Software-defined wireless networking opportunities and challenges for internet-of-things: a review

With the emergence of Internet-of-Things (IoT), there is now growing interest to simplify wireless network controls. This is a very challenging task, comprising information acquisition, information analysis, decision-making, and action implementation on large scale IoT networks. Resulting in research to explore the integration of software-defined networking (SDN) and IoT for a simpler, easier, and strain less network control. SDN is a promising novel paradigm shift which has the capability to enable a simplified and robust programmable wireless network serving an array of physical objects and applications. This paper starts with the emergence of SDN and then highlights recent significant developments in the wireless and optical domains with the aim of integrating SDN and IoT. Challenges in SDN and IoT integration are also discussed from both security and scalability perspectives.

Scalable RFID security framework and protocol supporting Internet of Things

Radio-frequency identification (RFID) is seen as one of the requirements for the implementation of the Internet-of-Things (IoT). However, an RFID system has to be equipped with a holistic security framework for a secure and scalable operation. Although much work has been done to provide privacy and anonymity, little focus has been given to performance, scalability and customizability issues to support robust implementation of IoT. Also, existing protocols suffer from a number of deficiencies such as insecure or inefficient identification techniques, throughput delay and inadaptability. In this paper, we propose a novel identification technique based on a hybrid approach (group-based approach and collaborative approach) and security check handoff (SCH) for RFID systems with mobility. The proposed protocol provides customizability and adaptability as well as ensuring the secure and scalable deployment of an RFID system to support a robust distributed structure such as the IoT. The protocol has an extra fold of protection against malware using an incorporated malware detection technique. We evaluated the protocol using a randomness battery test and the results show that the protocol offers better security, scalability and customizability than the existing protocols. © 2014 Elsevier B.V. All rights reserved.

Predictive data mining for converged internet of things: a mobile health perspective

Mobile Health (mHealth) is now emerging with Internet of Things (IoT), Cloud and big data along with the prevalence of smart wearable devices and sensors. There is also the emergence of smart environments such as smart homes, cars, highways, cities, factories and grids. Presently, it is difficult to quickly forecast or prevent urgent health situations in real-time as health data are analyzed offline by a physician. Sensors are expected to be overloaded by demands of providing health data from IoT networks and smart environments. This paper proposes to resolve the problems by introducing an inference system so that life-threatening situations can be prevented in advance based on a short and long term health status prediction. This prediction is inferred from personal health information that is built by big data in Cloud. The inference system can also resolve the problem of data overload in sensor nodes by reducing data volume and frequency to reduce workload in sensor nodes. This paper presents a novel idea of tracking down and predicting a personal health status as well as intelligent functionality of inference in sensor nodes to interface IoT networks

A framework for convergence of cloud services and Internet of things

Today, Cloud Computing and the Internet of things are two "major forces" that drive the development of new Information Technology (IT) solutions. Many Internet of things (IoT) based large-scale applications rely on a cloud platform for data processing and storage. However, big data generated or collected by large-scale geo-distributed devices needs to be transferred to the cloud, often becoming a bottleneck for the system. In this paper, we propose a framework that integrates popular cloud services with a network of loT devices. In the framework, novel methods have been designed for reliable and efficient data transportation. This framework provides a convergence of cloud services and devices that will ease the development of loT based, cloud-enabled applications. We have implemented a prototype of the framework to demonstrate the convergence of popular cloud services and IoT technologies.

Inference of personal sensors in the internet of things

Smartphone technology has become more popular and innovative over the last few years, and technology companies are now introducing wearable devices into the market. By emerging and converging with technologies such as Cloud, Internet of Things (IoT) and Virtualization, requirements to personal sensor devices are immense and essential to support existing networks, e.g. mobile health (mHealth) as well as IoT users. Traditional physiological and biological medical sensors in mHealth provide health data either periodically or on-demand. Both of these situations can cause rapid battery consumption, consume significant bandwidth, and raise privacy issues, because these sensors do not consider or understand sensor status when converged together. The aim of this research is to provide a novel approach and solution to managing and controlling personal sensors that can be used in various areas such as the health, military, aged care, IoT and sport. This paper presents an inference system to transfer health data collected by personal sensors efficiently and effectively to other networks in a secure and effective manner without burdening workload on sensor devices.

