Trickle Algorithm Research Articles

Power-Efficient Trickle Algorithm With Dynamic Adjustment of Idle Interval for Internet-of-Things Application

Power-Efficient Trickle Algorithm With Dynamic Adjustment of Idle Interval for Internet-of-Things Application

Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT

Objectives: The main objectives of this research endeavor encompass the development of the Adaptive RPL Optimization (ARPLO) model to enhance data transmission efficiency within IoT networks. This includes constructing a grid-based network structure optimized for data transfer, selecting the most suitable nodes as grid head nodes to maximize network lifespan while minimizing energy consumption, implementing an innovative objective function-driven approach to optimize parent node selection, and integrating an Adaptive Deep Neural Network (ADNN) to accurately classify medical data. Methods: The research methodology entails several key steps. A grid-based network structure is established with IoT nodes and root nodes, where a DODAG process incorporating DIO messages is utilized for node ranking. To enhance energy efficiency, the Trickle algorithm is employed for control message optimization. Grid head nodes are chosen based on metrics such as root node fairness, residual energy, and load influence index. The novel Middle Order Optimal Routing (MOOR) objective function is utilized to optimize routing decisions. ADNN is implemented for precise medical data classification. The proposed model's performance is evaluated through simulation in a Python environment. Findings: The research findings demonstrate that the ARPLO model yields notable benefits compared to existing models. It achieves higher energy efficiency, improved throughput, enhanced packet delivery ratio (PDR), and an extended network lifespan. The Trickle algorithm contributes to efficient control message optimization. The MOOR-based routing approach improves multimedia medical data transfer. Moreover, the integration of ADNN enhances the accuracy of data classification, particularly in healthcare applications. The research outcomes align with the broader field's existing values and reports while offering novel insights that contribute to enhancing the existing knowledge base. ARPLO protocol performance reveals that there is increase of throughput of 31.2%, PDR by 7.12%, lifetime of 10.7 % with reduction of energy consumption by 12.72%, control overhead by 31.01% and end-to-end delay by 33.01%. Novelty: The novelty of this research lies in its comprehensive approach that integrates a grid-based network structure, MOOR-based optimization, and ADNN-based classification. The incorporation of the Trickle algorithm for energy-efficient communication is an innovative feature. The introduction of new metrics for grid head node selection, along with the application of the MOOR objective function for multimedia medical data routing, showcases the research's innovative contributions. Keywords: Internet of Things (IoT), RPL (Routing Protocol for Low­Power and Lossy Networks), Optimization, Routing, Multimedia, Healthcare

Elastic Hop Count Trickle Timer Algorithm in Internet of Things

The Internet of Things (IoT) is a technology that allows machines to communicate with each other without the need for human interaction. Usually, IoT devices are connected via a network. A wide range of network technologies are required to make the IoT concept operate successfully; as a result, protocols at various network layers are used. One of the most extensively used network layer routing protocols is the Routing Protocol for Low Power and Lossy Networks (RPL). One of the primary components of RPL is the trickle timer method. The trickle algorithm directly impacts the time it takes for control messages to arrive. It has a listen-only period, which causes load imbalance and delays for nodes in the trickle algorithm. By making the trickle timer method run dynamically based on hop count, this research proposed a novel way of dealing with the difficulties of the traditional algorithm, which is called the Elastic Hop Count Trickle Timer Algorithm. Simulation experiments have been implemented using the Contiki Cooja 3.0 simulator to study the performance of RPL employing the dynamic trickle timer approach. Simulation results proved that the proposed algorithm outperforms the results of the traditional trickle algorithm, dynamic algorithm, and e-trickle algorithm in terms of consumed power, convergence time, and packet delivery ratio.

