A centrality-based optimization system for traffic management and dynamic routing in flying ad HOC networks and a method thereof

Abstract

The invention relates to an innovative traffic management system which combines eigenvector centrality measures and dynamic routing algorithms in order to avoid overload on the central nodes and improve the network performance in flying ad hoc networks (FANET), and to a method thereof.

Description

[0001] A CENTRALITY-BASED OPTIMIZATION SYSTEM FOR TRAFFIC MANAGEMENT AND DYNAMIC ROUTING IN FLYING AD HOC NETWORKS AND A METHOD THEREOF

[0002] Technical Field of the Invention

[0003] The invention relates to an innovative traffic management system which combines eigenvector centrality measures and dynamic routing algorithms in order to avoid overload on the central nodes and improve the network performance in flying ad hoc networks (FANET), and to a method thereof.

[0004] State of the Art

[0005] Flying ad-hoc networks (FANET) are systems that provide data transmission by communicating directly between mobile nodes in infrastructure-free media. These networks are used in critical applications such as military operations, disaster management, and emergency communications. Many existing routing algorithms and protocols have been developed to enable data transmission in FANETs. However, these algorithms encounter some technical challenges, especially in situations such as fastmoving nodes, network dynamics, and traffic load.

[0006] One of these algorithms, OLSR (Optimized Link State Routing), is a proactive routing protocol and constantly updates the link state of all nodes in the network to determine the most reliable routes. OLSR ensures the network always to have a ready routing table by periodically updating the link information of the nodes in the network. However, this approach causes a high control traffic and leads to latencies and packet losses as it cannot react quickly enough to the link changes, especially in fast-moving nodes.

[0007] Another algorithm, D Lite*, is an effective path updating protocol in dynamic media. D* Lite works based on a principle of recalculating only the affected paths for the changed links. This provides a performance advantage by reducing the computational overhead. However, the link updates increase under a heavy traffic, which may increase the complexity of the algorithm and reduce the performance thereof.JarmRout, on the other hand, is a multipath routing protocol and offers alternative ways to improve the link quality. This algorithm is designed to ensure the paths available in the network to run parallel to each other. However, managing conflicts and interactions between the paths may be challenging. In addition, the inability to provide an effective load compensation in case of a high network traffic negatively affects the communication performance.

[0008] Consequently, the existing routing protocols encounter technical problems such as disconnections in dynamic and fast-changing networks such as FANET, high latencies, and traffic overload on the central nodes. In particular, an overload on the central nodes reduces the communication quality of the network and causes bottlenecks, thereby weakening the overall resilience of the network. These challenges significantly limit the performance and reliability of FANETs.

[0009] Summary and Objectives of the Invention

[0010] The invention relates to an innovative traffic management system which combines eigenvector centrality measures and dynamic routing algorithms in order to avoid overload on the central nodes and improve the network performance in flying ad hoc networks (FANET).

[0011] An object of the invention is to avoid the traffic overload on the central nodes. By using eigenvector centrality measure, it detects the central nodes in the network and distributes the traffic load more efficiently, thereby reducing the overload.

[0012] Another object of the invention is to enhance network resilience against signal interruptions and malicious attacks by optimizing traffic load distribution and the protection of the central nodes.

[0013] Another object of the invention is to minimize the disconnections. By using dynamic routing algorithms, it quickly and effectively adapts to the link changes in fast-moving nodes.Another object of the invention is to optimize the energy consumption of the high-centrality nodes by making the eigenvector centrality-based transmission power adjustments.

[0014] Another object of the invention is to improve the data transmission performance. It uses hop penalties and link cost calculations to determine the most appropriate routing paths and reduce the latencies.

[0015] A further object of the invention is to optimize the data rate and transmission time by taking into account the use of multiple channels and signal-to-noise ratio (SNR) calculations.

[0016] Another object of the invention is to use the D* Lite algorithm to quickly update the varying link costs and to keep the performance of the network constantly up-to-date.

