Cloud computing is a very attractive option for service users and service providers for their businesses because of the benefits it provides. A major concern among service users regarding cloud adoption, however, is the unpredictability of performance in relation to the services provided. Even though guarantees in the form of service-level agreements are provided to users by service providers, real-time service-level degradability remains a critical concern; hence, there is a need for an approach that assists users to manage a service before it fails. The approaches proposed in the literature assess and evaluate the performance of the cloud infrastructure of providers, but this does not guarantee that a given service instance will meet the desired quality level because there may be factors other than the provider's infrastructure that will affect the level of quality of the service instance. In this paper, we present an approach that measures the quality of a service instance in real time and provides important analysis for service users as to whether they will achieve their desired objectives. This analysis also constitutes an important input for service users in the assessment and management of a service to avoid the failure to achieve objectives.

Cyber-Physical Systems (CPS) provides a smart infrastructure connecting abstract computational artifacts with the physical world. This paper presents some challenges for developing distributed real-time Cyber-Physical Systems. The focus is on one particular challenge, namely event modelling in distributed real-time CPS. A Web-of-Things based CPS framework for event handling and processing is proposed. To illustrate the application of the proposed framework, a case study for achieving demand response in a smart home is provided.

The function of Wireless Sensor Networks (WSNs) is to collect and store data sent to other nodes or servers. Current technologies allow validation during transit, but stop after the data reaches its destination. One of the challenges with these technologies is to ensure that the source of the data is preserved, once it leaves the WSN. This is important as the data can be used by other applications or distributed to other parties. Therefore, it needs to be ensured that the data source is identifiable and the data is valid. Sensors are susceptible to various types of attack, such as data modification, data insertion and deletion, or even physical capture and sensor replacement. Hence, security becomes an important issue with WSNs. The traditional algorithms are used for securing data transmission between sensor nodes. However these algorithms need millions of multiplication instructions to perform operations, and cannot efficiently protect the copyright of the valuable sensor data. Watermarking is one of the effective choices to overcome this challenge. Watermark adds a second line of defense to ensure that the data is valid, even if someone cracks the encryption. This paper proposes a watermarking technique for copyright protection of data in WSNs. It also provides performance evaluation of the technique to show its robustness against various types of attacks, like data deletion, packet replication and Sybil attacks.

Cloud computing is a new paradigm for service-based computing and is gaining popularity. An efficient way for the assurances of the expected service levels in cloud computing is to establish a tailor-made Service Level Agreement (SLA) and to ensure the commitment of SLAs by service providers. In this paper, we conduct a survey of the state of the art in cloud SLA assurance from two aspects -- pre- and post-interaction phases, based on which research gaps in existing approaches are identified. New research requirements for SLA assurance are then presented.

Over the past two decades, neural networks have been applied to develop short-term traffic flow predictors. The past traffic flow data, captured by on-road sensors, is used as input patterns of neural networks to forecast future traffic flow conditions. The amount of input patterns captured by the on-road sensors is usually huge, but not all input patterns are useful when trying to predict the future traffic flow. The inclusion of useless input patterns is not effective to developing neural network models. Therefore, the selection of appropriate input patterns, which are significant for short-term traffic flow forecasting, is essential. This can be conducted by setting an appropriate configuration of input nodes of the neural network; however, this is usually conducted by trial and error. In this paper, the Taguchi method, which is a robust and systematic optimization approach for designing reliable and high-quality models, is proposed for the purpose of determining an appropriate neural network configuration, in terms of input nodes, in order to capture useful input patterns for traffic flow forecasting. The effectiveness of the Taguchi method is demonstrated by a case study, which aims to develop a short-term traffic flow predictor based on past traffic flow data captured by on-road sensors located on a Western Australia freeway. Three advantages of using the Taguchi method were demonstrated: 1) short-term traffic flow predictors with high accuracy can be designed; 2) the development time for short-term traffic flow predictors is reasonable; and 3) the accuracy of short-term traffic flow predictors is robust with respect to the initial settings of the neural network parameters during the learning phase.

This paper proposes a novel neural network (NN) training method that employs the hybrid exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to improve the generalization capabilities of previously used methods for training NNs for short-term traffic flow forecasting. The approach uses exponential smoothing to preprocess traffic flow data by removing the lumpiness from collected traffic flow data, before employing a variant of the LM algorithm to train the NN weights of an NN model. This approach aids NN training, as the preprocessed traffic flow data are more smooth and continuous than the original unprocessed traffic flow data. The proposed method was evaluated by forecasting short-term traffic flow conditions on the Mitchell freeway in Western Australia. With regard to the generalization capabilities for short-term traffic flow forecasting, the NN models developed using the proposed approach outperform those that are developed based on the alternative tested algorithms, which are particularly designed either for short-term traffic flow forecasting or for enhancing generalization capabilities of NNs.

Intelligent Transportation Systems (ITS) use sensors to capture real-time information for managing road congestion and more efficient utilisation of road infrastructure by motorists. Due to privacy laws in Australia and other countries, the road transport authorities are only allowed to collect anonymous road usage data, thus limiting their real-time analysis of road usage. This paper proposes a novel vehicle tracking method using the new 802.11p technology for wireless access in vehicular environments for providing accurate data while preserving the privacy of the road user. The proposed architecture captures vehicle movements from the start of the journey to the destination without identifying the same vehicle on subsequent journeys. This gives accurate vehicle journey data that will be used to improvement travel times, road safety and lower fuel consumption for motorists.

