Human intrusion detection for high-speed railway perimeter under all-weather condition

Purpose – The paper aims to solve the problem of personnel intrusion identification within the limits of highspeed railways. It adopts the fusion method of millimeter wave radar and camera to improve the accuracy ofobject recognition in dark and harsh weather conditions.Design/methodology/approach – This paper adopts the fusion strategy of radar and camera linkage toachieve focus amplification of long-distance targets and solves the problem of low illumination by laser lightfilling of the focus point. In order to improve the recognition effect, this paper adopts the YOLOv8 algorithm formulti-scale target recognition. In addition, for the image distortion caused by bad weather, this paper proposesa linkage and tracking fusion strategy to output the correct alarm results.Findings – Simulated intrusion tests show that the proposed method can effectively detect human intrusionwithin 0–200 m during the day and night in sunny weather and can achieve more than 80% recognitionaccuracy for extreme severe weather conditions.Originality/value – (1) The authors propose a personnel intrusion monitoring scheme based on the fusion ofmillimeter wave radar and camera, achieving all-weather intrusion monitoring;(2) The authors propose a newmulti-level fusion algorithm based on linkage and tracking to achieve intrusion target monitoring underadverse weather conditions;(3) The authors have conducted a large number of innovative simulationexperiments to verify the effectiveness of the method proposed in this article.

Enhanced Mechanism for Link Failure Rerouting in Software-Defined Exchange Point Networks

Internet Exchange Point(IXP)is a system that increases network bandwidth performance.Internet exchange points facilitate interconnection among network providers,including Internet Service Providers(ISPs)andContent Delivery Providers(CDNs).To improve service management,Internet exchange point providers have adopted the Software Defined Network(SDN)paradigm.This implementation is known as a Software-Defined Exchange Point(SDX).It improves network providers’operations and management.However,performance issues still exist,particularly with multi-hop topologies.These issues include switch memory costs,packet processing latency,and link failure recovery delays.The paper proposes Enhanced Link Failure Rerouting(ELFR),an improved mechanism for rerouting link failures in software-defined exchange point networks.The proposed mechanism aims to minimize packet processing time for fast link failure recovery and enhance path calculation efficiency while reducing switch storage overhead by exploiting the Programming Protocol-independent Packet Processors(P4)features.The paper presents the proposed mechanisms’efficiency by utilizing advanced algorithms and demonstrating improved performance in packet processing speed,path calculation effectiveness,and switch storage management compared to current mechanisms.The proposed mechanism shows significant improvements,leading to a 37.5%decrease in Recovery Time(RT)and a 33.33%decrease in both Calculation Time(CT)and Computational Overhead(CO)when compared to current mechanisms.The study highlights the effectiveness and resource efficiency of the proposed mechanism in effectively resolving crucial issues inmulti-hop software-defined exchange point networks.

Irrigated Agriculture Facing the Challenge of Climate Change: Adaptation Strategies for Farmers in the Irrigated Perimeters of Môle Saint-Nicolas, Haiti

Môle Saint-Nicolas, like all other communes in the Republic of Haiti, faces increasing climate variability, impacting agricultural production and water resources. Consequently, there is a pressing need for adaptation to these climatic changes. This research aims to showcase the adaptation strategies deployed by farmers to cope with the increasing climate variability. Surveys were conducted through group and individual discussions with a randomly selected cohort of 150 farmers. Two types of analysis were performed: quantitative and qualitative. The quantitative data analysis was conducted using Statistical Package for the Social Sciences (SPSS) software. The findings reveal that farmers have perceived changes in rainfall patterns, temperature, wind, and their environment. These changes manifest as irregular rainfall, higher temperatures, prolonged drought periods, violent winds accompanied by rain, premature cessation of rains, and reduced flow from water sources. In response, the most common adaptation strategies adopted include selecting new cultivars, early-maturing varieties, crop rotation and diversification, canal dredging, new soil preparation methods, upstream water source protection, and micro-watershed management. The significance of this research lies in its contribution to enhancing farmers’ adaptive capacities by alerting stakeholders in the irrigated perimeters about the consequences of climate change, thereby incorporating the real needs of farmers in future projects.

