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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]

Intelligent unmanned clusters have played a crucial role in military reconnaissance, disaster rescue, border patrol, and other domains. Nevertheless, due to factors such as multipath propagation, electromagnetic interference, and frequency band congestion in high dynamic scenarios, unmanned cluster networks experience frequent topology changes and severe spectrum limitations, which hinder the provision of connected, elastic and autonomous network support for data interaction among unmanned aerial vehicle (UAV) nodes. To address the conflict between the demand for reliable data transmission and the limited network resources, this paper proposes an AODV routing protocol based on node energy consumption and mobility optimization (AODV-EM) from the perspective of network routing protocols. This protocol introduces two routing metrics: node energy based on node degree balancing and relative node mobility, to comprehensively account for both the balance of network node load and the stability of network links. The experimental results demonstrate that the AODV-EM protocol exhibits better performance compared to traditional AODV protocol in unmanned cluster networks with dense node distribution and high mobility, which not only improves the efficiency of data transmission, but also ensures the reliability and stability of data transmission. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) refers to a network of interconnected devices communicating through the internet. Routing is a crucial aspect of IoT for effective communication between devices since intermediate nodes collect and transmit sensor data for processing. IoT networks face limitations like poor connectivity, and devices are constrained in power, bandwidth, and memory. The Internet Engineering Task Force developed the Routing Protocol for Low power and lossy networks (RPL) to address these issues. In RPL, a Destination Oriented Directed Acyclic Graph (DODAG) is formed to start the data transfer. Rank is computed using routing metrics and an objective function during DODAG formation. The paper studies the approaches developed for rank calculation and proposes a new technique called the Entropy-based Weighted approach for RPL (EWRPL). The proposed approach combines the routing metrics and computes the rank for parent selection. In EWRPL, we have assigned weights to the routing metrics using the entropy method. The proposed approach considers four routing metrics-hop count, ETX, delay, and energy. The EWRPL approach was experimented with using the Cooja simulator and Contiki-OS. The study indicates that utilizing the EWRPL approach results in a higher Packet Delivery Ratio (PDR), lower latency, and reduced control message overhead compared to the two standard objective functions and the state-of-the-art equal weight approach. The technique can be used in various domains, such as home automation, healthcare, and industrial usage. [ABSTRACT FROM AUTHOR]

Opportunistic networks are characterized by intermittent connectivity and dynamic topologies, which pose significant challenges for efficient message delivery, resource management, and routing decision-making. This paper introduces the Fuzzy Control Routing Protocol, a novel approach designed to address these challenges by leveraging fuzzy logic to enhance routing decisions and improve overall network performance. The protocol considers buffer occupancy, angle to destination, and the number of unique connections of the target nodes to make context-aware routing decisions. It was implemented and evaluated using the FuzzyC framework for simulations and the opportunistic network environment simulator for realistic network scenarios. Simulation results demonstrate that the Fuzzy Control Routing Protocol achieves competitive delivery probability, efficient resource utilization, and low overhead compared to the Epidemic and MaxProp protocols. Notably, it consistently outperformed the Epidemic protocol across all metrics and exhibited comparable delivery probability to MaxProp while maintaining significantly lower overhead, particularly in low-density scenarios. The results demonstrate the protocol's ability to adapt to varying network conditions, effectively balance forwarding and resource management, and maintain robust performance in dynamic vehicular environments. [ABSTRACT FROM AUTHOR]

Security is the primary concern when creating security protocols for wireless sensor networks (WSN), which motivates many academics to find security solutions that effectively provide a few benefits, such as low consumption of electricity, flexible communication, and low cost. A few restrictions still remain, including the inability to exactly select the expected cluster, the sensor nodes' constrained functionality, and their poor efficiency. Thus, Egret-Harris optimization for hybrid secure routing and monitoring mechanisms in IoT-based WSNs (EHO optimized routing protocol in WSN) was introduced in this research. The created EHO algorithm combines the search, fitness function, and hunting phase features from Harris hawks and egrets to determine the best solution among all practical solutions while ensuring safe data transfer from the cluster heads (CHs) to the Base station (BS). Specifically, the EHO-enabled clustering is applied to the suggested model to efficiently choose the ideal group of CHs. Additionally, the EHO algorithm assists in choosing the best possible routes with minimal distance and less delay for facilitating energy-efficient transmission. With 100 nodes analyzed, the suggested EHO-WSN approach without any attacks achieved 22 alive nodes, a delay of 0.10 ms, a normalized energy of 0.346J, and a throughput of 0.64 bps, respectively. Additionally, in the presence of a Sybil attack, the suggested EHO-WSN technique achieves 14, 0.214 J, 0.010 ms, and 0.512 bps for an analysis involving 100 nodes. Compared to previous methods, the suggested EHO-WSN model without attack achieves a delay of 0.07 ms, a throughput of 0.30 bps, an energy of 0.374 J, and 37 alive nodes for 200 node evaluation. For 200 nodes under examination, the EHO-WSN technique yields superior results of attaining 12 alive nodes, a delay of 0.072 ms, a throughput of 0.181 bps, and a normalized energy of 0.330 J even in the presence of the Sybil attack and exceeded other traditional techniques. [ABSTRACT FROM AUTHOR]

