http://www.parkjonghyuk.net/lecture/2015-1st-lecture/networksecurity/Paper1.pdf

Security, privacy and trust in Internet of Things

A survey on trust management for Internet of Things

A survey of intrusion detection in Internet of Things

Proceedings of the World Congress on Engineering 2013, WCE 2013

Internet of Things (IoT) is an emerging paradigm which aims to inter-connect all smart physical devices, so that the devices together can provide smart services to the users. Some of the IoT applications include smart homes, smart cities, smart grids, smart retail, etc. Since IoT systems are built up with heterogeneous hardware and networking technologies, connecting them to the software/application level to extract information from large amounts of data is a complex task. In this paper, we have surveyed various architecture and protocols used in IoT systems and proposed suitable taxonomies for classifying them. We have also discussed the technical challenges, such as security and privacy, interoperability, scalability, and energy efficiency. We have provided an in-depth coverage of recent research works for every mentioned challenge. The objective of this survey is to help future researchers to identify IoT specific challenges and to adopt appropriate technology depending on the application requirements.

A Survey on Architecture, Protocols and Challenges in IoT

“Internet of Things” (IOT) has become the everyday buzz words in recent years. As part and parcel of the Smart environment where human beings and things interact intelligently, trust and mobility becomes the basic prerequisites. However, to unify trust and mobility, a security protocol must be used for information exchanges among human and things; as well as between things and things (such as between Wi-Fi Client and Wi-Fi AP). In this paper, we present an enhancement of a security protocol for bulk data transfer amongst embedded devices (similar to the practices in IOT). We also proposed a security framework for enhancing security, trust and privacy (STP) for embedded system infrastructure. We suggested the use of lightweight symmetric encryption (for data) and asymmetric encryption (for key exchange) protocols in Trivial File Transfer Protocol (TFTP). The target implementation of TFTP is for embedded devices such as Wi-Fi Access Points (AP) and remote Base Stations (BS). We have chosen Das U-Boot (Universal Boot loader) as the horizontal security platform for this new security implementation which is suitable for Smart Environment.

A lightweight and secure TFTP protocol for smart environment

Educational App for Children with Autism Spectrum Disorders (ASDs)

This paper presents an application of the Radial Basis Function Neural Network (RBFNN)-based identification of an essential oil extraction using Non-Linear Autoregressive Model with Exogenous Inputs (NARX) model. The dataset consisted of a Pseudo-Random Binary Sequence (PRBS) inputs as the control signal, and outputs depicting temperatures inside the distillation column. One Step Ahead (OSA) model fitting and residual tests demonstrated that the RBFNN-based NARX model was able to approximate the system well, while satisfying all validation criterias.

Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network

This paper reports on the study of interference effects from 2 types of Ultra Wide Band (UWB) radar signals on an OFDM transmission system by simulation. The UWB signals were: Gaussian monocycle pulse and UWB chirp signal. Average bit error rate (BER) degradation of the victim system was evaluated under in-band interference from the UWB signals in AWGN channel. Simulation results indicated that Gaussian monocycle pulse degrades the BER characteristic of an OFDM system more severe than a UWB chirp signal, where significant BER floor were observed at the victim receiver. The amplitude probability distributions (APD) of interfering UWB envelope signal at the victim receiver were analyzed.

Analysis of interference from UWB radar signals on a digital OFDM transmission system

An automated image processing system has the potential to aid ophthalmologist in diagnosing eye diabetic retinopathy (DR) diseases better, by detecting changes in retina features. This paper introduces the development of detection and classification system that provides DR stage classification based on exudates quantification in digital fundus images. The system can help ophthalmologist to perform early screening on diabetes patients. The exudates detection methods consist of two steps; rough and fine exudates segmentation. Rough segmentation is performed using morphology operation and column-wise neighborhoods operation, while fine segmentation is done using morphological reconstruction. As for DR stage classification, the segmented image is translated into DR numerical index, which later classified into the respective stage based on inference study using statistical analysis. The proposed method used fundus image database from Sungai Buloh Hospital, Malaysia. Together with other suitable retinal features extraction and classification methods, this segmentation method can form the basis of a fast and easy to use diagnostic support tool for diabetic retinopathy, which will give a huge advantage in terms of improved access to mass screening people for risk or presence of diabetes.

http://ieeexplore.ieee.org/iel7/6888664/6895028/06895049.pdf

Syed Farid Syed Adnan

Papers

A lightweight and secure TFTP protocol for smart environment

Educational App for Children with Autism Spectrum Disorders (ASDs)

Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network

Analysis of interference from UWB radar signals on a digital OFDM transmission system

A proposed diabetic retinopathy classification algorithm with statistical inference of exudates detection