3D Void Handling Geographic P2P-RPL for Indoor Multi-Hop IR-UWB Networks

IETF has standardized the point-to-point RPL (P2P-RPL) to ensure reliable and optimal P2P route discovery for low-power and lossy networks (LLNs). P2P-RPL propagates route discovery packets to all nodes in the network, which results in high routing communication overheads. Recently, other RPL-based P2P routing algorithms have been proposed to reduce such overheads, but still, quite an amount of overheads occur due to their flooding-based approach. In real life 3D environments, a larger number of nodes should be deployed to guarantee the full network connectivity, and thus the flooding strategy incurs higher overheads. In effort to alleviate high overheads, geographic routing is an attractive solution that exploits the nodes’ geographic locations in its next-hop routing selection. However, geographic routing inherently suffers from the local minimum (void) problem following greedy next-hop selection. Local minima occur more often in 3D space, and therefore, a reliable 3D void handling technique is required. In this paper, we propose greedy forwarding and void handling point-to-point RPL with adaptive trickle timer (GVA-P2P-RPL), which is a novel RPL-based P2P routing protocol that quickly discovers energy-efficient and reliable P2P routes in 3D networks. In GVA-P2P-RPL, P2P-RPL is modified to greedily forward routing packets when it is possible. IR-UWB-based 3D multi-hop self-positioning is conducted in advance to obtain the geographical location of each node. When local minima are encountered, routing packets are temporarily broadcast just like in the traditional P2P-RPL. A new trickle algorithm called adaptive trickle timer (ATT) is also presented to reduce route discovery time and provide better collision avoidance effects. The performance of GVA-P2P-RPL is compared with that of P2P-RPL, partial flooding-based P2P-RPL (PF-P2P-RPL) and ER-RPL. It shows significant improvements in route discovery overheads and route discovery time against these state-of-the-art RPL-based P2P routing methods in 3D environments. Performance evaluation in the special network case where a huge 3D void volume exists in the center is also presented to show the strong void recovery capability of the proposed GVA-P2P-RPL in 3D environments.

Learning automata and lexical composition method for optimal and load balanced RPL routing in IoT

Low power and lossy network, internet of things (IoT) motivates energy-efficient and load-balanced routing in the network layer to extend network lifetime. IoT application scenarios exploit the routing protocol for low-power and lossy networks (RPL) due to the significant potentials. The core components of RPL are the trickle algorithm and objective functions (OF) for creating destination oriented directed acyclic graph (DODAG) and data forwarding. The RPL needs more attention to avoid hotspot problems and unnecessary energy depletion. Most of the existing routing protocols take a single either hop count or ETX, or multiple routing decision metrics. However, the RPL cannot select appropriate link metrics efficiently against the dynamic and lossy environment without considering the relationship between those metrics. Thus, the proposed methodology takes important routing metrics, such as hop count, expected transmission count, and traffic-related metric, and composites the metrics using learning automata and lexical composition method. The special attention on network energy balancing through expected transmission energy (ETT) avoids a hotspot issue and inefficient routing energy. The proposed work supports multiple metrics-based OF with considerable routing overhead by tuning the trickle parameter. Moreover, the proposed work is evaluated to show its advantages over the dynamic and lossy network, IoT.

Improved Trickle Algorithm Toward Low Power and Better Route for the RPL Routing Protocol

The IPv6 Routing Protocol for Low Power and Lossy networks (RPL) is a routing protocol standardized by the Internet Engineering Task Force. According to the specification of the protocol, the Trickle algorithm is adopted for the dissemination of route construction information among nodes in such networks. Since the algorithm is originally designed for code propagation and maintenance in Wireless Sensor Networks, when using the algorithm for the route formation of the network there exist some problems, such as the route convergence time, the fairness issue among nodes, and the amount of power consumption. Therefore, the paper proposes an improved Trickle algorithm, namely FI-Trickle, by taking a new approach to simultaneously reduce the unfairness among nodes and the power consumption and to improve the packet delivery ratio of the network. The performance of FI-Trickle is verified via simulation with extensive experiments over various network sizes, interference conditions, and network topologies. For comparison purposes, the extensive experiments are applied not only to FI-Trickle, but also to various Trickle variants such as Trickle, Trickle-F, I-Trickle, and Drizzle. The simulation results show that FI-Trickle can use less power to achieve a similar fairness level as compared to Trickle-F. Also, in terms of the packet delivery ratio, as the network size grows, FI-Trickle can increasingly outperform Trickle-F, Trickle, Drizzle, and I-Trickle by up to 1%, 3%, 3%, and 4%, respectively. This end result implies that FI-Trickle can be a better Trickle candidate for the dissemination of the RPL message for large-scale networks.