Secure object tracking protocol for the internet of things

In this paper, we propose a secure object tracking protocol to ensure the visibility and traceability of an object along the travel path to support the Internet of Things (IoT). The proposed protocol is based on radio frequency identification system for global unique identification of IoT objects. For ensuring secure object tracking, lightweight cryptographic primitives and physically unclonable function are used by the proposed protocol in tags. We evaluated the proposed protocol both quantitatively and qualitatively. In our experiment, we modeled the protocol using security protocol description language (SPDL) and simulated SPDL model using automated claim verification tool Scyther. The results show that the proposed protocol is more secure and requires less computation compared to existing similar protocols.

Security framework and protocols for the internet of things

This study was conducted to protect security of human beings and businesses from intruders in the context of IoT which is next generation internet. We have developed a universal framework and a number of communication protocols to provide security that will make IoT cyber world safer for all t users.

Integrating internet-of-things with the power of cloud computing and the intelligence of big Data analytics- a three layered approach

This paper is written through the vision on integrating Internet-of-Things (IoT) with the power of Cloud Computing and the intelligence of Big Data analytics. But integration of all these three cutting edge technologies is complex to understand. In this research we first provide a security centric view of three layered approach for understanding the technology, gaps and security issues. Then with a series of lab experiments on different hardware, we have collected performance data from all these three layers, combined these data together and finally applied modern machine learning algorithms to distinguish 18 different activities and cyber-attacks. From our experiments we find classification algorithm RandomForest can identify 93.9% attacks and activities in this complex environment. From the existing literature, no one has ever attempted similar experiment for cyber-attack detection for IoT neither with performance data nor with a three layered approach.

A smart web service based on the context of things

Combining the Semantic Web and the Ubiquitous Web, Web 3.0 is for things. The Semantic Web enables human knowledge to be machine-readable and the Ubiquitous Web allows Web services to serve any thing, forming a bridge between the virtual world and the real world. By using context, Web services can become smarter-that is, aware of the target things' or applications' physical environments, or situations and respond proactively and intelligently. Existing methods for implementing context-aware Web services on Web 2.0 mainly enumerate different implementations corresponding to different attribute values of the context, in order to improve the Quality of Services (QoS). However, things in the physical world are extremely diverse, which poses new problems for Web services: it is difficult to unify the context of things and to implement a flexible smart Web service for things. This article proposes a novel smart Web service based on the context of things, which is implemented using a REpresentational State Transfer for Things (Thing-REST) style, to tackle the two problems. In a smart Web service, the user's description (semantic context) and sensor reports (sensing context) are two channels for acquiring the context of things which are then employed by ontology services to make the context of things machine-readable. With guidance of domain knowledge services, event detection services can analyze things' needs particularly, well through the context of things. We then propose a Thing-REST style to manage the context of things and user context, and to mashup Web services through three structures (i.e., chain, select, and merge) to implement smart Web services. A smart plant watering-service application demonstrates the effectiveness of our method. © 2012 ACM.

That social side of things: students' experiences of social and physical place

This paper highlights the experience of social and physical place as experienced by
students at aTAFE (Technical and Further Education) college in Victoria, Australia, and TAFE diploma graduates who transitioned to a degree course at a university in Victoria, Australia.

Enhancement of sensor data transmission by inference and efficient data processing

When wearable and personal health device and sensors capture data such as heart rate and body temperature for fitness tracking and health services, they simply transfer data without filtering or optimising. This can cause over-loading to the sensors as well as rapid battery consumption when they interact with Internet of Things (IoT) networks, which are expected to increase and de-mand more health data from device wearers. To solve the problem, this paper proposes to infer sensed data to reduce the data volume, which will affect the bandwidth and battery power reduction that are essential requirements to sensor devices. This is achieved by applying beacon data points after the inferencing of data processing utilising variance rates, which compare the sensed data with ad-jacent data before and after. This novel approach verifies by experiments that data volume can be saved by up to 99.5% with a 98.62% accuracy. Whilst most existing works focus on sensor network improvements such as routing, operation and reading data algorithms, we efficiently reduce data volume to reduce band-width and battery power consumption while maintaining accuracy by implement-ing intelligence and optimisation in sensor devices.

