Efficient Data Routing Research Articles

Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains

In the evolving landscape of the Internet of Things (IoT), ensuring the security and integrity of data transmission remains a paramount challenge. Routing Protocol for Low-Power and Lossy Networks (RPL) is commonly utilized in IoT networks to facilitate efficient data routing. However, RPL networks are susceptible to various security threats, with Sybil and flood attacks being particularly detrimental. Sybil attacks involve malicious nodes generating multiple fake identities to disrupt network operations, while flood attacks overwhelm network resources by inundating them with excessive traffic. This paper proposes a novel mitigation strategy leveraging Bloom filters and hash chains to enhance the security of RPL-based IoT networks against sybil and flood attacks. Extensive simulation and performance analysis demonstrate that this solution significantly reduces the impact of sybil and flood attacks while maintaining a low power consumption profile and low computational overhead.

Cost analysis of IPv6 distributed mobility management protocols in comparison with TFMIPv6.

The past decade has witnessed a significant evolution in the role of the Internet, transitioning from individual connectivity to an integral aspect of various domains. This shift has prompted a move in IP paradigms from hierarchical to distributed architectures characterized by decentralized structures. This transition empowers efficient data routing and management across diverse networks. However, traditional distributed mobility management (DMM) protocols, reliant on tunneling mechanisms, incur overheads, costs, and delays, exacerbating challenges in managing the exponential growth of mobile data traffic. This research proposes Tunnel-Free Mobility for IPv6 (TFMIPv6) as a solution to address the shortcomings of existing DMM protocols. TFMIPv6 eliminates the need for tunneling, simplifying routing processes and reducing latency. A comprehensive cost analysis and performance evaluation are conducted, comparing TFMIPv6 with traditional protocols such as MIPv6, PMIPv6, FMIPv6, and HMIPv6. The study reveals significant improvements with TFMIPv6. Signaling costs are reduced by 50%, packet delivery costs by 23%, and tunneling costs are completely eliminated (100%). Real-world network traffic datasets are used for simulation, providing statistical evidence of TFMIPv6's efficacy in supporting an uninterrupted movement of IPv6 data across networks.

Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids.

Efficient and reliable data routing is critical in Advanced Metering Infrastructure (AMI) within Smart Grids, dictating the overall network performance and resilience. This paper introduces Q-RPL, a novel Q-learning-based Routing Protocol designed to enhance routing decisions in AMI deployments based on wireless mesh technologies. Q-RPL leverages the principles of Reinforcement Learning (RL) to dynamically select optimal next-hop forwarding candidates, adapting to changing network conditions. The protocol operates on top of the standard IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), integrating it with intelligent decision-making capabilities. Through extensive simulations carried out in real map scenarios, Q-RPL demonstrates a significant improvement in key performance metrics such as packet delivery ratio, end-to-end delay, and compliant factor compared to the standard RPL implementation and other benchmark algorithms found in the literature. The adaptability and robustness of Q-RPL mark a significant advancement in the evolution of routing protocols for Smart Grid AMI, promising enhanced efficiency and reliability for future intelligent energy systems. The findings of this study also underscore the potential of Reinforcement Learning to improve networking protocols.

Enhancing quality-driven iot operations for smart cities: A comprehensive approach for routing, security, and optimization

This study introduces a tailored data acquisition and communication framework for IoT smart applications, focusing on enhancing efficiency and system performance. The proposed Quality-Driven IoT Routing (EQR-SC) for smart cities utilizes IoT-enabled wireless sensor networks. Additionally, a noteworthy contribution is the introduction of the Chaotic Firefly Optimization (CFOA) algorithm for IoT sensor cluster formation, potentially optimizing the organization and efficiency of IoT sensor networks in smart cities. Trust-based cluster Head Selection is enhanced by employing the Weighted Clustering Algorithm (WCA), which assigns weights to nodes based on trustworthiness and relevant metrics to select reliable cluster heads. The proposal of a lightweight data encryption technique enhances data security among IoT sensors, ensuring the privacy and integrity of transmitted information. To optimize pathfinding within the IoT platform, the research employs the Bellman-Ford algorithm, ensuring efficient data routing while accommodating negative edge weights when necessary. Finally, a thorough performance analysis, conducted through network simulation (NS2), provides insights into the effectiveness of the proposed OQR-SC technique, allowing for valuable comparisons with existing state-of-the-art methods.

DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing

Recent advances on deep learning models come at the price of formidable training cost. The increasing model size is one of the root causes, but another less-emphasized fact is that data scale is actually increasing at a similar speed as model scale, and the training cost is proportional to both of them. Compared to the rapidly evolving model architecture, how to efficiently use the training data (especially for the expensive foundation model pretraining) is both less explored and difficult to realize due to the lack of a convenient framework that focus on data efficiency capabilities. To this end, we present DeepSpeed Data Efficiency, a framework that makes better use of data, increases training efficiency, and improves model quality. Specifically, we propose and combine two data efficiency techniques: efficient data sampling via a general curriculum learning library, and efficient data routing via a novel random layerwise token dropping technique. For GPT-3 1.3B language model pretraining, our work achieves 12.5x less data/time/cost ($3.7K if rent on Azure), while still maintaining 95% of model quality compared to baseline with full data and cost ($46.3K). For GPT-3 1.3B and BERT-large pretraining, our work can also achieve the same model quality with up to 2x less data/time/cost, or achieve better model quality under same data/time/cost. DeepSpeed Data Efficiency is easy to use and tune, enabling us to easily apply it and verify its benefit on additional tasks including GPT-3 MoE model pretraining and small-scale GPT-2/ViT finetuning.

A Novel Hybridized Cluster-Based Geographical Opportunistic Routing Protocol for Effective Data Routing in Underwater Wireless Sensor Networks

Underwater wireless sensor nodes comprise hundreds to thousands of battery-operated sensor nodes with limited bandwidth. These networks are employed to transmit the data with enhanced quality of service (QoS). However, efficient data routing is the most challenging obstacle in many underwater applications. To solve the issues in underwater sensor nodes, the hybridized cluster-based geographical opportunistic routing protocol with distance vector establishment has been proposed to transmit the data efficiently. Primarily, the proposed methodology finds out the shortest path with minimal hop count whereas the void node can be updated with infinite hop count. Thereafter, the sleep/wake scheduling and waiting mechanism and periodic beaconing algorithm are incorporated into the proposed model to attain a higher packet delivery ratio with minimal energy consumption. This proper scheduling and optimal cluster routing enhance the continuous data transmission in underwater applications. The simulation result reveals that the proposed method achieves better energy efficiency and higher network lifetime when compared with the existing clustering methods.

Energy‐efficient adaptive clustering (EEAC) with rendezvous nodes and mobile sink

SummaryWireless sensor networks are an indispensable part of the present industrial and environmental scenario, with the everlasting challenges to increase network lifetime and reduce energy consumption. This paper presents an energy‐efficient adaptive clustering (EEAC) model, which is built upon the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with mobile sink and rendezvous nodes. The nodes are energy aware and participate in cluster head selection only if their current energy is higher than the average energy for the round. The idle nodes in the rendezvous region, which do not act as rendezvous nodes undergo clustering within themselves for the efficient routing of data. The EEAC inspires local clustering successfully conserves the energy of continuous long‐range transmission and enhances network lifetime. The proposed scheme proves to be remarkably better than the previous schemes, especially in large network cross‐sections. It improves alive nodes' performance for around 50% more rounds than the optimizing‐LEACH and the remaining energy curves improve around 500 rounds for 250 mx250 m network. For 350 mx350 m and 450 mx450 m networks, the results improved much in EEAC. In addition, the improvement in stability period is 450%, 578%, and 198% for the network cross‐section 150 mx150 m, 200 mx200m, and 250 mx250 m, respectively.

