Optimal Routing Path Research Articles

Hybrid Red Deer and Improved Fireworks Optimization Algorithm–based Clustering Protocol for improving network longevity with energy stability in WSNs

SummaryClustering of nodes in wireless sensor networks (WSNs) plays a dominant role in gathering environmental data from the specific area of monitoring over which they are deployed for achieving a reactive decision‐making process. The design and development of an energy‐efficient clustering strategy with a potential cluster head (CH) selection process is a herculean task. This development of the CH selection scheme is referred as a non‐deterministic polynomial (NP) hard problem as it needs to optimize different parameters that influence the selection of potential sensor nodes as CH. It needs to concentrate on the process of enhancing network lifespan with energy efficiency by selecting optimal routing path during data dissemination activity. In this paper, a Hybrid Red Deer and Fireworks Optimization Algorithm (HIRDIFOA)–based energy efficient clustering technique is proposed for extending network lifespan with maximized stability in the network energy. This proposed HRDFOA integrated the exploration capability of Improved Red Deer Optimization (IRDOA) with the maximized exploitation tendency of the Modified Firework Optimization Algorithm (MFWOA) during the CH selection process. It facilitated the CH selection by evaluating the fitness functions that integrate the factors of residual energy (RE), distance between sensor and CH, distance between CH and sink, and radius of communication. It significantly adopted MWFOA for achieving sink node mobility such that data can be reliably routed from CH to sink. The outcomes of HRDFOA confirm better throughput of 19.21% with reduced energy consumption of 17.42% and reduced end‐to‐end delay of 18.52% in contrast to the competitive CH selection schemes used for investigation.

Energy-Efficient Clustering in Wireless Sensor Networks Using Grey Wolf Optimization and Enhanced CSMA/CA.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

A Novel Energy-Efficient Routing Protocol for Industrial WSN Using Hybrid Coot-Ls Algorithm with LSTM-Based Dom Prediction

In industrial Wireless Sensor Networks (WSNs), energy efficiency and reliable data transmission are critical challenges that need to be addressed to ensure sustainable and robust network operations. This paper proposes a novel energy-efficient routing protocol that integrates a Hybrid COOT-LS (Coot- Levy Search) algorithm with Long Short-Term Memory (LSTM)-based Dominant Object Motion (DOM) prediction. The routing protocol leverages the strengths of Hybrid Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to enhance routing efficiency and reduce energy consumption. The Hybrid PSO and ACO algorithms are employed to optimize routing paths by balancing exploration and exploitation, considering multiple factors such as energy levels, node distance, and reliability. The COOT-LS algorithm further refines these paths by incorporating a Levy flight mechanism to enhance the search process. Additionally, the LSTM-based DOM prediction provides accurate forecasts of network conditions, enabling dynamic adjustments to routing strategies in real time. Simulation results demonstrate that the proposed protocol significantly improves network lifetime, reduces energy consumption, and enhances data transmission reliability compared to traditional routing protocols. This approach provides a robust and scalable solution for industrial WSN applications, ensuring efficient and reliable network performance in dynamic and complex industrial environments.

SD-GPSR: A Software-Defined Greedy Perimeter Stateless Routing Method Based on Geographic Location Information

To mitigate the control overhead of Software-Defined Mobile Ad Hoc Networks (SD-MANETs), this paper proposes a novel approach, termed Software-Defined Greedy Perimeter Stateless Routing (SD-GPSR), which integrates geographical location information. SD-GPSR optimizes routing functions by decentralizing them within the data plane of SD-MANET, utilizing the geographic location information of nodes to enhance routing efficiency. The controller is primarily responsible for providing location services and facilitating partial centralized decision-making. Within the data plane, nodes employ an enhanced distance and angle-based greedy forwarding algorithm, denoted as GPSR_DA, to efficiently forward data. Additionally, to address the issue of routing voids in the data plane, we employ the A* algorithm to compute an optimal routing path that circumvents such voids. Finally, we conducted a comparative analysis with several state-of-the-art approaches. The evaluation experiments demonstrate that SD-GPSR significantly reduces the control overhead of the network. Simultaneously, there is a notable improvement in both end-to-end latency and packet loss rate across the network.

