Self-organizing Mobile Networks Research Articles

Review of Routing Protocols for Mobile Ad Hoc Networks

Research on Mobile Ad Hoc Networks is of great significance in various fields including military, emergency response, Internet of Things (IoT), vehicular networking, aviation, and scientific research. This research contributes to the advancement and application of mobile communication technologies. Both domestic and international researchers have conducted in-depth studies on routing protocol design, which encompasses various aspects such as protocol design, topology management, link quality estimation, security, and energy efficiency. These areas represent current research hotspots and challenges. With the continuous development of wireless communication technologies and increasing demands for application, research on routing in Mobile Ad Hoc Networks remains highly significant with broad prospects for development. Therefore, research on routing strategies for Mobile Ad Hoc Networks has garnered widespread attention in academic circles worldwide, leading to numerous valuable research outcomes. This paper comprehensively analyzes the requirements related to the design of routing protocols for mobile self-organizing networks and comprehensively investigates the existing routing protocols, and classifies and investigates the routing protocols for mobile self-organizing networks in order to promote the research of next-generation mobile self-organizing network routing in China, which provides a reference.

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 adaptive intelligent routing algorithm based on deep reinforcement learning

A mobile ad hoc network (MANET) is a self-organizing network composed of multiple mobile nodes. These nodes achieve distributed communication and wireless resource sharing through autonomous collaboration. However, typical routing protocols for MANETs suffer from uneven network load, low network throughput, poor anti-destruction performance, and low transmission reliability. In order to meet the routing protocol characteristics of distributed adaptability, reliability and anti-destruction, balance of load and energy consumption and guarantee of high QoS in MANETs, this paper proposes an adaptive intelligent routing algorithm based on the PER-D3QN model of deep reinforcement learning. To achieve better routing strategy selection, the routing problem is first modeled, a distributed multi-agent reinforcement learning routing framework is designed, and a deep reinforcement learning reward function considering multiple factors is used. Next, the implementation process of the adaptive intelligent routing algorithm using the PER-D3QN model is described in detail. Finally, a mobile self-organizing network environment was constructed based on Networkx, and corresponding data was collected to simulate the algorithm. The simulation results show that the adaptive intelligent routing algorithm proposed in this paper can balance the energy consumption of the network and the load of nodes, its average remaining energy has increased by more than 10% and PL is reduced by at least 10% compared to other algorithms, while guaranteeing high QoS performance of routing, that is, reducing the average end-to-end delay of data packet transmission by over 22% compared to other algorithms and maintaining a high packet delivery ratio.

Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network

Due to the high mobility of nodes and the complexity of the mission environment, mission-oriented UAV networks are not only subject to frequent topology changes, but also to the risk of being compromised, hijacked and corrupted. As a result, an operating UAV network is essentially a Byzantine distributed system whose physical structure and node trustworthiness change over time. How to implement the global management of UAV networks to achieve a rational allocation of UAV network resources and reconfiguration of trusted networks is a problem worthy of in-depth study. The method proposed in this paper introduces a lightweight storage blockchain in the UAV network through two-stage consensus, firstly performing data consensus on the local state records of the nodes, then performing decision consensus on the data consensus results using algorithms such as fuzzy K-Modes clustering and global trustworthiness assessment, and finally recording the decision consensus results into a new block as the new configuration information of the UAV network. A lightweight storage blockchain-assisted trusted zone routing protocol (BC_TZRP) is designed to dynamically and adaptively build configurable trusted networks in a way that the blockchain continuously adds new blocks. Using QualNet simulation experimental software, an experimental comparison between the classical routing protocol for mobile self-organizing networks and the traditional consensus algorithm for blockchains is conducted. The results show that the approach has significant advantages in terms of packet delivery rate, routing overhead and average end-to-end delay, and can effectively improve the overall working life and fault tolerance of the UAV network.

A dynamic source routing protocol based on path reliability and link monitoring repair.

