Salahuddin A. Azad

Energy-Efficient Wireless MAC Protocols for Railway Monitoring Applications

Recent advances in wireless sensor networking (WSN) techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems. These have the potential for use in the monitoring of railway signaling systems and rail tracks. Energy efficiency is one of the most important design factors for the WSNs as the typical sensor nodes are equipped with limited power batteries. In earlier research, an energy-efficient cluster-based adaptive time-division multiple-access (TDMA) medium-access-control (MAC) protocol, named EA-TDMA, has been developed by the authors for the purpose of communication between the sensors placed in a railway wagon. This paper proposes another new protocol, named E-BMA, which achieves even better energy efficiency for low and medium traffic by minimizing the idle time during the contention period. In addition to railway applications, the EA-TDMA and E-BMA protocols are suitable for generic wireless data communication purposes. Both analytical and simulation results for the energy consumption of TDMA, EA-TDMA, BMA, and E-BMA have been presented in this paper to demonstrate the superiority of the EA-TDMA and E-BMA protocols.

An Efficient Transmission Scheme for Minimizing User Waiting Time in Video-On-Demand Systems

To take the advantage of skewed popularity of videos, efficient video-on-demand (VOD) systems are more likely to deliver the most popular videos through periodic broadcasting and the least popular videos through on-demand multicasting. While videos delivered through multicasting usually share a pool of server channels, broadcasting of each video demands one or more channels dedicated to it. Given a total number of available channels, distributing them for individual broadcasting and the multicasting pool to achieve the optimal average user waiting time is a nonlinear optimization problem. This letter addresses this problem by proposing a hybrid transmission scheme, which uses dynamic programming approach to ensure optimally for any given number of channels and request arrival rate

A novel batched multicast patching scheme for video broadcasting with low user delay

Reactive near-video-on-demand (NVOD) schemes provide instantaneous service, but the server bandwidth requirement becomes substantially high at high arrival rates. Proactive NVOID schemes are very attractive at high arrival rates but these introduce user delay, which can only be reduced by increasing the number of server streams. This paper proposes a new scheme, called batched multicast patching (BMP), which combines both reactive and proactive methods by multicasting patch streams to a batch of user requests for a video which is broadcast in a staggered manner. This scheme uses less number of server streams at both moderate and high arrival rates. The number of channels the client has to join concurrently is at most two. This scheme is less complex than other low user delay broadcasting protocols such as pyramid and harmonic broadcasting protocols.

Bitrate modeling of scalable videos using quantization parameter, frame rate and spatial resolution

The quality and bitrate modeling is essential to effectively adapt the bitrate and quality of videos when delivered to multiplatform devices over resource constraint heterogeneous networks. The recent model proposed by Wang et al. [1] estimates the bitrate and quality of videos in terms of the frame rate and quantization parameter. However, to build an effective video adaptation framework, it is crucial to incorporate the spatial resolution in the analytical model for bitrate and perceptual quality adaptation. Hence, this paper proposes an analytical model to estimate the bitrate of videos in terms of quantization parameter, frame rate, and spatial resolution. The model can fit the measured data accurately which is evident from the high Pearson correlation. The proposed model is based on the observation that the relative reduction in bitrate due to decreasing spatial resolution is independent of the quantization parameter and frame rate. This modeling can be used for rate-constrained bit-stream adaptation scheme which selects the scalability parameters to optimize the perceptual quality for a given bandwidth constraint.

User-Centered video quality assessment for scalable video coding of H.264/AVC standard

Scalable video coding of H.264/AVC standard enables adaptive and flexible delivery for multiple devices and various network conditions. Only a few works have addressed the influence of different scalability parameters (frame rate, spatial resolution, and SNR) on the user perceived quality within a limited scope. In this paper, we have conducted an experiment of subjective quality assessment for video sequences encoded with H.264/SVC to gain a better understanding of the correlation between video content and UPQ at all scalable layers and the impact of rate-distortion method and different scalabilities on bitrate and UPQ. Findings from this experiment will contribute to a user-centered design of adaptive delivery of scalable video stream.

