An Arrival and Departure Time Predictor for Scheduling Communication in Opportunistic IoT

Abstract

In this article, an Arrival and Departure Time Predictor (ADTP) for scheduling communication in opportunistic Internet of Things (IoT) is presented. The proposed algorithm learns about temporal patterns of encounters between IoT devices and predicts future arrival and departure times, therefore future contact durations. By relying on such predictions, a neighbour discovery scheduler is proposed, capable of jointly optimizing discovery latency and power consumption in order to maximize communication time when contacts are expected with high probability and, at the same time, saving power when contacts are expected with low probability. A comprehensive performance evaluation with different sets of synthetic and real world traces shows that ADTP performs favourably with respect to previous state of the art. This prediction framework opens opportunities for transmission planners and schedulers optimizing not only neighbour discovery, but the entire communication process.

Notice of Violation of IEEE Publication Principles: Free Parking Space Prediction and Reliability Analysis Based on Big Data Analysis

“…Based on the classical continuous time-queuing model, Caliskan et al [9] assumed that the arrival time obeys Poisson distribution and the given departure time obeys the exponential distribution, estimated the arrival rate by the maximum likelihood method, and forecasted the occupancy rate of the parking lot with the Gaussian process. Pozza et al [10] considered that arrival time and departure time are obtainable, and applied the classical continuous time-queuing model to estimate the probability that the parking lot is full. Based on the mean number of vehicles in parking lots, Dinh and Kim.…”