Javed Iqbal Bangash

VGDRA: A Virtual Grid-Based Dynamic Routes Adjustment Scheme for Mobile Sink-Based Wireless Sensor Networks

In wireless sensor networks, exploiting the sink mobility has been considered as a good strategy to balance the nodes energy dissipation. Despite its numerous advantages, the data dissemination to the mobile sink is a challenging task for the resource constrained sensor nodes due to the dynamic network topology caused by the sink mobility. For efficient data delivery, nodes need to reconstruct their routes toward the latest location of the mobile sink, which undermines the energy conservation goal. In this paper, we present a virtual grid-based dynamic routes adjustment (VGDRA) scheme that aims to minimize the routes reconstruction cost of the sensor nodes while maintaining nearly optimal routes to the latest location of the mobile sink. We propose a set of communication rules that governs the routes reconstruction process thereby requiring only a limited number of nodes to readjust their data delivery routes toward the mobile sink. Simulation results demonstrate reduced routes reconstruction cost and improved network lifetime of the VGDRA scheme when compared with existing work.

A Survey of Routing Protocols in Wireless Body Sensor Networks

Wireless Body Sensor Networks (WBSNs) constitute a subset of Wireless Sensor Networks (WSNs) responsible for monitoring vital sign-related data of patients and accordingly route this data towards a sink. In routing sensed data towards sinks, WBSNs face some of the same routing challenges as general WSNs, but the unique requirements of WBSNs impose some more constraints that need to be addressed by the routing mechanisms. This paper identifies various issues and challenges in pursuit of effective routing in WBSNs. Furthermore, it provides a detailed literature review of the various existing routing protocols used in the WBSN domain by discussing their strengths and weaknesses.

Reliability aware routing for intra-wireless body sensor networks

With the promise of cost effective, unobtrusive, and unsupervised continuous monitoring, wireless body sensor networks (WBSNs) have attracted a wide range of monitoring applications such as medical and healthcare, sport activity, and rehabilitation systems. Most WBSNs medical and healthcare applications are real-time and life-critical, which require strict guarantee of quality of service (QoS), in terms of latency, and reliability. Reliability in routing plays key role in providing the overall reliability in WBSNs. This paper presents reliability aware routing (RAR) for intra-WBSNs that aims to provide high reliability for reliability constraint data packets. It considers the high and dynamic path loss due to body postural movements and temperature rise of the implanted biomedical sensor nodes. We have used two network models in this paper: RAR without Relays (RAR) and RAR with Relays (RARR). The simulation results reveal that RARR outperforms the other state-of-the-art schemes while RAR has slightly low reliability at low data rates as compared to RARR but significantly higher than other state-of-the-art schemes.

Data-Centric Routing for Intra Wireless Body Sensor Networks

A significant proportion of the worldwide population is of the elderly people living with chronic diseases that result in high health-care cost. To provide continuous health monitoring with minimal health-care cost, Wireless Body Sensor Networks (WBSNs) has been recently emerged as a promising technology. Depending on nature of sensory data, WBSNs might require a high level of Quality of Service (QoS) both in terms of delay and reliability during data reporting phase. In this paper, we propose a data-centric routing for intra WBSNs that adapts the routing strategy in accordance with the nature of data, temperature rise issue of the implanted bio-medical sensors due to electromagnetic wave absorption, and high and dynamic path loss caused by postural movement of human body and in-body wireless communication. We consider the network models both with and without relay nodes in our simulations. Due to the multi-facet routing strategy, the proposed data-centric routing achieves better performance in terms of delay, reliability, temperature rise, and energy consumption when compared with other state-of-the-art.

VGDD: A virtual grid based data dissemination scheme for wireless sensor networks with mobile sink

Wireless sensor network (WSN) is considered as the enabling technology to bridge the gap between the physical and digital world. Some of the applications environments of WSN require a mobile sink to operate in the sensor field where delayed and/or partial data delivery might lead to inappropriate conclusions and thus require high quality of service in terms of latency and packets delivery ratio. Majority of existing mobile sink based data dissemination schemes aim to prolong network lifetime whereas few schemes improve the data delivery performance by employing multiple mobile sinks which add to the hardware and operating cost. In this paper we propose a virtual grid based data dissemination (VGDD) scheme that aims to optimize the tradeoff between network lifetime and data delivery performance while adhering to the low cost theme of WSN. Using the virtual structure, the proposed VGDD scheme follows a set of rules to disseminate sinks mobility updates in an energy efficient manner thereby maintaining nearly optimal routes. Furthermore, to cope with speeds variation of mobile sink, VGDD makes use of appropriate forwarder nodes for guaranteed data delivery. Simulation results reveal improved data delivery performance in terms of latency and data delivery ratio compared to existing work.

