Routing Protocol For Low-Power Research Articles

History-based consistency algorithm for the trickle-timer with low-power and lossy networks

Recently, the internet of things (IoT) has become an important concept which has changed the vision of the Internet with the appearance of IPv6 over low power and lossy networks (6LoWPAN). However, these 6LoWPANs have many drawbacks because of the use of many devices with limited resources; therefore, suitable protocols such as the Routing Protocol for low power and lossy networks (RPL) were developed, and one of RPL's main components is the trickle timer algorithm, used to control and maintain the routing traffic frequency caused by a set of control messages. However, the trickle timer suffered from the short-listen problem which was handled by adding the listen-only period mechanism. This addition increased the delay in propagating transmissions and resolving the inconsistency in the network. However, to solve this problem we proposed the history based consistency algorithm (HBC), which eliminates the listen-only period based on the consistency period of the network. The proposed algorithm showed very good results. We measured the performance of HBC trickle in terms of convergence time; which was mainly affected, the power consumption and the packet delivery ratio (PDR). We made a comparison between the original trickle timer, the E-Trickle, the optimized trickle and our HBC trickle algorithm. The PDR and the power consumption showed in some cases better results under the HBC trickle compared to other trickle timers and in other cases the results were very close to the original trickle indicating the efficiency of the proposed trickle in choosing optimal routes when sending messages.

DETONAR: Detection of Routing Attacks in RPL-Based IoT

The Internet of Things (IoT) is a reality that changes several aspects of our daily life, from smart home monitoring to the management of critical infrastructure. The “Routing Protocol for low power and Lossy networks” (RPL) is the only de-facto standardized routing protocol in IoT networks and is thus deployed in environmental monitoring, healthcare, smart building, and many other IoT applications. In literature, we can find several attacks aiming to affect and disrupt RPL-based networks. Therefore, it is fundamental to develop security mechanisms that detect and mitigate any potential attack in RPL-based networks. Current state-of-the-art security solutions deal with very few attacks while introducing heavy mechanisms at the expense of IoT devices and the overall network performance. In this work, we aim to develop an Intrusion Detection System (IDS) capable of dealing with multiple attacks while avoiding any RPL overhead. The proposed system is called DETONAR - DETector of rOutiNg Attacks in Rpl - and it relies on a packet sniffing approach. DETONAR uses a combination of signature and anomaly-based rules to identify any malicious behavior in the traffic (e.g., application and DIO packets). To the best of our knowledge, there are no exhaustive datasets containing RPL traffic for a vast range of attacks. To overcome this issue and evaluate our IDS, we propose RADAR - Routing Attacks DAtaset for Rpl: the dataset contains five simulations for each of the 14 considered attacks in 16 static-nodes networks. DETONAR’s attack detection exceeds 80% for 10 attacks out of 14, while maintaining false positives close to zero.

AIEMLA: artificial intelligence enabled machine learning approach for routing attacks on internet of things

The Internet of things (IoT) is emerging as a prime area of research in the modern era. The significance of IoT in the daily life is increasing due to the increase in objects or things connected to the internet. In this paper, routing protocol for low power and lossy networks (RPL) is examined on the Contiki operating system. This paper used RPL attack framework to simulate three RPL attacks, namely hello-flood, decreased-rank and increased-version. These attacks are simulated in a separate and simultaneous manner. The focus remained on the detection of these attacks through artificial neural network (ANN)-based supervised machine learning approach. The accurate detection of the malicious nodes prevents the network from the severe effects of the attack. The accuracy of the proposed model is computed with hold-out approach and tenfold cross-validation technique. The hyperparameters have been optimized through parameter tuning. The model presented in this paper detected the aforesaid attacks simultaneously as well as individually with 100% accuracy. This work also investigated other performance measures like precision, recall, F1-score and Mathews correlation coefficient (MCC).

A Dense Neural Network Approach for Detecting Clone ID Attacks on the RPL Protocol of the IoT.

