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The Internet of Things (IoT) technology is not just about the connection among billions of devices but also about enabling a wide range of new capabilities, architectures, or service paradigms. The scale of the IoT is set to have a major economic, social, and environmental impact. However, the implementation of IoT faces various challenges, including sensing ability, energy consumption, power supply, data security, privacy, and data accountability. In order to mitigate these challenges, more and more IoT systems are embracing the new IoT paradigm, such as millimeter-wave (mmWave), sensor-less sensing, backscattering Wi-Fi/RFID, wireless/vision hybrid, blockchain, fat client, and so on. These modes shed light on the emerging technologies that could revolutionize the conventional IoT systems.

Tumor oxygen concentration image is essential to oxygen-image guided, precise radiation therapy. Electron paramagnetic resonance imaging is an advanced oxygen imaging technique. However, the scanning time is still comparatively long, leading to motion artifacts for static imaging and low time resolution for dynamic imaging. Usually, a projection signal at a specific angle is obtained by averaging thousands of repeatedly collected projections to suppress random noise and achieve a high signal-to-noise ratio (SNR). Reducing the repetition times of projection collecting at a specific angle may effectively speed up the whole scanning process. However, the EPR images reconstructed by the conventional three-dimensional filtered backprojection (FBP) algorithm from these fast-scanned, low SNR projections are too noisy to be used for further image postprocessing. In the paper, we investigate the capability of an optimization-based algorithm in accurate reconstruction from noisy projections. We designed a total variation constrained, data divergence minimization model, derived its Chambolle-Pock (CP) solving algorithm, and then validated and evaluated the CP algorithm via mathematical and physical phantoms. The studies show that the CP algorithm may accurately reconstruct EPR images from fast-scanned, noisy projections, and thus the whole scanning process may be speeded up four times compared with the full scan time demanded by the FBP algorithm in the image reconstruction of the complex physical phantom.

Due to the simplicity of implementation, user-initiated Wi-Fi offloading becomes more and more popular, and naturally the benefits of users become the main optimization goal. We notice the intercontact and intra-contact durations could be uncertain in reality by reason of the user mobility and network dynamics. The two uncertain durations can cause great impact on the benefit of users; however, they were either ignored or simply assumed to be deterministic in most previous works. In this paper, for the first time, we study Wi-Fi offloading problem with uncertain contact durations. The aim is to guarantee the benefit of users (delay and payment) without damaging operator's benefit (amount of the offloaded traffic) at the same time. We propose a multi-armed bandit (MAB)-based online offloading scheme (MABOO) to solve the problem and prove the near-optimality of MABOO in terms of the utility theoretically. Extensive simulations show that MABOO always approaches the optimal scheme and achieves higher utility as well as offloads more traffic compared with the minimal payment and on-the-spot-offloading schemes.

As mobile social networks grow rapidly, influential user identification has attracted much more attention. Previous studies either need large message overhead to achieve global maxima in influence computation or focus on relatively stable network topology. To tackle the dynamic topology, we present an influential user identification scheme that fully exploits the active mobile users, in which the stable-state property could be leveraged under information potential construction scheme. We also propose an efficient routing algorithm for reaching the global maxima without depending on specific routing protocols. The proposed scheme is validated with extensive simulations using both synthetic random-walk and real-world mobility traces. The results demonstrate that it achieves considerable performance on influential user identification and route construction with little overhead. Furthermore, we present a case of mobile data offloading, and the results show that our scheme could reduce the efficient data traffic by up to 79.2%, compared with a baseline without data offloading.

In this paper, we report a new model in analysis of spherical thin-shell piezoelectric transducers for transient response, based on Fourier transform and the principle of linear superposition. We show that a circuit-network, a combination of a series of parallel-connected equivalent-circuits, can be used in description of a spherical thin-shell piezoelectric transducer. When excited by a signal with multiple frequency components, each circuit would have a distinctive radiation resistance and a radiation mass, arising from an individual frequency component. Each frequency component would act independently on the electric/mechanic-terminals. A cumulative signal-output from the mechanic/electric-terminals is measured as the overall acoustic/electric output. As a prototype example in testing the new model, we have designed two spherical shin-shell transducers, applied a gated sine electric-signal as the initial excitation, and recorded the experimental information. The transient response and the output signals are calculated based on the new model. The results of calculation are in good agreement with that of experimental observation.

Seat-level positioning of a smartphone in a vehicle can provide a fine-grained context for many interesting in-vehicle applications, including driver distraction prevention, driving behavior estimation, in-vehicle services customization, etc. However, most of the existing work on in-vehicle positioning relies on special infrastructures, such as the stereo, cigarette lighter adapter or OBD (on-board diagnostic) adapter. In this work, we propose iLoc, an infrastructure-free, in-vehicle, cooperative positioning system via smartphones. iLoc does not require any extra devices and uses only embedded sensors in smartphones to determine the phones’ seat-level locations in a car. In iLoc, in-vehicle smartphones automatically collect data during certain kinds of events and cooperatively determine the relative left/right and front/back locations. In addition, iLoc is tolerant to noisy data and possible sensor errors. We evaluate the performance of iLoc using experiments conducted in real driving scenarios. Results show that the positioning accuracy can reach 90% in the majority of cases and around 70% even in the worst-cases.

When we encounter a malicious rumor or an infectious disease outbreak, immunizing k nodes of the relevant network with limited resources is always treated as an extremely effective method. The key challenge is how we can insulate limited nodes to minimize the propagation of those contagious things. In previous works, the best k immunised nodes are selected by learning the initial status of nodes and their strategies even if there is no feedback in the propagation process, which eventually leads to ineffective performance of their solutions. In this paper, we design a novel vaccines placement strategy for protecting much more healthy nodes from being infected by infectious nodes. The main idea of our solution is that we are not only utilizing the status of changing nodes as auxiliary knowledge to adjust our scheme, but also comparing the performance of vaccines in various transmission slots. Thus, our solution has a better chance to get more benefit from these limited vaccines. Extensive experiments have been conducted on several real-world data sets and the results have shown that our algorithm has a better performance than previous works.