Optimization of Air Route Network Nodes to Avoid ″Three Areas″ Based on An Adaptive Ant Colony Algorithm

Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.

Optimization of Route Repair in Mobile Ad-hoc Network

A main shortcoming of mobile Ad-hoc network's reactive routing protocols is the large volume of far-reaching control traffic required to support the route discovery (RD) and route repair (RR) mechanism. Using a random mobility model, this paper derives the probability equation of the relative distance (RDIS) between any two mobile hosts in an ad-hoc network. Consequently, combining with average equivalent hop distance (AEHD), a host can estimate the routing hops between itself and any destination host each time the RD/RR procedure is triggered, and reduce the flooding area of RD/RR messages. Simulation results show that this optimized route repair (ORR) algorithm can significantly decrease the communication overhead of RR process by about 35%.

Route Optimization for Network Mobility Based Aeronautical Network Using Correspondent Router

The future aeronautical network will be based on IPv6 and the services over the aeronautical network will be classified into 3 domains: Air Traffic Services (ATS), Airline Operational Services (AOS) and Passenger Information and Entertainment Services (PIES), among which the ATS and AOS domains are important for aircraft safety and airline business operation. Some schemes have been proposed to provide IP mobility support for aeronautical network, and Network Mobility (NEMO) scheme is the most promising one. However, using NEMO technology will lead to sub-optimal routing, so route optimization technology is highly desired for NEMO. A route optimization scheme is proposed for the ATS and AOS domains, which introduces the Correspondent Routers to realize the optimal routing and employs an improved procedure to reduce the handoff delay. The route optimization for the PIES domain is also discussed to provide better performance for some special scenarios.

Geographic Drone-based Route Optimization Approach for Emergency Area Ad-Hoc Network

Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,localization,heterogeneous network,self-organization,and self-sufficient operation.In this background,the current study focuses on specially-designed communication link establishment for high connection stability of wireless mobile sensor networks,especially in disaster area network.Existing protocols focus on location-dependent communications and use networks based on typically-used Internet Protocol(IP)architecture.However,IP-based communications have a few limitations such as inefficient bandwidth utilization,high processing,less transfer speeds,and excessive memory intake.To overcome these challenges,the number of neighbors(Node Density)is minimized and high Mobility Nodes(Node Speed)are avoided.The proposed Geographic Drone Based Route Optimization(GDRO)method reduces the entire overhead to a considerable level in an efficient manner and significantly improves the overall performance by identifying the disaster region.This drone communicates with anchor node periodically and shares the information to it so as to introduce a drone-based disaster network in an area.Geographic routing is a promising approach to enhance the routing efficiency in MANET.This algorithm helps in reaching the anchor(target)node with the help of Geographical Graph-Based Mapping(GGM).Global Positioning System(GPS)is enabled on mobile network of the anchor node which regularly broadcasts its location information that helps in finding the location.In first step,the node searches for local and remote anticipated Expected Transmission Count(ETX),thereby calculating the estimated distance.Received Signal Strength Indicator(RSSI)results are stored in the local memory of the node.Then,the node calculates the least remote anticipated ETX,Link Loss Rate,and information to the new location.Freeway Heuristic algorithm improves the data speed,efficiency and determines the path and optimization problem.In comparison with other models,the proposed method yielded an efficient communication,increased the throughput,and reduced the end-to-end delay,energy consumption and packet loss performance in disaster area networks.

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

Load Balancing-Based Routing Optimization Mechanism for Power Communication Networks

In power communication networks, it is a challenge to decrease the risk of different services efficiently to improve operation reliability. One of the important factor in reflecting communication risk is service route distribution. However, existing routing algorithms do not take into account the degree of importance of services, thereby leading to load unbalancing and increasing the risks of services and networks. A routing optimization mechanism based on load balancing for power communication networks is proposed to address the abovementioned problems. First, the mechanism constructs an evaluation model to evaluate the service and network risk degree using combination of devices, service load, and service characteristics. Second, service weights are determined with modified relative entropy TOPSIS method, and a balanced service routing determination algorithm is proposed. Results of simulations on practical network topology show that the mechanism can optimize the network risk degree and load balancing degree efficiently.

Cooperative Channel and Optimized Route Selection in Adhoc Network

Over the last decade,mobile Adhoc networks have expanded dramati-cally in popularity,and their impact on the communication sector on a variety of levels is enormous.Its uses have expanded in lockstep with its growth.Due to its instability in usage and the fact that numerous nodes communicate data concur-rently,adequate channel and forwarder selection is essential.In this proposed design for a Cognitive Radio Cognitive Network(CRCN),we gain the confidence of each forwarding node by contacting one-hop and second level nodes,obtaining reports from them,and selecting the forwarder appropriately with the use of an optimization technique.At that point,we concentrate our efforts on their channel,selection,and lastly,the transmission of data packets via the designated forwarder.The simulation work is validated in this section using the MATLAB program.Additionally,steps show how the node acts as a confident forwarder and shares the channel in a compatible method to communicate,allowing for more packet bits to be transmitted by conveniently picking the channel between them.We cal-culate the confidence of the node at the start of the network by combining the reliability report for thefirst hop and the reliability report for the secondary hop.We then refer to the same node as the confident node in order to operate as a forwarder.As a result,we witness an increase in the leftover energy in the output.The percentage of data packets delivered has also increased.