At present, new data sharing technologies, such as those used in the Internet of Things (IoT) paradigm, are being extensively adopted. For this reason, intelligent security controls have become imperative. According to good practices and security information standards, particularly those regarding security in depth, several defensive layers are required to protect information assets. Within the context of IoT cyber-attacks, it is fundamental to continuously adapt new detection mechanisms for growing IoT threats, specifically for those becoming more sophisticated within mesh networks, such as identity theft and cloning. Therefore, current applications, such as Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), and Security Information and Event Management Systems (SIEM), are becoming inadequate for accurately handling novel security incidents, due to their signature-based detection procedures using the matching and flagging of anomalous patterns. This project focuses on a seldom-investigated identity attack—the Clone ID attack—directed at the Routing Protocol for Low Power and Lossy Networks (RPL), the underlying technology for most IoT devices. Hence, a robust Artificial Intelligence-based protection framework is proposed, in order to tackle major identity impersonation attacks, which classical applications are prone to misidentifying. On this basis, unsupervised pre-training techniques are employed to select key characteristics from RPL network samples. Then, a Dense Neural Network (DNN) is trained to maximize deep feature engineering, with the aim of improving classification results to protect against malicious counterfeiting attempts.

Reliable critical nodes detection for Internet of Things (IoT)

The 3D critical node (C-N) detection can play a vital role in algorithm development of security, surveillance, monitoring, topology detection, and situation-aware emergency navigation for the Internet of Things (IoT). However, 3D C-N detection problem in IoT raises some issues and also introduces new challenges. The existing state of the art in 3D C-N detection shows that rely on prior known anchor node, known coordinate, embedding of the 3D situation on a 2D geometrical structure like circles and presence of unreliable node and ignores the energy constraint in Low Power and Lossy Networks IoT. In this paper, we present a practical, distributed, and energy-efficient algorithm for reliable 3D C-N detection. The goal of the proposed mechanism is twofold, firstly a 3D critical nodes (C-N) detection algorithm is proposed which uses only Received Signal Strength Indicator information of neighbor. Secondly, a correlation-based algorithm for the reliability approach is proposed to increases the node resilience against malicious IoT nodes. The complexity of our proposed algorithms has a time complexity of $$\mathcal {O}(\log (N))$$ and computation cost $$\mathcal {O}(\delta (\log N))$$ where N is the number of nodes in networks, and $$\delta $$ is the total number of forward and the backward message from an individual node. To validate our work, we implemented our proposed approach with the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) based IoT routing protocol compare it with RPL and cryptographic approach Version Number and Rank Authentication (VeRa). The result shows that the proposed approach can detect 10–15% more C-N nodes. Result also shows that our proposed algorithm has better PDR than RPL based approach by 12% and less than VeRa (cryptographic approach) by 8% however our proposed approach consumes almost 50% less power than the VeRa.

Fault-tolerant AI-driven Intrusion Detection System for the Internet of Things

Internet of Things (IoT) has emerged as a key component of all advanced critical infrastructures. However, with the challenging nature of IoT, new security breaches have been introduced, especially against the Routing Protocol for Low-power and Lossy Networks (RPL). Artificial-Intelligence-based technologies can be used to provide insights to deal with IoT’s security issues. In this paper, we describe the initial stages of developing, a new Intrusion Detection System using Machine Learning (ML) to detect routing attacks against RPL. We first simulate the routing attacks and capture the traffic for different topologies. We then process the traffic and generate large 2-class and multi-class datasets. We select a set of significant features for each attack, and we use this set to train different classifiers to make the IDS. The experiments with 5-fold cross-validation demonstrated that decision tree (DT), random forests (RF), and K-Nearest Neighbours (KNN) achieved good results of more than 99% value for accuracy, precision, recall, and F1-score metrics, and RF has achieved the lowest fitting time. On the other hand, Deep Learning (DL) model, MLP, Naïve Bayes (NB), and Logistic Regression (LR) have shown significantly lower performance.

Multi-constraints based RPL objective function with adaptive stability for high traffic IoT applications

The internet of things technology is classified as a Low power and lossy network. These kinds of networks require a trustworthy routing protocol considered as the backbone for management and high quality of service achievements. IPv6 routing protocol for Low power and lossy network (RPL) was able to gain popularity compared to other routing protocols dedicated to IoT for its great flexibility through the objective function. Default objective functions implemented in the RPL core are based on a single metric. Consequently, the routing protocol can’t cope with different constraints and show congestion issues in high traffics. For that, we proposed in our paper multi-constraints-based objective function with adaptive stability (MCAS-OF), which uses novel strategies for Radio strength indicator, node energy consumption, hop count and a designed work-metric combination, new rank processing, and parent selection procedure. The network stability was also taken into account, since the multi constraints can lead to frequent parent changes, using an adaptive threshold. The proposal, evaluated under the COOJA emulator against standard-RPL and EC-OF, showed a packet delivery ratio improvement by 24% in high traffics, a decrease in the power consumption close to 44%, achieved less latency and DIO control messages, it also gives a good workload balancing by reducing the standard deviation of node’s power consumption.

