Routing In Internet Of Things Research Articles

The importance of nature‐inspired metaheuristic algorithms in the data routing and path finding problem in the internet of things

SummaryOver the last decade, the Internet of Things (IoT) has become ever more popular, as is evident from its role in changing the human lifestyle and conferring remarkable privileges for them. It has a significant presence in various crucial areas, including smart cities, smart factories, manufacturing, transportation, and healthcare. Massive amounts of data generated by IoT devices have the potential to endanger the lifetime of nodes in IoT‐based networks due to increased communication power consumption. It has become crucial to propose solutions for network‐based issues, such as quality of service, security, network heterogeneity, congestion avoidance, reliable routing, and energy conservation. To address the mentioned problems, routing protocols play a critical role in data transmission among heterogeneous items. In such environments, routing refers to constructing routes between mobile nodes. Since identifying optimal routes among IoT nodes and establishing an effective routing protocol in an IoT network are an NP‐hard issue, employing metaheuristic algorithms may be a viable solution to overcome this problem. Various IoT routing protocols based on metaheuristic algorithms have been presented in recent years, but there is still a lack of systematic study for reviewing the existing works. The current study emphasizes the impact of metaheuristic algorithms in the IoT routing problem, discusses the optimization models, presents a comprehensive comparison of protocols based on critical parameters, and eventually suggests some hints for future studies.

Optimal vs rotation heuristics in the role of cluster-head for routing in IoT constrained devices

Wireless Sensor Networks (WSNs) are crucial for implementing the Internet of Things (IoT) vision. A WSN can include distributed devices that can be battery powered, have a small amount of memory, have limited CPU processing capabilities and limited transmission range. WSNs are categorised into two main types that are homogeneous and heterogeneous. Homogeneous WSNs are composed of nodes that are all equal while heterogeneous ones are composed of nodes that can have different transmission rate and different initial energy. Collecting data in an energy efficient manner is one of the main goals when building wireless sensor networks since devices are battery powered and battery replacement can be difficult or impossible to be performed. Clustering protocols are one of the main approaches that have been used in order to collect data in an energy efficient way. A clustering protocol groups nodes into a set of clusters. Each cluster has a representative node that is referred to as Cluster Head (CH). This collects data from its cluster members and forwards them to an external Base Station, possibly in a multi-hop way among cluster heads. A rotation strategy for electing the cluster head is often used together with clustering in order to prolong the network lifetime. Rotation eliminates the overhead traffic that is needed for leader election and cluster formation since the old cluster heads directly designate new ones. In this work, we describe an Optimum Rotation Scheduling (ORS) that uses Integer Linear Programming in order to find the optimum rotation strategy. ORS assumes the WSN clusters are already formed by using some clustering protocol. A novel integer linear programming formulation is then used in order to define a cluster head rotation that produces the optimum cluster lifetime. Comparisons with existing heuristics and the provided optimum are then shown and discussed by means of extended simulations involving both homogeneous and heterogeneous WSNs.

RAARPL: End‐to‐end Reliability‐Aware Adaptive RPL routing protocol for Internet of things

SummaryThe RPL protocol has been accepted by the Internet Engineering Task Force as the only routing standard for the Internet of things (IoT). Although RPL has been significantly used in IoT routing, it still has extensive challenges. One of the most basic challenges is related to the reliability of routing. The severe limitations of resources, wireless communication, and variable topology, along with other limitations of IoT, have caused reliability in these networks to be a challenging topic. However, in RPL, no measures are provided to support the reliability of routing. To improve this issue, in this article, RPL was improved based on reliability requirements, and a new protocol was proposed called the Reliability‐Aware Adaptive RPL routing protocol (RAARPL). RAARPL selects the parents based on the evaluation of various criteria related to reliability and forms the network topology. In addition, the conditions of the paths were also considered in the decision‐making so that the reliability depends on the paths and parents. In order to maintain stability, RAARPL had controlled the parent selection and children assignment in a way that will prevent errors as much as possible. The simulation results by Cooja and compared with CLRPL and RPL protocols in various scenarios indicated the high efficiency of RAARPL in improving the reliability of data exchange and successful delivery ratio, reducing the instability of topology and the network throughput.

