Internet of Things – ICIOT 2018

Advances in the Internet of Things (IoT) computing paradigm have made it a popular solution covering many areas such as smart cities, connected vehicles, smart farming etc. to provide major economic benefits, reduced resource consumption, smarter environments and increased sharing of resources among other advantages. However, the security issues arising from the scale of connectivity and heterogeneity of resources in IoT make it a hot target for attackers, where centralized security solutions fall short. The vulnerabilities in providing proper device authentication and data integrity in IoT networks have been shown to introduce devastating effects. This calls for designing a data security architecture for IoT, which can accurately authenticate devices by anyone in the network in a decentralized manner and prevent unauthorized modification of the stored data. In this paper, we propose a blockchain-based approach for IoT systems that introduces transparency and tamper-resistance into data storage and retrieval in IoT networks. Evaluation of a developed prototype demonstrates that the proposed solution is promising to present a unified framework for IoT data security.

The Internet of Things (IoT) is the network of physical devices embedded with sensors, actuators, and connectivity which enables these objects to connect and exchange data. Cleary the IoT has a pervasive impact on the society and an increasing number of systems are now based on IoT. One of the key challenges in IoT is coping with the heterogeneous set of systems and the integration of these systems in the same communication network. Several studies have focused on this integration aspect and addressed this at different levels of abstraction. Unfortunately, the different approaches are scattered and fragmented over the different studies and it is not clear how to cope with the integration concern within a single IoT system but also across multiple IoT systems that need to be integrated. To this end this chapter provides a comprehensive and systematic approach for identifying the key integration concerns in the IoT system architecture and describing the currently provided solutions. For this we adopt a pattern-based approach in which generic architecture solution structures are provided to these recurring integration concerns. We illustrate our approach for addressing the integration of IoT based systems within the context of smart city engineering.

Wiki technology can support the collaborative accumulation of empirical knowledge and embodies the wisdom and experience information from a vast number of users. This paper proposes a novel Wiki mechanism for engineering empirical knowledge management, compares the new Wiki mechanism with the original one, introduces the differences between two mechanisms in the way of knowledge accumulation, and analyses two mechanisms from the maturity of knowledge structure.

We develop a graphical feature-based framework that collects data from different kinds of sensor networks, represents the sensor network data as a graph, extracts graphical features from the graph representation, and adds those features to a set of non-graphical features that are typical for the application. Our hypothesis is that the addition of a structural representation and transitional features will improve performance for the corresponding prediction tasks of different networks. We apply our graphical feature-based approach on smart phone GPS sensor data to predict activities performed by phone users. We represent the location category corresponding to each GPS value as a node and movement of users from one GPS location to another as an edge in graph. Then we extract graphical features such as existence of nodes and edges from the graph representation and add them to basic sensor data features coming from the smart phone. We find that using this augmented feature set improves activity recognition accuracy by 7.27% compared to using only basic non-graphical features with feature set augmented with existence of nodes performing the best.

The Internet of Things (IoT) requires a shift in our way of building applications, as it is aimed at providing many services to society in general. Non-developer people require increasingly complex IoT applications and support for their ever changing run-time requirements. Although service composition allows the combination of functionality into more complex behaviours, current approaches provide support for dealing with one IoT scenario at a time, as they allow the definition of only one workflow. In this paper, we present DX-MAN, an algebraic model for static service composition that allows the definition of composite services that encompass multiple workflows for run-time scenarios. We evaluate our proposal on an example in the domain of smart homes.

IoT applications are usually characterized by large-scale demand and the widespread use of mobile devices. Similarly, performing interaction among application and system components in a decoupled and elastic way, and enforcing Quality of Service (QoS) usually also become issues. Hence, paradigms such as pub/sub on top of cloud resources represent a suitable strategy for application development. However, management of QoS-aware resource allocation for pub/sub systems remains challenging, especially when system peers connect in an intermittent way. In this paper, we propose a new approach for resource allocation focusing on end-to-end performance in face of peers’ disconnections. We evaluate and demonstrate the benefits of our approach using simulations. QoS enforcement was achieved in almost all scenarios, and we have shown that our approach can help reasoning about efficient resource allocation.

Calibration is an important step towards building reliable IoT systems. For example, accurate sensor reading requires ADC calibration, and power monitoring chips must be calibrated before being used for measuring the energy consumption of IoT devices. In this paper, we present ProCal, a low-cost, accurate, and scalable power calibration tool. ProCal is a programmable platform which provides dynamic voltage and current output for calibration. The basic idea is to use a digital potentiometer connected to a parallel resistor network controlled through digital switches. The resistance and output frequency of ProCal is controlled by a software communicating with the board through the SPI interface. Our design provides a simple synchronization mechanism which prevents the need for accurate time synchronization. We present mathematical modeling and validation of the tool by incorporating the concept of Fibonacci sequence. Our extensive experimental studies show that this tool can significantly improve measurement accuracy. For example, for ATMega2560, the ADC error reduces from 0.2% to 0.01%. ProCal not only costs less than 2% of the current commercial solutions, it is also highly accurate by being able to provide extensive range of current and voltage values.