Underwater sensor nodes may move with the water current, resulting in a highly dynamic network topology, which brings about challenges for the routing in underwater acoustic network (UAN). To cope with the challenges of node mobility and energy efficiency in UAN, this paper proposed a distance prediction and multi-criteria decision based layering routing for underwater acoustic networks (DMD-LR). To predict the distance between nodes, this paper presented a gray Markov internode distance prediction model based on an improved gray prediction model. According to the prediction results based on the gray Markov distance prediction model, it provided an effective neighbor node determination rule. Further, it proposed a grey relational analysis model based on multi-criteria decision to calculate the grey relational degree (GRD) of neighbor nodes, and selected the neighbor node with the highest GRD as the optimal next hop. It conducted extensive experiments with the NS3 network simulation simulator to evaluate the performance of the DMD-LR protocol. The results show that the DMD-LR protocol has great advantages in improving packet delivery rate, reducing end-to-end delay, and lowering total energy consumption compared with the LEER, DBR, VBF, LR-NMP, and IATLR protocols. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks (UWSNs) are made up of several underwater wireless sensor nodes that are scattered throughout the marine environment and can be utilized for resource discovery, navigation, surveillance, data collecting, and disaster prediction. Energy efficiency becomes a significant issue in the UWSN's design due to the use of limited battery capacity and the difficulties of changing or charging the inbuilt batteries. To overcome the energy efficiency problem in routing, a hybrid approach is developed in this study for UWSNs. Initially, a Range-based Localization technique was used to locate sensor nodes, which used the received signal intensity and arrival time to provide reliable node range estimation. Accordingly, this work presents a Recursive Position Estimation (RPE) technique, which determines the sensor location for a given number of available anchor references. A path must be built between a sensor (or source) and the desired destination (or surface sink) for effective and reliable data transmission. Between the sensor nodes, a multi-hop communication channel is conceivable. The intermediate nodes deliver the packet data until it reaches the sink node in multi-hop- ping. To route packet data towards the destination, an Artificial Bee Colony (ABC) integrated Chaotic Particle-swarm Krill Herd (ABC-CPKH) technique is used, as the main contribution deals with the choice of cluster head. Due to the harsh underwater environment, its capabilities, and the open acoustic channel is vulnerable to threats and malicious attacks. The research suggested a Delphi detection system to reduce the threat problem in UWSN, which prevents undesired packets from being delivered to the destination, mainly, it prevents the data from the wormhole attack. PDR, end-to-end delay, energy consumption, network lifetime, localization energy, localization coverage, detection rate, etc., are performance metrics for the proposed study, which is simulated using Python software. The proposed method is compared to the existing IRL-WOA, EECRP, and DSIP methods, Compared to these methods, the proposed method provided 6%, 3.5% and 4% better performance for time complexity, energy consumption and delay. Consequently, future research can suggest new metaheuristic algorithms to more efficiently allocate resources and increase localization accuracy, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a novel methodology for the design of dual-stack IP routing protocols focused on neighborships and routing messages transport. The first part describes currently used dual-stack IP routing protocols, their history, and key features. The description of four identified combinations based on a number of neighbor sessions and protocol used for the transport of routing messages is presented afterward. These combinations are introduced as Protocol Integration Solution Classes (PISCs) in this paper. Colored Petri nets (CPNs) are selected as a formal method to create a model of each PISC. The following parts deal with a detailed description and analysis of the created Protocol Integration Solution Class models, which are also verified and compared with each other. Based on the comparison of each PISC model simulation step, the PISC 2 class is selected as the most efficient one. The last part of the paper formulates recommendations for the dual-stack IP routing protocol design based on the results presented in this paper. [ABSTRACT FROM AUTHOR]

Summary: The ability of mobile ad hoc networks (MANET) to be used as communication tools in a variety of industries, including healthcare, the military, smart traffic, and smart cities, has drawn special consideration. Traditional Manet's multicast routing methods seem to be inappropriate to massive with Adaptive systems because the problem is NP‐complete, resulting in an enchanting QoS restrictions. In order to conquer that the paper proficiently introduces the Conglomerate Crumb Horde Formicary Meta‐Heuristic (CCHFMO) with Asymmetrical Meander Diffie‐Hellman (AMDH) to tackle the major obstacles are multicast routing problems and lack of data protection. Initially, the fusion of crumb horde optimization (CHO) and formicary optimization (FO) is exploited to strengthen QoS limitations and reduce QoS data loss. However, the massive and dynamic nature of the network with the combination of more QoS restrictions, deficient security has become extremely difficult. Therefore, the research work establishes the asymmetrical meander Diffie‐Hellman (AMDH) to significantly improve performance and concealment while ensuring channel security during data transfer. Finally, the results demonstrated that by employing the novel optimization approaches, the MANET can increase data protection while still achieving high transmission rates and sophistication of communication. As a consequence, it adequately explicates the article to improve QoS performances. [ABSTRACT FROM AUTHOR]

A Mobile ad hoc Network (MANET) is a collection of wireless mobile nodes that temporarily establish a network without centralized administration or fixed infrastructure. Designing the routing of adequate routing protocols is a major challenge given the constraints of battery, bandwidth, multi-hop, mobility, and enormous network sizes. Recently, Swarm Intelligence (SI) methods have been employed in MANET routing due to similarities between swarm behavior and routing. These methods are applied to obtain ideal solutions that ensure flexibility. This paper implements an enhanced Particle Swarm Optimization (EPSO) algorithm that improves MANET performance by enhancing the routing protocol. The proposed algorithm selects the stable path by considering multiple metrics such as short distance, delay of the path, and energy consumption. The simulation results illustrate that the EPSO outperforms other existing approaches regarding throughput, PDR, and number of valid paths. [ABSTRACT FROM AUTHOR]

A Mobile ad hoc Network (MANET) is a collection of wireless mobile nodes that temporarily establish a network without centralized administration or fixed infrastructure. Designing the routing of adequate routing protocols is a major challenge given the constraints of battery, bandwidth, multi-hop, mobility, and enormous network sizes. Recently, Swarm Intelligence (SI) methods have been employed in MANET routing due to similarities between swarm behavior and routing. These methods are applied to obtain ideal solutions that ensure flexibility. This paper implements an enhanced Particle Swarm Optimization (EPSO) algorithm that improves MANET performance by enhancing the routing protocol. The proposed algorithm selects the stable path by considering multiple metrics such as short distance, delay of the path, and energy consumption. The simulation results illustrate that the EPSO outperforms other existing approaches regarding throughput, PDR, and number of valid paths.

