Pervasive surveillance-agent system based on wireless sensor networks: design and deployment

Nowadays, proliferation of embedded systems is enhancing the possibilities of gathering information by using wireless sensor networks (WSNs). Flexibility and ease of installation make these kinds of pervasive networks suitable for security and surveillance environments. Moreover, the risk for humans to be exposed to these functions is minimized when using these networks. In this paper, a virtual perimeter surveillance agent, which has been designed to detect any person crossing an invisible barrier around a marked perimeter and send an alarm notification to the security staff, is presented. This agent works in a state of 'low power consumption' until there is a crossing on the perimeter. In our approach, the 'intelligence' of the agent has been distributed by using mobile nodes in order to discern the cause of the event of presence. This feature contributes to saving both processing resources and power consumption since the required code that detects presence is the only system installed. The research work described in this paper illustrates our experience in the development of a surveillance system using WNSs for a practical application as well as its evaluation in real-world deployments. This mechanism plays an important role in providing confidence in ensuring safety to our environment

Intruder Localization and Tracking Using Two Pyroelectric Infrared Sensors

In this paper, we introduce a method to estimate the range of an intruder and track its trajectory by utilizing the received signal strength of the heat flux for pyroelectric infrared (PIR) sensors. To this end, we first develop a mathematical model of the received heat flux signal strength and the corresponding PIR signal for a moving intruder. The algorithm uses only two PIR sensors and the geometry of the field of views (FOVs) to perform the estimation and tracking process without any knowledge of the intruder's parameters. The tracking algorithm shows remarkable performance in estimating the intruder's parameters. The intruder heat flux was accurately estimated even at large separation distances as was the intruder path angle. Finally, the intruder's location was also very accurately estimated with sub-meter error for large separation distances

Advanced Occupancy Measurement Using Sensor Fusion

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control

Embedded sensor speed and width estimation

The goal of this report is to provide a novel system of estimating width and velocity of an object passing perpendicularly through a sensor field. By fixing four sensors on an axis at known angles the distance and velocity of an object can be estimated from the times of detection of each sensor using a system of equations. To develop a prototype of this system the scenario was modeled and simulated, a processor and sensors were selected, and algorithms for detection and estimation were developed. The goal of the report was to develop a self-contained sensor network and computing platform that would correctly estimate the speed of the object in miles per hour within 15% accuracy, and to estimate the width of the object within 20% accuracy. Due to prototype design errors, these requirements were not met, however useful algorithms and simulations were developed to lead towards successful future work.Electrical and Computer Engineerin

Development of early warning system for human wildlife conflict using deep learning, IoT and SMS

A Project Report Submitted in Partial Fullfilment of the Requirements for the Degree of Master of Science in Embedded and Mobile Systems of the Nelson Mandela African Institution of Science and TechnologyHuman-wildlife conflict is a significant challenge to communities living in areas close to wildlife game parks and reserves. It is more evident in the United Republic of Tanzania whose economy depends on agriculture and wildlife tourism as a significant source of income for her citizens and foreign exchange respectively. The proposed system is a low-power and low-cost early-warning system using deep learning, Internet of Things (IoT) and Short Message Service (SMS) to support human-wildlife conflict response teams in mitigating these problems. The proposed system comprises three basic units: sensing unit, processing unit, and alerting unit. The sensing unit consists of a Global Positioning System (GPS) module, a passive infrared (PIR) sensor, and a Raspberry Pi camera. The PIR sensor module detects animal nearby using its heat signature, the GPS collects and records the current system location while the Raspberry pi camera takes an image after the PIR sensor has detected the animal nearby using its heat signature. The processing unit with the main unit uses a Raspberry microcomputer to perform image inferencing using the “you look only once” (YOLO) algorithm and data processing. The last unit is an alerting unit that uses Global System for Mobile Communications module to send an alerting SMS message to the community response team leader and the human-wildlife conflict response team whenever wild animals are detected near the park’s border. Therefore, the system detects, identifies, and reports wild animals detected using SMS. General Packet Radio Service cellular network provides internet connectivity for the purpose of data collection to enable monitoring and storage in the cloud. An online visualization system was developed using google maps to show the location of wildlife detected by the camera trap. The park rangers track the wildlife online to acquire important information before the wildlife wanders out of the park. This system was developed using the open-source Raspberry pi which is cost effective even for low-income communities who are targeted by the syste

An analysis of automated solutions for the Certification and Accreditation of navy medicine information assets

