Low Power And Lossy Networks Research Articles

Efficient multicast forwarding in low power and lossy networks under RPL non‐storing mode

SummaryThe Internet of Things (IoT) continues to find novel applications across various domains. These applications require the support of efficient communication protocols in the Low Power and Lossy Networks (LLNs). The development of efficient multicast protocols is one such area that requires critical research attention. Efficient multicast protocols are especially important in the non‐storing mode of IPv6 Routing Protocol for LLNs (RPL), considering its applicability in real‐world IoT applications. However, the development of non‐storing RPL multicast protocols needs to handle the intricacies associated with source routing. Addressing these issues, this paper presents a novel multicast protocol named Multicast Forwarding in LLNs under Non‐storing mode (MFLN). MFLN uses Bloom Filters to minimize the overhead that comes with source routing. While retaining the best features of multicast protocols operating in the storing mode of RPL such as bidirectional traffic and cross‐layer optimization with ContikiMAC, MFLN also includes a novel metric named Mode Selection Metric (MSM) to decide the suitable link‐layer forwarding method. MSM makes this decision based on the ability of a node, in terms of its residual energy, to perform multiple link‐layer unicasts. Thus, MFLN saves the node battery from running out and at the same time reduces the duplicate packets created due to link‐layer broadcasts. The simulations conducted using Contiki‐COOJA indicate considerable improvements in terms of packet delivery ratio, energy consumption per delivered packet, packet transmissions, end‐to‐end delay, and normalized routing overhead for the proposed protocol.

Modeling end-to-end delays in TSCH wireless sensor networks using queuing theory and combinatorics

Wireless communication offers significant advantages in terms of flexibility, coverage and maintenance compared to wired solutions and is being actively deployed in the industry. IEEE 802.15.4 standardizes the Physical and the Medium Access Control (MAC) layer for Low Power and Lossy Networks (LLNs) and features Timeslotted Channel Hopping (TSCH) for reliable, low-latency communication with scheduling capabilities. Multiple scheduling schemes were proposed to address Quality of Service (QoS) in challenging scenarios. However, most of them are evaluated through simulations and experiments, which are often time-consuming and may be difficult to reproduce. Analytical modeling of TSCH performance is lacking, as only one-hop communication with simplified traffic patterns is considered in state-of-the-art. This work proposes a new framework based on queuing theory and combinatorics to evaluate end-to-end delays in multihop TSCH networks of arbitrary topology, traffic and link conditions. The framework is validated in simulations using OMNeT++ and shows below 6% root-mean-square error (RMSE), providing quick and reliable latency estimation tool to support decision-making and enable formalized comparison of existing scheduling solutions.

Li-MSD: A lightweight mitigation solution for DAO insider attack in RPL-based IoT

Many IoT applications run on a wireless infrastructure supported by resource-constrained nodes which is popularly known as Low-Power and Lossy Networks (LLNs). Currently, LLNs play a vital role in digital transformation of industries. The resource limitations of LLNs restrict the usage of traditional routing protocols and therefore require an energy-efficient routing solution. IETF’s Routing Protocol for Low-power Lossy Networks (RPL, pronounced “ripple”) is one of the most popular energy-efficient protocols for LLNs, specified in RFC 6550. In RPL, Destination Advertisement Object (DAO) control message is transmitted by a child node to pass on its reachability information to its immediate parent or root node. An attacker may exploit the insecure DAO sending mechanism of RPL to perform “DAO insider attack” by transmitting DAO multiple times. This paper shows that an aggressive DAO insider attacker can drastically degrade network performance. We propose a Lightweight Mitigation Solution for DAO insider attack, which is termed as “Li-MSD”. Li-MSD uses a blacklisting strategy to mitigate the attack and restore RPL performance, significantly. By using simulations, it is shown that Li-MSD outperforms the existing solution in the literature.