RL-IoT: Reinforcement Learning-Based Routing Approach for Cognitive Radio-Enabled IoT Communications

Internet of Things (IoT) devices are widely being used in various smart applications and being equipped with cognitive radio (CR) capabilities for dynamic spectrum allocation. Our objectives in this work are to achieve higher data rates and minimize end-to-end routing delays in CR-enabled IoT communication in order to maximize throughput. We propose a reinforcement learning (RL)-based routing approach in the cognitive radio network (CRN)-based IoT environment. The idea is to add the channel selection decision capability to the network layer in order to minimize packet collisions as well as end-to-end delay (EED). We perform a comprehensive performance evaluation of the proposed RL-IoT routing mechanism by simulating the cognitive radio-enabled Internet of Things (CR-IoT) communication environment in the cognitive radio cognitive network (CRCN) simulator and comparing the network performance achieved by our proposed mechanism with that of the recent AODV-based routing mechanism for IoT (AODV-IoT), ELD-CRN, and SpEED-IoT routing approaches. Our evaluation results show that the RL-IoT model performs better than existing approaches in terms of average data rate, throughput, packet collision, and EED.

Secure and Optimal LOADng Routing for IoT with Composite Routing Metric

Security is the one of the major challenges for routing the data between the source and destination in an Internet of Things (IoT) network. To overcome this challenge, a secure Lightweight On-demand Ad hoc Distance-vector—Next Generation (LOADng) Routing Protocol is proposed in this paper. As the LOADng protocol is the second version of Ad Hoc On-Demand Distance Vector (AODV) protocol, it retains most of the basic functionality and characteristics of AODV. During the route discovery process, the cyclic shift transposition algorithm (CSTA) is used to encrypt the control packets of the LOADng protocol to improve its security. CSTA approach only derives transposition and substitution without product cipher with respect to input data. Besides this, for choosing the best probable path between the source and destination, routing metrics such as link quality Indicator (LQI), hop count (HC) and queue length (QL) are included in the control packets. The data is then securely sent using CSTA using the optimal secure path selected. Experimental Results depict that the proposed secure and optimal LOADng (SO-LOADng) using CSTA encryption obtains better throughput, delivery ratio encryption time and decryption time than the existing state-of-art approaches.

Customised Intrusion Detection for an Industrial IoT Heterogeneous Network Based on Machine Learning Algorithms Called FTL-CID

Technological breakthroughs in the Internet of Things (IoT) easily promote smart lives for humans by connecting everything through the Internet. The de facto standardised IoT routing strategy is the routing protocol for low-power and lossy networks (RPL), which is applied in various heterogeneous IoT applications. Hence, the increase in reliance on the IoT requires focus on the security of the RPL protocol. The top defence layer is an intrusion detection system (IDS), and the heterogeneous characteristics of the IoT and variety of novel intrusions make the design of the RPL IDS significantly complex. Most existing IDS solutions are unified models and cannot detect novel RPL intrusions. Therefore, the RPL requires a customised global attack knowledge-based IDS model to identify both existing and novel intrusions in order to enhance its security. Federated transfer learning (FTL) is a trending topic that paves the way to designing a customised RPL-IoT IDS security model in a heterogeneous IoT environment. In this paper, we propose a federated-transfer-learning-assisted customised distributed IDS (FT-CID) model to detect RPL intrusion in a heterogeneous IoT. The design process of FT-CID includes three steps: dataset collection, FTL-assisted edge IDS learning, and intrusion detection. Initially, the central server initialises the FT-CID with a predefined learning model and observes the unique features of different RPL-IoTs to construct a local model. The experimental model generates an RPL-IIoT dataset with normal and abnormal traffic through simulation on the Contiki-NG OS. Secondly, the edge IDSs are trained using the local parameters and the globally shared parameters generated by the central server through federation and aggregation of different local parameters of various edges. Hence, transfer learning is exploited to update the server's and edges' local and global parameters based on relational knowledge. It also builds and customised IDS model with partial retraining through local learning based on globally shared server knowledge. Finally, the customised IDS in the FT-CID model enforces the detection of intrusions in heterogeneous IoT networks. Moreover, the FT-CID model accomplishes high RPL security by implicitly utilising the local and global parameters of different IoTs with the assistance of FTL. The FT-CID detects RPL intrusions with an accuracy of 85.52% in tests on a heterogeneous IoT network.