Effective Controller Placement in Software-Defined Internet-of-Things Leveraging Deep Q-Learning (DQL)

The controller is a main component in the Software-Defined Networking(SDN)framework,which plays a significant role in enabling programmability and orchestration for 5G and next-generation networks.In SDN,frequent communication occurs between network switches and the controller,which manages and directs traffic flows.If the controller is not strategically placed within the network,this communication can experience increased delays,negatively affecting network performance.Specifically,an improperly placed controller can lead to higher end-to-end(E2E)delay,as switches must traverse more hops or encounter greater propagation delays when communicating with the controller.This paper introduces a novel approach using Deep Q-Learning(DQL)to dynamically place controllers in Software-Defined Internet of Things(SD-IoT)environments,with the goal of minimizing E2E delay between switches and controllers.E2E delay,a crucial metric for network performance,is influenced by two key factors:hop count,which measures the number of network nodes data must traverse,and propagation delay,which accounts for the physical distance between nodes.Our approach models the controller placement problem as a Markov Decision Process(MDP).In this model,the network configuration at any given time is represented as a“state,”while“actions”correspond to potential decisions regarding the placement of controllers or the reassignment of switches to controllers.Using a Deep Q-Network(DQN)to approximate the Q-function,the system learns the optimal controller placement by maximizing the cumulative reward,which is defined as the negative of the E2E delay.Essentially,the lower the delay,the higher the reward the system receives,enabling it to continuously improve its controller placement strategy.The experimental results show that our DQL-based method significantly reduces E2E delay when compared to traditional benchmark placement strategies.By dynamically learning from the network’s real-time conditions,the proposed method ensures that controller placement remains efficient and responsive,reducing communication delays and enhancing overall network performance.

OTP-Based Software-Defined Cloud Architecture for Secure Dynamic Routing

In the current era,anyone can freely access the Internet thanks to the development of information and communication technology.The cloud is attracting attention due to its ability to meet continuous user demands for resources.Additionally,Cloud is effective for systems with large data flow such as the Internet of Things(IoT)systems and Smart Cities.Nonetheless,the use of traditional networking technology in the cloud causes network traffic overload and network security problems.Therefore,the cloud requires efficient networking technology to solve the existing challenges.In this paper,we propose one-time password-based software-defined cloud architecture for secure dynamic routing to mitigating the above-mention issues.The proposed cloud architecture provides a secure data path through dynamic routing using One-Time Internet Protocol(OTIP)algorithm between each layer.On the network side,we use software-defined technology to provide efficient network management and data security.We introduce a software-defined cloud architecture that applies OTIP algorithms for secure dynamic routing.We conduct a comparative analysis between general IP communication and proposed OTIP communication architecture.It evaluates the performance of OTIP algorithms.Finally,we examine the proposed software-defined cloud architecture,including how to apply OTIP in secure dynamic routing according to the results of the comparative analysis.

ON A PERIMETER-PRESERVING PLANE CURVE FLOW

The purpose of this paper is to investigate a new type of evolution problem for closed convex plane curves which will preserves the perimeter of the curve but expands the enclosed area and the final limiting curve is a circle in the Hausdorff metric in the plane.

On Reliability-optimized Controller Placement for Software-Defined Networks

By decoupling control plane and data plane,Software-Defined Networking(SDN) approach simplifies network management and speeds up network innovations.These benefits have led not only to prototypes,but also real SDN deployments.For wide-area SDN deployments,multiple controllers are often required,and the placement of these controllers becomes a particularly important task in the SDN context.This paper studies the problem of placing controllers in SDNs,so as to maximize the reliability of SDN control networks.We present a novel metric,called expected percentage of control path loss,to characterize the reliability of SDN control networks.We formulate the reliability-aware control placement problem,prove its NP-hardness,and examine several placement algorithms that can solve this problem.Through extensive simulations using real topologies,we show how the number of controllers and their placement influence the reliability of SDN control networks.Besides,we also found that,through strategic controller placement,the reliability of SDN control networks can be significantly improved without introducing unacceptable switch-to-controller latencies.