Intrusion Detection Systems in RPL-Based 6LoWPAN: A Systematic Literature Review

Drastic reduction in the manufacturing cost of sensors and actuators has resulted in considerable growth in the number of smart objects. The so-called Internet of Things (IoT) blends the real and virtual environments and removes time and distance barriers. It is widely perceived as a major enabler for the efficient and effective provision of services across a range of sectors. It has naturally attracted the interest of cyberattackers. Due to the heterogeneity, resource-constraints, scale, and internet connectivity of IoT devices, each IoT layer is prone to various threats. Intruders consider the network layer of IoT as the gateway and leverage vulnerabilities in the routing protocol to compromise the Confidentiality, Integrity, and Availability (CIA) of connected nodes. Researchers have proposed different security infrastructures to mitigate harm to IoT networks. One of these is the Intrusion Detection System (IDS). An IDS is an essential component for the network security layer and is widely adopted to reinforce the security of the IoT network. This systematic literature review explores the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) and its existing threats, classifies relevant IDS techniques and identifies areas requiring further investigation. We review 103 published papers in this domain.

Sinkhole Attack Detection and Avoidance Mechanism for RPL in Wireless Sensor Networks

The security issue is one of the main problems in Wireless Sensor Network (WSN) and Internet of Things (IoTs). RPL (Routing protocol for low power and lossy networks) is a standard routing protocol for WSN, is not to be missed from being attacks. The performance of RPL is reduced significantly after being attacked. Sinkhole attack is one of the most common attacks to WSN and RPL, threatening the network capability by discarding packets and disrupting routing paths. Therefore, this paper proposes a new Secured-RPL routing protocol to detect and avoid sinkhole attacks in the network, which is called Cross Layers Secured RPL (CLS-RPL). This routing protocol is enhanced of the existing RPL routing protocol. CLS-RPL is a cross-layer routing protocol that uses information from the data link layer in its security mechanism. CLS-RPL uses a new technique and concept in detecting a sinkhole attack that is based on eave-listening (overhearing) that allows a child node to eave-listening its parent transmission. If the child node does not hear any transmission from its parent node after sending several packets, this means its parent node is a sinkhole attacker. Otherwise, if the node hears transmission from its parent node, this means that its parent node is legitimate and continues to send more packets. CLS-RPL implements a simple security mechanism that provides a high packet delivery ratio. The finding shows that CLS-RPL provides 52% improvement in terms of packet delivery ratio when compared to RPL protocol.

Load balancing for RPL-based Internet of Things: A review

Low-Power and Lossy networks are an integral part of the IoT ecosystem. These networks are defined by their shared features such as having limited resources and high occurrence of packet loss. A routing protocol for such networks called Routing Protocol for Low-Power and Lossy Networks (RPL) was proposed in 2012. Even though RPL is now standardized and well-accepted by the community, it still has areas to be improved such as load balancing, stability, and support for mobility. This study focuses particularly on approaches proposed for the load balancing problem in RPL. In the literature, many researchers aimed to tackle this problem by creating different routing metrics that handle different objectives. This review makes a thorough assessment of these works, their strengths and shortcomings, and provides future directions on the issue.