A Wireless Sensor Network (WSN) is a significant technological advancement that might contribute to the industrial revolution. The sensor nodes that are part of WSNs are battery-powered. Energy is the most crucial resource for WSNs since batteries cannot be changed or refilled. Since WSNs are a finite resource, several techniques have been devised and used throughout time to preserve them. To extend the lifespan of WSNs, this study will provide an effective method for Cluster Head (CH) selections. Many researches are employing the Swarm-based optimization algorithm to Select the optimal CH. In this study, the Squirrel Search Algorithm (SSA) is utilized to select the optimal CH Selection in WSN. The general SSA has been modified in this study to address the exact need for CH choice in WSNs. The Improved Squirrel Search Algorithm (I-SSA) integrates a series of enhancements aimed at accelerating convergence and elevating solution quality. Notably, we've implemented Adaptive Population Initialization, Dynamic Step Size Control, and a Local Search Algorithm to augment the exploration and exploitation capabilities of the SSA. These enhancements collectively refine the algorithm's ability to navigate the search space effectively, resulting in more efficient convergence towards optimal solutions. The suggested formulation's goal function takes into account the CH balance average, factor, sink distance residual energy and intra-cluster distance. The simulations are run under a variety of circumstances. The MATLAB 2021a working setting is utilised for simulation. The proposed code of conduct SSA-C is compared with the existing protocols Grey Wolf Optimization (GWO), SSA, Chernobyl Disaster Optimizer (CDO), Sperm Swarm Optimization (SSO), A Metaheuristic Optimized Cluster head selection-based Routing Algorithm for WSNs (MOCRAW), Energy-Efficient Weighted Clustering (EEWC), and Multi-agent pathfinding using Ant Colony Optimization (MAP-ACO). The ISSA-C method achieved a Packet Delivery Ratio (PDR) of 88%, outperforming GWO, SSA, and MAP-ACO. It reduced energy consumption to 210 mJ, which is lower than other methods, and showed improved bit error rates. Cluster formation and head selection times were also reduced to 82 s and 67 s, respectively. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are instrumental in various modern applications, such as environmental monitoring and data collection. However, prolonging their lifespan and optimizing data transmission pose considerable challenges. This study introduces ARZSEP (Angle-based Routing in Zone-based SEP), a novel heterogeneous routing protocol aimed at addressing these challenges. ARZSEP optimizes resource allocation by employing angle-based zone formation, which categorizes sensor nodes based on angular coordinates relative to a central sink node. This approach effectively minimizes energy consumption during data transmission and enhances Cluster Head selection for data aggregation. The energy efficiency of ARZSEP is underpinned by a comprehensive model that takes into account distance-dependent energy dissipation, node types, and data packet size. Additionally, adaptive routing dynamically selects efficient routes based on real-time energy metrics, ensuring optimal data relay. ARZSEP builds on the foundation of ZSEP (Zone-based Stable Election Protocol), which optimizes energy consumption by dividing the network into zones and selecting cluster heads based on node energy levels within each zone. Through MATLAB simulations, ARZSEP's superiority over other protocols is demonstrated, showcasing significant improvements in various metrics. Specifically, ARZSEP achieved a Cumulative Packets Transmitted to BS of 222,045 bits and extended the Network Lifetime to 3821 rounds, among other notable results. Overall, ARZSEP contributes to extending the operational lifespan of WSNs, improving data routing efficiency, and optimizing energy usage. The proposed protocol shows improvements in the time for the first node to die by 104.4%, 49.4%, and 36.7%; the time for half the nodes to die by 62.8%, 33.0%, and 0.8%; and the time for all nodes to die by 20.7%, 26.1%, and 7.5%, compared to LEACH, SEP, and ZSEP respectively. [ABSTRACT FROM AUTHOR]

Flying Ad Hoc Networks (FANETs) are multi-hop, autonomous, highly dynamic Mobile Ad Hoc Networks (MANETs) composed of swarms of Unmanned Aerial Vehicles (UAVs). The high dynamics and instability of FANETs render communication nodes vulnerable to failure, attacks, and deception, posing significant challenges to reliable communication within these networks. Consequently, we design a topology-aware secure routing protocol, Perception-AODV (P-AODV), to enhance network security. Initially, we develop a communication model for wireless self-organizing networks, examining the impact of failed nodes on network stability. Addressing the network's inherent dynamics and the reconfiguration post-failure, we consider factors such as communication networks, spatial positioning, and energy constraints. By integrating a stable node-based ETX link evaluation approach with a method for calculating link effective generation time, we propose a multi-routing metric-based link selection strategy. Simulation experimental results show that our proposed P-AODV protocol has a higher packet delivery rate than existing methods. [ABSTRACT FROM AUTHOR]

Real-time status monitoring is crucial in ensuring the safety of railway tunnel traffic. The primary monitoring method currently involves deploying sensors to form a Wireless Sensor Network (WSN). Due to the linear characteristics of railway tunnels, the resulting sensor networks usually have a linear topology known as a thick Linear Wireless Sensor Network (LWSN). In practice, sensors are deployed randomly within the area, and to balance the energy consumption among nodes and extend the network's lifespan, this paper proposes a self-organizing network and routing method based on thick LWSNs. This method can discover the topology, form the network from randomly deployed sensor nodes, establish adjacency relationships, and automatically form clusters using a timing mechanism. In the routing, considering the cluster heads' load, residual energy, and the distance to the sink node, the optimal next-hop cluster head is selected to minimize energy disparity among nodes. Simulation experiments demonstrate that this method has significant advantages in balancing network energy and extending network lifespan for LWSNs. [ABSTRACT FROM AUTHOR]

