Routing Scheme Research Articles

Design and Analysis of a Bandwidth Aware Adaptive Multipath N-Channel Routing Protocol for 5G Internet of Things (IoT)

Large numbers of mobile wireless nodes that can move randomly and join or leave the network at any moment make up mobile ad-hoc networks. A significant number of messages are delivered during information exchange in populated regions because of the Internet of Things' (IoT) exponential increase in connected devices. Congestion can increase transmission latency and packet loss by causing congestion. More network size, increased network traffic, and high mobility that necessitate dynamic topology make this problem worse. An adaptive Multipath Multichannel Energy Efficient (AMMEE) routing strategy is proposed in this study, in which route selection strategies depend on forecasted energy consumption per packet, available bandwidth, queue length, and channel utilization. While multichannel uses a channel-ideal assignment process to lessen network collisions, multipath offers various paths and balances network strain. The link bandwidth is split up into a few sub-channels in the multichannel mechanism. To reduce network collisions, several source nodes simultaneously access the channel bandwidth. The cooperative multipath multichannel technique offers several paths from a single source or from several sources to the destination without colliding or becoming congested. The AMMEE routing approach is the basis for path selection. A load- and bandwidth-aware routing mechanism in the proposed AMMEE chooses the path based on node energy and forecasts their lifetime, which improves network dependability. The outcome demonstrates a comparative analysis of various multichannel medium access control (MMAC) techniques, including Parallel Rendezvous Multi Channel Medium Access Protocol (PRMMAC), Quality of Service Ad hoc On Demand Multipath Distance Vector (QoS-AOMDV), Q-learning-based Multipath Routing (QMR), and Topological Change Adaptive Ad hoc On-demand Multipath Distance Vector (TA-AOMDV) and the proposed AMMEE method. The results show that the AMMEE approach outperforms alternative systems. Doi: 10.28991/ESJ-2024-08-01-018 Full Text: PDF

POLAR: Adaptive and Non-invasive Join Order Selection via Plans of Least Resistance

Join ordering and query optimization are crucial for query performance but remain challenging due to unknown or changing characteristics of query intermediates, especially for complex queries with many joins. Over the past two decades, a spectrum of techniques for adaptive query processing (AQP)---including inter-/intra-operator adaptivity and tuple routing---have been proposed to address these challenges. However, commercial database systems in practice do not implement holistic AQP techniques because they increase the system complexity (e.g., intertwined planning and execution) and thus, complicate debugging and testing. Additionally, existing approaches may incur large overheads, leading to problematic performance regressions. In this paper, we introduce POLAR, a simple yet very effective technique for a self-regulating selection of alternative join orderings with bounded overhead. We enhance left-deep join pipelines with alternative join orders, perform regret-bounded tuple routing to find and validate "plans of least resistance", and then process the majority of tuple batches through these plans. We study different join order selection techniques, different routing strategies, and a variety of workload characteristics. Our experiments with a POLAR prototype in DuckDB show runtime improvements of up to 9x and less than 7% overhead for all benchmark queries, while outperforming state-of-the-art AQP systems by up to 15x.

Smart traffic routing and service allocation strategy to reduce water consumption in data centers through power reduction

Due to the growth of communication networks, energy consumption in information and communication technology industries is increasing dramatically. Among these industries, data centers are operating with a large number of processors and other components, which, due to heavy processing and mass data transmission, in addition to consuming high electrical power, also cause high thermal losses. Since water cooling systems are used for cooling different parts of these centers as well as for cooling the electric power generation unit, these centers are among the major consumers of water and electricity resources. In this article, while examining the important factors affecting water consumption in data centers, useful methods are suggested to reduce the consumption of electrical energy and water. Optimizing energy consumption in data centers is possible in three parts: routing, servicing and use of cooling equipment. For all three parts, improvement methods are suggested in this article. For this purpose, an optimization problem is designed and an algorithm is presented to solve it. In the proposed solution, an energy-smart method based on SDN technology is used for routing, virtual machines equipped with the possibility of reducing power consumption in no-load conditions are used for servicing, and two types of water-cooled and air-cooled systems are used for cooling equipment. is used. The simulation results show that depending on the cooling system, the proposed method reduces water consumption between 24% and 32% compared to the case where the proposed solution is not used.