Design and Implementation of a Mobility Support Adaptive Trickle (MSAT) Algorithm for RPL in Vehicular IoT Networks

Design and Implementation of a Mobility Support Adaptive Trickle (MSAT) Algorithm for RPL in Vehicular IoT Networks

Design and implementation of a mobility support adaptive trickle algorithm for RPL in vehicular IoT networks

Design and implementation of a mobility support adaptive trickle algorithm for RPL in vehicular IoT networks

Lightweight Sybil Attack Detection in IoT based on Bloom Filter and Physical Unclonable Function

Routing protocols play an important role in the communication and information distribution within an Internet of Things (IoT) system. RPL is one such popular routing protocol for IoT devices and systems. However, security in RPL is an afterthought, and it does not meet the demands of today’s complex cyberthreat landscape. Focusing on sybil attack detection in RPL-based IoT, we first propose a lightweight Bloom filter and physical unclonable function (PUF) based sybil attack detection mechanism (hereafter referred to as liteSAD). Our approach is designed to minimize memory cost as well as detection latency, without affecting the detection accuracy. Specifically, in liteSAD, Destination-Oriented Directed Acyclic Graph (DODAG) root generates a Bloom filter array through hashing each legitimate node’s identifier and PUF response, and distributes it through a new packet named BF-DAO. Upon receiving the BF-DAO packet, each legitimate node retrieves the Bloom filter array, updates its local copy, and employs it to detect sybil attack. We also propose a probabilistic DIO reply mechanism (i.e., proDIO) to reduce the number of broadcasted DIO packets in response to attack DIS packets. We investigate the setting of Bloom filter parameters that minimize the probability of false positive and time complexity while meeting the requirement of memory constraints in IoT devices. We also evaluate the performance of our mechanism liteSAD+proDIO through extensive simulation experiments, where the results demonstrate that liteSAD+proDIO can provide better performance in terms of detection rate, detection latency, miss detection rate, DIO Trickle timer, number of broadcasted DIO packets, and energy consumption. In summary, our major contributions are twofold: (i) the comprehensive analysis of RPL routing protocol, Trickle algorithm, and the impact of sybil attack; and (ii) the proposal of lightweight Bloom filter and PUF based sybil attack detection mechanism.

Adjusted and Robust Routing Update Algorithms for Internet of Things

In this paper, two enhanced Trickle-based route update and maintenance algorithms for Low-Power and Lossy Networks (LLNs), called Adaptive and Robust Trickle and Adjusted-Trickle algorithms (AR-Trickle and Trickle-A) are proposed. The purpose is to tackle the main limitations of the standardized route update Trickle algorithm in this type of networks, in addition to the load-balancing problem of the IPv6 Routing Protocol for LLNs (RPL). In the proposed algorithms, new approaches for dynamically choosing the values of the redundancy coefficient (k) and the minimum interval size (Imin) are introduced. In addition, new mechanisms for skipping intervals to double the current interval size (I) are introduced. Moreover, new probabilistic approaches for transmitting and suppressing scheduled transmissions are introduced. In addition, new techniques for selecting the listen-only period and transmission window are introduced. Moreover, in order to evaluate the proposed algorithms, extensive simulation experiments have been conducted under different conditions and scenarios. The experiments results have showed that Trickle-A outperforms the standard Trickle and achieves better efficiency, with high stability, reliability, and scalability for different applications. Thanks to the stepwise approach, this algorithm follows in dealing with network variations by gradually increasing and decreasing parameters and variables values. On the other hand, AR-Trickle outperforms both Trickle-A and the standard Trickle and has achieved strong performance, by effectively reducing control-plane overhead, convergence time, and power consumption by up to 68%, 47%, and 18% respectively, while maintaining nearly the same level of reliability in term of Packet Delivery Ratio (PDR).

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.

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.

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.

