding, scheduling are the usual WSN attacks that could quickly harm the system. A significant level of redundancy, network data's higher correlation, intrusion detection schemes for wireless sensor networks also have the drawbacks of poor identification rate, high computation overhead, and higher false alarm rate. MethodsInitially, the data's are taken from WSN-DS. In pre-processing, it confiscates the data redundancy and missing value restore sunder Color Wiener filtering (CWF). In feature selection, the optimal features are selected using tasmanian devil optimization (TDO) algorithm. Based on the optimum features, the intruders in WSN data are categorized into normal and anomalous data utilizing SAPVAGAN. Hence, honey badger algorithm (HBA) is proposed to optimize the SAPVAGAN, which detects the WSN intrusion accurately. ResultsThe proposed technique is performed in Python utilizing the WSN-DS dataset. Here, the performance measures, like recall, precision, f-measure, specificity, accuracy, RoC, computation time is evaluated. The proposed method provides 23.56%, 12.64%, and 15.63% higher accuracy, 23.14%, 16.78% and 20.04% lower computational time analyzed to the existing models, such as Intrusion Detection System in wireless sensor network using light GBM method (ECS-WSN-SLGBM), Intrusion Detection Scheme in wireless sensor network utilizing recurrent neural network (ECS-WSN-RNN) and Intrusion Detection Scheme for Wireless Sensor Networks utilizing whale optimized gate recurrent unit (ECS-WSN-WOGRU) respectively. ConclusionIt combines advanced techniques such as self-attention, provisional learning, and generative adversarial networks. By leveraging self-attention, the model captures important features and relationships in the WSN data. The provisional allows the model to adapt to changing network dynamics. The component generates realistic sensor data and accurately identifies malicious inputs. Overall, this innovative approach improves security and adaptability in WSNs.

ADAPTIVE SECURE AND EFFICIENT ROUTING PROTOCOL FOR ENHANCE THE PERFORMANCE OF MOBILE AD HOC NETWORK

Nowadays Mobile Ad Hoc Network (MANET) is an emerging area of research to provide various communication services to end users. Mobile Ad Hoc Networks (MANETs) are self-organizing wireless networks where nodes communicate with each other without a fixed infrastructure. Due to their unique characteristics, such as mobility, autonomy, and ad hoc connectivity, MANETs have become increasingly popular in various applications, including military, emergency response, and disaster management. However, the lack of infrastructure and dynamic topology of MANETs pose significant challenges to designing a secure and efficient routing protocol. This paper proposes an adaptive, secure, and efficient routing protocol that can enhance the performance of MANET. The proposed protocol incorporates various security mechanisms, including authentication, encryption, key management, and intrusion detection, to ensure secure routing. Additionally, the protocol considers energy consumption, network load, packet delivery fraction, route acquisition latency, packets dropped and Quality of Service (QoS) requirements of the applications to optimize network performance. Overall, the secure routing protocol for MANET should provide a reliable and secure communication environment that can adapt to the dynamic nature of the network. The protocol should ensure that messages are delivered securely and efficiently to the intended destination, while minimizing the risk of attacks and preserving the network resources Simulation results demonstrate that the proposed protocol outperforms existing routing protocols in terms of network performance and security. The proposed protocol can facilitate the deployment of various applications in MANET while maintaining security and efficiency.

Estimation of the Achievable Performance of Mobile Ad Hoc Networks with Optimal Link State Routing

The paper explores the challenges of constructing self-organizing wireless mobile ad hoc networks (MANETs) utilizing Optimal Link State Routing (OLSR) with MPR (MultiPoint Relay) optimization and quality control through the RSVP (Resource Reservation Protocol). Analytical expressions are presented for calculating the achievable network characteristics, including route acquisition time, network efficiency (routing overhead), packet transmission delay (end-to-end delay), and signal propagation losses between nodes assuming no packet collisions within the network nodes. The possibility of network scalability is analyzed depending on the scenarios of operation and the number of network nodes. Recommendations for the construction and scalability of self-organizing wireless networks are formulated based on the conducted evaluations and calculations.

