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The recent spate of cyber attacks towards Internet of Things (IoT) devices in smart homes calls for effective techniques to understand, characterize, and unveil IoT device activities. In this paper, we present a new system, named IoTAthena, to unveil IoT device activities from raw network traffic consisting of timestamped IP packets. IoTAthena characterizes each IoT device activity using an activity signature consisting of an ordered sequence of IP packets with inter-packet time intervals. IoTAthena has two novel polynomial time algorithms, sigMatch and actExtract. For any given signature, sigMatch can capture all matches of the signature in the raw network traffic. Using sigMatch as a subfunction, actExtract can accurately unveil the sequence of various IoT device activities from the raw network traffic. Using the network traffic of heterogeneous IoT devices collected at the router of a real-world smart home testbed and a public IoT dataset, we demonstrate that IoTAthena is able to characterize and generate activity signatures of IoT device activities and accurately unveil the sequence of IoT device activities from raw network traffic.

Multipath TCP (MPTCP) utilizes multiple paths for simultaneous data transmission to enhance performance. However, existing MPTCP protocols are still far from satisfactory in wireless networks because of their loss-based congestion control and the difficulty of managing multiple subflows. To overcome these problems, we redesign the coupled congestion control algorithm and scheduler to boost MPTCP in wireless heterogeneous networks. The main purpose is to promote transmission rate under lossy networks, while also provide stability when networks suffer physical link changes and asymmetric links. In this paper, inspired by Bottleneck Bandwidth and Round-trip propagation time (BBR), we first propose Coupled BBR that utilizes detected bandwidth to adjust the sending rate within an MPTCP connection. Coupled BBR provides high loss tolerance as well as balanced congestion among MPTCP subflows. Then, to further improve the performance, we propose an Adaptively Redundant and Predictive packet (AR&P) scheduler to improve adaptability and keep in-order packet delivery in highly dynamic network scenarios. Based on Linux kernel implementation and experiments in both testbed and real network scenarios, we show that the proposed scheme not only provides high throughput in wireless networks, but also improves robustness and reduces out-of-order packets in some harsh circumstances.

The present study was designed to evaluate the safety and efficacy of computed tomography-guided percutaneous microwave ablation (MWA) to treat pulmonary nodules under conscious analgosedation with sufentanil.February to May 2021, 124 patients with 151 pulmonary nodules were enrolled in this study. The patients underwent 124 sessions of MWA. Sufentanil (0.25 μg/kg) was injected intravenously before MWA.The technical success was 100% and no procedure-related deaths. The dosage of sufentanil was 16.6 ± 3.0 μg. The mean tumor diameter in the enrolled patients was 1.3 ± 0.8 cm. The intraoperative mean numerical rating scale (NRS) was 2.2 ± 1.7. Among the patients with NRS3, seven patients had nodules adjacent to the pleura, while in ten patients, they were not adjacent. The mean systolic, diastolic blood pressure, and heart rate of patients were 139.1 ± 23.5 mmHg, 77.8 ± 12.3, and 76.1 ± 13.4 times/min, respectively, before sufentanil injection. The mean lowest systolic, lowest diastolic blood pressure, and lowest heart rate intraoperative were 132.9 ± 22.0 mmHg, 76.1 ± 12.1, and 74.0 ± 13.5 times/min. Twenty-six patients had mild adverse events including nausea (6.45%, 8/124), dizziness (2.42%, 3/124), vomiting(4.03%, 5/124), nausea and dizziness (2.42%, 3/124), nausea with vomiting and dizziness (2.42%, 3/124), urinary retention (1.61%, 2/124) and respiratory depression (0.81%, 1/124).Sufentanil is a feasible, safe, and effective analgesic for MWA in patients with pulmonary nodules. It can be used for clinical promotion.

The development of lead-free ferroelectric ceramics exhibiting a high Curie point is regarded as a key aspect of improving the overall performance of high temperature sensing devices. Two-layer Dion-Jacobson phases have great potential for these applications, but the dielectric properties of many of these compounds have not been investigated and several are still erroneously considered to be non-polar. RbPrNb2O7, a two-layer Dion-Jacobson phase, has previously been identified as being non-polar at room temperature, with an orthorhombic crystal structure in space group Imma. Here, the structure of RbPrNb2O7 is re-examined and its ferroelectric (FE) character confirmed through ferroelectric, dielectric and piezoelectric measurements, using dense bulk ceramics. In addition, the microstructure and phase evolution of the compound were analysed by transmission electron microscopy, variable temperature X-ray powder diffraction and differential scanning calorimetry. RbPrNb2O7 is a ferroelectric at room temperature, exhibiting orthorhombic symmetry in space group I2cm. It undergoes a reversible FE ↔ FE phase transition at around 260 °C, with evidence for a diffuse apolar transition between 800 and 900 °C. Details of the subtle structural changes occurring at these transitions are discussed. RbPrNb2O7 possesses a high Curie point of ~1090 °C, making it a potential candidate for use in high temperature piezoelectric sensing.