Adaptive Control Packet Broadcasting Scheme for Faster 6TiSCH Network Bootstrapping

The IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH) Working Group released 6TiSCH minimal configuration (6TiSCH-MC) standard for 6TiSCH network bootstrapping. 6TiSCH-MC allocates only one shared cell per slotframe, known as a minimal cell, to transmit all types of control packets. Our Markov Chain-based probabilistic analysis reveals that few network parameters have a high impact on the performance of 6TiSCH network formation due to increasing congestion in the minimal cell with the increased number of nodes. This work aims to improve the 6TiSCH network formation process by reducing congestion in the minimal cell. For this, the Trickle algorithm, which is used for controlling the rate of routing information-carrying packet transmission is modified so that sufficient routing information can be provided without congesting the minimal cell. To reduce the congestion further, a slotframe window (SW)-based adaptive scheme is proposed by which nodes are restricted to transmit their control packets frequently. The proposed dynamic Trickle algorithm and SW-based scheme are implemented on Contiki-NG and evaluated using FIT IoT-LAB testbed. The experimental results show that both the proposed schemes together improve the joining time and energy consumption of the pledges compared to the state-of-the-art schemes. Additionally, both the proposed schemes provide fair control packet transmission opportunities among the nodes in a network.

Design and Implementation of an Adaptable Trickle Algorithm for Amelioration of RPL Usage in Internet of Things Networks

The efficient message routing is highly challenging in terms of low power and lossy networks (loT) for transmission of data with overhead and delay. The protocols used for routing need to be designed such that they should be working efficiently. Efficiency in calculated in terms of energy and delivery of packets. RPL protocol is also designed with the aim of making these two parameters efficient. Even then it contains drawbacks. Trickle algorithm is designed with a goal to reduce the drawbacks in RPL. Trickle algorithm is used in RPL protocols for creation of routes between nodes in the network with different intervals. Unfortunately, there exists some more downsides for the trickle algorithm, which made design of several algorithms inorder to analyse different drawbacks. In this paper, on analysing different types of trickle algorithms and locating the drawback in every algorithm, a novel algorithm is designed which helps in reduction of the drawbacks that are found. The description of this algorithm along with the simulation results done using Cooja 3.0 simulator is also discussed in this paper. The Simulation of the algorithm that is newly designed is done by assuming a network with different count of nodes and comparing the results with the previously introduced Trickle algorithms.

Load Balancing in RPL to Avoid Hotspot Problem for Improving Data Aggregation in IoT

Data aggregation plays a vital role in the Internet of Things (IoT), and it aggregates the collected sensor data from devices to suppress redundant data transmissions. Many-to-one traffic pattern in the IoT induces hotspot problem and inefficient data aggregation. The Routing protocol for low-power and lossy networks (RPL) in the network layer impacts the hotspot problem due to the frequent usage of forwarding nodes and load imbalance. The processes of network layer protocol, such as trickle algorithm and Objective Functions (OF) for Destination Oriented Directed Acyclic Graph (DODAG) construction, need more attention to avoid hotspot for efficient data aggregation. This work proposes a Load Balanced RPL (LoB-RPL) protocol to avoid hotspot creation using a composite metric based parent selection, DODAG construction, and local topology adaptive decision on trickle parameters. The LoBRPL improves the Minimum Hop with Hysteresis Objective Function(MRHOF) using the composite metric based parent selection and tunes the parameters of the Trickle algorithm. It ensures efficient maintenance of DODAG structure, hotspot avoidance, and unnecessary DIO transmissions. Beyond the advantages of composite metric based parent selection, consideration of dynamic parameters may induce frequent parent switching in RPL. To avoid frequent changes in the DODAG structure, the LoB-RPL optimally decides the parent switching threshold. Thus, the proposed work ensures a load-balanced and an energy-efficient RPL routing as well as data aggregation in the IoT environment. The LoB-RPL delivers outperforming results compared to the base RPL under various inter-packet interval time over 50 node topologies.

An Optimized Dynamic Trickle Algorithm for Media Technology

An omnipresent revolutionary technology is what the Internet of Things (IoT) has brought to this century, connecting objects in a sequence of domains around people, since it plays a concrete role in the media technologies used daily. IoT turns media technologies objects, whether they are automated or self-control with less or no human intervention activity. A unique IoT component is a Low power and Lossy Networks (LLNs). It is a resource-constrained, where the trickle algorithm is an essential component of the IPv6 routing protocol over low power and lossy network RPL. Its job is to control and manage the network routing traffic. RPL routing protocol is used widely in the IoT. One of the main components of RPL is the trickle timer algorithm. It intends to make the information exchanging between the IoT nodes more straightforward, scalable, and energy-efficient approach. RPL has usage limitations, where the listening time for this is short, so the nodes have not enough time to transmit, and thus, to reduce the network performance. Several enhancements have been suggested in order to overcome this short-flow. This work will address those variants and intends to enhance the current trickle algorithm and its variants in order to achieve more scalable and less power consumption.

