Trickle Timer Research Articles

A multi path routing protocol with efficient energy consumption in IoT applications real time traffic

The extensive utilization of IoT applications leads to the aggregation of a substantial volume of data, presenting a crucial challenge in terms of data routing within these networks. RPL intentionally surpasses the limitations sometimes observed in low-power and lossy networks, which are particularly prevalent in IoT networks. The RPL protocol is designed specifically for static networks that do not involve mobility or topological changes. The RPL protocol guarantees continuous connectivity between nodes and mitigates the risk of data loss in stationary IoT applications that do not involve mobility or alterations in network configuration. The article utilizes a mobility aid technology known as network performance stability using the intelligent routing protocol (nPSIR), which expands upon RPL. The Mobility Support Entity (nPSIR) facilitates the displacement of all nodes, with the exception of the root node, and ensures uninterrupted connection during mobility. Moreover, it deals with the situation where there is a physical barrier between two interconnected nodes in a changing environment. In order to achieve this objective, it employs a dynamic trickle timer that operates within two distinct ranges. Furthermore, it utilizes a neighbor link quality table, a mechanism for selecting the most beneficial parent node in the event of migration, a measure of confidence, the identification of crucial regions, and a blacklist. Multiple simulations validate that nPSIR effectively decreases hand-off delay and improves packet delivery, despite the minor drawbacks of increased signaling costs and power consumption. The delivery ratio decreases the quantity of lost data packets and surpasses both RPL as a responsive protocol and mRPL as a proactive protocol in relation to mobility.

Trickle timer modification for RPL in Internet of things

Trickle timer modification for RPL in Internet of things

A novel routing optimization strategy based on reinforcement learning in perception layer networks

Wireless sensor networks have become incredibly popular due to the Internet of Things’ (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for low-power and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm’s effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.

Review of routing protocol for low power and lossy network in the internet of things

The growth of internet of things (IoT) devices and apps has caused disruptions in key sectors such as smart environment, healthcare, and mission-critical tool. Security of IoT devices, networks, and infrastructure is the biggest challenge. RPL, a routing protocol optimised for low-power and lossy networks, is applied by the IoT to offer seamless information transfer among the myriad of interconnected entities. So, the paper delves into the nuts and bolts of RPL, covering its protocol settings, trickle timer, and objective function, as well as the IoT’s architecture and applications. This article also provides a framework for categorising past research on the effects of RPL-IoT on energy consumption, transportation, service quality, traffic congestion, and safety. The future of energy efficiency, congestion, objective function, mobility, stability, security, collaborative intrusion detection systems, active learning, and Key Management are all considered in this analysis.

Adaptive Trickle Timer for Efficient 6TiSCH Network Formation Using Q-Learning

The 6TiSCH (IPv6 over IEEE802.15.4e time-slotted channel hopping mode) wireless sensor network architecture utilizes control packets to construct network formation. These control packets are essential for establishing communication links between nodes and configuring network settings. The trickle timer algorithm is utilized to broadcast the DIO control packet. DIO carries information about the available parent nodes, which is then used to form the routing tree. Sensors transmit control packets in one cell on each TSCH slotframe, called the minimal cell. This leads to the problem that RPL trickle timer algorithm encounters congestion in DIO control packet transmission with other control messages, particularly in dense networks. Moreover, high traffic transmission also leads to high queue usage, which then drops the DIO control packet. Failed DIO transmission can increase network formation time and energy consumption. To address this issue, we propose Q-Trickle, an adaptive trickle timer algorithm based on Q-learning that determines the optimal policy for transmitting or suppressing DIO based on minimal cell and transmission queue conditions. Q-Trickle adaptively selects a redundancy constant value and transmission interval that promotes fair transmission distribution and considers network condition. Additionally, a control scheme over minimal cell transmission is formulated to lower transmission congestion and faster synchronization. The proposed methods were assessed using simulation and actual testing on the FIT IoT-LAB testbed. The results indicated that Q-Trickle performed better than the benchmark methods. Q-Trickle decreases joining time, energy consumption, and number of failed DIO compared with the original algorithm by -13%, -11%, and -43%, respectively.

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.

