Biblio

Despite the strict measures taken by authorities for children safety, crime against children is increasing. To curb this crime, it is important to improve the safety of children. School authorities can be severely penalized for these incidents, hence monitoring the school bus is significantly important in limiting these incidents. The developing worry of families for the security and insurance of their kids has started incredible interest in creating strong frameworks that give successful following and oversight of kids driving among home and school. Coordinated transport following permits youngsters to partake more in their normal schoolwork longer than trusting that a transport will be late with the assistance of notice and guarantees the security of every understudy. These days, reacting to the necessities existing apart from everything else, numerous instructive foundations have begun to push more towards a compelling global positioning framework of their vehicles that ensures the wellbeing of their understudies. Effective transport following is accomplished by procuring the geographic directions utilizing the GPS module and communicating the informationto a distant server. The framework depends on prepared to-utilize inactive RFID peruses. Make a message pop-up from the server script subsequent to checking the understudy's RFID tag be. The RFID examine exhibiting that the understudy boarded the vehicle to the specific trained professionals and the parent. Successful transport following permits school specialists, guardians, and drivers to precisely design their schedules while protecting kids from the second they get on until they get off the transport. The framework overall makes it conceivable to educate the administration regarding crises or protests. A variety of reports can be generated for different school-wide real-time bus and vehicle activities. This paper reviews the various smart security transport systems proposed for providing security features.

Vehicles play an integral part in the life of a human being by facilitating in everyday tasks. The major concern that arises with this fact is that the rate of vehicle thefts have increased exponentially and retrieving them becomes almost impossible as the responsible party completely alters the stolen vehicles, leaving them untraceable. Ultimately, tracking and monitoring of vehicles using on-vehicle sensors is a promising and an efficient solution. The Internet of Things (IoT) is expected to play a vital role in revolutionizing the Security and Safety industry through a system of sensor networks by periodically sending the data from the sensors to the cloud for storage, from where it can be accessed to view or take any necessary actions (if required). The main contributions of this paper are the implementation and results of the prototype of a vehicle tracking and monitoring system. The system comprises of an Arduino UNO board connected to the Global Positioning System (GPS) module, Neo-6M, which senses the exact location of the vehicle in the form of latitude and longitude, and the ESP8266 Wi-Fi module, which sends the data to the Application Programming Interface (API) Cloud service, ThingSpeak, for storage and analyzing. An Android based mobile application is developed that utilizes the stored data from the Cloud and presents the user with the findings. Results show that the prototype is not only simple and cost effective, but also efficient and can be readily used by everyone from all walks of life to protect their vehicles.

Cross-platform development is becoming widely used by developers, and writing for separate platforms is being replaced by developing a single code base that will work across multiple platforms simultaneously, while reducing cost and time. The purpose of this paper is to demonstrate cross-platform development by creating a cargo tracking system that will work on multiple platforms with web application by tracking cargo using Global Positioning System (GPS), since the transport business has played a vital role in the evolution of human civilization. In this system, Google Flutter technology is used to create a mobile application that works on both Android and iOS platforms at the same time, by providing maps to clients showing their cargo location using Google Map API, as well as providing a web-based application.

Security has become a major concern for women, children and even elders in every walk of their life. Women are getting assaulted and molested, children are getting kidnapped, elder citizens are also facing many problems like robbery, etc. In this paper, a smart security solution called smart wearable device system is implemented using the Raspberry Pi3 for enhancing the safety and security of women/children. It works as an alert as well as a security system. It provides a buzzer alert alert to the people who are nearby to the user (wearing the smart device). The system uses Global Positioning System (GPS) to locate the user, sends the location of the user through SMS to the emergency contact and police using the Global System for Mobile Communications (GSM) / General Radio Packet Service (GPRS) technology. The device also captures the image of the assault and surroundings of the user or victim using USB Web Camera interfaced to the device and sends it as an E-mail alert to the emergency contact soon after the user presses the panic button present on Smart wearable device system.

The Global Positioning System (GPS) can determine the position of any person or object on earth based on satellite signals. But when inside the building, the GPS cannot receive signals, the indoor positioning system will determine the precise position. How to achieve more precise positioning is the difficulty of an indoor positioning system now. In this paper, we proposed an ultra-wideband fingerprinting positioning method based on a convolutional neural network (CNN), and we collect the dataset in a room to test the model, then compare our method with the existing method. In the experiment, our method can reach an accuracy of 98.36%. Compared with other fingerprint positioning methods our method has a great improvement in robustness. That results show that our method has good practicality while achieves higher accuracy.

This paper proposes a method to synthesis acoustic wave (AW) filters with Chebyshev response automatically. Meanwhile, each AW resonator used to design the filter can be easily fabricated on the same piezoelectric substrate. The method is based on an optimization algorithm with constraints for constant electromechanical coupling coefficient ( kt2) to minimize the defined cost function. Finally, the experimental result for a surface acoustic wave (SAW) filter of global positioning system (GPS) frequency band based on the 42° lithium tantalate (LiTaO3) substrate validates the simulation results. The designed filter shows insertion loss (IL) and return loss (RL) better than 2.5dB and 18dB respectively in the pass-band, and out-band reflection larger than 30dB.