Secure Routing Strategy Based on Attribute-Based Trust Access Control in Social-Aware Networks

Secure Routing Strategy Based on Attribute-Based Trust Access Control in Social-Aware Networks

Block chain technology for secured dynamic optimal routing in WSN with optimized generative adversarial capsule network

SummaryOne of the key strategies in wireless sensor network (WSN) for sending data packets to the base station (BC) is known as routing. However, malicious node outbreaks will occur, which exaggerate the functions of WSNs during the routing procedure. A secure routing protocol is necessary to protect the effectiveness of WSN and routing fortification. In real‐time scenarios, the existing routing protocol is volatile dynamically; it makes difficult to identify the performance of unprotected routing nodes. In this manuscript, a unique routing strategy is proposed for secured dynamic optimal routing (SDOR) in WSN that combines block chain technology optimized with a generative adversarial capsule network. Every routing sensor node contains a secret key, which is used to construct a crypto hash signature (CHS) token for flow access. The proposed block chain‐based WSN is used to provide SDOR information using a self‐attention‐based generative adversarial capsule network that is improved using the flamingo search algorithm. Following that, the security on the proposed block chain WSN is assessed from six angles. Block chain token transactions are evaluated using measures including average latency, average energy usage, and throughput. The proposed SDOR‐SA‐GAC‐FSA‐CHS‐BWSN delivers 53.87%, 42.57%, and 32.87% lower average packet delay, 28.97%, 37.73%, and 34.75% lesser identification time analyzed to the existing methods, such as trusted distributed routing scheme for WSNs using block chain (SDOR‐DCNN‐SSOA‐BWSN), secure authentication with DSM‐KL ascertained presentation optimization of hybrid block chain‐enabled model for multi‐WSN (SDOR‐DSM‐KL‐BWSN), and optimizing the valid transaction utilizing reinforcement learning‐based block chain ecosystem in WSN (SDOR‐RIL‐BWSN).

RETRACTED ARTICLE: Optimizing IoT-enabled WSN routing strategies using whale optimization-driven multi-criterion correlation approach employs the reinforcement learning agent

RETRACTED ARTICLE: Optimizing IoT-enabled WSN routing strategies using whale optimization-driven multi-criterion correlation approach employs the reinforcement learning agent

Discarded Perseaamericana leaf-derived natural O, Mg, and Ca self-doped activated carbon and its applications as electrode materials for high-performance symmetric supercapacitors

Recently, there has been growing research interest in the fabrication of heteroatom-doped porous carbon from biomass. Herein, natural O, Mg, and Ca self-doped activated carbon was fabricated from discarded Persea americanaleaves via a one-step route strategy. By varying the carbonization temperature (500, 600, 700, and 800 °C), the as-fabricated DPAL-AC exhibits a high surface area (875.775 m2/g) and rich natural O, Mg, and Ca heteroatoms composition. The symmetric device product (DPAL-AC700) exhibits a high gravimetric capacitance of 209 F/gat 1 A/g. In addition, the device delivered an energy density of 28.4 Wh/kg at a power density of 135.5 W/kg. This work presents a novel approach to producing self-doped activated carbon with natural heteroatoms, which can enhance the performance of symmetric supercapacitors.

Routing Techniques in Network-On-Chip Based Multiprocessor-System-on-Chip for IOT: A Systematic Review

Routing techniques (RTs) play a critical role in modern computing systems that use network-on-chip (NoC) communication infrastructure within multiprocessor system-on-chip (MPSoC) platforms. RTs contribute greatly to the successful performance of NoC-based MPSoCs due to traffic congestion avoidance, quality-of-service assurance, fault handling and optimisation of power usage. This paper outlines our efforts to catalogue RTs, limitations, recommendations and key challenges associated with these RTs used in NoC-based MPSoC systems for the IoT domain. We utilized the PRISMA method to collect data from credible resources, including IEEE Xplore ®, ScienceDirect, Association for Computing Machinery and Web of Science. Out of the 906 research papers reviewed, only 51 were considered relevant to the investigation on NoC RTs. The study addresses issues related to NoC routing and suggests new approaches for in-package data negotiating. In addition, it gives an overview of the recent research on routing strategies and numerous algorithms that can be used for NoC-based MPSoCs. The literature analysis addresses current obstacles and delineates potential future avenues, recommendations, and challenges analyzing techniques to assess performance utilizing metrics within the TCCM framework.