Intelligent agent based virtual clustering and multi-context aware routing for congestion mitigation in secure RPL-IoT environment

RPL (Routing Protocol for Low power Lossy network) is a routing protocol for Internet of Things (IoT) network with low power consumption. RPL faces many security challenges because of its resource constrained nature. That security threats introduced heavy congestion in the network. If the congestions are not properly managed then it will reduce overall network performance, in addition it also reduces the network lifetime. In real time IoT application, the parent nodes attacks massive amount of nodes and establish the unbalanced structure that reduces network reliability. In this research we proposed Secure RPL (Sec-RPL) to perform congestion control RPL-IoT environment. For that we proposed five phases, (1). RPL node authentication initially, all the nodes are register their ID, IP, MAC, Rank and PUF in the sink node which generate secret key for that node by blowfish algorithm, and hash values are generated by lightweight hash one algorithm. (2). To increase network reliability we propose multi-context aware parent selection, the context decisions are made by grey set theory based decision making algorithm. For optimal parent selection we deploy Hybrid Cuttle Fish-Harris Hawker Optimizer (HC-HHO) algorithm. (3) Congestion detection and mitigation, for congestion monitoring we propose Multi-Intelligent Agent based Q-Learning (MIAQ) which mainly relies on three environmental status such as buffer status, generation status and loss status. For congestion mitigation we proposed virtual cluster based congestion mitigation (VC2M) method. (4). Deep packet analysis and attack detection, Lightweight Convolutional Neural Network with Transfer Learning (LiteCNN-TL) algorithm is deployed to analyze each packet in depth to detect malicious packets. (5). Adaptive Trickle Timer, Dynamic Rule base Trickle (DR-trickle) is deployed to adaptively changed the trickle timer based on multiple parameters (Traffic Intensity, RSSI, Received Time Interval). Finally, the simulation is conducted by NS3.26 network simulator. The result shows that the proposed system achieves better performance in terms of average control traffic overhead, packet delivery ratio, average energy consumption, delay, packet loss rate ad load balancing capacity with respect to network size and number of malicious nodes.

A Novel Approach for Enhancing the Network Life Time of Secure ML_RPL Routing Protocol in Internet of Things by Using PROB_ATT_SCHEME

Abstract-- IoT devices are having self-configuration in nature for establishing adhoc networks to monitor physical activities like environmental sensing, Intelligent Traffic Management Systems (ITMS) and so on even in unmanned areas. Benefits of these networks are numerous and few of them are no infrastructure and deploying spontaneously, cost effective set up of temporary and dynamic networks and so on. Moreover, characteristics like unreliable wireless links, resource constraints related to energy and power and dynamic topology, makes the protocols that are developed recently to ignore the security needs with the intention of satisfying quality of service (QoS). IoT makes the jobs easier and smarter, but still security mechanisms are challenging for resource constrained devices like denial of service attacks (Stuxnet), blackhole attacks are the examples of attacks which maliciously controls these devices causing damage. Remote Attestation (RA) is one tool which efficiently identifies a malicious entity in the network. However, the proposed tool not suitable for large size mobile IoT networks, where nodes are connected in heterogeneous manner with resource constraints for the nodes. It is understood that it is necessary to do the necessary research for extending the network life time of resources constraint nodes existing in the network along with secure connection between nodes of the network, In present work, proposed a probability-based attestation scheme (PROB_ATT) for improving routing and security in IoT network. With the help of the estimated probability value of network path, attestation value is included in the network along with data. With application of trickle time, attestation is verified and data transmission is performed. After estimation of probability within network data transmission is performed based on trust mechanism. Simulation analysis stated that PROB_ATTT scheme reduces energy consumption, packet loss ratio and delay as 20%, 10% and 5% respectively when compared with Machine Learning based RPL protocol. Whereas the other important performance metrics: energy efficiency, throughput and Packet delivery rate performance is improved around 20% than ML based RPL protocol. Keywords-- Attestation scheme, Probability attestation scheme (PROB_ATT), Security, Trust Mechanism, Trickle Timer, MAC layer,RPL,ML_RPL protocol

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.

