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Register a new userDistributed remote estimation over the collision channel with and without local communication
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The emergence of the Internet-of-Things and cyber-physical systems necessitates the coordination of access to limited communication resources in an autonomous and distributed fashion. Herein, the optimal design of a wireless sensing system with n sensors communicating with a fusion center via a collision channel of limited capacity k (k < n) is considered. In particular, it is shown that the problem of minimizing the mean-squared error subject to a threshold-based strategy at the transmitters is quasi-convex. As such, low complexity, numerical optimization methods can be applied. When coordination among sensors is not possible, the performance of the optimal threshold strategy is close to that of a centralized lower bound. The loss due to decentralization is thoroughly characterized. Local communication among sensors (using a sparsely connected graph), enables the on-line learning of unknown parameters of the statistical model. These learned parameters are employed to compute the desired thresholds locally and autonomously. Consensus-based strategies are investigated and analyzed for parameter estimation. One strategy approaches the performance of the decentralized approach with fast convergence and a second strategy approaches the performance of the centralized approach, albeit with slower convergence. A hybrid scheme that combines the best of both approaches is proposed offering a fast convergence and excellent convergent performance.
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We study distributed estimation of a high-dimensional static parameter vector through a group of sensors whose communication network is modeled by a fixed directed graph. Different from existing time-triggered communication schemes, an event-triggered asynchronous scheme is investigated in order to reduce communication while preserving estimation convergence. A distributed estimation algorithm with a single step size is first proposed based on an event-triggered communication scheme with a time-dependent decaying threshold. With the event-triggered scheme, each sensor sends its estimate to neighbor sensors only when the difference between the current estimate and the last sent-out estimate is larger than the triggering threshold. We prove that the proposed algorithm has mean-square and almost-sure convergence respectively, under an integrated condition of sensor network topology and sensor measurement matrices. The condition is satisfied if the topology is a balanced digraph containing a spanning tree and the system is collectively observable. Moreover, we provide estimates for the convergence rates, which are related to the step size as well as the triggering threshold. Furthermore, as an essential metric of sensor communication intensity in the event-triggered distributed algorithms, the communication rate is proved to decay to zero with a certain speed almost surely as time goes to infinity. We show that given the step size, adjusting the decay speed of the triggering threshold can lead to a tradeoff between the convergence rate of the estimation error and the decay speed of the communication rate. Specifically, increasing the decay speed of the threshold would make the communication rate decay faster, but reduce the convergence rate of the estimation error. Numerical simulations are provided to illustrate the developed results.
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