A major disaster would damage the communication infrastructure severely, resulting in further chaos and loss in the disaster stricken area. Rapid restoration of wireless/mobile communications is one of the most critical issues for disaster response. Wireless multihop networking by deploying low-cost relays is a promising solution to effectively extend network services to people in the disrupted areas after large-scale disasters have occurred. It is of great importance to accurately estimate the population distribution after a disaster and, based on that, judiciously place a limited number of relay nodes to maximize the population coverage ratio. In this article we present an intelligent post-disaster networking approach by exploiting crowd dynamics. First, we present a long short-term memory based neural network to predict the spatio-temporal population distribution after a disaster. The neural network is trained by using a real crowd dynamics dataset collected during the Kumamoto earthquake in 2016. Then, based on the fine-grained population estimation result, we present three simple algorithms for the budget-constrained population-aware relay placement problem. The proposed approach is evaluated in real-world scenarios. The results show that the estimation error for population distribution is reduced by 56~69 percent compared to the regressive models, and a large proportion of the population could be efficiently covered by a limited number of relays.

Learning from massive network data to produce cognitive knowledge for efficient resource management in 5G and beyond 5G (B5G) is still challenging. We are delighted to introduce the readers to this special section of the IEEE Transactions on Cognitive Communications and Networking (TCCN), which aims at exploring recent advances and addressing practical challenges in the intelligent resource management in 5G/B5G. We have received a total number of 30 submissions, and after a rigorous review process, 15 articles have been selected for publication, which are briefly discussed as follows.

Lead-free piezoelectric ceramics featuring high piezoelectricity and fatigue resistance are extremely important in practical applications. In this work, 0.85Bi0.5Na0.5TiO3–0.11Bi0.5K0.5−xRbxTiO3–0.04BaTiO3 (x = 0–0.20) ceramics are developed, and the effects of Rb content on the crystalline phase, domain structure, electric property, and fatigue resistance are investigated. XRD results show that all the ceramics exhibit the rhombohedral/tetragonal morphotropic phase boundary. The introduction of Rb at A-sites induces enhanced relaxor characteristics. Increasing the Rb content leads to apparently decreased size and increased dynamics of ferroelectric domains, as revealed by the PFM and TEM observations. Notably, the fatigue resistance of ceramics is greatly improved with the introduction of Rb. In particular, the composition of x = 0.10 exhibits almost fatigue-free behaviors in conjunction with high piezoelectricity of d33 ~ 203 pC/N, which can be attributed to the formation of tweed-like nanodomains with a size of 10–20 nm and stripe-like nanodomains with an average width of ~ 3.5 nm.

Spatial crowdsourcing (SC) enables task owners (TOs) to outsource spatial-related tasks to a SC-server who engages mobile users in collecting sensing data at some specified locations with their mobile devices. Data aggregation, as a specific SC task, has drawn much attention in mining the potential value of the massive spatial crowdsensing data. However, the release of SC tasks and the execution of data aggregation may pose considerable threats to the privacy of TOs and mobile users, respectively. Besides, it is nontrivial for the SC-server to allocate numerous tasks efficiently and accurately to qualified mobile users, as the SC-server has no knowledge about the entire geographical user distribution. To tackle these issues, in this paper, we introduce a fog-assisted SC architecture, in which many fog nodes deployed in different regions can assist the SC-server to distribute tasks and aggregate data in a privacy-aware manner. Specifically, a privacy-aware task allocation and data aggregation scheme (PTAA) is proposed leveraging bilinear pairing and homomorphic encryption. PTAA supports representative aggregate statistics (e.g., sum, mean, variance, and minimum) with efficient data update while providing strong privacy protection. Security analysis shows that PTAA can achieve the desirable security goals. Extensive experiments also demonstrate its feasibility and efficiency.