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

Hybrid Secure Cluster-Based Routing Algorithm for Enhanced Security and Efficiency in Mobile Ad Hoc Networks

ABSTRACT This research addresses critical gaps in Mobile Ad hoc Networks (MANETs) by proposing a hybrid secure cluster-based routing algorithm, focusing on enhancing network security, robustness, and reliability through multipath routing. Methodologically, the approach integrates Convolutional Neural Networks (CNN) for optimal path routing and Emperor Penguin Optimization (EPO) for clustering, introducing a novel combination for efficient cluster head selection. A novel contribution lies in the development of a prediction technique utilizing a trust assessment algorithm to calculate direct trust ratings at each node, incorporating fuzzy values between zero and one. Trust values are further influenced by node performance, adding a dynamic dimension to the trust evaluation process. Key novelties include the emphasis on energy efficiency, network longevity, remaining energy, security level, bandwidth, and packet delivery ratio as evaluation criteria. The proposed CNN-EPO model demonstrates superior results compared to traditional routing protocols, achieving a remarkable 95% energy efficiency, a heightened security level of 99%, and a throughput reaching up to 8 Mbps. Additionally, the Packet Delivery Ratio (PDR) attains close to 99% and routing overhead remains below 0.5, ensuring efficiency in challenging network scenarios with 50 adversaries. In summary, this research contributes a comprehensive solution to MANET challenges, introducing a novel hybrid routing algorithm, incorporating advanced methodologies for path optimization and clustering. These outcomes highlight how important the suggested strategy is to improve the existing state of the art in MANETs.

EWFAIGF: Design of an Efficient Model for Enhancing Energy Efficiency in IoT-Based Wireless Sensor Networks through Fuzzy AHP and Iterative Grey Wolf Jelly Fish Optimization

In the realm of Wireless Sensor Networks (WSNs), the quest for enhanced energy efficiency remains paramount, given their pivotal role in facilitating the Internet of Things (IoT) applications. Existing methodologies often grapple with the dual challenge of optimizing energy consumption while ensuring robust data transmission, thereby limiting their efficacy in dynamic, resource-constrained environments. This work introduces a novel paradigm that meticulously addresses these constraints, thereby heralding a significant leap towards optimizing WSNs' operational efficiency. At the core of our proposed model lies the integration of Fuzzy Analytic Hierarchy Process (Fuzzy AHP)-based clustering, which ingeniously segregates nodes by leveraging multi-criteria such as location, energy efficiency, and temporal performance. This clustering serves as a precursor to the application of an innovative Iterative Grey Wolf Jelly Fish Optimizer (GWJFO). The GWJFO stands out by its strategic prowess in delineating optimal routing paths, thus minimizing energy expenditure and enhancing data relay efficiency. Furthermore, our model is fortified with a Q Learning Method, ingeniously designed to identify and execute optimal alternate routing paths through a Make Before Break strategy. This addition not only mitigates potential faults but also significantly boosts computational efficiency and packet delivery performance. Empirical validation through real-time network simulations underscored the model's superiority, demonstrating a 9.5% improvement in communication speed, an 8.5% increase in energy efficiency, a 4.5% improvement in packet delivery performance under fault conditions, a 10.4% rise in throughput, and a 5.9% enhancement in network consistency over existing benchmarks. This groundbreaking work not only paves the way for more energy-efficient WSNs but also sets a new standard in achieving high-performance metrics essential for the next generation of IoT applications. The implications of these advancements extend beyond mere technical enhancements, offering a beacon for future research in the domain, potentially revolutionizing the way we deploy and manage sensor networks in an array of applications.

