Lossy Networks Research Articles

A trust aware security mechanism to detect sinkhole attack in RPL-based IoT environment using random forest – RFTRUST

The Internet of Things (IoT) plays a vital role in many application domains like battlefield surveillance, wildlife monitoring, disaster response, medical care, transportation, industry, smart home, smart cities, etc. However, this network is susceptible to various types of attacks due to its special features like sensing, intelligence, large scale, self-configuring, connectivity, heterogeneity, open and dynamic environment. It is significant to ensure security in the IoT network. In the scalable and dynamic IoT environment, conventional security mechanisms such as cryptography techniques, key management, intrusion detection system, anomaly detection, etc cannot be applicable, because it consumes more energy. Therefore, the IoT network requires a lightweight security mechanism for reliable and secure data transmission. A trust-based security solution solves many security-related problems. The proposed RFTrust model provides a trust-based lightweight solution for ensuring security in the IoT network. It is primarily designed to address the sinkhole attack in Routing Protocol for Low power and Lossy networks (RPL) based IoT environments. It enhances the trusted routing in the IoT environment by finding and removing sinkhole nodes in the network. The proposed model uses Random Forest (RF) and Subjective Logic (SL) to improve the network performance by identifying sinkhole attack. The mathematical analysis shows the applicability of the proposed model. The merits of the proposed work are highlighted by comparing performance with the existing similar protocols in terms of delivery ratio, throughput, average delay, energy consumption, false-positive rate, false-negative rate, and detection accuracy.

Energy harvesting effect on prolonging low-power lossy networks lifespan

Low-power lossy networks performance relies heavily on the wireless node battery status. Furthermore, Routing Protocol for Low-Power and Lossy Network routing protocol was not optimally designed with sustainable energy consumption in mind to suit these networks. Prolonging the lifespan of these networks is of utmost priority. This article introduces a solar energy harvesting module to power energy-constrained network devices and quantifies the effect of using harvested energy on prolonging their network lifetime when Routing Protocol for Low-Power and Lossy Network routing protocol is used. Simulation of the new developed module is conducted in three different scenarios using Contiki Cooja simulator sporting Zolertia Z1 motes. Furthermore, the harvested energy used was fed from a Cooja-based Simulation model of actual PV supercapacitor circuit design. All battery levels were set to 1% of their total capacity for all nodes in the network to speed up observing the energy harvesting effect. The performance evaluation results showed that the network with no-energy harvesting operated for time duration of 4:08:04 time units (i.e. hour:minute:second) with a dramatic decrease in connection between nodes in the network. However, the same network, when using the harvested energy to back up the battery operation, lasted for 6:40:01 in time units with improved connectivity, a total extended network lifetime of 2:31:97-time units. Furthermore, for the Routing Protocol for Low-Power and Lossy Network routing metrics, OF0 outperformed ETX in term of throughput, packet delivery ratio, energy consumption, and network connectivity. Results indicate that the developed harvested energy module fits perfectly for any Cooja-based simulation and mimics actual photovoltaic-based supercapacitor battery. It should also help researchers introduce and quantify accurately new energy consumption-based routing metrics for Routing Protocol for Low-Power and Lossy Network.

Highly Integrated PA-PIFA With a Wide Frequency Tuning Range

This letter presents a highly integrated power amplifier (PA) antenna with the capability of frequency tuning. A planar inverted-F antenna (PIFA) is used to directly match the PA transistor. This mitigates the lossy output matching network in the PA-integrated antenna (PAIA). In order to achieve a wide frequency tuning range, a combination of capacitors is loaded at the PIFA to control the antenna input impedances so that matching can be maintained with frequency tuning. The antenna analysis and design procedure of the tunable PAIA are detailed. For verification, a prototype is designed, fabricated, and measured. The experimental results show that the frequency tuning range of the proposed PAIA covers from 2 to 4 GHz. Over this frequency tuning range, the effective isotropic radiation power and power-added efficiency are higher than 40.5 dBm and 60% at the input power of 26.5 dBm, respectively.

Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks.

While IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) networks should be equipped to deal with the hard wireless challenges of industrial environments, the sensor networks are often still limited by the characteristics of the used physical (PHY) layer. Therefore, the TSCH community has recently started shifting research efforts to the support of multiple PHY layers, to overcome this limitation. On the one hand, integrating such multi-PHY support implies dealing with the PHY characteristics to fit the resource allocation in the TSCH schedule, and on the other hand, defining policies on how to select the appropriate PHY for each network link. As such, first a heuristic is proposed that is a step towards a distributed PHY and parent selection mechanism for slot bonding multi-PHY TSCH sensor networks. Additionally, a proposal on how this heuristic can be implemented in the IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH) protocol stack and its Routing Protocol for Low-power and Lossy network (RPL) layer is also presented. Slot bonding allows the creation of different-sized bonded slots with a duration adapted to the data rate of each chosen PHY. Afterwards, a TSCH slot bonding implementation is proposed in the latest version of the Contiki-NG Industrial Internet of Things (IIoT) operating system. Subsequently, via extensive simulation results, and by deploying the slot bonding implementation on a real sensor node testbed, it is shown that the computationally efficient parent and PHY selection mechanism approximates the packet delivery ratio (PDR) results of a near-optimal, but computationally complex, centralized scheduler.

Multicast DIS attack mitigation in RPL-based IoT-LLNs

The IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) was standardised by the IETF ROLL Working Group to address the routing issues in the Internet of Things (IoT) Low-Power and Lossy Networks (LLNs). RPL builds and maintains a Destination Oriented Directed Acyclic Graph (DODAG) topology using pieces of information propagated within the DODAG Information Object (DIO) control message. When a node intends to join the DODAG, it either waits for DIO or sends a DODAG Information Solicitation (DIS) control message Multicast to solicit DIOs from nearby nodes. Nevertheless, sending Multicast DIS messages resets the timer that regulates the transmission rate of DIOs to its minimum value, which leads to the network’s congestion with control messages. Because of the resource-constrained nature of RPL-LLNs, the lack of tamper resistance, and the security gaps of RPL, malicious nodes can exploit the Multicast DIS solicitation mechanism to trigger an RPL-specification-based attack, named DIS attack. The DIS attack can have severe consequences on RPL networks, especially on control packets overhead and power consumption. In this paper, we use the Cooja–Contiki simulator to assess the DIS attack’s effects on both static and dynamic PRL networks. Besides, we propose and implement a novel approach, namely RPL-MRC, to improve the RPL’s resilience against DIS Multicast. RPL-MRC aims to reduce the response to DIS Multicast messages. Simulation results demonstrate how the attack could damage the network performance by significantly increasing the control packets overhead and power consumption. On the other hand, the RPL-MRC proposed mechanism shows a significant enhancement in reducing the control overhead and power consumption for different scenarios.

A Softwarized Intrusion Detection System for the RPL-based Internet of Things networks

Internet of Things (IoT) constitutes a pivotal contributor to the Industry 4.0 (I 4.0) vision, technologically transforming production and societies. It enables novel services through the seamless integration of devices, such as motes carrying sensors, with the Internet. However, the broad adoption of IoT technologies is facing security issues due to the direct access to the devices from the Internet, the broadcasting nature of the wireless media, and the potential unattended operation of relevant deployments. In particular, the Routing over Low Power and Lossy Networks (RPL) protocol, a prominent IoT solution, is vulnerable to a large number of attacks, both of general-purpose and RPL-specific nature, while the resource-constraints of the corresponding devices are making attack mitigation even more challenging, e.g., in terms of involved control overhead and detection accuracy.In this paper, we introduce ASSET, a novel Intrusion Detection System (IDS) for RPL with diverse profiles to tackle the above issues that mitigate at least 13 attacks. At the same time, other solutions go up to eight. ASSET, inspired by the network softwarization paradigm, supports a novel, extendable workflow, bringing together three anomaly-detection and four RPL specification-based mechanisms, a novel attacker identification process, as well as multiple attack mitigation strategies. Our IDS also supports an adaptable control & monitoring protocol, trading overhead for accuracy, depending on the network conditions. The proof-of-concept experiments show that ASSET entails a low overhead for the different modes of operation it supports (i.e., 6.28 percent on average) compared to other solutions reaching up to 30 percent. At the same time, it also keeps the power consumption at acceptable levels (from 0.18 up to 1.54 percent more). Moreover, it provides 100 percent accuracy for specific attacks and can identify the attacker in far more attacks than any other similar solution.