Fuzzy based clustering and improved ant colony optimization for collecting data via mobile sink in wireless sensor networks

<span>Energy efficient routing of data from sensor to base station (BS) is attained utilizing the clustering of sensor nodes, thus minimizing the number of hops and circling the task of the cluster head (CH) sporadically. In addition, the clusters near to the BS take a substantial load over multi-hop communication. The hot spot problem affects wireless sensor networks (WSNs) with BS nodes, which is caused by sensor nodes close to the BS allowing for increased traffic load. So, the entire network lifespan is minimized owing to the element some nodes drain their energy resources much faster equated to the break. To solve these issues fuzzy based clustering and form the optimal route (FCOR) by mobile sink (MS) approach for efficient data collection is proposed. The fuzzy logic method is used for elected the CH by node remaining energy, node connectivity and node distance parameters. Discovering an optimal movable trajectory for the MS is serious so as to attain energy efficiency. Improved ant colony optimization (IACO) method is a better solution to discovering an optimal traversal route. Simulation results proves that FCOR increases the energy efficiency, throughput and minimized the network delay in the WSN.</span>

A trusted security approach to detect and isolate routing attacks in mobile ad hoc networks

BackgroundSecure routing is of utmost importance in protecting mobile devices from external threats. However, the dynamic characteristics of Mobile Ad hoc Networks (MANETs) pose significant challenges in achieving a secure routing path. ObjectivesThis paper aims to propose a Trust and Anonymous Model (TAM) for efficient and secure data routing in MANETs. The objective is to address the limitations of existing routing approaches and enhance the security of the network. MethodsThe TAM protocol is designed with a Two-Tier Security Mechanism (TTSM). In the first level of security, trustable nodes are selected based on their ability to process control messages. The recommended trust value of a node is determined by estimating the speed at which it processes control messages, with higher energy nodes being considered more trustable. In the second level of security, the original node identity is concealed, and data is transmitted through selected trusted nodes with duplicate identities generated using a factorial recursive function. This ensures secure transmission, as malicious observers are unable to identify participating nodes in the routing operation. ResultsThe proposed TAM model is evaluated and compared with state-of-the-art schemes such as A Multi-attribute-based Trusted Routing for Embedded devices (EMBTR) and a cognitive energy-efficient-based trusted model (CEMT). Experimental evaluation demonstrates that the TAM model achieves better performance in terms of energy consumption, delay, packet delivery rate, and false node detection rate, thereby improving network optimality. ConclusionThe Trust and Anonymous Model (TAM) with its Two-Tier Security Mechanism (TTSM) presents a promising approach for secure data routing in MANETs. By selecting trustable nodes and utilizing duplicate identities, the TAM model enhances network security while achieving improved performance compared to existing schemes. The findings of this study contribute to the advancement of secure routing techniques in MANETs and provide insights for future research in this domain.

CVFP: Energy and trust aware data routing protocol based on Competitive Verse Flower Pollination algorithm in IoT

Internet of Things (IoT) is a network of things that are interconnected to provide a variety of services. These things can include smartphones, actuators, sensors, computers, and wearables. The IoT enables the collection and exchange of data between these objects. The expansion of smart networks is what has given the Internet of Things its global influence. Routing is an important strategy that involves the creation of routes and the transmission of data packets from the source to the intended recipient in the network. Energy and trust are considered to be the two most important parts that provide secure data transmission in a network. However, secure routing in the highly distributed and dynamic environment of IoT still results in a great challenge due to the heterogeneity of devices. Hence, an efficient secure data routing mechanism is designed with the proposed Competitive Verse Flower Pollination (CVFP) algorithm that considers trust, energy, delay, distance, and link lifetime (LLT) parameters to find an optimal routing path. Moreover, the selection of optimal path is made by involving CH nodes such that nodes have higher energy are chosen in the routing strategy. The quality of the link is measured using the LLT constraint in order to decide whether data can be routed or to perform a route maintenance mechanism to ensure reliability in data routing. The performance of the proposed algorithm is analyzed using the simulated data. The proposed algorithm attains a lower delay of 0.204 s, higher residual energy of 0.093 J, a maximum throughput of 94.59%, and a higher trust of 0.713.

