## 1 Introduction
For example, the linkcorrelation between links \(S\to R1\) and \(S\to R2\) can be calculated as: \(c^{S}_{R1,R2}=\frac{N^{CR}}{N^{R}_{R2}}\), where \(N^{CR}\) denotes the number of common packet receptions of two links and \(N^{R}_{R2}\) denotes the number of received packets at R2. With real-world experiments and analysis, however, we observe that the beacon-based approach has the following drawbacks: First, it lacks accuracy. Link correlation is often used to estimate performance metrics (e.g., the expected number of transmissions) of data transmissions. More specifically, link correlation should be essentially the correlation of data packet receptions/losses. However, beacon messages are different from data packets in both packet length and transmission interval, which, as we will discuss in Section 2, are two important factors impacting link correlation. Second, BCM relies only on the historical statistics. It cannot provide timely (short-term) link correlation measurement. For WSN-based applications requiring real-time communications, a timely link correlation measurement is necessary [1, 18]. Third, periodically exchanging beacons incur considerable measurement overhead. Before each batch of data packet transmissions, the protocols exchange a large number of beacon messages for measuring the link correlation. These extra transmissions may even balance out the benefits by considering link correlation in the protocols. To address the above-mentioned problems, in this paper, we propose a lightweight and accurate link correlation estimation approach (LACE). Compared with BCM, LACE has three salient features: First, instead of beacon messages, LACE directly exploits data packet reception/loss traces for measuring the desired data packet-level link correlation. Second, LACE also combines the PHY layer information, the received signal strength indicator (RSSI), to further calibrate the measurement results. Since RSSI indicates the immediate link behaviors, we can extract short-term link correlation with the RSSI trace. By combining both the RSSI-based result and the data reception trace-based statistical result, we can get a timely, yet accurate estimation of the link correlation. Third, LACE uses beacon messages only in the startup session. After that, LACE depends solely on the data reception history and the immediately measured RSSI traces. Considering that RSSI measurements are fast and cheap [19] and that we have almost eliminated the beacon exchanges, the estimation overhead is greatly reduced compared to BCM. We implement LACE in TinyOS 2.1.2 with TelosB motes as a stand-alone interface. Then, we incorporate LACE into existing link correlation-based OR and flooding protocols to study its impact on end-to-end protocol performance (the number of transmissions and delay). Simulation and testbed experimental results show that LACE: (1) provides more accurate link correlation measurement; (2) greatly reduces the measurement overhead; and (3) that LACE-based protocols outperform their counterparts with BCM. The contribution of this paper is summarized as follows:  
* We demonstrate the inaccuracy of beacon-based measurement and give the reasoning (Section 2). * We propose a simple model to bridge the PHY layer information (packet length and transmission rate) and packet-level link correlation (Section 3)
* We propose a novel link correlation estimation approach, LACE. LACE combines both link layer and PHY layer information for lightweight and accurate link correlation estimation (Section 3). * We implement LACE in TinyOS 2.1.2 and incorporate it into existing protocols. Experimental results show that LACE provides more accurate link correlation measurement and that LACE-based protocols outperform existing BCM-based protocols (Section 4).