A Comprehensive Review on Secure Routing in Internet of Things: Mitigation Methods and Trust-Based Approaches

Internet of Things (IoT) is a network of “things,” connected via Internet, to collect and exchange data. These “things” can be sensors, actuators, smartphones, wearables, computers, or any object that is interconnected to provide specific services. Similarly, wireless sensor network (WSN), as a part of IoT, forwards the gathered data after sensing any event. The scalability and heterogeneity of IoT offer limited protection and is prone to diverse attacks, including WSN-inherited attacks. Moreover, IPv6 routing protocol for low power and lossy networks (RPL), a de facto routing protocol for IoT networks, also suffers from certain vulnerabilities based on its features and functionalities. Researchers have proposed various mitigation mechanisms for secure networks and routing in IoT. Recently, trust-based approaches have gained tremendous interest from the research community to embed security in IoT networks and routing protocols. In the existing literature, several trust models have been introduced according to the security needs of the IoT system, such as SecTrust, DCTM-IoT, CTRUST, etc. In this research, security issues and requirements of IoT networks and RPL routing protocol are studied with respect to various attacks, such as Blackhole, Spoofing, Rank, etc. Additionally, various mitigation methods and significance of trust models in IoT for secure routing are analyzed. Further, trust metrics in IoT environments, including the open issues and research challenges, as well as the implication of trust as a security paradigm in IoT networks and routing protocols are discussed.

A fault tolerant routing scheme for advanced metering infrastructure: an approach towards smart grid

The wireless sensor network is gaining significant attention because of its ubiquitous deployability nature. In general, the role of the wireless sensor network is remarkable in smart city applications for data sensing, collecting, and transmitting. Advanced metering infrastructure is an automatic system for the reading of electricity consumption by individual users. The reading of data from meters normally communicated over a wireless medium. Apart from collecting and sensing, routing is one of the major attributes in the AMI network. Successful communication is possible when the link and node are in the proper state. In this paper, we propose a fault-tolerant routing mechanism named Grid Topological Routing scheme to provide an efficient route in case of encountering a faulty path. The simulation shows better performance against the Routing Protocol for Low-Power and Lossy Networks and Ad-Hoc On-Demand Vector algorithm in terms of power consumption and packet delivery ratio. This model is explicitly designed for the smart grid in India.

Time Slotted Channel Hopping and ContikiMAC for IPv6 Multicast-Enabled Wireless Sensor Networks.

Smart buildings benefit from IEEE 802.15.4e time slotted channel hopping (TSCH) medium access for creating reliable and power aware wireless sensor and actuator networks (WSANs). As in these networks, sensors are supposed to communicate to each other and with actuators, IPv6 multicast forwarding is seen as a valuable means to reduce traffic. A promising approach to multicast, based on the Routing Protocol for Low Power and Lossy Networks (RPL) is Bidirectional Multicast RPL Forwarding (BMRF). This paper aimed to analyze the performance of BMRF over TSCH. The authors investigated how an adequate TSCH scheduler can help to achieve a requested quality of service (QoS). A theoretical model for the delay and energy consumption of BMRF over TSCH is presented. Next, BMRF’s link layer (LL) unicast and LL broadcast forwarding modes were analyzed on restricted and realistic topologies. On topologies with increased interference, BMRF’s LL broadcast on top of TSCH causes high energy consumption, mainly because of the amount of energy needed to run the schedule, but it significantly improves packet delivery ratio and delay compared to ContikiMAC under the same conditions. In most cases, the LL unicast was found to outperform the LL broadcast, but the latter can be beneficial to certain applications, especially those sensitive to delays.

A firefly algorithm for power management in wireless sensor networks (WSNs)

In wireless sensor networks (WSNs), designing a stable, low-power routing protocol is a major challenge because successive changes in links or breakdowns destabilize the network topology. Therefore, choosing the right route in this type of network due to resource constraints and their operating environment is one of the most important challenges in these networks. Therefore, the main purpose of these networks is to collect appropriate routing information about the environment around the network sensors while observing the energy consumption of the sensors. One of the important approaches to reduce energy consumption in sensor networks is the use of the clustering technique, but in most clustering methods, only the criterion of the amount of energy of the cluster or the distance of members to the cluster has been considered. Therefore, in this paper, a method is presented using the firefly algorithm and using the four criteria of residual energy, noise rate, number of hops, and distance. The proposed method called EM-FIREFLY is introduced which selects the best cluster head with high attractiveness and based on the fitness function and transfers the data packets through these cluster head to the sink. The proposed method is evaluated with NS-2 simulator and compared with the algorithm-PSO and optimal clustering methods. The evaluation results show the efficiency of the EM-FIREFLY method in maximum relative load and network lifetime criteria compared to other methods discussed in this article.