MF‐DLB: Multimetric forwarding and directed acyclic graph‐based load balancing for geographic routing in FANETs

SummaryFlying ad hoc network (FANET) comprising unmanned aerial vehicles (UAVs) emerges as a promising solution for numerous military and civil applications. Transferring data collected from the environment to the ground station (GS) is a primary concern for meeting the communication demands of most of these applications. However, the highly mobile UAVs with limited communication range, resulting in frequent topology change and intermittent connectivity, make data routing challenging. In such scenarios, geographic routing is a viable solution due to its scalability and robustness. However, the basic forwarding mechanism of geographic routing favors the neighboring UAV nearest to the destination, impacted substantially by link failures and routing holes in a dynamic environment. Additionally, routing decisions ignoring the current load over UAVs contribute to performance degradation due to the high concentration of data traffic near the GS. Thus, to address these issues, a geographic routing protocol named MF‐DLB comprising multimetric forwarding (MF) and a directed acyclic graph‐based load balancing (DLB) scheme is proposed to enhance packet forwarding in FANETs. MF takes account of multiple metrics related to connectivity, geographic progress, link lifetime, and residual energy to select the next hop with a stable communication link while effectively bypassing the routing holes. The second scheme, DLB, focuses on proactively maintaining routing paths near GS for load distribution among underutilized nodes to address the congestion problem. Simulations performed in network simulator ns‐3 confirm the outperformance of MF‐DLB over other related routing schemes in terms of different performance metrics.

Two-Echelon Location–Routing Problem of Perishable Products Based on the Integrated Mode of In-Store Pick-Up And Delivery

The integrated service mode of in-store pick-up and delivery has become common in the post-epidemic period owing to the combined online and offline purchases of perishable products. This study investigates the diverse requirements of in-store pick-up and delivery customers. Then, it establishes a two-echelon location–routing model for a perishable food distribution network to minimize total cost as an objective. An adaptive large neighborhood search (ALNS) algorithm was also developed to solve the foregoing problem. To test the algorithm, instances from those of Solomon are derived. The proposed ALNS algorithm was found to achieve satisfactory performance with respect to speed and accuracy by comparing its results with those of the CPLEX software for a 12-node small-scale instance. The applicability and stability of the ALNS algorithm were further verified using different types of instances with more nodes. Different proportions of in-store pick-up and delivery customers were set, and the total cost of location–routing schemes under these proportions was compared. The results show that an integrated service type compared with the single delivery service mode and single in-store pick-up service mode can save 7.98% and 11.44% of the total cost, respectively.

Recent trends and future directions of congestion management strategies for routing in IoT-based wireless sensor network: a thematic review

Recent trends and future directions of congestion management strategies for routing in IoT-based wireless sensor network: a thematic review

On Maximizing the Probability of Achieving Deadlines in Communication Networks

We consider the problem of meeting deadline constraints in wireless communication networks. Fulfilling deadlines depends heavily on the routing algorithm used. We study this dependence generically for a broad class of routing algorithms. For analyzing the impact of routing decisions on deadline fulfillment, we adopt a stochastic model from operations research to capture the source-to-destination delay distribution and the corresponding probability of successfully delivering data before a given deadline. Based on this model, we propose a decentralized algorithm that operates locally at each node and exchanges information solely with direct neighbors in order to determine the probabilities of achieving deadlines. A modified version of the algorithm also improves routing tables iteratively to progressively increase the deadline achievement probabilities. This modified algorithm is shown to deliver routing tables that maximize the deadline achievement probabilities for all nodes in a given network. We tested the approach by simulation and compared it with routing strategies based on established metrics, specifically the average delay, minimum hop count, and expected transmission count. Our evaluations encompass different channel quality and small-scale fading conditions, as well as various traffic load scenarios. Notably, our solution consistently outperforms the other approaches in all tested scenarios.

Incentive Mechanism for Privacy-Preserving Collaborative Routing Using Secure Multi-Party Computation and Blockchain.

Traffic congestion results from the spatio-temporal imbalance of demand and supply. With the advances in connected technologies, incentive mechanisms for collaborative routing have the potential to provide behavior-consistent solutions to traffic congestion. However, such mechanisms raise privacy concerns due to their information-sharing and execution-validation procedures. This study leverages secure Multi-party Computation (MPC) and blockchain technologies to propose a privacy-preserving incentive mechanism for collaborative routing in a vehicle-to-everything (V2X) context, which consists of a collaborative routing scheme and a route validation scheme. In the collaborative routing scheme, sensitive information is shared through an off-chain MPC protocol for route updating and incentive computation. The incentives are then temporarily frozen in a series of cascading multi-signature wallets in case vehicles behave dishonestly or roadside units (RSUs) are hacked. The route validation scheme requires vehicles to create position proofs at checkpoints along their selected routes with the assistance of witness vehicles using an off-chain threshold signature protocol. RSUs will validate the position proofs, store them on the blockchain, and unfreeze the associated incentives. The privacy and security analysis illustrates the scheme's efficacy. Numerical studies reveal that the proposed incentive mechanism with tuned parameters is both efficient and implementable.