A Novel Insider Attack and Machine Learning Based Detection for the Internet of Things

Due to the widespread functional benefits, such as supporting internet connectivity, having high visibility and enabling easy connectivity between sensors, the Internet of Things (IoT) has become popular and used in many applications, such as for smart city, smart health, smart home, and smart vehicle realizations. These IoT-based systems contribute to both daily life and business, including sensitive and emergency situations. In general, the devices or sensors used in the IoT have very limited computational power, storage capacity, and communication capabilities, but they help to collect a large amount of data as well as maintain communication with the other devices in the network. Since most of the IoT devices have no physical security, and often are open to everyone via radio communication and via the internet, they are highly vulnerable to existing and emerging novel security attacks. Further, the IoT devices are usually integrated with the corporate networks; in this case, the impact of attacks will be much more significant than operating in isolation. Due to the constraints of the IoT devices, and the nature of their operation, existing security mechanisms are less effective for countering the attacks that are specific to the IoT-based systems. This article presents a new insider attack, named loophole attack , that exploits the vulnerabilities present in a widely used IPv6 routing protocol in IoT-based systems, called RPL (Routing over Low Power and Lossy Networks). To protect the IoT system from this insider attack, a machine learning based security mechanism is presented. The proposed attack has been implemented using a Contiki IoT operating system that runs on the Cooja simulator, and the impacts of the attack are analyzed. Evaluation on the collected network traffic data demonstrates that the machine learning based approaches, along with the proposed features, help to accurately detect the insider attack from the network traffic data.

Network efficient topology for low power and lossy networks in smart corridor design using RPL

PurposeThis paper aims to determine the network efficient topology for low power and lossy networks (LLNs) using routing protocol for LLN (RPL) with respect to the increase in network size and propose a novel approach to overcome the shortcomings of the existing models.Design/methodology/approachThe authors have used Contiki OS/Cooja simulator to conduct experiments on primarily four topologies (star, bus/linear, ring/eclipse and random). They have implemented RPL protocol using Sky motes for each topology from 10, 20, 30 and up to 70 nodes. Consequently, after 24 h of experimentation, the readings have been noted and, alongside, a comprehensive comparative analysis has been performed based on the network density and metric parameters: packet delivery ratio (PDR), expected transmission (ETX) and power consumption. Further, a hybrid model is proposed where the additional factors of mobility, multiple sink and a combination of static and mobile nodes are introduced. The proposed model is then compared with the star model (all static nodes and star topology) and the dynamic model (all mobile nodes) to analyze the efficiency and network performance for different network sizes (28, 36, 38 and 44 nodes). The mobility is introduced using BonnMotion tool in Contiki OS.FindingsSimulation results have shown that the star topology is most network efficient when compared with bus/linear, ring/eclipse and random topologies for low density and high scalable network. But when the same setup is compared with the proposed hybrid model, the proposed model shows a significant improvement and gives the best and efficient network performance with highest PDR (average improvement approximately 44.5%) and lowest ETX (average improvement approximately 49.5%) comparatively.Practical implicationsAlso, these findings will benefit the deployment of smart devices used in advanced metering infrastructure, road side units and in various industrial applications such as traffic monitoring system, electronic toll collection and traffic analysis in the smart grid infrastructure.Originality/valueThe impact of topology is significant and detailed analysis is required to understand the impact of different topologies of the nodes in the network for the present and the future scenarios. As very few research studies have discussed this gap, this research paper is quintessential and shall open novel future potential direction. Also, the proposed approach of hybrid model with mobility has not been considered in the literature yet.

A Secure Model to Protect Healthcare IoT System from Version Number and Rank Attack

Recently the entire world is witnessing a pandemic outbreak of COVID-19. Resultantly, the healthcare sector battles the challenges like inadequacy of staff members, feeble patient monitoring services, etc. IoT technology provides complete digitization. However, this healthcare IoT falls prey to various attacks and security threats. The main cause of the attacks is the IoT communication and routing protocols. Routing protocol for low power lossy network (RPL) provides the potent routing mechanism in the 6LoWPAN network. However, RPL is an unsecured protocol; hence providing security to IPV6-RPL connected devices is a challenging task. This paper modeled the rules for RPL protocol signatures like ETX, beacon interval, and energy consumption in a novel attack detection system. The simulation results proved that the proposed system detects the conventional as well as RPL specific attacks precisely version number and rank attacks in healthcare IoT systems with higher accuracy and detection rate.

