Routing In Internet Of Things Research Articles

IFKC and Optimized DeTrac Deep Convolutional Neural Network Based Location Prediction in the Mobile IOT

Wireless Sensor Network (WSN) is a noteworthy technological advancement in the Internet of Things (IoT). Due to its severe resource constraints, this network necessitates the development of energy-efficient routing strategies. In the context of the Internet of Things, a cluster-based routing problems frequently struggle with unequal power usage. Therefore, this paper proposes an Improved Fractional Rough Fuzzy K-means Clustering and Optimized DeTraC Deep Convolutional Neural Network based Location Prediction in Mobile Internet of Things (IFKC-ODTN-LP-MIoT) system to address unequal power consumption in cluster-based IoT routing. By proposing Improved Fractional Rough Fuzzy K-means (IF-RFKM) for cluster head selection and an optimized DeTraC Deep Convolutional Neural Network (DT-DCNN) the system aims to enhance energy efficiency for location prediction. Then Adolescent Identity Search Algorithm (AISA) optimizes DT-DCNN parameters for improved accuracy. The experimental results demonstrate significant improvements in Computational efficiency, network lifetime and delay compared to the existing approaches.

Advancements in IoT Routing and Energy Efficiency: A Comprehensive Review of Algorithms and Technologies

The rapid expansion of the Internet of Things (IoT) has led to a significant increase in connected devices, resulting in growing challenges related to efficient data transmission, energy consumption, and network scalability. Routing protocols and energy-efficient algorithms play a pivotal role in addressing these challenges, enabling IoT systems to optimize performance while minimizing power usage. This review provides a comprehensive analysis of recent advancements in IoT routing techniques and energy-efficient solutions, focusing on multi-objective optimization algorithms such as fractional gravitational search, grey wolf optimization, and hybrid salp swarm-differential evolution algorithms. Additionally, we explore context-aware routing, cluster-based methods, and the integration of machine learning to enhance decision-making in dynamic IoT environments. The review also highlights the critical role of energy-efficient routing in IoT applications, such as smart agriculture, healthcare, and smart cities, emphasizing its importance in prolonging network lifetime and reducing overall operational costs. The potential of blockchain technology and AI-driven algorithms to enhance security and energy efficiency in IoT networks is discussed, alongside future directions in green routing solutions. By addressing the complexities of IoT routing and energy management, this review aims to provide insights into the latest research trends and guide future innovations in IoT systems.

FL-DSFA: Securing RPL-Based IoT Networks against Selective Forwarding Attacks Using Federated Learning.

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy.

Chaos Reptile Search Algorithm Based Clustering and Routing for Internet of Things assisted Wireless Sensor Network

Chaos Reptile Search Algorithm Based Clustering and Routing for Internet of Things assisted Wireless Sensor Network

Enhancing IoT Routing Security and Efficiency: Towards AI-Enabled RPL Protocol

In the rapidly evolving landscape of the Internet of Things (IoT), the security and efficiency of network routing are paramount. The standard Routing Protocol for Low-Power and Lossy Networks (RPL) faces challenges in the form of various cyber-attacks, impacting its reliability and performance. To address this problem, we propose a Novel Algorithm for Network Traffic Analysis and Response (NANTAR), a novel algorithm that optimizes the security and efficiency of RPL routing using AI-based techniques. Through evaluation against traditional RPL, NANTAR has demonstrated remarkable improvements in key performance metrics: a 20% increase in throughput, latency reductions of 20-30%, and more efficient resource utilization. Notably, NANTAR maintains robust network performance with scaling, evidenced by 15-20% lower false positive and negative rates. The algorithm’s efficacy is further highlighted by its high detection rates in the face of diverse attacks and its power consumption efficiency, which is crucial for IoT devices. NANTAR’s adaptability and seamless integration across various IoT platforms affirm its potential as a solution for securing IoT ecosystems, with results indicating significant improvements in key operational metrics.

Load aware active low energy adaptive clustering hierarchy for IoT-WSN

Clustering is a primary process that takes place in an IoT based wireless sensor network environment commences from the deployment phase. Due to the heterogeneity and resource constrained nature of internet of things (IoT) networks, dynamic clustering, cluster head selection, and routing are required to optimize the network and to improve the overall network performance. Load aware active low energy adaptive clustering hierarchy (LAALEACH) work is an attempt to introduce novel components to the standard LEACH protocol. The main objective of LAALEACH work is to achieve a load aware active routing in IoT based wireless sensor network environments. Rapid load estimator, load pattern tracker, and load aware active routing are the contributed modules introduced in this LAALEACH work. Most recent related works are analyzed and the proposed modules are devised in a way to overcome the issues in the existing methods. Standard network performance parameters such as throughput, packet delivery rate, communication delays, and energy consumption are measured by the OPNET based simulation during the experiments. Obtained improvements in the overall performance is the accomplishment of LAALEACH work.

