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

Smart Home Systems Based on Internet of Things

Smart home systems achieved great popularity in the last decades as they increase the comfort and quality of life. Most smart home systems are controlled by smartphones and microcontrollers. A smartphone application is used to control and monitor home functions using wireless communication techniques. We explore the concept of smart home with the integration of IoT services and cloud computing to it, by embedding intelligence into sensors and actuators, networking of smart things using the corresponding technology, facilitating interactions with smart things using cloud computing for easy access in different locations, increasing computation power, storage space and improving data exchange efficiency. In this chapter we present a composition of three components to build a robust approach of an advanced smart home concept and implementation

PRIVATE SECURITY SURVEILLANCE SYSTEM

Security is an essential need for man. Without the sense of security, daily human activities will be greatly affected. Most people go to great lengths to ensure there is a presence of security in their environment, often employing dedicated personnel to keep watch over them and their property. This paper proposes a design of a microcontroller based electronic security system which helps to detect possible intruders to a home. This security system is designed to reduce the need of having personnel stationed as security guards over a home. It has the primary unit called the Area Watch Unit (AWU) consisting of a motion detection unit that effectively detects motion around specified perimeters which is then followed by a computer vision to identify and classify what caused the motion. A facial recognition algorithm is run on the face extracted from the image captured after the object that caused the motion is identified and classified as human. Access is then granted to the individual if the results from the facial recognition is positive otherwise a message is sent to the owner of the home indicating a possible intruder is present. There is also a Final Recovery Unit (FRU) which sends a message to the owner of the home and sounds an alarm whiles flashing lights in the event that the Area Watch Unit (AWU) is by-passed without authority

Veracity in power consumption of smart home

This project is intended to develop for EDP Portugal from Cside for the newly launched smart home platform called EDP re:dy ( Remote Energy Dynamics) Home Automation devices with digital controlling platform from anywhere with optimum power consumption pattern in order to make energy efficient home.During the realization of this project,the veracity of re:dy smart meter has been analyzed with different load factors (capacitive, nductive) in order to observe the harmonics distortion pattern with various home appliances and traditional energy meters.Gathering all collected data in SDP (Service Delivery latform) online platform to analyze the sequence of energy values and according to that edp box will give effective power consumption pattern to the client's minimum tariff and energy saving pattern via reducing the power dissipation.As a final conclusion of the project, based on obtained results an effective method for smart home automation with edp re:dy box associated with MAC address of equipment added and controlled via online carrier/subscriber portal

Active and assisted living ecosystem for the elderly

A novel ecosystem to promote the physical, emotional and psychic health and well-being of the elderly is presented. Our proposal was designed to add several services developed to meet the needs of the senior population, namely services to improve social inclusion and increase contribution to society. Moreover, the solution monitors the vital signs of elderly individuals, as well as environmental parameters and behavior patterns, in order to seek eminent danger situations and predict potential hazardous issues, acting in accordance with the various alert levels specified for each individual. The platform was tested by seniors in a real scenario. The experimental results demonstrated that the proposed ecosystem was well accepted and is easy to use by seniors

