Abstract

As an important part of Internet of Things, Radio Frequency Identification (RFID) system employs low-cost RFID tag to communicate with everything containing animate and inanimate objects. This technology is widely used in the e-healthcare applications. However, the malicious communication environment makes people more and more worried. In order to overcome the hazards in the network, RFID authentication schemes for e-healthcare have been proposed by researchers. But since the computation ability of the tag is relatively weak, it is necessary to put forward a lightweight and secure scheme for medical systems. Moreover, cloud is widely accepted by people and used in many kinds of systems. So we propose a novel and lightweight RFID authentication scheme with cloud for e-healthcare applications. We use an enhanced formal security model to prove the security of our scheme. In this model the channel between the server and the reader is considered to be insecure and informal analysis is used to prove the security of the proposed scheme. Through the formal and informal analysis, our scheme not only resists the common attacks, but also keeps mutual authentication, information integrity, forward untraceability and backward untraceability. Moreover, both the tag and the reader can reach the anonymity. Our scheme is only hash-based and suitable to realize various security requirements. Compared to recent schemes of the same sort, it is more applicable in e-healthcare.

Abstract

We propose two new authentication schemes for the cloud that support private attribute-based authentication services. The basic scheme is non-anonymous attribute-based authentication scheme. The extended scheme of the basic scheme is fully anonymous attribute-based authentication scheme to realize full anonymity and unlinkability services. In the proposed schemes, a user is authenticated by the remote server if the intersection of the set of his/her assigned attributes and the server’s required attributes exceeds a satisfactory predefined level. Unlike existing attribute-based encryption and signature schemes that require the user to perform significant amount of elliptic curve bilinear pairings and modular exponentiations, and require the user to hold a significantly long decryption/signature key, in our schemes the user is not required to perform any bilinear pairings. With a fixed length private key, independent of the number of attributes, the cloud user performs only few exponentiations by which he/she is able to authenticate himself/herself to the remote server and establish a session key with the server with the condition that he/she satisfies a predefined level of the server’s attributes requirement. Therefore, our schemes are suitable for implementation on devices with limited resources. We provide the rigorous security of the proposed schemes and complexity analysis of our schemes. Finally, the security and performance comparisons of our schemes with the existing related schemes show that our schemes outperform other existing schemes.

Abstract

The rapid proliferation of mobile networks has made security an important issue, particularly for transaction oriented applications. Recently, Jo et al. presented an efficient authentication protocol for wireless mobile networks and asserted that their proposed approach provides all known security functionalities including session key (SK) security under the assumption of the widely-accepted Canetti–Krawczyk (CK) model. We reviewed Jo et al.’s proposed roaming protocol and we demonstrate that it fails to provide the SK-security under the CK-adversary setting. We then propose an enhancement to Jo et al.’s roaming protocol to address the security drawback found in Jo et al.’s protocol. In the enhanced roaming protocol, we achieve the SKsecurity along with reduced computation, communication and storage costs. We also simulate the enhanced roaming protocol using NS2 for end-to-end delay and network throughput, and the simulation results obtained demonstrate the efficiency of our protocol.

Abstract

The prolonged usage of non-renewable resources like petroleum and coal have adverse affect on the environment and has led to energy crisis in the world. In order to mitigate the situation, efficient strategies have been proposed for generation, distribution and consumption of energy obtained from renewable sources such as tidal, wind and solar power. With the advent of Smart Grids being developed world wide, the most widely accepted strategy is Demand-Response management. In this strategy, the customers or end-users are incentivized to change their energy-utility behavior with time in response to fluid price changes or to induce lower energy consumption during peak demand time. The system is controlled by a cloud of servers that monitor the demand- supply chain over a network all the time. This brings up the issue of security within the operations of the system. Current security mechanisms such as Rivest-Shamir-Alderman (RSA) public key encryption, Advanced Encryption Standard (AES) symmetric encryption, Elliptic Curve Cryptography (ECC) public-key cryptosytem and the recently proposed works are not future- proof in the world of post-quantum cryptography. This paper proposes a lattice based cryptographic scheme to ensure proper security in the system. The proposed scheme has been proven secure against major known attacks

Abstract

Thermoelectric generators (TEGs) are interesting energy harvesters of otherwise wasted heat. Here, a polymer‐assisted generic process and its mechanics to obtain sputtered thermoelectric (TE) telluride material‐based 3D tubular structures with unprecedented length (up to seamless 4 cm and further expandable) are shown. This length allows for large temperature differences between the hot and the cold ends, a critical but untapped enabler for high power generation. Compared with a flat slab, better area efficiency is observed for a rolled tube and compared with a solid rod architecture, a rolled tube uses less material (thus making it lightweight and cost effective) and has competitive performance advantage due to a smaller contact area. It is also shown that a tubular architecture thermopile‐based TEG is able to generate up to 5 μW of power (eight pairs of p‐ and n‐type thermopiles) through a temperature …