IoT-based waste management: hybrid optimal routing and waste classification model.

Internet of Things (IoT) makes connectivity between physical devices which are embedded with sensors, software, and connectivity that let them to communicate and transfer data. This technology makes it possible to collect and transfer data from a vast network device, opening the door to the development of automatic and more efficiency systems. The term "waste management" refers to all of the responsibilities essential to regulate trash, from the point of gathering through reusing and monitoring. Reducing the hazardous consequences of such garbage on the environment and human health is the goal of waste management. By considering these hazardous consequences, this research work is interested in working on an efficient waste management system. The utilization of IoT devices enables municipalities to optimize waste management operations by leveraging data insights. This information aids in scheduling waste collections more effectively and planning optimal routes. Therefore, the research work proposes an IoT-based waste management system with two vital processes such as IoT routing and waste management. At first, routing in IoT is done by proposing hybrid optimization algorithm named Snake Optimization Updated Beluga Whale Optimization algorithm (SOUBWO) under constraints such as distance, energy, link quality, delay, and trust. Secondly, waste management is worked on by following steps including pre-processing, segmentation, feature extraction, and classification. The waste images collected by IoT devices are transmitted from source node (SN) to destination node (DN) by optimal routing. Those transmitted waste images are pre-processed by Wiener filtering process. Consequently, the pre-processed images are segmented by employing proposed Balanced Iterative Reducing and Clustering Using Hierarchies-Altered Distance Metrics (BIRCH-ADM) algorithm. Subsequently, features such as multi-text on histogram feature, proposed Local Gabor XOR Pattern (LGXP)-based feature using novel image processing techniques, and statistical features are extracted. Finally, these extracted features are efficiently classified by hybrid classification model which is formed by integrating conventional deep maxout and Bidirectional-Long Short Term Memory (Bi-LSTM) networks. The effectiveness of the proposed approach is validated through various analyses, including performance and statistical analyses. Moreover, the proposed scheme demonstrates minimal energy consumption, with a recorded value of 0.123. In contrast, conventional methodologies exhibit higher energy consumption, with values such as SOA = 0.237, BWO = 0.146, BES = 0.183, SMO = 0.158, CHOA = 0.174, and PSO = 0.189, respectively. By this hybrid classification model, the process of classification on waste is effectively done and moreover its effectiveness is proved by various analyses.

Secure data dissemination and routing in Internet of Things

Secure data dissemination and routing in Internet of Things

F-EVM: improving routing in Internet of Things using fuzzy MAIRCA approach and fuzzy eigenvector method

F-EVM: improving routing in Internet of Things using fuzzy MAIRCA approach and fuzzy eigenvector method

Secure multi-path routing for Internet of Things based on trust evaluation.

In the realm of the Internet of Things (IoT), ensuring the security of communication links and evaluating the safety of nodes within these links remains a significant challenge. The continuous threat of anomalous links, harboring malicious switch nodes, poses risks to data transmission between edge nodes and between edge nodes and cloud data centers. To address this critical issue, we propose a novel trust evaluation based secure multi-path routing (TESM) approach for IoT. Leveraging the software-defined networking (SDN) architecture in the data transmission process between edge nodes, TESM incorporates a controller comprising a security verification module, a multi-path routing module, and an anomaly handling module. The security verification module ensures the ongoing security validation of data packets, deriving trust scores for nodes. Subsequently, the multi-path routing module employs multi-objective reinforcement learning to dynamically generate secure multiple paths based on node trust scores. The anomaly handling module is tasked with handling malicious switch nodes and anomalous paths. Our proposed solution is validated through simulation using the Ryu controller and P4 switches in an SDN environment constructed with Mininet. The results affirm that TESM excels in achieving secure data forwarding, malicious node localization, and the secure selection and updating of transmission paths. Notably, TESM introduces a minimal 12.4% additional forwarding delay and a 5.46% throughput loss compared to traditional networks, establishing itself as a lightweight yet robust IoT security defense solution.