An Improved Shape Annealing Algorithm for the Generation of Coated Deoxyribonucleic Acid Origami Nanostructures

Abstract In recent years, the field of structural DNA nanotechnology has advanced rapidly due to transformative design tools. Although these tools have been revolutionary, they still bear one overall limitation of requiring users to fully conceptualize their designs before designing. Recently, a simple computational casting technique was developed using generative optimization strategies to automate the DNA origami nanostructure design. This approach employs a shape annealing algorithm, which creates a formal language of honeycomb nanostructures with shape grammars and drives designs from the language toward a desired configuration using simulated annealing. This initial demonstration of the approach can generate novel scaffold routing schemes for creating solid or hollow structures constrained by the boundaries of polyhedral meshes. The results from the initial approach, particularly from the hollow structures, reveal a challenging design space. This simple technique generates novel scaffold routing schemes that do not replicate the overall polyhedral mesh shape and are limited in their ability to control scaffold path exploration in the design space. This paper demonstrates an approach for achieving different levels of consistent effective wall thicknesses and improving the quality of mesh coverage for hollow structures that can be tuned and optimized by introducing a more refined computational casting technique. We achieve these improvements through changes in the simulated annealing algorithm by adding a Hustin move set algorithm that dynamically adjusts the performance of the overall design and redefining how these hollow designs are articulated. This work illustrates how the technique can navigate a challenging design space to generate effective hollow designs.

LBDR: A load-balanced deadlock-free routing strategy for chiplet systems

Chiplet integration technology offers enhanced performance and reduced costs by breaking down a large chip into smaller chiplets, which are then interconnected using advanced packaging technology. However, the current chiplet interconnects give priority to correctness concerns rather than load balancing issues. While prioritizing correctness aims to prevent deadlocks, a system that is free of deadlocks can still experience load imbalances that compromise its overall performance. This paper presents the LBDR strategy, which is a load-balanced deadlock-free routing strategy for chiplet systems, and effectively enhances the system performance. Firstly, we develop a novel boundary nodes selection criterion and a heuristic algorithm based node binding mechanism to maintain a balanced load on vertical links for each chiplet’s outbound traffic. Secondly, we select turn restrictions at the boundary nodes to ensure that inbound traffic flows evenly into the chiplet, while maintaining a deadlock-free system. Experiments using the Garnet in gem5 simulator show that this strategy outperforms existing methods in terms of load balancing, throughput, and latency, and is applicable to a variety of traffic scenarios.

An adaptive routing strategy in P2P-based Edge Cloud

P2P-based Edge Cloud (PEC) is widely used in Internet of Things (IoT). Inevitably, the sensor data routing technology has a significant impact on the performance of PEC. Due to its prevalence and complexity, the existing routing technologies in PEC need to be optimized. Specifically, key factors such as overall network traffic, user access latency, and resource utilization of edge nodes should be considered to adapt to the dynamic requirements of user request services and network topology. In order to address the challenges produced by these factors, an adaptive routing in P2P-based Edge Cloud is proposed, which is named ARPEC. In our approach, a target edge node selection scheme based on message activity and network topology is proposed, aiming to minimize the load on edge node and user access latency. Furthermore, to minimize system overhead, sensor data routing is mapped to minimum cost maximum flow (MCMF) graph. On this basis, a target edge node selection algorithm based on a grey linear regression combination prediction model is designed, and an incremental MCMF algorithm based on belief propagation (BP) is proposed. The evaluation results show that our approach can effectively improve PEC transmission performance and user experience.

Efficient routing method for IoT networks using bee colony and hierarchical chain clustering algorithm

Hierarchical chain communication is an efficient technique for saving sensor energy while maintaining load balance and scalability. However, the long delay caused by the formation of long chains is a significant research gap in this paradigm. In this article, a two-level hierarchical chain routing scheme based on the bee colony algorithm called PEG_ABC is proposed to reduce transmission delay and take advantage of the benefits of chain communication. Initially, the remaining sensor energy, Euclidean distance between sensors, and Euclidean distance between sensors and base station are simultaneously adjusted to the fitness function of the bee colony algorithm to select the most suitable chain leader at the first level. Also, in our proposed method at the second level, the collected data in the leader nodes of each level is transferred to the BS through end-to-end path formation between the leaders based on the bee colony algorithm. At this level, the data transmission delay and remaining energy in the leader nodes adjust the fitness function of the bee colony algorithm. Simulation results show the efficiency of the proposed PEG_ABC scheme compared to literature research in terms of energy consumption, network lifetime, number of packets sent to BS, and transmission delay. Also, this article presents a relationship based on extensive simulation results to determine the optimal number of clusters in hierarchical chain communication.

