Traffic congestion is not only a cause of nuisance for general commuters but also a factor that has a measurable impact on the economy if not handled proactively. When congestion increases, the waiting time for commuters increases which results in wasted fuel and wasted time. Wasted fuel adds to the import bill of a country and lost time results in loss of productivity. Traffic can be regulated at various points in order to reduce congestion and eliminate bottleneck areas. In this paper, we propose the use of conventional scheduling to regulate traffic at intersections in order to reduce congestion. We propose minimum destination distance first (MDDF) and minimum average destination distance first (MADDF) algorithms and compare them with some of the relevant existing scheduling algorithms. The proposed algorithms can not only be easily implemented on low cost hardware but also show better performance and outperform the existing algorithms that are considered, based on simulation results. Simulation results show that the MDDF and MADDF algorithms reduce the traffic congestion at intersections by up to 80% in some cases compared to static traffic lights.

Wireless Sensor Networks (WSN) have emerged as a suitable solution for real-time vehicle data collection in many ITS applications. We propose the use of magnetometers in conjunction with WSN for real-time vehicle data collection. Magnetometers when used as sensor nodes offer advantages over other vehicle sensing technologies that include cost-effectiveness, energy efficiency, resistance to changes in environmental conditions, ease of re-deployment and flexibility. In this paper we present our prototype of a wireless magnetic sensor, algorithms for vehicle detection, vehicle speed estimation and vehicle length estimation and quantify their performance. Results from field evaluations prove the feasibility of our proposed solution for accurate vehicle data collection.

A novel neuro-evolutionary Cartesian genetic programming-based frame size estimator for multimedia streaming applications has been proposed in this letter. The frame size obtained from the proposed estimator is used to calculate and allocate the bandwidth required for frame transmission. Bandwidth calculation and allocation is done via various probabilistic and linear regression methods. To obtain conclusive results regarding the feasibility of the proposed system, different test case scenarios have been exploited. The bandwidth allocation efficiency for the technique has been compared with previously proposed methods to evaluate its effectiveness in precise bandwidth utilization. Compared to other contemporary techniques, our technique gives approximately 98% efficient frame size prediction and bandwidth allocation.

The requirement for correct bandwidth allocation and management in a multitude of different communication mediums has generated some exceedingly tedious challenges that need to be addressed both intelligently and with innovative solutions. Current advances in high speed broadband technologies have manifold increased the amount of bandwidth required during successful multimedia streaming. The progressive growth of Neuro-Evolutionary techniques have presented themselves as worthy options to address many of the challenges faced during multimedia streaming. In this paper a Neuro-Evolutionary technique called the Recurrent Cartesian Genetic Programming Evolved Artificial Neural Network(RCGPANN) is presented for prediction of future frame sizes. The proposed technique takes into account the traffic size trend of the historically transmitted data for future frame size prediction. The predicted frame size forms the basis for estimation of the amount of bandwidth necessary for transmission of future frame. Different linear regression and probabilistic approaches are employed to estimate the allocated bandwidth, while utilizing the predicted frame size. Our proposed intelligent traffic size prediction along with bandwidth estimation and management results in a 98% increased efficiency.

The advancement in renewable energy sector being the focus of research these days, a novel neuro evolutionary technique is proposed for modeling wind power forecasters. The paper uses the robust technique of Cartesian Genetic Programming to evolve ANN for development of forecasting models. These Models predicts power generation of a wind based power plant from a single hour up to a year - taking a big lead over other proposed models by reducing its MAPE to as low as 1.049% for a single day hourly prediction. Results when compared with other models in the literature demonstrated that the proposed models are among the best estimators of wind based power generation plants proposed to date.

One of the major problems, caused by traffic congestion, owes its existence to the unwanted delay experienced by the priority vehicles. The evaluation of two scheduling algorithms as adaptive traffic control algorithms has been proposed here to reduce this unwanted delay. One of these algorithms is the earliest deadline first (EDF) algorithm, whereas the other is the fixed priority (FP) algorithm. The performance of both algorithms as adaptive traffic light control algorithms is evaluated for isolated traffic intersections. A comparative study is performed here, where the performance of these algorithms is compared against a fixed static traffic light controller. Moreover, their performance is also compared against each other. Conclusive results from the simulation of the algorithms reveal that the number of stops, average delay, and mean trip time of the priority vehicles is significantly reduced by the implementation of these algorithms. Furthermore, it has been shown that the overall performance of EDF is much better than FP in terms of improvement of different performance measures for congestion reduction of priority vehicles.

Cloud computing is an emerging and rapid growing field of Infrastructure as a Service (IaaS), it has to deal with resource allocation and power management issues. This paper proposes CGPANN to accurately forecast the client's requests for a very short term duration of 1 second. A forecasting accuracy as high as 99.81% has been attained that verifies the accuracy of the proposed model. The experimental results show that the model outperforms all the contemporary models proposed in past.

This work explores developmental plasticity in neural networks for forecasting the trends in the daily foreign currency exchange rates. With this work we achieved an efficient artificial neural network (ANN) based dynamic prediction model that make use of the trends in the historical daily prices of the foreign currency to predict the future daily rates while modifying its structure with the trends. The plasticity in ANN is explored to achieve a prediction model that is computationally robust and efficient. The system performance analysis prove that the prediction model proposed is efficient, computationally cost effective and unique in terms of its least dependency on the amount of previous data required for the future prediction. The prediction model achieved accuracy as high as 98.852 percent, in predicting a single day's data from ten days data history, over a span of 1000 days (3 years). Further exploration demonstrated that when the problem domain for the network was changed to predict daily currency prices for multiple chunks of days a much better accuracy was achieved. This performance proved the robustness of the model proposed in this work for a modified problem domain.