Advanced Authentication and Energy-Efficient Routing Protocol for Wireless Body Area Networks

Recently, wireless body area network (WBAN) becomes a hot research topic in the advanced healthcare system. The WBAN plays a vital role in monitoring the physiological parameters of the human body with sensors. The sensors are small in size, and it has a small-sized battery with limited life. Hence, the energy is limited in the multi-hop routing process. The patient data is collected by the sensor, and the data are transmitted with high energy consumption. It causes failure in the data transmission path. To avoid this, the data transmission process should be optimized. This paper presents an advanced authentication and energy-efficient routing protocol (AAERP) for optimal routing paths in WBAN. Patients’ data are aggregated from the WBAN through the IoMT devices in the initial stage. To secure the patient’s private data, a hybrid mechanism of the elliptic curve cryptosystem (ECC) and Paillier cryptosystem is proposed for the data encryption process. Data security is improved by authenticating the data before transmission using an encryption algorithm. Before the routing process, the data encryption approach converts the original plain text data into ciphertext data. This encryption approach assists in avoiding intrusions in the network system. The encrypted data are optimally routed with the help of the teamwork optimization algorithm (TOA) approach. The optimal path selection using this optimization technique improves the effectiveness and robustness of the system. The experimental setup is performed by using Python software. The efficacy of the proposed model is evaluated by solving parameters like network lifetime, network throughput, residual energy, success rate, number of packets received, number of packets sent, and number of packets dropped. The performance of the proposed model is measured by comparing the obtained results with several existing models.

Cosine deep convolutional neural network for Parkinson’s disease detection and severity level classification using hand drawing spiral image in IoT platform

In recent years, the Internet of Things (IoT) is an emerging technology that plays a vital role in the early detection and diagnosis of Parkinson’s disease-affected patients using various healthcare monitoring systems at any time. The utilization of IoT in real-time by monitoring Parkinson’s disease-affected patients in-home care to enhance the quality of life. Thus in this research, a hybrid deep learning classifier Cosine Deep Convolutional Neural network (CosineDCNN) is developed for early detection and classification of Parkinson’s disease. Initially, routing is performed using the designed Sine Cosine Geese Migration Optimization (SCGA) to determine the optimal routing path to transmit the hand-drawing spiral images to the base station. At the base station, the hand-drawing spiral images are pre-processed, and augmented, and the features are extracted from them for Parkinson’s disease detection and severity classification using CosineDCNN. The results obtained from the experiment proved that the CosineDCNN achieved superior performance with 89.98 % accuracy, 87.41 % Positive Predictive Value (PPV), 89.77 % True Negative Rate (TNR), 87.82 % Negative Predictive Value (NPV), and 89.84 % True Positive Rate (TPR) as compared with other techniques. Moreover, the routing performance of the SCGA model is also computed, which recorded a minimum of 0.283mS delay and a maximum of 0.507 J energy.

HOEEACR: Hybrid Optimized Energy-Efficient Adaptive Clustered Routing for WSN

Wireless Sensor Networks have been used for sensing and gathering data about an environment from a remote location for many years in a variety of engineering applications. In WSN, nodes must overcome energy consumption to function efficiently. To resolve these issues and extend the usefulness of the network, clustering and routing algorithms are promising. One of the main issues with WSNs is that they lack restricted energy sources. To overcome this issue, a Hybrid Optimized Energy-Efficient Adaptive Clustered Routing approach (HOEEACR) has been proposed for WSNs. This paper presents the Genetic Bee Colony (GBC) Algorithm for Cluster Head Selection by considering distances to neighbors, residual energy, node degrees, and node centralities. Furthermore, an optimal routing path for the cluster heads is found by utilizing the Aquila with African Vulture Optimization (AAVO) algorithm. The AAVO optimizes network performance using residual energy, node degree, and distance. The proposed HOEEACR method has been extensively tested to ensure network lifetime and energy efficiency. According to experimental results, the proposed HOEEACR method consistently outperforms existing techniques on a variety of performance metrics.

