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

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.

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.

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.

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.

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.

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.

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.

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.

Control layer resource management in SDN-IoT networks using multi-objective constraint

Software Defined Networking (SDN) and Internet of Things (IoT) integration has thrown many critical challenges. Specifically, in heterogeneous SDN-IoT ecosystem, optimized resources utilization and effective management at the control layer is very difficult. This mainly affects the application specific Quality of Service (QoS) and energy consumption of the IoT network. Motivated from this, we propose a new Resource Management (RM) method at the control layer, in distributed SDN-IoT networks. This paper starts with reasons that why at control layer RM is more complex in the SDN-IoT ecosystem. After-that, we highlight motivated examples that necessitate to investigate new RM methods in SDN-IoT context. Further, we propose a novel method to compute controller performance. Theoretical analysis is conducted to prove that the proposed method is better than the existing methods.

PUF-based secure checker protocol for Networked RFID Systems

Radio Frequency Identification (RFID) system is anemerging technology for automating object identification. TheNetworked RFID System (NRS) is a component of a distributedobject identification network which facilitates automated supplychain management. It also makes the Internet of Things (IoT)concept a reality. To increase the business feasibility of NRSimplementation, the system should be able to ensure the visibilityand traceability of the object throughout the chain using achecker protocol. By doing so, the protocol will check thegenuineness of the object and the genuineness of the object’sprevious travel path on-site. While doing so, the protocol needs toensure the security requirement of the system. To this end, wepropose a secure checker protocol for NRS which will use a PUF(Physically Unclonable Function) and simple cryptographicprimitives. The protocol provides security (protect privacy of thepartners, injection of fake objects, non-repudiation, andunclonability), visibility and traceability for NRS. It is alsosuitable for passive tags.

Secure object tracking protocol for networked RFID systems

Networked systems have adapted Radio Frequency identification technology (RFID) to automate their business process. The Networked RFID Systems (NRS) has some unique characteristics which raise new privacy and security concerns for organizations and their NRS systems. The businesses are always having new realization of business needs using NRS. One of the most recent business realization of NRS implementation on large scale distributed systems (such as Internet of Things (IoT), supply chain) is to ensure visibility and traceability of the object throughout the chain. However, this requires assurance of security and privacy to ensure lawful business operation. In this paper, we are proposing a secure tracker protocol that will ensure not only visibility and traceability of the object but also genuineness of the object and its travel path on-site. The proposed protocol is using Physically Unclonable Function (PUF), Diffie-Hellman algorithm and simple cryptographic primitives to protect privacy of the partners, injection of fake objects, non-repudiation, and unclonability. The tag only performs a simple mathematical computation (such as combination, PUF and division) that makes the proposed protocol suitable to passive tags. To verify our security claims, we performed experiment on Security Protocol Description Language (SPDL) model of the proposed protocol using automated claim verification tool Scyther. Our experiment not only verified our claims but also helped us to eliminate possible attacks identified by Scyther.

Big data sensing and service: a tutorial

As the advance of the Internet of Things (IoT), more M2M sensors and devices are connected to the Internet. These sensors and devices generate sensor-based big data and bring new business opportunities and demands for creating and developing sensor-oriented big data infrastructures, platforms and analytics service applications. Big data sensing is becoming a new concept and next technology trend based on a connected sensor world because of IoT. It brings a strong impact on many sensor-oriented applications, including smart city, disaster control and monitor, healthcare services, and environment protection and climate change study. This paper is written as a tutorial paper by providing the informative concepts and taxonomy on big data sensing and services. The paper not only discusses the motivation, research scope, and features of big data sensing and services, but also exams the required services in big data sensing based on the state-of-the-art research work. Moreover, the paper discusses big data sensing challenges, issues, and needs.

V for variety: Lessons learned from complex smart cities data harmonization and integration

With emerging trends for Internet of Things (IoT) and Smart Cities, complex data transformation, aggregation and visualization problems are becoming increasingly common. These tasks support improved business intelligence, analytics and enduser access to data. However, in most cases developers of these tasks are presented with challenging problems including noisy data, diverse data formats, data modeling and increasing demand for sophisticated visualization support. This paper describes our experiences with just such problems in the context of Household Travel Surveys data integration and harmonization. We describe a common approach for addressing these harmonizations. We then discuss a set of lessons that we have learned from our experience that we hope will be useful for others embarking on similar problems. We also identify several key directions and needs for future research and practical support in this area.

Remembrance of things past? Change, development and paternalism

As part of a retrospective study of effects of organizational change on interpersonal relations, this paper discusses change talk among Australian employees of an American multinational manufacturing enterprise. Interviewees tended to feel pushed into change, discussing its effects in terms of the difficulties of adolescence and earlier experiences of sudden independence. Over time, what had been a simple and firm us and them division in intergroup relations between management and unions/workers had become more fluid and subtle, and perhaps more mature. Interview data are interpreted and then re-interpreted in terms of theories of team development, nostalgia, and paternalism. It is argued that each interpretation makes differing, but complementary, assumptions about the nature of time. If developmental, progressive assumptions of organizational change are relaxed, further attention can be given to theorizing and researching subtleties in talk of the past.