## 1 Introduction  
Recent advancements in Information Technology (IT) have opened the door to the creation of numerous and diverse devices and services. Miniaturized wireless or wired sensor devices allow not only the automation of manual work but also data collection from regions that humans are not able to access directly. Such sensors possess routing functions for autonomous topology configuration and basic sensing functions for data collection. The number of sensors required for a particular topology ranges from dozens to hundreds and even thousands. Research on the effective transfer and application service of big data that are collected in these topologies is currently being actively conducted in many areas, including government and organizations. In the case of big data, three large topologies are considered [1; 2; 3; 4; 5; 6; 7; 8; 9; 10].  
The first is Fixed Topology (FT), which is structured in Fixed Sensor Nodes (FSNs) or Static Sensor Nodes (SSNs) where the sensor nodes are in a fixed location according to the user settings. FT is useful for a targeted area that requires fixed observation and cyclical data collection from the same location [6].  
The second topology considered is Mobile Topology (MT) which is structured in Mobile Sensor Nodes (MSNs) with autonomous mobility. MT is suitable for a targeted area where data collection is difficult due to low accessibility by humans or other devices. It can also be used when flexible monitoring is required [6].  
The third consideration is Hybrid Topology (HT), which is a mixed composition of FSNs and MSNs. HT comes handy for targeted areas that require fixed observation as well as active sensing following human movement or in other exceptional situations.  
The big data collected through sensor nodes in FTs, MTs, and HTs are transferred to a sink node, this sink mode transfers big data to middleware or to a direct server. Big data are employed by diverse users, companies, and public organizations depending on their goals and objective. Application services include national safety, telecommunication systems, energy savings, process control, traffic control, healthcare, and distributed robots.  
Data transfer processing is critical because drastic battery consumption of a single sensor node due to false data processing of big data in FTs, MTs, and HTs can have a negative influence in the whole topology. This has led to multiple studies on the development of various simulators and topology reconfigurations, which have created tools for determining the appropriate sensor nodes, sensor deployment, and load-balancing techniques for an observation area [4; 6; 7; 11].  
Despite the research and development surrounding such simulators, topology configuration for effective collection and transfer of big data has not been plausible due to its limited function and the topology reconfiguration and control of the load-balancing that do not reflect the constructed topology condition.  
Therefore, in this paper, we propose the Sustainable Load-balancing Scheme (SLS) that considers the battery consumption rate of each sensor to achieve effective data transfer. SLS can maximize the topology lifespan by considering the battery remains and consumption rate of sensors in FTs, MTs, and HTs, and the routing condition of big data.  
This paper is organized as follows.  
Section 2 reviews the literature on simulators that were previously developed as a topology design and test tool for big data of sensors. This section also reviews the literature on load-balancing techniques for topology reconfiguration and control. Section 3 describes the proposed load-balancing, which considers the composition, visualization, and battery consumption rate of the SLS sensor nodes. Sections 4 and 5 describe the design and implementation of SLS, respectively. Section 6 examines performance improvements by comparing previous simulators and measures the topology lifetime in the SLS application. Finally, Section 7 contains the overall summary, conclusion, and future research tasks.