A classy energy efficient spider monkey optimization based clustering and data aggregation models for wireless sensor network

SUMMARYEstablishment of energy efficient and reliable data routing in wireless sensor network (WSN) is one of the most critical and challenging task in the recent days. Also, the overall performance and lifetime of WSN is highly depends on the energy level of sensor nodes, hence it is most essential to save the energy of network. For this purpose, the different types of clustering and data aggregation mechanisms are developed in the conventional works, which are focusing on improving both the energy conservation and lifetime of network. Yet, it facing the challenges of increased computational complexity, inefficient routing of data, high controlling overhead, and reduced reliability. Thus, the proposed work objects to develop a novel energy efficient mechanism by integrating the functionalities of advanced clustering, path selection, and data aggregation methodologies. Here, the spider monkey optimization based energy efficient routing protocol is developed for optimally selecting the cluster head (CH) based on certain parameters of energy, distance, and weight value. In this framework, the data transmission is performed between the source to destination nodes through the relay nodes and CHs, which helps to minimize the energy consumption of network. Then, the Classy Bellman‐Ford algorithm is deployed for identifying the best paths having shortest distance with the sink nodes. Consequently, an anticipated data aggregation mechanism is utilized for ensuring the security and reliability of data transmission in WSN. For evaluation assessment, various performance metrics have been utilized to validate the results of proposed methodology, and also the obtained values are compared with some other recent state‐of‐the‐art models for proving the betterment of proposed mechanism.

An optimized ticket manager based energy‐aware multipath routing protocol design for IoT based wireless sensor networks

SummaryRecently, wireless sensor networks (WSNs) and Internet of Things (IoTs) have emanated as an indispensable assets that play a critical role in revolutionizing the field of data communication. Owing to the evolution of communication standards, research trends in IoT based wireless sensor networks have been rapidly progressing toward achieving effective data routing with a prolonged network lifetime and minimized energy consumption. In this article, an optimized ticket manager based energy‐aware multipath routing protocol (TMERP) is proposed. The proposed protocol design comprises three important functional entities: ticket manager (TM), routing planner (RP), and backup node (BN). The TM is responsible for controlling and monitoring all the constraints related to networking. Then, the RP minimizes the overall complexity of the optimal resource allocation by avoiding an end‐to‐end delay. Finally, the BN facilitates efficient data routing through the optimal selection of routing paths using the node trust evaluation and backup process to minimize data loss. Hence, the proposed multipath routing system has a distinct advantage in enhancing the network lifetime constraint with minimal energy consumption owing to the collective performance of its functional entities. The simulation results of the experimental studies show that the proposed protocol design achieved an improved performance in terms of network energy, throughput, and network operational lifetime by 39.3%, 47.9%, and 10.5%, respectively when compared with similar existing protocols.

Incorporation of Energy Efficient Computational Strategies for Clustering and Routing in Heterogeneous Networks of Smart City

For decision-making and governance, smart cities depend on tracking data collected via a substantial percentage of wireless sensing nodes. However, several limitations affect Wireless Sensor Network (WSN)-based Internet of Things (IoT) services, such as low battery life, recurrent connectivity problems due to multi-hop connections, and a limited channel capacity. Furthermore, in many systems, clustering and routing are handled independently, which prevents the adaptation of effective strategies for optimal energy usage and prolonged network lifespan. This research gathers data from heterogeneous IoT nodes linked via WSN and distributed across a smart infrastructure. There are two interrelated problems to be addressed with respect to energy efficiency computations: clustering and routing. We provide a new clustering strategy through which efficient routing of critical and regular data is handled. As a result, both clustering and routing have been significantly strengthened, which balances the communication load across different sectors of the smart infrastructure network. Minkowski distance and ranking strategy are used for routing and selecting cluster heads, respectively. Deterministic distributed–time division multiple access (DD-TDMA) scheduling is employed to balance the communication load across the network. The experimental results show that the proposed work outperforms some of the popular cluster-based routing strategies.

A Data Transmission Scheme Using K-Means and Fuzzy Logic for IOT Sensor Based Forest Fire Detection System

Forest fires are natural disasters and effective mitigation of these fires require early warning systems. Deployment of sensors that are robust against environmental conditions, collection of sensor data in an energy efficient manner and at real time at the central server, and accurate detection of fire’s existence are all critical elements for implementing such systems. In this paper, we focus on second issue which is energy efficient routing of sensor data in a Internet-of-Things (IoT) sensor network that is deployed for forest fire detection. A Fuzzy-based Cluster Head selection technique for WSN in detecting forest fire is presented. In the proposed scheme, the nodes are divided into clusters using K-means clustering and then the cluster heads are determined by using a fuzzy logic scheme. Unlike traditional parameters, distance from the centroid and the remaining energy levels are used as parameters to select the cluster head. Simulation tool is used to implement the proposed technique. The simulation results suggest that the proposed cluster head selection approach outperforms the existing schemes