[0017] A further object of the invention is to provide both fast and endurable communication by selecting the most appropriate routing method based on the network conditions.

[0018] Another object of the invention is to prevent bottlenecks by distributing the traffic load among the decentralized nodes and to improve the overall performance of the network.

[0019] Description of the Drawings

[0020] Fig. 1 is a view showing a flow diagram of the method of the invention.

[0021] Fig. 2 is a view showing the conventional orientation made before the method of the invention.

[0022] Fig. 3 is a view showing the orientation in the method of the invention.

[0023] Description of the References in the Figures

[0024] 1001. determining the identity of the nodes and making an initial configuration in order to initialize all nodes by the network management unit and uniquely identity each node in the network

[0025] 1002. Generating a network topology by identifying the devices and links in the network by the network topology generating module1003. Determining the inter-node link costs based on the factors including SNR, transmission time of an average-sized packet and hop count by the network topology generating module

[0026] 1004. Calculating the eigenvector centralities to determine the importance of each node in the network by the node centrality calculating module

[0027] 1005. Adjusting the transmission power and interframe spaces of the node based on the centrality value by the parameter configuration calculating module

[0028] 1006. Selecting the most appropriate routing strategy based on the current state of the network so as to quickly adapt the network to the varying conditions by the traffic management module

[0029] 1007. Updating OpenFlow routing tables based on changes in topology by the OpenFlow controller,

[0030] 1008. Transmitting the data packets over the network with the new routing tables generated based on the routing algorithm selected by the routing algorithm module

[0031] Detailed Description of the Invention

[0032] The invention relates to an innovative traffic management system which combines eigenvector centrality measures and dynamic routing algorithms in order to avoid overload on the central nodes and improve the network performance in flying ad hoc networks (FANET).

[0033] The OpenFlow Controller is used to dynamically manage the network traffic by running on the server. In the invention, the routing tables of the UAVs are updated using OpenFlow.

[0034] An UAV (an unmanned aerial vehicle) can act as one of the endpoints in the communication of the system. They are vehicles which may fly autonomously and connect to a network.

[0035] The MAVLink protocol is located on the UAV and provides communication between the flight control equipment and ground control station of the UAVs.

[0036] The network topology generating module runs on the server and provides a graph representing the links between the UAVs and the physical structure of the network. Thenetwork topology generating module determines the link quality using the path loss calculations and signal-to-noise ratio (SNR) values.

[0037] The node centrality calculating module runs on the server and calculates the eigenvector centrality to determine the importance of each node in the network.

[0038] The parameter configuration calculating module runs on the server and adjusts the new transmission power and interframe space values of the nodes as expressed in Equation-2 based on the eigenvector centrality calculation result calculated by the node centrality calculating module.

[0039] The traffic management module runs on the server, analyzes the traffic in the network and makes the routing decisions. In a hybrid routing mode, on the other hand, one mode is selected depending on the situation, according to the advantages offered by two different routing modes (flexible and fast mode).

[0040] The channel allocation module runs on the server and allocates the channels to avoid collisions between different nodes.

[0041] The routing algorithm module runs on the server and finds an optimal path of the packets for the destination using the D* Lite algorithm. It takes into account the factors including the traffic load and the link quality.

[0042] The energy efficiency control unit runs on the UAV and measures the total energy consumption of the system. It takes into account the factors including hop count, link cost, and node energy levels.

[0043] The quality of service (QoS) control unit runs on the server and continuously monitors QoS metrics such as latency, jitter (timing fluctuation), packet loss rate, and checks whether they are below the targeted values.

