We present a new simple Internet of Things (IoT) device that we call “Smart Bottle Cap”, which enables a standard bottle to become a user-controllable liquid pouring system. It consists of a mini vacuum pump to start the liquid flowing, a microcontroller to control the liquid flow, a BLE module to connect it to a smartphone, an accelerometer to detect the tilt angle of the bottle, an LED for drawing the attention of users, and a 3.7 V LiPo battery. The device's novel point is that a flow control mechanism built into a standard bottle cap makes the system suitable for general use and enables it to be easily extended.

The United Nations (UN) reports that the global population reached 7 billion in 2011, and today, it stands at about 7.3 billion. This dramatic increase has been driven largely by the extension of people's lifetime. The urban population has been also increasing, which causes a lot of issues for cities, such as congestion and increased demand for resources, including energy, water, sanitation, education, and healthcare services. A smart city has been expected a lot to solve those issues. The concept of a smart city is not new. Due to the progress of information and communication technology (ICT), including the Internet of Things (IoT) and big data (BD), the concept of a smart city has been being realized in various aspects. This paper introduces the concept and definition of a smart city. Then it explains the ambient intelligence that supports a smart city. Moreover, it introduces several key components of a smart city.

We focus on the construction of the digital signature scheme for local broadcast, which allows the devices with limited resources to securely transmit broadcast message. A multi-group authentication scheme that enables a node to authenticate its membership in multi verifiers by the sum of the secret keys has been proposed for limited resources. This paper presents a transformation which converts a multi-group authentication into a multi-group signature scheme. We show that the multi-group signature scheme converted by our transformation is existentially unforgeable against chosen message attacks (EUF-CMA secure) in the random oracle model if the multi-group authentication scheme is secure against impersonation under passive attacks (IMP-PA secure). In the multi-group signature scheme, a sender can sign a message by the secret keys which multiple certification authorities issue and the signature can validate the authenticity and integrity of the message to multiple verifiers. As a specific configuration example, we show the example in which the multi-group signature scheme by converting an error correcting code-based multi-group authentication scheme.

The Internet of Things (IoT) is defined as a global infrastructure for the Information Society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving, interoperable information and communication technologies by ITU-T. Data may be communicated in low-power and lossy environments, which causes complicated security issues. Furthermore, concerns are raised over access of personally identifiable information pertaining to IoT devices, network and platforms. Security and privacy concerns have been main barriers to implement IoT, which needs to be resolved appropriate security and privacy measures. This paper describes security threats and privacy concerns of IoT, surveys current studies related to IoT and identifies the various requirements and solutions to address these security threats and privacy concerns. In addition, this paper also focuses on major global standardization activities for security and privacy of Internet of Things. Furthermore, future directions and strategies of international standardization for theInternet of Thing's security and privacy issues will be given. This paper provides guidelines to assist in suggesting the development and standardization strategies forward to allow a massive deployment of IoT systems in real world.

This paper proposes Deep Neural Network (DNN)-based Voice Conversion (VC) using input-to-output highway networks. VC is a speech synthesis technique that converts input features into output speech parameters, and DNN-based acoustic models for VC are used to estimate the output speech parameters from the input speech parameters. Given that the input and output are often in the same domain (e.g., cepstrum) in VC, this paper proposes a VC using highway networks connected from the input to output. The acoustic models predict the weighted spectral differentials between the input and output spectral parameters. The architecture not only alleviates over-smoothing effects that degrade speech quality, but also effectively represents the characteristics of spectral parameters. The experimental results demonstrate that the proposed architecture outperforms Feed-Forward neural networks in terms of the speech quality and speaker individuality of the converted speech.

Image sensor communication (ISC), a type of visible light communication, is an emerging wireless communication technology that uses LEDs to transmit a signal and uses an image sensor in a camera to receive the signal. This paper discusses the present status of and future trends in ISC by describing the essential characteristics and features of ISC. Moreover, we overview the products and expected future applications of ISC.

This paper proposes secure regenerating codes that are composed of non-secure regenerating codes and a new all-or-nothing transform. Unlike the previous analysis of secure regenerating codes, the security of the proposed codes is analyzed in the sense of the indistinguishability. The advantage of the proposed codes is that the overhead caused by the security against eavesdropping is much less than that of previous secure regenerating codes. The security of the proposed codes against eavesdropping mainly depends on the new all-or-nothing transform.

Network coding (NC) is considered a new paradigm for distributed networks. However, NC has an all-or-nothing property. In this paper, we propose a sparse recovery approach using sparse sensing matrix to solve the NC all-or-nothing problem over a finite field. The effectiveness of the proposed approach is evaluated based on a sensor network.

With the popularization of Internet of things (IoT), the interaction between human and IoT has become a daily life. In this interaction, the objects of IoT usually require access to personal data, which are often sensitive. We propose a lightweight privacy-preserving model based on the trust evaluation that it can effectively protect privacy based on simple threshold detection. The key issue we address in this work is how to construct trust model so that non trusted objects were prevented from accessing private data. This work can be considered as a lightweight approach to access control for privacy-preservation. The main algorithm in the proposed model is a kind of dynamic self-adjusting trust evaluation mechanism that uses a combination of interaction information occurs between the human and the Internet of things, between the human and the human. According to the given threshold, the trust model can determine the data level of object access in the IoT. We have implemented a prototype of the proposed scheme, thereby demonstrating the feasibility of the proposed scheme on resource-constrained devices.

