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Loop-free routing approaches based on distance information combine a Local Ordering Condition (LOC) with a distributed reordering computation (DRC). The  LOC  allows routers to determine whether they can  independently change next hops to destinations without creating a routing loop, and the DRC permits routers re-establish their ordering relative to a destination when the LOC is not satisfied.  A new type of DRC called Spreading Computations  is  shown to be  inherently more efficient than  DRCs used in existing  loop-free routing protocols.  A new  routing algorithm based on spreading computations  is presented and  compared with OSPF, DSDV, and RIPv2 using the ns-3 simulator. The results show that using spreading computations results in faster convergence speeds  after link or router recoveries or failures.

Energy conservation and optimized traffic delay are crucial challenges in Internet of Things (IoT) systems, particularly in wireless sensor networks (WSNs). This study presents a novel approach to address these issues through a fuzzy‐based routing protocol. Traditional methods often struggle to effectively enhance routing protocols with optimized packet size control. Researchers have proposed a combination of machine learning and evolutionary techniques to overcome this limitation and enhance energy efficiency in WSNs. The new approach called MOSPFNN (Multi‐Objective Spider Prey‐localized Fuzzy Neural Network) leverages fuzzy logic control (FLC) to select optimal paths for traffic‐aware multipath routing. Additionally, a new meta‐heuristic algorithm of SP optimizer is employed to develop an energy‐efficient spatial routing protocol with superior convergence and minimal local optima. The proposed protocol incorporates optimal FNN for congestion monitoring and traffic‐aware routing. Simulation results validate that new protocol outperforms existing methods in terms of average end‐to‐end delay and packet delivery ratio (PDR). Using MOSPFNN, traffic with different priority levels can achieve a successful PDR rate of over 92% and network lifetime of above 2% than previous network within a reasonable timeframe. This research contributes to advancing energy‐efficient and traffic delay‐aware routing protocols in IoT systems. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are cutting-edge technology that can be used in many fields requiring critical information. However, limited resource constraints, contextual connectivity, and lifecycle requirements drive designers to seek more efficient WSN infrastructures. Unbalanced energy utilization of sensor hubs during information steering in Wireless Sensor Networks (WSN). Accordingly, one of the principal configuration difficulties of remote sensor networks is to limit the energy utilization of sensor hubs. Therefore, many routing schemes are designed to efficiently utilize sensor nodes' limited Energy. These schemes generally use low-power paths to transmit data. It turns out that using the same path is suboptimal given the network lifetime. To overcome this problem, a new method is introduced that uses a Multi-Route Clustering Protocol Using Timeslot Transmission (MRCP-TTDPS) with dynamic path selection. It consumes Energy on path selection. In the first step, multipath routing is configured to detect path quality, so this method uses Multipath Optimized Routing (MPRS) to send high-quality path data. The second stage uses Cluster-Based Optimal Path Selection (CORS) to establish the best energy path. The third stage develops energy consumption models. The amount of the hubs with the most noteworthy leftover Energy is chosen as the group top of each round. Every standard hub partakes in a group framed by neigh exhausting bunch heads. Every sensor hub sends identified information to the bunch head in each round. The group head sends the data to the base station. The results show that the model is better than the path model regarding path identifier energy utilization and path quality. [ABSTRACT FROM AUTHOR]

With the emergence of new applications in mobile networks, users demand higher network stability and lower data transmission delays. When the network address of a mobile user changes, the data transmission path in the wired network may need to be switched to maintain service continuity. Traditional mobility support methods typically rely on a single switching path for all mobile data flows. However, if this path cannot meet the requirements of all the flows, it may lead to network congestion or a decline in user experience. To overcome this limitation, this paper proposes a mobility handover decision method based on multi-topology. It enables the dynamic allocation of mobile data flows across different switching paths within multiple logical topologies. The method models a multi-topology selection problem aimed at minimizing average packet transmission delay and packet loss rate, while considering network conditions and the Quality of Service (QoS) requirements for each flow. By solving the dual problem of the original optimization, a near-optimal solution is achieved. The proposed scheme and algorithm were implemented and tested using the Mininet network simulator. Results show that the proposed approach achieves low average packet transmission delay, low average packet loss rate, and high throughput, compared to traditional single-path switching methods and existing multipath routing methods. [ABSTRACT FROM AUTHOR]

Recently, with the development of nanotechnology and the emergence of new materials, Wireless Nanosensor Networks (WNSNs) have been presented. To address the problems of link instability in WNSNs and poor adaptability of the EBCNF framework to network topology changes, a reliable multi-path routing based on link stability called RMRLS is proposed. Based on the EBCNF framework, this protocol introduces a path similarity judgment model to judge the similarity between paths. In the route establishment process, the link stability evaluation model is used to measure the stability of each path and as a criterion for path selection. Then alternative route is selected through the routing similarity judgment model. Optimal path and backup path are two routing paths to cope with changes in the network topology. The simulation results show that the RMRLS protocol has advantages in energy efficiency, data packet transmission success rate and average throughput, which are 28.84% lower, 53.8% higher, and more than double compared to EBCNF at a distance of 10 mm, respectively. These findings proved the RMRLS protocol's capacity to significantly boost the stability and reliability of the network. [ABSTRACT FROM AUTHOR]