The two most essential factors for mobile self-organizing networks applicable to drones are reliability and stability. In harsh communication environments, such as mountainous regions and natural disasters, the use of satellites and terrestrial communication stations has severe time delays due to the high speed of UAVs, resulting in frequent communication interruptions with UAVs. Therefore, UAVs need to establish self-organizing networks for communication and information sharing. High-speed movement will lead to rapid changes in the network topology, resulting in established links being in an unstable connection state and even frequent routing errors, thus preventing the establishment of stable communication links. In order to improve the communication quality of UAVs under high-speed movement, we propose a dynamic source routing protocol based on path reliability and monitoring repair mechanism (DSR-PM). The model performs data transmission by filtering the best reliability path. The link state information is monitored during transmission and broken links are repaired in time to ensure the communication stability and reliability of the links and improve the data transmission efficiency. We simulated the approach in NS2 software and the simulation results show that the DSR-PM protocol effectively reduces parameters such as overhead, packet loss and delay, improves network throughput, and provides better communication performance.

A dynamic multi-sink routing protocol for static and mobile self-organizing wireless networks: A routing protocol for Internet of Things

With the rapid advent of using various devices like smart phones, vehicles etc, the connection of these devices with the help of Internet connectivity has emerged to IoT paradigm. The interconnection of smart objects under the various real world constraints like communication technologies, network scalability, node mobility, energy consumption etc, is a big challenge and requires designing new robust, adaptive, dynamic, and configurable routing protocols. With the arrival of the 5G and the future Internet, the latency time will be extremely reduced, this motivated us to propose a new protocol that entrust internet to transport a large part of control and data traffic of the network. In this paper, a dynamic multi-sink routing protocol (DMS-RP) is proposed for self-organizing multi-hop WSNs, iMANETs and IoT, it uses a sink-based auto-clustering of the network without using a dedicated clustering algorithm and it allows a multipoint-to-multipoint communication for local areas as well as for distant separated areas. Three multi-hop forwarding techniques of the proposed protocol are studied: BASIC, MULTI−PARENT and MULTI−PARENT+SNR. We have implemented the proposed protocol as a new module for NS-3 simulator. The simulation results show that the proposed protocol achieves a largely better performance in terms of packet delivery, network delay, overhead, energy and network lifetime when compared with one of common multi-hop self-organizing routing protocols (AODV). Furthermore, the protocol analysis shows its high efficiency regarding scalability for static as well as for mobile networks where the studied performances are not significantly affected by the network size which makes it a suitable routing protocol for large-scale network architectures.

Software model for studying the features of wireless connections in Flying Ad-Hoc Networks (FANETs)

Over the last decade, earlier named self-organizing mobile computer networks, MANET (Mobile Ad Hoc Network), including those based on unmanned aerial vehicles (UAV) – FANET (Flying Ad Hoc Network), have become widespread. In FANET, the UAV is connected to the network using wireless technology. Due to the constant dynamics of the nodes of such FANET, in this network, there are sometimes lost connections between the nodes. Modern wireless technologies such as WiFi, ZigBee, AirMAX, and others, have different technical characteristics that significantly affect the ability to build FANET. In the article developed a software model that allows you to study the statistical characteristics of mobile networks. The motion of each object (network node) is described using a quaternion model. The software model is developed in the Python programming language. The obtained software model for the given conditions allows us to estimate the basic statistical characteristics of FANET and to make recommendations to their construction and application.

Modeling Analysis and Algorithm Optimization Design of Vehicle Networking Based on Information Transmission

Vehicle Internet is the embodiment of mobile self-organizing network in the field of transportation.It is regarded as a key technology to reduce the incidence of traffic accidents, improve traffic efficiency and passenger driving experience. It has become the central development direction of the country. Because it is widely influenced by government industries and universities, the effective transmission of information is the core and problem of automotive Internet. This article starts with the application needs of vehicle Internet, designed to address different security-related needs in messaging and non-secure messaging applications, modeling analysis and algorithm optimization design combined with the network attributes of the vehicle Internet. High-speed mobility of vehicle nodes, self-organization and the existence of multi-hop networks, the information transmission on the vehicle Internet is very different from the traditional mobile AD hoc network, at the same time, with the continuous expansion of in-vehicle application Internet, different application scenarios have different service requirements, different services require the vehicle information system’s Internet to guarantee different modes of transmission. Therefore, this paper studies application-oriented information transmission technology. It improves the efficiency of the entire information transmission system, and it is of great significance to the development of vehicle Internet.