Routing attack induced anomaly detection in IoT network using RBM-LSTM

The network of resource constraint devices, also known as the Low power and Lossy Networks (LLNs), constitutes the edge tire of the Internet of Things applications like smart homes, smart cities, and connected vehicles. The IPv6 Routing Protocol over Low power and lossy networks (RPL) ensures efficient routing in the edge tire of the IoT environment. However, RPL has inherent vulnerabilities that allow malicious insider entities to instigate several security attacks in the IoT network. As a result, the IoT networks suffer from resource depletion, performance degradation, and traffic disruption. Recent literature discusses several machine learning algorithms to detect one or more routing attacks. However, IoT infrastructures are expanding, and so are the attack surfaces. Therefore, it is essential to have a solution that can adapt to this change. This paper introduces a comprehensive framework to detect routing attacks within Low Power and Lossy Networks (LLNs). The proposed solution leverages deep learning by combining Restricted Boltzmann Machine (RBM) and Long Short-Term Memory (LSTM). The framework is trained on 11 network parameters to understand and predict normal network behavior. Anomalies, identified as deviations from the forecast trends, serve as indicators of potential routing attacks and thus address vulnerabilities in the RPL.

TurboBlom: A light and resilient key predistribution scheme with application to Internet of Things.

In the Internet of Things (IoT), there are often devices that are computationally too constrained to establish a security key using traditional key distribution mechanisms such as those based on the Diffie-Hellman key exchange. To address this, current solution commonly rely on key predistribution schemes (KPSs). Among KPSs, the Blom scheme provably provides the highest resilience against node capture attacks. This, however, comes at high computational overhead, because the Blom scheme requires many multiplications over a large finite field. To overcome this computational overhead, we present TurboBlom, a novel amendment of the Blom scheme. TurboBlom circumvents the need for field multiplications by utilizing specialized generator matrices, such as random zero-one matrices. We demonstrate that, through this approach, TurboBlom can significantly reduce the computational overhead of the Blom scheme by orders of magnitude. In our next key finding, we demonstrate that TurboBlom offers a level of resilience against node capture that is virtually on par with the Blom scheme. Notably, we prove that the gap between the resilience of the two schemes is exponentially small. These features of TurboBlom (i.e., low computational overhead and high resilience) make it suitable for computationally constrained devices. Such devices exist in abundance in IoT, for example, as part of Low Power and Lossy Networks (LLNs). To demonstrate a sample application of TurboBlom, we show how to use it to enable sender authentication in the Routing Protocol for LLNs (RPL), a standard routing protocol for IoT.

Trust-Based Optimized Reporting for Detection and Prevention of Black Hole Attacks in Low-Power and Lossy Green IoT Networks.

The Internet of Things (IoT) is empowering various sectors and aspects of daily life. Green IoT systems typically involve Low-Power and Lossy Networks (LLNs) with resource-constrained nodes. Lightweight routing protocols, such as the Routing Protocol for Low-Power and Lossy Networks (RPL), are increasingly being applied for efficient communication in LLNs. However, RPL is susceptible to various attacks, such as the black hole attack, which compromises network security. The existing black hole attack detection methods in Green IoT rely on static thresholds and unreliable metrics to compute trust scores. This results in increasing false positive rates, especially in resource-constrained IoT environments. To overcome these limitations, we propose a delta-threshold-based trust model called the Optimized Reporting Module (ORM) to mitigate black hole attacks in Green IoT systems. The proposed scheme comprises both direct trust and indirect trust and utilizes a forgetting curve. Direct trust is derived from performance metrics, including honesty, dishonesty, energy, and unselfishness. Indirect trust requires the use of similarity. The forgetting curve provides a mechanism to consider the most significant and recent feedback from direct and indirect trust. To assess the efficacy of the proposed scheme, we compare it with the well-known trust-based attack detection scheme. Simulation results demonstrate that the proposed scheme has a higher detection rate and low false positive alarms compared to the existing scheme, confirming the applicability of the proposed scheme in green IoT systems.

A Survey on Routing Solutions for Low-Power and Lossy Networks: Toward a Reliable Path-Finding Approach

Low-Power and Lossy Networks (LLNs) have grown rapidly in recent years owing to the increased adoption of Internet of Things (IoT) and Machine-to-Machine (M2M) applications across various industries, including smart homes, industrial automation, healthcare, and smart cities. Owing to the characteristics of LLNs, such as Lossy channels and limited power, generic routing solutions designed for non-LLNs may not be adequate in terms of delivery reliability and routing efficiency. Consequently, a routing protocol for LLNs (RPL) was designed. Several RPL objective functions have been proposed to enhance the routing reliability in LLNs. This paper analyses these solutions against performance and security requirements to identify their limitations. Firstly, it discusses the characteristics and security issues of LLN and their impact on packet delivery reliability and routing efficiency. Secondly, it provides a comprehensive analysis of routing solutions and identifies existing limitations. Thirdly, based on these limitations, this paper highlights the need for a reliable and efficient path-finding solution for LLNs.