Forecasting the electrical load requirements is an important research objective for maintaining a balance between the demand and generation of electricity. This paper utilizes a neuro-evolutionary technique known as Cartesian Genetic Programming evolved Recurrent Neural Network (CGPRNN) to develop a load forecasting model for very short term of half an hour. The network is trained using historical data of one month on half hourly basis to predict the next half hour load based on the 12 and 24 hours data history. The results demonstrate that CGPRNN is superior to other networks in very short term load forecasting in terms of its accuracy achieving 99.57 percent. The model was developed and evaluated on the data collected from the UK Grid station.

A new recurrent neural network model which has the ability to learn quickly is explored to devise a load forecasting and management model for the highly fluctuating load of London. Load forecasting plays an significant role in determining the future load requirements as well as the growth in the electricity demand, which is essential for the proper development of electricity infrastructure. The newly developed neuroevolutionary technique called Recurrent Cartesian Genetic Programming evolved Artificial Neural Networks (RCGPANN) has been used to develop a peak load forecasting model that can predict load patterns for a complete year as well as for various seasons in advance. The performance of the model is evaluated using the load patterns of London for a period of four years. The experimental results demonstrate the superiority of the proposed model to the contemporary methods presented to date.

A fast learning neuro-evolutionary technique that evolves Artificial Neural Networks using Cartesian Genetic Programming (CGPANN) is used to detect the presence of breast cancer. Features from breast mass are extracted using fine needle aspiration (FNA) and are applied to the CGPANN for diagnosis of breast cancer. FNA data is obtained from the Wisconsin Diagnostic Breast Cancer website and is used for training and testing the network. The developed system produces fast and accurate results when compared to contemporary work done in the field. The error of the model comes out to be as low as 1% for Type-I (classifying benign sample falsely as malignant) and 0.5% for Type-II (classifying malignant sample falsely as benign).

This paper introduces a collision avoidance technique for Vehicular Ad hoc Networks (VANETs). An alarm is generated as a warning by the adjacent vehicle depending upon the active speed of impact (ASI) a vehicle expects to encounter after both the adjacent vehicles expect to apply brakes, while in full throttle exactly at the same time. A novel technique of measuring the vehicles' stopping time is introduced by manipulation of co-efficient of decreasing speed (CODS) by the receiving vehicle. Depending upon the received information of CODS, initial speed and other associated factors, the vehicle is given a pre-emptive signal to maintain a safety margin by decreasing or increasing the current vehicle speed. An efficient data packet protocol known as Adjacent Vehicle Collision Avoidance version 1.0 (AVCA ver 1) is introduced in order to update the receiving vehicle with minimum possible data-rate and the requisite amount of data.

VANETs (Vehicular Ad-Hoc Networks) usually utilize the double-ring model in estimating the channel effects. Our proposal exploits the double-ring model in presence of line of sight (LOS) components to observe the complex envelope of the received Frequency of Variation in Channel (FVC) effect. The effect is observed in presence of a Rician faded channel. Constant variation in FVC is a requisite condition for every vehicle communicating with another vehicle as variation in FVC is not only an unwanted effect but also responsible to generate a time-variant condition. This paper suggests several factors that are responsible for achieving a constant level of FVC by taking different possibilities of vehicle velocities relative to another vehicle during inter-vehicular communication. As a result, new techniques can be thought of to compensate the effect of FVC for inter-vehicular communication.

The IEEE 802.11 standard conformant wireless communication stations have multi-rate transmission capability. To achieve greater communication efficiency, multi-rate capable stations use rate-adaptation to select appropriate transmission rate according to variations in the channel quality. This paper proposes a novel rate-adaptation scheme where the decision of rate selection relies on the mutual feedback of a transmitter and receiver pair. As an important feature of a rate-adaptation scheme, our proposed solution is highly responsive when compared with existing rate-adaptation scheme. Using the mutual-feedback, it has been made possible to achieve frame-loss differentiation with just three frames, avoiding the use of RTS/CTS and further delays in this process. Unlike the previous approaches, the feedback is delivered to the transmitter without any changes to the standard frame format. Various performance tests assert the suitability of our proposed solution in different test scenarios over existing rate-adaptation schemes.

Multi-media applications with demanding throughput requirements often raise issues of capacity limitations in wireless networks. High data rate (HDR) wireless personal area networks (WPANs) can operate in a mesh configuration to extend from the legacy single-hop communication to multiple hops. Although the range of WPANs is increased in a mesh configuration, there are capacity constraints because the superframe time is shared by multiple piconet clusters instead of being dedicated for a single piconet cluster just like in the legacy single-hop case. Furthermore, the distributed superframe sharing can cause fairness issues because there is no mechanism to determine the requirement of each mesh piconet coordinator (MPNC) and monitor a fair allocation to each MPNC. Here, the authors propose a centralised resource allocation approach and define three resource allocation policies for meshed HDR WPANs. The proposed approach is augmented by a traffic estimation strategy to predict a suitable fraction of superframe time for each cluster. Simulation results prove the effectiveness of the proposed resource allocation policies and the fairness index is shown to increase upto 42% with the proposed approach in some cases.