Towards developing a machine learning-metaheuristic-enhanced energy-sensitive routing framework for the internet of things

The heterogeneous nature, communication constraints, multi-hop data transmissions, and inherent challenges of wireless connections have caused routing and data transmission processes to play a vital role in the Internet of Things (IoT) infrastructures efficiency and stability. Energy efficiency and optimal load equilibrium between sensing devices are fundamental challenges due to the limitation of energy resources in IoT devices. Considering the cruciality of prolonging IoT networks and the NP-hard nature of energy optimization in heterogeneous and distributed IoT infrastructures, this research represents a hybrid energy-aware protocol for data routing in IoT. It is deployed based on Machine Learning (ML) and metaheuristic HTOA (Heat Transfer Optimizer). The proposed protocol adopts a four-phase approach for fuzzy clustering, predicting energy consumption based on Support Vector Regression (SVR) and time series techniques, selecting optimal cluster heads (CH) considering energy and centralization factors, and routing and data transmission from each sensor to and from CH to the sink using Ant Colony Optimization (ACO) algorithm. The simulation outputs revealed that our scheme performs more efficiently in energy consumption, end-to-end delay (EtED), load balancing, overhead, and network lifetime in all designed scenarios compared to other methods.

Threat analysis model to control IoT network routing attacks through deep learning approach

Most of the recent research has focused on the Internet of Things (IoT) and its applications. The open interface and network connectivity of the interconnected systems under the IoT network make them vulnerable to hackers. A model has been proposed to identify and classify IoT routing attacks. To generate IoT routing datasets, the Cooja simulator is used at first. The IoT routing dataset is then augmented into larger volumes using ADASYN, which is also used to solve the class imbalance problems. A deep learning hybrid model based on a Long-Short-Term Memory (LSTM) network and adaptive Mayfly Optimization Algorithm (LAMOA) was presented for the classification of IoT attacks. The adaptive MOA adjusts the weights in the various layers of the LSTM network and the Fully Connected Layer with SoftMax Classification. As part of the validation process, the proposed model was also compared with benchmark datasets NSL-KDD, BoT-IoT, and IoT-23. Using benchmark datasets, LAMOA achieved 99.94% accuracy for multiclassifications, 99.92% accuracy for binary classifications, and 98.42% accuracy for real-time datasets. Compared to other models, our proposed model significantly improves the accuracy of each attack's classification by 5–10%.

An Intrusion Detection System for RPL-Based IoT Networks

The Internet of Things (IoT) has become very popular during the last decade by providing new solutions to modern industry and to entire societies. At the same time, the rise of the industrial Internet of Things (IIoT) has provided various benefits by linking infrastructure around the world via sensors, machine learning, and data analytics. However, the security of IoT devices has been proven to be a major concern. Almost a decade ago, the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) was designed to handle routing in IoT and IIoT. Since then, numerous types of attacks on RPL have been published. In this paper, a novel intrusion detection system (IDS) is designed and implemented for RPL-based IoT. The objective is to perform an accurate and efficient detection of various types of routing and denial-of-service (DoS) attacks such as version number attack, blackhole attack, and grayhole attack, and different variations of flooding attacks such as Hello flood attack, DIS attack, and DAO insider attack. To achieve this, different detection strategies are combined, taking advantage of the strengths of each individual strategy. In addition, the proposed IDS is experimentally evaluated by performing a deep analysis of the aforementioned attacks in order to study the impact caused. This evaluation also estimates the accuracy and effectiveness of the IDS performance when confronted with the considered attacks. The obtained results show high detection accuracy. Furthermore, the overhead introduced in terms of CPU usage and power consumption is negligible. In particular, the CPU usage overhead is less than 2% in all cases, whereas the average power consumption increase is no more than 0.5%, which can be considered an insignificant impact on the overall resource utilisation.

