Noor Azurati Ahmad

Dynamic WLAN Fingerprinting RadioMap for Adapted Indoor Positioning Model

As a result of Smartphone usage increment a sharp growth in demand for indoor environment computing especially for Location Based Services (LBS) has been occurred. The basic concept of LBS is to determine the mobile users’ location, which is important for services such as tracking or navigation in Civil defense and Healthcare. Currently, there are many techniques used to locate a mobile user in indoor environment. WLAN is considered as one of the best choices for indoor positioning due to its low cost, simple configuration and high accuracy. Although the WLAN Received Signal Strength Indicator (RSSI) fingerprinting method is the most accurate positioning method, it has a serious drawback because it’s Radio Map (RM) become outdated when environmental change occurs. In addition, recalibrating the RM is a time consuming process. This paper presents a novel adapted indoor positioning model which uses the path loss propagation model of the wireless signal to overcome the outdated RM. The experimental results demonstrate that the proposed adapted model is highly efficient in solving the problems mentioned especially in a dynamically changing environment.

Adaptive indoor positioning model based on WLAN-fingerprinting for dynamic and multi-floor environments

The Global Positioning System demonstrates the significance of Location Based Services but it cannot be used indoors due to the lack of line of sight between satellites and receivers. Indoor Positioning Systems are needed to provide indoor Location Based Services. Wireless LAN fingerprints are one of the best choices for Indoor Positioning Systems because of their low cost, and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS) differently, and peoples’ presence in LOS between access points and mobile device affects the RSS. This research proposes a new Adaptive Indoor Positioning System model (called DIPS) based on: a dynamic radio map generator, RSS certainty technique and peoples’ presence effect integration for dynamic and multi-floor environments. Dynamic in our context refers to the effects of people and device heterogeneity. DIPS can achieve 98% and 92% positioning accuracy for floor and room positioning, and it achieves 1.2 m for point positioning error. RSS certainty enhanced the positioning accuracy for floor and room for different mobile devices by 11% and 9%. Then by considering the peoples’ presence effect, the error is reduced by 0.2 m. In comparison with other works, DIPS achieves better positioning without extra devices.

Adapted WLAN Fingerprint Indoor Positioning System (IPS) Based on User Orientations

Location determination techniques for IPS are divided into proximity, triangulation, and fingerprint techniques. WLAN-Fingerprint has become one of the best alternative methods because it is already widely available inside buildings and provides a high level of accuracy. However, the WLAN signal is highly influenced by the surrounding environment, such as walls, ceilings, and people. People who hold mobile devices (MDs) significantly affect IPS performance. This paper therefore proposes a new method to overcome the effect of user orientation on the accuracy of IPS by introducing an adaptation of the signal strength values based on user orientation. The average position error when using the initial radio map (RM) is found to be 8.84 m. After implementing the RM adaptation, the average error decreased to 3.5 m. This proposed method can therefore improve accuracy by 60.4% and decrease the required RM database memory with more additions of simple computations.

Malware Forensic Analytics Framework Using Big Data Platform

The dramatically increased threats such as malware attacks to our cyber world have given us the vital sign to strengthen the security in a more proactive way. Thus, in recent research we proposed an integrated malware forensic analytics framework that will expose the future threats of malware attacks. This framework incorporates malware collections, malware analytics and visualization of discovered malware attacks. In this paper, we present the design and implementation of the framework which focuses on analytics and visualization, and utilized the emerging technology of big data platform. The implementation of the framework shows promising results in presenting descriptive analytics and predicting the future attacks using machine learning algorithms. We also demonstrate the feasibility of Hortonworks Cybersecurity Package (HCP) in supporting the proposed framework. Finally, we discussed the future work that can be further investigated in improving the implementation of the framework.

