A trusted distributed routing scheme for wireless sensor networks using blockchain and meta‐heuristics‐based deep learning technique

Wireless sensor network (WSN) has key constraint on energy of sensor nodes i.e. each nodes have finite amount of energy. Seeing energy as key constraint WSN must has robust energy optimized routing algorithm. Mostly routing approach use shortest path and minimal energy depletion model. Network operate on these routing scheme early partitioned due to unbalanced energy consumption of sensor nodes i.e. a single shortest route may used most of the time leads to faster consumption of energy of sensor nodes coming in route path. In current paper attempts has made to develop energy efficient routing scheme based on optimization techniques "particle swarm optimization (PSO) and genetic algorithm (GA)". We implemented all four routing scheme i.e. shortest path approach, PSO approach, GA approach and hybrid PSO-GA approach for small size as well as large size sensor network. Simulation results depicts hybrid PSO-GA routing scheme raised 12% to 23%, 8% to 15% and 5% to 13% lifetime compared to shortest path approach, PSO approach and GA approach respectively for large scale network. For large scale network hybrid PSO-GA raised 9% to 16%, 6% to 11% and 5% to 9% packet delivery ratio compared to shortest path approach, PSO approach and GA approach respectively.

Scalable routing in wireless sensor network (WSN) is required for future applications to provide high-quality and robust transmission. In this paper we propose a Polychromatic Sets based scalable Routing (PSR) scheme for WSNs. PSR is a hierarchical routing scheme designed to scale to the network size. This scheme firstly partitions the network into multiple clusters which are organized as a multilevel hierarchical model. Then intra-cluster or inter-cluster routing is applied to increase the scalability. The clustering techniques and routing scheme are validated by comparing with existing protocols. Simulation results clearly show the superior throughput and considerable low packets delay of PSR against AODV and FSR in terms of scalability and robustness to network size.

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Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities.

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).

For Wireless Multimedia Sensor Networks (WMSN), how to increase the throughput in constrained environments has become the main challenge. Multipath routing is a promising solution because it can improve the channel utilization rate, reduce transmission delay and balance the transmission load. To pursue high efficiency for a multipath routing scheme, we need to take the characteristics of wireless environments and sensor networks into careful considerations. Based on such considerations, this paper introduces a new routing scheme which divides the whole network topology into many districts and simultaneously forwards data through these districts without interfering with each other - to achieve interference-free transmissions. Our scheme will adjust the load of each district according to the energy status of their nodes; it does not establish fixed paths in advance and will hence maintain high performance even when the topology changes rapidly. Simulation results show that, when compared with related routing schemes, our scheme can achieve higher performance at real-time transmission and meanwhile distribute transmission loads more evenly to most of the nodes in the network.

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An optimal routing and data aggregation scheme for wireless sensor networks is proposed in this paper. The objective is to maximize the network lifetime by jointly optimizing data aggregation and routing. We adopt a model to integrate data aggregation with the underlying routing scheme and present a smoothing approximation function for the optimization problem. The necessary and sufficient conditions for achieving the optimality are derived and a distributed gradient algorithm is designed accordingly. We show that the proposed scheme can significantly reduce the data traffic and improve the network lifetime. The distributed algorithm can converge to the optimal value efficiently under all network configurations.

Amid the rapid development of information and communication technologies, there is an increasing demand for efficient and reliable data routing in next-generation computer and telecommunication systems. Wireless Sensor Networks (WSNs), which are widely applied across various domains – from environmental monitoring to healthcare and industrial automation – require a customized approach to routing strategy selection due to their constrained energy resources, dynamic topologies, and high node densities. Given the growing diversity of network architectures and the rising performance requirements, it becomes essential to identify optimal routing mechanisms capable of balancing delivery latency, throughput, energy consumption, and fault tolerance. This paper presents a structured review and analysis of recent studies dedicated to data transmission characteristics in WSNs. Particular attention is given to the importance of routing strategy classification in the context of designing an efficient WSN architecture, as it enables not only the systematization of existing solutions but also the justification of the most appropriate approach based on the network parameters. A comprehensive classification of routing strategies is shown, including: by route discovery method (proactive, reactive, hybrid), by network architecture (flat, hierarchical, location-based); by operational model (multipath, query-based, negotiation-based, QoS-based, and data-centric (further categorized into coherent and non-coherent approaches); by communication initiator (source-based and destination-based). The study explores the implementation specifics of each strategy, highlighting their advantages and potential limitations in the context of WSNs operating under energy and computational constraints. It has been found that the choice of an appropriate routing strategy significantly affects data transmission efficiency, network stability, delivery delays, and energy expenditure – key parameters for the practical deployment of WSNs across application areas ranging from environmental monitoring to industrial systems. The results obtained in this review may be used to improve routing algorithms and adapt them to specific operational conditions of sensor networks.