A process.http://archive.org/details/annalysisofutoma109452039Approved for public release; distribution is unlimited

Machine learning for smart building applications: Review and taxonomy

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field

VCare: A Personal Emergency Response System to Promote Safe and Independent Living Among Elders Staying by Themselves in Community or Residential Settings

‘Population aging’ is a growing concern for most of us living in the twenty first century, primarily because many of us in the next few years will have a senior person to care for - spending money towards their healthcare expenditures AND/OR having to balance a full-time job with the responsibility of care-giving, travelling from another city to be with this elderly citizen who might be our parent, grand-parent or even community elders. As informal care-givers, if somehow we were able to monitor the day-to-day activities of our elderly dependents, and be alerted when wrong happens to them that would be of great help and lower the care-giving burden considerably. Information and Communication Technology (ICT) can certainly help in such a scenario, with tools and techniques that ensure safe living for the individual we are caring for, and save us from a lot of worry by providing us with anytime access into their lives or activities, and as a result check their functional state. However, we should be mindful of the tactics that could be adopted by harm causers to steal data stored in these products and try to curb the associated service costs. In short, we are in need of robust, cost-effective, useful, and secure solutions to help elders in our society to ‘age gracefully’. This work is a little step taken towards that direction. ‘Population aging’ is a growing concern for most of us living in the twenty first century, primarily because many of us in the next few years will have a senior person to care for - spending money towards their healthcare expenditures AND/OR having to balance a full-time job with the responsibility of care-giving, travelling from another city to be with this elderly citizen who might be our parent, grand-parent or even community elders. As informal care-givers, if somehow we were able to monitor the day-to-day activities of our elderly dependents, and be alerted when wrong happens to them that would be of great help and lower the care-giving burden considerably. Information and Communication Technology (ICT) can certainly help in such a scenario, with tools and techniques that ensure safe living for the individual we are caring for, and save us from a lot of worry by providing us with anytime access into their lives or activities, and as a result check their functional state. However, we should be mindful of the tactics that could be adopted by harm causers to steal data stored in these products and try to curb the associated service costs. In short, we are in need of robust, cost-effective, useful, and secure solutions to help elders in our society to ‘age gracefully’. This work is a little step taken towards that direction. Advisor: Tadeusz Wysock

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-the-art data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored

Employing multi-modal sensors for personalised smart home health monitoring.

Smart home systems are employed worldwide for a variety of automated monitoring tasks. FITsense is a system that performs personalised smart home health monitoring using sensor data. In this thesis, we expand upon this system by identifying the limits of health monitoring using simple IoT sensors, and establishing deployable solutions for new rich sensing technologies. The FITsense system collects data from FitHomes and generates behavioural insights for health monitoring. To allow the system to expand to arbitrary home layouts, sensing applications must be delivered while relying on sparse "ground truth" data. An enhanced data representation was tested for improving activity recognition performance by encoding observed temporal dependencies. Experiments showed an improvement in activity recognition accuracy over baseline data representations with standard classifiers. Channel State Information (CSI) was chosen as our rich sensing technology for its ambient nature and potential deployability. We developed a novel Python toolkit, called CSIKit, to handle various CSI software implementations, including automatic detection for off-the-shelf CSI formats. Previous researchers proposed a method to address AGC effects on COTS CSI hardware, which we tested and found to improve correlation with a baseline without AGC. This implementation was included in the public release of CSIKit. Two sensing applications were delivered using CSIKit to demonstrate its functionality. Our statistical approach to motion detection with CSI data showed a 32% increase in accuracy over an infrared sensor-based solution using data from 2 unique environments. We also demonstrated the first CSI activity recognition application on a Raspberry Pi 4, which achieved an accuracy of 92% with 11 activity classes. An application was then trained to support movement detection using data from all COTS CSI hardware. This was combined with our signal divider implementation to compare CSI wireless and sensing performance characteristics. The IWL5300 exhibited the most consistent wireless performance, while the ESP32 was found to produce viable CSI data for sensing applications. This establishes the ESP32 as a low-cost high-value hardware solution for CSI sensing. To complete this work, an in-home study was performed using real-world sensor data. An ESP32-based CSI sensor was developed to be integrated into our IoT network. This sensor was tested in a FitHome environment to identify how the data from our existing simple sensors could aid sensor development. We performed an experiment to demonstrate that annotations for CSI data could be gathered with infrared motion sensors. Results showed that our new CSI sensor collected real-world data of similar utility to that collected manually in a controlled environment