Lossy Networks Research Articles

On Packet Reordering in Time-Sensitive Networks

Time-sensitive networks (IEEE TSN or IETF DetNet) may tolerate some packet reordering. Re-sequencing buffers are then used to provide in-order delivery, the parameters of which (timeout, buffer size) may affect worst-case delay and delay jitter. There is so far no precise understanding of per-flow reordering metrics nor of the dimensioning of re-sequencing buffers in order to provide worst-case guarantees, as required in such networks. First, we show that a previously proposed per-flow metric, reordering late time offset (RTO), determines the timeout value. If the network is lossless, another previously defined metric, the reordering byte offset (RBO), determines the required buffer. If packet losses cannot be ignored, the required buffer may be larger than RBO, and depends on jitter, an arrival curve of the flow at its source, and the timeout. Then we develop a calculus to compute the RTO for a flow path; the method uses a novel relation with jitter and arrival curve, together with a decomposition of the path into non order-preserving and order-preserving elements. We also analyse the effect of re-sequencing buffers on worst-case delay, jitter and propagation of arrival curves. We show in particular that, in a lossless (but non order-preserving) network, re-sequencing is “for free”, namely, it does not increase worst-case delay nor jitter, whereas in a lossy network, re-sequencing increases the worst-case delay and jitter. We apply the analysis to evaluate the performance impact of placing re-sequencing buffers at intermediate points and illustrate the results on two industrial test cases.

Probabilistic Transmission Scheme for Distributed Filtering Over Randomly Lossy Sensor Networks in the Presence of Eavesdropper

This paper studies distributed secure estimation in sensor networks with packet losses and an eavesdropper. The message is transmitted through communication channels between sensors, which can be overheard by the eavesdropper with a certain probability. Two types of sensor networks under two cooperative filtering algorithms are considered and probabilistic transmission schemes to defend against the eavesdropper are proposed. For collectively detectable sensor networks under consensus Kalman filter, a sufficient distributed detectability condition on the transmission probabilities is identified to guarantee that the estimation errors of sensors are statistically bounded. Furthermore, a necessary and sufficient security condition is obtained to guarantee unboundedness of the eavesdroppers estimation error. For neighborhood-detectable nodes under standard Kalman filter, a sufficient distributed detectability condition and a necessary security condition are provided on the transmission probabilities. Simulation examples are given to illustrate the results.

Qubit teleportation between non-neighbouring nodes in a quantum network

Future quantum internet applications will derive their power from the ability to share quantum information across the network1,2. Quantum teleportation allows for the reliable transfer of quantum information between distant nodes, even in the presence of highly lossy network connections3. Although many experimental demonstrations have been performed on different quantum network platforms4–10, moving beyond directly connected nodes has, so far, been hindered by the demanding requirements on the pre-shared remote entanglement, joint qubit readout and coherence times. Here we realize quantum teleportation between remote, non-neighbouring nodes in a quantum network. The network uses three optically connected nodes based on solid-state spin qubits. The teleporter is prepared by establishing remote entanglement on the two links, followed by entanglement swapping on the middle node and storage in a memory qubit. We demonstrate that, once successful preparation of the teleporter is heralded, arbitrary qubit states can be teleported with fidelity above the classical bound, even with unit efficiency. These results are enabled by key innovations in the qubit readout procedure, active memory qubit protection during entanglement generation and tailored heralding that reduces remote entanglement infidelities. Our work demonstrates a prime building block for future quantum networks and opens the door to exploring teleportation-based multi-node protocols and applications2,11–13.

DRiPLOF: An RPL Extension for Multi-Interface Wireless Sensor Networks in Interference-Prone Environments.