A Survey: Typical Security Issues of Software-Defined Networking

Software-Defined Networking (SDN) has been a hot topic for future network development, which implements the different layers of control plane and data plane respectively. Despite providing high openness and programmability, the “three-layer two-interface” architecture of SDN changes the traditional network and increases the network attack nodes, which results in new security issues. In this paper, we firstly introduced the background, architecture and working process of SDN. Secondly, we summarized and analyzed the typical security issues from north to south: application layer, northbound interface, control layer, southbound interface and data layer. Another contribution is to review and analyze the existing solutions and latest research progress of each layer, mainly including: authorized authentication module, application isolation, DoS/DDoS defense, multi-controller deployment and flow rule consistency detection. Finally, a conclusion about the future works of SDN security and an idealized global security architecture is proposed.

Quality of Service Improvement with Optimal Software-Defined Networking Controller and Control Plane Clustering

The controller is indispensable in software-defined networking(SDN).With several features,controllers monitor the network and respond promptly to dynamic changes.Their performance affects the quality-of-service(QoS)in SDN.Every controller supports a set of features.However,the support of the features may be more prominent in one controller.Moreover,a single controller leads to performance,single-point-of-failure(SPOF),and scalability problems.To overcome this,a controller with an optimum feature set must be available for SDN.Furthermore,a cluster of optimum feature set controllers will overcome an SPOF and improve the QoS in SDN.Herein,leveraging an analytical network process(ANP),we rank SDN controllers regarding their supporting features and create a hierarchical control plane based cluster(HCPC)of the highly ranked controller computed using the ANP,evaluating their performance for the OS3E topology.The results demonstrated in Mininet reveal that a HCPC environment with an optimum controller achieves an improved QoS.Moreover,the experimental results validated in Mininet show that our proposed approach surpasses the existing distributed controller clustering(DCC)schemes in terms of several performance metrics i.e.,delay,jitter,throughput,load balancing,scalability and CPU(central processing unit)utilization.

Estimating the minimum in-stream flow requirements via wetted perimeter method based on curvature and slope techniques

Under the assumptions of triangular cross section channel and uniform stable flow, an analytical solution of the minimum ecological in-stream flow requirement （MEIFR） is deduced. Based on the analytical solution, the uncertainty of the wetted perimeter method is analyzed by comparing the two techniques for the determination of the critical point on the relationship curve between wetted perimeter, P and discharge, Q. It is clearly shown that the results of MEIFR based on curvature technique （corresponding to the maximum curvature） and slope technique （slope being 1） are significantly different. On the P-Q curve, the slope of the critical point with the maximum curvature is 0.39 and the MEIFR varied prominently with the change of the slope threshold. This indicates that if a certain value of the slope threshold is not available for slope technique, curvature technique may be a better choice. By applying the analytical solution of MEIFR in the losing rivers of the Western Route South-to-North Water Transfer Project in China, the MEIFR value via curvature technique is 2.5%-23.7% of the multi-year average annual discharge, while that for slope technique is 11%-105.7%. General conclusions would rely on the more detailed research for all kinds of cross-sections.

Perimeter traffic control strategy based on macroscopic fundamental diagrams

A perimeter traffic signal control strategy is proposed based on the macroscopic fundamental diagram theory(MFD)to solve the signal control problem in oversaturated states.First,the MFD of a specific regional network can be derived using VSS IM simulation software.Secondly,the maximum number of cumulative vehicles that the network can accommodate is determined based on the MFD.Then,through monitoring the influx flow,the number of vehicles existing in and exiting from the network,a perimeter traffic control model is proposed to optimize the signal timing of the boundary intersections.Finally,a virtual network simulation model is established and three different kinds o f traffic demand are loaded into the network.Simulation results show that a fer the strategy implementation,the number o f vehicles accumulating in the network can be kept near the optimal value,while the number o f both entering and exiting vehicles increases significantly and the road network can be maintained at a large capacity.Simultaneously,the queue length at the approach of the border intersections is reasonably controlled and vehicles entering and exiting the network can maintain a more efficient and stable speed.The network performance indices such as the average traffic delay and average number of stops can be improved to a certain degree,thus verifying the effectiveness and feasibility of the perimeter control strategy.