Complex methodology of parameters management of military radio networks in the conditions of uncertainty of the radioelectronic situation

The experience of the Joint Forces operation (Anti-terrorist operation in Donetsk and Luhansk oblasts) shows that the current order of management of military radio communication systems does not always meet the modern requirements for them. The classic centralized approach to the management of channel and network resources of military radio systems does not quite meet modern requirements, so the authors of this article proposed to take mobile self-organizing networks as a basic principle of construction. The authors propose a comprehensive method of managing the parameters of military radio networks in conditions of uncertainty of the electronic environment, the essence of which is to ensure the maintenance of the specified values of the performance of military radio communication systems at the appropriate level. The article is based on the control principle, which is described in the reference network model of open systems interaction, but with some additions and changes. This approach generally allows for end-to-end management of channel and network resources of military radio systems in a complex electronic environment. In the article, the device of fuzzy logic, the theory of electronic suppression, neural networks, the theory of noise protection, the theory of antennas, noise-resistant coding are used. The proposed complex methodology should be used in the development of software for modules (units) for the assessment of advanced radio communications, based on open architecture interfaces version SCA 2.2, which will: use effective signal-code structures to ensure noise immunity of channels; to ensure efficient use of the radio frequency resource of programmable radio communication means; increase the speed of evaluation of communication channels; reduce the use of computing resources of radio communications with programmable architecture.

The continuous maximal covering location problem in large-scale natural disaster rescue scenes

This study proposes a continuous maximal covering location problem (C-MCLP) that is often confronted in the rescuing scenes of natural disasters such as earthquakes, floods, and storms. The aim of the research is to optimize (dynamically and rapidly) the continuous locations of the communication hub-centers (e.g., moving vehicles or boats) of the self-organizing mobile network that is quickly established in such signal-free fields. The proposed C-MCLP well represents the real emergency rescues, but it is more complex to solve than the traditional discrete MCLP models, where the hub facilities are typically immobile and placed only within a limited set of candidate sites. We developed two mixed-integer linear programming (MILP) models for the C-MCLP. The first model is the single-period C-MCLP model, which is applicable to a stochastic rescuing environment where the rescue teams (RTs) do not have planned movements and can move towards any direction. The second one is the multi-period C-MCLP model, which is for cases where RTs have planned movements in multiple periods/phases. We introduced a new linearization method for the non-linear Euclidean distance with a controllable approximation error allowance, by which the proposed models are linearized and can be solved optimally using commercial MIP solvers such as CPLEX and Lingo. To solve large-sized problems, we provide a MILP-based fix-and-optimize heuristic approach to obtain near-optimal solutions with high computational efficiency. Then we conduct simulation experiments to verify the proposed models and heuristic approach with an intended time-limit setting on small-sized and large-sized test problem instances, respectively, with up to 1000 nodes of rescue teams. Finally, experimental results are analyzed and compared with those obtained using the traditional k-means clustering algorithms, which confirm that the proposed models and approach are applicable for the C-MCLPs in emergency rescue scenes, and can yield rapid and good solutions.

AUV Link Mobile Ad-Hoc Network Examination

Wireless optical communication systems allow reliable communication in the ultraviolet (UV) range, and protected from intentional suppression and interception, both in the presence and in the absence of direct visibility between the transmitter and the receiver. Currently, the actual and insufficiently explored direction is the development and research of mobile ad-hoc networks (MANET) with a UV communication channel. To analyze the capacity of a mobile network of UV communications with different spatial orientation of network nodes, it is necessary to take into account not only their coordinates and elevation angles but also azimuths. NLOS UV bandwidth models have been developed using three types of modulation: on-off keying (OOK), pulse-phase modulation (PPM), digital pulse interval modulation (DPIM) with a different number of guard intervals. The noted advantages of DPIM, which are most important for the implementation of a mobile self-organizing network with a UV channel, are: it does not require symbolic synchronization of the transmitter and receiver, it requires a smaller frequency band compared to PPM, it has a higher energy efficiency compared to OOK. A parametric approximation of losses in the NLOS UV channel has been performed, taking into account the azimuthal deviations of the transmitter and receiver for specific parameters of the communication system. Has been done a performance simulation of the MANET network with an ultraviolet communication channel Has been calculated values of the bit error rate (BER), and bit rate for a system with different types of modulation with low, medium, and high noise levels in the channel. With the use of parametric approximation of losses in the channel, are analyzed the BER and the bitrate of the UV communication system with an azimuthal deviation within 30 degrees. Based on the obtained simulation results, prospects for the further development of mobile self-organizing networks with a UV channel are indicated to improve their reliability and performance.