The Routing Protocol for Low-power and Lossy Networks (RPL) is a popular routing layer protocol for multi-hop Wireless Sensor Networks (WSNs). However, typical RPL configurations are based on decade-old assumptions, leading to a mismatch with: (1) advances in wireless hardware; and (2) growing wireless contention. To soften the impact of external stressors (i.e., jamming and interference), we extended RPL to exploit the capabilities of modern multi-interfaced wireless devices. More specifically, our main contribution is the design, development, and evaluation of a novel RPL Objective Function (OF) which, through simulations, is compared to traditional single-interface approaches and a state-of-the-art multi-interface approach. We examine two scenarios, with and without the injection of jamming, respectively. Our proposed OF is shown to outperform, or otherwise perform similar to, all alternatives considered. In normal conditions, it auto-selects the best interface whilst incurring negligible protocol overhead. In our jamming simulations, it provides stable end-to-end delivery ratios exceeding 90%, whereas the closest alternative averages 65% and is considerably less stable. Given we have open-sourced our development codebase, our solution is an ideal candidate for adoption by RPL deployments that expect to suffer interference from competing technologies or are unable to select the best radio technology a priori.

An innovative RPL objective function for broad range of IoT domains utilizing fuzzy logic and multiple metrics

The indispensable part of IoT is the Low-power and Lossy Network (LLN), which is required to connect plenty of resource-constrained (e.g., power and memory) wireless devices. Interestingly, the IPv6 Routing Protocol for LLNs (i.e., RPL), which is the standard routing protocol for IoT, relies primarily on constructing a destination-oriented directed acyclic graph governed by a variety of routing metrics that help in choosing the Objective Function (OF), which is liable for selecting the best preferred parent of each node which in turn gets involved in the route establishment towards the destination. In fact, establishing an OF based on a single metric, as the majority of research considers, has flaws or drawbacks, as it only benefits a few IoT applications. To put it another way, the network lifetime is improved for a few IoT apps but degraded for the majority. In this article, we address not only the limitation of using a single metric, but also the limitations of those works employed a composite metric by putting forward a cross-layer design and accordingly developing a fuzzy logic system that brings together four input metrics, namely, hop count, energy consumption, latency, and received signal strength indicator as a new OF, abbreviated as FL-HELR-OF. The simulation findings, acquired by the Cooja simulator, prove the effectiveness of our new OF, which particularly outperforms other existing studies concerning the packet delivery ratio, control message overhead, latency, energy consumption, and average hop count.

EQ-RPL: An Energy-Efficient and Quality-Aware Routing Protocol for IoT-Based Low-Power and Lossy Networks

<p>In recent years, the advances in sensing and communication technologies have led to the rapid development of various applications of the Internet of Things (IoT). The devices in IoT form an autonomous network architecture, in which the device has a limited battery power and the link has a low reliability. This kind of network is called the low-power and lossy network. In this paper, we propose a routing protocol for low-power and lossy networks. The proposed protocol introduces a novel rank value to construct a proper destination-oriented directed acyclic graph for the source node to transmit packets to the destination node. The proposed rank value is mainly derived from the expected transmission count, which is widely used to represent the link quality. Moreover, we consider the residual energy as the metric for a node to select the proper node to relay the packet to the destination node. We conducted simulations for performance evaluation, showing that the proposed routing protocol improves the packet delivery ratio, especially for the environment with a high bit error rate. The result also validated that our approach achieves the balance of energy consumption of nodes, compared to the existing approach.</p> <p> </p>

Effect of DIS Attack on 6TiSCH Network Formation

The 6TiSCH standard provides minimum latency and reliability in mission-critical IoT applications. To optimize resource allocation during 6TiSCH network formation, IETF released the 6TiSCH minimal configuration (6TiSCH-MC) standard. 6TiSCH-MC considered IETF’s IPv6 Routing Protocol for Low power and Lossy network (RPL) as a routing protocol for both upward and downward routing. In RPL, new joining nodes or joined nodes transmit DODAG Information Solicitation (DIS) requests to get routing information from the network. However, we observe that malicious node(s) can severely affect 6TiSCH networks by sending multiple DIS requests. In this letter, we show and experimentally evaluate on real devices the impact of the DIS attack during 6TiSCH networks formation. We show that the attacker does not need expensive resources or access to the network’s sensitive information to execute the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DIS attack</i> . Our testbed experiments show that the DIS attack significantly degrades the nodes’ joining time and energy consumption, increasing them by 34% and 16%, respectively, compared to normal functioning during 6TiSCH network formation.