A Novel Optimization based Energy Efficient and Secured Routing Scheme using SRFIS-CWOSRR for Wireless Sensor Networks

Ensuring the reliable and energy efficient data routing in Wireless Sensor Networks (WSNs) is still remains one of the challenging and demanding tasks due to its dynamic architecture. For this purpose, the different types of routing methodologies and security schemes have been developed in the conventional works. However, it faced the problems related to increased network overhead, high cost consumption, reduced Quality of Service (QoS), and inefficient bandwidth utilization. The main contribution of this work is to implement an optimization based secured routing methodology for establishing an energy efficient data communication in WSNs. For clustering the nodes, the parameters such as residual energy, trust score, and mobility have been considered, which also helps to simplify the networking operations. Moreover, the outliers in the network are detected with the help of Spatial Temporal Fuzzy Inference System technique, which generates the set of inference rules based on the distance, energy, and mobility measures. Also, the Crow-Whale Optimized Secured Robin Routing (CWOSRR) is used to enable the data transmission in the network through the optimized path, which avoids the loss of information. During the simulation analysis, the results of the proposed technique is validated and compared by using various performance measures.

Establishing Self-Healing and Seamless Connectivity among IoT Networks Using Kalman Filter

The Internet of Things (IoT) is the extension of Internet connectivity into physical devices and to everyday objects. Efficient mobility support in IoT provides seamless connectivity to mobile nodes having restrained resources in terms of energy, memory and link capacity. Existing routing algorithms have less reactivity to mobility. So, in this work, a new proactive mobility support algorithm based on the Kalman Filter has been proposed. Mobile nodes are provided with a seamless connectivity by minimizing the switching numbers between point of attachment which helps in reducing signaling overhead and power consumption. The handoff trigger scheme which makes use of mobility information in order to predict handoff event occurrence is used. Mobile nodes new attachment points and its trajectory is predicted using the Kalman-Filter. Kalman-Filter is a predictor-estimator method used for movement prediction is used in this approach. Kalman Filtering is carried out in two steps: i) Predicting and ii) Updating. Each step is investigated and coded as a function with matrix input and output. Self-healing characteristics is being considered in the proposed algorithm to prevent the network from failing and to help in efficient routing of data. Proposed approach achieves high efficiency in terms of movement prediction, energy efficiency, handoff delay and fault tolerance when compared to existing approach.

Performance Analysis of Wireless Sensor Network by Variation in Cluster Head Selection through mLEACH-CS Algorithm

Abstract: The energy depletion at the nodes due to large volume of data transmission and data reception for large scale Wireless Sensor Networks (WSN) is one of the important areas of concern. Unbalanced energy consumption due to data traffic overhead at the nodes near the base station (BS) results in early node death and reduction in network lifetime. To balance the load at the wireless nodes during clustering, a novel hybrid clustering and routing protocol has been proposed based on mLEACH (modified Low-Energy Adaptive Clustering Hierarchy) and Cuckoo Search (CS) algorithm. Initially, mLEACH protocol is utilized to solve a novel equation designed to select the cluster heads by rotation at every round of data transmission based on the maximum permissible cluster heads in the network. In the second phase, CS algorithm is implemented for uniform distribution of the cluster heads in the network for efficient multi-hop data routing in the network based on several weighted factors. The performance of the proposed algorithm is compared with other algorithms in literature in terms of energy consumption and network lifetime for different positions of the base station and varied number of cluster heads in the model application area. The proposed mLEACH-CS algorithm shows a significant improvement by 60% considering 300 nodes in the WSN architecture while it decreased to 40% for denser network architecture. Optimal choice of the number and position of the cluster heads in certain network architecture has proved beneficial for designing an energy efficient wireless network with improved network lifetime.

