Network Coding for Multihop Wireless Networks: Joint Random Linear Network Coding and Forward Error Correction with Interleaving for Multihop Wireless Networks

Optimising the throughput performance for wireless networks is one of the challenging tasks in the objectives of communication engineering, since wireless channels are prone to errors due to path losses, random noise, and fading phenomena. The transmission errors will be worse in a multihop scenario due to its accumulative effects. Network Coding (NC) is an elegant technique to improve the throughput performance of a communication network. There is the fact that the bit error rates over one modulation symbol of 16- and higher order- Quadrature Amplitude Modulation (QAM) scheme follow a certain pattern. The Scattered Random Network Coding (SRNC) system was proposed in the literature to exploit the error pattern of 16-QAM by using bit-scattering to improve the throughput of multihop network to which is being applied the Random Linear Network Coding (RLNC). This thesis aims to improve further the SRNC system by using Forward Error Correction (FEC) code; the proposed system is called Joint RLNC and FEC with interleaving. The first proposed system (System-I) uses Convolutional Code (CC) FEC. The performances analysis of System-I with various CC rates of 1/2, 1/3, 1/4, 1/6, and 1/8 was carried out using the developed simulation tools in MATLAB and compared to two benchmark systems: SRNC system (System-II) and RLNC system (System- III). The second proposed system (System-IV) uses Reed-Solomon (RS) FEC code. Performance evaluation of System IV was carried out and compared to three systems; System-I with 1/2 CC rate, System-II, and System-III. All simulations were carried out over three possible channel environments: 1) AWGN channel, 2) a Rayleigh fading channel, and 3) a Rician fading channel, where both fading channels are in series with the AWGN channel. The simulation results show that the proposed system improves the SRNC system. How much improvement gain can be achieved depends on the FEC type used and the channel environment.Indonesian Government and the University of Bradfor

Interference Management with Dynamic Resource Allocation Method on Ultra-Dense Networks in Femto-Macrocellular Network

Ultra-Dense Network (UDN) which is formed from femtocells densely deployed is known as one of key technologies for 5th generation (5G) cellular networks. UDN promises for increased capacity and quality of cellular networks. However, UDN faces more complex interference problems than rarely deployed femtocells, worse on femtocells that are located on cell edge area of macrocell. Therefore, mitigating or reducing effects of interferences is an important issue in UDN. This paper focuses on interference management using dynamic resource allocation for UDN. Types of interference considered in this study are cross-tier (macrocell-to-femtocell) and co-tier (femtocellto-femtocell) interferences for uplink transmission. We consider several scenarios to examine the dynamic resource allocation method for UDN in case of femtocells deployed in the whole area of microcell and in the cell edge area of macrocell. Simulation experiment using MATLAB program has been carried out. The performance parameters that are collected from the simulation are Signal to Interference and Noise Ratio (SINR), throughput, and Bit Error Rate (BER). The obtained simulation results show that system using dynamic resource allocation method outperforms conventional system and the results were consistent for the collected performance parameters. The dynamic resource allocation promises to reduce the effects of interference in UDN

Addressing Semantic Interoperability, Privacy and Security Concerns in Electronic Health Records

The use of Electronic Health Records (EHR) in healthcare has the potential of reducing medical errors, minimizing healthcare cost and significantly improving the healthcare service quality. However, there is a barrier in healthcare data and information exchange between various healthcare systems due to the lack of interoperability. Also, with the implementation of EHR system, there are security and privacy concerns in the storage and transferring data entities.  The healthcare interoperability problem remains an issue of further research and this paper proposes a semantic interoperability framework for solving  this problem by allowing healthcare stakeholders and organizations (doctors, clinics, hospitals)using various healthcare standards to exchange data and its semantics, which can be understood by both machines and humans. Moreover, the proposed framework takes into consideration the security aspects in the semantic interoperability framework by utilizing data encryption and other technologies to secure the communication for the EHR information while ensuring real time data availability.                                                                                                  Keywords:. Semantic interoperability; Interoperability standards; Electronic Health records(EHR); Artifical Intelligence Techniques. Natural Language Processing (NLP), Word2Vec, skip gram, CBO

Similarity Analyzer for Semantic Interoperability of Electronic Health Records Using Artificial Intelligence (AI)

The introduction of Electronic Health Records (EHR) has opened possibilities for solving interoperability issues within the healthcare sector. However, even with the introduction of EHRs, healthcare systems like hospitals and pharmacies remain isolated with no sharing of EHRs due to semantic interoperability issues. This paper extends our previous work in which we proposed a framework that dealt with semantic interoperability and security of EHR. The extension is the proposal of a cloud-based similarity analyzer for data structuring, data mapping, data modeling and conflict removal using Word2vec Artificial Intelligence (AI) technique. Different types of conflicts are removed from data in order to model data into common data types which can be interpreted by different stakeholder

Building a semantic RESTFul API for achieving interoperability between a pharmacist and a doctor using JENA and FUSEKI