Development and validation of the physical disability sexual and body esteem scale

The Physical Disability Sexual and Body Esteem (PDSBE) scale was developed to assess respondents' capacity to feel positive about their sexuality and their body while living with a physical impairment. The current paper presents four studies that were conducted to develop and assess the psychometric properties of the 10 item PDSBE scale. The first study was an exploratory factor analysis involving 348 participants with physical disabilities. The factors were: Sexual Esteem, Attractiveness to Others, and Body Esteem. The second study was a confirmatory factor analysis, with a total of 338 participants. This analysis confirmed the factor structure established in study 1. The third study revealed good test-retest reliability on a total of 47 participants with physical disabilities. The final study was conducted with 748 participants with physical disabilities and showed that the PDSBE had high reliability and good convergent and divergent validity. Mean scores showed that women scored significantly higher than men on the total PDSBE scale and on the Sexual Esteem and Attractiveness to Others subscales. Future applications for the PDSBE scale are discussed.

Validity and reliability of a physical activity recall instrument among overweight and non-overweight men and women

This study examined the validity and reliability of survey items measuring one-week recall of physical activity (PA) and examined differences by weight status. A sub-aim of the study was to objectively assess the intensity of activity that most closely matches self-report. A questionnaire was administered to adults twice, three days apart. It was again administered after subjects wore a MTI/CSA accelerometer for seven days (n = 118). Several metabolic equivalent (MET) thresholds were applied to the accelerometer data. Agreement between test and re- test estimates of sufficient physical activity for health benefits (150 min/week) was high (% agreement > 90%). Correlations (rho) between total reported PA (mins/day) and accelerometer data were 0.29 (p < 0.05) among men and 0.25 (p < 0.05) among women. Among men, self- reported duration of moderate PA (3-5.9 METS) and accelerometer data were significantly correlated (rho = 0.40, p < 0.01), with no differences by weight status. Among women, a significant relationship was found only for those who were not overweight (rho = 0.52, p < 0.001). A significant correlation between self-reported duration of vigorous PA (6+ METS) and accelerometer data was only found for overweight men (rho = 0.40, p < 0.05). When lower MET thresholds were applied to the accelerometer data, women's reported duration of moderate-intensity PA was most strongly correlated with moderate PA (accelerometer) defined as 2.0-5.9 METS (rho = 0.39, p < 0.01). The recall instrument provides a consistent measure of physical activity and validation coefficients were similar to those obtained for other physical activity recall questionnaires. However, the ability to measure PA by self-report may vary by weight status.

Development and reliability of a self-report questionnaire to examine children's perceptions of the physical activity environment at home and in the neighbourhood

Background: Environmental factors are increasingly being implicated as key influences on children's physical activity. Few studies have comprehensively examined children's perceptions of their environment, and there is a paucity of literature on acceptable and reliable scales for measuring these. This study aimed to develop and test the acceptability and reliability of a scale which examined a broad range of environmental perceptions among children.
Methods: Based on constructs from ecological models, a survey incorporating items on children's perceptions of the physical and social environment at home and in the neighbourhood was developed. This was administered on two occasions, nine days apart, to a sample of 39 children aged 11 years (54% boys), attending a metropolitan Australian elementary school. The acceptability of the survey was determined by the proportion of missing responses to each item. The test-retest reliability of individual items, scores and scales were determined using Kappa statistics and percent agreement for categorical variables, and intraclass correlation coefficients (ICC) for continuous variables.
Results: There were few missing responses to each question, with only 4% of all responses missing. Although some Kappa values were low, all categorical variables showed acceptable reliability when examined for percent agreement between test and retest (range 68%–100% agreement). Continuous variables all showed moderate to good ICC values (range 0.72–0.92).
Conclusion: Findings suggest this questionnaire is reliable and acceptable to children for assessing environmental perceptions relevant to physical activity among 11-year-old children.