Abstract The recent development of Web‐of‐Things (WoT) and Cyber–Physical Systems (CPS) raises a new requirement of connecting abstract computational artifacts with the physical world. This requires both new theories and engineering practices that model cyber and physical resources in a unified framework, a challenge that few current approaches are able to tackle. The solution must break the boundary between the cyber world and the physical world by providing a unified infrastructure that permits integrated models addressing issues from both worlds simultaneously. This paper proposes a framework to integrate WoT and CPS. A case study is presented to demonstrate the advantage of the framework. Copyright © 2010 John Wiley & Sons, Ltd.

Quality of Service (QoS) is one of the most active research areas in networking. The most fundamental requirement for QoS routing is the ability to find and maintain a network path that provides the required network resources between two or more nodes. In this paper, we present a distributed collaborative multicast QoS routing architecture that uses a semi-greedy probing heuristic to quickly find a QoS path between a joining node and the multicast tree. The proposed architecture will enable the routers along the path to intelligently and dynamically discover a QoS path. Any router that receives a probe will only know its neighbours and it will create a link to the previous router from where the probe comes from. The proposed architecture is a tree-initiated QoS search and the first QoS packet to reach the joining node will be used as the QoS path. Analysis of this method shows that the path search time and message overhead is lower than other similar schemes.

Current diagnosis, treatment and healthcare delivery processes in Australia are dominated by long established westernized clinically driven methods of patient-practitioner interaction. Consequently this dominant healthcare provider influence contributes to risk of miscommunication, misinformation in patient records and reciprocal misunderstandings that go unrecognised as such. For Indigenous communities, inadequate health literacy (HL) and a pervasive semantic disconnect are major barriers. Overcoming these barriers in the primary care setting presents opportunities to deliver appropriate timely and more effective care. We propose an e-health framework that enhances the Patient-Practitioner Interview Encounter (PPIE) through the use of a patient-centric linguistic interface using semantic mappings between Aboriginal English (AE) and Standard Australian English (SAE). This will ameliorate communications and interactions, so meeting the needs of all stakeholders (Patients, Physicians, Nurses, Allied Health Professionals and their Non-Critical Carers) engaged in Indigenous patient-centric primary care. It provides healthcare practitioners and their Indigenous T2DM patients with a new platform for two-way educative sharing and knowledge exchange that will increase mutually productive treatment, care and management expectations.

The global Type 2 Diabetes Mellitus (T2DM) epidemic imposes a heavy burden on communities that are ethnically vulnerable to the disease and further disadvantaged by socio-economic circumstance and cultural communications barriers. Aboriginal communities in rural and remote Western Australia are representative of these high-risk groups. Indigenous patients needing continuous management of T2DM are also experiencing disproportionate risk of co-morbidities and hospitalizations compared with non-indigenous patients. Type 2 Diabetes Mellitus (T2DM) is often described as `the lifestyle disease'. Within clinical care and patient quality of life management domains, T2DM presents both the healthcare practitioner and the patient with a mosaic of complexities. Information processing demands for self-management of diabetes are extensive, requiring constant self monitoring and assessment of the illness state in order to apply per instance and per condition the most appropriate form of control. In this work we introduce a primary care communications concept tool centered upon optimization of the Patient-Practitioner Interview Encounter (PPIE). The target beneficiary is the Aboriginal T2DM patient living in Western Australia. A vital part of our design effort is therefore dedicated to understanding and responding to the cultural domain barriers, challenges and opportunities of this specific health care environment.

Modern multimedia communication tools must have high security, high availability and high quality of service (QoS). Any security implementation will directly impact on QoS. This paper will investigate how end-to-end security impacts on QoS in Voice over Internet Protocol (VoIP). The QoS is measured in terms of lost packet ratio, latency and jitter using different encryption algorithms, no security and just the use of IP firewalls in Local and Wide Area Networks (LAN and WAN). The results of laboratory tests indicate that the impact on the overall performance of VoIP depends upon the bandwidth availability and encryption algorithm used. The implementation of any encryption algorithm in low bandwidth environments degrades the voice quality due to increased loss packets and packet latency, but as bandwidth increases encrypted VoIP calls provided better service compared to an unsecured environment.

Over the last couple of months a large number of distributed denial of service (DDoS) attacks have occurred across the world, especially targeting those who provide Web services. IP traceback, a counter measure against DDoS, is the ability to trace IP packets back to the true source/s of the attack. In this paper, an IP traceback scheme using a machine learning technique called intelligent decision prototype (IDP), is proposed. IDP can be used on both probabilistic packet marking (PPM) and deterministic packet marking (DPM) traceback schemes to identify DDoS attacks. This will greatly reduce the packets that are marked and in effect make the system more efficient and effective at tracing the source of an attack compared with other methods. IDP can be applied to many security systems such as data mining, forensic analysis, intrusion detection systems (IDS) and DDoS defense systems.