S-Octopus: A Novel Scalable Secure Position-Based Routing Protocol for MANETs

Mobile Ad-Hoc Networks (MANET) are self-organized wireless networks that are becoming progressively popular. Determining an efficient route leading from a source to a specific destination in these networks is an essential issue since nodes are continuously moving. Furthermore, finding a secure route is a difficult area to deal with since adversaries might insert themselves into these routes unless a strict secure routing procedure is implemented. In this paper, a novel scalable secure routing protocol called S-Octopus has been proposed. Via dividing the network area into sectors and utilizing restricted directional flooding, our protocol intents to achieve improved scalability. Moreover, S-Octopus seeks to enhance robustness against the single point of failure and compromise by introducing several Sector Certificate Authority servers. Together with S-Octopus a location service and a misbehavior detection system have been proposed. Using GloMoSim simulator, S-Octopus security and performance have been evaluated and compared with the basic Authenticated Routing for Ad-Hoc Networks (ARAN) as well as Zone-based Authenticated Routing for Ad-Hoc Networks (ARANz). Simulation results assure that S-Octopus is able to effectively initiate and maintain secure routes in MANETs. Results also confirm that S-Octopus has significantly mitigated the scalability problem by achieving the maximum packet delivery fraction and the minimum network and routing loads within fairly large networks with high-mobility nodes and large malicious node percentage. Thus, S-Octopus is a good choice for MANET established among students on a campus or peers at a conference, where keys and certificates might be previously deployed.

Network Topology Inference Based on Timing Meta-Data

A set of low-cost sensors is deployed to infer the network topology of a self-organizing wireless network. The sensors operate in a non-invasive fashion, extracting only the timings of data packets and acknowledgment (ACK) packets from all nodes in a network. The meta-data also reports the source node of each packet, but not the destination nodes or the contents of the packets. A central processor collects the meta-data from the sensors, and the goal is for the processor to infer the network topology based solely on such information. Prior work leveraged causality metrics to identify which links are active. If the data timings and ACK timings of two nodes – say node 1 and node 2, respectively – are causally related, this may be taken as evidence that node 1 is communicating to node 2 (which sends back ACK packets to node 1). This paper starts with the observation that packet losses can weaken the causality relationship between data and ACK timing streams. To obviate this problem, a new Expectation Maximization (EM)-based algorithm is introduced – EM-causality discovery algorithm (EM-CDA) – which treats packet losses as latent variables. EM-CDA iterates between the estimation of packet losses and the evaluation of causality metrics. The method is validated through extensive experiments in wireless sensor networks on the NS-3 simulation platform.

Анализ особенностей и методов определения маршрутов доставки данных в беспроводных самоорганизующихся сетях на основе беспилотных летательных аппаратов

Currently, one of the promising areas in the development of network technologies is the wireless self-organizing networks based on the unmanned aerial vehicles, i.e., FANET (Flying Ad-Hoc Networks), which most important task in the course of their operation lies in organizing the effective data exchange. The distinctive properties of wireless self-organizing networks with altering topology are leading to the fact that technical solutions and methods of determination of the data delivery routes used in telecommunication networks with the traditional fixed architecture turn out to be inefficient in the special FANET networks and are not able to provide the required performance. At the same time, wireless self-organizing networks based on the unmanned aerial vehicles possess their own techniques used in data routing, which are subject to the requirements that take into account characteristic differences inherent in the networks of this type, including high mobility and low density of the nodes, dynamic and frequent topology alterations. Features and methods for determining data delivery routes were analyzed in the wireless self-organizing networks, which basis (nodes) were the unmanned aerial vehicles

Optimized Routing of UAVs Using Bio-Inspired Algorithm in FANET: A Systematic Review