YesInteroperability within different healthcare systems (clinics/hospitals/pharmacies) remains an issue of further research due to a barrier in sharing of the patient’s Electronic Health Record (EHR) information. To solve this problem, cross healthcare system collaboration is required. This paper proposes an interoperability framework that enables a pharmacist to access an electronic version of the patient’s prescription from the doctor using a RESTFul API with ease. Semantic technology standards like Web Ontology Language (OWL), RDF (Resource Description Framework) and SPARQL (SPARQL Protocol and RDF Query Language) were used to implement the framework using JENA semantic framework tool to demonstrate how interoperability is achieved between a pharmacy and a clinic JENA was used to generate the ontology models for the pharmacy called pharmacy.rdf and clinic called clinic.rdf. The two models contain all the information from the two isolated systems. The JENA reasoner was used to merge the two ontology models into a single model.rdf file for easy querying with SPARQL. The model.rdf file was uploaded into a triple store database created using FUSEKI server. SPARQL Endpoint generated from FUSEKI was used to query the triple store database using a RESTFul API. The system was able to query the triple store database and output the results containing the prescription name and its details in JSON and XML formats which can be read by both machines and humans.Supported by a Institutional Links grant, ID 261865161, under the Newton-Ristekdikti Fund partnership. The grant is funded by the UK Department for Business, Energy and Industrial Strategy and Indonesia Ministry of Research, Technology and Higher Education and delivered by the British Council

An intelligent edge computing based semantic gateway for healthcare systems interoperability and collaboration

YesThe use of Information and Communications Technology (ICTs) in healthcare has the potential of minimizing medical errors, reducing healthcare cost and improving collaboration between healthcare systems which can dramatically improve the healthcare service quality. However interoperability within different healthcare systems (clinics/hospitals/pharmacies) remains an issue of further research due to a lack of collaboration and exchange of healthcare information. To solve this problem, cross healthcare system collaboration is required. This paper proposes a conceptual semantic based healthcare collaboration framework based on Internet of Things (IoT) infrastructure that is able to offer a secure cross system information and knowledge exchange between different healthcare systems seamlessly that is readable by both machines and humans. In the proposed framework, an intelligent semantic gateway is introduced where a web application with restful Application Programming Interface (API) is used to expose the healthcare information of each system for collaboration. A case study that exposed the patient's data between two different healthcare systems was practically demonstrated where a pharmacist can access the patient's electronic prescription from the clinic.British Council Institutional Links grant under the BEIS-managed Newton Fund

Joint random linear network coding and convolutional code with interleaving for multihop wireless network

NoAbstract: Error control techniques are designed to ensure reliable data transfer over unreliable communication channels that are frequently subjected to channel errors. In this paper, the effect of applying a convolution code to the Scattered Random Network Coding (SRNC) scheme over a multi-hop wireless channel was studied. An interleaver was implemented for bit scattering in the SRNC with the purpose of dividing the encoded data into protected blocks and vulnerable blocks to achieve error diversity in one modulation symbol while randomising errored bits in both blocks. By combining the interleaver with the convolution encoder, the network decoder in the receiver would have enough number of correctly received network coded blocks to perform the decoding process efficiently. Extensive simulations were carried out to study the performance of three systems: 1) SRNC with convolutional encoding, 2) SRNC; and 3) A system without convolutional encoding nor interleaving. Simulation results in terms of block error rate for a 2-hop wireless transmission scenario over an Additive White Gaussian Noise (AWGN) channel were presented. Results showed that the system with interleaving and convolutional code achieved better performance with coding gain of at least 1.29 dB and 2.08 dB on average when the block error rate is 0.01 when compared with system II and system III respectively

Intelligent and energy efficient mobile smartphone gateway for healthcare smart devices based on 5G

NoThe healthcare sector is now blending with Information and Communications Technology (ICT) using Internet of Things (IoT) to potentially minimise medical errors and reduce healthcare cost. Patients are now embedded with smart devices like body sensors and wearable devices which can monitor their health without the need for a doctor in physical contact. Such smart devices have the downside of low battery power and are unable to transmit their data to the medical personnel when the patient is on the move away from the smart home/smart clinic fixed gateway. A mobile gateway is required which moves with the patient to process the smart device data without depleting the smartphone battery. This paper proposes an Intelligent and Energy Efficient SG based smartphone Gateway for healthcare smart devices (IEE5GG). In IEE5GG, the 5G architecture is adopted and the patient's smartphone is used as a gateway where multiple smart devices are connected e.g. via Bluetooth. To save energy, requests to the smartphone can either be executed on the smartphone gateway or offloaded and executed in the Mobile Edge Computing (MEC) cloud at close proximity to the smartphone in the 5G Base Station (BS) central Unit (gNB-CU) while considering the transmission power, Quality of Service (QoS), smartphone battery level and Central Processing Unit (CPU) load. Results show that the proposed IEE5GG framework saves up to 38% of energy in the healthcare mobile gateway smartphone and reduces healthcare application service time by up to 41%.British Council Institutional Links grant under the BEIS-managed Newton Fund

Fall detection system for elderly using Arduino, Gyroscope and GPS Module

N