The adoption of UAVs in defence and civilian sectors necessitates robust communication networks. This paper presents a routing protocol for Cognitive Radio Unmanned Aerial Vehicles (CR-UAVs) in Flying Ad-hoc Networks (FANETs). The protocol is engineered to optimize route selection by considering crucial parameters such as distance, speed, link quality, and energy consumption. A standout feature is the introduction of the Central Node Resolution Factor (CNRF), which enhances routing decisions. Leveraging the Received Signal Strength Indicator (RSSI) enables accurate distance estimation, crucial for effective routing. Moreover, predictive algorithms are integrated to tackle the challenges posed by high mobility scenarios. Security measures include the identification of malicious nodes, while the protocol ensures resilience by managing multiple routes. Furthermore, it addresses route maintenance and handles link failures efficiently, cluster formation, and re-clustering with joining and leaving new nodes along with the predictive algorithm. Simulation results showcase the protocol's self-comparison under different packet sizes, particularly in terms of end-to-end delay, throughput, packet delivery ratio, and normalized routing load. However, superior performance compared to existing methods, particularly in terms of throughput and packet transmission delay, underscoring its potential for widespread adoption in both defence and civilian UAV applications. [ABSTRACT FROM AUTHOR]

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Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mobility has ultimate applications in Wireless Sensor Networks (WSNs) including several areas such as wildlife intensive care, health monitoring, flood and fire detection, and much more. Mobile WSNs (MWSNs) have captivated customer attention in recent centuries because of their applications in numerous areas. Mobile WSNs are source constraints and request performance studies by many nodes' movement outlines. Usually, routing algorithms in MWSN have been examined for specified movement. However, for instantaneous network scenarios, designing an efficient routing protocol/algorithm and analysing changes in numerous movement outlines on routing approaches are important to provide efficient results. Hence, keeping in the assessment of the aforesaid problem, an Energy Proficient Mobile Routing (EPMR) protocol is proposed. All the simulations have been completed in MATLAB on varied constraints to assess the effectiveness of EPMR and state-of-the-art routing protocols. Simulation outcomes demonstrate that EPMR provides improved performance than Distributed Efficient Multi-hop Clustering (DEMC), Geographic Robust Clustering (GRC), Mobility Aware Routing (MAR), Distributed Efficient Clustering Approach (DECA), and Improved Energy Mobile Routing (ECMR). EPMR enhances packet delivery ratio by 13–15%, reduces packet loss percentage by 12–14%, extends throughput by 14–16%, decreases overhead by 13–14%, minimizes average end-to-end delay by 13–15%, minimizes energy consumption by 16–18%, and extends network lifetime 17–19% on sensors' mobility. EPMR achieves better outcomes with the Random Waypoint Mobility (RWPM) model than the Random Walk Mobility (RWM) model and Pathway Mobility (PM) model. [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]

Recent developments and the widespread use of IoT-enabled technologies has led to the Research and Development (R&D) efforts in green communication. Traditional dynamic-source routing is one of the well-known protocols that was suggested to solve the information dissemination problem in an IoT environment. However, this protocol suffers from a high level of energy consumption in sensor-enabled device-to-device and device-to-base station communications. As a result, new information dissemination protocols should be developed to overcome the challenge of dynamic-source routing, and other similar protocols regarding green communication. In this context, a new energy-efficient routing protocol (EFRP) is proposed using the hybrid adopted heuristic techniques. In the densely deployed sensor-enabled IoT environment, an optimal information dissemination path for device-to-device and device-to-base station communication was identified using a hybrid genetic algorithm (GA) and the antlion optimization (ALO) algorithms. An objective function is formulated focusing on energy consumption-centric cost minimization. The evaluation results demonstrate that the proposed protocol outperforms the Greedy approach and the DSR protocol in terms of a range of green communication metrics. It was noticed that the number of alive sensor nodes in the experimental network increased by more than 26% compared to the other approaches and lessened energy consumption by about 33%. This leads to a prolonged IoT network lifetime, increased by about 25%. It is evident that the proposed scheme greatly improves the information dissemination efficiency of the IoT network, significantly increasing the network's throughput. [ABSTRACT FROM AUTHOR]

Addressing latency concerns and ensuring high-quality video services in Heterogeneous Mobile Adhoc Networks (H-MANETs) are paramount challenges. This paper presents a pioneering solution: the Pre-emptive Priority Queueing based Multipath Routing algorithm (PPQM). Our approach prioritizes video traffic within OpenFlow switches, directing it across multiple paths in H-MANETs. Integrating the PPQ module within Cluster Heads operating in the software-defined networking (SDN) architecture is central to our design. We rigorously evaluate delay for each path by employing an M/M/1 queueing policy based on a Poisson arrival process and an exponential service time distribution. Utilizing Burke's theorem, our calculation spans the entire route from the cluster head to a sink node. By meticulously assessing the delay characteristics of individual paths, our model facilitates the selection of the most optimal path to minimize overall delay and enhance network performance. Our proposed model amalgamates clustering, FIFO with M/M/1 queueing, and SDN techniques. In a comprehensive evaluation against existing technologies, the implementation of PPQM demonstrates superior performance in crucial Quality of Service (QoS) metrics, including end-to-end delay, queue size, waiting time, throughput, and response time. Furthermore, our research achieves a significant 4.2% improvement in QoS metrics compared to contemporary approaches, highlighting the effectiveness of the PPQM algorithm in enhancing network performance. This research contributes a robust solution for advancing QoS in H-MANETs, demonstrating the efficacy of the PPQM algorithm compared to contemporary approaches. [ABSTRACT FROM AUTHOR]