TUBER: Time-aware UAV-based energy-efficient reconfigurable routing scheme for smart wireless livestock sensor network.

This paper is a follow-up to a recent work by the authors on recoverable UAV-based energy-efficient reconfigurable routing (RUBER) scheme for addressing sensor node and route failure issues in smart wireless livestock sensor networks. Time complexity and processing cost issues connected to the RUBER scheme are consequently treated in this article by proffering a time-aware UAV-based energy-efficient reconfigurable routing (TUBER) scheme. TUBER scheme employs a synchronized clustering-with-backup strategy, a minimum-hop neighborhood recovery mechanism, and a redundancy minimization technique. Comparative network performance of TUBER was investigated and evaluated with respect to RUBER and UAV-based energy-efficient reconfigurable routing (UBER) schemes. The metrics adopted for this comparative performance analysis are Cluster Survival Ratio (CSR), Network Stability (NST), Energy Dissipation Ratio (EDR), Network Coverage (COV), Packet Delivery Ratio (PDR), Fault Tolerance Index (FTI), Load Balancing Ratio (LBR), Routing Overhead (ROH), Average Routing Delay (ARD), Failure Detection Ratio (FDR), and Failure Recovery Ratio (FRR). With reference to best-obtained values, TUBER demonstrated improvements of 36.25%, 24.81%, 34.53%, 15.65%, 38.32%, 61.07%, 31.66%, 63.20%, 68.96%, 66.19%, and 78.63% over RUBER and UBER in terms of CSR, NST, EDR, COV, PDR, FTI, LBR, ROH, ARD, FDR, and FRR, respectively. These experimental results confirmed the relative effectiveness of TUBER against the compared routing schemes.

Intelligent data routing strategy based on federated deep reinforcement learning for IOT-enabled wireless sensor networks

The high-speed data routing in the Internet of Things (IoT)-enabled Wireless Sensor Networks (WSNs) is vital in improving network performance. Existing routing protocols face difficulties dealing with frequent node relocations, energy efficiency optimization, scalability, and dynamic network environments. The inadequacy of conventional routing algorithms in handling the issues provided by frequent node relocations, energy efficiency optimization, scalability, and dynamic network settings in IoT-enabled WSNs is identified as the problem. Traditional protocols are inflexible in the face of node relocations, whereas machine learning-based techniques frequently miss energy efficiency factors. Reinforcement learning-based techniques presume static topologies and the potential of federated learning in WSN routing is largely untapped. The goal is to provide adaptive and energy-efficient routing while considering message overhead, temporal complexity, data sum rate, communication delay, and scalability. The research adopts federated deep reinforcement learning (FDRL) to address these limitations for routing the high-speed data packets in IoT-enabled WSNs. The proposed research technique uses FDRL, which enables distributed decision-making and adaptive routing in dynamic network situations. The fundamental instant data load is separated into cluster pairs in the proposed technique, each with one strong and one weak sensor node. This split enables optimal network load balancing and resource use. The FDRL architecture is used to disperse the learning process across numerous nodes or access points, allowing for localized decision-making while taking advantage of global coordination. The simulations are conducted to validate the effectiveness of the FDRL Routing in terms of packet loss, latency, energy efficiency, and scalability. The results show that the proposed federated DRL-based intelligent data routing approach outperforms traditional protocols and other current routing strategies.

An Accuracy-Aware Energy-Efficient Multipath Routing Algorithm for WSNs.

In the fields of industrial production or safety monitoring, wireless sensor networks are often content with unreliable and time-varying channels that are susceptible to interference. Consequently, ensuring both transmission reliability and data accuracy has garnered substantial attention in recent years. Although multipath routing-based schemes can provide transmission reliability for wireless sensor networks, achieving high data accuracy simultaneously remains challenging. To address this issue, an Energy-efficient Multipath Routing algorithm balancing data Accuracy and transmission Reliability (EMRAR) is proposed to balance the reliability and accuracy of data transmission. The multipath routing problem is formulated into a multi-objective programming problem aimed at optimizing both reliability and power consumption while adhering to data accuracy constraints. To obtain the solution of the multi-objective programming, an adaptive artificial immune algorithm is employed, in which the antibody initialization method, antibody incentive calculation method, and immune operation are improved, especially for the multipath routing scheme. Simulation results show that the EMRAR algorithm effectively balances data accuracy and transmission reliability while also saving energy when compared to existing algorithms.