This article is devoted to enhancing the mathematical models for traffic engineering routing and load balancing to ensure differentiated quality of service for packet flows with varying priorities in communication networks. The research focuses on formulating updated load-balancing conditions considering packet-flow priorities and conducting a comparative analysis of different models to identify the most effective solution. The study introduces a multipath routing model based on the type of service and presents numerical research to evaluate the proposed solutions in varying network conditions. The findings allowed us to select the model that offers the most precise regulation of service differentiation for different priority flows by optimizing the load-balancing strategy. This model directs higher-priority packet flows through less heavily loaded links and routes, while lower-priority flows are sent through more heavily loaded paths. Moreover, the routes for higher-priority flows are designed to have fewer hops than those for lower-priority flows. This approach improves network resource utilization and simplifies the architecture for delivering differentiated quality of service, potentially reducing the need for the manual configuration of congestion management mechanisms. The study concludes that the proposed enhanced model is the most effective for practical implementation in modern networks, offering a robust solution for managing diverse priority traffic. [ABSTRACT FROM AUTHOR]

The expansion of megaconstellation networks (MCNs) represents a promising solution for achieving global Internet coverage. To meet the growing demand for satellite services, multipath routing allows the simultaneous establishment of multiple transmission paths, enabling the transmission of flows in parallel. Nevertheless, the mobility of satellites and time-varying link states presents a challenge for the discovery of optimal paths and traffic scheduling in multipath routing. Given the inflexibility of traditional static deep reinforcement learning (DRL)-based routing algorithms in dealing with time-varying constellation topologies, DRL-based multipath routing (DMR) enabled by a graph neural network (GNN) is proposed as a means of enhancing the transmission performance of MCNs. DMR decouples the stochastic optimization problem of multipath routing under traffic and bandwidth constraints into two subproblems: multipath routing discovery and multipath traffic scheduling. Firstly, the minimum hop count-based multipath route discovery algorithm (MHMRD) is proposed for the computation of multiple available paths between all source and destination nodes. Secondly, the GNN-based multipath traffic scheduling scheme (GMTS) is proposed as a means of dynamically scheduling the traffic on each available path for each data stream, based on the state information of ISLs and traffic demand. Simulation results demonstrate that the proposed scheme can be scaled to constellations with different configurations without the necessity for repeated training and enhance the throughput, completion ratio, and delay by 42.64%, 17.39%, and 3.66% in comparison with the shortest path first algorithm (SPF), respectively. [ABSTRACT FROM AUTHOR]

Currently, distributed routing protocols are constrained by offering a single path between any pair of nodes, thereby limiting the potential throughput and overall network performance. This approach not only restricts the flow of data but also makes the network susceptible to failures in case the primary path is disrupted. In contrast, routing protocols that leverage multiple paths within the network offer a more resilient and efficient solution. Multipath routing, as a fundamental concept, surpasses the limitations of traditional shortest path first protocols. It not only redirects traffic to unused resources, effectively mitigating network congestion, but also ensures load balancing across the network. This optimization significantly improves network utilization and boosts the overall performance, making it a widely recognized efficient method for enhancing network reliability. To further strengthen network resilience against failures, we introduce a routing scheme known as Multiple Nodes with at least Two Choices (MNTC). This innovative approach aims to significantly enhance network availability by providing each node with at least two routing choices. By doing so, it not only reduces the dependency on a single path but also creates redundant paths that can be utilized in case of failures, thereby enhancing the overall resilience of the network. To ensure the optimal placement of nodes, we propose three incremental deployment algorithms. These algorithms carefully select the most suitable set of nodes for deployment, taking into account various factors such as node connectivity, traffic patterns, and network topology. By deploying MNTC on a carefully chosen set of nodes, we can significantly enhance network reliability without the need for a complete overhaul of the existing infrastructure. We have conducted extensive evaluations of MNTC in diverse topological spaces, demonstrating its effectiveness in maintaining high network availability with minimal path stretch. The results are impressive, showing that even when implemented on just 60% of nodes, our incremental deployment method significantly boosts network availability. This underscores the potential of MNTC in enhancing network resilience and performance, making it a viable solution for modern networks facing increasing demands and complexities. The algorithms OSPF, TBFH, DC and LFC perform fast rerouting based on strict conditions, while MNTC is not restricted by these conditions. In five real network topologies, the average network availability of MNTC is improved by 14.68%, 6.28%, 4.76% and 2.84%, respectively, compared with OSPF, TBFH, DC and LFC. [ABSTRACT FROM AUTHOR]