Physical Identification Based Trust Path Routing Against Sybil Attacks on RPL in IoT Networks

Routing protocol for low power and lossy networks (RPL) is a popular protocol used in IPv6 based wireless Internet of Things (IoT) networks. Wireless IoT devices using RPL without centralized security management are extremely vulnerable to Sybil attacks, as a malicious node can forge multiple identities and easily conduct denial-of-service (DoS) attacks. Several countermeasures have been investigated to prevent DoS caused by Sybil attacks in RPL, but crucial limitations still remain. In this letter, a physical identification based trust path routing (PITrust) scheme that uses the received signal strength indicator (RSSI) and a centralized trust scheme to improve Sybil node detection is proposed, which is shown to improve the detection rate and packet delivery ratio when compared to other schemes.

A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things.

The IETF Routing Over Low power and Lossy network (ROLL) working group defined IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to facilitate efficient routing in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). Limited resources of 6LoWPAN nodes make it challenging to secure the environment, leaving it vulnerable to threats and security attacks. Machine Learning (ML) and Deep Learning (DL) approaches have shown promise as effective and efficient mechanisms for detecting anomalous behaviors in RPL-based 6LoWPAN. Therefore, this paper systematically reviews and critically analyzes the research landscape on ML, DL, and combined ML-DL approaches applied to detect attacks in RPL networks. In addition, this study examined existing datasets designed explicitly for the RPL network. This work collects relevant studies from five major databases: Google Scholar, Springer Link, Scopus, Science Direct, and IEEE Xplore® digital library. Furthermore, 15,543 studies, retrieved from January 2016 to mid-2021, were refined according to the assigned inclusion criteria and designed research questions resulting in 49 studies. Finally, a conclusive discussion highlights the issues and challenges in the existing studies and proposes several future research directions.

Investigation of RPL in 6LoWPAN based elderly care monitoring system

Wireless Sensor Network (WSN) is a self-structured network containing small, energy-constrained wireless nodes that act together to accomplish difficult tasks. Wearable sensors, one of the WSNs play a significant role in healthcare applications, especially patient monitoring. With a miniature size, wearable sensors have less space dedicated for energy sources. So it is important for wearable sensors to be manufactured as energy efficient and reliable and it must ensure quality of service in providing the data. Remote health care monitoring has two limitations such as adoption of mobility and the usage of low power consumption devices. To overcome these limitations, appropriate routing protocol can be used in Low Power Lossy Networks (LLNs). IPV6 Routing Protocol for Low Power Lossy Networks (RPL) is one of the routing protocols standardized to be applied in Internet of things network with wireless sensors. The current research article investigates the performance of RPL with three Objective Functions (OF), Minimum Rank with Hysteresis Objective Function (MRHOF) with Energy as metric, MRHOF with Expected Transmission count (ETX) as metrics and Objective Function zero(OF0) with hop count as metric, in elderly health care monitoring system. The study considered two scenarios case 1 has all static nodes while case 2 has few dynamic nodes. The performance was evaluated in terms of metrics control overhead, convergence time, Packet Delivery Ratio (PDR), Latency and energy consumption and the OF optimum for reliability, mobility and energy consumption is determined. The results of the simulation showed that, in mobile scenario OF0 converged at a fast rate than the MRHOF, which increases the life time. OF0 also consumed the least energy and it increased the life time of the node. As far as PDR is concerned, OF0 had low PDR when the nodes were mobile and ETX performed well.