QDVGDD: Query-Driven Virtual Grid based Data Dissemination for wireless sensor networks using single mobile sink

In wireless sensor networks, efficient resource management is a major concern for the battery operated sensor nodes. Data collection using mobile sink(s) is considered as a good strategy to prolong network lifetime and improve network coverage. Most of the existing mobile sink based data collection schemes operate in event driven or periodic sensing modes. There are several application environments, which dictate query driven data collection using a mobile sink e.g., a mobile sink might require reinforced data reporting from one particular network segment compared to others. In this regard, the existing query driven data collection schemes either impose too many constraints on network operation or poorly perform when delivering the requested data to a mobile sink with variable speed. In this paper we propose Query-Driven Virtual Grid based Data Dissemination (QDVGDD) scheme that aims to improve data delivery performance to a mobile sink. The proposed scheme makes use of a virtual infrastructure thereby causing minimal network control overheads while delivering the requested data with high quality of service to the mobile sink. We carried out extensive simulation works in NS-2.35 to evaluate the performance of our QDVGDD at different sink’s speeds and network sizes. Simulation results reveal improved performance of QDVGDD in terms of data delivery latency, data delivery ratio, average energy consumption, and estimated network traffic as compared to other state-of-the-art.

Impact of Beacon Order and Superframe Order on IEEE 802.15.4 for Nodes Association in WBAN

IEEE 802.15.4, Low data Rate low power Wireless Personal Area Network (LR-WPAN) standard uses fixed duty cycle of the superframe operation. Fixed duty cycle operation of the stated standard suffers from several network issues like Personal Area Network (PAN) tedious initialization interval, nodes association duration, number of associated nodes, average network delay, throughput and nodes energy etc. Low duty cycle control operation with lower energy consumption of the network is one of the attractive feature of IEEE 802.15.4. Two superframe parameters Beacon Order (BO) and Superframe Order (SO) decide the beacon interval and duty cycle of the standard and has a strong impact on the performance of other network parameters. In this paper we analyze the IEEE 802.15.4 MAC with brief simulations by varying the two superframe parameters BO and SO to accomplish the finest performance of the WBAN network.

Low level visio-temporal features for violence detection in cartoon videos

Cartoons are an informative way for creating awareness; children take keen interest in watching cartoons and spend leisure time in front of television. Unfortunately there is an inclination towards violence and other objectionable scenes in cartoon videos that have very bad impact on the developing personality of children. Extensive use of such violent scenes is one of the factors of increase of violence in society. Parents rely on cartoons as a mean of amusement for their children without analyzing its content which can pose serious negative impacts on child behavior. To protect children from violent content means are require for automatic detection of violence content in cartoon videos. In this paper different low-level visual features are evaluated for violence detection in cartoon videos using indigenously developed dataset that is categorized as violent and non-violent. From our results it has been observed that low-level visual features could not be used efficiently for the detection of violence. However these features are very helpful in identifying the character and situation that if combined with a knowledgebase capability can be used for detection of violence and other objectionable elements in the cartoon videos.

Volterra neural analysis of fetal cardiotocographic signals

A novel technique for electronic fetal monitoring is presented. It is based on training a Volterra based neural network to classify the fetal states based on a recorded cardiotocography (CTG) data base. The dataset consists of measurements of fetal heart rate and uterine contraction. The CTG signals have 21 attributes and three fetal states, normal, suspect and Pathologic. The Volterra based neural networks (VNN) employ Volterra series expansion for the input vectors and can produce explicit equations describing any multi-input multi-output (MIMO) system. Moreover, VNN has fast and uniform convergence. Simulations have demonstrated the efficiency of the proposed technique in electronic fetal monitoring. The VNN was able to classify the fetal states in a very low number of iterations with negligible error (practically zero). The conventional neural networks, on the other hand, have failed to achieve a reliable convergence.