Elastic optical networks (EON) are recognized as a promising approach to handle the increasing demands of Internet traffic in future optical networks, as they enable the efficient provisioning of connections with varying bandwidth requirements. EON's flexibility and efficiency are enhanced by aspects such as routing, modulation, and spectrum allocation. In this paper, routing and adaptive modulation are taken into account where a strategy for combining multipath routing with distance adaptive modulation format in EON based on a meta-heuristic approach called the Genetic algorithm (GA) has been proposed. Moreover, the proposed scheme enhances the EON flexibility while considerably reducing blocking probability. Further, to compare the effectiveness of multipath routing with GA and single-path routing with respect to blocking probability, two networks, NSFNET and DT14, are used in the simulation. The suggested approach outperforms other algorithms when used with distance adaptive modulation, providing the most optimal performance. This enables the system to perform better, enabling adaptability and flexibility while reducing the blocking probability. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a scenario in which protected and unprotected services coexist in an elastic optical network under dynamic traffic. In the investigated scenario, unprotected services can reuse the reserved idle bandwidth to provide protection to the protected services. Under this scenario, we propose a new heuristic algorithm that enables such reuse as well as define and introduce a new assignment problem in elastic optical networks, named a Transmission Spectrum Assignment (T-SA) problem. In this paper, we consider a scenario in which services may be routed using the multipath routing approach. Additionally, protection using bandwidth squeezing is also considered. We assess our proposal through simulations on three different network topologies and compare our proposal against the classical protection approach, in which bandwidth reuse is not allowed. For the simulated range of network loads, the maximum (minimum) blocking probability reduction obtained by our proposal is approximately 48% (10%) in the European topology, 46% (7%) in the NSFNET topology, and 32% (6%) in the German topology. [ABSTRACT FROM AUTHOR]

Summary: IoT‐assisted WSN contains various nodes, which are placed on a huge scale that increases complications. Thus, the challenges and issues of these networks fluctuate as compared to WSNs. Hence, sensor nodes are imperative unit that runs on less energy resources. Hence, devising a robust and energy‐effective protocol for increasing network lifetime is a complex task. This paper devises a novel hybrid optimization‐driven approach for selecting cluster head (CH) in IoT‐assisted WSNs. Initially, the simulation of IoT nodes is done by configuration. Thereafter, the Cluster Head selection is done using a newly devised optimization technique, namely the Taylor‐Political optimizer (Taylor‐PO). Thus, fitness is newly developed by adapting certain attributes like energy, delay, inter and intra‐cluster distance, Link Lifetime (LLT), predicted energy, and delay. Here, the multipath routing is accomplished using Tunicate Swarm gray wolf optimization (TSGWO). Thus, the proposed Taylor‐PO is offered for effective Cluster Head selection along with multipath routing using TSGWO. The proposed Taylor‐PO offered improved performance with smallest delay of 0.006 sec, highest energy of 2.368 J, highest throughput of 494.043 kbps. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) plays a vital role in the world of Internet and integrates computing devices. The digital machines provide unique identifiers. The capacity to send data through the network using a better path is one of the capabilities of digital machines. IoT generates huge data based on fast aggregation data and processes these data efficiently. In this paper, a multi-objective hybrid woodpecker and flamingo search optimization algorithm is proposed for finding optimum Cluster Head (CH)-based energy-aware routing protocol in IoTs environment (Hyb-WFSOA-CHS-IoT). Here, the proposed Hyb-WFSOA-CHS-IoT model executes routing method through CH. The woodpecker and flamingo search optimization algorithm is used as the intelligent CH selection that consumes same energy as sensors. Hybrid woodpecker and flamingo search optimization algorithm (Hyb-WFSOA) is examined by using fitness functions, like distance, delay, energy consumption, throughput. The proposed Hyb-WFSOA-CHS-IoT method is activated in MATLAB software. Then, the performance of the proposed system is examined with different metrics, like delay, packet delivery ratio, throughput, network lifetime, energy consumption. Therefore, the proposed method attains lower delay 28.38%, 32.34% and 47.45%, higher delivery ratio 19.34%, 23.12% and 18.96% and lower energy consumption 11.25%, 7.90% and 12.88% compared with existing methods, like multi-objective fractional gravitational search algorithm for energy-efficient routing on IoT (FGSA-CHS-IoT), multi-objective sunflower-based gray wolf optimization algorithm for multiple path routing on IoT network (SFG-CHS-IoT) and energy-aware routing in the IoT with improved grasshopper metaheuristic algorithm with chaos theory and fuzzy Logic (FLGOA-CHS-IoT). [ABSTRACT FROM AUTHOR]