V-RPL: An effective routing algorithm for low power and lossy networks using multi-criteria decision-making techniques

The rapid advancement of technology and the advent of IoT technology which can connect everything around us is one of the most exciting breakthroughs all around the world. The need to handle thousands of fixed and mobile nodes in this network in addition to meeting several strict constraints have made the researchers start addressing the different challenges of this technology. One of the most significant challenges in this regard is providing an efficient and optimized routing protocol which on one side is able to manage the mobility of nodes and dynamic topology of this network and on the other side it can take the different aspects of the network performance into account. The aspects such as energy constraint for the battery-powered nodes, traffic congestion and end-to-end delay. One of the well-known routing protocols which has been proposed and standardized during recent years for IoT networks is RPL. Although this method works well and can support different types of IoT networks, it still suffers from various drawbacks particularly for mobile networks which cause the algorithm not to work effectively and efficiently. In this research, we present a new routing algorithm called V-RPL, which can outperform many of the proposed routing algorithms in terms of packet delivery ratio (PDR), end-to-end delay and energy consumption. The proposed algorithm is based on RPL and takes multiple parameters into account, simultaneously and utilizes AHP and VIKOR techniques for multi-criteria decision making to find the best possible route for each node in a mobile network. The performance of V-RPL has been evaluated in different situations using Contiki simulator and obtained results signify a remarkable improvement in different aspects compared to other proposed algorithms.

A New Objective Function Based on Additive Combination of Node and Link Metrics as a Mechanism Path Selection for RPL Protocol

Since its development by IETF, the IPv6 routing protocol for low power and lossy networks (RPL) remains the subject of several researches. RPL is based on objective function as a mechanism selection of paths in the network. However, the default objective functions standardized selects the routes according to a single routing metric that leads to an unoptimized path selection and a lot of parent changes. Thus, we propose in this paper weighted combined metrics objective function (WCM-OF) and non-weighted combined metrics objective function (NWCM-OF) that are based both on additive link quality and energy metrics with equal weights or not to achieve a tradeoff between reliability and saved energy levels. The proposed objective functions were implemented in the core of Contiki operating system and evaluated with Cooja emulator. Results show that the proposed objective functions improved the network performances compared to default objective functions.

Lightweight Path Recovery in IPv6 Internet-of-Things Systems

In an Internet-of-Things system supported by Internet Protocol version 6 (IPv6), the Routing Protocol for Low-Power and Lossy Networks (RPL) presents extensive applications in various network scenarios. In these novel scenarios characterized by the access of massive devices, path recovery, which reconstructs the complete path of the packet transmission, plays a vital role in network measurement, topology inference, and information security. This paper proposes a Lightweight Path recovery algorithm (LiPa) for multi-hop point-to-point communication. The core idea of LiPa is to make full use of the spatial and temporal information of the network topology to recover the unknown paths iteratively. Specifically, spatial and temporal information refer to the potential correlations between different paths within a time slot and path status during different time slots, respectively. To verify the effect of our proposal, we separately analyze the performance of leveraging temporal information, spatial information, and their composition by extensive simulations. We also compare LiPa with two state-of-the-art methods in terms of the recovery accuracy and the gain–loss ratio. The experiment results show that LiPa significantly outperforms all its counterpart algorithms in different network settings. Thus, LiPa can be considered as a promising approach for packet-level path recovery with minor loss and great adaptability.

Performance improvement of Routing Protocol for Low‐Power and Lossy Networks protocol in an Internet of Things‐based smart retail system

SummaryThe Internet of Things (IoT) technology improvement has led to concepts such as smart retail and supply chain management systems. The main problem with IoT‐based smart retail and supply chain management systems is the need for a high‐speed network for responding quickly to many transactions within the network. If the network is not able to respond to all transactions quickly, it will not be possible to handle all requests in time. Also, the network must be stable and reliable to ensure that all requests are received and answered. And finally, they must have a very low power consumption metric to reduce costs. This paper suggests a Multi‐Sink Routing Protocol for Low‐Power and Lossy Networks (RPL) topology for network node placement to achieve network performance improvement. The proposed topology is modeled in a simulated environment using an Instant Contiki operating system by Cooja simulator and is evaluated regarding the different performance metrics including packet delivery ratio (PDR), power consumption, node participation, and control traffic overhead. Also, the network behavior is analyzed by the Wireshark tool. The results show achieving better performance and improving the level of confidence.

