# CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams
## 1 Introduction  
With the development of the Internet of Things (IoT) industry, the application scope and complexity of emerging sensor networks (ESNs) have reached unprecedented heights. Against the background of the IoT, ESNs are highly dependent on a huge and complex network, which brings great reliability risks and presents many new problems and challenges to research on topology control in wireless sensor networks [1]. By deploying massive numbers of sensor nodes in the monitoring region, ESNs can collect the information of perceived objects effectively and deliver the fusion solution to the end user through wireless communication. Most applications of ESNs are focused on the field of military reconnaissance [2], environmental monitoring [3] and disaster relief [4], control and command in autonomous vehicles and robots [5], etc. However, the static sensor nodes are usually equipped with low processing and limited power capabilities, and often communicate over unreliable, short-range radio links. Since the communication range of sensor nodes is limited, they often are arranged in a self-organized manner and adopt hop-by-hop communication to exchange data. The prominent characteristics in the target monitoring applications of ESNs includes: many-to-one communication, the sensor nodes with greater density, and limited resources. Thus any communication protocol should strive for the timeliness and reliability of data transmission [5]. Among the factors that affect the performance of wireless sensor networks, topology control is one of the key problems [6]. If there is no topology control in wireless sensor networks, there will be the following problems [7]:* Each node communicates with the maximum transmission power, and the limited node energy will be consumed quickly, thus reducing the lifetime of network. * Large communication radius can increase the signal interference between nodes, which will affect the communication quality and reduce the network throughput. * A lot of redundant edges exist in the generated network topology and result in network topology information redundancy. Large and complex routing calculation will bring about a waste of valuable computing resources. * Non-uniform sparse link between nodes will impact on the network connectivity, reliability and survivability, especially in military application, which will directly affect the network security. How to optimize the network topology, enhance the adaptability and robustness of the network topology, and provide a good topology support for the upper layer communication protocol is an important issue in the research of topology control and optimization. The existing literature shows that there are many constraints and challenges in the topology control of wireless sensor networks:  
* Nodes' dispersion [8]. In the face of different network applications, the nodes are distributed in two ways: predetermined and random dispersion. For the former, the data transmission can be carried out along a pre-designed path. For the latter, the distribution of nodes is not uniform and it is necessary to carry out reasonable clustering to ensure the network connectivity and energy efficiency. * Energy consumption [9]. The lifetime of a network node depends on its battery life, and the communication and computation of the energy reserve form is very important. Because a node failure will change the topology, it may lead to the failure of the original algorithm. * Heterogeneous nodes [10; 11]. In many studies, it is usually assumed that the nodes are isomorphic, but in the face of different applications, the roles and capabilities of nodes may be different, which will lead to the emergence of many new problems in topology control. * Tolerance [12]. The fault tolerance mechanism in the topology control is necessary, which can ensure the function of the network at the time some nodes out of action; for instance, lack of energy, physical damage or environmental interference. * Scalability [13; 14].