Performance of cellular CDMA with voice/data traffic with an SIR based admission control

We analyze the performance of an SIR based admission control strategy in cellular CDMA systems with both voice and data traffic. Most studies In the current literature to estimate CDMA system capacity with both voice and data traf-Bc do not take signal-tlFlnterference ratio (SIR) based admission control into account In this paper, we present an analytical approach to evaluate the outage probability for voice trafllc, the average system throughput and the mean delay for data traffic for a volce/data CDMA system which employs an SIR based admission controL We show that for a dataaniy system, an improvement of about 25% In both the Erlang capacity as well as the mean delay performance is achieved with an SIR based admission control as compared to code availability based admission control. For a mixed voice/data srtem with 10 Erlangs of voice traffic, the Lmprovement in the mean delay performance for data Is about 40%.Ah, for a mean delay of 50 ms with 10 Erlangs voice traffic, the data Erlang capacity improves by about 9%.

Mobile agent based TCP attacker identification in MANET using the traffic history (MAITH)

TCP attacks are the major problem faced by Mobile Ad hoc Networks (MANETs) due to its limited network and host resources. Attacker traceback is a promising solution which allows a victim to identify the exact location of the attacker and hence enables the victim to take proper countermeasure near attack origins, for forensics and to discourage attackers from launching the attacks. However, attacker traceback in MANET is a challenging problem due to dynamic network topology, limited network and host resources such as memory, bandwidth and battery life. We introduce a novel method of TCP attacker Identification in MANET using the Traffic History - MAITH. Based on the comprehensive evaluation based on simulations, we showed that MAITH can successfully track down the attacker under diverse mobile multi-hop network environment with low communication, computation, and memory overhead.

Traffic engineered NoC for streaming applications

Streaming applications demand hard bandwidth and throughput guarantees in a multiprocessor environment amidst resource competing processes. We present a Label Switching based Network-on-Chip (LS-NoC) motivated by throughput guarantees offered by bandwidth reservation. Label switching is a packet relaying technique in which individual packets carry route information in the form of labels. A centralized LS-NoC Management framework engineers traffic into Quality of Service (QoS) guaranteed routes. LS-NoC caters to the requirements of streaming applications where communication channels are fixed over the lifetime of the application. The proposed NoC framework inherently supports heterogeneous and ad hoc system-on-chips. The LS-NoC can be used in conjunction with conventional best effort NoC as a QoS guaranteed communication network or as a replacement to the conventional NoC. A multicast, broadcast capable label switched router for the LS-NoC has been designed. A 5 port, 256 bit data bus, 4 bit label router occupies 0.431 mm(2) in 130 nm and delivers peak bandwidth of 80 Gbits/s per link at 312.5 MHz. Bandwidth and latency guarantees of LS-NoC have been demonstrated on traffic from example streaming applications and on constant and variable bit rate traffic patterns. LS-NoC was found to have a competitive AreaxPower/Throughput figure of merit with state-of-the-art NoCs providing QoS. Circuit switching with link sharing abilities and support for asynchronous operation make LS-NoC a desirable choice for QoS servicing in chip multiprocessors. (C) 2013 Elsevier B.V. All rights reserved.

Multi-agent Reinforcement Learning for Traffic Signal Control

Optimal control of traffic lights at junctions or traffic signal control (TSC) is essential for reducing the average delay experienced by the road users amidst the rapid increase in the usage of vehicles. In this paper, we formulate the TSC problem as a discounted cost Markov decision process (MDP) and apply multi-agent reinforcement learning (MARL) algorithms to obtain dynamic TSC policies. We model each traffic signal junction as an independent agent. An agent decides the signal duration of its phases in a round-robin (RR) manner using multi-agent Q-learning with either is an element of-greedy or UCB 3] based exploration strategies. It updates its Q-factors based on the cost feedback signal received from its neighbouring agents. This feedback signal can be easily constructed and is shown to be effective in minimizing the average delay of the vehicles in the network. We show through simulations over VISSIM that our algorithms perform significantly better than both the standard fixed signal timing (FST) algorithm and the saturation balancing (SAT) algorithm 15] over two real road networks.

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

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. Crown Copyright (C) 2015 Published by Elsevier B.V.

Simple lattice Boltzmann model for traffic flows

A lattice Boltzmann model with 5-bit lattice for traffic flows is proposed. Using the Chapman-Enskog expansion and multi-scale technique, we obtain the higher-order moments of equilibrium distribution function. A simple traffic light problem is simulated by using the present lattice Boltzmann model, and the result agrees well with analytical solution.

Probabilistic graphical models for semi-supervised traffic classification

Traffic classification using machine learning continues to be an active research area. The majority of work in this area uses off-the-shelf machine learning tools and treats them as black-box classifiers. This approach turns all the modelling complexity into a feature selection problem. In this paper, we build a problem-specific solution to the traffic classification problem by designing a custom probabilistic graphical model. Graphical models are a modular framework to design classifiers which incorporate domain-specific knowledge. More specifically, our solution introduces semi-supervised learning which means we learn from both labelled and unlabelled traffic flows. We show that our solution performs competitively compared to previous approaches while using less data and simpler features. Copyright © 2010 ACM.