The ubiquity of crowdsourcing has reshaped the static sensor-enabled data sensing paradigm with cost efficiency and flexibility. Still, most existing triangular crowdsourcing systems only work under the centralized trust assumption and suffer from various attacks mounted by malicious users. Although incorporating the emerging blockchain technology into crowdsourcing provides a possibility to mitigate some of the issues, how to concretely implement the crucial components and their functionalities in a verifiable and privacy-aware manner remains unaddressed. In this paper, we present BRPC, a blockchainbased decentralized system for general crowdsourcing. BRPC integrates the confident-aware truth discovery algorithm to provide task requesters with reliable task truths while evaluating each worker’s data quality. To mitigate biased evaluation of malicious requesters, we propose a privacy-aware verification protocol leveraging the Threshold Paillier Cryptosystem, with which a certain number of workers can collaboratively verify the evaluation results without knowing any sensory data. Furthermore, we define the three roles of a user and elaborate a comprehensive reputation evaluation model enforced by smart contracts for its trustworthy running. Financial and social incentives are both offered to motivate users’ honest participation. Finally, we implement a prototype of BRPC and deploy it on the Ethereum blockchain. Theoretical analyses and experiment results show its security and practicality.

The 1932 Ms 7.6 earthquake struck the active Changma fault in the NE Tibetan Plateau, and produced a distinct surface rupture along the fault zone. However, the segmentation and termination of the surface rupture zone are still unclear. In this paper, the active tectonic analyses of multiple satellite images complemented by field investigations present the 120-km-long surface rupture zone, which can be divided into five discrete first-order segments, ranging from 14.4 to 39.56 km in length, linked by step-overs. Our results also indicate that the 1932 rupture zone could jump across step-overs 0.3–4.5 km long and 2.2–5.4 km wide in map view, but was terminated by a 6.3-km-wide restraining step-over at the eastern end. The left-lateral slip rates along the mid-eastern and easternmost segments of the Changma fault are 3.43 ± 0.5 mm/yr and 4.49 ± 0.5 mm/yr since 7–9 ka, respectively. The proposed tectonic models suggest that the slip rates on the Changma fault are similar to the slip rate on the eastern segment of the Altyn Tagh fault system near the junction point with the Changma fault. These results imply that the Changma fault plays a leading role in the slip partitioning of the easternmost segment of the Altyn Tagh fault system.

This special issue focuses on the research challenges of computational intelligence technologies toward trustworthy IoT in the Big Data Era.

The Internet-of-Things (IoT) has inspired numerous new applications ever since its invention. Nevertheless, its development and utilization have always been restricted by the limited resources in various application scenarios. In this paper, we study the problem of resource provisioning for real-time IoT applications, i.e. , applications that process concurrent data streams from data sources in the network. We investigate joint application placement and data routing to support IoT applications that have both quality-of-service and robustness requirements. We formulate four versions of the provisioning problem, spanning across two important classes of real-time applications (parallelizable and non-parallelizable), and two provisioning scenarios (single application and multiple applications). All versions are proved to be NP-hard. We propose fully polynomial-time approximation schemes for three of the four versions, and a randomized algorithm for the forth. Through simulation experiments, we analyze the impact of parallelizability and robustness on the provisioning performance, and show that our proposed algorithms can greatly improve the quality-of-service of the IoT applications.

In this paper, we aim to study networking problems from a whole new perspective by leveraging emerging deep learning, to develop an experience-driven approach, which enables a network or a protocol to learn the best way to control itself from its own experience (e.g., runtime statistics data), just as a human learns a skill. We present design, implementation and evaluation of a deep reinforcement learning (DRL)-based control framework, DRL-CC (DRL for Congestion Control), which realizes our experience-driven design philosophy on multi-path TCP (MPTCP) congestion control. DRL-CC utilizes a single (instead of multiple independent) agent to dynamically and jointly perform congestion control for all active MPTCP flows on an end host with the objective of maximizing the overall utility. The novelty of our design is to utilize a flexible recurrent neural network, LSTM, under a DRL framework for learning a representation for all active flows and dealing with their dynamics. Moreover, we, for the first time, integrate the above LSTM-based representation network into an actor-critic framework for continuous (congestion) control, which leverages the emerging deterministic policy gradient to train critic, actor, and LSTM networks in an end-to-end manner. We implemented DRL-CC based on the MPTCP implementation in the Linux kernel. The experimental results show that 1) DRL-CC consistently and significantly outperforms a few well-known MPTCP congestion control algorithms in terms of goodput without sacrificing fairness, 2) it is flexible and robust to highly-dynamic network environments with time-varying flows, and 3) it is friendly to regular TCP.