A new load balancing clustering method for the RPL protocol

Internet of things (IoT) is a network of different interconnected objects that are capable to collect and exchange data without human interaction. IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) is the common IoT routing protocol. One of the main drawbacks of the RPL protocol is lack of support of load balancing leads to unfair distribution of traffic load in the network which may decrease network efficiency. In this paper for load balancing, we proposed a new method called C-Balance based on cluster ranking to increase the network lifetime. In this method, two ranks are calculated for each node. The first rank is used to identify clusters and cluster heads and the second rank is used to select parents of each cluster head to forward packets towards the destination. To calculate these ranks, several metrics are used including Expected Transmission Count, hop count, residual energy and number of children. To investigate the performance of the proposed method, it has been simulated with Cooja simulator in the form of nodes with mobility and non-mobility scenarios plus using a random topology network with 20, 40 and 60 nodes experiments. The results are compared with OF0 and MRHOF standard objective functions as well as the QU-RPL method. The final results in both scenarios show that the proposed method in the field of energy consumption, network lifetime and load balancing has improved compared to the other methods. In terms of end-to-end delay, the proposed method has more delay compared to the standard objective functions and QU-RPL method. The calculation of the mean packet delivery ratio (PDR) of these four methods also shows that the proposed method has an acceptable performance. Final results indicate that on average, there is a 30–45% improvement in energy consumption, 15–23% reduction in average number of children and 22–48% improvement in network lifetime are obtained compared to the other methods. Finally, there is about 12% progress for PDR compared to the OF0.

Optimizing RPL performance based on the selection of best route between child and root node using E-MHOF method

IETF has proposed the routing protocol for low power and lossy networks (RPL) for IOT as view as light weight routing protocol. In RPL, the objective function (OF) is used to select the best route between child and root node. Several researches have been conducted in order to, enhance OF according to number parameters such as number of hops, remaining energy and expected number of transmissions (ETX), without a consideration to other challenges such as congestion node problem and latency. So, to overcome these challenges a new technique called “Enhance-Minimum Rank with Hysteresis Objective Function (MHOF)” is proposed in this paper, to select the ideal path between the child and root node. The technique is consisted of three layers: parent selection layer in which parent is selected based on three parameters (ETX, RSSI and nodes’ residual energy), path selection layer in which the best route is chosen according to the minimum of (average ETX value) and maximum of (average remaining energy value) of all nodes in the selected route. The last layer is child node minimization, which utilized to solve the congestion node energy problem by using two parameters (RSSI reference and threshold value). The proposed method has been implemented and evaluated by using Cooja simulator software. The simulation results have shown that selected path with E-MHOF is increased the network lifetime and reduced latency in comparison with MHOF.

CTrust‐RPL: A control layer‐based trust mechanism for supporting secure routing in routing protocol for low power and lossy networks‐based Internet of Things applications

AbstractInternet of Things (IoT) fostered a new epoch of innovation by interconnecting digital devices to make human life more convenient and attractive. These smart objects are largely deployed as low power and lossy networks (LLNs) and use routing protocol for LLNs (RPL) for routing. Unfortunately, it is extremely vulnerable to a large variety of external and internal attacks to cause devastating and calamitous effects. However, this article's scope revolves around internal attacks only, where nodes are already part of a legitimate network. Various trust‐based mechanisms have been proposed to secure the RPL protocol from insider attackers. Existing trust mechanisms cause high energy depletion due to complex computation on the node level, which consequently decreases the performance of LLNs. Therefore, this article presents a novel hierarchical trust‐based mechanism “CTrust‐RPL” by assessing the trust of nodes based on their forwarding behaviors. This study ships complex trust‐related computations to the higher layer, known as the controller, to save computational, storage, and energy resources at the node level. We also compare the proposed mechanism with a state‐of‐the‐art technique calledSec‐trust. Our mechanism demonstrates superior performance in detecting and isolating blackhole attacks. The results depict thatCTrust‐RPLdetects and isolates 10% more malicious nodes thanSec‐trustin the same time‐lapse. The average packet loss ratio difference is less for our proposed mechanism, with 35% more energy efficiency.