Summary Wireless sensor networks (WSNs) contain different sensors, which collect various data in the monitoring area. In general, one of the significant resources in WSNs is energy, which prolongs the network's lifetime. The energy‐efficient routing algorithms reduce energy consumption and enhance the survival cycle of WSNs. Thus, this work developed the optimization‐based WSN routing and deep learning (DL)–enabled energy prediction scheme for efficient routing in WSNs. Initially, the WSN simulation is carried out, and then, the node with minimum energy consumption is chosen as the cluster head (CH). Here, the proposed rat hawk optimization (RHO) algorithm is established for finding the best CH, and the RHO is the integration of rat swarm optimization (RSO) and fire hawk optimization (FHO). Furthermore, the routing is accomplished by the developed fractional rat hawk optimization (FRHO) using the fitness function includes delay, distance, link lifetime, and predicted energy of a network for predicting the finest route. Here, the fractional calculus (FC) is incorporated with the RHO to form the FRHO. The energy prediction is achieved by deep recurrent neural network (DRNN). The energy, delay, and throughput evaluation metrics are considered for revealing the efficiency of the proposed system, and the proposed system achieves the best results of 0.246 J, 0.190 s, and 67.13 Mbps, respectively. [ABSTRACT FROM AUTHOR]

Summary: Wireless body area networks (WBANs) are becoming widely considered in remote healthcare monitoring applications. However, WBAN nodes have limited resources; therefore, effective and reliable routing protocols are pivotal research challenges. Furthermore, balancing the traffic load among sensor nodes is also highly required to increase network stability. In recent years, many interesting routing solutions have been proposed for WBANs; however, the significant feature in terms of stability and energy in these solutions has not been sufficiently addressed. Therefore, in this context, the Hot‐Sspot Aaware Multicost‐based Energy‐efficient Routing (H‐SAMER) protocol is proposed in this paper. The suggested protocol used multieffective cost function for next‐hop node selection based on the data type, where the patient's data are categorized into three classes: normal data, on‐demand data, and emergency data. Furthermore, the H‐SAMER protocol adds awareness to the transmission of control packets by adding the cost value to the RREQ packets. Thus, the simulation scenario shows that the H‐SAMER saves energy 8.57%, 88%, 98%, 97%, and 100% greater than E‐HARP, EH‐RCP, CO‐LAEEBA, EECBSR, and ELR‐W, respectively. Moreover, the first node death of the proposed H‐SAMER is delayed to round number 7500, which proves H‐SAMER to be a stable and reliable effective solution for WBANs. [ABSTRACT FROM AUTHOR]

With technological advancements in networking and wireless communication, the flying ad hoc network has emerged as a cost-effective relay network. Networks of unmanned aerial vehicles (UAVs) can be cooperatively organized as an ad hoc network for effectively accomplishing complex tasks. Such a UAV network is deployed as a relay network that forwards and exchanges accurate and reliable end-to-end in-formation. However, the design of routing protocols is rendered difficult by flying constraints and extreme topology changes over a short time period. The performance of the routing protocols in such networks is considerably affected by high-speed nodes and extreme topology changes. Evaluating the routing protocol in a UAV network gives insight regarding the performance of the entire network. This paper evaluates, analyzes, and compares the performances of three routing protocols: Ad hoc On-demand Distance Vector (AODV), Optimized Link State Routing (OLSR), and Destination-Sequenced Distance-Vector (DSDV). In most of the simulated cases, the AODV outperformed the OLSR and DSDV. The number of nodes, i.e., the network size, largely affected the throughput, packet delivery ratio (PDR), and end-to-end delay (E2E) of the network. However, the OLSR improved the performance of the average E2E; therefore, it is suitable for delay-sensitive applications. However, AODV outperformed the other protocols in terms of throughput and PDR because it reacts to topology changes. DSDV delivered the poorest PDR and throughput. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are crucial in collecting environmental information through sensor nodes. However, limited energy resources pose a challenge, necessitating efficient routing algorithms to minimize energy consumption. Failure to address issues can consume energy and reduce network lifespan and overall efficiency. This research paper presents a cutting-edge approach for minimizing the consumption of energy within WSN through the implementation of an optimal routing method. The approach involves two steps: first, clustering sensor nodes using the pond skater algorithm (PSA) to select cluster head (CHs) for routing; second, by leveraging the ant colony optimization (ACO) algorithm, this study introduces an innovative technique that empowers a mobile sink to gather packets from given CHs and transmit effectively, send them back to the base station (BS). Notably, the authors make a significant contribution by introducing a different variant of the PSA algorithm to select CH. This novel approach aims to curtail the consumption of energy within WSN significantly. The authors also present an ACO-based head traversal for cluster method, resembling the traveling salesman problem coding, for minimized energy consumption. The study's primary objectives include reducing energy consumption, minimizing packet delivery ratio, and prolonging the lifetime of the WSN. The assessment efficacy of the proposed method was achieved by regressive simulations using MATLAB on diverse scenarios. Through meticulous comparative analyses with several efficient algorithms, the method proposed here has shown significant performance in network lifetime comparison of PSACO in terms of Alive nodes with number of rounds PSO: 17.65%, GWO: 25%, CS: 33.33%, CBR-ICWSN: 66.66%, CCP-IC: 17.65%. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, the implementation of routing protocols is crucial owing to their limited computational capacities. Tree routing is a suitable method in wireless sensors owing to its minimal routing overhead. However, single-hop metric schemes, such as hop count, cause congestion at specific nodes, whereas multiple metric schemes cannot control dynamically changing network environments. To address these issues, we propose a scheme to implement enhanced tree routing with adaptive metrics based on hop count. This approach assigns different weights to metrics to select suitable parent nodes based on hop count. The parent-selection algorithm utilizes hop count, buffer occupancy, and received signal strength indicator (RSSI) as metrics. This study evaluates the performance through simulation scenarios to analyze whether improvements can be made to address problems encountered in traditional tree routing. The performance metrics include packet delivery speed, throughput, and end-to-end delay, which vary depending on the volume of network traffic. [ABSTRACT FROM AUTHOR]