To reduce the server load and communication costs of machine-to-machine (M2M) systems, sensor data are aggregated in M2M gateways. Aggregation logic is typically programmed in the C language and embedded into the firmware. However, developing aggregation programs is difficult for M2M service providers because it requires gateway-specific knowledge and consideration of resource issues, especially RAM usage. In addition, modification of aggregation logic requires the application of firmware updates, which are risky. We propose a rule-based sensor data aggregation system, called the complex sensor data aggregator (CSDA), for M2M gateways. The functions comprising the data aggregation process are subdivided into the categories of filtering, statistical calculation, and concatenation. The proposed CSDA supports this aggregation process in three steps: the input, periodic data processing, and output steps. The behaviors of these steps are configured by an XML-based rule. The rule is stored in the data area of flash ROM and is updatable through the Internet without the need for a firmware update. In addition, in order to keep within the memory limit specified by the M2M gateway's manufacturer, the number of threads and the size of the working memory are static after startup, and the size of the working memory can be adjusted by configuring the sampling setting of a buffer for sensor data input. The proposed system is evaluated in an M2M gateway experimental environment. Results show that developing CSDA configurations is much easier than using C because the configuration decreases by 10%. In addition, the performance evaluation demonstrates the proposed system's ability to operate on M2M gateways.

We propose an edge-preserving multiscale image decomposition method using filters for non-equispaced signals. It is inspired by the domain transform, which is a high-speed edge-preserving smoothing method, and it can be used in many image processing applications. One of the disadvantages of the domain transform is sensitivity to noise. Even though the proposed method is based on non-equispaced filters similar to the domain transform, it is robust to noise since it employs a multiscale decomposition. It uses the Laplacian pyramid scheme to decompose an input signal into the piecewise-smooth components and detail components. We design the filters by using an optimization based on edge-preserving smoothing with a conversion of signal distances and filters taking into account the distances between signal intervals. In addition, we also propose construction methods of filters for non-equispaced signals by using arbitrary continuous filters or graph spectral filters in order that various filters can be accommodated by the proposed method. As expected, we find that, similar to state-of-the-art edge-preserving smoothing techniques, including the domain transform, our approach can be used in many applications. We evaluated its effectiveness in edge-preserving smoothing of noise-free and noisy images, detail enhancement, pencil drawing, and stylization.

The booming developments in embedded sensor technique, wireless communication technology, and information processing theory contribute to the emergence of Internet of Things (IoT), which aims at perceiving and connecting the physical world. In recent years, a growing number of Internet-connected sensors have published their real-time state about the real-world objects on the Internet, which makes the content-based sensor search a promising service in the Internet of Things (IoT). However, classical search engines focus on searching for static or slowly varying data, rather than object-attached sensors. Besides, the existing sensor search systems fail to support the search mode based on a given measurement range. Furthermore, accessing all available sensors to find sought targets would result in tremendous communication overhead. Thus an accurate matching estimation mechanism is proposed to support the search mode based on a given search range and improve the efficiency and applicability of existing sensor search systems. A time-dependent periodical prediction method is presented to periodically estimate the sensor output, which combines with the during the period feedback prediction method that can fully exploit the verification information for enhancing the prediction precision of sensor reading to efficiently serve the needs of sensor search service. Simulation results demonstrate that our prediction methods can achieve high accuracy and our matching estimation mechanism can dramatically reduce the communication overhead of sensor search system.

With the flourish of applications based on the Internet of Things (IoT), privacy issues have been attracting a lot of attentions. Although the concept of privacy homomorphism was proposed along with the birth of the well-known RSA cryptosystems, cryptographers over the world have spent about three decades for finding the first implementation of the so-called fully homomorphic encryption (FHE). Despite of, currently known FHE schemes, including the original Gentry's scheme and many subsequent improvements as well as the other alternatives, are not appropriate for IoT-oriented applications because most of them suffer from the problems of inefficient key size and noisy restraining. In addition, for providing fully support to IoT-oriented applications, symmetric fully homomorphic encryptions are also highly desirable. This survey presents an analysis on the challenges of designing secure and practical FHE for IoT, from the perspectives of lightweight requirements as well as the security requirements. In particular, some issues about designing noise-free FHE schemes would be addressed.

Location-based services (LBSs) are useful for many applications in internet of things(IoT). However, LBSs has raised serious concerns about users' location privacy. In this paper, we propose a new location privacy attack in LBSs called hidden location inference attack, in which the adversary infers users' hidden locations based on the users' check-in histories. We discover three factors that influence individual check-in behaviors: geographic information, human mobility patterns and user preferences. We first separately evaluate the effects of each of these three factors on users' check-in behaviors. Next, we propose a novel algorithm that integrates the above heterogeneous factors and captures the probability of hidden location privacy leakage. Then, we design a novel privacy alert framework to warn users when their sharing behavior does not match their sharing rules. Finally, we use our experimental results to demonstrate the validity and practicality of the proposed strategy.