The growing trend of video traffic and ubiquitous Internet of Things applications have created substantial challenges for the host-centric communication model of today's Internet. As the major consumers of video applications and IoT devices, smart homes have become one of the critical components in the Internet ecosystem. In this article, we introduce a novel concept of intelligent and programmable home routers to power smart homes in the information-centric networking (ICN) architecture, which advocates a new content- centric communication paradigm for efficient content distribution and improved cybersecurity. The main features of the envisioned home ICN routers include at-edge cooperative caching for efficient content distributions, content modeling for security monitoring, and a delegation model of security operations between intelligent home ICN routers with resource-constrained IoT devices. The envisioned home ICN routers with much desired characteristics are a promising direction for addressing the rising challenges in operating, managing, and securing millions of smart home networks in the ICN architecture.

This study was conducted to explore the high-intensity focused ultrasound (HIFU) prepared antigen-sensitized dendritic cells (DC) and the induction of cytotoxic T lymphocyte (CTL) killing effects by DC and to observe their anti-tumor immunity effects on BALB/c mice.GM-CSF and IL-4 were used to culture the mouse bone marrow-derived DC. HIFU was used to prepare CT-26 tumor cell antigen-sensitive DC vaccines. The capability of T cell proliferation was detected byDC in the HIFU group, tumor cell freeze-thawing group, tumor supernatant group, and phosphate buffer solution (PBS) group could induce T cell proliferation in vitro. However, the ability to induce T cell proliferation of DC in the HIFU group and tumor cell freeze-thawing group was significantly higher than those in the tumor supernatant and PBS groups (P0. 05). CTL induced in vitro by DC in the HIFU group, and the tumor cell freeze-thawing group had significant cytotoxicity to colon cancer, being significantly different from those in the tumor supernatant and PBS groups (P0.05). There was no significant difference between the cytotoxicity of CTL induced in vitro in the HIFU group and the tumor cell freeze-thawing group (P0.05). Additionally, significant differences in the occurrence time of the tumor, its weight and volume on the 20HIFU prepared antigen-sensitized DC could cause substantial proliferation of T cells and CTL with strong anti-tumor effects. The DC-based tumor vaccine prepared using HIFU irradiation affected active immunization on the tumor occurrence in vitro and was better than the DC-based tumor vaccine prepared using the repeated-freezing-thawing method.

The drastic growth of mobile traffic greatly challenges the capacity of mobile infrastructures. Dense deployment of low-power small cells helps alleviate the congestion in the radio access network, yet it also introduces large complexity for network management. Software-defined radio access network has been proposed to tackle the added complexity. However, existing software-defined solutions rely on a fully centralized control plane to make decisions for the whole network, which greatly limits the scalability and responsiveness of the control plane. In this paper, we propose a hierarchical software-defined radio access network architecture. The proposed architecture leverages the hierarchical structure of radio access networks, deploying additional local controllers near the network edge. Utilizing the intrinsic locality in radio access networks, it offloads control tasks from the central controller to local controllers with limited overhead introduced. Under the architecture, a distributed optimization framework is proposed, and a typical optimization problem is studied to illustrate the effectiveness of the proposed architecture and framework. Both analysis and experiments validate that the proposed architecture and framework can improve the network objective during the optimization, meanwhile balancing load and improving scalability and responsiveness.

In this article, we introduce the concept of FoT, a paradigm for on-demand IoT. On-demand IoT is an IoT platform where heterogeneous connected things can be accessed and managed via a uniform platform based on real-time demands. Realizing such a platform faces challenges including heterogeneity, scalability, responsiveness, and robustness, due to the large-scale and complex nature of an IoT environment. The FoT paradigm features the incorporation of fog computing power, which empowers not only the IoT applications, but more importantly the scalable and efficient management of the system itself. FoT utilizes a flat-structured virtualization plane and a hierarchical control plane, both of which extend to the network edge and can be reconfigured in real time, to achieve various design goals. In addition to describing the detailed design of the FoT paradigm, we also highlight challenges and opportunities involved in the deployment, management, and operation of such an on-demand IoT platform. We hope this article can shed some light on how to build and maintain a practical and extensible control back-end to enable large-scale IoT that empowers our connected world.

With the rapid growth of smartphones, mobile crowdsensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data efficiently. Many auction-based incentive mechanisms have been proposed to stimulate smartphone users to participate in the mobile crowdsensing applications and systems. However, none of them has taken into consideration both the bid privacy of smartphone users and the social cost. In this paper, we design two frameworks for privacy-preserving auction-based incentive mechanisms that also achieve approximate social cost minimization. In the former, each user submits a bid for a set of tasks it is willing to perform; in the latter, each user submits a bid for each task in its task set. Both frameworks select users based on platform-defined score functions. As examples, we propose two score functions, linear and log functions, to realize the two frameworks. We rigorously prove that both proposed frameworks achieve computational efficiency, individual rationality, truthfulness, differential privacy, and approximate social cost minimization. In addition, with log score function, the two frameworks are asymptotically optimal in terms of the social cost. Extensive simulations evaluate the performance of the two frameworks and demonstrate that our frameworks achieve bid-privacy preservation although sacrificing social cost.