Resilient of routing processes is one of the biggest challenges for data transmission in mobile networks without infrastructure. Communication under current routing protocols is through a communication path that, although the shortest, may not perform satisfactorily in terms of resilient. Routing and communication within such a path may take place using nodes that are malicious or inappropriate in the communication process due to malicious or poor technical state. This paper presents a new algorithm for various uses of mobile ad hoc networks not only in edge networks with infrastructure but also with the possibility of being used in the cloud solutions. We have modified decentralized blockchain technology and artificial intelligence using deep learning methods that have been implemented in routing processes. The objective of this algorithm was to select the most resilient communication path from the source to the destination node. Such a communication path selection consisted of selecting the nodes that were most suitable in terms of resilience, where the selection nodes was provided through a network and technical parameters. The key quality of service metrics, throughput, total delay, number of delivered signaling and data packets and the ratio between them were used to evaluate the proposed resilient routing algorithm. Modified resilient routing protocols achieved improvement in all the analyzed parameters compared to the original routing protocols. The improvement in these parameters led to an increase in the resilience of the routing process based on the actual data obtained from each node in the network and previous communications. [ABSTRACT FROM AUTHOR]

Delay tolerant networks (DTNs) are a kind of sporadically connected mobile networks in which the network is intermittent, and end-to-end path is hard to establish. However, as devices in DTNs may often have limited energy and buffer, the network performance will be inevitably affected, especially in our application domain, i.e. the post-disaster scenario. Thus, to start with, we present an appropriate energy and buffer efficient routing protocol (EBRout) for efficient message transmission over a smartphone based DTN. Due to limited battery and storage capacity in mobile devices, a major problem in DTNs is to convince forwarder nodes to participate in forwarding messages. Thus, for improving cooperation among the nodes, an incentivizing scheme is proposed which works in two steps. As the first step, we propose an optimization model to find the minimum incentive. Next, we propose a blockchain-based incentive allocation model that uses Ethereum platform built on top of a DTN-Blockchain integrated environment. The use of blockchain helps to create an immutable and globally accessible record for incentive allocation. The performance of the entire scheme is estimated through extensive simulation in ONE simulator, Python PuLP and Ethereum platform. Performance analyses indicate that the average incentive paid using our proposed optimization model is much lower than the average incentive paid without using the optimization model. Also, the results substantiate the efficiency of the proposed scheme over the competing schemes, in terms of delivery ratio, energy and message overhead without negotiating the blockchain performance in terms of processing time and gas consumption. [ABSTRACT FROM AUTHOR]

Sea-air cross-domain networks consist of underwater and surface subnets. In order to fully utilize resources, enable multiple applications to share the same physical infrastructure, and make different data packets coexist within the same network, differentiated transmission strategies are needed to meet application demands. However, existing routing protocols often fail to provide personalized services based on application requirements. To address this issue, a task-oriented routing protocol for sea-air cross-domain networks was proposed. The protocol adjusted the calculation method of forwarding factors based on the types of tasks, thereby selecting the most suitable next-hop node for specific task types. Furthermore, a preprocessing layer was added to the protocol to facilitate communication between heterogeneous networks. Simulation results show that compared to other typical protocols, the proposed protocol achieves optimal transmission strategies based on specific task requirements.

Wireless sensor networks (WSNs) are critical in manufacturing contexts because they provide continuous tracking, digitization, and data collection. However, they frequently come under threat to security concerns and have limited energy resources due to the growing number of devices and data in Industrial manufacturing, thereby affecting the quality of data transfer. Consequently, previous works have concentrated on predicting novel methods and processes to offer security in WSNs but the threat persists. This study has significance because it solves two major issues in WSNs: security and energy efficiency. This study aims to improve network lifetime, save operational costs, and increase the security and reliability of industrial monitoring systems by proposing a secure and energy-efficient routing protocol. The study suggested a Machine Learning-based Secure Routing Protocol (MLSRP) for WSN to obtain better energy efficiency and overall performance to deliver an efficient tightened security for WSN in comparison to the existing approaches along with reduced data loss. According to a Multi-Criteria-based Decision Making (MCDM) paradigm, the MLSRP performs clustering, cluster head election, and data routing while analyzing certain network elements that affect the node, route, and data quality. The proposed framework was implemented and simulated using the NS2 simulator tool, and the outcomes are compared with the existing system for performance analysis. The proposed approach for secured and accurate data transfer achieves 98.78%. The study could have practical consequences for industries desiring efficient, secure, and long-lasting IoT solutions.

