Innovative Hybrid Framework for Routing and Clustering in Wireless Sensor Networks with Quantum Optimization

A wireless sensor network (WSN) is made up of sensor nodes that communicate via radio waves in order to conduct sensing functions. In WSN, the location of the base station is critical. Although base stations are fixed, they may move in response to data received from sensor nodes under specific conditions. Clustering is a highly efficient approach of minimising energy use. The issues of extending the life of WSNs and optimising their energy consumption have been addressed in this paper. It has been established that integrating mobile sinks into wireless sensor networks extends their longevity. Thus, this research proposes an optimal clustering and routing technique for optimising the energy usage and lifetime of WSNs. To minimise energy consumption, this research employs movable and stationary sink nodes. The K-Medoid clustering model is used to generate the initial number of nodes in the various clusters. After that, the cluster head is chosen using a hybrid Interval Type-2 Fuzzy technique that takes three aspects into account: residual energy, node centrality, and neighbourhood. A highly efficient backup cluster head (CH) collecting system can provide in significant energy savings while also prolonging the system’s life. Finally, better Reinforcement learning combined with a Genetic algorithm routing protocol is used to ensure effective data delivery. The suggested approach’s efficacy is evaluated in comparison to earlier approaches utilising residual node energy, delay or average delay, packet delivery ratio, throughput, network longevity, average energy consumption, and multiple alive nodes. In experiments, the proposed strategy outperforms existing strategies.

Wireless Sensor Networks (WSNs) are essential in fields such as agriculture, education, industry, and environmental monitoring. However, given their limited energy supplies and occasional maintenance, creating energy-efficient network topologies and routing methods is a considerable problem. This work presents an approach to enhance the longevity of networks in WSN. This approach employs advanced fuzzy C-means clustering, modified particle swarm optimization (MPSO) for cluster head selection, and routingbased adaptive zebra optimization (AZO). The adaptive zebra optimization approach relies on efficient routing paths chosen for every relay nodes to send data to the base station (BS). The proposed method illustrates that the relay node facilitates and optimizes communication between the sink and the cluster head, thereby enhancing energy efficiency. Ultimately, the quality of service (QoS) in WSNs improves, enabling the system to sustain long performance. The suggested technique demonstrated superior performance compared to prior methods for energy consumption, throughput, node viability, end-to-end latency (E2E), and packet delivery ratio (PDR).

ABSTRACTRecent advancements in information technology have led to the widespread adoption of the Internet of Things (IoT) across various applications. Wireless sensor networks (WSNs), consisting of low‐cost, compact sensors, are crucial for IoT systems, enabling data collection for tasks like surveillance and tracking. A major challenge in WSNs is achieving energy efficiency while extending network lifetime (NLT), necessitating effective clustering and routing strategies. Numerous existing methodologies for energy‐efficient clustering and routing exhibit potential; however, they are hindered by constraints including inadequate adaptability to fluctuating network conditions, suboptimal selection of s cluster heads (CHs), and uneven energy consumption, resulting in diminished network longevity and efficacy. These issues require novel strategies to improve overall performance. To tackle this issues, this research presents a novel hybrid technique combining fuzzy logic with barnacles mating optimization (FL‐BMO) to identify the most optimal CHs by evaluating critical criteria like average sink distance, average intracluster distance, residual energy, and CH balance factor. The FL‐BMO methodology utilizes fuzzy logic to address uncertainties in sensor data, and the BMO algorithm, modeled after barnacle mating patterns, offers a resilient and adaptable optimization process, markedly enhancing energy efficiency and network longevity. In addition, an innovative natural‐inspired hybrid cross‐layer sunflower optimization routing (NiHCLR‐SFO) technique has been introduced that entails optimal routing path selection. This approach balances exploration and exploitation during a route selection process, integrating multiple layers of the network functionality which eventually results in improved routing efficiency and network throughput. Such a hybrid approach has been implemented in MATLAB. The proposed method is compared with fuzzy reinforcement learning based data gathering (FRLDG), neuro‐fuzzy‐emperor penguin optimization (NF‐EPO), bio‐inspired cross‐layer routing (BiHCLR), and fuzzy rule‐based energy‐efficient clustering and immune‐inspired routing (FEEC‐IIR) protocols. From these comparisons, it was observed that the method propagates definite NLT gains reaching 39.74%, 32.92%, 15.95%, and 4.8076%, respectively. The proposed method outperforms the existing approaches (FRLDG, NF‐EPO, FEEC‐IIR, and BiHCLR) across several performance parameters: 99% packet delivery ratio (PDR), 2.8 ms of end‐to‐end delay time (E2ED), 1 Mbps of throughput, 30 mJ of energy consumption, 6000 rounds NLT, 2% bit error rate (BER), 1.25 buffer occupancy ratio, and 0.5% of packet loss ratio (PLR).