Compact Learning Model for Dynamic Off-Chain Routing in Blockchain-Based IoT

Dynamic off-chain routing in payment channel network (PCN)-based Internet of Things (IoT) is attracting increasing research attention. However, there are two major issues in dynamic routing in PCN-based IoT with resource-limited devices. The first issue is how to achieve high long-term transaction efficiency in PCN with dynamic channel capacities. The second issue is how to achieve a lightweight routing algorithm deployed on IoT devices while achieving high transaction efficiency, i.e., successful payment amount and success ratio. Therefore, in this paper, we propose a compact deep reinforcement learning (DRL) algorithm to learn the joint dynamic and lightweight routing policy for maximizing long-term transaction efficiency. To obtain optimal performance in dynamic routing problems for off-chain systems, a proximal policy optimization algorithm is employed to create an actor–critic learning structure for training the teacher DRL model. To obtain a compact and efficient student DRL model, an adaptive pruning technique is utilized for pruning unnecessary parameters of networks in the teacher model adaptively without affecting its learning ability. Furthermore, knowledge distillation is leveraged to improve the performance of the student network. Thus, a compact and efficient student DRL model can be developed and implemented to maximize the long-term transaction efficiency in off-chain systems on resource-limited IoT devices. The simulation results demonstrate that the proposed DRL algorithm outperforms the other baseline algorithms in PCN transaction efficiency while requiring only 10% of the computation and storage resources compared with that of the original teacher model.

Dingo Optimization Based Cluster Based Routing in Internet of Things.

The Wireless Sensor Network (WSN) is a collection of distinct, geographically distributed, Internet-connected sensors, which is capable of processing, analyzing, storing, and exchanging collected information. However, the Internet of Things (IoT) devices in the network are equipped with limited resources and minimal computing capability, resulting in energy conservation problems. Although clustering is an efficient method for energy saving in network nodes, the existing clustering algorithms are not effective due to the short lifespan of a network, an unbalanced load among the network nodes, and increased end-to-end delays. Hence, this paper proposes a novel cluster-based approach for IoT using a Self-Adaptive Dingo Optimizer with Brownian Motion (SDO-BM) technique to choose the optimal cluster head (CH) considering the various constraints such as energy, distance, delay, overhead, trust, Quality of Service (QoS), and security (high risk, low risk, and medium risk). If the chosen optimal CH is defective, then fault tolerance and energy hole mitigation techniques are used to stabilize the network. Eventually, analysis is done to ensure the progression of the SADO-BM model. The proposed model provides optimal results compared to existing models.

Secured location-aware mobility-enabled RPL

Although there have been many studies on the Internet of Things (IoT), there are still major challenges for IoT to become ubiquitous. So far, the mobility management challenge has not been addressed well. Routing Protocol for Low-Power and Lossy Networks (RPL), which is known as the de-facto for routing in IoT, does not support mobile nodes. Some studies have tried to address this challenge, but they either caused a very high Packet Loss Rate (PLR) or produced lots of control packets. Also, they have not considered the security aspects of their work which is crucial for real-world applications. In this study, a novel extension for the RPL called Secured Location-Aware Mobility-enabled RPL (SLM-RPL) is proposed to facilitate the mobility management of RPL while considering security precautions. From the mobility management point of view, according to extensive evaluations, SLM-RPL greatly reduces the hand-off delay and PLR compared to other mobility management schemes, even in big, dense, or highly dynamic networks. Therefore, SLM-RPL is shown to be the best option to be used in IoT applications, especially loss-sensitive ones. Also, SLM-RPL produces small numbers of control packets and has a low memory overhead. Moreover, from the security point of view, a probability-based method has been proposed and embedded in SLM-RPL, which is shown to be able to reduce the negative impacts of DODAG Information Solicitation (DIS) attacks by more than 99%. Also, a performable attack on SLM-RPL called False-Location-Injection (FLI) attack has been introduced, and a lightweight hybrid-structured Intrusion Detection System (IDS) has been provided to counter this attack as well as Sybil, Rank, Sinkhole, and impersonation attacks. The proposed IDS uses a voting-based approach which, when the ψ parameter is adjusted, can mitigate the impact of False-Reporting and collusion attacks. According to the evaluations, the proposed IDS can counter the mentioned attacks in the presence of Collusion attackers in different scenarios with Accuracy ≥0.99.