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node's energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node's residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10–20% compared to the benchmark algorithms. [ABSTRACT FROM AUTHOR]

For remote health monitoring, activity tracking, and other applications in healthcare such as sports, Wireless Body Area Networks (WBANs) have become a feasible technology. However, the limited resources and dynamic nature of WBANs pose significant challenges to designing efficient and reliable routing protocols. To address these challenges, the proposed work suggests a thermal-aware, energy-efficient, and congestion-aware routing protocol (TECRP) for WBAN. TECRP focuses on improving transmission in both inter-WBAN and intra-WBAN scenarios. It addresses three key Quality of Service (QoS) parameters: energy efficiency, node temperature, and congestion, aiming to enhance overall WBAN communication efficiency. To achieve all these parameters, the algorithm is considered a multi-objective problem. The analysis shows that the temperature rises and delay increases with the number of data transmission, but the multi-objective approach helps to mitigate such effects. The result analysis shows that the path loss values fluctuate with increasing data transmission and network traffic. But the temperature rise increases with more data transmission and larger packets. On the other hand, the Packet Delivery Ratio (PDR) decreases with an increase in data transmission and with larger packet sizes. This shows that with higher congestion ratios, a higher likelihood of packet loss is seen. But in overall performance, the proposed TECRP shows better efficiency and congestion management as compared to other existing state-of-art-models and achieves high PDR, and minimizes packet loss. The proposed approach shows 0.42% improvement in energy efficiency as compared to existing approaches. [ABSTRACT FROM AUTHOR]

One of the networking technologies is called a wireless sensor network, and what it does is link sensor nodes by using wireless communication as the channel of connection. A haphazard and random distribution of nodes is carried out throughout the deployment process within the network area. As a result, the distances between the nodes do not remain constant. The nodes have a high sensing capacity, but their range of perception and battery life are also severely limited. The wireless data may be sensed by all of the nodes inside their respective sensing regions. Consequently, there are a variety of new applications that make use of WSN applications, particularly in surveillance monitoring applications such as those used in hospitals, buildings, forests, and airspace monitoring. The transmission of the data to the appropriate server or data centre that is linked with the cloud or the internet is experiencing greater difficulties as a result of the growing amount of monitoring data. This indicates that a significant quantity of data is lost as a result of the fact that wireless transmission is affected by environmental factors such as precipitation, humidity, and wind storms. It is vital to create and execute an innovative routing protocol to deliver greater throughput and raise the overall Quality of Service of the WSN applications in order to solve the difficulties that have been outlined above. This study paper presents a metaheuristic algorithm based optimal routing protocol for WSN application, which boosts the overall quality of service of any surveillance monitoring applications. To achieve this goal, the protocol was designed for wireless sensor networks. It does so in an effective manner, resulting in decreased delay and energy consumption as well as enhanced throughput and PDR. Based on the findings of the experiments, it has been hypothesised that the proposed metaheuristic algorithm-based routing protocol is capable of performing better routing for any type of surveillance monitoring and remote monitoring, as well as successfully transmitting any type of data, including numerical, video, and image data. [ABSTRACT FROM AUTHOR]

Energy limitation is one of the essential parameters in the design of a Wireless body area networks (WBANs) as it is important to improve the lifetime of the network. WBAN routing is an effective approach for establishing energy efficiency sets and assign time slots for the network. Many algorithms that deal with interference model treats the whole WBAN as a minimum interference unit and increase their lifetime cycle. In this research, we report an effective low-energy adaptive clustering hierarchy (LEACH) routing protocol using MATLAB simulation and related C++ simulation codes to enhance the overall performance of the network by improving the energy efficiency and network lifetime cycles. Furthermore, the study sheds light up on the comparison of the protocol and proposes a modified protocol for WBAN. Based on the results obtained from conducting different configurations in the proposed design, the base station should be situated near the network to insure high network performance. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) concept is being extensively embraced in several industries, such as Intelligent Transportation Systems (ITS), to improve effectiveness and sustainability by utilizing advanced transportation resource data. Researching routing protocols is essential for maximizing the efficiency of dynamic Vehicular Ad Hoc Networks (VANETs) in ITS.VANETS utilize wireless technology, including both cellular and non-cellular, to facilitate the transmission of information. The former offers minimal latency but is expensive to operate, but the latter is preferable for lower-data-rate applications decreases costs, and is more energy-efficient. Developing countries are currently enhancing their 5G assistance, but the financial viability of Low Earth Orbit (LEO) satellite technology remains low. Underdeveloped nations require cost-effective, comprehensive wireless communication solutions in both urban and rural regions. One of the prominent choices for the Low Power Wide Area Network (LPWAN) in the 5G ecosystem is the Long Range Wide Area Network (LoRaWAN). This paper presents an enhanced routing protocol that is secure against attacks on the network and offers improved performance metrics. The protocol is designed for information routing in the proposed Vehicular Communication System (VCS). The VCS is tested to verify its efficacy in detecting blind spots. A proof-of-concept prototype for VCS is developed to integrate LoRaWAN technology with an enhanced routing protocol. This integration aims to enhance signal strength, improving communication reliability, network coverage, and security. Furthermore, the system is engineered to optimize vehicle energy efficiency, rendering it well-suited for communication in underdeveloped urban and rural regions. [ABSTRACT FROM AUTHOR]

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Wireless body area networks (WBANs) are an emerging field in the domain of healthcare which are typically composed of biomedical sensors. These sensors are implanted inside or attached to the human body for monitoring a patient's condition and providing accurate treatment to patients. In WBANs, energy efficiency is a critical concern due to the restricted battery capacity of the sensors. Extending the network lifetime and reducing the energy consumption of these sensor nodes can significantly impact the reliability and effectiveness of WBANs in monitoring patients' health. An efficient routing protocol based on energy-related parameters is crucial in designing these networks. Although many routing protocols have been proposed for routing in WBANs, sufficient features have not been properly handled in these methods. To overcome these issues, a novel routing protocol named Simple Energy Efficient and Bandwidth Aware (SEBA) routing protocol is proposed for routing in WBANs. The proposed scheme considers multiple metrics of the network node, such as remaining energy, energy harvesting, draining rate energy, available bandwidth, and number of hops in a route selection to minimize energy consumption, increase network lifetimes, and enhance the reliability of data transmission in WBANs. Additionally, the SEBA uses a novel mechanism to change the route dynamically based on energy consumption. This mechanism plays a significant role in reducing the number of route errors, route discoveries, and distributing energy consumption among sensor nodes. The experimental results reveal that the proposed scheme performs well in terms of average network throughput, packet delivery ratio, normalized routing load, average end-to-end delay, energy consumption, and network lifetime compared with the existing AMCRP, EEMP, and EAR protocols. [ABSTRACT FROM AUTHOR]