Multiobjective-energy centric honey badger optimization based routing for wireless body area network

Wireless Body Area Network (WBAN) is an interconnection of tiny biosensors that are organized in/on several parts of the body. The developed WBAN is used to sense and transmit health-related data over the wireless medium. Energy efficiency is the primary challenges for increasing the life expectancy of the network. To address the issue of energy efficiency, one of the essential approaches i.e., the selection of an appropriate relay node is modelled as an optimization problem. In this paper, energy efficient routing optimization using Multiobjective-Energy Centric Honey Badger Optimization (M-ECHBA) is proposed to improve life expectancy. The proposed M-ECHBA is optimized by using the energy, distance, delay and node degree. Moreover, the Time Division Multiple Access (TDMA) is used to perform the node scheduling at transmission. Therefore, the M-ECHBA method is used to discover the optimal routing path for enhancing energy efficiency while minimizing the transmission delay of WBAN. The performances of the M-ECHBA are analyzed using life expectancy, dead nodes, residual energy, delay, packets received by the Base Station (BS), Packet Loss Ratio (PLR) and routing overhead. The M-ECHBA is evaluated with some classical approaches namely SIMPLE, ATTEMPT and RE-ATTEMPT. Further, this M-ECHBA is compared with the existing routing approach Novel Energy Efficient hybrid Meta-heuristic Approach (NEEMA), hybrid Particle Swarm Optimization-Simulated Annealing (hPSO-SA), Energy Balanced Routing (EBR), Threshold-based Energy-Efficient Routing Protocol for physiological Critical Data Transmission (T-EERPDCT), Clustering and Cooperative Routing Protocol (CCRP), Intelligent-Routing Algorithm for WBANs namely I-RAW, distributed energy-efficient two-hop-based clustering and routing namely DECR and Modified Power Line System (M-POLC). The dead nodes of M-ECHBA for scenario 3 at 8000 rounds are 4 which is less when compared to the dead nodes of EBR.

Reliable paths prediction with intelligent data plane monitoring enabled reinforcement learning in SD-IoT

Legacy routing with conventional distributed networking in the Internet-of-Things (IoT) networks needs to work on efficiently handling service requests, leading to compromised Quality of Service (QoS) for user demands. In this research, we introduce Reliable routing based on Reinforcement learning in SD-IoT Networks (RRSN), an intelligent routing technique leveraging Software-Defined Networking (SDN) in IoT networks. RRSN utilizes a hybrid mechanism that combines Machine Learning (ML) and Reinforcement Learning (RL) within SDN’s centralized control to optimize QoS-aware reliable routing. In addition, network telemetry is utilized for intelligent network monitoring in the data plane to get the underlying network’s status efficiently. The proposed approach consists of three main modules: a Support Vector Regression (SVR) machine learning algorithm to predict link reliability, a traffic classifier for QoS-based traffic classification, and a reliability-aware reinforcement learning algorithm to compute optimal routing paths for IoT sensors. SVR algorithm has a high prediction accuracy, is lightweight, less susceptible to noisy data, and its computational complexity is independent of the dimensions of the input space. RRSN leverages intelligent network monitoring with RL, global view, and centralized SDN control to compute and implement optimal routes in data plane switches efficiently. We conduct extensive Proof-Of-Concept (POC) experimental evaluations, comparing RRSN with state-of-the-art models, including POC experiments in real Internet topologies. The results demonstrate that RRSN outperforms existing schemes in network performance metrics such as throughput, jitter, packet loss ratio, and delay, showcasing its effectiveness in improved QoS for IoT networks.

Reliable data transmission for a VANET-IoIT architecture: A DNN approach

The challenges and resilience of vehicular ad hoc network (VANET) and deep neural network (DNN) hybrid architectures in terms of reliability in smart cities have attracted much global interest stemming from the rollout of the next generation of intelligent networks. In this paper, we propose a novel distributed DNN (D-DNN) scheme with blockchain to support the Internet of Intelligent Things (IoIT) infrastructure in the VANET environment of the future. In particular, because the communication links between edge nodes are very unstable in VANETs, a new neuro-fuzzy server that serves the dual roles of finding reliable links between edge nodes and performing optimal routing path selection is proposed. Next, a blockchain layer is employed at the edge nodes, which are initially scrutinized before establishing communication links to ensure reliability during data transfer. Then, the proposed D-DNN (PD-DNN) scheme is applied to enhance the performance of the VANET-IoIT architecture by improving the data flow and convergence rate and mitigating erratic variations in output. To address reliability concerns, the coverage probability (CP) metric is investigated as a measure of network connectivity. Furthermore, we present an analysis of the PD-DNN scheme in comparison with the traditional DNN (T-DNN) scheme. Finally, simulation results for VANET-IoIT scenarios show that, subject to data protection and privacy constraints, the CP values corresponding to different communication links are improved to a greater extent under our scheme than under the traditional scheme, demonstrating the feasibility of the proposed scheme.