Video Processing Analysis For Non-Invasive Fatigue Detection And Quantification

Fatigue is a common symptom of weakness either physically or mentally. These symptoms may led to a drop in motivation, weakened sensitivity, slowing of responsiveness and inability to give full attention. All of these problems can cause adverse effects, such as accidents, especially those that require full attention as drivers of vehicles, and rail operators, the pilot of an aircraft or ship operators. This research investigates systems to detect and quantify the signs of fatigue using non-invasive facial analytics. There are four main algorithms that represent the major contribution from the PhD research. These algorithms encompass facial fatigue detection and quantification system as a whole. Firstly, a new technique to detect the face is introduced. This face detection algorithm is an affiliation of colour skin segmentation technique, connected component of binary image usage, and learning machine algorithm. The introduced face detection algorithm is able to reduce the false positive detection rate by a very significant margin. For the facial fatigue detection and quantification, the major fatigue signs features are from the eye activity. A new algorithm called the , Interdependence and Adaptive Scale Mean Shift (IASMS) is presented. The IASMS is able to quantify the state of eye as well as to track non-rigid eye movement. IASMS integrates the mean shift tracking algorithm with an adaptive scale scheme, which is used to track the iris and quantify the iris size. The IASMS is associated with face detection algorithm, image enhanced scheme, eye open detection technique and iris detection method in the initialisation process. This proposed method is able to quantify the eye activities that represent the blink rate and the duration of eye closure. The third contribution is yawning analysis algorithm. Commonly yawning is detected based on a wide mouth opening. Frequently however this approach is thwarted by the common human reaction to hand-cover the mouth during yawning. In this research, a new approach to analyse yawning which takes into account the covered mouth is introduced. This algorithm combines with a new technique of mouth opening measurements, covered mouth detection, and facial distortion (wrinkles) detection. By using this proposed method, yawning is still able to detect even though the mouth is covered. In order to have reliable results from the testing and evaluating of the developed fatigue detection algorithm, the real signs of fatigue are required. This research develops a recorded face activities database of the people that experience fatigue. This fatigue database is called as the Strathclyde Fatigue Facial (SFF). To induce the fatigue signs, ethically approved sleep deprivation experiments were carried out. In these experiments twenty participants, and four sessions were undertaken, which the participant has to deprive their sleep in 0, 3, 5, and 8 hours. The participants were subsequently requested to carry out 5 cognitive tasks that are related to the sleep loss. The last contribution of this research is a technique to recognise the fatigue signs. The existing fatigue detection system is based on single classification. However, this work presents a new approach for fatigue recognition which the fatigue is classified into levels. The levels of fatigue are justified based on the sleep deprivation stages where the SFF database is fully used for training, testing and evaluation of the developed fatigue recognition algorithm. This fatigue recognition algorithm is then integrated into a Fatigue Monitoring Tool (FMT) platform. This FMT has been used to test the participant that carried out the tasks as ship crew in shipping bridge simulator

Investigation on Design and Development Methods for Internet of Things

The thesis work majorly focuses on the development methodologies of the Internet of Things (IoT). A detailed literature survey is presented for the discussion of various challenges in the development of software and design and deployment of hardware. The thesis work deals with the efficient development methodologies for the deployment of IoT system. Efficient hardware and software development reduces the risk of the system bugs and faults. The optimal placement of the IoT devices is the major challenge for the monitoring application. A Qualitative Spatial Reasoning (QSR) and Qualitative Temporal Reasoning (QTR) methodologies are proposed to build software systems. The proposed hybrid methodology includes the features of QSR, QTR, and traditional databased methodologies. The hybrid methodology is proposed to build the software systems and direct them to the specific goal of obtaining outputs inherent to the process. The hybrid methodology includes the support of tools and is detailed, integrated, and fits the general proposal. This methodology repeats the structure of Spatio-temporal reasoning goals. The object-oriented IoT device placement is the major goal of the proposed work. Segmentation and object detection is used for the division of the region into sub-regions. The coverage and connectivity are maintained by the optimal placement of the IoT devices using RCC8 and TPCC algorithms. Over the years, IoT has offered different solutions in all kinds of areas and contexts. The diversity of these challenges makes it hard to grasp the underlying principles of the different solutions and to design an appropriate custom implementation on the IoT space. One of the major objective of the proposed thesis work is to study numerous production-ready IoT offerings, extract recurring proven solution principles, and classify them into spatial patterns. The method of refinement of the goals is employed so that complex challenges are solved by breaking them down into simple and achievable sub-goals. The work deals with the major sub-goals e.g. efficient coverage of the field, connectivity of the IoT devices, Spatio-temporal aggregation of the data, and estimation of spatially connected regions of event detection. We have proposed methods to achieve each sub-goal for all different types of spatial patterns. The spatial patterns developed can be used in ongoing and future research on the IoT to understand the principles of the IoT, which will, in turn, promote the better development of existing and new IoT devices. The next objective is to utilize the IoT network for enterprise architecture (EA) based IoT application. EA defines the structure and operation of an organization to determine the most effective way for it to achieve its objectives. Digital transformation of EA is achieved through analysis, planning, design, and implementation, which interprets enterprise goals into an IoT-enabled enterprise design. A blueprint is necessary for the readying of IT resources that support business services and processes. A systematic approach is proposed for the planning and development of EA for IoT-Applications. The Enterprise Interface (EI) layer is proposed to efficiently categorize the data. The data is categorized based on local and global factors. The clustered data is then utilized by the end-users. A novel four-tier structure is proposed for Enterprise Applications. We analyzed the challenges, contextualized them, and offered solutions and recommendations. The last objective of the thesis work is to develop energy-efficient data consistency method. The data consistency is a challenge for designing energy-efficient medium access control protocol used in IoT. The energy-efficient data consistency method makes the protocol suitable for low, medium, and high data rate applications. The idea of energyefficient data consistency protocol is proposed with data aggregation. The proposed protocol efficiently utilizes the data rate as well as saves energy. The optimal sampling rate selection method is introduced for maintaining the data consistency of continuous and periodic monitoring node in an energy-efficient manner. In the starting phase, the nodes will be classified into event and continuous monitoring nodes. The machine learning based logistic classification method is used for the classification of nodes. The sampling rate of continuous monitoring nodes is optimized during the setup phase by using optimized sampling rate data aggregation algorithm. Furthermore, an energy-efficient time division multiple access (EETDMA) protocol is used for the continuous monitoring on IoT devices, and an energy-efficient bit map assisted (EEBMA) protocol is proposed for the event driven nodes