Effect of People around User to WLAN Indoor Positioning System Accuracy

The ability to check the location of people or mobile devices (MD) in indoor environment has a large number of application. Indoor Positioning System (IPS) utilizes many existing technologies such as radio frequencies, magnetic fields, acoustic signals, thermal, optical or other sensors. WLAN IPS become one of the most favorite solutions because it is already widely exist and provide good accuracy. Fingerprint is one of the methods in WLAN IPS. The performance of this method is greatly affected by received signal strength indicator (RSSI). In fact RSSI value is very dynamic and influenced by environmental conditions inside the room such as walls, ceiling and also human presence. This paper presented an experiment to explore the effect of many people around MD to the RSSI and position error of IPS. People around MD at certain distance and position will be barrier for WLAN signal, therefore the RSSI will decrease. The average of position error because of that effect is 11.34 meter.

Defense Mechanisms against Machine Learning Modeling Attacks on Strong Physical Unclonable Functions for IOT Authentication: A Review

Security component in IoT system are very crucial because the devices within the IoT system are exposed to numerous malicious attacks. Typical security components in IoT system performs authentication, authorization, message and content integrity check. Regarding authentication, it is normally performed using classical authentication scheme using crypto module. However, the utilization of the crypto module in IoT authentication is not feasible because of the distributed nature of the IoT system which complicates the message cipher and decipher process. Thus, the Physical Unclonable Function (PUF) is suggested to replace crypto module for IoT authentication because it only utilizes responses from set of challenges instead of cryptographic keys to authenticate devices. PUF can generate large number of challenge-response pairs (CRPs) which is good for authentication because the unpredictability is high. However, with the emergence of machine learning modeling, the CRPs now can be predicted through machine learning algorithms. Various defense mechanisms were proposed to counter machine learning modeling attacks (ML-MA). Although they were experimentally proven to be able to increase resiliency against ML-MA, they caused the generated responses to be instable and incurred high area overhead. Thus, there is a need to design the best defense mechanism which is not only resistant to ML-MA but also produces reliable responses and reduces area overhead. This paper presents an analysis on defense mechanisms against ML-MA on strong PUFs for IoT authentication.

The effect of people presence on WLAN RSS is governed by influence distance

Recently, there is high demand for implementing Location Based Services (LBS) indoor. Many technologies and methods have been investigated for this issue since GPS could not prove accurate positioning indoor. Although WLAN-fingerprinting is considered as one of the most accurate IPS method, its accuracy vulnerable to WLAN RSS fluctuation. This fluctuation occurs due to the physical obstacles presence. Many research papers highlighted peoples presence near to mobile device (MD) between it and Access point (AP) as RSS attenuate factor, but it is hard to find a research paper investigated this attenuation factor as in the case of walls and ceilings. People presence effect was estimated in previous work as -5dBm on closed distance from MD but it still need a lot of investigation. This paper raises the existence of people presence influence distance as a new concept related to people presence effect. In addition to this, it provides experimental work in order to prove the truth of this claim. The obtained results proved the truth of this claim and the existence of the influence distance became a fact.

Energy harvesting in wireless sensor networks: A survey

With the emergence of Internet-of-Things (IoT) technologies, the application of Wireless Sensor Network (WSN) has become pervasive in our physical environment. These technologies rely on the WSN to capture environmental data and transmit it through the network to the specified location or storage. Given the massive data that captured by the WSN and the data transmission that happen in the process, there is a challenge for any developers and researchers in WSN in terms of a continuous operation of the WSN device. This is because WSNs are battery-powered and often deployed in a remote location that is hard to access for a battery replacement. The purpose of this paper is to survey the energy harvesting for improving the lifetime of the wireless sensor networks powered by the environmental energy. Energy harvesting takes advantages of the stray energy from the environment as the sustainable source of power.

Planar Textile Antennas Performance Under Wearable and Body Centric Measurements

This study proposes three types of planar textile antennas namely planar straight dipole, diamond dipoles and CPW monopole for body centric communication. All the proposed antennas are made from entirely textile where fleece and Shieldit fabrics are used as substrates and conducting parts, respectively. The proposed antennas have been thoroughly measured under wearable and body centric measurements. Investigations including bending, wetness and SAR were performed to test the performance of the proposed antennas for practical realization in body centric applications. Bending was found not to give any obvious performance deviation. However, wetness has caused major performance disruption since the antennas are made from non-waterproof material. Nevertheless, the original performance is recovered once the antennas are dried out. SAR investigation was also performed, and both antennas give significant SAR values when positioned near to human body.