In this paper we argue that achieving symmetric errors is the key to an improved understanding of clock synchronization. We present a clock synchronization algorithm with drift compensation that implements this symmetric error paradigm. The performance of the algorithm is evaluated by measurements in an indoor testbed using the TinyNode hardware platform. We show that the remaining error is symmetric and in the range of the clock granularity.

No abstract

Abstract-Clock synchronization is an enabling service for a wide range of applications and protocols in both wired and wireless networks. We study the implications of clock drift and communication latency on the accuracy of clock synchronization when scaling the network diameter. Starting with a theoretical analysis of synchronization protocols, we prove tight bounds on the synchronization error in a model that assumes independently and randomly distributed communication delays and slowly changing drifts. While this model is more optimistic than traditional worst-case analysis, it much better captures the nature of real-world systems such as wireless networks.The bound on the synchronization accuracy, which is roughly the square-root of the network diameter, is achieved by the novel PulseSync protocol. Extensive experiments demonstrate that PulseSync is able to meet the predictions from theory and tightly synchronizes large networks. This contrasts against an exponential growth of the skew incurred by the state-of-the-art protocol for wireless sensor networks. Moreover, PulseSync adapts much faster to network dynamics and changing clock drifts than this protocol.

Long-term location tracking, where trajectory compression is commonly used, has gained high interest for many applications in transport, ecology, and wearable computing. However, state-of-the-art compression methods involve high spacetime complexity or achieve unsatisfactory compression rate, leading to rapid exhaustion of memory, computation, storage and energy resources. We propose a novel online algorithm for errorbounded trajectory compression called the Bounded Quadrant System (BQS), which compresses trajectories with extremely small costs in space and time using convex-hulls. In this algorithm, we build a virtual coordinate system centered at a start point, and establish a rectangular bounding box as well as two bounding lines in each of its quadrants. In each quadrant, the points to be assessed are bounded by the convex-hull formed by the box and lines. Various compression error-bounds are therefore derived to quickly draw compression decisions without expensive error computations. In addition, we also propose a light version of the BQS version that achieves O(1) complexity in both time and space for processing each point to suit the most constrained computation environments. Furthermore, we briefly demonstrate how this algorithm can be naturally extended to the 3-D case.Using empirical GPS traces from flying foxes, cars and simulation, we demonstrate the effectiveness of our algorithm in significantly reducing the time and space complexity of trajectory compression, while greatly improving the compression rates of the state-of-the-art algorithms (up to 47%). We then show that with this algorithm, the operational time of the target resourceconstrained hardware platform can be prolonged by up to 41%.

No abstract