Challenge-based collaborative intrusion detection in software-defined networking: An evaluation

Software-Defined Networking(SDN)is an emerging architecture that enables a computer network to be intelligently and centrally controlled via software applications.It can help manage the whole network environment in a consistent and holistic way,without the need of understanding the underlying network structure.At present,SDN may face many challenges like insider attacks,i.e.,the centralized control plane would be attacked by malicious underlying devices and switches.To protect the security of SDN,effective detection approaches are indispensable.In the literature,challenge-based collaborative intrusion detection networks(CIDNs)are an effective detection framework in identifying malicious nodes.It calculates the nodes'reputation and detects a malicious node by sending out a special message called a challenge.In this work,we devise a challenge-based CIDN in SDN and measure its performance against malicious internal nodes.Our results demonstrate that such a mechanism can be effective in SDN environments.

On the use of the genetic programming for balanced load distribution in software-defined networks

As a new networking paradigm,Software-Defined Networking(SDN)enables us to cope with the limitations of traditional networks.SDN uses a controller that has a global view of the network and switch devices which act as packet forwarding hardware,known as“OpenFlow switches”.Since load balancing service is essential to distribute workload across servers in data centers,we propose an effective load balancing scheme in SDN,using a genetic programming approach,called Genetic Programming based Load Balancing(GPLB).We formulate the problem to find a path:1)with the best bottleneck switch which has the lowest capacity within bottleneck switches of each path,2)with the shortest path,and 3)requiring the less possible operations.For the purpose of choosing the real-time least loaded path,GPLB immediately calculates the integrated load of paths based on the information that receives from the SDN controller.Hence,in this design,the controller sends the load information of each path to the load balancing algorithm periodically and then the load balancing algorithm returns a least loaded path to the controller.In this paper,we use the Mininet emulator and the OpenDaylight controller to evaluate the effectiveness of the GPLB.The simulative study of the GPLB shows that there is a big improvement in performance metrics and the latency and the jitter are minimized.The GPLB also has the maximum throughput in comparison with related works and has performed better in the heavy traffic situation.The results show that our model stands smartly while not increasing further overhead.

Orchestrating Network Functions in Software-Defined Networks

Software.defined networking(SDN) enables third.part companies to participate in the network function innovations. A number of instances for one network function will inevitably co.exist in the network. Although some orchestration architecture has been proposed to chain network functions, rare works are focused on how to optimize this process. In this paper, we propose an optimized model for network function orchestration, function combination model(FCM). Our main contributions are as following. First, network functions are featured with a new abstraction, and are open to external providers. And FCM identifies network functions using unique type, and organizes their instances distributed over the network with the appropriate way. Second, with the specialized demands, we can combine function instances under the global network views, and formulate it into the problem of Boolean linear program(BLP). A simulated annealing algorithm is designed to approach optimal solution for this BLP. Finally, the numerical experiment demonstrates that our model can create outstanding composite schemas efficiently.

Software-Defined Networking

Software- defined networking （SDN） is a promising technology for next-generation networking and has attracted much attention from academics, network equipment manufacturer, network operators, and service providers. It has found center, and enterprise networks. applications in mobile, data The SDN architecture has a centralized, programmable control plane that is separate from the data plane. SDN also provides the ability to control and manage virtualized resources and networks without requiring new hardware technologies. This is a major shift in networking technologies.

A movement-assisted software-defined sensor network with NFV support

A flexible and controllable movement-assisted software-defined sensor network(MA-SDSN)based on the software-defined network(SDN)and network function virtualization(NFV)is proposed.First,a three-layer fundamental architecture is proposed to overcome the inherent distributed management and rigidity of the conventional wireless sensor networks.Furthermore,the platform for research and development of MA-SDSN is established,and the dumb node(DN),the software-defined node(SN)and the movement-assisted node(MN)are designed and implemented.Then,the southbound application programming interface(API)is designed to provide a series of frames for communication between controllers and sensor nodes.The northbound API is developed and demonstrated overall and in detail.The functions of the controller are presented including topology discovery,dynamic networking,packet processing,mobility management and virtualization.Followed by the MA-SDSN network model,a Markov chain-based movement-assisted weighted relocation(MMWR)topology control algorithm is proposed to redeploy the MNs based on the node status and weight.Simulation results and analysis indicate that the proposed algorithm based on the MA-SDSN extends network lifetime with a lower average power consumption.