Comparative Study of Routing Protocols for IoT Networks

Abstract: The Internet of things (IoT) is widely used for communication between portable and intelligent heterogeneous devices, like laptops, smartphones, computers, etc. IoT networks are popular in the modern era because they allow data to be exchanged anywhere when connected to the internet. IoT networks have several challenges, including those related to routing, connectivity, privacy, security, and other issues. The major challenge is routing in terms of choosing the best route for sharing data in IoT networks; IoT routing algorithms use more time and energy. In this paper, we review relevent literature and patents and also various routing approaches are categorized into groups, like multicast, clustering, emergency application, traffic, location, tree, and residual energy based approach, and they are compared based on several parameters, like energy consumption, network lifetime, path length, packet delivery ratio, and network latency. In terms of performance metrics, like energy consumption, network lifetime, reliability, efficiency, and packet delivery ratio, the comparison shows that Routing Protocol for Low Power and Lossy Networks (RPL), Efficient Tree-based Self-organizing Protocol (ETSP), Collection Tree Protocol (CTP), and Fast Multi-constrained Multicast Routing Algorithms (FAMOUS) are the best protocols. The best approach is a tree-based one since it solves the larger problem in the hierarchy with the least amount of time complexity.

Secure routing in the Internet of Things (IoT) with intrusion detection capability based on software-defined networking (SDN) and Machine Learning techniques

Routing and security are the two main prerequisites for ensuring the correct operation of wireless networks. The importance of these cases doubles in wide networks such as IoT. This paper presents an algorithm to improve Secure Routing in IoT called SRAIOT. This algorithm uses a hierarchical structure to determine the connections between network components and data transfer routing. In SRAIOT, the network structure is managed hierarchically and through SDN. For this purpose, the IoT network is first divided into a set of subnets using the SDN solution, communication control and authentication are managed using the controller nodes of each subnet. The communication between two objects (located in different subnets) will be possible if their identity is confirmed through the controller nodes related to them. On the other hand, in order to identify the sources of attacks and network security threats, the controller nodes in each subnet monitor the network traffic pattern using an ensemble learning model and identify possible attacks in their subnet. The performance of SRAIOT was tested in the simulation, and the results were compared with previous methods. The results of these tests show that SRAIOT improves network performance regarding routing and detecting attacks.

Performance evaluation and comparative analysis of CrowWhale-energy and trust aware multicast routing algorithm

Multipath routing helps to establish various quality of service parameters, which is significant in helping multimedia broadcasting in the Internet of Things (IoT). Traditional multicast routing in IoT mainly concentrates on ad hoc sensor networking environments, which are not approachable and vigorous enough for assisting multimedia applications in an IoT environment. For resolving the challenging issues of multicast routing in IoT, CrowWhale-energy and trust-aware multicast routing (CrowWhale-ETR) have been devised. In this research, the routing performance of CrowWhale-ETR is analyzed by comparing it with optimization-based routing, routing protocols, and objective functions. Here, the optimization-based algorithm, namely the Spider Monkey Optimization algorithm (SMO), Whale Optimization Algorithm (WOA), Dolphin Echolocation Optimization (DEO) algorithm, Water Wave Optimization (WWO) algorithm, Crow Search Algorithm (CSA), and, routing protocols, like Ad hoc On-Demand Distance Vector (AODV), CTrust-RPL, Energy-Harvesting-Aware Routing Algorithm (EHARA), light-weight trust-based Quality of Service (QoS) routing, and Energy-awareness Load Balancing-Faster Local Repair (ELB-FLR) and the objective functions, such as energy, distance, delay, trust, link lifetime (LLT) and EDDTL (all objectives) are utilized for comparing the performance of CrowWhale-ETR. In addition, the performance of CrowWhale-ETR is analyzed in terms of delay, detection rate, energy, Packet Delivery Ratio (PDR), and throughput, and it achieved better values of 0.539 s, 0.628, 78.42%, 0.871, and 0.759 using EDDTL as fitness.