ETXRE: Energy and delay efficient routing metric for RPL protocol and wireless sensor networks

AbstractInternet of Things is an emerging paradigm based on interconnecting physical and virtual objects with each other and to the Internet. Most connected things fall into the category of constrained devices, with restricted resources (processing power, memory, and energy). These low‐power and lossy networks (LLNs) are known for their instability, high loss rates and low data rates, which makes routing one of the most challenging problems in low‐cost communications. A routing protocol for low‐power and lossy networks (RPL) is a proactive dynamic routing protocol based on IPv6. This protocol defines an objective function (OF) that utilises a set of metrics to select the best possible path to the destination. Minimum rank hysteresis objective function (MRHOF) and objective function zero (OF0) are the most basic OFs, where the first one selects the path to the sink based on the expected transmission count (ETX) metric, and OF0 is based on the hop count (HC). These two metrics prioritise either brute performance (i.e. ETX) or simplicity (i.e. HC). Therefore, using a single metric with an OF can either limit the performance or have an inefficient impact on load management and energy consumption. To overcome these challenges, a routing metric based on MRHOF OF which takes into consideration the link‐based routing metric (i.e. ETX) and node‐based metric (i.e. remaining energy) for route selection is provided. Expected transmission count remaining energy (ETXRE) is evaluated through 36 scenarios with different parameters. Preliminary results show that ETXRE outperforms ETX and RE in terms of end‐to‐end delay by an average of at least 17%, packet delay by 13% and consumes 10% less energy.

Multidirectional Trust-Based Security Mechanisms for Sinkhole Attack Detection in the RPL Routing Protocol for Internet of Things

The Internet of Things (IoT) has gained popularity in recent years by connecting physical objects to the Internet, enabling innovative applications. To facilitate communication in low-power and lossy networks (LLNs), the IPv6-based routing protocol for LLNs (RPL) is widely used. However, RPL’s lack of specified security models makes it vulnerable to security threats, particularly sinkhole attacks. Existing sinkhole attack detection techniques suffer from high detection delays and false positives. To overcome these limitations, in our research we propose a multidirectional trust-based detection approach for sinkhole attacks in the RPL routing protocol. Our model introduces a novel architecture that considers trust in parent, child, and neighbor directions, reducing detection delays. We enhance detection efficiency and reduce false positives by combining fuzzy logic systems (FLSs) and subjective logic (SL). Additionally, we introduce a new trust weight variable derived from Shannon's entropy method and multiattribute utility theory. We adaptively adjust the SL coefficient based on network conditions, replacing the constant coefficient value of SL theory. Our approach is compared to the most recent techniques, and we assess different indicators, such as false-positive rate, false-negative rate, packet delivery ratio, throughput, average delay, and energy consumption. Our results demonstrate superior performance in all these metrics, highlighting the effectiveness of our approach.

An Investigation Schoolwork of IoT Systems In Interior Routing Outbreak

Internet of Things (IoT) deployments mostly relies on the establishment of Low-Power and Lossy Networks (LLNs) among a large number of constraint devices. The Internet Engineering Task Force (IETF) provides an effective IPv6-based LLN routing protocol, namely the IPv6 Routing Protocol for Low Power and Lossy Network (RPL). RPL provides adequate protection against external security attacks but stays vulnerable to internal routing attacks such as a rank attack. Malicious RPL nodes can carry out a rank attack in different forms and cause serious network performance degradation. An experimental study of the impact of the decreased rank attack on the overall network performance is presented in this paper. In also besides, it is important to understand the main influencing factors in this context. In this study, several some many network scenarios were considered with varying network sizes, attacker properties, and topological setups. The experimental results indicate a noticeable adverse effect of the rank attack on the average PDR, delay, ETX, and beacon interval. However, such impact was varied according to network size, attacker position, attacker neighbor count, number of attack-affected nodes, and overall hops increase. The results give a practical reference to the overall performance of RPL networks under rank attacks.