MSE-RPL: Mobility Support Enhancement in RPL for IoT Mobile Applications

Due to the expansion of IoT applications which causes the generation of a massive amount of data, data routing is one of the most important challenges in these networks. The Routing Protocol for Low-power and Lossy Networks (RPL) was developed to cope with the Low-power and lossy network constraints, which play a significant role in IoT networks. Although most IoT applications involve mobility and topology change that makes mobility support a substantial need to prevent disconnection of nodes and data loss, the RPL is designed for static networks. This article proposes a mobility support method called MSE as an extension of RPL. The MSE supports mobility of all nodes except the root node, and it provides a seamless connection during the mobility. It also manages a situation when a physical obstacle settles between two paired nodes in a dynamic environment. To this end, it uses a dynamic trickle timer with two different ranges, a neighbor link quality table, a function to select the best parent in case of mobility, confidence, critical zones, and a blacklist. Simulations in multiple scenarios indicate that MSE, despite causing a slight increase in signaling cost and power consumption, significantly reduces hand-off delay, increases Packet Delivery Ratio, reduces the number of lost data packets, and outperforms both RPL as a reactive and mRPL as a proactive protocol regarding mobility.

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.

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.

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.

Efficient and Stateless P2P Routing Mechanisms for the Internet of Things

Arbitrary point-to-point (P2P) routing is becoming a necessity in a multitude of Internet-of-Things (IoT) applications, including building automation, smart cities, and smart manufacturing. For this reason, new P2P routing protocols such as lightweight on-demand ad hoc distance-vector routing protocol next generation (LOADng) and ad hoc on-demand distance vector routing-based RPL protocol (AODV-RPL) are being standardized. Both protocols are inspired by AODV, and hence, they might suffer from broadcast storms caused by flooding route requests (RREQs) and related issues. Thus, while AODV-RPL takes advantage of RPL mechanisms, LOADng uses blind flooding to forward RREQs. However, both lack effective techniques for avoiding unnecessary RREQ transmissions when routes are found. In this article, we first deploy stopping Trickle timers to ensure scalable, efficient, and reliable dissemination of RREQs. Second, we devise and present new techniques to suppress unnecessary RREQs with optimizations for radio duty-cycled networks. Finally, the two mechanisms are combined in a third proposal for better efficiency. These mechanisms provide individually and collectively great enhancements to P2P routing protocols, such as AODV-RPL and LOADng, while staying backward compatible with their base specifications. The performance of the proposed mechanisms when applied to LOADng has been validated using both extensive time-accurate simulations and large-scale public testbeds. The obtained results have shown the effectiveness of the proposed mechanisms with around 50% less overhead and savings in energy consumption, along with a 20% gain in route discovery ratio at the expense of an increase in discovery delays.

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.

A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications

IPv6 routing protocol for low‐power and lossy networks (RPL) has been developed as a routing agent in low‐power and lossy networks (LLN), where nodes’ resource constraint nature is challenging. This protocol operates at the network layer and can create routing and optimally distribute routing information between nodes. RPL is a low‐power, high‐throughput IPv6 routing protocol that uses distance vectors. Each sensor‐to‐wire network router has a collection of fixed parents and a preferred parent on the path to the Destination‐oriented directed acyclic graph (DODAG) graph’s root in steady‐state. Each router part of the graph sends DODAG information object (DIO) control messages and specifies its rank within the graph, indicating its position within the network relative to the root. When a node receives a DIO message, it determines its network rank, which must be higher than all its parents’ rank, and then continues sending DIO messages using the trickle timer. As a result, DODAG begins at the root and eventually extends to encompass the whole network. This paper is the first review to study intrusion detection systems in the RPL protocol based on machine learning (ML) techniques to the best of our knowledge. The complexity of the new attack models identified for RPL and the efficiency of ML in intelligent and collaborative threats detection, and the issues of deploying ML in challenging LLN environments underscore the importance of research in this area. The analysis is done using research sources of “Google Scholar,” “Crossref,” “Scopus,” and “Web of Science” resources. The evaluations are assessed for studies from 2016 to 2021. The results are illustrated with tables and figures.