A Lightweight Mitigation Approach against a New Inundation Attack in RPL-Based IoT Networks

Internet of Things (IoT) networks are being widely deployed for a broad range of critical applications. Without effective security support, such a trend would open the doors to notable security challenges. Due to their inherent constrained characteristics, IoT networks are highly vulnerable to the adverse impacts of a wide scope of IoT attacks. Among these, flooding attacks would cause great damage given the limited computational and energy capacity of IoT devices. However, IETF-standardized IoT routing protocols, such as the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL), have no relevant security-provision mechanism. Different variants of the flooding attack can be easily initiated in RPL networks to exhaust network resources and degrade overall network performance. In this paper, a novel variant referred to as the Destination Information Object Flooding (DIOF) attack is introduced. The DIOF attack involves an internal malicious node disseminating falsified information to instigate excessive transmissions of DIO control messages. The results of the experimental evaluation demonstrated the significant adverse impact of DIOF attacks on control overhead and energy consumption, which increased by more than 500% and 210%, respectively. A reduction of more than 32% in Packet Delivery Ratio (PDR) and an increase of more than 192% in latency were also experienced. These were more evident in cases in which the malicious node was in close proximity to the sink node. To effectively address the DIOF attack, we propose a new lightweight approach based on a collaborative and distributed security scheme referred to as DIOF-Secure RPL (DSRPL). It provides an effective solution, enhancing RPL network resilience against DIOF attacks with only simple in-protocol modifications. As the experimental results indicated, DSRPL guaranteed responsive detection and mitigation of the DIOF attacks in a matter of a few seconds. Compared to RPL attack scenarios, it also succeeded in reducing network overhead and energy consumption by more than 80% while maintaining QoS performance at satisfactory levels.

Increasing efficiency for routing in internet of things using Binary Gray Wolf Optimization and fuzzy logic

In the field of information and communication technology, the Internet of Things is regarded as a brand-new and important technology. The introduction of new protocols in this area is caused by the presence of devices in these networks with constrained resources and relatively low computing power. One of the most well-known routing protocols for low-power devices is the RPL protocol. This algorithm cannot take into account all of the required routing goals at once. This article introduces a proposed data-oriented RPL algorithm that divides data during routing according to their content. This can decrease the amount of duplicate data transferred through the network, shorten the communication system's delay, conserve the node's limited energy, and prolong the network's lifespan. The effectiveness of RPL can be increased by selecting the best route utilizing the Binary Gray Wolf Optimization. The best parent node in the routing procedure is chosen using an objective function during the tree construction phase. This objective function is built using fuzzy logic and the Binary Gray Wolf Optimization in the suggested technique. The results of Matlab 2022a and OMNET environment tests have shown that the proposed method has increased the efficiency of energy consumption and reduced the period of instability and end-to-end delay. that the ratio of the instability period in the proposed method is much less than the other three methods, so that the ratio of the instability period is 57% for the proposed method in the ORPL and Qos RPL methods, it is equal to 80%, and in the RPL method it is equal to 89%. This problem shows that the proposed method is more stable, or, in other words, it has been active for a longer period of time with the maximum number of nodes.

Securing IoT Attacks: A Machine Learning Approach for Developing Lightweight Trust-Based Intrusion Detection System

The routing process in the Internet of Things (IoT) presents challenges in industrial applications due to its complexity, involving multiple devices, critical decision-making, and accurate data transmission. The complexity further increases with dynamic IoT devices, which creates opportunities for potential intruders to disrupt routing. Traditional security measures are inadequate for IoT devices with limited battery capabilities. Although RPL (Routing Protocol for Low Energy and Lossy Networks) is commonly used for IoT routing, it remains vulnerable to security threats. This study aims to detect and isolate three routing attacks on RPL: Rank, Sybil, and Wormhole. To achieve this, a lightweight trust-based secured routing system is proposed, utilizing machine learning techniques to derive values for devices in new networks, where initial trust values are unavailable. The system demonstrates successful detection and isolation of attacks, achieving an accuracy of 98.59%, precision of 98%, recall of 99%, and f-score of 98%, thereby reinforcing its effectiveness. Attacker nodes are identified and promptly disabled, ensuring a secure routing environment. Validation on a generated dataset further confirms the reliability of the system.

