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.

QoS Routing Optimization Based on Deep Reinforcement Learning in SDN

To enhance the efficiency and expediency of issuing e-licenses within the power sector, we must confront thechallenge of managing the surging demand for data traffic. Within this realm, the network imposes stringentQuality of Service (QoS) requirements, revealing the inadequacies of traditional routing allocation mechanismsin accommodating such extensive data flows. In response to the imperative of handling a substantial influx of datarequests promptly and alleviating the constraints of existing technologies and network congestion, we present anarchitecture forQoS routing optimizationwith in SoftwareDefinedNetwork (SDN), leveraging deep reinforcementlearning. This innovative approach entails the separation of SDN control and transmission functionalities, centralizingcontrol over data forwardingwhile integrating deep reinforcement learning for informed routing decisions. Byfactoring in considerations such as delay, bandwidth, jitter rate, and packet loss rate, we design a reward function toguide theDeepDeterministic PolicyGradient (DDPG) algorithmin learning the optimal routing strategy to furnishsuperior QoS provision. In our empirical investigations, we juxtapose the performance of Deep ReinforcementLearning (DRL) against that of Shortest Path (SP) algorithms in terms of data packet transmission delay. Theexperimental simulation results show that our proposed algorithm has significant efficacy in reducing networkdelay and improving the overall transmission efficiency, which is superior to the traditional methods.

Risk-Balanced Routing Strategy for Service Function Chains of Cyber-Physical Power System Considering Cross-Space Cascading Failure

Cyber-physical power system(CPPS)has significantly improved the operational efficiency of power systems.However,cross-space cascading failures may occur due to the coupling characteristics,which poses a great threat to the safety and reliability of CPPS,and there is an acute need to reduce the probability of these failures.Towards this end,this paper first proposes a cascading failure index to identify and quantify the importance of different information in the same class of communication services.On this basis,a joint improved risk-balanced service function chain routing strategy(SFC-RS)is proposed,which is modeled as a robust optimization problem and solved by column-and-constraint generation(C-CG)algorithm.Compared with the traditional shortest-path routing algorithm,the superiority of SFC-RS is verified in the IEEE 30-bus system.The results demonstrate that SFC-RS effectively mitigates the risk associated with information transmission in the network,enhances information transmission accessibility,and effectively limits communication disruption from becoming the cause of cross-space cascading failures.

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.

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.

A comprehensive survey on Segment Routing Traffic Engineering

Traffic Engineering(TE)enables management of traffic in a manner that optimizes utilization of network resources in an efficient and balanced manner.However,existing TE solutions face issues relating to scalability and complexity.In recent years,Segment Routing(SR)has emerged as a promising source routing paradigm.As one of the most important applications of SR,Segment Routing Traffic Engineering(SR-TE),which enables a headend to steer traffic along specific paths represented as ordered lists of instructions called segment lists,has the capability to overcome the above challenges due to its flexibility and scalability.In this paper,we conduct a comprehensive survey on SR-TE.A thorough review of SR-TE architecture is provided in the first place,reviewing the core components and implementation of SR-TE such as SR Policy,Flexible Algorithm and SR-native algorithm.Strengths of SR-TE are also discussed,as well as its major challenges.Next,we dwell on the recent SR-TE researches on routing optimization with various intents,e.g.,optimization on link utilization,throughput,QoE(Quality of Experience)and energy consumption.Afterwards,node management for SR-TE are investigated,including SR node deployment and candidate node selection.Finally,we discuss the existing challenges of current research activities and propose several research directions worth of future exploration.

Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

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.