Flying Ad hoc Network (FANET) is a self-organizing wireless network that constitutes swarms of flying nodes, namely Unmanned Aerial Vehicles (UAV), and communicates in close proximity. It has various distinguishing characteristics that set it apart from other ad hoc networks, posing some issues, particularly in routing. UAVs are highly dynamic and have frequent topology changes. Hence, the network urges an efficient routing technique to coordinate the node swarms and enhance the evaluation metrics of the network. The biological behavior of various living organisms, such as animals, insects, microbes, and humans, inspires researchers to solve various routing problems in ad hoc networks. Decentralized self-organized swarms of UAVs closely resemble the biological system. Therefore, the Bio-Inspired Algorithms (BIA) resolve a wide range of routing challenges in FANET. A Systematic Literature Review (SLR) is adopted to survey FANET routing methods based on non-hybrid and hybrid BIAs to properly comprehend the existing bio-inspired strategies used in FANET routing. The review will be beneficial for the researchers in the specified area. To our knowledge, no SLR has been conducted about the FANET routing protocol that employs BIA. This paper examines (1) the characteristics and features of existing routing algorithms, (2) the need of both non-hybrid and hybrid BIA for effective and optimal routing, (3) an analysis of the method’s simulation tools, evaluation metrics, and mobility models, (4) the current issues and scope of the study related to the specified method.

Development of a mathematical model of video monitoring based on a self-organizing network of unmanned aerial vehicles

The article presents the development of a mathematical model of video monitoring based on a self-organizing network of unmanned aerial vehicles. The necessity of developing models and algorithms for providing geoecological monitoring using a wireless self-organizing network based on unmanned aerial vehicles is shown. Models are presented that allow calculating the speed of information transfer in the network and reducing the number of failures in the process of transmitting video data. With the help of models, it is possible to substantiate the power of network transmitting devices, at which the losses of transmitted packets are significantly reduced. The practical use of the model contributes to the achievement of the required quality of video surveillance in a wireless self-organizing network of unmanned aerial vehicles in the process of geoecological monitoring.

Adaptive Safety Shields for Reinforcement Learning-Based Cell Shaping

Adjusting the remote electrical tilt (RET) of antennas is one of the important actions targeting run-time optimization of key performance indicators (KPIs) related to service quality in wireless self-organizing networks (SONs). Reinforcement learning (RL) is one of the preferred Machine Learning methods for automating the choice of RET for all the antennas managed by a company in a region. The automated system should ensure that the system will operate within a safe region to maintain a minimum defined service quality. The safe region of operation is typically customizable based on the targeted service quality at any point in time. This customizable nature of the safe region necessitates automated learning of adaptive safety shields for steering the RL agent away from unsafe regions. This paper presents an adaptive safety shield framework that is capable of learning such shields during the training phase of the RL agent. Our adaptive safety shield framework has been evaluated in different RET scenarios, and we have shown the benefits of our proposed framework over the Baseline method currently in use and a vanilla RL-based method in terms of both safety and performance metrics.

Simulation Test Research on Typical Simulator NS-2 in Urban Vehicle Ad Hoc Network Based on Big Data

Abstract Vehicle ad hoc network (VANET) has gradually become a prominent research topic in the fields of wireless networks and intelligent vehicles. VANETs are unique mobile ad hoc networks with vehicles as their mobile nodes, presenting distinctive performance characteristics compared to traditional wireless self-organizing networks. In recent years, VANETs have gained significant attention in the wireless network and intelligent transportation domain. As an integral aspect of autonomous driving technology, vehicle-to-everything (V2X) communication spans multiple disciplines and is closely related to intelligent transportation, assisted driving, active safety, and smart vehicles. Evaluating VANET protocols and applications in real-world settings can be challenging. Therefore, utilizing simulation tools for VANET research is an effective approach. In this paper, we have designed and developed an optimized platform that uses IEEE 802.11a and IEEE 802.11p protocols for communication within a simulated urban traffic environment created with NS-2. The simulation results confirm the feasibility and rationale of applying the IEEE 802.11p protocol to wireless vehicular ad hoc networks. Within a distance of 300 m, at 0.0000 s, 14 key packets have not arrived in IEEE 802.11a, and 8 packets have not arrived in IEEE 802.11p; at 8.0000 s, 38 key packets have not arrived in IEEE802.11a, and 6 packets have not arrived in IEEE802.11p. Comparing the performance of IEEE 802.11a and IEEE 802.11p, the study concluded that the use of the 802.11p protocol in urban mobile environments can improve reliability and reduce average packet latency.

