{"title":"iTRPL:基于多代理强化学习的智能可信 RPL 协议","authors":"Debasmita Dey, Nirnay Ghosh","doi":"10.1016/j.adhoc.2024.103586","DOIUrl":null,"url":null,"abstract":"<div><p>Routing Protocol for Low Power and Lossy Networks (RPL) is the de-facto routing standard in IoT networks. It enables nodes to collaborate and autonomously build ad-hoc networks modeled by tree-like destination-oriented direct acyclic graphs (DODAG). Despite its widespread usage in industry and healthcare domains, RPL is susceptible to insider attacks. Although the state-of-the-art RPL ensures that only authenticated nodes participate in DODAG, such hard security measures are still inadequate to prevent insider threats. This entails a need to integrate soft security mechanisms to support decision-making. This paper proposes <em>iTRPL</em>, an intelligent and behavior-based framework that incorporates trust to segregate honest and malicious nodes within a DODAG. It also leverages multi-agent reinforcement learning (MARL) to make autonomous decisions concerning the DODAG. The framework enables a parent node to compute the trust for its child and decide if the latter can join the DODAG. It tracks the behavior of the child node, updates the trust, computes the rewards (or penalties), and shares them with the root. The root aggregates the rewards/penalties of all nodes, computes the overall return, and decides via its <span><math><mi>ϵ</mi></math></span>-Greedy MARL module if the DODAG will be retained or modified for the future. A simulation-based performance evaluation demonstrates that <em>iTRPL</em> learns to make optimal decisions with time.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"iTRPL: An intelligent and trusted RPL protocol based on Multi-Agent Reinforcement Learning\",\"authors\":\"Debasmita Dey, Nirnay Ghosh\",\"doi\":\"10.1016/j.adhoc.2024.103586\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Routing Protocol for Low Power and Lossy Networks (RPL) is the de-facto routing standard in IoT networks. It enables nodes to collaborate and autonomously build ad-hoc networks modeled by tree-like destination-oriented direct acyclic graphs (DODAG). Despite its widespread usage in industry and healthcare domains, RPL is susceptible to insider attacks. Although the state-of-the-art RPL ensures that only authenticated nodes participate in DODAG, such hard security measures are still inadequate to prevent insider threats. This entails a need to integrate soft security mechanisms to support decision-making. This paper proposes <em>iTRPL</em>, an intelligent and behavior-based framework that incorporates trust to segregate honest and malicious nodes within a DODAG. It also leverages multi-agent reinforcement learning (MARL) to make autonomous decisions concerning the DODAG. The framework enables a parent node to compute the trust for its child and decide if the latter can join the DODAG. It tracks the behavior of the child node, updates the trust, computes the rewards (or penalties), and shares them with the root. The root aggregates the rewards/penalties of all nodes, computes the overall return, and decides via its <span><math><mi>ϵ</mi></math></span>-Greedy MARL module if the DODAG will be retained or modified for the future. A simulation-based performance evaluation demonstrates that <em>iTRPL</em> learns to make optimal decisions with time.</p></div>\",\"PeriodicalId\":55555,\"journal\":{\"name\":\"Ad Hoc Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ad Hoc Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1570870524001975\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ad Hoc Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1570870524001975","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
iTRPL: An intelligent and trusted RPL protocol based on Multi-Agent Reinforcement Learning
Routing Protocol for Low Power and Lossy Networks (RPL) is the de-facto routing standard in IoT networks. It enables nodes to collaborate and autonomously build ad-hoc networks modeled by tree-like destination-oriented direct acyclic graphs (DODAG). Despite its widespread usage in industry and healthcare domains, RPL is susceptible to insider attacks. Although the state-of-the-art RPL ensures that only authenticated nodes participate in DODAG, such hard security measures are still inadequate to prevent insider threats. This entails a need to integrate soft security mechanisms to support decision-making. This paper proposes iTRPL, an intelligent and behavior-based framework that incorporates trust to segregate honest and malicious nodes within a DODAG. It also leverages multi-agent reinforcement learning (MARL) to make autonomous decisions concerning the DODAG. The framework enables a parent node to compute the trust for its child and decide if the latter can join the DODAG. It tracks the behavior of the child node, updates the trust, computes the rewards (or penalties), and shares them with the root. The root aggregates the rewards/penalties of all nodes, computes the overall return, and decides via its -Greedy MARL module if the DODAG will be retained or modified for the future. A simulation-based performance evaluation demonstrates that iTRPL learns to make optimal decisions with time.
期刊介绍:
The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to:
Mobile and Wireless Ad Hoc Networks
Sensor Networks
Wireless Local and Personal Area Networks
Home Networks
Ad Hoc Networks of Autonomous Intelligent Systems
Novel Architectures for Ad Hoc and Sensor Networks
Self-organizing Network Architectures and Protocols
Transport Layer Protocols
Routing protocols (unicast, multicast, geocast, etc.)
Media Access Control Techniques
Error Control Schemes
Power-Aware, Low-Power and Energy-Efficient Designs
Synchronization and Scheduling Issues
Mobility Management
Mobility-Tolerant Communication Protocols
Location Tracking and Location-based Services
Resource and Information Management
Security and Fault-Tolerance Issues
Hardware and Software Platforms, Systems, and Testbeds
Experimental and Prototype Results
Quality-of-Service Issues
Cross-Layer Interactions
Scalability Issues
Performance Analysis and Simulation of Protocols.