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In this paper, we study a class of decentralized online convex optimization problems with time-varying loss functions over multi-agent networks. We propose a decentralized online subgradient method by using only the signs of the relative states of neighbors, which considerably reduces the sensing and communication requirements for the agents. We show that, despite the loss of information, the proposed algorithm can still achieve O (T) regret bound (T is the time horizon), which matches that of the standard distributed online subgradient method with exact relative state information. We further investigate the scenario of using noisy signs, where the measurements of the directions of the relative states are perturbed by noise. We show that the regret bound is not affected as long as the noise is not too large, which manifests certain noise-tolerance property of the proposed algorithm. Additionally, we extend the …