Simultaneous optimization of transit network and public bicycle station network

The traditional manner to design public transportation system is to sequentially design the transit network and public bicycle network. A new public transportation system design problem that simultaneously considers both bus network design and public bicycle network design is proposed. The chemical reaction optimization(CRO) is designed to solve the problem. A shortcoming of CRO is that, when the two-molecule collisions take place, the molecules are randomly picked from the container.Hence, we improve CRO by employing different mating strategies. The computational results confirm the benefits of the mating strategies. Numerical experiments are conducted on the Sioux-Falls network. A comparison with the traditional sequential modeling framework indicates that the proposed approach has a better performance and is more robust. The practical applicability of the approach is proved by employing a real size network.

LOA-RPL:Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

Energy conservation is a significant task in the Internet of Things(IoT)because IoT involves highly resource-constrained devices.Clustering is an effective technique for saving energy by reducing duplicate data.In a clustering protocol,the selection of a cluster head(CH)plays a key role in prolonging the lifetime of a network.However,most cluster-based protocols,including routing protocols for low-power and lossy networks(RPLs),have used fuzzy logic and probabilistic approaches to select the CH node.Consequently,early battery depletion is produced near the sink.To overcome this issue,a lion optimization algorithm(LOA)for selecting CH in RPL is proposed in this study.LOA-RPL comprises three processes:cluster formation,CH selection,and route establishment.A cluster is formed using the Euclidean distance.CH selection is performed using LOA.Route establishment is implemented using residual energy information.An extensive simulation is conducted in the network simulator ns-3 on various parameters,such as network lifetime,power consumption,packet delivery ratio(PDR),and throughput.The performance of LOA-RPL is also compared with those of RPL,fuzzy rule-based energyefficient clustering and immune-inspired routing(FEEC-IIR),and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm(RISARPL).The performance evaluation metrics used in this study are network lifetime,power consumption,PDR,and throughput.The proposed LOARPL increases network lifetime by 20%and PDR by 5%–10%compared with RPL,FEEC-IIR,and RISA-RPL.LOA-RPL is also highly energy-efficient compared with other similar routing protocols.

A Novel Method for Routing Optimization in Software-Defined Networks

Software-defined network(SDN)is a new form of network architecture that has programmability,ease of use,centralized control,and protocol independence.It has received high attention since its birth.With SDN network architecture,network management becomes more efficient,and programmable interfaces make network operations more flexible and can meet the different needs of various users.The mainstream communication protocol of SDN is OpenFlow,which contains aMatch Field in the flow table structure of the protocol,which matches the content of the packet header of the data received by the switch,and completes the corresponding actions according to the matching results,getting rid of the dependence on the protocol to avoid designing a new protocol.In order to effectively optimize the routing forSDN,this paper proposes a novel algorithm based on reinforcement learning.The proposed technique canmaximize numerous objectives to dynamically update the routing strategy,and it has great generality and is not reliant on any specific network state.The control of routing strategy is more complicated than many Q-learning-based algorithms due to the employment of reinforcement learning.The performance of the method is tested by experiments using the OMNe++simulator.The experimental results reveal that our PPO-based SDN routing control method has superior performance and stability than existing algorithms.

Dynamic Routing Optimization Algorithm for Software Defined Networking

Time and space complexity is themost critical problemof the current routing optimization algorithms for Software Defined Networking(SDN).To overcome this complexity,researchers use meta-heuristic techniques inside the routing optimization algorithms in the OpenFlow(OF)based large scale SDNs.This paper proposes a hybrid meta-heuristic algorithm to optimize the dynamic routing problem for the large scale SDNs.Due to the dynamic nature of SDNs,the proposed algorithm uses amutation operator to overcome the memory-based problem of the ant colony algorithm.Besides,it uses the box-covering method and the k-means clustering method to divide the SDN network to overcome the problemof time and space complexity.The results of the proposed algorithm compared with the results of other similar algorithms and it shows that the proposed algorithm can handle the dynamic network changing,reduce the network congestion,the delay and running times and the packet loss rates.