A Wireless Sensor Network (WSN) is a system of sensor nodes that communicate with one another and with their surroundings. Wireless sensor nodes are now used as the foundation for the higher-level industrial internet of things applications. Any type of real-time Internet of Things (IoT) application, as long as the data is collected utilizing the device's sensors. WSN are severely limited in terms of energy. Routing is one of the most challenging components of WSNs. Unbalanced energy utilization during data packet routing operations is one of the most serious difficulties with WSNs. This issue must be considered because the energy levels of sensor nodes are limited. We used WSNs multipath routing to save energy, improve scalability, and balance load. Many investigations are being undertaken to increase sensor node energy efficiency and network lifetime. More attention must be devoted to the clustering and routing components of communication in order to conserve energy. Clustering, also known as node clustering is a network organizing approach which helps with energy efficiency, workload distribution, data collection, and a connected hierarchy. The network is made up of numerous sensor nodes that must connect with one another in order for data to reach its goal, and when messages are not sent to intermediary nodes, problems arise. With multipath routing, numerous paths between the source and destination can be created, improving the possibility of data reaching the target and providing additional copies via alternate paths. The proposed routing solution uses less energy and extends the life of the network while compared with well-known approaches i.e., LEACH, ECPF, CHEF, UCR, DFLC, ACAWT, and Gupta. According to the simulation results and overall analysis, the proposed technique is much better than all the previous approaches under different number of nodes and simulation time in terms of end-to-end time reduced by 17.09% to 68.13%, energy consumption reduced by 13.23% to 29.12%, packet loss rate declined by 16.33% to 82.38% and drop rate of alive sensor nodes are much better shows high network resilience.

The Internet of Things (IoT) is an emerging technology that has recently gained significant interest, especially with the dramatic increase in connected devices. However, IoT networks are not yet standardized, and the design of such networks faces many challenges, including scalability, flexibility, reliability, and availability of such networks. Routing is among the significant problems facing IoT network design because of the dramatic increase in connected devices and the network requirements regarding availability, reliability, latency, and flexibility. To this end, this work investigates deploying a multipath routing scheme for dense IoT networks. The proposed method selects a group of routes from all available routes to forward data at a maximum rate. The choice of data transmission routes is a complex problem for which numerical optimization methods can be used. A novel method for selecting the optimum group of routes and coefficients of traffic distribution along them is proposed. The proposed method is implemented using dynamic programming. The proposed method outperforms the traditional route selection methods, e.g., random route selection, especially for dense IoT networks. The model significantly reduced the number of intermediate nodes involved in routing paths over dense IoT networks by 34%. Moreover, it effectively demonstrated a significant decrease of 52% in communication overhead and 40% in data delivery time in dense IoT networks compared to traditional models. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) is a network of smart things that connects people and objects for gathering and exchanging information by means of embedded sensor. IoT‐based wireless sensor network (WSN) nodes are monitored as a resource parameter at the variety of methods, including resource calculation, energy resource, and storage resource, with multipath routing protocols necessary to enable extended network lifespan and achieve high energy utilization. The existing routing protocols did not provide optimal path from the multipath route. Hence, in this manuscript, a Hybrid Marine Predators and Border Collie Optimization algorithm is proposed for multipath routing in IoTs. The main aim of this endeavor is to "increase network lifetime." IoT is simulated initially and multipath routing initiates in internet of things network. Multipath routing is achieved by proposed Hyb‐MP‐BCOA; this is the combination of Hybrid Marine Predators Algorithm and Border Collie Optimization algorithm. The proposed Hyb‐MP‐BCOA algorithm chooses the optimum path from the multiple path obtainable for routing, depending upon fitness parameter, like context awareness, network lifetime, residual energy, trust, and delay. Based on the fitness parameter, the optimal path is selected by using Hyb‐MP‐BCOA algorithm. Finally, the proposed Hyb‐MP‐BCOA‐MR‐IoT model is applied in MATLAB, and efficacy of proposed technique is estimated by using various performance metrics, like drop, normalized network energy, lifetime of network, delay, throughput, energy consumed, packet delivery ratio, and number of sensor nodes versus aggregation level. The proposed method attains lower delay of 14.285%, 50%, 53.846%, and 57.142%, lower packet drop of 14.285%, 50%, 53.846%, and 57.142%, lower energy consumption compared with existing methods such as multiobjective sun flower optimization with gray wolf optimizer (GWO) for multipath routing in IoT (SFO‐GWO‐MR‐IoT), multiobjective Whale Lion Fireworks optimization algorithm and fitness function for multipath routing in IoT (WLFA‐MR‐IoT), multipath routing in IoT using shuffled frog leaping algorithm (SFLA‐MR‐IoT), and multipath routing in IoT using hybrid Whale Optimization Algorithm‐Moth Flame Optimization (MFO)(hyb‐WOA‐MFO‐MR‐IoT). Finally, the safe communication is attained in the IoT network. [ABSTRACT FROM AUTHOR]