Securing Low-Power Blockchain-enabled IoT Devices against Energy Depletion Attack

Blockchain-enabled Internet of Things (IoT) envisions a world with rapid development and implementations to change our everyday lives based on smart devices. These devices are attached to the internet that can communicate with each other without human interference. A well-known wireless network in blockchain-enabled IoT frameworks is the Low Power and Lossy Network (LLN) that uses a novel protocol known as Routing protocol for low power and lossy networks (RPL) to provide effective and energy-efficient routing. LLNs that run on RPL are inherently prone to multiple Denial of Service (DoS) attacks due to the low cost, shared medium, and resource-constrained nature of blockchain-enabled IoT devices. A Spam DODAG Information Solicitation (DIS) attack is one of the novel attacks that drains the energy source of legitimate nodes and ends up causing the legitimate nodes to suffer from DoS. To address this problem, a mitigation scheme named DIS Spam Attack Mitigation (DISAM) is proposed. The proposed scheme effectively mitigates the effects of the Spam DIS attack on the network’s performance. The experimental results show that DISAM detects and mitigates the attack quickly and efficiently.

A Novel Cryptography-Based Multipath Routing Protocol for Wireless Communications

Communication in a heterogeneous, dynamic, low-power, and lossy network is dependable and seamless thanks to Mobile Ad-hoc Networks (MANETs). Low power and Lossy Networks (LLN) Routing Protocol (RPL) has been designed to make MANET routing more efficient. For different types of traffic, RPL routing can experience problems with packet transmission rates and latency. RPL is an optimal routing protocol for low power lossy networks (LLN) having the capacity to establish a path between resource constraints nodes by using standard objective functions: OF0 and MRHOF. The standard objective functions lead to a decrease in the network lifetime due to increasing the computations for establishing routing between nodes in the heterogeneous network (LLN) due to poor decision problems. Currently, conventional Mobile Ad-hoc Network (MANET) is subjected to different security issues. Weathering those storms would help if you struck a good speed-memory-storage equilibrium. This article presents a security algorithm for MANET networks that employ the Rapid Packet Loss (RPL) routing protocol. The constructed network uses optimization-based deep learning reinforcement learning for MANET route creation. An improved network security algorithm is applied after a route has been set up using (ClonQlearn). The suggested method relies on a lightweight encryption scheme that can be used for both encryption and decryption. The suggested security method uses Elliptic-curve cryptography (ClonQlearn+ECC) for a random key generation based on reinforcement learning (ClonQlearn). The simulation study showed that the proposed ClonQlearn+ECC method improved network performance over the status quo. Secure data transmission is demonstrated by the proposed ClonQlearn + ECC, which also improves network speed. The proposed ClonQlearn + ECC increased network efficiency by 8-10% in terms of packet delivery ratio, 7-13% in terms of throughput, 5-10% in terms of end-to-end delay, and 3-7% in terms of power usage variation.

TFUZZY-OF: a new method for routing protocol for low-power and lossy networks load balancing using multi-criteria decision-making

The internet of things (IoT) based on a network layer perspective includes low-power and lossy networks (LLN) that are limited in terms of power consumption, memory, and energy usage. The routing protocol used in these networks is called routing over low-power and lossy networks (RPL). Therefore, the IoT networks include smart objects that need multiple routing for their interconnections which makes traffic load balancing techniques indispensable to RPL routing protocol. In this paper, we propose a method based on fuzzy logic and the technique for the order of prioritization by similarity to the ideal solution (TOPSIS) as a well-known multi-criteria decision-making method to solve the load balancing problem by routing metrics composition. For this purpose, a combination of both link and node routing metrics namely hop count, expected transmission count, and received signal strength indicator is used. The results of simulations show that this method can increase the quality of services in terms of packet delivery ratio and average end-to-end delay.

