Wireless magnetic sensor network for road traffic monitoring and vehicle classification

Efficiency of transportation of people and goods is playing a vital role in economic growth. A key component for enabling effective planning of transportation networks is the deployment and operation of autonomous monitoring and traffic analysis tools. For that reason, such systems have been developed to register and classify road traffic usage. In this paper, we propose a novel system for road traffic monitoring and classification based on highly energy efficient wireless magnetic sensor networks. We develop novel algorithms for vehicle speed and length estimation and vehicle classification that use multiple magnetic sensors. We also demonstrate that, using such a low-cost system with simplified installation and maintenance compared to current solutions, it is possible to achieve highly accurate estimation and a high rate of positive vehicle classification

A Hybrid Modified Ant Colony Optimization - Particle Swarm Optimization Algorithm for Optimal Node Positioning and Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been widely deployed in hostile locations for environmental monitoring. Sensor placement and energy management are the two main factors that should be focused due to certain limitations in WSNs. The nodes in a sensor network might not stay charged when energy draining takes place; therefore, increasing the operational lifespan of the network is the primary purpose of energy management. Recently, major research interest in WSN has been focused with the essential aspect of localization. Several types of research have also taken place on the challenges of node localization of wireless sensor networks with the inclusion of range-free and range-based localization algorithms. In this work, the optimal positions of Sensor Nodes (SNs) are determined by proposing a novel Hybrid M-ACO – PSO (HMAP) algorithm. In the HMAP method, the improved PSO utilizes learning strategies for estimating the relay nodes\u27 optimal positions. The M-ACO assures the data conveyance. A route discovers when it relates to the ideal route irrespective of the possibility of a system that includes the nodes with various transmission ranges, and the network lifetime improves. The proposed strategy is executed based on the energy, throughput, delivery ratio, overhead, and delay of the information packets

Efficient Time of Arrival Calculation for Acoustic Source Localization Using Wireless Sensor Networks

Acoustic source localization is a very useful tool in surveillance and tracking applications. Potential exists for ubiquitous presence of acoustic source localization systems. However, due to several significant challenges they are currently limited in their applications. Wireless Sensor Networks (WSN) offer a feasible solution that can allow for large, ever present acoustic localization systems. Some fundamental challenges remain. This thesis presents some ideas for helping solve the challenging problems faced by networked acoustic localization systems. We make use of a low-power WSN designed specifically for distributed acoustic source localization. Our ideas are based on three important observations. First, sounds emanating from a source will be free of reflections at the beginning of the sound. We make use of this observation by selectively processing only the initial parts of a sound to be localized. Second, the significant features of a sound are more robust to various interference sources. We perform key feature recognition such as the locations of significant zero crossings and local peaks. Third, these features which are compressed descriptors, can also be used for distributed pattern matching. For this we perform basic pattern analysis by comparing sampled signals from various nodes in order to determine better Time Of Arrivals (TOA). Our implementation tests these ideas in a predictable test environment. A complete system for general sounds is left for future wor

Efficient Time of Arrival Calculation for Acoustic Source Localization Using Wireless Sensor Networks

Acoustic source localization is a very useful tool in surveillance and tracking applications. Potential exists for ubiquitous presence of acoustic source localization systems. However, due to several significant challenges they are currently limited in their applications. Wireless Sensor Networks (WSN) offer a feasible solution that can allow for large, ever present acoustic localization systems. Some fundamental challenges remain. This thesis presents some ideas for helping solve the challenging problems faced by networked acoustic localization systems. We make use of a low-power WSN designed specifically for distributed acoustic source localization. Our ideas are based on three important observations. First, sounds emanating from a source will be free of reflections at the beginning of the sound. We make use of this observation by selectively processing only the initial parts of a sound to be localized. Second, the significant features of a sound are more robust to various interference sources. We perform key feature recognition such as the locations of significant zero crossings and local peaks. Third, these features which are compressed descriptors, can also be used for distributed pattern matching. For this we perform basic pattern analysis by comparing sampled signals from various nodes in order to determine better Time Of Arrivals (TOA). Our implementation tests these ideas in a predictable test environment. A complete system for general sounds is left for future wor

Efficient Time of Arrival Calculation for Acoustic Source Localization Using Wireless Sensor Networks

Acoustic source localization is a very useful tool in surveillance and tracking applications. Potential exists for ubiquitous presence of acoustic source localization systems. However, due to several significant challenges they are currently limited in their applications. Wireless Sensor Networks (WSN) offer a feasible solution that can allow for large, ever present acoustic localization systems. Some fundamental challenges remain. This thesis presents some ideas for helping solve the challenging problems faced by networked acoustic localization systems. We make use of a low-power WSN designed specifically for distributed acoustic source localization. Our ideas are based on three important observations. First, sounds emanating from a source will be free of reflections at the beginning of the sound. We make use of this observation by selectively processing only the initial parts of a sound to be localized. Second, the significant features of a sound are more robust to various interference sources. We perform key feature recognition such as the locations of significant zero crossings and local peaks. Third, these features which are compressed descriptors, can also be used for distributed pattern matching. For this we perform basic pattern analysis by comparing sampled signals from various nodes in order to determine better Time Of Arrivals (TOA). Our implementation tests these ideas in a predictable test environment. A complete system for general sounds is left for future wor

Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

ReSCon '09, Research Student Conference: Book of Abstracts

The second SED Research Student Conference (ReSCon2009) was hosted over three days, 22-24 June 2009, in the Lecture Centre at Brunel University. The conference consisted of technical presentations, a poster session and social events. The abstracts and presentations were the result of ongoing research by postgraduate research students from the School of Engineering and Design at Brunel University. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.Mohamed Elgendi, Björn Eskofier, Socrates Dokos, Derek Abbot