A Multi-Agent Reinforcement Learning-Based Collaborative Jamming System: Algorithm Design and Software-Defined Radio Implementation

In multi-agent confrontation scenarios, a jammer is constrained by the single limited performance and inefficiency of practical application. To cope with these issues, this paper aims to investigate the multi-agent jamming problem in a multi-user scenario, where the coordination between the jammers is considered. Firstly, a multi-agent Markov decision process (MDP) framework is used to model and analyze the multi-agent jamming problem. Secondly, a collaborative multi-agent jamming algorithm (CMJA) based on reinforcement learning is proposed. Finally, an actual intelligent jamming system is designed and built based on software-defined radio (SDR) platform for simulation and platform verification. The simulation and platform verification results show that the proposed CMJA algorithm outperforms the independent Q-learning method and provides a better jamming effect.

EARS: Intelligence-Driven Experiential Network Architecture for Automatic Routing in Software-Defined Networking

Software-Defined Networking(SDN)adapts logically-centralized control by decoupling control plane from data plane and provides the efficient use of network resources.However,due to the limitation of traditional routing strategies relying on manual configuration,SDN may suffer from link congestion and inefficient bandwidth allocation among flows,which could degrade network performance significantly.In this paper,we propose EARS,an intelligence-driven experiential network architecture for automatic routing.EARS adapts deep reinforcement learning(DRL)to simulate the human methods of learning experiential knowledge,employs the closed-loop network control mechanism incorporating with network monitoring technologies to realize the interaction with network environment.The proposed EARS can learn to make better control decision from its own experience by interacting with network environment and optimize the network intelligently by adjusting services and resources offered based on network requirements and environmental conditions.Under the network architecture,we design the network utility function with throughput and delay awareness,differentiate flows based on their size characteristics,and design a DDPGbased automatic routing algorithm as DRL decision brain to find the near-optimal paths for mice and elephant flows.To validate the network architecture,we implement it on a real network environment.Extensive simulation results show that EARS significantly improve the network throughput and reduces the average packet delay in comparison with baseline schemes(e.g.OSPF,ECMP).

A Game-Theoretic Approach to Elastic Control in Software-Defined Networking

Elastic control could balance the distributed control plane in Software-Defined Networking(SDN). Dynamic switch migration has been proposed to achieve it. However, existing schemes mainly focus on how to execute migration operation, but not why. This paper designs a decision-making mechanism based on zero-sum game theory to reelect a new controller as the master for migrated switches. It first chooses a switch for migration in the heavy controller which invites its neighbors as the game players to compete for the master role of this switch in the game-playing field(GPF) which is an occasional and loose domain for game-playing. Second, based on the concept of GPF, we design a decentralized strategy to play the game and determine which player as the final master. We implement it by extending the Open Flow protocol. Finally, numerical results demonstrate that our distributed strategy can approach elastic control plane with better performance.

Contribution of Geomatics to the Delimitation of Protection Perimeters: Case of Agbo River in Agboville (South-East of Côte d’Ivoire)

The present study aims to delimit protection perimeters around the “Agbo” river in Agboville in order to reduce the risks of pollution of this water resource. The methodological approach consisted first all in assessing the vulnerability of the resource and then in determining the protection perimeters based on the vulnerability map and previous studies. Five parameters (slope, land use, soil type, annual runoff and drainage density) were used. These parameters were then weighted using the Saaty multicriteria analysis method. The vulnerability map reveals four classes (very low, low, medium and high) with a dominance of the low class (41.35% of the area). Three protection perimeters were delineated (immediate, close and remote). The immediate protection perimeter is delimited at a distance of 200 m around the SODECI water intake on the “Agbo” river and covers the areas with a strong influence on the vulnerability to pollution of the river. The closer protection perimeter at a distance of 1000 m, is represented by the zones with strong influence on the vulnerability of the resource and the direction of water flow in the watershed. Finally, the remote protection perimeter covers a large part of the catchment area with a surface area of 510.36 km2 and takes into account the zones that have an influence on the vulnerability to pollution of the resource. These delimited protection perimeters can serve as a guide for the management and protection of the water intake in Agboville as well as for land use planning in this area.