In online surveys, many people are not willing to provide true answers due to privacy concerns. Thus, anonymity is important for online message collection. Existing solutions let each member blindly shuffle the submitted messages by using the IND-CCA2 secure cryptosystem. In the end, all messages are randomly shuffled and no one knows the message order. However, the heavy computational overhead and linear communication rounds make it only useful for small groups. In this paper, we propose an efficient anonymous message submission protocol aimed at a practical group size. Our protocol is based on a simplified secret sharing scheme and a symmetric key cryptosystem. We propose a novel method to let all members secretly aggregate their messages into a message vector such that a member knows nothing about other members' message positions.We provide a theoretical proof showing that our protocol is anonymous under malicious attacks. We then conduct a thorough analysis of our protocol, showing that our protocol is computationally more efficient than existing solutions and results in a constant communication rounds with a high probability.

Nowadays, it remains a challenge to achieve a simultaneous improvement of dielectric constant, energy density, and temperature stability in dielectric ceramics. In this work, it was found that such drawback could be effectively resolved by using KTaO3 to substitute Na0.5Bi0.5TiO3. In (1-x)Na0.5Bi0.5TiO3-xKTaO3 ceramics, the temperature coefficient of capacitance (TCC) of x = 0.10 composition is lower than 15% in the temperature range of 25–390 °C, with a high dielectric constant of 2008 and a low dielectric loss of 0.0044 at 150 °C. Meanwhile, a large discharged energy density of ~2.50 J/cm3 with the variation less than± 3% in 5–190 °C was obtained under a low electric field of 18 kV/mm in x = 0.20 composition, accompanied with a high energy efficiency of> 80%. These results reveal that this ceramic system is a promising candidate for temperature-stable dielectric and energy storage capacitors applications.

Spectrum auctions provide a platform for licensed spectrum users to share their underutilized spectrum with unlicensed users. Existing spectrum auctions either adopt the protocol interference model to characterize interference relationship as binary relationship or only lease channels that are not used by the primary user (PU) to secondary users (SUs). In this paper, we design spectrum auctions under the physical interference model, which allow PU and SUs to transmit simultaneously. Specifically, we consider both single-minded and multi-minded cases, and design auctions SPA-S and SPA-M, respectively. We prove that both auctions are truthful, individually rational, and computationally efficient. Extensive simulation results demonstrate that, these designed auctions achieve higher spectrum utilization, buyer satisfaction ratio, and revenue than a representative existing spectrum auction adapted for the physical interference model.

In this paper, we propose a new network abstraction, termed critical network flow , which models the bandwidth requirement of modern Internet applications and services. A critical network flow defines a conventional flow in a network with explicit requirement on its aggregate bandwidth, or the flow value as commonly termed. Unlike common bandwidth-guaranteed connections whose bandwidth is only guaranteed during normal operations, a critical network flow demands strictly enforced bandwidth guarantee during various transient network states, such as network reconfiguration or network failures. Such a demand is called the bandwidth criticality of a critical network flow, which is characterized both by its flow value and capability to satisfy bandwidth guarantee in the transient states.We study algorithmic solutions to the accommodation of critical network flows with different bandwidth criticalities, including the basic case with no transient network state considered, the case with network reconfiguration, and the case with survivability against link failures. We present a polynomial-time optimal algorithm for each case. For the survivable case, we further present a faster heuristic algorithm. We have conducted extensive experiments to evaluate our model and validate our algorithms.

The ever-increasing mobile traffic has inspired deployment of capacity and performance enhancing network services within mobile networks. Owing to recent advances in network function virtualization, such network services can be flexibly and cost-efficiently deployed in the mobile network as software components, avoiding the need for costly hardware deployment. Nevertheless, this complicates network planning by bringing the need for service function chaining. In this paper, we study mobile network planning through a software-defined approach, considering both quality-of-service and reliability of different classes of traffic. We define and formulate the traffic steering problem for service function chaining in mobile networks, which turns out to be $\mathcal {NP}$ -hard. We then develop a fast approximation scheme for the problem, and evaluate its performance via extensive simulation experiments. The results show that our algorithm is near-optimal, and achieves much better performance compared with baseline algorithms.