Two kinds of sensors will be discussed in some sort of wireless sensor networks (WSNs). One is named a normal sensor (called A_nodes) with fixed initial energy, which can get perception data from the surrounding environment and have functions of storage and forwarding. The other is named relay sensor (called B_node) with sufficient energy, which only can store data and forward data. Cluster heads (called A_heads) are chosen among A_nodes by probability mode to create clusters first. Local adjustments would be done inter-cluster. Then the sink is selected as the root and a backbone shortest path tree with hops limited is built dynamically from A_heads, B_Nodes and sink. Inside the backbone shortest path aggregation tree, two steps are done. The first step, select some B_node as cluster head (denoted as B_node) and make local adjustment intra-clusters if it is possible. The second step, adjust the shortest path aggregation tree. By using both particle swarm optimization (PSO) and ant colony optimization (ACO), a traveling salesman problem (TSP) cycle with shorter path length is obtained (this method is noted as TSP_PSO_ACO). This TSP cycle consists of all nodes in the aggregation backbone tree. Along this cycle, energy is offered to nodes except sink. The above strategy would be discussed for its feasibility and time complexity. The experimental results imply that the energy consumption is reduced by using local adjustment. The relative failure rate of nodes can be reduced in the later stage of WSN survival. Offering energy to a small number of nodes several times has no obvious impact on the overall energy consumption and survival time of WSN. The case with some small-sized obstacles in WSN area is also discussed. There are little affections on TSP cycles both from results on theoretical analysis and simulation if there are some obstacles with small size in WSN area.

To provide proper coverage of their random deployment regions, wireless sensor networks (WSN) should employ abundant nodes. In this paper we correct such situations by employing some mobile robots as mobile nodes in WSN which can actively move to desired locations for repairing the broken networks. According to the pre-research work we know that the number of nodes employed by WSN is closely relevant to quality of service (QoS) in sensing coverage when the nodes are randomly deployed in the target region. A modified particle swarm optimization (PSO) named particle swarm genetic optimization (PSGO), which imports selection and mutation operators in PSO to overcome the premature fault of classical PSO, is proposed to redeploy the mobile robots according to the node density for repairing the sensing coverage hole after their initial random deployment. It is suggested by the simulated experiment results that the WSN employing the mobile robots can improve the QoS in sensing coverage than the stationary WSN by redeploying mobile robots according to the node density.

Wireless sensor networks (WSNs) have widespread applications in vital fields, including environmental surveillance, medical care, and smart urban settings. Nevertheless, the restricted battery capacity of sensor nodes renders energy consumption an ongoing hindrance, limiting both the performance and operational lifespan of a network. To mitigate this challenge, this study proposes an enhanced clustering and routing scheme, the Black-Kite Optimization–Time Of Arrival (BKA-TOA) algorithm, which jointly optimizes cluster head selection and data transmission. The proposed BKA-TOA integrates the bio-inspired Black-Kite Optimization (BKA) with relay selection based on the Time of Arrival (TOA) technique. A multidimensional fitness function is constructed by incorporating inter-node distance, energy variance, and cluster head distribution to achieve robust clustering, balanced energy consumption, and improved scalability. Extensive simulations conducted in Matlab R2024a are used to benchmark the proposed algorithm against Particle Swarm Optimization (PSO), Improved Ant Colony–minimum spanning tree (IACO-MST), and Energy-Efficient Uneven Clustering (EEUC). The experimental results indicate that BKA-TOA significantly reduces node mortality, improves residual energy preservation, and prolongs the operational lifetime of WSNs compared with competing algorithms.

Lifetime is a vital performance metric for wireless sensor networks (WSNs), with current hierarchical routing protocols suffering from the premature death of overloaded nodes, affecting the network’s lifetime. Spurred by this problem, we propose a low-energy first electoral multipath alternating multihop (LEMH) routing scheme. To avoid overloading the nodes, these are given a contention radius that adaptively changes with the topology and affords the nodes to combine the remaining energy to vote for those participating in the cluster heads election process. Then, the running nodes decide the final cluster head based on two-dimensional competition parameters. Finally, the traditional multihop communication shortcomings are addressed by formulating an energy balance lifetime maximization routing problem based on sequential quadratic programming (SQP) under alternating multihop. This problem is optimally solved using particle swarm optimization (PSO), providing an improved multihop transmission scheme for WSNs. The simulation results demonstrate that LEMH effectively extends the network’s survival time and improves performance.

Over the past few years, the devices in Wireless Sensor Networks (WSN) are growing exponentially due to the emergence of many sophisticated applications. This tremendous growth leads to serious security challenges, and the devices of WSN should be protected from various attacks. WSN can be configured dynamically without fixed infrastructure and the devices can be talked with one another in an ad-hoc manner. Due to the dynamic nature of WSN, routing is considered as the challenging task that should be performed efficiently with robust routing mechanism. Even though many routing schemes have been emerged for WSN, they are not well scalable in very large-scale environment. This work introduces multi path routing strategy, and the routing will be selected based on trusted nodes. First, the trusted nodes are identified using trusted metrics of each node in the network. These metrics are calculated based on the threshold value of nodes. Then, secure routing is established by isolating node capturing attacks from the path. The performance of the work is analyzed in terms of packet loss, computational time and throughput. The paper compares the performance with the state-of-the-art routing schemes such as EMBTR (Enhanced Multi Attribute Based Attack Resistance), TSRM (Trust based secure routing model), and TARF (Trust-aware routing framework for WSNs). The outcome of the simulation shows that the proposed scheme outperforms the other state-of-the-work in terms of computational cost, throughput, and delay.