Navigating Routing Challenges in Internet of Things (IoT): A Focus on Routing Protocols and Wireless Sensor Networks

This paper states the routing issues in the Internet of Things (IoT). The main point of focus is especially on routing protocols in IoT and wireless sensor networks (WSN). “Everything which is connected to the internet is alive”, is going to be the new rule for the future. The future is the Internet of Things (IoT), the world is changing every day with new technologies and inventions. The Internet of Things (IoT) has known as a future scenario of technology in human life. IoT expands the concept of the Internet from a network of rather homogenous devices such as computers to a network of heterogeneous devices such as home appliances and the conventional network is mostly replaced with IoT. Devices are the main users of the IoT framework an important part is IEEE 802.15.4, a standard that operates at the PHY layer and MAC layer of IoT devices. The IoT Ecosystem includes a large number of integrated heterogeneous low-power, low-cost devices, and secure connections are needed between them [15]. They communicate with each other to gather, share and forward the information in a multi-hop manner, this requires a complex routing between them. WSN, new technology in the IoT system comprised of a large collection of sensors, a mesh network can be used to individually gather data and send data through a router to the internet in an IoT system [6]. an enormous number of sensors are used and its growth is also fast, they are collecting inexperienced data and transforming it into meaningful and useful information. considering the full usability of these devices and a huge number of them, produce routing challenges [16]. Routing in IoT is the most critical part because we are working with devices that have restricted device resources. the optimum goal is to have enhanced routing protocols and efficient performance with such limited resource devices [15, 18].

Compound Metric Assisted Trust Aware Routing for Internet of Things through Firefly Algorithm

Compound Metric Assisted Trust Aware Routing for Internet of Things through Firefly Algorithm

An efficient Clustered IoT (CIoT) routing protocol and control overhead minimization in IoT network

Internet of Things (IoT) is a novel networking communication field in which the nodes act as physical objects. These nodes might be stationary or mobile. The majority of networking communication problems occur due to nodes’ mobility. Due to the mobility of IoT networks, excessive control overhead messages are generated. These messages are generated due to the frequent exchange of some control information, such as device identity, location information, device mobility, device direction, and others. In general, network clustering helps to alleviate the problem of generating high control overhead messages. Therefore, many proposed or enhanced Cluster-Based Routing (CBR) protocols address the shortcoming of traditional CBR protocols. In CBR protocols, each cluster selects only one node based on some parameters, and this node is called a LEaDeR (LEDR). The LEDR acts as a coordinator for its associated cluster. Furthermore, the rest of the nodes located in the cluster are called Smart DEVices (SDEV). A problem occurs when the SDEVs and the LEDR exchange their control overhead messages with each other. The exchanging of these messages utilizes the available network resources. This article proposes a Clustered IoT (CIoT) routing protocol, the CIoT routing protocol applies in a grid clustered topology. The proposed CIoT protocol aims to efficiently propagate messages between any pair of devices. The CIoT protocol selects only one route among a set of candidate routes, and the selected route is verified and evaluated based on some constraints. Also, this article proposes a Predictive Generated Hello Messages Algorithm (PGHMA). The PGHMA aims to minimize the generated control overhead messages produced by SDEVs and LEDRs. In comparison to other CBR protocols, simulation results demonstrate the effectiveness of the CIoT algorithms by increasing network throughput by 78% and decreasing end-to-end latency by 12.5%. Also, the PGHMA algorithm significantly outperforms other algorithms in terms of reducing the number of generated HELLO messages by 5.9%. Finally, we implement and evaluate our proposed algorithms by comparing them to other related algorithms published in the literature.

Congestion and Energy Aware Multipath Load Balancing Routing for LLNs

The Internet of Things (IoT) is presently in its golden era with its current technological evolution towards digital transformation. Low-power and Lossy Networks (LLNs) form the groundwork for IoT, where the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is designated by Internet Engineering Task Force as the benchmark protocol for routing. Although RPL, with its unique capabilities, has addressed many IoT routing requirements, Load balancing and Congestion control are the outliers. This paper builds on the RPL protocol and proposes a multipath Congestion and Energy Aware RPL (CEARPL) that alleviates the load balancing and congestion concerns associated with RPL and improves the network performance. For congestion avoidance, a Congestion and Energy Aware Objective Function (CEA-OF) is suggested during parent selection that considers multiple metrics like Child Count metric, Estimated Lifetime metric, and Queue Occupancy metric, to equally distribute the traffic in LLNs. The Queue Occupancy metric is used to detect congestion in the network, and a Multipath routing strategy is utilized to mitigate the congestion in the network. A comparison of the performance of CEA-RPL was made against the existing Objective Functions of RPL, OFO, and MRHOF, as well as COM-OF, utilizing Contiki OS 3.0's Cooja emulator. CEA-RPL projected superior results with power consumption lowering by 33%, end-to-end delay decreasing by 30%, queue loss ratio reducing by 49%, and packet receiving rate and network lifetime improving by 7% and 49%, on an average, respectively.