Method of training routes of data transmission on mobile radio networks

A method for training data transmission routes in wireless self-organized networks is proposed. Features of construction of networks of this class are described. The main tasks of functioning of the control system of wireless self – organized networks are shown. The main teaching methods used to predict changes in data transmission routes are analyzed. The efficiency of application in certain fields and non-compliance with the requirements for the method being developed are explained. The essence of forecasting and direct connection with the process of learning data transmission routes is described. The routing system is shown as a necessary component for uninterrupted operation of wireless self-organized networks. The essence and requirements for the teaching method are shown. The learning unit of the forecasting subsystem is considered. Options for increasing the efficiency of scalar optimization are shown. The essence of the method is to learn the parameters (total latency; network routes; minimum bandwidth; reliability; load; load) of data transmission routes using the scalar optimization method, designed to dynamically select the most efficient adaptability function used in each new generation of evolutionary algorithms. Optimization problems with auxiliary criteria and reinforcement training are analyzed. The “Evolutionary Algorithm and Reinforced Learning” algorithm allows you to control the execution process of the evolutionary algorithm. The Hierarchical-if-and-only-if function problem is described and its efficiency when working with different algorithms is shown. The parameters used in the work correspond to the parameters of the research, which makes it possible to compare the results with previous results. In the course of work on the method its efficiency is reflected and the comparative analysis with similar methods of multicriteria optimization is carried out.

Обеспечение безопасной маршрутизации в беспроводных самоорганизующихся сетях

Purpose of the study: development of a distributed routing algorithm to ensure security in wireless self-organizing networks from intruder attacks on the network. Methods: application of algorithms, graph theory, discrete optimization and heuristic approaches based on the results of numerical experiments. Results: the paper proposes a modified distributed routing algorithm for self-organizing networks. The algorithm, presented in this paper, is based on a distributed version of Dijkstra’s algorithm, designed to detect the shortest paths without loops on a graph under conditions of changing the weight of its edges. The loop freedom in this case is achieved by storing at each node an additional table containing the penultimate nodes on the shortest routes to all nodes, which allows the node to build a tree of shortest routes with itself as a root. In the modification of the algorithm, these tables are used by the nodes to check the correspondence of the declared route and the return route, which makes it possible to recognize and exclude from the network an intruder who carries out attacks to disrupt the correct routing mechanism. The effectiveness of the proposed algorithm for protection against routing attacks, in particular, black hole attacks, is confirmed by the results of testing on a program model.

Decentralized Self-Organizing Networks for Poor Reconfigurability of Loom Networks