Seamless channel transition for popular video broadcasting

The principal goal of near video-on-demand system is to minimize the average user waiting time. The user waiting time of popular videos can be reduced significantly by using broadcasting schemes such as fast broadcasting. Instead of allocating channels uniformly to all the videos, channels are nonuniformly distributed according to the relative popularity of the videos to minimize the average user waiting time. Since the demand for videos changes from time to time, the number of channels allocated to a video needs to be changed dynamically. The channel transition should be seamless so that the users currently watching the video do not experience any disruption due to this transition. Existing seamless channel transition scheme pads a dummy video stream at the end of the original video stream to produce exact correspondence between segments, but this approach causes some wastage of bandwidth. This paper proposes an improved seamless channel transition scheme which minimizes the wastage of bandwidth by preloading a postfix of the original video stream in the client buffer.

Bandwidth borrowing schemes for instantaneous video-on-demand systems

A controlled multicast scheme provides instantaneous service, but limited server bandwidth causes some user requests to be either delayed or rejected when insufficient free bandwidth is available. Two borrowing schemes are proposed for instantaneous video-on-demand (VOD) that reduce the user request blocking rate by borrowing bandwidth from ongoing video streams when there is insufficient free bandwidth for the server to deliver a new video stream. Both these new schemes have proved to be successful in reducing blocking rate and increasing bandwidth utilization at the expense of temporarily degrading the video quality.

Identification of typical load profiles using K-means clustering algorithm

Typical load profile (TLP) describes the hourly values of electricity consumption on a daily basis, and is associated to a certain consumer category, for certain specific operating conditions. TLPs can be defined for residential, small industrial, commercial or services consumers, for warm season and cold season, for week days and weekends. In this paper, the daily load curves of a residential feeder are grouped using K-Means clustering algorithm to classify the load curves. The paper further explores the relationship between load profiles and seasonal periods to identify season types. The paper also obtains truncated discrete Fourier transform coefficients for the load curves to reduce the dimensionality of the clustering problem. Application of K-Means clustering on the discrete Fourier coefficients exhibits results that are identical to the clusters of the original load curves.

Demand Forecasting in Smart Grid

Changes in temperature, rainfall, icefall, sea level, and the frequency and severity of extreme events are raising a question that how much energy we should produce to meet the world demand. The smart grid is a new paradigm that enables two-way communications between the electricity providers and consumers. Smart grid emerged due to the initiatives by the engineers to make the power grid more stable, reliable, efficient, and secure. The smart grid creates the opportunity for the electricity consumers to play a bigger role in their power usage and motivates them to use power sensibly and efficiently. Hence, in the implementation of smart grid, demand management going to play a vital role. Demand scheduling is an effective way to implement demand management at the customer side. It is an automated and intelligent method to shift a portion of the demand from peak to off peak so that the demand curve is flattened. To optimize the demand scheduling, the accurate energy usage pattern of the consumers is essential. This is where the demand forecasting comes into play. This chapter investigates how effectively the machine learning algorithms can forecast the electricity demand to facilitate electricity demand management. For the experiments, a real-life dataset is considered which was collected locally at Rockhampton, Australia. From the experimental experience, it is concluded that support vector machine is the most reliable machine learning tool for accurate prediction of electricity demand.

Securing the Smart Grid: A Machine Learning Approach

The demand of electricity is increasing in parallel with the growth of the world population. The existing power grid, which is over 100 years old, is facing many challenges to facilitate the continuous flow of electricity from large power plants to the consumers. To overcome these challenges, the power industry has warmly accepted the new concept smart grid which has been initiated by the engineers. This movement will be more beneficial and sustainable to the extent if we can offer a secure smart grid. Machine learning, representing a comparatively new era of Information Technology, can make smart grid really secure. This chapter provides an overview of the smart grid and a practical demonstration of maintaining the security of smart grid by incorporating machine learning.