A wireless body area network (WBAN) is a type of wireless sensor network that plays a crucial role in monitoring patient healthcare. In this network, sensor nodes are typically placed inside or outside the patient's body. These nodes are capable of transmitting data to the sink node when any functional changes in the patient are detected. The routing and energy efficiency of the network nodes are challenging tasks in WBAN, as each sensor node operates on a battery. Therefore, in multi-hop routing designing a robust routing protocol has a significant impact on reducing energy consumption during the selection of the next hop. In this paper, a simple novel routing protocol named simple energy-aware and reliable (SEAR) routing protocol is proposed to transmit reliable data packets in a WBAN. The proposed routing scheme considers the remaining energy of sensor nodes, priority data, and hop count to the sink node as significant metrics for dynamically selecting the best forwarder node. Furthermore, the proposed protocol utilizes the route reliability factor (RRF) to select the optimal route among all possible routes between the source sensor node and the sink node. RRF selects the route with the maximum route residual energy and minimum hop count. As a result, SEAR has the ability to provide effective single-hop and multi-hop routing data transmission to improve the reliability of data transmission, decrease the energy consumption of the sensor nodes, and prolong the lifetime of the network. The simulation results show that the performance of the SEAR routing protocol outperforms the existing routing protocols for the metrics: packet loss ratio, throughput, end-to-end delay, normalized routing load, energy consumption, and network lifetime. The results indicate that the proposed protocol improves total energy consumption by 18.76% and 10.89% when compared to EERR-RLFL and AMCRP respectively. Meanwhile, SEAR reduces the average end-to-end delay by 17% and 9%, packet loss ratio by 29.48% and 17.69%, and normalized routing load by 31.17% and 20.91%. Additionally, SEAR achieves up to 16% and 9.71% higher throughput compared to EERR-RLFL and AMCRP routing protocols respectively. Overall, the results obtained indicate that the proposed SEAR routing scheme significantly enhances the overall performance of the network. [ABSTRACT FROM AUTHOR]

Recently, wireless sensor networks have been widely used for environmental and structural safety monitoring. However, node batteries cannot be replaced or easily recharged in harsh environments. Maximizing network lifetime remains a challenging issue in designing WSN routing. This paper introduces GTIACO, a novel metaheuristic clustering protocol. It employs an optimal cluster head function to determine cluster number and utilizes game theory for selecting optimal cluster heads. To optimize inter-cluster routing, improved ant colony optimization (ACO) is introduced to construct gathering paths from clusters to the base station. Both blind pathways, pheromone concentration, and angle factors are considered to improve path exploration and transmission efficiency in ant colonies. To assess network performance, various scenarios involving different base station placements and network densities are examined. Experimental results demonstrate GTIACO's superiority over LEACH, ACO, SEP, and PRESPE protocols in network lifetime, stability, energy, and throughput. The proposed GTIACO shows an improvement of at least 4.3% in network lifetime and 32.8% in network throughout. It exhibits superior stability and transmission efficiency across diverse network densities. [ABSTRACT FROM AUTHOR]

The growing ubiquity of Internet of Things (IoT) devices within smart homes demands the use of advanced strategies in IoT implementation, with an emphasis on energy efficiency and security. The incorporation of Artificial Intelligence (AI) within the IoT framework improves the overall efficiency of the network. An inefficient mechanism of parent selection at the network layer of IoT causes energy drain in the nodes, particularly near the sink node. As a result, nodes die earlier, causing network holes that further increase the control message overhead as well as the energy consumption of the network, compromising network security. This research introduces an AI‐based approach to parent selection of the Routing Protocol for Low Power and Lossy networks (RPL) at the network layer of IoT to enhance security and energy efficiency. A novel objective function, named Energy and Parent Load Objective Function (EA‐EPL), is also proposed that considers the composite metrics, including energy and parent load. Extensive experiments are conducted to assess EA‐EPL against OF0 and MRHOF algorithms. Experimental results show that EA‐EPL outperformed these algorithms in improving energy efficiency, network stability, and packet delivery ratio. The results also demonstrate a significant enhancement in the overall efficiency of IoT networks and increased security in smart home environments. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANETs) face inherent challenges in maintaining reliable data transmission because of their dynamic and unpredictable nature. This research conducts a comprehensive reliability analysis of data transmission in MANETs, emphasizing the influence of dynamic conditions such as node mobility, changing network topologies, and fluctuating channel conditions. Through the utilization of mathematical models and simulations, the study assesses the overall reliability of data transmission, considering scenarios with diverse node densities, mobility patterns, and network sizes. Existing routing protocols, error correction mechanisms, and adaptive transmission strategies are examined for their effectiveness in reducing the impact of dynamic conditions on reliability. The research not only examines the current state of protocols but also explores potential enhancements and introduces novel approaches to enhance data transmission reliability under dynamic conditions. The findings offer a nuanced understanding of the challenges and opportunities related to reliable communication in MANETs. This research provides crucial insights for designing resilient communication systems in dynamic and mobile network scenarios, offering valuable guidance to researchers, network designers, and practitioners involved in optimizing the performance of MANETs in real-world applications. [ABSTRACT FROM AUTHOR]