Hybrid Marine Predators Optimization and Improved Particle Swarm Optimization-Based Optimal Cluster Routing in Wireless Sensor Networks(WSNs)

Wireless Sensor Networks(WSNs)play an indispensable role in the lives of human beings in the fields of environment monitoring,manufacturing,education,agriculture etc.,However,the batteries in the sensor node under deployment in an unattended or remote area cannot be replaced because of their wireless existence.In this context,several researchers have contributed diversified number of cluster-based routing schemes that concentrate on the objective of extending node survival time.However,there still exists a room for improvement in Cluster Head(CH)selection based on the integration of critical parameters.The meta-heuristic methods that concentrate on guaranteeing both CH selection and data transmission for improving optimal network performance are predominant.In this paper,a hybrid Marine Predators Optimization and Improved Particle Swarm Optimizationbased Optimal Cluster Routing(MPO-IPSO-OCR)is proposed for ensuring both efficient CH selection and data transmission.The robust characteristic of MPOA is used in optimized CH selection,while improved PSO is used for determining the optimized route to ensure sink mobility.In specific,a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of MPOA.The high-speed ratio,unit speed rate and low speed rate strategy inherited by MPOA facilitate better exploitation by preventing solution from being struck into local optimality point.The simulation investigation and statistical results confirm that the proposed MPOIPSO-OCR is capable of improving the energy stability by 21.28%,prolonging network lifetime by 18.62%and offering maximum throughput by 16.79%when compared to the benchmarked cluster-based routing schemes.

Bee Colony Optimization Algorithm for Routing and Wavelength Assignment Based on Directional Guidance in Satellite Optical Networks

With the development of satellite communication,in order to solve the problems of shortage of on-board resources and refinement of delay requirements to improve the communication performance of satellite optical networks,this paper proposes a bee colony optimization algorithm for routing and wavelength assignment based on directional guidance(DBCO-RWA)in satellite optical networks.In D-BCORWA,directional guidance based on relative position and link load is defined,and then the link cost function in the path search stage is established based on the directional guidance factor.Finally,feasible solutions are expanded in the global optimization stage.The wavelength utilization,communication success probability,blocking rate,communication hops and convergence characteristic are simulated.The results show that the performance of the proposed algorithm is improved compared with existing algorithms.

Transit Network Design Using GIS and Ant Colony Optimization in Sanandaj

The public transit system in Sanandaj has been under review and modification for the last several years. The goal is to reduce the traffic congestion and the share of private car usage in the city and increase the very low share of the public transit. The bus routes in Sanandaj are not connected. There is no connected transit network with the ability to transfer between the routes in locations outside of the downtown terminal. The routes mostly connect the downtown core directly to the peripheries without providing travel options for passengers between peripheries. Although there has been some improvement in the transit system, lack of service in many populated districts of Sanandaj and town nearby makes the transit system unpopular and unreliable. This research is an attempt to provide solutions for the transit network design (TND) problem in Sanandaj using the capabilities of GIS and artificial intelligence methods. GIS offers several tools that enable the decision-makers to investigate the spatial correlations between different features. One of the contributions of this research is developing a transit network design with utilizing a spectrum of GIS software modeling functionalities. The visual ability of GIS is used to generate TNDs. Many studies focus on artificial intelligence as the main method to generate the TNDs, but the focus of this research is to combine GIS and artificial intelligence capabilities in order to generate a multi-objective GIS-based procedure to construct different bus network designs and explore and evaluate them to find the suitable transit network alternative.

Study on the Routing Technology of Wireless Sensor Network Based on Ant Colony Optimization

This article puts forward the routing algorithm of wireless sensor network based on ant colony optimization. The algorithm uses the characteristics of ant colony algorithm that is easy to realize local work, integrates link quality into the pheromone formation and supports multiple routes. When choosing routing, the probability is calculated that the node is selected as the next hop according to the pheromone concentration on the route. The ant colony optimization is self-organized, dynamic and multi-path, so it is very suitable for the routing of wireless sensor network. This algorithm has low routing cost, good self-adaption and supports multiple paths. It can balance energy consumption of the network and prolong the survival time of the network. The thesis makes comparative analysis of the simulation experiment and experimental result, proves that the ant colony algorithm can find the optimal routing in wireless sensor network and reaches the design objective of routing algorithm of wireless sensor network.

Energy optimal routing for long chain-type wireless sensor networks in underground mines

Wireless sensor networks are useful complements to existing monitoring systems in underground mines. They play an important role of enhancing and improving coverage and flexibility of safety monitoring systems.Regions prone to danger and environments after disasters in underground mines require saving and balancing energy consumption of nodes to prolong the lifespan of networks.Based on the structure of a tunnel,we present a Long Chain-type Wireless Sensor Network（LC-WSN）to monitor the safety of underground mine tunnels.We define the optimal transmission distance and the range of the key region and present an Energy Optimal Routing（EOR）algorithm for LC-WSN to balance the energy consumption of nodes and maximize the lifespan of networks.EOR constructs routing paths based on an optimal transmission distance and uses an energy balancing strategy in the key region.Simulation results show that the EOR algorithm extends the lifespan of a network,balances the energy consumption of nodes in the key region and effectively limits the length of routing paths,compared with similar algorithms.