Clustering is an effective method for reducing energy consumption in wireless sensor networks (WSNs). In a multihop clustered network scenario, each sensor node transmits data to its own cluster head (CH), and the CH aggregates the data from its member nodes and forwards it to the base station (BS) via other CHs. However, the “hot spot” problem is prone to occur in clustered WSNs because CHs closer to the BS have heavier intercluster forwarding tasks. To address this problem, this paper proposes an unequal clustering algorithm based on interval type-2 TSK fuzzy logic theory (UCT2TSK). The relative distance to the BS (RDB), residual energy (RE), and node density (ND) are considered as the inputs of an interval type-2 fuzzy logic system (FLS). Through fuzzy reasoning, outputs are acquired that can be used to optimize the CHs and determine the cluster sizes. Simulation results verify that UCT2TSK can effectively balance energy consumption and enhance energy efficiency because it has better performance in network lifetime and network throughput than other classical and recent clustering algorithms.

<span>The wireless sensor networks (WSNs) believed to be as a hot research area due to their ranges in various fields of applications. Clustering is the more efficient techniques considered to resolve the problems of energy consumption in WSNs. The clustering has assured its effectiveness, specifically with high dynamic networks at which all nodes are represented in the clustering and all the data value can be collecting by sensor nodes with a cluster head (CH). The energy consumption as well as network traffic are balancing, the (CH) must be exchanged between all sensor nodes and the clustering size could be closely determined at various portions of the network. The key point at issue in WSN is to improve the energy-efficient algorithms with Grid based clustering for extending the lifetime of the networks, reducing cost, in addition to increasing the reliability of the network. The sensor nodes prepared with a power sources at limited period, so that the Grid Based Clustering algorithm (GBC) which efficiently employing sensor nodes energy have the ability to preserve an extended network lifetime. Sensor node Performances of the network mainly depend on the routing protocols, which are application based, four routing protocols LEACH, OLSR, AODV, and DSR in clustering WSN are estimated with Qualnet simulator. The results demonstrate that DSR outperforms in term of throughput, and end to end delay which compared to others while OLSR have highest value of half number of alive nodes per round.</span>

Recent trend to steer Wireless Sensor Networks towards a broad outdoor environment allows to monitor harsh areas and prevent a disaster situation. To guarantee an extensive work of the system and smooth cooperation among involved devices a new clustering approach needs to be found. In this paper, we present a new algorithm for an Event-oriented Focal Weight-based clustering for Wireless Sensor Network which will be able to monitor various parameters (physical, biological, and chemical) of the outdoor environment and signal the current situation further to the network. Shown results demonstrate the applicability of the algorithm for the large amount of nodes with various power levels. The use of a dual-band transceiver and advanced processing elements allow to accommodate new features in the nodes and guarantee a high performance with the low power consumption which is crucial for the outdoor system with no human attendance.

The technology of Heterogeneous Wireless Sensor Networks (HWSNs) is critical to the efficient operation and deployment of a variety of real-time Internet of Things (IoT) and Mobile Ad hoc Networks (MANETs). It is crucial for reducing overall energy dissipation and ensuring consistent energy distribution throughout the network. Bio-inspired hybrid optimization algorithms are emerging as a possible option for overcoming basic difficulties in Wireless Sensor Networks (WSNs), with a focus on sensor lifespan restrictions. A significant topic that must be considered prior to network configuration is attaining energy efficiency and optimal communication. Several papers have been published on the use of bio-inspired algorithms in WSNs. Few articles, however, addressed the hybrid strategy for routing and clustering in WSNs with communication. This research focuses on hybrid bio-inspired optimization algorithms and elaborates on their taxonomy and problem domains in WSNs. Furthermore, we explored and investigated the hybridization of the Whale Optimization Algorithm (WOA) with other meta-heuristic algorithms such as the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and others. This review can assist researchers in exploring the uses of such algorithms within and outside of this study area.