A Statistical Analysis of Wireless Body Area Network using Clustering Based Routing Protocols

Recently, research on wireless body-area sensor networks (WBASN) or wireless body area networks (WBANs) has gained great importance in medical applications, and now plays an important role in patient monitoring. Clustering is the most important task in wireless sensor networks (WSNs) by data aggregation through each cluster head (CH). The development of technology has seen comfort in the domestic and professional life of an individual. However, such survival was not able to meet medical emergencies during the pandemic COVID-19 and other health surveillance scenarios. As a result, it is important to design an energy-efficient routing system for WBAN. Existing routing algorithms focus more on energy efficiency than security. A Secure Optimal Path-Routing (SOPR) protocol is proposed to identify and discover routes in WBAN that are secure against black-hole attacks. In this paper the sensor's wireless capability has been tested with a simulator to detect and transmit signals across a wide range of frequencies and to ensure that data is transferred successfully. CARF reduces the maximum work done by relay nodes in case of overloaded nodes and thereby increases the energy distribution in the network. The proposed CARF design reduces packet loss and time delay, thereby increasing network lifetime compared to other fuzzy and heuristic methods used for routing in WSNs. The proposed algorithm maintains more than 75% of the residual energy in the network at a minimum initial energy level of 1 J to enable data syncing. A SAC-TA approach for data aggregation in WSNs is presented. False injection attacks are identified based on traffic analysis at the time of the route discovery process. One-time key generation algorithm is introduced to eliminate malicious nodes from the network. The presented paper proposes a congestion-free routing framework for data transmission from source to sink nodes in WSNs. Congestion-aware routing mechanisms modeled using fuzzy sets (CARF) include operations such as establishing non-localized nodes, by which more paths can be created for traffic distribution, and predicting an optimal congestion.

Energy Efficient Routing in Wireless Mesh Networks using Multi-Objective Dwarf Mongoose Optimization Algorithm

Wireless Mesh Networks (WMNs) are part of wireless technologies that are known for their flexibility and extended coverage. Wireless applications have reached their peak in applications related to various fields such as healthcare, image processing, and so on. However, delay and energy efficiency are considered the two aspects that diminish the performance of WMNs. To overcome the aforementioned issues, this research introduces an effective routing method using Multi-Objective Dwarf Mongoose Optimization Algorithm (MO-DMOA). The MO-DMOA performs routing by considering the multiple paths using an enriched population resource. The nomadic behaviour of MO-DMOA helps in detecting the optimal routing path with minimized over-exploitation. The proposed MO-DMOA is evaluated with different routing schemes such as Load Balance and Interference Avoid-Partially Overlapped Channels Assignment (LBIA-POCA) framework, and Multi-Objective Dyna Q-based Routing (MODQR). The outcomes obtained through the experimental analysis show that the proposed approach acquires a better throughput of 13.5×105kbps for 22 flows, whereas the existing LBIA-POCA achieves a throughput 60× 103 kbps

Energy Efficient Routing in Wireless Mesh Networks using Multi-Objective Dwarf Mongoose Optimization Algorithm