RPL rank based‐attack mitigation scheme in IoT environment

SummaryDue to the new technologies of the Internet of Things (IoT), connected objects have become smart interactive devices. However, despite their imminent advantages, whether for the daily life of users or in professional contexts, and the various security solutions proposed in literature in particular, the security of IoT networks is still a paramount issue given to the amount of confidential data that circulates through these technologies. In other words, among the IoT protocols that suffer from security issues, routing protocol for low power and lossy networks (RPL) is considered to be one of the most widely used protocols in the deployment of constrained IoT networks with limited resources (storage and energy constraints for example). On this basis, this article investigates in the first part a security flaw in the RPL protocol by carrying out an attack against the rank value of the RPL in an IoT environment. In the second part, a mitigation scheme to avoid it using a trust threshold is proposed. The malicious behavior of the attack and the effectiveness of the proposed scheme were assessed by detailed simulations using the Cooja simulator, in terms of successful detection rate, average network hops and global energy consumption. To conclude, a fair comparison between the different simulation results has been adopted using the Friedman statistical test, and a discussion has been presented with some future works concerning security against rank attack.

An optimization‐based congestion control for constrained application protocol

SummaryThe Constrained Application Protocol (CoAP) is a lightweight web transfer protocol designed based on the REST architecture standardized by the Internet Engineering Task Force (IETF) to meet and accommodate the requirements of the constrained Internet of Things (IoT) environments. Managing congestion control in a resource‐constrained lossy network with a high bit error rate is a significantly challenging task that needs to be addressed. The primary congestion control mechanism defined by CoAP specification leverages on basic binary exponential backoff and often fails to utilize the network dynamics to the best of its traffic conditions. As a result, CoCoA has been introduced for better IoT resource utilization. In addition, CoCoA retransmission timeout (RTO) for network dynamics is based on constant coefficient values. The resource‐constrained nature of IoT networks poses new design challenges for congestion control mechanisms. In this paper, we propose a new particle swarm optimization (PSO)‐based congestion control approach called psoCoCoA as a variation of CoCoA. The psoCoCoA applies random and optimal parameter‐driven simulation to optimize default CoAP parameters and update the fitness and velocity positions to adapt to the traffic conditions. This process is performed for different traffic scenarios by varying the retransmission and max‐age values by using the optimization‐based algorithm. We carried out extensive simulations to validate the congestion control performance for CoAP with Observe, CoCoA, and psoCoCoA with different network topologies. The results indicate that psoCoCA outperforms or very similar to CoCoA and achieves better performance compared to CoAP with Observe under different network scenarios.

ASRPH: Application Specific Routing Protocol for Health care

While proceeding towards the age of Internet of Things (IoT), Wireless Sensor Network (WSN) is going to play an indispensable role in the overall networking architecture. Applications like health care, smart-city etc. are extensively using WSN frameworks to collect and analyse real-time data along with taking necessary actions based on the application’s requirements. While designing the routing protocol for WSN, the uniqueness and requirements of the application have the potential to play a dominant role. Generalizing the networking framework while ignoring application-specific requirements would lead towards designing an inefficient routing protocol. Considering these scenarios, we have proposed an application-specific routing protocol for the health care which would ensure the increase in network lifetime while maintaining the maximum throughput in data transmission. The proposed routing protocol has shown an improved performance in terms of metrics like throughput, network lifetime, end to end delay, packet delivery ratio and packet loss while comparing with some of the traditional routing protocols e.g. Routing Protocol for Low-Power and Lossy Networks and Collection Tree Protocol.

Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment.

Forest fire monitoring is very much needed for protecting the forest from any kind of disaster or anomaly leading to the destruction of the forest. Now, with the advent of Internet of Things (IoT), a good amount of research has been done on energy consumption, coverage, and other issues. These works did not focus on forest fire management. The IoT-enabled environment is made up of low power lossy networks (LLNs). For improving the performance of routing protocol in forest fire management, energy-efficient routing protocol for low power lossy networks (E-RPL) was developed where residual power was used as an objective function towards calculating the rank of the parent node to form the destination-oriented directed acyclic graph (DODAG). The challenge in E-RPL is the scalability of the network resulting in a long end-to-end delay and less packet delivery. Additionally, the energy of sensor nodes increased with different transmission range. So, for obviating the above-mentioned drawbacks in E-RPL, compressed data aggregation and energy-based RPL routing (CAA-ERPL) is proposed. The CAA-ERPL is compared with E-RPL, and the performance is analyzed resulting in reduced packet transfer delay, less energy consumption, and increased packet delivery ratio for 10, 20, 30, 40, and 50 nodes. This has been evaluated using a Contiki Cooja simulator.

