# Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
The random graph theory that is used in modeling WSNs can be called a random geometric graph, in which an edge can only exist between two adjacent nodes [12]. To make the model more appropriate to the real situation of WSNs, [PERSON] et al. [13] took the reliability of links into consideration to present a novel random geometric graph coverage model. Moreover, [PERSON] et al. [14] described a range-dependent model for WSNs by using random graph theory, of which the probability of an edge existing was expressed as a function of the distance between its two end vertices in the fading environment. In this work, by taking the randomness of opportunistic node connections into account, we propose a novel random graph model named Opportunistic Connection Random Graph (OCRG) for WSNs. Inspired by the work of [PERSON] et al. [15], who first proposed a scheme to address the connectivity between a designated source and destination in random graphs, in which they assumed that at each step one edge could be tested to see if it existed or not and an optimal policy that minimized the total expected number of steps determined while establishing whether the designated source and destination were connected. As a matter of fact, this work mainly focuses on the connectivity between any source node and the mobile sink in the OCRG, and based on the opportunistic node connections, a data collection scheme is proposed, in this scheme, all nodes can use the calculated optimal path to forward their sensing data to the mobile sink. In the scheme, first, the mobile sink can launch a data collection task anywhere in network at any time by broadcasting a tag message that declares its data collection period, and each node that receives this message will forward its source information (including its whole working time in this data collection period, its status transition frequency in this period and all its neighbors' IDs) included in a probe message to the mobile sink. Here the energy efficient probe message forwarding mechanism is designed to minimize the overall energy consumption. On receiving these probe messages, the mobile sink first constructs an opportunistic connection random graph by analyzing the source information included in them, then calculates the optimal path from itself to each node in this random graph, and hence a spanning tree could be generated where the mobile sink play as the root node, and finally it broadcasts this spanning tree so that each node can obtain the optimal path from itself to the mobile sink, and according to which to forward the sensing data. In addition, a routing protocol which adapts to different nodes operating statuses is proposed to improve the reliability of data transmission when nodes forwarding their sensing data along the calculated optimal path. The experimental results show that this scheme can effectively improve the reliability of the data transmission while prolonging the network lifetime. The main contributions of this paper are summarized as follows:  
1. An opportunistic connection random graph is constructed to reflect the opportunistic node connections caused by an asynchronous working-sleeping cycle strategy;
2. For each node, an energy efficient probe message forwarding mechanism is used to minimize the overall energy consumption in network when forwarding the source information to the mobile sink;
3. A routing protocol which adapts to different nodes operating statuses is proposed to improve the reliability of data transmission when nodes are forwarding their sensing data along the calculated optimal path. The rest of the paper is organized as follows: Section 2 describes the related work. Section 3 gives the system model. Section 4 presents the proposed data collection scheme in details. Simulation results and performance evaluations are shown in Section 5. Finally, Section 7 summarizes this work and gives some future research directions.