Novel Attacks and Defenses for Enterprise Internet-of-Things (E-IoT) Systems

This doctoral dissertation expands upon the field of Enterprise Internet-of-Things (E-IoT) systems, one of the most ubiquitous and under-researched fields of smart systems. E-IoT systems are specialty smart systems designed for sophisticated automation applications (e.g., multimedia control, security, lighting control). E-IoT systems are often closed source, costly, require certified installers, and are more robust for their specific applications. This dissertation begins with an analysis of the current E-IoT threat landscape and introduces three novel attacks and defenses under-studied software and protocols heavily linked to E-IoT systems. For each layer, we review the literature for the threats, attacks, and countermeasures. Based on the systematic knowledge we obtain from the literature review, we propose three novel attacks and countermeasures to protect E-IoT systems. In the first attack, we present PoisonIvy, several attacks developed to show that malicious E-IoT drivers can be used to compromise E-IoT. In response to PoisonIvy threats, we describe Ivycide, a machine-learning network-based solution designed to defend E-IoT systems against E-IoT driver threats. As multimedia control is a significant application of E-IoT, we introduce is HDMI-Walk, a novel attack vector designed to demonstrate that HDMI\u27s Consumer Electronics Control (CEC) protocol can be used to compromise multiple devices through a single connection. To defend devices from this threat, we introduce HDMI-Watch, a standalone intrusion detection system (IDS) designed to defend HDMI-enabled devices from HDMI-Walk-style attacks. Finally, this dissertation evaluates the security of E-IoT proprietary protocols with LightingStrike, a series of attacks used to demonstrate that popular E-IoT proprietary communication protocols are insecure. To address LightningStrike threats, we introduce LGuard, a complete defense framework designed to defend E-IoT systems from LightingStrike-style attacks using computer vision, traffic obfuscation, and traffic analysis techniques. For each contribution, all of the defense mechanisms proposed are implemented without any modification to the underlying hardware or software. All attacks and defenses in this dissertation were performed with implementations on widely-used E-IoT devices and systems. We believe that the research presented in this dissertation has notable implications on the security of E-IoT systems by exposing novel threat vectors, raising awareness, and motivating future E-IoT system security research