Development of Swarm Technology for Mobile Self-Organizing Networks. Identification of Dynamic Models

Статья посвящена беспилотным летательным аппаратам, а именно организации летательных аппаратов в группы. Актуальность заключается в том, что группа летательных аппаратов, образующих так называемый рой, может выполнять более сложные задачи, чем одиночный аппарат. При этом в рое возникает проблема по обеспечению минимального расстояния между аппаратами. Дистанция между БПЛА зависит от множества факторов - как от управления аппаратами, так и от изменений внешней среды. Авторами предлагается рассматривать динамическую модель роя БПЛА как колебательную систему, движущуюся со скоростью ведущего аппарата, что используется в большинстве реализаций поведения роя. В этом случае минимальное расстояние между дронами можно рассматривать как сумму номинального расстояния, обеспечиваемого системой управления и амплитудой колебаний дрона при попытках сохранить это расстояние. Рой БПЛА как колебательная система обладает рядом особенностей. Во-первых, ряд параметров колебательной системы меняется во времени. Например, меняются массы дронов вследствие уменьшения количества топлива, расходования полезного груза, потери частей дрона. Во-вторых, меняется структура роя, связанная с убытием и прибытием дронов в рой, сменой порядка построения дронов и т. п. В-третьих, рой может состоять из разных по конструкции, характеристикам и оснащению дронов. Для описания структуры и количественных характеристик авторы предлагают использовать теоретико-множественный подход. Используется формализованное описание топологической модели роя беспилотных летательных аппаратов, полностью учитывающее указанные выше особенности. Полученная модель учитывает не только динамические свойства, но и изменчивость структуры роя, а также медленное изменение динамических параметров БПЛА.

Machine Learning for QoE Prediction and Anomaly Detection in Self-Organizing Mobile Networking Systems

Existing mobile networking systems lack the level of intelligence, scalability, and autonomous adaptability required to optimally enable next-generation networks like 5G and beyond, which are expected to be Self Organizing Networks (SONs). It is anticipated that machine learning (ML) will be instrumental in designing future “x”G SON networks with their demanding Quality of Experience (QoE) requirements. This paper evaluates a methodology that uses supervised machine learning to predict the QoE level of the end user experiences and uses this information to detect anomalous behavior of dysfunctional network nodes (eNodeBs/base stations) in self-organizing mobile networks. An end-to-end network scenario is created using the network simulator ns-3, where end users interact with a remote host that is accessed over the Internet to run the most commonly used applications like file downloads and uploads and the resulting output is used as a dataset to implement ML algorithms for QoE prediction and eNodeB (eNB) anomaly detection. Three ML algorithms were implemented and compared to study their effectiveness and the scalability of the methodology. In the test network, an accuracy score greater than 99% is achieved using the ML algorithms. As suggested by the ns-3 simulation the use of ML for QoE prediction will help network operators understand end-user needs and identify network elements that are failing and need attention and recovery.