Congestion and Energy Aware Multipath Load Balancing Routing for LLNs

The Internet of Things (IoT) is presently in its golden era with its current technological evolution towards digital transformation. Low-power and Lossy Networks (LLNs) form the groundwork for IoT, where the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is designated by Internet Engineering Task Force as the benchmark protocol for routing. Although RPL, with its unique capabilities, has addressed many IoT routing requirements, Load balancing and Congestion control are the outliers. This paper builds on the RPL protocol and proposes a multipath Congestion and Energy Aware RPL (CEARPL) that alleviates the load balancing and congestion concerns associated with RPL and improves the network performance. For congestion avoidance, a Congestion and Energy Aware Objective Function (CEA-OF) is suggested during parent selection that considers multiple metrics like Child Count metric, Estimated Lifetime metric, and Queue Occupancy metric, to equally distribute the traffic in LLNs. The Queue Occupancy metric is used to detect congestion in the network, and a Multipath routing strategy is utilized to mitigate the congestion in the network. A comparison of the performance of CEA-RPL was made against the existing Objective Functions of RPL, OFO, and MRHOF, as well as COM-OF, utilizing Contiki OS 3.0's Cooja emulator. CEA-RPL projected superior results with power consumption lowering by 33%, end-to-end delay decreasing by 30%, queue loss ratio reducing by 49%, and packet receiving rate and network lifetime improving by 7% and 49%, on an average, respectively.

Multi-Modal Industrial IoT Networks: Recent Advances and Future Challenges

AbstractWhile the ongoing fourth industrial revolution continues to be a major driver behind wireless communication technologies, some environments are so prohibitive that even state-of-the-art solutions can barely achieve ubiquitous wireless connectivity (if at all). For example, in industrial sites with large metal constructions (such as petrochemical plants), highly localized and time-varying changes in wireless link quality are quite common. Oddly enough, much of the capabilities needed to deal with such effects are already present at the physical layer (PHY), but remain largely unexploited by higher protocol layers. In fact, little Industrial Internet of Things (IoT) (IIoT) research has considered harnessing the full multi-modal capabilities of modern multi-PHY/multi-band IoT hardware in general. As such, in this vision paper, we: (1) analyze recent advances towards enabling multi-modal IIoT through link- and routing layer operations; and (2) describe challenges and opportunities for future IIoT deployments, based on the design choices that emerged from said analysis. In summary, we identify a combination of a modified/extended Time-Slotted Channel Hopping (TSCH) link layer, using either fixed or variable duration timeslots, together with a Parent-Oriented (PO) Routing Protocol for Low-Power and Lossy Networks (LLNs) (RPL) approach to be the most promising way forward.

An enhanced opportunistic rank-based parent node selection for sustainable & smart IoT networks

The Internet of Things (IoT) is a network of interconnected devices that includes low-end devices (sensors) and high-end devices (servers). The routing protocol used the Low-Power and Lossy Networks (RPL) protocol, which was designed to collect data in Low-Power and Lossy Networks (LLN) efficiently and reliably. The RPL rank property specifies how sensor nodes are placed in Destination Oriented Directed Acyclic Graphs (DODAG) based on an Objective Function (OF). The OF includes information such as the Expected Transmission Count (ETX) and packet delivery rate. The rank property aids in routing path optimization, reducing control overhead, and maintaining a loop-free topology by using rank-based data path validation. However, it causes many issues, such as optimal parent selection, next-hop node selection, and network instability. We proposed an Enhanced Opportunistic Rank-based Parent Node Selection for Sustainable and Smart IoT Networks to address these issues. The optimal parent node is determined by forecasting the expected energy of each node using Received Signal Strength (RSS) and an enhanced reinforcement learning algorithm. The proposed method addresses the issue of selecting the next-hop neighbor node and improves routing stability. Furthermore, when a large number of new nodes try to join the sustainable IoT-based smart cities, the proposed technique provides optimal load balance.

