Amulti-hop routing protocol based on the Secretary Bird Optimization Algorithm (SBOA) is proposed to address the issues of limited node energy and unbalanced energy consumption in a three-dimensional (3D) Bridge Wireless Sensor Network (BWSN). This protocol offers a comprehensive solution that optimizes network lifetime and energy distribution. The method for calculating the optimal number of cluster heads (CHs) in 3D BWSN is presented. Secondly, the network is clustered using the Fuzzy C-means (FCM) algorithm, and suitable CHs are selected via the entropy weight method. Thirdly, during the inter-cluster routing phase, SBOA is utilized to find the optimal path between CHs and base stations (BS). Finally, a re-clustering mechanism is proposed to control the frequency of updating the cluster structure, enhancing the energy efficiency of the proposed protocol. The experimental results demonstrate that the proposed protocol improves network lifetime, energy efficiency, and energy consumption balance by 135%, 7%, and 59.3%, respectively, compared to current mainstream methods. Therefore, the proposed protocol exhibits good adaptability in 3D BWSN.

The Bridge Health Monitoring System (BHMS) has been widely implemented and advocated globally to replace traditional bridge management and maintenance practices. Employing wireless sensors for collecting and transmitting bridge monitoring data via a self-organizing bridge wireless sensor network (BWSN) is crucial for ensuring the efficient operation and cost-effectiveness of BHMS. To validate and enhance the performance of BWSN, this study develops a bridge space model (SM-SSB) based on the Simple-supported Beam Bridge, and introduces a Critical-TOPSIS Clustering (CTC) routing algorithm on SM-SSB, employing multi-criteria decision-making (MCDM) for optimal cluster head (Optimal-CH) and relay node (Optimal-RN) selection efficiently. The CTC algorithm’s main benefit is its holistic approach to considering factors such as the distance between nodes and the base station (BS), node densityC node residual energy, and the frequency of CH selection. Utilizing the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) algorithm enhanced by the CRITIC (CRiteria Importance Though Intercrieria Correlation) method (referred to as CRITIC-TOPSIS) sorting all nodes and then proceeds to quickly opt for the Optimal-CH and the Optimal-RN within the subspace of each bridge space model simultaneously. In the Matlab environment, the CTC algorithm’s performance is benchmarked against LEACH, Fuzzy-TOPSIS, CTC, EEM-CRP, and AROA algorithms, examining metrics like dead node distribution, network life cycle, packet reception rate, and variance of energy consumption. Simulation outcomes indicate that the CTC algorithm achieves a balanced energy consumption across nodes, significantly boosting the energy efficiency of bridge sensor networks and fulfilling the BWSN routing algorithm’s conditions.

Field observation systems are mainly deployed in the harsh natural environment. These systems principally focus on observation and study within the station currently, which leads to problems such as the inability to form combined network observation and quite challenging to answer the scientific questions of wider regions and scales. To form Field Observation Instruments Networks (FOINs) and accelerate the general automation rate as well as in real-time data exchange in field observation, a multi-objective decision-making mehod named Entropy-based TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) Clustering routing algorithm (ETC) for FOIN is proposed in this paper. The ETC algorithm can select the optimal cluster head (Optimal-CH) through multi-objective decision-making and mainly solves the problem that some existing multi-objective optimization algorithms cannot dynamically and objectively allocate weights. The ETC algorithm was compared with some latest work and similar kinds of work from network lifespan, the number of CH and energy consumption in the Matlab simulations experiments. The result shows that the ETC algorithm performs well, enhancing energy conservation and extending the existence of FOIN.

Field stations and observation systems in cold and arid areas are mostly distributed in harsh natural environments which lead to problems such as the poor real-time data collection, transmission and processing, lower accuracy of data and failure to form integrated network observation, etc. To solve these problems which severely restrict scientific monitoring and research in these areas, a Layered and Heterogeneous Clustering routing algorithm LHC is proposed for field observation instrument networks based on the classical clustering routing algorithm LEACH. First, the LHC algorithm adopts the mechanism of heterogeneous node energy to divide nodes into advanced nodes and normal nodes. The advanced nodes have more initial energy than normal nodes, which increases their probability to be elected as cluster heads (CHs). Then, a hierarchical structure is used to divide the network into several layers, each layer elects a fixed number of CHs, and the distribution of CHs is improved. Finally, by analyzing the influence of the residual energy of the node and the distance between the node and the base station (BS) of the network, the mechanism based on energy and distance factors is introduced into each round of CH election to improve the CH election method. In Matlab experiments, the improved LHC algorithm was compared with LEACH, SEP, and DEEC through analysis and comparison from the aspects of network life cycle, energy consumption and CH number. The experimental results show that the LHC algorithm has the advantages of uniform CH distribution and balanced node energy consumption which effectively improve the energy efficiency and data transmission capacity, and prolong the life cycle of the observing instrument network. The LHC algorithm provides an important routing protocol for observing instrument network and real-time reliable data transmission.

Wireless sensors have the advantages of distributed and flexible deployment, real-time monitoring, multi-sensor collaboration, self-organization, and self-adaptation. They can be widely used in bridge structural health monitoring systems (BHMS). At the same time, wireless sensors are still limited by battery life and communication distance; extending the life cycle of wireless sensor networks (WSN) employing optimal deployment and effective improvement of routing algorithms is a research hotspot for domestic and foreign engineering and academic circles. In this paper, a WSN clustering routing algorithm based on bridge spatial model and multi-objective fuzzy inference (BSMOFC) is proposed to solve the problem that the existing research has not considered the spatial characteristics of bridges and the reasonable choice of cluster head (CH) in the clustering routing algorithm. Firstly, a bridge space model is designed to simulate the three-dimensional deployment of WSN nodes; secondly, the CH selection process of the WSN clustering routing algorithm is defined as a multi-objective optimization problem, and the multi-objective Non-Dominated Sorted Whale Optimization Algorithm (NSWOA) is used to solve the set of Pareto solutions. All the solutions are defined as the candidate cluster heads (C-CHs), and then a fuzzy inference system is used to choose the final cluster heads (F-CHs). Finally, the simulation platform MATLAB completes the performance verification of the BSMOFC algorithm. The simulation results show that the BSMOFC algorithm can effectively extend the WSN lifetime and provide a new practical method for selecting a Pareto optimal solution.

China possesses vast and valuable natural resources in its expansive cold and arid regions, but the natural environment is harsh. Field stations and observation systems primarily emphasize in-station observations and research. Nevertheless, these systems face challenges such as limited energy resources, inability to cope with the dynamic nature of the environment, and obstacles in conducting extensive and large-scale scientific research. To advance the automation level of field observation data, enhance real-time data transmission performance, and bolster the adaptive capabilities of Field Observation Instrument Networks (FOINs), a novel method named a routing protocol based on multi-objective mayfly optimization algorithm (MMA) for solar energy dynamically supply of FOIN (DSFOI-MMA) is proposed in this paper. The proposed protocol first designs an energy harvesting model based on the dynamic nature of solar energy to ensure energy supply for low-energy nodes in the network, and then dynamically elects the optimal cluster head via MMA. Secondly, the idea of virtual complete graphs is introduced to construct inter-cluster transmission paths. Finally, the Matlab simulation platform is utilized to validate the effectiveness of the DSFOI-MMA. Extensive experiments and analyses have been performed to compare with existing major approaches in terms of network lifetime, number of cluster heads, etc. The results demonstrate the excellent performance of the developed protocol, which enhances the automation of monitoring data and provides a reliable guarantee of experimental data for in-depth scientific research and development in cold and arid regions.