Adaptive and Fair Route Update Algorithm for Low Power and Lossy Networks in the Internet of Things

Internet of Things (IoT) refers to the network of physical objects embedded with sensors, software, and other technologies for the purpose of exchanging data with other devices over the internet. One of the routing protocols accredited to the IoT applications in Low Power and Lossy Networks is the Routing Protocol for Low Power and Lossy Networks (RPL). Trickle timer algorithm is considered one of the major algorithms in the RPL used to control the flow of control messages through the network. Performance evaluation has shown that the trickle algorithm is suffering from load balancing problems. In this study, an adaptive algorithm, called the Fair-K algorithm, is introduced for maintaining routing information in Low Power and Lossy Networks. It differs from the standard Trickle in two ways. First, the Fair-K algorithm provides an elastic selection of redundancy value based on the node degree in the network. Second, it provides a new scheme in order to solve the bottleneck problem in the network. The performance evaluation of the Fair-K algorithm is validated using extensive simulation experiments. The results indicate that the Fair-K algorithm reduces the convergence time by 80%, power consumption by 20%, and the total delay by 78% while maintaining approximately the same packet delivery ratios.

Sybil Attack in RPL-Based Internet of Things: Analysis and Defenses

Over the past few years, Internet of Things (IoT) has emerged as a promising paradigm that connects various physical devices to the Internet, and contributes to the development of countless next-generation applications. As a major enabler for IoT, IPv6-based low-power and lossy networks (LLNs) have been receiving considerable attention as a mature solution for scalable data collection in a ubiquitous computing and communication infrastructure. In order to provide efficient point-to-multipoint and multipoint-to-point communication, a novel routing protocol for LLNs, also well known as RPL, has been proposed and standardized. Nonetheless, due to devices' constraints on processing power, memory, and energy, and the lack of specific security models of the RPL routing protocol, LLNs become an ideal target for various security attacks. In this article, we propose a Gini index-based countermeasure, also called GINI, to effectively detect and mitigate sybil attack in RPL-based LLNs, where the malicious node multicasts an excessive number of DODAG information solicitation (DIS) messages with different fictitious identities to cause the legitimate nodes to restart the Trickle algorithm frequently and broadcast a large number of DODAG information object (DIO) messages to quickly drain the limited energy resource of legitimate nodes. We also present a simple analytical model and its numerical results in terms of detection rate. We evaluate the proposed GINI countermeasure through extensive simulation experiments using OMNeT++ and compare its performance with two existing schemes, SecRPL and two-step detection. The simulation results show that the proposed GINI countermeasure can not only improve the detection rate and detection latency but also reduce energy consumption, indicating a viable approach against sybil attack in the IoT. For continuous improvement and future research, we further discuss the proposed GINI countermeasure in terms of design features, design constraints, and possible extensions.

A²-Trickle: Adaptive & Aligned Trickle for Rapid and Reliable Dissemination in Low-Power Wireless Networks

Trickle is a scalable and robust flooding algorithm designed for low-power multihop wireless networks. It aims to reduce redundant packet transmissions and collisions while maintaining information consistency, and has been popular due to its simple yet efficient operation on most general topologies. However, Trickle fails to achieve reliability and low latency under certain, but not uncommon, scenarios. To tackle this problem, we propose A 2 -Trickle , a light-weight enhancement to the Trickle algorithm that guarantees rapid and reliable dissemination under any topology. A 2 -Trickle aligns the interval boundary at propagation times without synchronizing the global clock, and adapts to the network topology for nodes that could suffer from Trickle’s naive suppression mechanism. A 2 -Trickle is implemented on a real embedded device and is evaluated in various scenarios and topologies through both testbed experiments and simulations. The results reveal that A 2 -Trickle adapts to the network and enables faster and more energy-efficient dissemination while maintaining >99% reliability.