MTCEE-LLN: Multilayer Threshold Cluster-Based Energy-Efficient Low-Power and Lossy Networks for Industrial Internet of Things

Internet of Things (IoT) is a new technology with multiple smart connected sensors capable of processing, storing, and computing. Industrial IoT (IIoT) is used in industrial applications, such as infrastructure, medical, logistics, and energy efficiency in smart grids. The network lifetime will be extended when sensor node energy usage is effectively controlled. This article proposed a multilayer threshold cluster-based energy-efficient low power and lossy networks (MTCEE-LLN) protocol for IIoT devices to decrease the network data traffic, sensor node energy consumption (EC) and also extends the network lifetime. The proposed scheme works in three phases: 1) network creation; 2) intra clustering; and 3) intercluster routing. The MTCEE-LLN forms equal-sized cluster in each transmission and elects the cluster head (CH). It maintains the destination-oriented directed acyclic graph (DODAG) to performs data transmission from the downward layer to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ DODAG}}_{\mathrm{ root}}$ </tex-math></inline-formula> . Furthermore, it aggregates the data packets in the cluster node to increases the network lifetime by reducing the number of redundant data packet transmissions. The proposed routing protocol has been evaluated based on different performance parameters such as packet loss rate (PLR), EC, control packet rate (CPR), and Node Failure Ratio. The simulated result proves its effectiveness compared to other traditional routing protocols.

Securing RPL Using Network Coding: The Chained Secure Mode (CSM)

As the de facto routing protocol for many Internet of Things (IoT) networks nowadays, and to assure the confidentiality and integrity of its control messages, the Routing Protocol for Low Power and Lossy Networks (RPL) incorporates three modes of security: the Unsecured Mode (UM), Preinstalled Secure Mode (PSM), and the Authenticated Secure Mode (ASM). While the PSM and ASM are intended to protect against external routing attacks and some replay attacks (through an optional replay protection mechanism), recent research showed that RPL in PSM is still vulnerable to many routing attacks, both internal and external. In this paper, we propose a novel secure mode for RPL, the Chained Secure Mode (CSM), based on the concept of intraflow Network Coding (NC). The CSM is designed to enhance RPL resilience and mitigation capability against replay attacks while allowing the integration with external security measures such as Intrusion Detection Systems (IDSs). The security and performance of the proposed CSM were evaluated and compared against RPL in UM and PSM (with and without the optional replay protection) under several routing attacks: the Neighbor attack (NA), Wormhole (WH), and CloneID attack (CA), using average packet delivery rate (PDR), End-to-End (E2E) latency, and power consumption as metrics. It showed that CSM has better performance and more enhanced security than both the UM and PSM with the replay protection, while mitigating both the NA and WH attacks and significantly reducing the effect of the CA in the investigated scenarios.

The DAO Induction Attack: Analysis and Countermeasure

We study the destination advertisement object (DAO) induction attack, a new attack against Internet Protocol version 6 (IPv6) routing protocol for low-power and lossy networks (RPL), the standard routing protocol for the Internet of Things (IoT). In the DAO induction attack, a compromised node in the network periodically transmits a special control message. Each of these crafted control messages induces many nodes in the network to transmit in response. We show that transmitting these unnecessary messages can significantly increase the power consumption of nodes, hence reduce the lifetime of battery-operated IoT devices. In addition, we show that the attack severely impacts end-to-end latency and packet delivery ratio, two important network performance metrics. For instance, in a network with 50 nodes, our simulation results show that the attack increases the average end-to-end latency and packet loss ratio by 410% and 260%, respectively. To counter the attack, we propose a lightweight solution. We show that our solution imposes no overhead when the network is in its normal operation (i.e., it is not under attack) and can quickly detect the attack even when the network experiences high packet loss rates.

