Car Security System

The project entitled 'Car Security System'is a system which is designed to provide high security in vehicles. This system is based on arduino controller with GSM and sensor networks. Driver’s drowsiness is one of the important factor which leads to vehicle’s accident. Eye blink detection technique helps to prevent this by controlling the vehicle’s speed. Sensors involved in the project help in detecting the occurrence of accident and also help informing the family with the accident location. The human movement is detected using the PIR sensors and the Tilt sensor will trigger arduino if the vehicle is being towed. The limit switch will provide the condition of the door which is open or closed. The system triggers an alarm detecting the presence of person or towing vehicle or opening of the door in a specific interval of time and the GSM call to user gets activated

A Home Security System Based on Smartphone Sensors

Several new smartphones are released every year. Many people upgrade to new phones, and their old phones are not put to any further use. In this paper, we explore the feasibility of using such retired smartphones and their on-board sensors to build a home security system. We observe that door-related events such as opening and closing have unique vibration signatures when compared to many types of environmental vibrational noise. These events can be captured by the accelerometer of a smartphone when the phone is mounted on a wall near a door. The rotation of a door can also be captured by the magnetometer of a smartphone when the phone is mounted on a door. We design machine learning and threshold-based methods to detect door opening events based on accelerometer and magnetometer data and build a prototype home security system that can detect door openings and notify the homeowner via email, SMS and phone calls upon break-in detection. To further augment our security system, we explore using the smartphone’s built-in microphone to detect door and window openings across multiple doors and windows simultaneously. Experiments in a residential home show that the accelerometer- based detection can detect door open events with an accuracy higher than 98%, and magnetometer-based detection has 100% accuracy. By using the magnetometer method to automate the training phase of a neural network, we find that sound-based detection of door openings has an accuracy of 90% across multiple doors

Motion Detecting Method And Device

In some embodiments, a motion detecting device is configured to detect whether one or more movement events have occurred. The motion detecting device can include: (a) a processing module configured to run on a computational unit; and (b) a sensing device having: (1) one or more pressure sensors configured to provide two or more pressure measurements; and (2) a transmitter electrically coupled to the one or more pressure sensors and configured to transmit the two or more pressure measurements to the computational unit. The processing module is configured to use the two or more pressure measurements to determine whether the one or more movement events have occurred. The sensing device can be configured to be placed in at least one of ductwork of a heating, ventilation, and air conditioning system or an air handler of the heating, ventilation, and air conditioning system. Other embodiments are disclosed.Georgia Tech Research Corporatio

Determining Enclosure Breach Electromagnetically

A structure breach may be determined. A sensor, provided in the structure, may be driven with a constant frequency signal. The sensor may comprise a first conductive element and a second conductive element. The first conductive element may be substantially parallel with the second conductive element. A standing wave pattern may be induced on the sensor by the constant frequency signal reflecting off a termination point of the sensor. A least one characteristic of the sensor caused by the voltage standing wave pattern may be measured. A breach occurrence in the structure may be determined when the measured at least one characteristic varies from a previously determined value by a predetermined amount. The first conductive element and the second conductive element may be sandwiched between two layers comprising the structure. The structure may comprise a shipping container floor. The detected breach may comprise an opening greater than nine square inches.Georgia Tech Research Corporatio

IoTSan: Fortifying the Safety of IoT Systems

Today's IoT systems include event-driven smart applications (apps) that interact with sensors and actuators. A problem specific to IoT systems is that buggy apps, unforeseen bad app interactions, or device/communication failures, can cause unsafe and dangerous physical states. Detecting flaws that lead to such states, requires a holistic view of installed apps, component devices, their configurations, and more importantly, how they interact. In this paper, we design IoTSan, a novel practical system that uses model checking as a building block to reveal "interaction-level" flaws by identifying events that can lead the system to unsafe states. In building IoTSan, we design novel techniques tailored to IoT systems, to alleviate the state explosion associated with model checking. IoTSan also automatically translates IoT apps into a format amenable to model checking. Finally, to understand the root cause of a detected vulnerability, we design an attribution mechanism to identify problematic and potentially malicious apps. We evaluate IoTSan on the Samsung SmartThings platform. From 76 manually configured systems, IoTSan detects 147 vulnerabilities. We also evaluate IoTSan with malicious SmartThings apps from a previous effort. IoTSan detects the potential safety violations and also effectively attributes these apps as malicious.Comment: Proc. of the 14th ACM CoNEXT, 201

Deception in context: coding nonverbal cues, situational variables and risk of detection

There are many situations in which deception may arise and understanding the behaviors associated with it are compounded by various contexts in which it may occur. This paper sets out a coding protocol for identifying cues to deception and reports on three studies, in which deception was studied in different contexts. The contexts involved manipulating risks (i.e., probability) of being detected and reconnaissance, both of which are related to terrorist activities. Two of the studies examined the impact of changing the risks of deception detection, whilst the third investigated increased cognitive demand of duplex deception tasks including reconnaissance and deception. In all three studies, cues to deception were analyzed in relation to observable body movements and subjective impressions given by participants. In general, the results indicate a pattern of hand movement reduction by deceivers, and suggest the notion that raising the risk of detection influences deceivers? behaviors. Participants in the higher risk condition displayed increased negative affect (found in deceivers) and tension (found in both deceivers and truth-tellers) than those in lower risk conditions