An Equivalent-Circuit Topology for Lossy Non-Symmetric Reciprocal Two-Ports

An equivalent-circuit topology for two-port lossy non-symmetric reciprocal networks is proposed. The circuit topology is based on the eigenstate decomposition. The proposed circuit consists of two immittances and two transformers with a single complex turns ratio, all obtained from the eigenvalues and eigenvectors of the admittance or impedance matrix of the network in a straightforward way. The interconnection of the circuit elements is simple and compact, and the real parts of its immittances are always positive. To verify its behavior, the equivalent circuit of two different leaky-wave antenna unit cells is extracted: a series-fed coupled patch radiating element, and the complementary strip-slot. Simulated and measured data are provided for the study cases, highlighting the satisfactory behavior of the equivalent circuit over a very broad bandwidth. A simple asymmetry model is also proposed that can be used advantageously in the analysis and design of non-symmetric two-ports, like unit cells of leaky-wave antennas.

Performance Evaluation of IPv6 Routing Protocol for Low Power and Lossy Networks Based on Objective Functions for Media Technology

The Internet Engineering Task Force (IETF) has proposed the use of the IPv6 Routing Protocol for the Low-Power and Lossy Networks (RPL). RPL has been seen to be a major routing protocol related to the Internet of Things (IoT) that could be used for wireless networks with lower power consumption and liable to a higher packet loss. The RPL construct-related network topology uses the Objective Functions (OF) for choosing the route from the source to a destination node, which has been based on constraints and metrics. The IETF designed two OFs for the RPL protocol, i.e., Objective Function Zero (OF0) and Minimum Rank with Hysteresis Objective Function (MRHOF). In this study, the researchers have investigated the RPL performance with the help of the OFs in a medium density. This performance has been judged using different network parameters like Packet Delivery Ratio (PDR) and Power Consumption (PC). Furthermore, in this study, the number of nodes has been maintained as 40 and 50. The RX ratio has varied between 20, 60, and 100%, in the grid, and random topologies. The results of the study have also indicated that the OF0 protocol has showed a better performance concerning the power consumption compared to the MRHOF, while both the OFs have showed a similar PDR performance.

Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks

In home and building automation applications, wireless sensor devices need to be connected via unreliable wireless links within a few hundred milliseconds. Routing protocols in Low-power and Lossy Networks (LLNs) need to support reliable data transmission with an energy-efficient manner and short routing convergence time. IETF standardized the Point-to-Point RPL (P2P-RPL) routing protocol, in which P2P-RPL propagates the route discovery messages over the whole network. This leads to significant routing control packet overhead and a large amount of energy consumption. P2P-RPL uses the trickle algorithm to control the transmission rate of routing control packets. The non-deterministic message suppression nature of the trickle algorithm may generate a sub-optimal routing path. The listen-only period of the trickle algorithm may lead to a long network convergence time. In this paper, we propose Collision Avoidance Geographic P2P-RPL, which achieves energy-efficient P2P data delivery with a fast routing request procedure. The proposed algorithm uses the location information to limit the network search space for the desired route discovery to a smaller location-constrained forwarding zone. The Collision Avoidance Geographic P2P-RPL also dynamically selects the listen-only period of the trickle timer algorithm based on the transmission priority related to geographic position information. The location information of each node is obtained from the Impulse-Response Ultra-WideBand (IR-UWB)-based cooperative multi-hop self localization algorithm. We implement Collision Avoidance Geographic P2P-RPL on Contiki OS, an open-source operating system for LLNs and the Internet of Things. The performance results show that the Collision Avoidance Geographic P2P-RPL reduced the routing control packet overheads, energy consumption, and network convergence time significantly. The cooperative multi-hop self localization algorithm improved the practical implementation characteristics of the P2P-RPL protocol in real world environments. The collision avoidance algorithm using the dynamic trickle timer increased the operation efficiency of the P2P-RPL under various wireless channel conditions with a location-constrained routing space.

