An area autonomous routing protocol based on multi-objective optimization methods for field observation instrument network
Graphical abstract
Introduction
China’s cold and arid regions account for more than two-thirds of the total land area. The ecological environment is fragile but contains indispensable resources for the national economy and has a prominent strategic position [1]. Field stations deployed in cold and arid areas acquire scientific data through long-term and continuous monitoring of the areas by the Field Observation Instrument Network (FOIN) to realize in-depth studies of the monitored areas. Although the limited energy of nodes is improved by an auxiliary power supply of solar cells, the harsh environment leads to the problem of insufficient battery charging. The efficient use of energy is still one of the key problems to be resolved in the FOIN. Additionally, the existing field observation stations mainly focus on in-station observation and research, resulting in a series of problems such as delayed information acquisition, poor real-time performance, low data acquisition and processing accuracy. These problems seriously restrict the in-depth research and further development of cold and arid regions [2]. Hence, it is necessary and challenging to design an efficient routing protocol according to the characteristics of FOIN.
The Wireless Sensor Network (WSN) is a widely used network consisting of a large number of low-cost, energy-limited sensor nodes which are functional—that is, which monitor the monitoring area instantaneously and at the same time send monitoring data to the Base Station (BS) [3]. Depending on the research and analysis of field observation stations, both FOIN and WSN have many comparable characteristics like self-organization, dynamic topology, multi-hop networks, and wireless transmission [4]. Consequently, the FOIN routing protocol is researched by utilizing WSN theory and technology.
Designing of WSN routing protocols based on clustering, sensor nodes are hierarchically organized into clusters by clustering. The cluster head (CH) is responsible for gathering data from its member nodes and transmitting data to the BS after fusion, which reduces the amount of data transmission and effectively extends the network lifecycle. While CHs consume plenty of energy because of multi-tasking. It is necessary to consider the methods of CH election, the cluster formation, and the communication between CH and BS when introducing the idea of clustering in the design of the FOIN routing protocol. Meanwhile, CH election is a multi-objective optimization problem, which is affected by various factors such as energy, distance, node density, transmission delay, etc. To optimize CH election, balance energy consumption, and prolong the lifecycle of the network, an area autonomous routing protocol based on multi-objective optimization methods for FOIN is proposed, which provides powerfully technical support for observation of comprehensive connected network and more in-depth scientific research in cold and arid areas.
The main work done is as follows in this paper:
- (1)
The optimal number of CH is calculated, and the network is evenly partitioned.
- (2)
BS utilizes the non-dominated sorting genetic algorithm II (NSGAII) or Particle Swarm Optimization algorithm (PSO) to obtain the Pareto optimal solution set of the CH election problem, and then the entropy method comprehensively scores the Pareto optimal solution set, thereby selecting the optimal CHs in each partition.
- (3)
A relay function is defined to select a reasonable relay node to forward data.
This paper is structured as follows. The related literatures are reviewed in Section 2. The FOIN model is reported in Section 3. Section 4 introduces multi-objective optimization problems based on Pareto optimal solutions, defines the multi-objective optimization functions for CH election in FOIN and details the FOI-MOC protocol (FOI-MOC-NSGAII, FOI-MOC-PSO). Simulation results and comparative analysis with the other algorithms are illustrated in Section 5. Section 6 states the concluding remarks and future work, respectively.
Section snippets
Related works
The clustering algorithms mentioned in related works are classified and organized in terms of whether they adopt intelligent optimization algorithms, specifically conventional clustering algorithm and intelligent optimization clustering algorithm. Intelligent optimization clustering algorithm is further divided into normal optimization clustering algorithm and multi-objective optimization clustering algorithm, as shown in Fig. 1.
Model of FOIN
Fig. 2 demonstrates a real-world implementation of the FOIN clustering routing algorithm. According to the operating process, the instrument network node mounted in the observation region elects the CHs to form related clusters, and the member nodes in the cluster transmit data to corresponding CH, which then fuses the data and transmits it to the BS.
Multi-objective optimization problems based on Pareto optimal solutions
Some topics need to be introduced in order to have the basics for this study, namely the basic concepts and definitions of a multi-objective optimization problem.
Simulation and analysis
To analyze the performance of the FOI-MOC protocol, the simulations are developed in MATLAB 2018b. The LEACH protocol, EEUC protocol, PECRP protocol, and ECDC protocol are compared and analyzed under the same experimental conditions.
Conclusion
Since there are similarities between FOIN and current WSNs with relatively mature technologies, an area autonomous routing protocol based on multi-objective optimization methods FOI-MOC is designed for FOIN based on related technologies of WSN, such as cluster structure, network partitioning, and multi-hop communication mechanism. The protocol defines the CH election problem as a multi-objective optimization problem and constructs different objective functions problem based on three influencing
CRediT authorship contribution statement
Jiuyuan Huo: Funding acquisition, Resources, Methodology, Supervision, Writing – review & editing. Shubin Lu: Investigation, Formal analysis, Methodology, Writing – original draft. Jiguang Yang: Writing – review & editing. Lei Wang: Supervision, Review & editing. Hamzah Murad Mohammed AL-Neshmi: Supervision, Review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work was supported by the National Nature Science Foundation of China (61862038, 62262038), the Double-First Class Major Research Programs, Educational Department of Gansu Province, China (GSSYLXM-04), the Gansu Province Science and Technology Program - Innovation Fund for Small and Medium-Sized Enterprises, China (21CX6JA150), the Lanzhou Talent Innovation and Entrepreneurship Technology Plan Project, China (2021-RC-40), and the Foundation of a Hundred Youth Talents Training Program of
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