An Efficient Secure Cryptography Scheme for New ML-based RPL Routing Protocol in Mobile IoT Environment

Internet of Things (IoT) offers reliable and seamless communication for the heterogeneous dynamic lowpower and lossy network (LLNs). To perform effective routing in IoT communication, LLN Routing Protocol (RPL) is developed for the tiny nodes to establish connection by using deflaut objective functions: OF0, MRHOF, for which resources are constraints like battery power, computation capacity, memory communication link impacts on varying traffic scenarios in terms of QoS metrics like packet delivery ratio, delay, secure communication channel. At present, conventional Internet of Things (IoT) are having secure communication channels issue for transmission of data between nodes. To withstand those issues, it is necessary to balance resource constraints of nodes in the network. In this paper, we developed a security algorithm for IoT networks with RPL routing. Initially, the constructed network in corporates optimizationbased deep learning (reinforcement learning) for route establishment in IoT. Upon the establishment of the route, the ClonQlearn based security algorithm is implemented for improving security which is based onaECC scheme for encryption and decryption of data. The proposed security technique incorporates reinforcement learning-based ClonQlearnintegrated with ECC (ClonQlearn+ECC) for random key generation. The proposed ClonQlearn+ECCexhibits secure data transmission with improved network performance when compared with the earlier works in simulation. The performance of network expressed that the proposed ClonQlearn+ECC increased the PDR of approximately 8% - 10%, throughput of 7% - 13%, end-to-end delay of 5% - 10% and power consumption variation of 3% - 7%.

Nonuniform Dual-Rate Extended Kalman-Filter-Based Sensor Fusion for Path-Following Control of a Holonomic Mobile Robot with Four Mecanum Wheels

This paper presents an extended Kalman-filter-based sensor fusion approach, which enables path-following control of a holonomic mobile robot with four mecanum wheels. Output measurements of the mobile platform may be sensed at different rates: odometry and orientation data can be obtained at a fast rate, whereas position information may be generated at a slower rate. In addition, as a consequence of possible sensor failures or the use of lossy wireless sensor networks, the presence of the measurements may be nonuniform. These issues may degrade the path-following control performance. The consideration of a nonuniform dual-rate extended Kalman filter (NUDREKF) enables us to estimate fast-rate robot states from nonuniform, slow-rate measurements. Providing these estimations to the motion controller, a fast-rate control signal can be generated, reaching a satisfactory path-following behavior. The proposed NUDREKF is stated to represent any possible sampling pattern by means of a diagonal matrix, which is updated at a fast rate from the current, existing measurements. This fact results in a flexible formulation and a straightforward algorithmic implementation. A modified Pure Pursuit path-tracking algorithm is used, where the reference linear velocity is decomposed into Cartesian components, which are parameterized by a variable gain that depends on the distance to the target point. The proposed solution was evaluated using a realistic simulation model, developed with Simscape Multibody (Matlab/Simulink), of the four-mecanum-wheeled mobile platform. This model includes some of the nonlinearities present in a real vehicle, such as dead-zone, saturation, encoder resolution, and wheel sliding, and was validated by comparing real and simulated behavior. Comparison results reveal the superiority of the sensor fusion proposal under the presence of nonuniform, slow-rate measurements.

State Estimation Fusion for Linear Microgrids over an Unreliable Network

Microgrids should be continuously monitored in order to maintain suitable voltages over time. Microgrids are mainly monitored remotely, and their measurement data transmitted through lossy communication networks are vulnerable to cyberattacks and packet loss. The current study leverages the idea of data fusion to address this problem. Hence, this paper investigates the effects of estimation fusion using various machine-learning (ML) regression methods as data fusion methods by aggregating the distributed Kalman filter (KF)-based state estimates of a linear smart microgrid in order to achieve more accurate and reliable state estimates. This unreliability in measurements is because they are received through a lossy communication network that incorporates packet loss and cyberattacks. In addition to ML regression methods, multi-layer perceptron (MLP) and dependent ordered weighted averaging (DOWA) operators are also employed for further comparisons. The results of simulation on the IEEE 4-bus model validate the effectiveness of the employed ML regression methods through the RMSE, MAE and R-squared indices under the condition of missing and manipulated measurements. In general, the results obtained by the Random Forest regression method were more accurate than those of other methods.