The growing use of smart phones has also given opportunity to the intruders to create malicious apps thereby the security and privacy concerns of a novice user has also grown. This research focuses on the privacy concerns of a user who unknowingly installs a malicious apps created by the programmer. In this paper we created an attack scenario and created an app capable of compromising the privacy of the users. After accepting all the permissions by the user while installing the app, the app allows us to track the live location of the Android device and continuously sends the GPS coordinates to the server. This spying app is also capable of sending the call log details of the user. This paper evaluates two leading smart phone operating systems- Android and IOS to find out the flexibility provided by the two operating systems to their programmers to create the malicious apps.

BYOD (Bring Your Own Device) trends allows employees to use the smartphone as a tool in everyday work and also as an attendance device. The security of employee attendance system is important to ensure that employees do not commit fraud in recording attendance and when monitoring activities at working hours. In this paper, we propose a combination of fingerprint, secure android ID, and GPS as authentication factors, also addition of anti emulator and anti fake location module turn Mobile Attendance System into Mobile Secure Attendance System. Testing based on scenarios that have been adapted to various possible frauds is done to prove whether the system can minimize the occurrence of fraud in attendance recording and monitoring of employee activities.

Wearables are now ubiquitous items equipped with a multitude of sensors such as GPS, accelerometer, or Bluetooth. The raw data from this sensors are typically used in a health context. However, we can also use it for security purposes. In this paper, we present a solution that aims at using data from the sensors of a wearable device to identify the password a user is typing on a keyboard by using machine learning algorithms. Hence, the purpose is to determine whether a malicious third party application could extract sensitive data through the raw data that it has access to.

The Internet of Things (IoT) is an emerging technology, an extension of the traditional Internet which make everything is connected each other based on Radio Frequency Identification (RFID), Sensor, GPS or Machine to Machine technologies, etc. The security issues surrounding IoT have been of detrimental impact to its development and has consequently attracted research interest. However, there are very few approaches which assess the security of IoT from the perspective of an attacker. Penetration testing is widely used to evaluate traditional internet or systems security to date and it normally spends numerous cost and time. In this paper, we analyze the security problems of IoT and propose a penetration testing approach and its automation based on belief-desire-intention (BDI) model to evaluate the security of the IoT.

Vehicular ad hoc networks (VANETs) are designed to provide traffic safety by exploiting the inter-vehicular communications. Vehicles build awareness of traffic in their surroundings using information broadcast by other vehicles, such as speed, location and heading, to proactively avoid collisions. The effectiveness of these VANET traffic safety applications is particularly dependent on the accuracy of the location information advertised by each vehicle. Therefore, traffic safety can be compromised when Sybil attackers maliciously advertise false locations or other inaccurate GPS readings are sent. The most effective way to detect a Sybil attack or correct the noise in the GPS readings is localizing vehicles based on the physical features of their transmission signals. The current localization techniques either are designed for networks where the nodes are immobile or suffer from inaccuracy in high-interference environments. In this paper, we present a RSSI-based localization technique that uses mobile nodes for localizing another mobile node and adjusts itself based on the heterogeneous interference levels in the environment. We show via simulation that our localization mechanism is more accurate than the other mechanisms and more resistant to environments with high interference and mobility.

Spoofing is a serious threat to the widespread use of Global Navigation Satellite Systems (GNSSs) such as GPS and can be expected to play an important role in the security of many future IoT systems that rely on time, location, or navigation information. In this paper, we focus on the technique of multi-receiver GPS spoofing detection, so far only proposed theoretically. This technique promises to detect malicious spoofing signals by making use of the reported positions of several GPS receivers deployed in a fixed constellation. We scrutinize the assumptions of prior work, in particular the error models, and investigate how these models and their results can be improved due to the correlation of errors at co-located receiver positions. We show that by leveraging spatial noise correlations, the false acceptance rate of the countermeasure can be improved while preserving the sensitivity to attacks. As a result, receivers can be placed significantly closer together than previously expected, which broadens the applicability of the countermeasure. Based on theoretical and practical investigations, we build the first realization of a multi-receiver countermeasure and experimentally evaluate its performance both in authentic and in spoofing scenarios.

User uses smartphones for web surfing and browsing data. Many smartphones are embedded with inbuilt location aware system called GPS [Global Positioning System]. Using GPS user have to register and share his all private information to the LBS server. LBS is nothing but Location Based Service. Simply user sends the query to the LBS server. Then what is happening the LBS server gives a private information regarding particular user location. There will be a possibility to misuse this information so using mobile crowd method hides user location from LBS server and avoid sharing of privacy information with server. Our solution does not required to change the LBS server architecture.

The Department of Energy seeks to modernize the U.S. electric grid through the SmartGrid initiative, which includes the use of Global Positioning System (GPS)-timing dependent electric phasor measurement units (PMUs) for continual monitoring and automated controls. The U.S. Department of Homeland Security is concerned with the associated risks of increased utilization of GPS timing in the electricity subsector, which could in turn affect a large number of electricity-dependent Critical Infrastructure (CI) sectors. Exploiting the vulnerabilities of GPS systems in the electricity subsector can result to large-scale and costly blackouts. This paper seeks to analyze the risks of increased dependence of GPS into the electric grid through the introduction of PMUs and provides a systems engineering perspective to the GPS-dependent System of Systems (S-o-S) created by the SmartGrid initiative. The team started by defining and modeling the S-o-S followed by usage of a risk analysis methodology to identify and measure risks and evaluate solutions to mitigating the effects of the risks. The team expects that the designs and models resulting from the study will prove useful in terms of determining both current and future risks to GPS-dependent CIs sectors along with the appropriate countermeasures as the United States moves towards a SmartGrid system.