2020/07/07 by Xingran Chen, Chen, Xingran, Xinyu Liao +3 · 2 citations
Computer Science · Engineering · Medicine · #Age of Information Optimization #Congenital Heart Disease Studies #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #IoT Networks and Protocols #Networking and Internet Architecture (cs.NI) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2007.03652
openalex publication_date 2020/07/07 · openalex created_date 2021/02/01 · openalex updated_date 2026/07/28
Efficient sampling and remote estimation are critical for a plethora of wireless-empowered applications in the Internet of Things and cyber-physical systems. Motivated by such applications, this work proposes decentralized policies for the real-time monitoring and estimation of autoregressive processes over random access channels. Two classes of policies are investigated: (i) oblivious schemes in which sampling and transmission policies are independent of the processes that are monitored, and (ii) non-oblivious schemes in which transmitters causally observe their corresponding processes for decision making. In the class of oblivious policies, we show that minimizing the expected time-average estimation error is equivalent to minimizing the expected age of information. Consequently, we prove lower and upper bounds on the minimum achievable estimation error in this class. Next, we consider non-oblivious policies and design a threshold policy, called error-based thinning, in which each source node becomes active if its instantaneous error has crossed a fixed threshold (which we optimize). Active nodes then transmit stochastically following a slotted ALOHA policy. A closed-form, approximately optimal, solution is found for the threshold as well as the resulting estimation error. It is shown that non-oblivious policies offer a multiplicative gain close to 3 compared to oblivious policies. Moreover, it is shown that oblivious policies that use the age of information for decision making improve the state-of-the-art at least by the multiplicative factor 2. The performance of all discussed policies is compared using simulations. The numerical comparison shows that the performance of the proposed decentralized policy is very close to that of centralized greedy scheduling.