[0044] The resilience control unit runs on the server and evaluates the resilience by taking into account the number of attackers, the network size and the traffic load factors. It uses a metric, the package delivery rate (FDR), for the resilience measurements.The process steps for the operation of the system are as follows:

[0045] Determining the identity of the nodes and making an initial configuration in order to initialize all nodes by the network management unit and uniquely identity each node in the network (1001),

[0046] Generating a network topology by identifying the devices and links in the network by the network topology generating module (1002)

[0047] Determining the inter-node link costs based on the factors including SNR, transmission time of an average-sized packet and hop count by the network topology generating module (1003),

[0048] Calculating the eigenvector centralities to determine the importance of each node in the network by the node centrality calculating module (1004),

[0049] Adjusting the transmission power (TX power) and interframe space (IFS) of the node based on the centrality value by the parameter configuration calculating module (1005),

[0050] Selecting the most appropriate routing strategy based on the current state of the network so as to quickly adapt the network to the varying conditions by the traffic management module (1006)

[0051] Updating OpenFlow routing tables based on changes in topology by the OpenFlow controller (1007),

[0052] Transmitting the data packets over the network with the new routing tables generated based on the routing algorithm selected by the routing algorithm module (1008).

[0053] The routing strategies are a flexible mode, a fast mode and a hybrid mode in the process step of selecting (1006) the most appropriate routing strategy based on the current state of the network so as to quickly adapt the network to the varying conditions by the traffic management module.The flexible mode adopts a dynamic approach to optimize the flow of traffic in the network. This mode continuously analyzes the current status of the network (such as traffic density, congestion, latency) and instantly determines the most appropriate routing rules. The fast mode is a strategy which aims to make and implement the routing decisions quickly, usually based on simple predefined rules. The hybrid mode is an approach that combines the advantages of the flexible and fast modes. Depending on the traffic, it may switch between both modes or use a combination of both.

[0054] The system works with the cooperation of all nodes in the network. The Open Flow Controller is used to dynamically manage the network traffic. UAVs are the endpoints providing the communication of the system. These vehicles, which may fly autonomously and connect to a network, are equipped with a variety of sensors and communication equipment. It has the ability to communicate wirelessly with other UAVs and the ground station. The MAVLink Protocol provides communication between the UAVs and the ground stations. The network topology creation module creates a network topology with CentAir, first of all, by determining the positions of all UAVs in the network and their link characteristics with each other. This topology is like a map that shows the link quality and signal strength between UAVs. Thus, the system obtains information on which UAVs may communicate directly with each other and which routes are more reliable.

[0055] Each UAV in the network has a different importance for the system. The node centrality calculating module uses a method called eigenvector centrality with CentAir to determine the importance of each UAV in the communication network. In this way, the central nodes where the network traffic is concentrated and the peripheral nodes that carry less load are determined. UAVs with a high centrality value are the points where the network traffic is concentrated and are usually central nodes where there is more packet latency and loss. The parameter configuration calculating module determines the optimal communication parameters (transmission power, interframe space) for each UAV. These parameters are adjusted according to the centrality value of the UAV and its relationship with the other UAVs in the network. The Node Centrality Calculating Module and Parameter Configuration Calculating Function are used to compensate the traffic of the network and to increase the resilience and the energy efficiency.

[0056] The traffic management module analyzes the traffic in the network by CentAir and ensures the packets to reach the destination in a shortest and most reliable way. Thesystem is able to quickly adapt to changes in network conditions, using both flexible and fast routing strategies. For example, when a UAV malfunctions or the link quality decreases, the system automatically determines an alternative path. In this process, the routing algorithm module uses the D* Lite algorithm as the routing algorithm.

[0057] The channel allocation module allocates CentAir and UAVs to different channels in order to prevent them from colliding with each other using the same frequency at the same time. In this way, each UAV uses its own channel when interacting with the other UAVs.

[0058] The energy efficiency control unit, the service quality control unit and the resilience control units are used to monitor and control the more effective and reliable operation of UAV networks, and thus the system continuously measures and controls the energy efficiency, the service quality and the safety metrics.

[0059] The invention will use a powerful mathematical concept such as eigenvector centrality measure to determine the importance of each node in the network. This metric will show how critical a role a node plays in the network, taking into account the degree of centrality of its neighbors. Simply, a high eigenvector centrality indicates that a node is one of the most important nodes in the network. This means that the node has a great influence on the other nodes. A low eigenvector centrality, on the other hand, indicates that one node is less efficient than the other nodes in the network. This means that the node's influence on the other nodes is limited.