An Improved Energy Efficient Solution for Routing in IoT

The concept of the Internet of Things (IoT) is an innovation paradigm that suggests linking physical devices together to share data and work toward a common goal. Nodes in an IoT network are anticipated to communicate with one another and with third-party Internet services. However, not all network nodes have direct Internet access due to high deployment costs. As a result, additional network nodes should be using Internet-connected nodes as gateways to transmit data to online services. Due to its central role in establishing connections between nodes in a network and distributing data packets, routing protocols have emerged as a significant challenge in IoT use cases. The work of routing is complicated in mobile IoT scenarios due to the topology changes brought on by the mobility of nodes. To better handle the mobile nature of the devices, existing Iot routing solutions typically exhibit severe constraints and poor performance in such cases. This study proposes an improvement to the existing protocols; the Cluster Formation Protocol with Neuro Fuzzy Rules, in light of the fact that main routing techniques for low-power network are unable to achieve adequate performance in the shown IoT context. The energy efficiency of the routing process in WSN can be improved with the help of cluster based routing thanks to the proposed efficient routing algorithms. In addition, the efficiency of clustering & cluster based routing protocols can be enhanced by employing intelligent approaches like fuzzy rules, temporal constraints, and categorization via deep learning. Therefore, the safe transfer of data packets from of the source point to the sink node necessitates careful consideration in the development of cluster dependent routing algorithms. The cluster dependent routing method presented is developed, evaluated with respect to a number of criteria, and shown to be effective.

Trust and Mobility-Based Protocol for Secure Routing in Internet of Things.

In the Internet of Things (IoT), the de facto Routing Protocol for Low Power and Lossy Networks (RPL) is susceptible to several disruptive attacks based on its functionalities and features. Among various RPL security solutions, a trust-based security is easy to adapt for resource-constrained IoT environments. In the existing trust-based security for RPL routing attacks, nodes’ mobility is not considered or limited to only the sender nodes. Similarly, these trust-based protocols are not evaluated for mobile IoT environments, particularly regarding RPL attacks. Hence, a trust and mobility-based secure routing protocol is proposed, termed as SMTrust, by critically analysing the trust metrics involving the mobility-based metrics in IoT. SMTrust intends to provide security against RPL Rank and Blackhole attacks. The proposed protocol is evaluated in three different scenarios, including static and mobile nodes in an IoT network. SMTrust is compared with the default RPL objective function, Minimum Rank with Hysteresis Objective Function (MRHOF), SecTrust, DCTM, and MRTS. The evaluation results indicate that the proposed protocol outperforms with respect to packet loss rate, throughput, and topology stability. Moreover, SMTrust is validated using routing protocol requirements analysis to ensure that it fulfils the consistency, optimality, and loop-freeness.

THC-RPL: A lightweight Trust-enabled routing in RPL-based IoT networks against Sybil attack.

The Internet of Things (IoT) and its relevant advances have attracted significant scholarly, governmental, and industrial attention in recent years. Since the IoT specifications are quite different from what the Internet can deliver today, many groundbreaking techniques, such as Mobile Ad hoc Networks (MANETs) and Wireless Sensor Networks (WSN), have gradually been integrated into IoT. The Routing Protocol for Low power and Lossy network (RPL) is the de-facto IoT routing protocol in such networks. Unfortunately, it is susceptible to numerous internal attacks. Many techniques, such as cryptography, Intrusion Detection System (IDS), and authorization have been used to counter this. The large computational overhead of these techniques limits their direct application to IoT nodes, especially due to their low power and lossy nature. Therefore, this paper proposes a Trust-based Hybrid Cooperative RPL protocol (THC-RPL) to detect malicious Sybil nodes in an RPL-based IoT network. The proposed technique is compared and evaluated with state-of-the-art and is found to outperform them. It detects more attacks while maintaining the packet loss ratio in the range of 15-25%. The average energy consumption of the nodes also remains in the ratio of 60-80 mj. There is approximately 40% more energy conservation at node level with an overall 50% increase in network lifetime. THC-RPL has 10% less message exchange and 0% storage costs.