Wireless acoustic sensor networks (WASN) usually demand uninterrupted and reliable data transmissions and an efficient path from the source nodes to the destination nodes, thus ensuring reliable delivery of sensitive or critical data with the use of multipath routing protocols. This paper presents a novel agent-drivenWASN relying on a set of static and mobile agents. In the proposed technique, the location address is functioned into the operating node and the destination node. Calculations of the midpoint between the operating and the destination nodes are followed by identifying the shortest path based on a reference axis, using a mobile agent and a location address. Such an approach aims to ensure maximum utilization of the communicating paths. Based on the information concerning partial topology of the network, the destination node computes the weight factor and multiple paths using node distances, energy ratios and efficiencies of specific links. Then, the destination node selects the appropriate path from the multiple paths available, to transmit the data. This article also analyzes the performance of the proposed system with various parameters taken into consideration and shows its efficiency in comparison to other existing techniques. [ABSTRACT FROM AUTHOR]

Summary: Parallel transmission in multiple access networks using MultiPath TCP (MPTCP) greatly enhances the throughput. However, critical packet disorder is commonly observed due to traffic fluctuation and path diversity. Although several predictive scheduling algorithms have been proposed to solve this problem, they cannot accommodate prediction accuracy and real‐time adaptation simultaneously in a dynamic network environment. The time overhead in modifying scheduling parameters to adapt to network changes leads to performance degradation in throughput and packet disorder. In this study, we propose a scheduling algorithm called Utilising Reinforcement Learning to Schedule Subflows in MPTCP (URLM). We apply reinforcement learning to select an optimal scheduling parameter in real time, which brings significant time benefits for modifying the parameters. The simulation comparison experiments show that URLM reduces the average number of out‐of‐order packets and the time overhead in adapting to network changes while improving global throughput. [ABSTRACT FROM AUTHOR]

Abstract Cloud Mobile Ad-hoc Networks (Cloud-MANETs) is a framework that can access and deliver cloud services to MANET users through their smart devices. MANETs is a pool of self-organized mobile gadgets that can communicate with each other with no support from a central authority or infrastructure. The main advantage of MANETs is its ability to manage mobility while data communication between different users in the system occurs. In MANETs, clustering is an active technique used to manage mobile nodes. The security of MANETs is a key aspect for the fundamental functionality of the network. Addressing the security-related problems ensures that the confidentiality and integrity of the data transmission is secure. MANETs are highly prone to attacks because of their properties.In clustering schemes, the network is broken down to sub-networks called clusters. These clusters can have overlapping nodes or be disjointed. An enhanced node referred to asthe Cluster Head (CH) is chosen from each set to overseetasks related to routing. It decreases the member nodes’ overhead and improvesthe performance of the system. The relationship between the nodes and CH may vary randomly, leading to re-associations and re-clustering in a MANET that is clustered. An efficient and effective routing protocol is required to allow networking and to find the most suitable paths between the nodes. The networking must be spontaneous, infrastructure-less, and provide end-to-end interactions. The aim of routing is the provision of maximum network load distribution and robust networks. This study focused on the creation of a maximal route between a pair of nodes, and to ensure the appropriate and accurate delivery of the packet. The proposed solution ensured that routing can be carried out with the lowest bandwidth consumption. Compared to existing protocols, the proposed solution had a control overhead of 24, packet delivery ratio of 81, the lowest average end-to-end delay of 6, and an improved throughput of 80,000, thereby enhancing the output of the network. Our result shows that multipath routing enables the network to identify alternate paths connecting the destination and source. Routing is required to conserve energy and for optimum bandwidth utilization.

Abstract In response to the fast and intensive traffic changes and the presence of redundant links in the network topology in data centre networks, multipath routing has become the dominant approach. Existing multipath routing algorithms are still inadequate in terms of adapting to dynamic changes in the network state quickly and the cooperation of path selection and sub-flow assignment. Therefore, this paper proposes a sub-flow adaptive multipath routing algorithm for data centre networks (SAMP). The deep deterministic policy gradient (DDPG) algorithm is introduced. DDPG combines deep learning (DL) and reinforcement learning (RL) to implement different network state changes quickly, especially in the process of topology, to achieve dynamic migration of the optimised decisions that have been learned. An adaptive multipath routing model for subflows is established to accomplish collaborative scheduling of path selection and subflow assignment based on the real-time state of the network. The experimental results show that the algorithm can be well adapted to the data centre network, and when compared with several traditional methods, it reduces the latency by an average of 31.3%, improves the task transmission success rate by an average of 14.9%, and increases the throughput by 37.1%.

