# CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams
## 1 Introduction
Large numbers of nodes are distributed in a sensing area, hence the design of a topology control scheme must be adapted to large-scale wireless sensor networks. * Connectivity [15]. The connectivity of the network is constrained by the topology changes, node failure and random distribution of nodes. * Coverage [16]. Due to the limitation of the communication radius of the sensor nodes, the area coverage is an important problem in the topology control of wireless sensor networks, especially for the mobile WSNs. * Data fusion [17; 18]. Considering the redundancy of the data collected by monitoring nodes, data fusion can be used to reduce the energy consumption by means of decease the packets being transmitted. Besides, it can also improve data accuracy. Thus the topology design should can provides an essential support for data fusion. * Quality of service [19; 20]. In some applications, the perception of the data should be issued within a certain time interval. Otherwise the data will be invalid. Especially for some important information, the reliability of data transmission is also very important, and the topology control algorithm should be designed to meet the requirement for different quality of service. By using a reasonable topology control method, the system can work under a stable state for a long time and to improve the efficiency of data fusion. Therefore, it is needed to design a data aggregation protocol to integrate topology control, MAC and routing efficiently. Meanwhile, the communication path between neighbor nodes should be optimized to prolong the lifetime of network. On the basis of existing research, a collaborative data aggregation in emerging sensor networks using a bio-level Voronoi diagram (CoDA) is proposed. The main idea is that all nodes in the network are distributed in the bio-level Voronoi diagram. The sensor nodes situated in the lower level of the diagram are responsible for listening and gathering data, and should be organized by some optimal clustering method. In each cluster, a cluster head is selected for gathering the data from its members and sending to the sink. In the upper level of the diagram, the relay nodes are deployed and constructed as the backbone network for forwarding data of all sensors to the sink node, in which the region covered by these nodes is relatively large and cluster heads adopt the manner of multi-hop for inter cluster communication so as to realize the integration of intra cluster data transmission to the backbone node. In the stage of inter cluster communication, a particle swarm optimization algorithm is addressed to seek optimal transmission path which could simultaneously reach the minimization of the maximum next hop distance between two nodes in the routing path and the minimization of the maximum hop count, so the minimization of whole network energy consumption was realized. Considering the time synchronization problem in multi-hop transmission mode, this paper proposes a multi-hop precision time synchronization model, which can achieve the balance between the synchronization accuracy and energy consumption. The specific contributions of this paper include:  
* A literature survey about various existing energy saving protocols and topology control approaches in ESNs, and analyze their advantages and disadvantages. * An effective bio-level Voronoi diagram model based on Voronoi-cluster and relay nodes for ESNs is proposed. * A collaborative data aggregation in emerging sensor networks using bio-level Voronoi diagram is proposed, which is an energy-efficient data aggregation protocol and integrates topology control, MAC and routing. * Performance analysis of the proposed algorithm and an evaluation of the algorithm with respect to other traditional routing protocols. The rest of this paper is outlined as follows. In Section 2 an overview of related works are presented. In Section 3, a collaborative data aggregation model using a bio-level Voronoi diagram is introduced with the presence of inhomogeneous time delays affecting the different hops.