Congestion-adaptive data collection with accuracy guarantee in cyber-physical systems

Data collection by wireless sensor networks is a fundamental and critical function for cyber-physical systems (CPS) to estimate the state of the physical world. However, unstable network conditions impose great challenges in guaranteeing data accuracy, which is essential for reliable state estimation of the physical phenomena. For underlying sensor networks, without efficiently resolving congestion in data transmission, packet loss at congested nodes can considerably increase the estimation error. However, previous congestion control schemes relying on reducing transmitted data samples also increase the estimation error. Thus, we propose a Congestion-Adaptive Data Collection scheme (CADC) to efficiently resolve the network congestion while guaranteeing the overall data estimation accuracy. CADC mitigates congestion by adaptive lossy compression with guarantee that a given overall data estimation error bound is satisfied. Besides, since a CPS application may have different priorities for different data items, we further propose a weighted CADC scheme such that the data with higher priority has less distortion. Extensive experimental results demonstrate the effectiveness and efficiency of our CADC schemes.