IEEE 802.1 Qbv time-aware shaper (TAS) has been developed for delivery of time-critical periodic frames of time-triggered (TT) flows over Ethernet under strict end-to-end latency and jitter bounds. Efficient routing and flow scheduling are critical for successful deployments of TAS systems. Although many routing and flow scheduling schemes for TAS have been proposed so far, most of them are restricted to single path routing and zero jitter delivery. Multipath routing and nonzero jitter bound could provide more flexibility in scheduling thus increase scheduling success. However they have been rarely studied in TAS scheduling. In this work, online incremental routing and scheduling schemes utilizing multipath routing and nonzero jitter bound are proposed based on no-wait packet switching mechanism for TAS. Two routing and scheduling schemes with nonzero jitter bound are proposed, i.e., incremental single path routing and scheduling with nonzero jitter bound (ISPRS-NZJ) and incremental multipath routing and scheduling with nonzero jitter bound (IMPRS-NZJ). Both schemes are based on the proposed resource-centric scheduling approach in which information on feasible free time-slices in the paths for a TT flow is updated according to flow scheduling result and used for scheduling of next flow. Paths for a flow are searched by depth-first way and cumulative free time-slices (CFTSs) in each link of a path are found and used in ISPRS-NZJ. In IMPRS-NZJ, combined cumulative free time-slices (CCFTSs) are obtained from CFTSs and used for scheduling. Numerical results show that scheduling with nonzero jitter bound or multipath routing could reduce schedule failure rates compared with typical single path scheduling with zero jitter bound. Also it is shown that scheduling with nonzero jitter bound is more effective in reducing schedule failure rates than scheduling with multipath routing.

In recent days, the usage of data transmission has increased in Wireless Sensor Network (WSN) environments due to its dynamic nature. However, WSNs face many issues during data transmission, such as less energy efficiency, less security, and less network lifetime. Here, in this research it presents secure and energy-efficient clustering and routing techniques for an edge-assisted WSN environment to address these problems. The proposed work includes four major processes: Quad tree-based network construction, energy-efficient clustering, RL-based duty cycling, and secure multipath routing. This work constructs the network based on a quad-tree structure to increase network management performance and reduce complexity. After network construction, authentication of sensors is performed by considering ID and location using the Lightweight Encryption Algorithm (LEA), which provides high security by eliminating illegitimate sensor nodes. Then, this research model performs clustering using Tasmanian Devil Optimization (TDO), which selects optimal CH and performs clustering. In contrast, the CH selection and clustering are performed dynamically by considering time and event metrics, which increases communication efficiency and reduces energy depletion. To reduce energy consumption, This research model performs duty cycling using the Improved Twin Delayed Deep Deterministic Policy Gradient (ITD3) algorithm, increasing the network lifetime. Finally, A secure multipath routing is performed using a game theory-based Generative Adversarial Network (GTGAN). During routing, GTGAN ranks the selected multipath based on their hop counts. The highest-ranked paths are chosen for transmitting an emergency message, and medium-ranked paths are selected for non-emergency message transmission, which reduces data loss due to energy depletion. Here, all the transactions are stored on the blockchain for increased security. The NS-3.26 network simulator conducts the simulation of this research, and the performances are evaluated based on various performance metrics, proving that the proposed work achieves superior performance compared to existing works.

Wireless acoustic sensor networks (WASN) usually demand uninterrupted and reliable data transmissions and an efficient path from the source nodes to the destination nodes, thus ensuring reliable delivery of sensitive or critical data with the use of multipath routing protocols. This paper presents a novel agent-driven WASN relying on a set of static and mobile agents. In the proposed technique, the location address is functioned into the operating node and the destination node. Calculations of the midpoint between the operating and the destination nodes are followed by identifying the shortest path based on a reference axis, using a mobile agent and a location address. Such an approach aims to ensure maximum utilization of the communicating paths. Based on the information concerning partial topology of the network, the destination node computes the weight factor and multiple paths using node distances, energy ratios and efficiencies of specific links. Then, the destination node selects the appropriate path from the multiple paths available, to transmit the data. This article also analyzes the performance of the proposed system with various parameters taken into consideration and shows its efficiency in comparison to other existing techniques.

Every day, WSNs (Wireless Sensor Networks) based healthcare systems is witnessing improvement in providing advice to the health condition and living habits of people. Healthcare applications based on WSNs face crucial problems of security and are vulnerable to assaults. In this study, a privacy-preserving method using the multipath routing and principles of secret keys are suggested for healthcare applications based on WSNs. Multiple paths to the sink will be computed using ECSO (Enhanced Cuckoo Search Optimization). The proposed algorithm utilizes distance and also the traffic rate of paths in the form of a parameter for discovering the optimum paths utilizing ECSO. Among these chosen paths, just one ideal path is chosen based on node energy using BO (Butterfly Optimization) that helps in decreasing the energy demands of the network. Encrypted data will be sent to the sink using the selected path. It is found from the results that the secret key merged with multipath routing is helpful in achieving security of privacy in healthcare systems based on WSNs. The results of simulation performance metrics are throughput, End to End delay, PDR, Energy Consumption, encryption time.