Secured Protocol with Collaborative IoT-Enabled Sustainable Communication Using Artificial Intelligence Technique

In recent years, 5G and the Internet of Things (IoT) have been integrated into a variety of applications to support sustainable communication systems. In the presence of intermediate hardware, IoT devices collect the network data and transfer them to cloud technologies. The interconnect machines provide essential information to the connected devices over the Internet. Many solutions have been proposed to address the dynamic and unexpected characteristics of IoT-based networks and to support smart developments. However, more work needs to explore efficient quality-aware data routing for distributed processing. Additionally, to handle the massive amount of data created by smart cities and achieve the transportation objectives for resource restrictions, artificial intelligence (AI)-oriented approaches are necessary. This research proposes a secured protocol with collaborative learning for IoT-enabled sustainable communication using AI techniques. This approach increases systems’ reaction times in critical conditions and also controls the smart functionalities for inter-device communication. Furthermore, fitness computing can help in balancing the contribution of quality-aware metrics to achieve load balancing and efficient energy consumption. To deal with security, IoT communication is broken down into stages, resulting in a more dependable network for unpredictable environments. The simulation results of the proposed protocol have been compared to existing approaches and improved the performance of response time by 17%, energy consumption by 14%, number of re-transmissions by 16%, and computing overhead by 16%, under a varying number of nodes and data packets.

An analysis of architecture, framework, security and challenging aspects for data aggregation and routing techniques in IoT WSNs

Internet has evolved into a promising technology after going through various transitional phases in the past decades. It was invented in the early nineties, with the web being static, public and shared. The final transitional phase is the internet of services, where the content, web services, Extensible Markup Language (XML), productivity and commerce tools were created by the user bringing improved websites and services. Later on, with affordable mobile broadband, Android phones and tablets, people could access the internet and be in touch with the world through social media platforms and mobile applications. IoT is a network of physical components, vehicles, household appliances and other items embedded with devices, software, sensor systems, actuators and connectivity, connecting and transferring data. The process of gathering data from heterogeneous sensors for preventing recurrent transmissions simultaneously offering quality aggregated information at the sink node is collectively called as data aggregation and routing process. There is a transmission of only most complex information to the sink node. The continuous use of data aggregation and routing technique increases the energy, bandwidth and memory requirements. The challenging aspects in IoT WSN are energy consumption, bandwidth and memory utilized for data aggregation and routing process. So, it is very much important to focus on these parameters so that the network has a greater lifetime and Quality of service. The tradeoff between energy saving, precision of data and latency can be achieved only by utilizing data redundancy, data similarity, data aggregation and routing algorithms. The aim of the research is to do the analysis of the IoT WSNs on the basis of the architecture, framework, and challenges related to security, data aggregation and routing techniques. This research work motivates the researchers to know about the challenges in data aggregation and routing in IoT WSNs. An efficient data aggregation and routing technique in IoT WSNs help to improve the QoS parameters namely, throughput, end to end delay, routing overhead, packet delivery ratio and energy consumption. Data aggregation technique in IoT WSNs help in saving the energy of the nodes in the network. Thus makes the network efficient in terms of energy and other QoS parameters. Because of the high density of nodes in IoT WSNs, same data is sensed by a lot of nodes, which results in redundancy. Using data aggregation technique, the redundancy can be eliminated while routing packets from source nodes to base station.

Genetic algorithm based sink mobility for energy efficient data routing in wireless sensor networks

In a sink mobility based Wireless Sensor Network, the sink node moves on a trajectory in the network region and collects the data of the sensor nodes in the vicinity. Sink mobility reduces the distance from average source node to sink and saves network transmission energy. This paper proposes a Genetic Algorithm (GA) based sink mobility technique for WSN. Network region is divided into the optimal number of clusters and a sink movement trajectory is built over there. The GA process determines the optimal sink locations on the trajectory for each cluster. The moving sink stops at the optimal sink locations and gathers data from the nodes of the related clusters. An optimal sink location consumes minimum node energy in data transmission. For determining the optimal sink location for a cluster, the GA initializes a population of chromosomes. Further, a network energy consumption model is proposed that implements the fitness evaluation operator of the GA process. The developed GA model converges into a set of optimal sink locations on the trajectory for each cluster. The results depict that the GA based sink mobility provides increase in network lifetime than other protocols.