Sahibzada Ali Mahmud

M2M communication in Smart Grids: Implementation scenarios and performance analysis

The idea of mass deployment of an Advanced Metering Infrastructure (AMI) for Smart Grids has been explored and evaluated in this paper. Since smart meters with a wireless interface that can connect to the utility providers server via a backhaul network forms the basic building block of an AMI, it is a good paradigm for an M2M application in Smart Grids. The relevant standardization efforts by 3GPP and ETSI are discussed along with other perceived application scenarios for M2M communication. An example architecture is then proposed and evaluated with clusters of smart meters that transfer their data via UMTS network to a server. The performance evaluation is carried out considering network throughput, latency, and the data generated by the smart meters using both TCP and UDP traffic. The results indicate that there is a substantial reduction in delay when UDP traffic is considered when compared to TCP traffic generated by the smart meters.

Breast cancer detection using cartesian genetic programming evolved artificial neural networks

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).

A Survey of Cluster-based Routing Schemes for Wireless Sensor Networks

In recent times, wireless sensor networks (WSNs) have become progressively more attractive and have found their way into a wide variety of applications and systems because of their low cost, self-organizing behavior, and sensing ability in harsh environments. A WSN is a collection of nodes organized into a network. Routing is a vital technology in WSNs and can be roughly divided into two categories: flat routing and hierarchical routing. In a flat routing topology, all nodes have identical functionality and carry out the same task in the network. Nodes in a hierarchical topology do different tasks in WSNs and are usually arranged into clusters. In this paper, a survey on cluster-based routing schemes for WSNs has been done and comparisons made on the basis of performance factors such as latency, scalability, and energy awareness. Strengths and limitations of each scheme are presented, and open issues that must be addressed in the design of cluster-based routing algorithms are discussed.

Rate-adaptation for multi-rate IEEE 802.11 WLANs using mutual feedback between transmitter and receiver

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.

The Power to Deliver: Trends in Smart Grid Solutions

Looking at the communication industry, one observes how drastically the communication horizon has changed. From letters to e-mails and SMS, from phone calls to video chat and live conferencing, from phone booths to smart phones: since the digitization of communication, a new era of consumer choice has been inaugurated. The potential exists for similar transformation and opportunity in the provision of electricity, embodied in a concept known as the “smart grid”. Smart grid is the electric delivery network from electrical generation to the end user that makes use of the latest advances in wireless communication and intelligent information management systems to ameliorate the electric system robustness, reliability, efficiency, and security. Like the telecommunications and the genesis of the Internet, technology holds the key to the smart grid and its realization.

Earliest-deadline-based scheduling to reduce urban traffic congestion

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.

Zone Routing Protocol: How does it perform the other way round?

In order to have the benefits offered by Proactive Routing and Reactive Routing while reducing the undesirable effects of both respectively, Hybrid Routing is suggested to be used in ad-hoc and Mobile Ad-hoc Networks (MANETs). One such hybrid routing protocol is the Zone Routing Protocol (ZRP). ZRP attempts to reduce the initial route discovery delay in case of reactive routing, and the control traffic in case of proactive routing by dividing the network into zones. Within each zone proactive routing is used while between zones, reactive routing is used. In this paper we evaluate the performance of ZRP when instead of proactive routing, reactive routing is used within zones and proactive routing is used between zones. Apparently this approach might indicate that the whole purpose of using hybrid routing is defeated. However, we take into account the parameters of throughput and delay while evaluating the performance. We also compare our reverse approach with the conventional ZRP to check the effect on routing traffic.

A Survey on Femtocells: Benefits Deployment Models and Proposed Solutions

Femtocell networks are considered to be a viable option that can fulfill the demands of high speed voice and datatraffic for the indoor users. It uses the services of the existing broadband connection to connect to the operator corenetwork. The cellular network operators need to modify the existing single tier macrocell network in order to providethe services of femtocells to its users. In this paper, we present a survey on femtocell deployment. Various aspects offemtocell networks in a diverse domain are discussed. A detailed analysis of the previous approaches is given tohighlight their pros and the cons.

Shortest Processing Time Scheduling to Reduce Traffic Congestion in Dense Urban Areas

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.

Classification of Arrhythmia Types Using Cartesian Genetic Programming Evolved Artificial Neural Networks

Cartesian Genetic programming Evolved Artificial Neural Network (CGPANN) is explored for classification of different types of arrhythmia and presented in this paper. Electrocardiography (ECG) signal is preprocessed to acquire important parameters and then presented to the classifier. The parameters are calculated from the location and amplitudes of ECG fiducial points, determined with a new algorithm inspired by Pan-Tompkins’s algorithm [14]. The classification results are satisfactory and better than contemporary methods introduced in the field.