A Stochastic Model for Performance Evaluation of Hybrid Network Architectures of IoT with an Improved Design

Hybridisation of networks is a trend that will continue to evolve for more efficient communication solutions. Hybrid power line communication (PLC) and wireless networks deployed for the Internet of Things (IoT) applications have improved the efficiency of low-power and lossy networks (LLNs). State-of-the-art solutions on hybrid networks are mainly application specific and does not provide a mathematical model to evaluate the network performance. Furthermore, the parallel PLC and wireless networks result in more power consumption due to the additional circuitry required to support and switch between two networks. There is a need for a generalized study on hybrid network architectures. A mathematical model must be developed to evaluate the performance of hybrid networks. Unlike parallel PLC and wireless networks, some intermediate solutions must be proposed that can offer the benefits of wired and wireless networks without increasing power consumption and circuit complexity. The paper presents a generalized study on hybrid network architectures. A hybrid network has been proposed that can offer the benefits of wired and wireless networks in the best possible way. A novel Markov-based stochastic model has been developed to evaluate the reliability of hybrid network designs. The analytical results obtained from Continuous Time Markov Chains (CTMC) have been validated by simulation using MATLAB. Spectral and energy efficiency have been evaluated for the hybrid designs along with a trade-off analysis. The proposed hybrid network (with 50% jump nodes being hybridized) is 33.53% more reliable for data transmission than parallel wired and wireless networks after 100 hours of operation.

RPL objective function for multihop PLC network

The increasing number of devices in the Internet of things (IoT) is beginning to exhaust the wireless channel. As an alternative, power line communication (PLC) appears promising and has the potential of providing easy deployment at low cost. On one hand, PLC resembles wireless low-power and lossy networks (LLN). Its performance is time-varying and sensitive to noise, interference, and asymmetry owing to coexisting electronic appliances. However, when running the standard IPv6 routing protocol for LLN (RPL) on PLC, the network suffers significant reliability loss because RPL does not reflect the unique characteristics of PLC. To address this problem, we propose PLC-OF, an objective function (OF) for RPL over PLC. PLC-OF exploits PLC's physical layer (PHY) diversity as a routing metric distinct from other approaches in wireless. PLC-OF finds the most suitable path to prepare for sudden interference or congestion in power lines. Evaluation results from real testbed experiments show that PLC-OF better tolerates noisy medium compared to existing objective functions. Particularly, PLC-OF has better reliability (≈10%) and robustness with less channel usage (≈30%) than that of ETX-based MRHOF in highly congested environments.

An improved RPL algorithm for low-power and lossy networks

The routing protocol for low-power and lossy networks (RPL), standardized by Internet Engineering Task Force (IETF), is mainly designed to use for Low-power and Lossy Networks (LLNs). To solve the problems of several important routing metrics are not evaluated, the optimal path may contain long single hop links, lack of scientific multi-routing metrics evaluation method and mechanism to balance the parent child number (especially the parent with one hop away from root), this paper proposes an improved RPL algorithm for LLN (I-RPL). First of all, we propose the evaluated routing metrics: child number of parent, candidate parent number, hop count, ETX and energy consumption index. Meanwhile, we improve the path ETX calculation method to avoid selecting optimal path containing long single hop links. Then we design a novel lexical method to synthetically evaluate candidate parents. Meanwhile, based on the evaluation results of candidate parents, we design a novel objective function and a new calculation node rank method which can also be used for selecting the optimal path. Finally, evaluation results show that I-RPL outperforms ETXOF and several other improvements in terms of packet delivery ratio, latency, etc.

BE-RPL: Balanced-load and Energy-efficient RPL

Internet of Things (IoT) empowers imaginative applications and permits new services when mobile nodes are included. For IoT-enabled low-power and lossy networks (LLN), the Routing Protocol for Low-power and Lossy Networks (RPL) has become an established standard routing protocol. Mobility under standard RPL remains a difficult issue as it causes continuous path disturbance, energy loss, and increases the end-to-end delay in the network. In this unique circumstance, a Balanced-load and Energy-efficient RPL (BE-RPL) is proposed. It is a routing technique that is both energy-efficient and mobility-aware. It responds quicker to link breakage through received signal strength-based mobility monitoring and selecting a new preferred parent reactively. The proposed system also implements load balancing among stationary nodes for leaf node allocation. Static nodes with more leaf nodes are restricted from participating in the election for a new preferred parent. The performance of BE-RPL is assessed using the COOJA simulator. It improves the energy use, network control overhead, frame acknowledgment ratio, and packet delivery ratio of the network.