Robot SLAM with Ad hoc wireless network adapted to search and rescue environments

An innovative multi-robot simultaneous localization and mapping (SLAM) is proposed based on a mobile Ad hoc local wireless sensor network (Ad-WSN). Multiple followed-robots equipped with the wireless link RS232/485 module act as mobile nodes, with various on-board sensors, Tp-link wireless local area network cards, and Tp-link wireless routers. The master robot with embedded industrial PC and a complete robot control system autonomously performs the SLAM task by exchanging information with multiple followed-robots by using this self-organizing mobile wireless network. The PC on the remote console can monitor multi-robot SLAM on-site and provide direct motion control of the robots. This mobile Ad-WSN complements an environment devoid of usual GPS signals for the robots performing SLAM task in search and rescue environments. In post-disaster areas, the network is usually absent or variable and the site scene is cluttered with obstacles. To adapt to such harsh situations, the proposed self-organizing mobile Ad-WSN enables robots to complete the SLAM process while improving the performances of object of interest identification and exploration area coverage. The information of localization and mapping can communicate freely among multiple robots and remote PC control center via this mobile Ad-WSN. Therefore, the autonomous master robot runs SLAM algorithms while exchanging information with multiple followed-robots and with the remote PC control center via this local WSN environment. Simulations and experiments validate the improved performances of the exploration area coverage, object marked, and loop closure, which are adapted to search and rescue post-disaster cluttered environments.

GA-AdaBoostSVM classifier empowered wireless network diagnosis

Self-healing is one of the most important parts in self-organizing mobile communication network. It focuses on detecting the decline of service quality and finding out the cause of network anomalies and repairing it with high automation. Diagnosis is a particularly important task which identifies the fault cause of problematic cells or regions. To perform the diagnosis, this paper presents two modified ensemble classifiers by using Support Vector Machine (SVM) with different kernels, i.e., SVM with the radial basis function (RBF) kernel (RBFSVM in short) and SVM with the linear kernel (LSVM in short), as component classifier in Adaptive Boosting (AdaBoost), and we call the two ensemble classifiers as Adaptive Boosting based on RBFSVM (AdaBoostRBFSVM in short) and Adaptive Boosting based on linear kernel (AdaBoostLSVM in short). Different with previous AdaBoostSVM classifiers using weak component classifiers, in this paper, the performance of the classifiers is adaptively improved by using moderately accurate SVM classifiers (the training error is less than 50%). To solve the accuracy/diversity dilemma in AdaBoost and get good classification performance, the training error threshold is regulated to adjust the diversity of classifier, and the parameters of SVM (regularization parameter C and Gaussian width σ) are changed to control the accuracy of classifier. The accuracy and diversity will be well balanced through reasonable parameter adjustment strategy. Results show that the proposed approaches outperform individual SVM approaches and show good generalization performance. The AdaBoostLSVM classifier has higher accuracy and stability than LSVM classifier. Compared with RBFSVM, the undetected rate and diagnosis error rate of AdaBoostRBFSVM decrease slightly, but the false positive rate does reduce a lot. It means that the AdaBoostRBFSVM classifier is indeed available and can greatly reduce the number of normal class samples that have been wrongly classified. Therefore, the two ensemble classifiers based on the SVM component classifier can improve the generalization performance by reasonably adjusting the parameters. To set the parameter values of component classifiers in a more reasonable and effective way, genetic algorithm is introduced to find the set of parameter values for the best classification accuracy of AdaBoostSVM, and the new ensemble classifier is called AdaboostSVM based on genetic algorithm (GA-AdaboostSVM in short) (including AdaboostLSVM based on genetic algorithm and AdaboostRBFSVM based on genetic algorithm). Results show that GA-AdaboostSVM classifiers have a lower overall error than AdaboostSVM classifiers. Genetic algorithm could help to achieve a more optimal performance of the ensemble classifiers.

Analysis of Efficiency for AODV and OLSR Routing Protocols in Flying Ad Hoc Networks

Сети FANET (Flying Ad Hoc Networks) относятся к классу самоорганизующихся мобильных сетей (MANET - Mobile Ad Hoc Networks), где в качестве сетевых узлов используются беспилотные летательные аппараты. Главная проблема таких сетей - обеспечение приемлемого качества передачи видеопотока с борта летающего узла на наземную станцию ввиду высокой мобильности узлов, непредсказуемого и быстрого изменения сетевой топологии и особенностей работы протоколов маршрутизации данных. Производится обзор эффективности используемых для сетей MANET протоколов маршрутизации в условиях функционирования сети FANET. Для оценки их эффективности была выбрана основная метрика качества обслуживания - средний коэффициент доставки пакетов (PDRavr). Для измерения этой метрики в программной среде NS-3 проводилось имитационное моделирование передачи пакетов данных в FANET для трех видов маршрутизации: статическая, AODV и OLSR. Исследование проводилось при различных параметрах: размер стороны квадрата m, в пределах которого движутся летучие узлы; количество узлов n; характер расположения и перемещения узлов r. Это позволило максимально приблизить условия имитационного моделирования к условиям реально действующей сети.