This paper discusses my research on human settlement system and personal air purification system based on mobile internet, internet of things, cloud computing, and big data analysis technologies under the condition that air pollution in China seriously affects people’s normal life. The design and development of personal air purification system is mainly designed by optimizing the principle of anion air purification. The design background, design principles, design innovations, and design results of the system are described in detail. The problems encountered in the R&D process and solutions are described in detail in conjunction with actual products. Since the product was introduced, this product has been widely recognized by the market and has achieved a lot of honor and research patent achievements.

The use of deep learning algorithms, as a core element of artificial intelligence, has attracted increased attention from industrial and academic institutes recently. One important use of deep learning is to predict the next user action inside an intelligent home environment that is based on Internet of Things (IoT). Recent researcher discusses the benefit of using deep learning based on different datasets to assist their result. However, assuring the best performance to satisfy real-time applications leads us to use a real-world dataset to make sure that the designed system meets the requirements of real-time applications. This paper uses the MavPad dataset which was gathered from distributed sensors and actuators in a real-world environment. The authors use simulation to investigate the performance of a multilayer neural network that predicts future human actions. The authors also present a hardware implementation of the deep learning model on an FPGA. The results showed that the hardware implementation demonstrated similar accuracy with significantly improved performance compared to the software-based implementation due to the exploitation of parallel computing and using optimization techniques to map the designed system into the target device. Additionally, our implementation of FPGA-based neural network system supports its future utilization for other applications.

The increasing presence of heterogeneous Internet of Things devices inside the home brings with it added convenience and value to the householders. At the same time, these devices tend to be Internet-connected and continuously monitor and collect data about the residents and their daily lifestyle activities. Such data can be of a sensitive nature, given that the house is the place where privacy is naturally expected. To gain insight into this state of affairs, we empirically investigate the privacy policies of 87 different categories of commercial smart home devices in terms of data being collected. This is done using a combination of manual and data mining techniques. The overall contribution of this work is a model that identifies and categorizes smart connected home data in terms of its collection mode, collection method, and collection phase. Our findings bring up several implications for smart connected home privacy, which include the need for better security controls to safeguard the privacy of the householders.

Securing Access to IOT devices is a challenging task as IoT devices are resource-constrained devices in terms of processing, storage, and networking capacity. Because of their fast spreading and deployment, significant disadvantages are seen in today’s authentication and access control schemes. This paper proposes a blockchain-based solution which allows for authentication and secure communication to IOT devices. Our solution benefits greatly from the intrinsic features of blockchain and also builds on existing authentication schemes. Specifically, our proposed blockchain-based solution, architecture, and design allow for accountability, integrity, and traceability with tamper-proof logs. The paper provides overall system design and architecture, and details on testing and implementation of a realistic scenario as a proof of concept.

At present URI based web service orchestration poses a fundamental limitation on the nature of resulting web service applications restricting them to client-server paradigm and the partnership determination as a static design time activity. This naïve simplicity limits the scope and capability of orchestration technology that shows up sorely in our inability orchestrate IoT devices using application level web service protocols/technology. This work suggests and demonstrates a middleware (named here as Open Interaction Middleware Services-OIMS) based web service orchestration approach that unfetter the client-server nature of orchestration and introduces run time establishment of provider-consumer relationship. OIMS mediated orchestration permits speech-act based specification and orchestration of partners of the collaboration. OIMS functionality deserves to be a part of the WS infrastructure eventually. Decoupled service orchestration permit creation of novel application layer level web services based protocols and applications – that has the merits of both bus and broker based protocols combined - such as in the case of Internet of Things (IoT) monitoring & integration. This work illustrates the proposed approach using a case study in integrating IoT devices belonging to multi class IP network that provides several important quality attributes such as loose coupling and scalability to the resulting environment.

Responding to rapid growth of Internet of Things (IoT) services and devices, many IoT platforms and frameworks are presented for successful IoT realization. Recently, Open Connectivity Foundation (OCF) which consists of a group of industry leaders emerges to create new standards for IoT platform and deliver an open source implementation and a certification program. IoTivity open-source software framework is one of the project sponsored by the OCF which has been developed to provide interoperability among heterogeneous IoT devices. It enables seamless Device-to-Device (D2D) connectivity and targets various application domains such as home and manufacturing automation, health care, and social networks. IoTivity uses, however, rather lots of memory in view of small devices which have limited hardware resources and runs Real-Time Operating System (RTOS). In this paper, we propose a light-weight IoT framework, RT-OCF that optimizes memory consumption and provides a memory tracer to prevent memory leaks. It has a layered architecture which consists of resource layer, messaging layer, and platform adaptation layer together with modules for security and utility. RT-OCF is able to run not only on Linux but also on TizenRT, an open source RTOS platform runnable on ARTIK053 board. The experiment performed on both Linux and TizenRT shows that more than 20% of peak memory is reduced when compared with IoTivity while preserving a packet latency for GET operations.