Wireless sensor networks (WSNs) are formed using a cluster of sensor nodes (SNs), deployed randomly to perform sensing operations in an area under observation. Due to the unavailability of an external power source, the energy efficiency considered as one of the critical issues in WSNs. Selection of a sensor node (SN) from a wireless sensor network (WSN) cluster to serve as an aggregator or cluster head (CH), considered as an efficient method to increase the lifetime of wireless sensor network (WSN). In this paper, we have proposed an energy efficient CH selection scheme for WSN, to enhance the lifetime and average residual energy of a single WSN cluster. The proposed strategy nominates a group SNs as CHs, based on their channel condition with the base station (BS) and their residual energy. The proposed algorithm is helpful in solving the problem of unbalanced energy consumption in WSNs. Furthermore, the mechanism of using mobile sink during the hand-off stage helps to overcome the delay in data transmission. Moreover, the incorporation of energy harvesting significantly increases the lifetime of WSN. In comparison to a state-of-art technique available in the literature, our scheme shows a 33% increase in lifetime and presents a steady decrease in residual energy for the same rounds of data transmission.

Wireless sensor networks (WSNs) has acquired more attention because of applications in various fields. The sensors have limited power sources as they cannot be replaced because of hostile nature. In this study, Genetic Algorithm (GA) based approaches for routing and clustering in WSNs have been proposed. The significant purpose of the process is to enhance lifetime of sensor and Quality of Service (QoS). The results illustrate that proposed algorithms such as LECR-GA or NLCR-GA works better than the existing algorithm with respect to several performance metrics such as number of packets, energy consumption acknowledged by the base station.

Wireless Sensor Network consists of a large population of sensor nodes, capable of computation, communication and sensing. Limited energy is the inherent limitation of wireless sensor network; most routing algorithms for sensor network focus only on energy efficient paths. In wireless sensor networks, unreliability of the links and the limited resources create considerable obstacles to routing. Even in the presence of static nodes, the channel conditions may vary because of multipath fading. Due to this, path break may occur more frequently and a considerable amount of energy is consumed to set-up new path. Ideally, routing algorithm should consider not only energy efficient path but also the robustness of path. In this paper, we are introducing two new metric like threshold and energy cost to find link quality and energy efficient path respectively. These two metric gives rise to the design of Distributed Link Quality Aware Routing (DLQAR) for wireless sensor network. DLQAR is designed to generate routing paths in a decentralized manner, while considering the energy efficiency and robustness of paths. Network lifetime, energy consumption and Quality of Service (QoS) parameters are used to analyze effectiveness of proposed algorithm. Comparative analysis of DLQAR with the widely used AODV shows that energy cost along with link quality should be considered to minimize energy consumption and maximize QoS parameters.

Industrial Internet of Things (IIoT) finds use in several industrial applications like robots, medical devices, and software-defined manufacturing processes. Apart from the promising benefits of the IIoT networks, several challenging issues need to be resolved, such as network connectivity, security, privacy, heterogeneity, scheduling, and energy efficiency. Due to the large-scale deployment and heterogeneity of the nodes in IIoT networks, some energy-limited nodes have existed in the IIoT networks, resulting in reduced network lifetime. At the same time, security and privacy are also considered as the major issues that exist in the design of IIoT, which can be addressed using secure routing techniques. In this view, this study develops a trust-aware multiobjective metaheuristic optimization-based secure clustering with route planning (TAMOMO-SCRP) technique for cluster-based IIoT environment. The presented TAMOMO-SCRP technique mainly focuses on the design of bald eagle search (BES) algorithm for clustering and routing processes. The proposed TAMOMO-SCRP model derives a fitness function for accomplishing maximum energy efficiency and security. For an effective clustering process, the TAMOMO-SCRP model designs an objective function involving four parameters such as trust level (TL), communication cost (CC), residual energy (RE), and node degree (ND). Besides, the route selection process is based on the fitness function with two variables, namely, queue length and link quality. For assessing the enhanced performance of the TAMOMO-SCRP model, a wide range of experiments were carried out to get the outcomes of network life time(NLT) as 39451,half network die (HND) as 25950 and Stability period (SP) 8000 time calculated no. of alive nodes. The achieved outcomes make sure the better performance of the TAMOMO-SCRP technique against the other recent approaches.