Trust-based secure routing in IoT network based on rider foraging optimization algorithm

Due to the mobility and frequent topology changes, routing the data poses a challenging issue in the Internet of Things (IoT). Security poses a fundamental requirement factor in most IoT applications particularly, secure routing among the IoT nodes. The aim of this paper is to develop secure routing in IoT with minimal delay, minimal energy consumption and high throughput. Thus, a new algorithm, named Rider Foraging Optimization algorithm (RFO) is designed by the incorporation of standard Rider Optimization algorithm (ROA) and Bacterial Foraging optimization algorithm (BFO) that render an optimal solution, which is the optimal path for transmitting the information. Initially, the trust of the nodes is evaluated and the cluster head (CH) is chosen based on Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol to securely transmit the data. Then, the proposed RFO algorithm selects the optimal path using the fitness factors, such as trust, energy, and delay constraints. The results revealed that the energy consumption of the nodes is reduced and the lifetime of the network is enhanced through the implementation of the proposed RFO, which attained a maximal throughput of 1, and minimal delay and energy consumption of 0.1169 and 0.0002, respectively.

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.

Deep Reinforcement Extreme Learning Machines for Secured Routing in Internet of Things (IoT) Applications

Multipath TCP (SMPTCP) has gained more attention as a valuable approach for IoT systems. SMPTCP is introduced as an evolution of Transmission Control Protocol (TCP) to pass packets simultaneously across several routes to completely exploit virtual networks on multi-homed consoles and other network services. The current multipath networking algorithms and simulation software strategies are confronted with sub-flow irregularity issues due to network heterogeneity, and routing configuration issues can be fixed adequately. To overcome the issues, this paper proposes a novel deep reinforcement-based extreme learning machines (DRLELM) approach to examine the complexities between routes, pathways, sub-flows, and SMPTCP connections in different topologies. Using DRLELM, throughput of the network is estimated. The extreme learning machines (ELM) preserves the run time wastage in multipath networks with faster convergence. Also, the Novel multipath TCP routing protocol integrates the logistic chaotic algorithm for the secured data transmission. Final results shows that the proposed framework outperformed other existing algorithms.

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

The Internet of Things (IoT) is getting important and interconnected technologies of the world, consisting of sensor devices. The internet is smoothly changing from an internet of people towards an Internet of Things, which permits various objects to connect to another wirelessly. The energy consumption of the IoT routing protocol can affect the network life span. In addition, the high volume of data produced by IoT will result in transmission collision, security issues, and energy dissipation due to increased data redundancy because tiny sensors are usually hard to recharge after they are deployed. Generally, to save energy, data aggregation reduces data redundancy at each node by turning some nodes into sleep mode and others into wake mode. Therefore, it is important to group the nodes with high data similarity using the fuzzy matrix. Then, the data received from the member nodes at the Cluster Head (CH) are analyzed using a fuzzy similarity matrix for clustering. In the next step, after clustering, some nodes are chosen from all groups as redundant nodes. The sleep scheduling mechanism is then applied to reduce data redundancy, network traffic jamming, and transmission costs. We have proposed an Energy-Efficient Data Aggregation Mechanism (EEDAM) secured by blockchain, which uses a data aggregation mechanism at the cluster level to save energy. As edge computing is used to provide on-demand trusted services to IoT with minimum delay, blockchain is integrated inside a cloud server, so the edge is validated by the blockchain to provide secure services to IoT. Finally, we performed simulations to calculate the performance of the proposed mechanism and compared it with the conventional energy-efficient algorithms. The simulation results show that the proposed structural design can successfully reduce the amount of data, provide proper security to the IoT, and extend the wireless sensor network (WSN).

A reliable link quality-based RPL routing for Internet of Things

A reliable link quality-based RPL routing for Internet of Things