With the advancement of urban rail transit towards intelligence, the demand for urban rail transit communication has increased significantly, but the traditional urban rail transit vehicle–ground communication system has been unable to meet the future vehicle–ground communication requirements. To improve the performance of vehicle–ground communication, the paper proposes a reliable low-latency multipath routing (RLLMR) algorithm for urban rail transit ad hoc networks. First, RLLMR combines the characteristics of urban rail transit ad hoc networks and uses node location information to configure a proactive multipath to reduce route discovery delay. Second, the number of transmission paths is adaptively adjusted according to the quality of service (QoS) requirements for vehicle–ground communication, and then the optimal path is selected based on the link cost function to improve transmission quality. Third, in order to enhance the reliability of communication, a routing maintenance scheme has been added, and the static node-based local repair scheme is used in routing maintenance to reduce the maintenance cost and time. The simulation results show that compared with traditional AODV and AOMDV protocols, the proposed RLLMR algorithm has good performance in improving latency and is slightly inferior to the AOMDV protocol in improving reliability. However, overall, the throughput of the RLLMR algorithm is better than that of the AOMDV. [ABSTRACT FROM AUTHOR]

Developments in computer communications and networks enabled the deployment of exciting new areas. Meanwhile, Efficient network and communication technologies also promote the development of routing protocols. AODV(Ad hoc On-Demand Distance Vector Routing, AODV) routing protocol has invulnerability to different mobile attributes of agent nodes and network damage scenarios. However, this invulnerability performance can not meet the needs of mobile multi-agent cooperative communication. Therefore, this paper proposes an improved responsive multipath routing protocol MD_AODV(Multipath Destroy-resistance AODV, MD_AODV). This routing protocol is based on AODV routing protocol. It is mainly improved in three aspects: multichannel concurrency mechanism, node load balancing mechanism and shortest path maintenance mechanism. Firstly, in this research, an improved multipath routing protocol is modeled and implemented, which is based on OPNET Network simulation platform. Then, network models with different degrees of impairment are built. Combines the mobile attributes of mobile multi-agent task coordination to carry out invulnerability simulation experiments. These experiments study the invulnerability of the improved multipath routing protocol. Finally, this paper evaluates and compares the routing invulnerability of MD_AODV, AODV and AOMDV(Ad-hoc On-Demand Multipath Distance Vector, AOMDV). The evaluation results show that MD_AODV routing has better invulnerability when the network is severely damaged. [ABSTRACT FROM AUTHOR]

In the recent ten years, with the development of new applications through Internet such as multimedia or networked control applications, users need more and more quality of service (QoS). However, the requested QoS is not the same depending on the application. Most of the new models to manage internet traffic are based on specific QoS criteria which should be optimized. This paper presents main multiprotocol label switching (MPLS) approaches such as MPLS adaptive traffic engineering (MATE), load distribution in MPLS (LDM) and load balancing over widest disjoints paths (LBWDP) that are new models for traffic engineering. It also introduces periodic multi-step (PEMS) algorithm that adapts the offered quality depending on the class of the routed traffic.

Retraction statement. The publishers of International Journal of Pervasive Computing and Communications wish to retract the article Vishal Sharad, H., Desai, S.R. and Krishnrao, K.Y. (2023), “Energy-aware multipath routing in WSN using improved invasive weed elephant herd optimization”, International Journal of Pervasive Computing and Communications, Vol. 19 No. 3, pp. 451-474. https://doi.org/10.1108/IJPCC-09-2021-0229 An internal investigation into a series of submissions has uncovered evidence that the peer review process was compromised. As a result of these concerns, the findings of the article cannot be relied upon. This decision has been taken in accordance with Emerald’s publishing ethics and the COPE guidelines on retractions. The authors of this paper would like to note that they do not agree with the content of this notice. The publishers of the journal sincerely apologize to the readers.

Cloud Mobile Ad-hoc Networks (Cloud-MANETs) is a framework that can access and deliver cloud services to MANET users through their smart devices. MANETs is a pool of self-organized mobile gadgets that can communicate with each other with no support from a central authority or infrastructure. The main advantage of MANETs is its ability to manage mobility while data communication between different users in the system occurs. In MANETs, clustering is an active technique used to manage mobile nodes. The security of MANETs is a key aspect for the fundamental functionality of the network. Addressing the security-related problems ensures that the confidentiality and integrity of the data transmission is secure. MANETs are highly prone to attacks because of their properties.In clustering schemes, the network is broken down to sub-networks called clusters. These clusters can have overlapping nodes or be disjointed. An enhanced node referred to asthe Cluster Head (CH) is chosen from each set to overseetasks related to routing. It decreases the member nodes' overhead and improvesthe performance of the system. The relationship between the nodes and CH may vary randomly, leading to re-associations and re-clustering in a MANET that is clustered. An efficient and effective routing protocol is required to allow networking and to find the most suitable paths between the nodes. The networking must be spontaneous, infrastructure-less, and provide end-to-end interactions. The aim of routing is the provision of maximum network load distribution and robust networks. This study focused on the creation of a maximal route between a pair of nodes, and to ensure the appropriate and accurate delivery of the packet. The proposed solution ensured that routing can be carried out with the lowest bandwidth consumption. Compared to existing protocols, the proposed solution had a control overhead of 24, packet delivery ratio of 81, the lowest average end-to-end delay of 6, and an improved throughput of 80,000, thereby enhancing the output of the network. Our result shows that multipath routing enables the network to identify alternate paths connecting the destination and source. Routing is required to conserve energy and for optimum bandwidth utilization. [ABSTRACT FROM AUTHOR]