A Green Routing Protocol with Wireless Power Transfer for Internet of Things

The usually constrained resources and lossy links scenarios of Internet of Things (IoT) applications require specific protocol suite, as the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). Due to its flexibility, RPL can support efficiently vertical applications such as environmental monitoring, smart city and Industry 4.0. In this paper, we propose a new Objective Function (OF) for RPL based on a composite metric considering jointly the residual energy of a node (parent) together with the energy that a neighbor node (child) can transfer to the parent according to the Wireless Power Transfer (WPT) concept. Specifically, we consider simultaneous wireless information and power transfer (SWIPT) technique, which enables both the energy harvesting and information decoding from the same radio frequency (RF) signal, in order to influence the selection of the best path according to the proposed energy efficient metric in RPL. Performance evaluation on a realistic scenario pointed out a remarkable energy saving to prolong the network lifetime, by selecting the best path toward the sink node, with respect to the OFs usually considered in the literature.

ODeSe: On-Demand Selection for multi-path RPL networks

The Internet of Things (IoT) has become ubiquitous due to its flexibility, ease-of-use, and reduced cost. As a consequence, the industry is adopting these systems in its transformation into Industry 4.0. However, the strict Quality of Service (QoS) requirements of the industry are not met with the default best-effort provisions of the IoT. Most industrial applications require strict guarantees in terms of end-to-end network reliability and latency. For instance, consecutive packet losses can lead to communication disruptions in supply chain systems. Therefore, adaptations are being made to fulfill these requirements with the IEEE Std 802.15.4-2015 Time slotted Channel Hopping (TSCH) link-layer standard and the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) standard at the Internet Engineering Task Force (IETF). However, even by employing such industrial protocols, it is still difficult to achieve the expected QoS levels. Considering that RPL constructs and maintains a single-path from a source to a destination if there are potential issues on this path (e.g., queue overflow, variable wireless link quality) packets may suffer unexpected delays and even drops. If we consider a multi-path implementation where each node can replicate a packet into several paths, the transmission reliability improves since each packet copy is used to forward the packet information. However, uncontrolled replication can lead to network flooding, resulting in excessive power consumption. In this article, we present On-Demand Selection (ODeSe), a novel multi-path routing algorithm, which improves our previous work, the Common Ancestor (CA) algorithms, by selecting the most suitable upward forwarders. Moreover, we use the Packet Automatic Repeat reQuest, Replication and Elimination, and Overhearing (PAREO) functions in order to improve network reliability and availability. In order to control the number of relay nodes during a transmission, ODeSe forces each node of the same relay level to select the same Preferred Parent (PP) and the same Alternative Parent (AP). Thus, we address the trade-off between the total number of traversed nodes in the network and high reliability. Using the Cooja network simulator running the Contiki OS, we compare ODeSe against single-path RPL and multi-path RPL with different alternative parent selection algorithms. The results demonstrate that ODeSe outperforms single-path RPL in terms of reliability, and multi-path RPL in terms of energy consumption while maintaining a 99.14% packet delivery ratio.

Routing protocol for low power and lossy network–load balancing time-based

Recently 6G/IoT emerged the latest technology of traditional wireless sensor network devices for 6G/IoT-oriented infrastructure. The construction of 6G/IoT utilizes the routing protocol for low power and lossy networks (RPL) protocol in the network layer. RPL is a proactive routing protocol with an IPV6 distance vector. The enormous number of connected smart devices and a huge amount of common information and services have shown the important need for an effective load balancing mechanism to distribute the load between nodes. The motivation of this research is to observe some of the load balancing challenges and problems and propose a solution. This paper proposes a new mechanism called Load Balancing Time Based (LBTB). The proposed LBTB is composed of the node count of neighbors and the remaining node power. The proposed LBTB deployed a modified edition of trickle timer algorithm to act as the constructor of the Destination Oriented Directed Acyclic Graph (DODAG) and controls the messages distribution between nodes. The simulation of the experiments performed using Cooja 2.7 on different network densities (low, medium, and high) under reception of success ratios (80%). Grid and random network topologies were deployed. The performance of RPL using the LBTB algorithm was measured using metrics including convergence time, the packet delivery ratio (PDR), power consumption, and delay. We compared the results with the LBSR and the standard algorithms. The results of the simulation showed that the average enhancement of the performance as follows: 68% convergence time, 16% power consumption, and 56% delay. In addition, the results showed that the PDR in some cases were better using the LBTB algorithm.