{"title":"RPL协议下基于安全群智能的恶意低功耗有损网络路径选择框架","authors":"Hanin Almutairi , Salem AlJanah , Ning Zhang","doi":"10.1016/j.iot.2025.101776","DOIUrl":null,"url":null,"abstract":"<div><div>Low-power and Lossy Networks (LLNs) face persistent challenges, including dynamic topologies, unreliable links, limited energy, and constrained computational resources. These issues are exacerbated under malicious conditions such as Packet Dropping Attacks (PDAs), where conventional routing and security mechanisms fall short due to their high computational overhead. To address these challenges, this paper proposes the Secured Swarm Intelligence-based Path Selection (S-SIPaS) framework, designed to enhance reliability and security in Malicious LLNs (MLLNs). S-SIPaS builds on our previous SIPaS framework by integrating a lightweight trust model and a novel Secured Ant Colony Objective Function (S-ACOF) into the RPL protocol. S-ACOF applies Ant Colony Optimisation (ACO) principles to compute globally optimal, trustworthy paths while reducing energy consumption and control overhead. A key feature of S-SIPaS is its three-phase trust model: monitoring, trust measurement, and trust determination, which detects and isolates malicious nodes based on packet-forwarding behaviour, without relying on cryptographic techniques.</div><div>The framework combines multiple routing metrics, including physical distance, energy level, link quality, and trust score, enabling adaptive and efficient path selection in dynamic LLNs. Simulation results show that S-SIPaS improves Packet Delivery Ratio (PDR) by up to 51% over existing methods, especially in high-density and high-attack scenarios.</div><div>Despite strong performance, the framework has limitations: (i) it requires C1-class nodes (e.g., Z1); (ii) evaluation is limited to simulations; and (iii) it currently addresses only PDA threats and static topologies. Overall, S-SIPaS offers an effective, scalable, and secure routing solution for enhancing MLLNs and IoT systems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101776"},"PeriodicalIF":7.6000,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Secured Swarm Intelligence-based Path Selection framework for Malicious Low-power and Lossy Networks under RPL protocol\",\"authors\":\"Hanin Almutairi , Salem AlJanah , Ning Zhang\",\"doi\":\"10.1016/j.iot.2025.101776\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Low-power and Lossy Networks (LLNs) face persistent challenges, including dynamic topologies, unreliable links, limited energy, and constrained computational resources. These issues are exacerbated under malicious conditions such as Packet Dropping Attacks (PDAs), where conventional routing and security mechanisms fall short due to their high computational overhead. To address these challenges, this paper proposes the Secured Swarm Intelligence-based Path Selection (S-SIPaS) framework, designed to enhance reliability and security in Malicious LLNs (MLLNs). S-SIPaS builds on our previous SIPaS framework by integrating a lightweight trust model and a novel Secured Ant Colony Objective Function (S-ACOF) into the RPL protocol. S-ACOF applies Ant Colony Optimisation (ACO) principles to compute globally optimal, trustworthy paths while reducing energy consumption and control overhead. A key feature of S-SIPaS is its three-phase trust model: monitoring, trust measurement, and trust determination, which detects and isolates malicious nodes based on packet-forwarding behaviour, without relying on cryptographic techniques.</div><div>The framework combines multiple routing metrics, including physical distance, energy level, link quality, and trust score, enabling adaptive and efficient path selection in dynamic LLNs. Simulation results show that S-SIPaS improves Packet Delivery Ratio (PDR) by up to 51% over existing methods, especially in high-density and high-attack scenarios.</div><div>Despite strong performance, the framework has limitations: (i) it requires C1-class nodes (e.g., Z1); (ii) evaluation is limited to simulations; and (iii) it currently addresses only PDA threats and static topologies. 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引用次数: 0
摘要
低功耗和有损网络(lln)面临着持续的挑战,包括动态拓扑、不可靠的链路、有限的能量和受限的计算资源。这些问题在诸如丢包攻击(Packet drop Attacks, pda)之类的恶意情况下更加严重,在这种情况下,传统的路由和安全机制由于其高计算开销而无法发挥作用。为了应对这些挑战,本文提出了基于安全群体智能的路径选择(S-SIPaS)框架,旨在提高恶意lln (mlln)的可靠性和安全性。S-SIPaS建立在我们以前的SIPaS框架的基础上,通过将轻量级信任模型和新颖的安全蚁群目标函数(S-ACOF)集成到RPL协议中。S-ACOF应用蚁群优化(ACO)原理来计算全局最优,可信赖的路径,同时降低能耗和控制开销。S-SIPaS的一个关键特征是它的三相信任模型:监控、信任测量和信任确定,它根据数据包转发行为检测和隔离恶意节点,而不依赖于加密技术。该框架结合了物理距离、能量水平、链路质量和信任评分等多种路由指标,实现了动态lln中自适应、高效的路径选择。仿真结果表明,与现有方法相比,S-SIPaS可将PDR (Packet Delivery Ratio)提高51%,特别是在高密度和高攻击场景下。尽管性能强大,但该框架仍有局限性:(i)它需要c1类节点(例如,Z1);(ii)评估仅限于模拟;(iii)它目前只处理PDA威胁和静态拓扑。总体而言,S-SIPaS为增强mln和物联网系统提供了有效、可扩展和安全的路由解决方案。
A Secured Swarm Intelligence-based Path Selection framework for Malicious Low-power and Lossy Networks under RPL protocol
Low-power and Lossy Networks (LLNs) face persistent challenges, including dynamic topologies, unreliable links, limited energy, and constrained computational resources. These issues are exacerbated under malicious conditions such as Packet Dropping Attacks (PDAs), where conventional routing and security mechanisms fall short due to their high computational overhead. To address these challenges, this paper proposes the Secured Swarm Intelligence-based Path Selection (S-SIPaS) framework, designed to enhance reliability and security in Malicious LLNs (MLLNs). S-SIPaS builds on our previous SIPaS framework by integrating a lightweight trust model and a novel Secured Ant Colony Objective Function (S-ACOF) into the RPL protocol. S-ACOF applies Ant Colony Optimisation (ACO) principles to compute globally optimal, trustworthy paths while reducing energy consumption and control overhead. A key feature of S-SIPaS is its three-phase trust model: monitoring, trust measurement, and trust determination, which detects and isolates malicious nodes based on packet-forwarding behaviour, without relying on cryptographic techniques.
The framework combines multiple routing metrics, including physical distance, energy level, link quality, and trust score, enabling adaptive and efficient path selection in dynamic LLNs. Simulation results show that S-SIPaS improves Packet Delivery Ratio (PDR) by up to 51% over existing methods, especially in high-density and high-attack scenarios.
Despite strong performance, the framework has limitations: (i) it requires C1-class nodes (e.g., Z1); (ii) evaluation is limited to simulations; and (iii) it currently addresses only PDA threats and static topologies. Overall, S-SIPaS offers an effective, scalable, and secure routing solution for enhancing MLLNs and IoT systems.
期刊介绍:
Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.
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Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.