Verification of Configuration Management Changes in Self-Organizing Networks

The verification of configuration management (CM) changes is an essential operation in a mobile self-organizing network (SON). Usually, a verification approach operates in three steps: it divides the network into verification areas, triggers an anomaly detection algorithm for those areas, and finally generates CM undo requests for the abnormally performing ones. Those requests set CM parameters to a previous stable state. However, the successful completion of this process can be quite challenging, since there are factors that might negatively impact its outcome. For instance, if a temporal degradation occurs during the optimization of a cell, a verification mechanism may wrongly assume that it is anomalous, and interrupt the optimization process. Furthermore, a verification strategy experiences difficulties when it faces verification collisions, i.e., conflicting undo requests that cannot be simultaneously deployed. At first, it has to determine whether the collision is a false positive one. Then, it has to resolve it by finding out which requests have the highest probability of restoring the network performance. In addition, it needs to consider the time that is given for rolling back CM changes. In this paper, we contribute to the area of SON verification by providing a solution that addresses those problems. Our approach makes use of constraint optimization techniques to resolve collisions as well as find the appropriate order for deploying undo requests. In addition, we use a minimum spanning tree-based clustering technique to eliminate false positive collisions. Further, we evaluate our solution in a simulation study in which we show its positive effect on the network performance.

A Movement-Assisted Deployment of Collaborating Autonomous Sensors for Indoor and Outdoor Environment Monitoring.

Using mobile robots or unmanned vehicles to assist optimal wireless sensors deployment in a working space can significantly enhance the capability to investigate unknown environments. This paper addresses the issues of the application of numerical optimization and computer simulation techniques to on-line calculation of a wireless sensor network topology for monitoring and tracking purposes. We focus on the design of a self-organizing and collaborative mobile network that enables a continuous data transmission to the data sink (base station) and automatically adapts its behavior to changes in the environment to achieve a common goal. The pre-defined and self-configuring approaches to the mobile-based deployment of sensors are compared and discussed. A family of novel algorithms for the optimal placement of mobile wireless devices for permanent monitoring of indoor and outdoor dynamic environments is described. They employ a network connectivity-maintaining mobility model utilizing the concept of the virtual potential function for calculating the motion trajectories of platforms carrying sensors. Their quality and utility have been justified through simulation experiments and are discussed in the final part of the paper.

An Adaptive Energy Aware Algorithm for Reliable Transmission Sensor Networks

Route maintenance and repair process plays an important role in mobile selforganizing network. On the basis of considering route stability and energy aware routing protocol, a new scheme is proposed in this paper. Firstly, signal strength, rest energy and port queue length are periodically and alternately detected; secondly, cross-layer method is adopted for route maintenance, and the alternate route is actively established for data transmission according to the routing repair mechanism “Make-before-Break”; finally, relevant experiment is carried out in NS2 to simulate and compare the proposed protocol and other similar routing protocols such as RSEA and LAER. The experiment result shows that the new scheme has good performance in the aspects of data packet delivery ratio, overhead control and end-to-end delay.

Design Methodology for Self-organized Mobile Networks Based

The methodology proposed in this article enables a systematic design of routing algorithms based on schemes of biclustering, which allows you to respond with timely techniques, clustering heuristics proposed by a researcher, and a focused approach to routing in the choice of clusterhead nodes. This process uses heuristics aimed at improving the different costs in communication surface groups called biclusters. This methodology globally enables a variety of techniques and heuristics of clustering that have been addressed in routing algorithms, but we have not explored all possible alternatives and their different assessments. Therefore, the methodology oriented design research of routing algorithms based on biclustering schemes will allow new concepts of evolutionary routing along with the ability to adapt the topological changes that occur in self-organized data networks.