Mobile adhoc networks are the only way for communication when we have catastrophic situations in remote areas. There is typically no central authority responsible for maintaining and configuring the Mobile ad-hoc network. Designing a MANET is fraught with difficulty due to the network's weakness and control overhead. The topology of the network changes due to the mobility of the nodes. Further link instability issues are also encountered. The aim of this work is to propose efficient yet light weight protocols with the help of fuzzy logic namely Stable On-Demand Multiple-Path Routing Protocol (SODRP) for MANET. By enhancing the performance and efficiency of packet delivery ratio, delay, and control overhead, the proposed protocols provide a new method for overcoming stability on demand. The experimental results show that SODRP gives packet delivery ratio improves on an average by 10.74% on 10 nodes test bed, packet delivery ratio improves on an average by 11.87% on 20 nodes, and throughput increases on an average by 12.64% as compared with Ad hoc On-Demand Distance Vector (AODV) protocol. The objective is to increase the network's delivery ratio and throughput. [ABSTRACT FROM AUTHOR]

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.

The Internet of things (IoT) is made up of huge sets of networked low-power devices. Regardless of the application specificities, those devices are generally tasked to provide highly important services that can be reached out through the internet. Communications with IoT networks cannot be achieved without the support of efficient underlying routing schemes. However, the constraints related to scarce resources, as well as the wireless and lossy nature of IoT networks may tremendously affect the communications reliability. Consequently, routing efficiency could be affected. Moreover, the exposure to internal and external cyber-attacks, especially those that threaten data and service availability, complicates even further routing and security missions in such a constrained context. In this paper, we propose an efficient solution that enhances both the communications reliability and security in the heterogeneous IoT-enabled low power and lossy networks. The solution relies on three variants of adaptive and secure multipath routing for IPv6 routing protocol for low-power and lossy networks (RPL). To the best of our knowledge, this is the first solution that deals with multipath routing security issue for RPL protocol, in IoT contexts. The proposed solution is evaluated with Cooja simulator of Contiki OS. The assessment results show obviously the effectiveness of the proposed solution for improved communication reliability and security, at low costs. [ABSTRACT FROM AUTHOR]

Summary: Recently, 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. [ABSTRACT FROM AUTHOR]

The wireless sensor networks (WSNs) are special network which has purpose of gathering information in certain area. Multipath routing is the paramount path of addressing QoS and energy balancing concerns in low power and lossy networks (LLNs) especially in IoT technology. Most of the extant efforts bring the limited number of disjoint paths into play, and intersect the traffic among them pursuant to a compound metric or centralized optimization problem. This paper proposes a multi-class multipath routing protocol for LLNs (called M2RPL), that construct a braided multipath routing graph based on the standard RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) protocol, and an energy balanced optimal rate assignment mechanism (EBORA) that solves a local multi class optimization problem for minimum cost traffic rate assignment. Moreover, the algorithm tries to maximize network lifetime by equalizing the energy dissipation rates of equi-level nodes. The simulation results expose the efficiency of the proposed framework, leading to an average 15% increase in lifetime, and improved QoS performance in terms of delay and reliability, compared to some well-known algorithms. [ABSTRACT FROM AUTHOR]

Wireless sensor networks applications have been used in many areas that are difficult to access by humans. However, Routing remains the key challenges in sensor networks as it is important for the timely delivery of sensed datato the base station. In recent years, Multipath Routing has been used to ensure reliable and scalable data transmission in WSN. Although many multipath routing algorithms have been proposed, very few protocols have been focused on the Quality of Service (QoS) based routing. This paper proposes a QoS-aware, multipath routing protocol in which sensor nodes are clustered using the hybrid Particle Swarm Optimization-Cuckoo Search Optimization algorithm. The proposed protocol then chooses multiple stable paths (optimized network routing) using the Cluster Heads to transmit data based on multi-hop communication. Unlike the existing protocols, it relies on paths that do not affect QoS for rapid data transmission. It also extends the network lifetime by changing the Cluster Heads periodically based on the residual energy and uses the optimal number of paths to data transmission unlike the existing QoS Centric protocols. The performance of the proposed protocol is evaluated using NS-2 Simulator in different scenarios. The proposed protocol outperforms current protocols in terms of QoS parameters such as throughput, packet delivery ratio, end-to-end delay, and network lifetime, according to simulation results. [ABSTRACT FROM AUTHOR]