[0060] The proposed method is based on the eigenvector centrality metric to quantitatively assess the importance of the nodes in a network. Eigenvector centrality is a value calculated by considering the links of a node with the other nodes in the network and the quality of these links. Eigenvector centrality is calculated by the following equation:

[0061] vjeM(vt)

[0062]

[0063] Equation-1

[0064] xv. a numerical value that indicates how important a particular node (v ) in a network is. This value is called the eigenvector centrality of that node.Mv. A set off all other nodes that have a direct link to a particular node (Vi). In other words, it refers to all the neighbors of a node.

[0065] A: A constant that shows the architecture of the network and the density of links between the nodes. This value is calculated as the maximum eigenvalue of the adjacency matrix of the network.

[0066] The eigenvector centrality of a node is directly proportional to the average of the eigenvector centralities of the other nodes adjacent to that node. That is, if the neighbors of a node are also important (i.e. They have a high eigenvector centrality), then that node itself is considered important. This indicates that important nodes in the network are often interconnected. Thus, this invention will make it possible to identify the nodes with a degree of centrality above a certain threshold value and to adjust the transmission power (tx power) and interframe space (SIFS) of these nodes. This threshold value is determined by the architecture of the network and the requirements of the application.

[0067] For the nodes with a high centrality, a new transmission power value is calculated using the following equation. This new value will be lower than the initial transmission power of the node.

[0068]

[0069] — Pi XViX Pp

[0070] Equation-2

[0071] Pv. The new transmission power value after being adjusted according to the eigenvector centrality of the node.

[0072] PtThe initial transmission power value of the node before any adjustments are made. PpThis is a multiplier used to compensate the load distribution in the network, allowing the transmission power of the nodes with a high eigenvector centrality to be further reduced.

[0073] In the study, the 802.11n standard and the use of channels in the 2.4 GHz band were taken into account. This invention provides a multigraph for the link between the two nodes, containing the edges duplicated based on the number of the channels used. Forexample, considering the channels 1, 6, and 11 in the 2.4 GHz band, 2 additional duplicate edges are formed for each link. To determine the appropriate data rate for the links between nodes, the signal-to-noise ratio (SNR) is first calculated. This calculation is used to evaluate the quality of the data to be transmitted for each link. Thereafter, the link costs are calculated as follows:

[0074] 1 . Calculating the Average Packet Transmission Time:

[0075] The transmission time of an average-sized packet (P) at a specified data rate (

[0076]

[0077] L^) is calculated. This time represents how much time the link will take during the data transfer.

[0078] 2. Addition of the Hop Penalty:

[0079] A hop penalty (Q is added to each link. This penalty makes the routing algorithm prefer paths with fewer hops on the network.

[0080] The link cost is expressed by the following formula:

[0081] P

[0082] Dij = ~Lrij+^

[0083]

[0084] Equation-3

[0085] Dij The link cost between the node

[0086]

[0087] and Vj.

[0088] Lij The data rate determined based on the SNR between these nodes.

[0089] P Average package size.

[0090] < : Hop penalty.

[0091] These concepts are used in routing algorithms to optimize network traffic and find the best path. The technique offered by the invention will allow to quickly adapt to changes in network topology using a dynamic routing algorithm such as D Lite*. The advantage of D* Lite is that it reduces the computational burden, as only the varying link costs need to be updated. In this way, the network will be constantly updatable and resistant to disconnections.In addition, thanks to the hybrid routing mode proposed by the invention, the most appropriate routing strategy may be selected according to the network conditions. This mode will increase the flexibility of the network by using a combination of both resilient and fast routing methods according to various scenarios. Thus, both low latency and high data transmission rate will be achieved.