In the current IoT (Internet of Things) environment, more and more Things: devices, objects, sensors, and everyday items not usually considered computers, are connected to the Internet, and these Things affect and change our social life and economic activities. By using IoTs, service providers can collect and store personal information in the real world, and such providers can gain access to detailed behaviors of the user. Although service providers offer users new services and numerous benefits using their detailed information, most users have concerns about the privacy and security of their personal data. Thus, service providers need to take countermeasures to eliminate those concerns. To help eliminate those concerns, first we conduct a survey regarding users' privacy and security concerns about IoT services, and then we analyze data collected from the survey using structural equation modeling (SEM). Analysis of the results provide answers to issues of privacy and security concerns to service providers and their users. And we also analyze the effectiveness and effects of personal information management and protection functions in IoT services.

A demonstration of power enhancement by nonlinear oscillation in a millimeter-sized electrostatic vibrational energy harvester for the future Internet of Things is presented. To enable nonlinearity in microelectromechanical system (MEMS) devices, we selected a gold spring as a component of the MEMS structure for its lower Young's modulus than conventional materials, a ductile characteristic, and an electrical conductivity. The mechanical characteristics of the fabricated MEMS device related to the nonlinear phenomenon were examined. The charging characteristics of an ethylene tetrafluoroethylene copolymer (ETFE) electret film for electrostatic induction were also evaluated. Nonlinear oscillation for the millimeter-sized energy harvester with the ETFE electret was confirmed experimentally by applying external vibration. The oscillation resulted in a bandwidth two times broader than that by linear oscillation. The normalized harvester effectiveness of the nonlinear oscillation was 5.1 times higher than that of the linear one.

In order to further optimize the power consumption of Pseudo-CMOS inverter, this paper proposes a Re-Pull-Down transistor scheme. Two additional transistors are used to build another pull-down network. With this design, the quiescent current of the inverter can be reduced while the ratioless nature is preserved. Based on the reduced input gate area, two output transistors are set wider to compensate for the pull-up speed. The simulation result shows that, compared with Pseudo-CMOS inverter, the maximum quiescent current of the Re-Pull-Down transistor scheme inverter is reduced by 37.6% in the static analysis. Besides, the average power consumption is reduced by 30.8% in the 5-stage ring oscillator test.

In this paper, we report the design of an organic thin-film transistor (OTFT) driver circuit for the actuator of an organic fluid pump, which can be integrated in a portable-size fully-organic artificial lung. Compared to traditional pump designs, lightness, compactness and scalability are achieved by adopting a creative pumping mechanism with a completely organic-material-based system concept. The transportable fluid volume is verified to be flexibly adjustable, enabling on-demand controllability and scalability of the pump's fluid-flow rate. The simulations, based on an accurate surface-potential OTFT compact model, demonstrate that the necessary driving waveforms can be efficiently generated and adjusted to the actuator requirements. At the actuator-driving-circuit frequency of 0.98Hz, an all-organic fluid pump with 40cm length and 0.2cm height is able to achieve a flow rate of 0.847L/min, which satisfies the requirements for artificial-lung assist systems to a weakened normal lung.

In this paper, we propose a novel architecture for a deep learning system, named k-degree layer-wise network, to realize efficient geo-distributed computing between Cloud and Internet of Things (IoT). The geo-distributed computing extends Cloud to the geographical verge of the network in the neighbor of IoT. The basic ideas of the proposal include a k-degree constraint and a layer-wise constraint. The k-degree constraint is defined such that the degree of each vertex on the h-th layer is exactly k(h) to extend the existing deep belief networks and control the communication cost. The layer-wise constraint is defined such that the layer-wise degrees are monotonically decreasing in positive direction to gradually reduce the dimension of data. We prove the k-degree layer-wise network is sparse, while a typical deep neural network is dense. In an evaluation on the M-distributed MNIST database, the proposal is superior to a state-of-the-art model in terms of communication cost and learning time with scalability.

A frequency-domain (FD) uniform asymptotic solution (FD-UAS) which is useful for engineering applications is newly derived for the two-dimensional scattered magnetic field by a coated conducting cylinder covered with a thin lossy medium. The FD-UAS is uniform in the sense that it remains valid within the transition region adjacent to the shadow boundary, and it smoothly connects a geometric optical ray (GO) solution and a geometrical theory of diffraction (GTD) solution exterior to the transition region, respectively. We assume that the thickness of a coating medium is thin as compared with one wavelength of a cylindrical wave radiated from a magnetic line source. This uniform asymptotic solution is represented by a combination of scattered field component solutions, namely, the GO solution composed of a direct GO (DGO) and a reflected GO, the extended uniform GTD (extended UTD) solution made up of a DGO and a pseudo surface diffracted ray (pseudo SD), the modified UTD solution representing SD series, and the GTD solution for a lowest order SD. The FD-UAS is valid for a source point and/or an observation point located either near the coating surface or in the far-zone. The effectiveness and usefulness of the FD-UAS presented here are confirmed by comparing with both the exact solution and the conventional UTD shadow region solution.