Abstract

Nearly sixty years back when Jack Kilby built the first integrated circuit (IC), it was also the beginning of today's advanced and matured complementary metal oxide semiconductor (CMOS) technology whose arts and science of miniaturization has enabled Moore's Law to double up the number of devices in a given area in every two years. It has also been possible because CMOS technology has consistently adopted new materials and processes. High performance (data processing speed in computational devices), energy efficiency (for portable devices) and ultra-large-scale-integration (ULSI) density - all these features have been added to every major technology generation in additive manner. As we go forward and embrace Internet of Everything (IoE) where people, process, device and data are going to be seamlessly connected, we may want to ask ourselves a few fundamental questions about the future

Abstract

The design and fabrication of a rolled dielectric elastomer actuator is described and the parametric dependence of the displacement and blocked force on the actuator geometry, elastomer layer thickness, voltage, and number of turns is analyzed. Combinations of different elastomers and carbon nanotube electrodes are investigated and optimized to meet performance characteristics appropriate to tactile display applications, namely operation up to 200 Hz with a combination of a 1 N blocked force and free displacement of 1 mm, all within a volume of less than 1 cm3. Lives in excess of 50 000 cycles have been obtained. Key to meeting these objectives is control of the multilayering fabrication process, the carbon nanotube electrode concentration, the selection of a soft elastomer with low viscous losses, and a proof‐testing procedure for enhancing life cycle reliability.

Abstract

A master equation has been constructed for a global system–bath interaction in the both absence and presence of non-Markovian noise. For the memoryless case, it has been exactly solved for a paradigmatic class of two qubit states in high- and zero-temperature thermal environment. For the non-Markovian model, it has been solved for zero-temperature bath. The evolution of quantum coherence and entanglement has been observed in the presence of the above-mentioned interactions. We show that the global part of the system–bath interaction compensates for the decoherence, resulting in slowdown of coherence and entanglement decay. For an appropriately defined limiting case, both coherence and entanglement show freezing behavior for the high-temperature bath. In case of zero-temperature bath, the mentioned interaction not only stabilizes the non-classical correlations, but also enhances them …

Abstract

The focus of this paper is on evaluating different spatial interpolation methods for the construction of radio environment map using field measurements obtained by cyclostationary-based mobile sensors. Mobile sensing devices employing cyclostationary detectors provide lot of advantages compared to the widely used energy detectors, such as robustness to noise uncertainty and ability to distinguish among different primary user signals. However, mobile sensing results are not available at locations between the sensors making it difficult for a secondary user (possibly without a spectrum sensor) to decide whether to use primary user resources at that location. To overcome this, spatial interpolation of test statistics measured at limited number of locations can be carried out to create a channel occupancy map at unmeasured locations between the sensors. For this purpose, different spatial interpolation techniques for the cyclostationary test statistic have been employed in this paper such as inverse distance weighting, ordinary kriging, and universal kriging. The effectiveness of these methods is demonstrated by applying them on extensive real-world field measurement data obtained by mobile-phone-compliant spectrum sensors. The field measurements were carried out using four mobile spectrum sensors measuring eight digital video broadcasting-terrestrial (DVB-T) channels at more than hundred locations encompassing roughly one-third of the area of the city of Espoo in Finland. The accuracy of the spatial interpolation results based on the field measurements is determined using the cross-validation approach with the widely used root mean square error as the metric. Field measurement results indicate that reliable results with spatial coverage can be achieved using kriging for cyclostationary based test statistics. Comparison of spatial interpolation results of cyclostationary test statistics is also carried out with those of energy values obtained during the measurement campaign in the form of received signal strength indicator. Comparison results clearly show the performance improvement and robustness obtained using cyclostationary based detectors instead of energy detectors.

Abstract

With the success of deep learning based approaches in tackling challenging problems in computer vision, a wide range of deep architectures have recently been proposed for the task of visual odometry (VO) estimation. Most of these proposed solutions rely on supervision, which requires the acquisition of precise ground-truth camera pose information, collected using expensive motion capture systems or high-precision IMU/GPS sensor rigs. In this work, we propose an unsupervised paradigm for deep visual odometry learning. We show that using a noisy teacher, which could be a standard VO pipeline, and by designing a loss term that enforces geometric consistency of the trajectory, we can train accurate deep models for VO that do not require ground-truth labels. We leverage geometry as a selfsupervisory signal and propose "Composite Transformation Constraints (CTCs)", that automatically generate supervisory signals for training and enforce geometric consistency in the VO estimate. We also present a method of characterizing the uncertainty in VO estimates thus obtained. To evaluate our VO pipeline, we present exhaustive ablation studies that demonstrate the efficacy of end-to-end, self-supervised methodologies to train deep models for monocular VO. We show that leveraging concepts from geometry and incorporating them into the training of a recurrent neural network results in performance competitive to supervised deep VO methods.