Novel Authentication and Secure Trust based RPL Routing in Mobile sink supported Internet of Things

ABSTRACT In the modern era, prevalence of the Internet of Things (IoT) devices that have de facto protocol as IPv6 routing protocol for low power and lossy networks (RPL). Yet, RPL protocol is vulnerable to many attacks such as rank attack, password spoofing and more. To this end, most of the works have focused their research on securing the RPL-based IoT network. However, still there exist downsides such as high energy consumption, lack of effective authentication and high packet losses. Motivated by these preceding defects, this paper proposes the Novel Authentication and Secure Trust-based RPL Routing in Mobile sink-supported Internet of Things (SecRPL-MS). At first, SecRPL-MS performs a registration process where all IoT nodes in the network register themselves in the security entity. In this work, the frequent death of IoT nodes is alleviated through deploying mobile sink in the network. If any grid member (GM) node wants to transmit their data to the grid head (GH) node, then it must undergo authentication process. Secure routing is adopted in RPL by utilising the sail fish optimisation algorithm. Each GM node encrypts its sensed data using the prince algorithm before transmitting it to the GH node. The moving points are selected for the mobile sink using the Quantum Inspired Neural Network (QINN) algorithm. This proposed SecRPL-MS performance is evaluated using the Network Simulator 3 (NS3) in terms of the Packet Delivery Ratio (%), Delay (ms), Energy Consumption (mJ), Key Generation Time (ms) and Malicious Node Detection Accuracy (%). The proposed SecRPL-Ms outperforms 23% of malicious node detection accuracy when compared to existing systems, which represent the proposed SecRPL-MS system providing high security by mitigating the following attacks such as rank attack, Sybil attack, blackhole attack and man in the middle attack.

LA-Trickle: A novel algorithm to reduce the convergence time of the wireless sensor networks

Wireless sensor networks (WSNs) are very important to realize the Internet of Things by connecting physical objects to the Internet. RPL (Routing Protocol for Low Power and Lossy Networks) has been provided by the IETF (Internet Engineering Task Force) for routing on wireless sensor networks. RPL uses the Trickle algorithm to schedule the transmission of control messages. The environment of WSNs is often highly variable and the environmental conditions for nodes are different. If network nodes are equipped with a learning automaton, network convergence time can be reduced. The use of learning automata allows each network node to adjust its parameters to environmental conditions by receiving environmental feedback to perform better. The main aim of this article is to reduce the network convergence time. We equipped the Trickle algorithm with a learning automaton, which determines how many times the algorithm is repeated with the minimum interval to resolve the inconsistency, based on environmental conditions. Since repeating the Trickle algorithm with the minimum interval increases the local repair speed and reduces the network convergence time. We simulated a large number of networks of different sizes and densities. According to the simulation results, we observed that in the proposed method, the network convergence time was reduced compared with the other methods. Also, due to fewer changes in network topology, energy consumption for network reconstruction was reduced.

Improved RPL Protocol for Low-Power and Lossy Network for IoT Environment

Improved RPL Protocol for Low-Power and Lossy Network for IoT Environment

A novel Network Partitioning Attack against Routing Protocol in Internet of Things

The Internet of Things (IoT) environment comprises large scale deployment of constrained devices like RFIDs and sensors making routing an essential requirement. These constrained devices are part of the Low power and Lossy Networks (LLNs) in the IoT. The Internet Engineering Task Force standardized IPv6 Routing Protocol for Low power and Lossy Networks (RPL) as the routing protocol for LLNs in IoT. IoT-LLNs are organized as multiple Destination Oriented Directed Acyclic Graphs (DODAGs). The root node in the DODAG organizes and maintains the DODAG through broadcast based configuration information dissemination process. The root node also connects the various nodes in the DODAG to the rest of the IoT network. Hence, for any node in the IoT-LLN, connectivity with the root node is important. We present a novel Network Partitioning Attack (NPA) scenario against RPL in IoT-LLNs wherein a malicious node segregates a section of the IoT-LLN from the root node and thus from the rest of the IoT network. In this paper, we discuss in-depth the vulnerabilities of RPL, which result in the proposed Network Partitioning Attack. We present an analytical model to demonstrate the impact of the attack on the performance of the routing protocol and validate the model through simulation. We also suggest mechanisms to mitigate the attack.