The intelligent weaving of looms requires networking and intelligent industrial production systems that use industrial equipment with network communication and data computing, creating a “smart factory”. However, limited by the development of industrial technology, existing industrial loom equipment does not have sufficient communication or computing capabilities: communications primarily rely on bus and Ethernet communication, which is limited to wired connections; and computations primarily rely on the host computers and cloud computers that only provide weak real-time computing. Currently, the remote management system used in the loom industry can be constructed using a cloud platform, but machine-machine interconnections between the loom control terminals are ignored. To solve the problem of poor reconfigurability in loom networks that rely on cloud servers, this study analyzes the networking requirements of a loom network and uses mesh network technology to build a wireless self-organizing loom network. A mesh network can be formed with clouds, loom equipment, and other equipment. By constructing an experimental platform with a decentralized self-organizing network to build a remote loom monitoring system, an anti-destruction experiment of the developed self-organizing loom network is performed. After the self-organizing network is constructed, key nodes in the network are disconnected, and the network is automatically reconfigured to resume normal work. The reconfigurability and stability of the network are demonstrated via destructive experiments. Results show that the self-organizing network construction method developed in this study can build a loom self-organizing network, which is anti-destructive and mitigate the poor reconfigurability of existing loom networks. Compared with the wireless communication network of the previous loom remote monitoring system, the loom interconnection self-organizing network is more reliable and anti-interference, which can better meet the network demand of intelligent manufacturing for loom equipment. This study provides an effective example of loom networking technology for the industry and is important when promoting the construction of smart loom factories.

Security Analysis for a VANET Privacy Protection Scheme

Vehicular ad hoc network (VANET) is a self-organizing wireless sensor network model, which is extensively used in the existing traffic. Due to the openness of wireless channel and the sensitivity of traffic information, data transmission process in VANET is vulnerable to leakage and attack. Authentication of vehicle identity while protecting vehicle privacy information is an advantageous way to improve the security of VANET. We propose a scheme based on fair blind signature and secret sharing algorithm. In this paper, we prove that the scheme is feasible through security analysis.

A review on application of MANET-IoT

Mobile Ad-hoc Networks (MANETs) will be very important for the Internet of Things (IoT). MANET is a decentralized selforganizing wireless network. Every device in MANET is completely free and can move in any direction. The interaction of the Internet of Things with wireless sensors and Mobile Ad-hoc Networks allows for the development of a new MANET-IoT framework. Innovative MANET-IoT systems are created through the interaction between Wireless Sensor Networks (WSN) and the IoT, which creates numerous design difficulties. Several application domains use MANET-based IoT systems. These systems increase user mobility while reducing costs. At the same time, new complex problems are emerging in the area of networking. This paper analyzes the literature on various MANET-IoT applications using wireless sensor networks (WSNs).

Stability Verification of Self-Organized Wireless Networks with Block Encryption

Stability Verification of Self-Organized Wireless Networks with Block Encryption

Distributed robust H∞ adaptive fault-tolerant control of amorphous and flat air-to-ground wireless self-organizing network system

PurposeThe paper takes the air-ground integrated wireless ad hoc network-integrated system as the research object, this paper aims to propose a distributed robust H∞ adaptive fault-tolerant control algorithm suitable for the system to distribute to solve the problem of control and communication failure at the same time.Design/methodology/approachIn the paper, the authors propose a distributed robust H∞ adaptive fault-tolerant control algorithm suitable for the air-ground integrated wireless ad hoc network-integrated system.FindingsThe results show that the integrated system has good robustness and fault tolerance performance indicators for flight control and wireless signal transmission when confronted with external disturbances, internal actuator failures and wireless network associated failures and the flight control curve of the quadrotor unmanned aerial vehicle (UAV) is generally smooth and stable, even if it encounters external disturbances and actuator failures, its fault tolerance performance is very good. Then in the range of 400–800 m wireless communication distance, the success rate of wireless signal loop transmission is stable at 80%–100% and the performance is at least relatively improved by 158.823%.Originality/valueThis paper takes the air-ground integrated wireless ad hoc network-integrated system as the research object, based on the robust fault-tolerant control algorithm, the authors propose a distributed robust H∞ adaptive fault-tolerant control algorithm suitable for the system and through the Riccati equation and linear matrix inequation method, the designed distributed robust H∞ adaptive fault-tolerant controller further optimizes the fault suppression factor γ, so as to break through the limitation of only one Lyapunov matrix for different fault modes to distribute to solve the problem of control and communication failure at the same time.