[0092] Consequently, the invention intends to improve the performance of FANETs by combining centrality measure, dynamic path update algorithms, and hybrid routing modes to solve problems encountered in the existing routing systems. This innovative approach considers energy efficiency while increasing the resilience of the network and provides a solid foundation for future applications. These innovations will also make the network more resistant to jamming attacks and improve its overall performance.

Claims

CLAIMS1. A centrality-based optimization system for traffic management and dynamic routing in flying ad hoc networks, characterized in that it comprises:- at least one OpenFlow controller, which dynamically manages the network traffic by running on a server and updates the routing tables of UAVs,- at least one UAV with endpoints providing the communication of the system, which may fly autonomously and connect to a network, - a MAVLink protocol, which provides communication between the flight control equipment of the UAVs and the ground control station, as it is located onboard an UAV,- at least one network topology generating module which generates a graph representing the links between the UAVs and the physical structure of the network by running on a server, and determines the appropriate data rate for the links using the path loss calculations and signal-to-noise ratio values,- at least one node centrality calculating module, which determines the central nodes on which the network traffic is concentrated and the peripheral nodes carrying less load, by calculating the eigenvector centrality in order to determine the importance of each node in the network by running on a server,- at least one parameter configuration calculating module which adjusts the new transmission power and interframe space values of the nodes according to the eigenvector centrality calculation result calculated by the node centrality calculation module, by running on a server- at least one traffic management module which analyzes the traffic in the network and makes the routing decisions, by running on a server, - at least one channel allocation module which allocates the channels to avoid collisions between different nodes, by running on a server, - at least one routing algorithm module running on the server, which determines the most appropriate path of packets to the destination by means of the D* Lite algorithm by evaluating the traffic load and link quality factors,- at least one energy efficiency control unit which measures the total energy consumption of the system by running on an UAV and evaluating the factors including hop count, link cost and node energy levels, - at least one service quality control unit which continuously monitors QoS metrics such as latency, timing fluctuation, packet loss rate and checks whether they are below the targeted values, by running on a server, - at least one resilience control unit which detects the packet losses and disconnections and uses a metric that is a packet delivery rate for the resilience measurement.

2. A centrality-based optimization method for traffic management and dynamic routing in flying ad hoc networks, characterized in that it comprises the process steps of:- determining the identity of the nodes and making an initial configuration in order to initialize all nodes by the network management unit and uniquely identity each node in the network (1001 ),- generating a network topology by identifying the devices and links in the network by the network topology generating module (1002), - determining the inter-node link costs based on the factors including SNR, transmission time of an average-sized packet and hop count by the network topology generating module (1003),- calculating the eigenvector centralities to determine the importance of each node in the network by the node centrality calculating module (1004),- adjusting the transmission power and interframe spaces of the node based on the centrality value by the parameter configuration calculating module (1005),- selecting the most appropriate routing strategy based on the current state of the network so as to quickly adapt the network to the varying conditions by the traffic management module (1006),- updating OpenFlow routing tables based on changes in topology by the OpenFlow controller (1007),- transmitting the data packets over the network with the new routing tables generated based on the routing algorithm selected by the routing algorithm module (1008).

3. A centrality-based optimization method for traffic management and dynamic routing in ad hoc networks according to Claim 2, characterized in that the link pcosts are calculated using the equation £ ,- = — I- in the process step ofLijdetermining (1003) the inter-node link costs based on the factors including SNR, transmission time of an average-sized packet and hop count by the network topology generating module.

4. A centrality-based optimization method for traffic management and dynamic routing in ad hoc networks according to Claim 2, characterized in that the routing strategies are a flexible mode, a fast mode and a hybrid mode in the process step of selecting (1006) the most appropriate routing strategy based on the current state of the network so as to quickly adapt the network to the varying conditions by the traffic management module.

5. A centrality-based optimization system for traffic management and dynamic routing in ad hoc networks according to Claim 1, characterized in that nodes comprises a parameter configuration calculating module adjusting the transmission power and interframe space values using the equation Pv. = Pt- *ViPp ■

Images