Expediency of Penetration Ratio and Evaluation of Mean Throughput for Safety and Commercial Applications in VANETs

Vehicular ad hoc network (VANET) applications are principally categorized into safety and commercial applications. Efficient traffic management for routing an emergency vehicle is of paramount importance in safety applications of VANETs. In the first case, a typical example of a high dense urban scenario is considered to demonstrate the role of penetration ratio for achieving reduced travel time between source and destination points. The major requirement for testing these VANET applications is a realistic simulation approach which would justify the results prior to actual deployment. A Traffic Simulator coupled with a Network Simulator using a feedback loop feature is apt for realistic simulation of VANETs. Thus, in this paper, we develop the safety application using traffic control interface (TraCI), which couples SUMO (traffic simulator) and NS2 (network simulator). Likewise, the mean throughput is one of the necessary performance measures for commercial applications of VANETs. In the next case, commercial applications have been considered wherein the data is transferred amongst vehicles (V2V) and between roadside infrastructure and vehicles (I2V), for which the throughput is assessed.

Simultaneous Perturbation Newton Algorithms for Simulation Optimization

We present a new Hessian estimator based on the simultaneous perturbation procedure, that requires three system simulations regardless of the parameter dimension. We then present two Newton-based simulation optimization algorithms that incorporate this Hessian estimator. The two algorithms differ primarily in the manner in which the Hessian estimate is used. Both our algorithms do not compute the inverse Hessian explicitly, thereby saving on computational effort. While our first algorithm directly obtains the product of the inverse Hessian with the gradient of the objective, our second algorithm makes use of the Sherman-Morrison matrix inversion lemma to recursively estimate the inverse Hessian. We provide proofs of convergence for both our algorithms. Next, we consider an interesting application of our algorithms on a problem of road traffic control. Our algorithms are seen to exhibit better performance than two Newton algorithms from a recent prior work.

Reinforcement learning with average cost for adaptive control of traffic lights at intersections

We propose for the first time two reinforcement learning algorithms with function approximation for average cost adaptive control of traffic lights. One of these algorithms is a version of Q-learning with function approximation while the other is a policy gradient actor-critic algorithm that incorporates multi-timescale stochastic approximation. We show performance comparisons on various network settings of these algorithms with a range of fixed timing algorithms, as well as a Q-learning algorithm with full state representation that we also implement. We observe that whereas (as expected) on a two-junction corridor, the full state representation algorithm shows the best results, this algorithm is not implementable on larger road networks. The algorithm PG-AC-TLC that we propose is seen to show the best overall performance.

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.

Reinforcement Learning With Function Approximation for Traffic Signal Control

We propose, for the first time, a reinforcement learning (RL) algorithm with function approximation for traffic signal control. Our algorithm incorporates state-action features and is easily implementable in high-dimensional settings. Prior work, e. g., the work of Abdulhai et al., on the application of RL to traffic signal control requires full-state representations and cannot be implemented, even in moderate-sized road networks, because the computational complexity exponentially grows in the numbers of lanes and junctions. We tackle this problem of the curse of dimensionality by effectively using feature-based state representations that use a broad characterization of the level of congestion as low, medium, or high. One advantage of our algorithm is that, unlike prior work based on RL, it does not require precise information on queue lengths and elapsed times at each lane but instead works with the aforementioned described features. The number of features that our algorithm requires is linear to the number of signaled lanes, thereby leading to several orders of magnitude reduction in the computational complexity. We perform implementations of our algorithm on various settings and show performance comparisons with other algorithms in the literature, including the works of Abdulhai et al. and Cools et al., as well as the fixed-timing and the longest queue algorithms. For comparison, we also develop an RL algorithm that uses full-state representation and incorporates prioritization of traffic, unlike the work of Abdulhai et al. We observe that our algorithm outperforms all the other algorithms on all the road network settings that we consider.

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%.

Impact of water drops onto the junction of a hydrophobic texture and a hydrophilic smooth surface

Wettability gradient surfaces play a significant role in control and manipulation of liquid drops. The present work deals with the analysis of water drops impacting onto the junction line between hydrophobic texture and hydrophilic smooth portions of a dual-textured substrate made using stainless steel material. The hydrophobic textured portion of the substrate comprised of unidirectional parallel groove-like and pillar-like structures of uniform dimensions. A high-speed video camera recorded the spreading and receding dynamics of impacting drops. The drop impact dynamics during the early inertia driven impact regime remains unaffected by the dual-texture feature of the substrate. A larger retraction speed of drop liquid observed on the hydrophobic portion of the substrate during the impact of low velocity drops makes the drop liquid on the higher wettability portion to advance further (secondary drop spreading). The net horizontal drop velocity towards the hydrophilic portion of the dual-textured substrate decreases with increasing drop impact velocity. The available experimental results suggest that the movement of bulk drop liquid away from the impact point during drop impact on the dual-textured substrate is larger for the impact of low inertia drops. (C) 2010 Elsevier B.V. All rights reserved.

Throughput Maximization of Delay-Constrained Traffic in Wireless Energy Harvesting Sensors

A wireless Energy Harvesting Sensor (EHS) needs to send data packets arriving in its queue over a fading channel at maximum possible throughput while ensuring acceptable packet delays. At the same time, it needs to ensure that energy neutrality is satisfied, i.e., the average energy drawn from a battery should equal the amount of energy deposited in it minus the energy lost due to the inefficiency of the battery. In this work, a framework is developed under which a system designer can optimize the performance of the EHS node using power control based on the current channel state information, when the EHS node employs a single modulation and coding scheme and the channel is Rayleigh fading. Optimal system parameters for throughput optimal, delay optimal and delay-constrained throughput optimal policies that ensure energy neutrality are derived. It is seen that a throughput optimal (maximal) policy is packet delay-unbounded and an average delay optimal (minimal) policy achieves negligibly small throughput. Finally, the influence of the harvested energy profile on the performance of the EHS is illustrated through the example of solar energy harvesting.

An instantaneous control model for flow control in an ATM network

This paper is concerned with the optimal flow control of an ATM switching element in a broadband-integrated services digital network. We model the switching element as a stochastic fluid flow system with a finite buffer, a constant output rate server, and a Gaussian process to characterize the input, which is a heterogeneous set of traffic sources. The fluid level should be maintained between two levels namely b1 and b2 with b1control on the ATM network is considered