Traffic Engineering in Dynamic Hybrid Segment Routing Networks

The emergence of Segment Routing(SR)provides a novel routing paradigm that uses a routing technique called source packet routing.In SR architecture,the paths that the packets choose to route on are indicated at the ingress router.Compared with shortest-path-based routing in traditional distributed routing protocols,SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress router.Despite the advantages of SR,it may be difficult to update the existing IP network to a full SR deployed network,for economical and technical reasons.Updating partial of the traditional IP network to the SR network,thus forming a hybrid SR network,is a preferable choice.For the traffic is dynamically changing in a daily time,in this paper,we propose a Weight Adjustment algorithm WASAR to optimize routing in a dynamic hybrid SR network.WASAR algorithm can be divided into three steps:firstly,representative Traffic Matrices(TMs)and the expected TM are obtained from the historical TMs through ultrascalable spectral clustering algorithm.Secondly,given the network topology,the initial network weight setting and the expected TM,we can realize the link weight optimization and SR node deployment optimization through a Deep Reinforcement Learning(DRL)algorithm.Thirdly,we optimize the flow splitting ratios of SR nodes in a centralized online manner under dynamic traffic demands,in order to improve the network performance.In the evaluation,we exploit historical TMs to test the performance of the obtained routing configuration in WASAR.The extensive experimental results validate that our proposed WASAR algorithm has superior performance in reducing Maximum Link Utilization(MLU)under the dynamic traffic.

The Information Modeling and Intelligent Optimization Method for Logistics Vehicle Routing and Scheduling with Multi-objective and Multi-constraint

The vehicle routing and scheduling (VRS) problem with multi-objective and multi-constraint is analyzed, considering the complexity of the modern logistics in city economy and daily life based on the system engineering. The objective and constraint includes loading, the dispatch and arrival time, transportation conditions,total cost,etc. An information model and a mathematical model are built,and a method based on knowledge and biologic immunity is put forward for optimizing and evaluating the programs dimensions in vehicle routing and scheduling with multi-objective and multi-constraints. The proposed model and method are illustrated in a case study concerning a transport network, and the result shows that more optimization solutions can be easily obtained and the method is efficient and feasible. Comparing with the standard GA and the standard GA without time constraint,the computational time of the algorithm is less in this paper. And the probability of gaining optimal solution is bigger and the result is better under the condition of multi-constraint.

An Ant Colony Algorithm Based on Cross-Layer Design for Routing and Wavelength Assignment in Optical Satellite Networks

This paper introduces an ant colony routing and wavelength assignment algorithm based on cross-layer design(CL-ACRWA),which can overcome the adverse effects of Doppler wavelength shift on data transmission in optical satellite networks. Firstly, a cross-layer optimization model is built, which considers the Doppler wavelength shift, the transmission delay as well as wavelength-continuity constraint. Then an ant colony algorithm is utilized to solve the cross-layer optimization model, resulting in finding an optimal light path satisfying the above constraints for every connection request. The performance of CL-ACRWA is measured by the communication success probability, the convergence property and the transmission delay. Simulation results show that CL-ACRWA performs well in communication success probability and has good global search ability as well as fast convergence speed. Meanwhile, the transmission delay can meet the basic requirement of real-time transmission of business.

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.

Optimized QoS Routing Algorithm

QoS routing is one of the key technologies for providing guaranteed service in IP networks. The paper focuses on the optimization problem for bandwidth constrained QoS routing, and proposes an optimal algorithm based on the global optimization of path bandwidth and hop counts. The main goal of the algorithm is to minimize the consumption of network resource, and at the same time to minimize the network congestion caused by irrational path selection. The simulation results show that our algorithm has lower call blocking rate and higher throughput than traditional algorithms.

Optimal Route Selection Method Based on Vague Sets

Optimal route selection is an important function of vehicle trac flow guidance system. Its core is to determine the index weight for measuring the route merits and to determine the evaluation method for selecting route. In this paper, subjective weighting method which relies on driver preference is used to determine the weight and the paper proposes the multi-criteria weighted decision method based on vague sets for selecting the optimal route. Examples show that, the usage of vague sets to describe route index value can provide more decision-making information for route selection.