Wireless Mesh Networks (WMNs) are part of wireless technologies that are known for their flexibility and extended coverage. Wireless applications have reached their peak in applications related to various fields such as healthcare, image processing, and so on. However, delay and energy efficiency are considered the two aspects that diminish the performance of WMNs. To overcome the aforementioned issues, this research introduces an effective routing method using Multi-Objective Dwarf Mongoose Optimization Algorithm (MO-DMOA). The MO-DMOA performs routing by considering the multiple paths using an enriched population resource. The nomadic behaviour of MO-DMOA helps in detecting the optimal routing path with minimized over-exploitation. The proposed MO-DMOA is evaluated with different routing schemes such as Load Balance and Interference Avoid-Partially Overlapped Channels Assignment (LBIA-POCA) framework, and Multi-Objective Dyna Q-based Routing (MODQR). The outcomes obtained through the experimental analysis show that the proposed approach acquires a better throughput of 13.5×105kbps for 22 flows, whereas the existing LBIA-POCA achieves a throughput 60× 103 kbps

An Optimized Routing Scheme in SDN-Based IoT Using the Lion Optimization Algorithm

The Internet of Things (IoT) speaks to the current and future state of the Internet. Numerous things (objects) related to the Internet generate a large amount of information that requires a ton of labor and prepare tasks to move to valuable data. In addition, for the association and control of this enormous information, the original ideas in the project and the executives of the IoT system need to expedite and improve its presentation. A software-defined networking (SDN) system is another worldview that has recently emerged to cover all the complexities in the traditional system architecture by summarizing all the controls and management functions from the basic devices (things in IoT). The open-flow protocol is used to make messages between switches and controllers. It is a correspondence conference, through which the controller integrates the path of the network pocket with switches. Therefore, data packets should be directed to the optimum path to improve system operation. The best or optimal path for packet routing in the SDN data plane is found using the Lion Optimization Algorithm (LOA), which is proposed in this study. Response times and packet loss rates can both be decreased by choosing the best route between the hosts. The suggested LOA outperforms current optimal path detection methods in terms of performance, delay, packet delivery rate, performance, and pocket loss rate, according to simulation findings.

A new intelligent cross-domain routing method in SDN based on a proposed multiagent reinforcement learning algorithm

PurposeA cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.Design/methodology/approachFirst, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.FindingsExperimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.Originality/valueMessage transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

A Hyper Heuristic Algorithm for Efficient Resource Allocation in 5G Mobile Edge Clouds

Emergence of intelligent devices and mobile edge clouds (MECs) in 5G networks has exponentially increased the number of applications that demand low latency services. However, their resource heterogeneity, limited computing power and storage including congestion in the ultra-dense 5G network, make the real-time services challenging. Existing works are limited either by addressing application delay requirements or computational load balancing. This paper develops an efficient resource allocation framework for selecting optimal servers and routing paths in the 5G MEC network by jointly optimizing latency, computational, and network load variances. First, we formulate the above multi-objective problem as a mixed-integer non-linear programming problem. Further, we adopt a hyper-heuristic (AWSH) algorithm by leveraging the combined powers of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> nt Colony, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</b> hale, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> ine-Cosine, and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> enry Gas Solubility Optimization algorithms. The proposed AWSH algorithm works at the higher level, and it explores and exploits one of the three lower-level heuristics in each iteration to efficiently capture the dynamically varying environmental parameters and thereby address the resource allocation problem. Their collaborative effort helps to achieve a global optimum in allocating resources of 5G MEC network. Simulation results prove the superiority of the AWSH algorithm compared to state-of-the-art solutions in terms of service latency, successful offloading ratio, and load balancing.

Whale optimized routing path selection and 128 bit secured key management for maritime safety

The critical aspect in advancing global trade lies in ensuring security across marine trading systems. This research significantly contributes to monitoring ships by assessing operational performance parameters. These parameters are sensed through ship-attached sensors, and further transmitted to the cloud via an Internet of Things (IoT) Module. To optimize the efficiency of this data transfer to the cloud, the proposed work employs a Whale Optimization Algorithm-based Radial Basis Function Neural Network (WOA-RBFNN). The utilization of this algorithm enhances the overall effectiveness of the system. Furthermore, to address security concerns, a 128-bit Two Fish Algorithm-based key management is introduced, providing enhanced data protection. This dual-focus approach not only optimizes the efficiency of data transmission but also fortifies the system against potential cyber threats. These enhancements collectively contribute to the successful and secure transmission of information to the cloud advancing the security and efficiency of marine trading systems.