Collision Observation-based Optimization of Low-power and Lossy IoT Network using Reinforcement Learning

The Internet of Things (IoT) has numerous applications in every domain, e.g., smart cities to provide intelligent services to sustainable cities. The next-generation of IoT networks is expected to be densely deployed in a resource-constrained and lossy environment. The densely deployed nodes producing radically heterogeneous traffic pattern causes congestion and collision in the network. At the medium access control (MAC) layer, mitigating channel collision is still one of the main challenges of future IoT networks. Similarly, the standardized network layer uses a ranking mechanism based on hop-counts and expected transmission counts (ETX), which often does not adapt to the dynamic and lossy environment and impact performance. The ranking mechanism also requires large control overheads to update rank information. The resource-constrained IoT devices operating in a low-power and lossy network (LLN) environment need an efficient solution to handle these problems. Reinforcement learning (RL) algorithms like Q-learning are recently utilized to solve learning problems in LLNs devices like sensors. Thus, in this paper, an RL-based optimization of dense LLN IoT devices with heavy heterogeneous traffic is devised. The proposed protocol learns the collision information from the MAC layer and makes an intelligent decision at the network layer. The proposed protocol also enhances the operation of the trickle timer algorithm. A Q-learning model is employed to adaptively learn the channel collision probability and network layer ranking states with accumulated reward function. Based on a simulation using Contiki 3.0 Cooja, the proposed intelligent scheme achieves a lower packet loss ratio, improves throughput, produces lower control overheads, and consumes less energy than other state-of-the-art mechanisms.

Low Power Trickle (LP-Trickle) Timer Algorithm: an Improved Solution for Low Power and Lossy Network in Media Technology Context

Internet of Things (IoT) has become a part of everyday life. It connects the items used on a daily basis, in order to render them more efficient and beneficial. Media technology items, which are fundamental components of the connected network in IoT, can be linked through a variety of IoT procedures. Among the ones that consider power consumption, there is the routing protocol for low power and lossy network (RPL), whose latest version is the IPv6 protocol for low power and lossy networks (LLNs) mechanisms. RPL is composed of control messages, objective functions, and the trickle timer algorithm. The emphasis of this investigation is on the trickle timer algorithm, which is engaged by the RPL, in order to organize the frequency of control messages. However, this algorithm comes with issues related to power consumption, overhead, and convergence time. With this undertaking, an enhanced current trickle timer algorithm is recommended to bring down the power consumption level of the network. This undertaking involves an alteration to the redundancy constant, in order to render it randomly selected at every juncture. This straightforward alteration will effectively lower the network’s power consumption level.

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.

Design and analysis of RPL objective functions using variant routing metrics for IoT applications

The main objective of Internet of Things (IoT) is to connect almost all the devices anywhere and everywhere in the world. IoT network is heterogeneous in nature, hence routing the data packets in this network is a big challenge. Routing Protocol for Low Power Lossy Network (RPL), has been designed by Internet Engineering Task Force (IETF) for such type of network. The existing design of RPL Objective Function (OF) is insufficient to cover all the issues of IoT applications. In this paper, the proposed OFs designs using various routing metrics are used to enhance the performance of the IoT applications. The analysis for various scenarios for these designs shows that only traditional hop and Expected Transmission Count (ETX) routing parameters will not fit for the smart applications need. The routing metric selection according to the application requirement is the principal idea of the proposed design. Three metrics ETX, Content and Energy, single and combination with each other are used to enhance the design of objective function of RPL for IoT applications. The enhanced triggering technique is added in these designs for the improvement of RPL. This technique will eliminate the cumulative effect of the short-listen problem of default trickle timer. The result analysis done using Cooja simulator along with Contiki Operating System (OS) states that, all the designs are performing well in one or other manner than the traditional OF. Energy combined with Content (EC) and aggregation with Enhanced timer (EC_En_Timer) design gives better result for Packet Delivery Ratio (PDR) and Latency Delay (LD) as compared to default OF design. Residual Energy (RE) combined with ETX (EE) and conjunction with Enhanced timer (EE_En_Timer) design works well for energy consumption. Overhead is very less in RE and ETX design. Conversion time is reduced by almost 50% in an En_Timer design. Higher PDR and low delay values of EC and EC_En_Timer design encourages its use in health monitoring application where reliability is essential. Low energy consumption results of RE, EE and EE_En_Timer designs are comfortable for forest monitoring application, as energy is a crucial aspect. This comparative result outcome will help to fulfill the IoT application requirements.