Evaluation of the submarine debris-flow hazard risks to planned subsea pipeline systems: a case study in the Qiongdongnan Basin, South China Sea

The ever-increasing deepwater oil and gas development in the Qiongdongnan Basin,South China Sea has initiated the need to evaluate submarine debris-flow hazard risks to seafloor infrastructures.This paper presents a case study on evaluating the debris-flow hazard risks to the planned pipeline systems in this region.We used a numerical model to perform simulations to support this quantitative evaluation.First,one relict failure interpreted across the development site was simulated.The back-analysis modeling was used to validate the applicability of the rheological parameters.Then,this model was applied to forecast the runout behaviors of future debris flows originating from the unstable upslope regions considered to be the most critical to the pipeline systems surrounding the Manifolds A and B.The model results showed that the potential debris-flow hazard risks rely on the location of structures and the selection of rheological parameters.For the Manifold B and connected pipeline systems,because of their remote distances away from unstable canyon flanks,the potential debris flows impose few risks.However,the pipeline systems around the Manifold A are exposed to significant hazard risks from future debris flows with selected rheological parameters.These results are beneficial for the design of a more resilient pipeline route in consideration of future debris-flow hazard risks.

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.

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.

Study on Rural Solid Waste Collection and Transportation Mode and Route Optimization Strategy in the Cold Regions of Northern China

Rural vitalization is a major strategy for reform and development of agriculture and rural areas in China,the key task of which is improving rural living environment.Imperfect rural solid waste(RSW)collection and transportation system exacerbates the pollution of RSW to rural living environment,while it has not been established and improved in the cold region of Northern China due to climate and economy.Through the analysis of the current situation of RSW source separation,collection,transportation and disposal in China,an RSW collection and transportation system suitable for the northern cold region was developed.Considering the low winter temperature in the northern cold region,different requirements for RSW collection,transportation and terminal disposal,scattered source points and single terminal disposal nodes in rural areas,the study focused on determining the number and location of transfer stations,established a model for transfer stations selection and RSW collection and transportation routes optimization for RSW collection and transportation system,and proposed the elite retention particle swarm optimization–genetic algorithm(ERPSO–GA).The rural area of Baiquan County was taken as a representative case,the collection and transportation scheme of which was given,and the feasibility of the scheme was clarified by simulation experiment.

Intelligent fire information system based on 3D GIS

Background This work aims to build a comprehensive and effective fire emergency management system based on the Internet of Things(IoT)and achieve an actual intelligent fire rescue.A smart fire protection information system was designed based on the IoT.A detailed analysis was conducted on the problem of rescue vehicle scheduling and the evacuation of trapped persons in the process of fire rescue.Methods The intelligent fire visualization platform based on the three-dimensional(3D)Geographic Information Science(GIS)covers project overview,equipment status,equipment classification,equipment alarm information,alarm classification,alarm statistics,equipment account information,and other modules.The live video accessed through the visual interface can clearly identify the stage of the fire,which facilitates the arrangement of rescue equipment and personnel.The vehicle scheduling model in the system primarily used two objective functions to solve the Pareto Non-Dominated Solution Set Optimization:emergency rescue time and the number of vehicles.In addition,an evacuation path optimization method based on the Improved Ant Colony(IAC)algorithm was designed to realize the dynamic optimization of building fire evacuation paths.Results The experimental results indicate that all the values of detection signals were significantly larger in the smoldering fire scene at t=17s than the initial value.In addition,the probability of smoldering fire and the probability of open fire were relatively large according to the probability function of the corresponding fire situation,demonstrating that this model could detect fire.Conclusions The IAC algorithm reported here avoided the passages near the fire and spreading areas as much as possible and took the safety of the trapped persons as the premise when planning the evacuation route.Therefore,the IoT-based fire information system has important value for ensuring fire safety and carrying out emergency rescue and is worthy of popularization and application.

ROUTE OPTIMIZING ALGORITHM OF AIRPORT SURFACE BASED ON GIS

The GIS technique is used for airport surface management to study the optimization of airplane taxiway for arrival and departure flights. The shortest paths are designed for just-arrived and ready-for-departing flights of the airport. Additionally, whether the flights could confront each other head-to-head on the taxiway is judged. In regard to the airport′s security and efficiency, airplanes must continuously taxi along the shortest route and the head-to-head confrontation should not occur. Two schemes are designed: One is to change the taxiing velocity of arrival flights, the other is to delay the starting time of departure flights. This algorithm is approved by a practical example.