Probabilistic Prediction based Scheduling for Delay Sensitive Traffic in Internet of Things

Abstract

This paper proposes a probabilistic prediction based approach for providing Quality of Service (QoS) to delay sensitive traffic for Internet of Things (IoT). A joint packet scheduling and dynamic bandwidth allocation scheme is proposed to provide service differentiation and preferential treatment to delay sensitive traffic. The scheduler focuses on reducing the waiting time of high priority delay sensitive services in the queue and simultaneously keeping the waiting time of other services within tolerable limits. The scheme uses the difference in probability of average queue length of high priority packets at previous cycle and current cycle to determine the probability of average weight required in the current cycle. This offers optimized bandwidth allocation to all the services by avoiding distribution of excess resources for high priority services and yet guaranteeing the services for it. The performance of the algorithm is investigated using MPEG-4 traffic traces under different system loading. The results show the improved performance with respect to waiting time for scheduling high priority packets and simultaneously keeping tolerable limits for waiting time and packet loss for other services.

A Markovian Model for Internet of Things Application

“…We review some of the next. Markov chain analysis is conducted in [9,13,14,15]. In [9], the authors consider a finite-capacity queue with two traffic classes; normal and emergency.…”

“…Basic performance measures are obtained including queue length and blocking probability for each traffic class. In [13], the performance of a finite capacity slot-based IoT node with three traffic classes is analyzed. The traffic classes include high, medium and low priority classes.…”

Performance Analysis of IoT System using Sleep Server Technique

“…For C = ∞ obviously no queue arises; the M/M/∞ and more generally, the M/G/∞ may be used to model. The state of the system is completely characterized by the number of jobs in the system [8][9][10][11][12][13][14]. Let P n denote the equilibrium probability that there are n jobs in the system.…”