DERNNet:Dual Encoding Recurrent Neural Network Based Secure Optimal Routing in WSN

A Wireless Sensor Network(WSN)is constructed with numerous sensors over geographical regions.The basic challenge experienced while designing WSN is in increasing the network lifetime and use of low energy.As sensor nodes are resource constrained in nature,novel techniques are essential to improve lifetime of nodes in WSN.Nodes energy is considered as an important resource for sensor node which are battery powered based.In WSN,energy is consumed mainly while data is being transferred among nodes in the network.Several research works are carried out focusing on preserving energy of nodes in the network and made network to live longer.Moreover,this network is threatened by attacks like vampire attack where the network is loaded by fake traffic.Here,Dual Encoding Recurrent Neural network(DERNNet)is proposed for classifying the vampire nodes s node in the network.Moreover,the Grey Wolf Optimization(GWO)algorithm helps for transferring the data by determining best solutions to optimally select the aggregation points;thereby maximizing battery/lifetime of the network nodes.The proposed method is evaluated with three standard approaches namely Knowledge and Intrusion Detection based Secure Atom Search Routing(KIDSASR),Risk-aware Reputation-based Trust(RaRTrust)model and Activation Function-based Trusted Neighbor Selection(AF-TNS)in terms of various parameters.These existing methods may lead to wastage of energy due to vampire attack,which further reduce the lifetime and increase average energy consumed in the network.Hence,the proposed DERNNet method achieves 31.4%of routing overhead,23%of end-to-end delay,78.6%of energy efficiency,94.8%of throughput,28.2%of average latency,92.4%of packet delivery ratio,85.2%of network lifetime,and 94.3%of classification accuracy.

Optimized routing strategy for complex network with multiple priorities

Different loads in the network require distinct Qo S standard, while present routing strategies for complex networks ignored this fact. To solve this problem, we designed a routing strategy RS-MP with multiple priorities by which packets are classified into privileged-packets and common-packets. In RS-MP, privileged-packets route by the Shortest Path Algorithm, and do not need to queue up. Common-packets＇ routes are determined by a new factor BJmax of the network. The BJmax stands for the largest betweenness centrality. By minimizing BJmax, the throughout capacity of the network can be maximized. The simulation results show that RS-MP can guarantee privileged-packets with the shortest path length and smallest delay, and maximized throughout capacity for common packets in the no-congestion state.

A Hybrid TCNN Optimization Approach for the Capacity Vehicle Routing Problem

A novel approximation algorithm was proposed for the problem of finding the minimum total cost of all routes in Capacity Vehicle Routing Problem (CVRP). CVRP can be partitioned into three parts: the selection of vehicles among the available vehicles, the initial routing of the selected fleet and the routing optimization. Fuzzy C-means (FCM) can group the customers with close Euclidean distance into the same vehicle according to the principle of similar feature partition. Transiently chaotic neural network (TCNN) combines local search and global search, possessing high search efficiency. It will solve the routes to near optimality. A simple tabu search (TS) procedure can improve the routes to more optimality. The computations on benchmark problems and comparisons with other results in literatures show that the proposed algorithm is a viable and effective approach for CVRP.

A Bio-Inspired Routing Optimization in UAV-enabled Internet of Everything

Internet of Everything(IoE)indicates a fantastic vision of the future,where everything is connected to the internet,providing intelligent services and facilitating decision making.IoE is the collection of static and moving objects able to coordinate and communicate with each other.The moving objects may consist of ground segments and ying segments.The speed of ying segment e.g.,Unmanned Ariel Vehicles(UAVs)may high as compared to ground segment objects.The topology changes occur very frequently due to high speed nature of objects in UAV-enabled IoE(Ue-IoE).The routing maintenance overhead may increase when scaling the Ue-IoE(number of objects increases).A single change in topology can force all the objects of the Ue-IoE to update their routing tables.Similarly,the frequent updating in routing table entries will result more energy dissipation and the lifetime of the Ue-IoE may decrease.The objects consume more energy on routing computations.To prevent the frequent updation of routing tables associated with each object,the computation of routes from source to destination may be limited to optimum number of objects in the Ue-IoE.In this article,we propose a routing scheme in which the responsibility of route computation(from neighbor objects to destination)is assigned to some IoE-objects in the Ue-IoE.The route computation objects(RCO)are selected on the basis of certain parameters like remaining energy and mobility.The RCO send the routing information of destination objects to their neighbors once they want to communicate with other objects.The proposed protocol is simulated and the results show that it outperform state-of-the-art protocols in terms of average energy consumption,messages overhead,throughput,delay etc.