The Age of Information (AoI) quantifies the freshness of status updates in a remote monitoring system. This paper introduces a new technique to calculate the average AoI efficiently using stochastic hybrid systems (SHS) analysis. In this paper, the relative freshness of packets in a queueing system is used to define the discrete states of the SHS Markov chain. We validate the average AoI for M/M/1/1 and M/M/1/2 queuing systems with First Come First Serve (FCFS) queuing discipline using this relative freshness approach. Furthermore, we extend our analysis to dual-queue status update systems. We also validate the known results for the packet-dropping probability and the probability that a packet is informative or successfully delivered.

This paper utilizes a novel approach to analyzing remote monitoring systems by using the Age of Information (AoI) as a metric to measure the timeliness of status updates. The study focuses on a scenario with a single source and destination queueing system, two parallel heterogeneous servers, and no additional buffer, where the analysis of AoI or PAoI is difficult because of the out-of-order reception of packets. To analyze the performance of the system, we use stochastic hybrid systems (SHS) approach. We evaluate the AoI and Peak AoI (PAoI) of the system using different queueing disciplines, including Last Come First Serve (LCFS) and LCFS with probabilistic routing. We use Proactive Obsolete Packet Management (POPMAN) method to identify obsolete packets that can be discarded in advance, which saves server processing time and receive packets in order at the receiver. The study compares the performance of this system to a similar system with two homogeneous servers. The results show that by using the POPMAN method, there is an improvement in the AoI and PAoI for LCFS queueing system and the performance of LCFS with probabilistic routing using the POPMAN approach is same as the traditional scheme. Additionally, the study analyzes various parameters, such as the probability of preempted packets, the probability of obsolete packets, the probability that a packet is informative or successfully delivered, and optimal splitting probabilities for probabilistic routing.

The problem of resource allocation in modern day networks is becoming increasingly complex and challenging. One such problem is the design of medium access control protocol that treats the channel as a resource for fair and efficient allocation. In this context game theory and its various versions have been successfully applied to solve network-resource allocation problems. Further, recent advances in physical layer technology has enabled Multi-packet reception (MPR), a technique that allows a receiver to simultaneously receive more than one packet at a time. The goal of this paper is to demonstrate that Minority Game (MG) with arbitrary cutoff is powerful paradigm that can be successfully applied to design of a Medium Access Control (MAC) protocol for channels supporting MPR. A detailed mathematical treatment of evolution, dynamics and convergence of the MG based MAC has been carried out. The analysis is validated against simulation results. We demonstrate that the performance of the MG-MAC outperforms slotted Aloha. It can be further improved by introducing the notion of pseudo MPRthreshold. To the best of our knowledge ours is the first attempt to carry out a detailed analytical treatment of MG with arbitrary cutoff and apply it in the domain of networking.

A document by Y.Arun Kumar Reddy. Click on the document to view its contents.

Device-to-Device (D2D) communication, a state-ofthe-art for next Generation communication paradigm, facilitates direct file transfer among neighboring nodes, without involving cellular base station. In this paper, we consider a purchase model related to content distribution. In this context, we propose a novel D2D content sharing scheme using the frameworks of Evolutionary Minority Game(EMG) and Multiple Choice Minority Game(MCMG). The performance of our content purchase model, in terms of its convergence , content downloading efficiency, etc., has been studied through extensive simulation. In particular, we studied the system performance by varying different system parameters, such as pricing parameters, cellular data rates, and cluster size. We demonstrate that our self-organizing game model achieves near-optimal performance, without requiring excessive coordination and signaling overhead.