Mehr Yahya Durrani

Co-UWSN: cooperative energy-efficient protocol for underwater WSNs

Sensor networks feature low-cost sensor devices with wireless network capability, limited transmit power, resource constraints, and limited battery energy. Cooperative routing exploits the broadcast nature of wireless medium and transmits cooperatively using nearby sensor nodes as relays. It is a promising technique that utilizes cooperative communication to improve the communication quality of single-antenna sensor nodes. In this paper, we propose a cooperative transmission scheme for underwater sensor networks (UWSNs) to enhance the network performance. Cooperative diversity has been introduced to combat fading. Cooperative UWSN (Co-UWSN) is proposed, which is a reliable, energy-efficient, and high throughput routing protocol for UWSN. Destination and potential relays are selected that utilize distance and signal-to-noise ratio computation of the channel conditions as cost functions. This contributes to sufficient decrease in path losses occurring in the links and transferring of data with much reduced path loss. Simulation results show that Co-UWSN protocol performs better in terms of end-to-end delay, energy consumption, and network lifetime. Selected protocols for comparison are energy-efficient depth-based routing (EEDBR), improved adaptive mobility of courier nodes in threshold-optimized depth-based routing (iAMCTD), cooperative routing protocol for UWSN, and cooperative partner node selection criteria for cooperative routing Coop (Re and dth).

Online Knowledge-Based Model for Big Data Topic Extraction

Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half.

SPARCO: Stochastic Performance Analysis with Reliability and Cooperation for Underwater Wireless Sensor Networks

Reliability is a key factor for application-oriented Underwater Sensor Networks (UWSNs) which are utilized for gaining certain objectives and a demand always exists for efficient data routing mechanisms. Cooperative routing is a promising technique which utilizes the broadcast feature of wireless medium and forwards data with cooperation using sensor nodes as relays. Here, we present a cooperation-based routing protocol for underwater networks to enhance their performance called Stochastic Performance Analysis with Reliability and Cooperation (SPARCO). Cooperative communication is explored in order to design an energy-efficient routing scheme for UWSNs. Each node of the network is assumed to be consisting of a single omnidirectional antenna and multiple nodes cooperatively forward their transmissions taking advantage of spatial diversity to reduce energy consumption. Both multihop and single-hop schemes are exploited which contribute to lowering of path-losses present in the channels connecting nodes and forwarding of data. Simulations demonstrate that SPARCO protocol functions better regarding end-to-end delay, network lifetime, and energy consumption comparative to noncooperative routing protocol—improved Adaptive Mobility of Courier nodes in Threshold-optimized Depth-based routing (iAMCTD). The performance is also compared with three cooperation-based routing protocols for UWSN: Cognitive Cooperation (Cog-Coop), Cooperative Depth-Based Routing (CoDBR), and Cooperative Partner Node Selection Criteria for Cooperative Routing (Coop Re and dth).

Unordered rule discovery using Ant Colony Optimization

In this article, a novel unordered classification rule list discovery algorithm is presented based on Ant Colony Optimization (ACO). The proposed classifier is compared empirically with two other ACO-based classification techniques on 26 data sets, selected from miscellaneous domains, based on several performance measures. As opposed to its ancestors, our technique has the flexibility of generating a list of IF-THEN rules with unrestricted order. It makes the generated classification model more comprehensible and easily interpretable. The results indicate that the performance of the proposed method is statistically significantly better as compared with previous versions of AntMiner based on predictive accuracy and comprehensibility of the classification model.

VerSig: a new approach for online signature verification

This paper introduces, VerSig, a new proposed scheme for online signature verification. The proposed scheme is based on creation of a signature envelope by employing dynamic time warping method. This envelope provides the basis for decision of forged and authentic signatures. The scheme only uses basic features such as X, Y coordinates of the signature. A well known and standardized Japanese handwritten dataset (provided for ICDAR 2013 signature verification competition) is used to evaluate the performance of proposed method. Proposed method is compared with state of art methods and observed to offer significant improvements in terms of overall accuracy of prediction.