Landmark-based routing (LR) provides a promising approach for scalable point-to-point routing in wireless sensor networks (WSNs). Though various approaches have been proposed for landmark-based routing, they either introduce significant computational complexity or are inefficient in realistic, dynamic environments. In this paper, we identify three design principles that could form the basis of efficiency: algorithmic simplicity, update efficiency, and application awareness. Motivated by these principles, we present XLR, a new, flexible and comprehensive framework that tackles the inefficiency of landmark-based routing. XLR consists of four components: Relay Selection (RS), Parametric P-Norm distance function (PPN), Efficient Update with Coordinate Difference (EUCD) and General Forwarding (GF). The key advantage of XLR is that any subset of XLR's components can be independently incorporated into most landmark-based routing protocols.We perform extensive simulations to demonstrate that: (i) RS, a simple method, yields good performance comparable with previous methods, (ii) PPN increases LR performance considerably, (iii) EUCD reduces coordinate update overhead by up to 39%, and (iv) our GF outperforms previous approaches that consider factors such as link quality, delay and power consumption independently.

Wireless Sensor Networks (WSNs) are made up of small nodes which have the ability to sense, compute and communicate wirelessly. Essential design issue in WSN routing is energy awareness as the sensors are energy constrained. Various routing, power management and data dissemination protocols designed for WSNs are available in the literature. Energy awareness and reliable data transmissions are handled in Medium Access Control (MAC) and Network layers. Decisions to achieve data reliability and energy efficiency trade-off were considered in layers. This study considers mobility, energy and link qualities to ward off poor link connectivity which reduce retransmissions and prolong WSN life. To achieve this, a fuzzy based cross layer protocol is proposed using an enhanced MAC protocol to provide better contention during mobility of the node and a network layer protocol based on link quality and mobility is proposed. Input to the fuzzy system is link quality and mobility. Output is decision for cluster head selection.

These Wireless Sensor Networks are widely used in applications such as environmental monitoring, smart cities, and industrial automation, where energy efficiency remains a critical challenge due to limited battery resources of sensor nodes. This paper proposes a Hybrid Energy-Efficient Intelligent model that integrates deep learning, swarm optimization, and reinforcement learning to minimize energy consumption and enhance network lifetime. The model combines a Bidirectional Long Short-Term Memory model for energy prediction, Particle Swarm Optimization for optimal cluster head selection, and an Actor-Critic reinforcement learning model for adaptive routing. The proposed approach used temporal energy patterns to prevent premature node failures, balances energy distribution through optimized clustering, and dynamically selects routing paths to reduce communication overhead. Extensive simulation results demonstrate that the proposed HEEIF framework achieves up to 80% energy reduction, improves network lifetime by 20-25%, and maintain a high packet delivery ratio of 94-96% compared to conventional methods such as LEACH, LSTM-based, and RL-based approaches. Additionally, the framework improves throughput, reduces delay, and enhances load balancing. The results indicate that integrating prediction, optimization, and adaptive learning that allows the scalable and efficient solution for next-generation energy-aware WSNs.

Wireless Sensor Networks (WSN) play a vital role in several real-time applications ranging from military to civilian. Despite the benefits of WSN, energy efficiency becomes a major part of the challenging issue in WSN, which necessitate proper load balancing amongst the clusters and serves a wider monitoring region. The clustering technique for WSN has several benefits: lower delay, higher energy efficiency, and collision avoidance. But clustering protocol has several challenges. In a large-scale network, cluster-based protocols mainly adapt multi-hop routing to save energy, leading to hot spot problems. A hot spot problem becomes a problem where a cluster node nearer to the base station (BS) tends to drain the energy much quicker than other nodes because of the need to implement more transmission. This article introduces a Jumping Spider Optimization Based Unequal Clustering Protocol for Mitigating Hotspot Problems (JSOUCP-MHP) in WSN. The JSO algorithm is stimulated by the characteristics of spiders naturally and mathematically modelled the hunting mechanism such as search, persecution, and jumping skills to attack prey. The presented JSOUCP-MHP technique mainly resolves the hot spot issue for maximizing the network lifespan. The JSOUCP-MHP technique elects a proper set of cluster heads (CHs) using average residual energy (RE) to attain this. In addition, the JSOUCP-MHP technique determines the cluster sizes based on two measures, i.e., RE and distance to BS (DBS), showing the novelty of the work. The proposed JSOUCP-MHP technique is examined under several experiments to ensure its supremacy. The comparison study shows the significance of the JSOUCP-MHP technique over other models.