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Differential privacy (DP) has gained popularity in truth discovery recently due to its strong privacy guarantee. However, existing DP mechanisms for streaming data publication are not suitable for truth discovery as they fail to consider the different reliabilities of individuals, while the DP-based approaches for truth discovery are not suitable for streaming data because they ignore the correlations between truths over time. Directly applying these existing methods to streaming crowdsourced data would lead to low accuracy of the discovered truth. To solve this problem, in this paper, we propose an edge computing based privacy-preserving truth discovery mechanism, named PrivSTD, for streaming crowdsourced data to realize high accuracy of discovered truth while protecting the privacy of workers. Specifically, edge servers are introduced between the untrusted cloud server and workers to securely calculate the local truths and workers’ reliabilities. A truth-dependent budget recycle mechanism is proposed for each edge server to adaptively determine the perturbed timestamp and allocate the privacy budget according to the changing pattern of local truths. Besides, a reliability-based perturbation mechanism is proposed to reduce the perturbation magnitude on the basis of worker's reliability. We theoretical analyze the data utility and computation cost of PrivSTD, and prove that PrivSTD can satisfy $w$ w -event ($\epsilon,\delta$ ε , δ )-differential privacy. Extensive experimental results on synthetic and real-world datasets demonstrate that PrivSTD achieves better utility than the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Sign language recognition (SLR) bridges the communication gap between the hearing-impaired and the ordinary people. However, existing SLR systems either cannot provide continuous recognition or suffer from low recognition accuracy due to the difficulty of sign segmentation and the insufficiency of capturing both finger and arm motions. The latest system, SignSpeaker, has a significant limit in recognizing two-handed signs with only one smartwatch. To address these problems, this paper designs a novel real-time end-to-end SLR system, called DeepSLR, to translate sign language into voices to help people “hear” sign language. Specifically, two armbands embedded with an IMU sensor and multi-channel sEMG sensors are attached on the forearms to capture both coarse-grained arm movements and fine-grained finger motions. We propose an attention-based encoder-decoder model with a multi-channel convolutional neural network (CNN) to realize accurate, scalable, and end-to-end continuous SLR without sign segmentation. We have implemented DeepSLR on a smartphone and evaluated its effectiveness through extensive evaluations. The average word error rate of continuous sentence recognition is 10.8 percent, and it takes less than 1.1s for detecting signals and recognizing a sentence with 4 sign words, validating the recognition efficiency and real-time ability of DeepSLR in real-world scenarios. [ABSTRACT FROM AUTHOR]

Blendshape representations are widely used in facial animation. Consistent semantics must be maintained for all the blendshapes to build the blendshapes of one character. However, this is difficult for real characters because the face shape of the same semantics varies significantly across identities. Previous studies have handled this issue by asking users to perform a set of predefined expressions with specified semantics. We observe that facial emotions can be used to define semantics. Herein, we propose a real-time technique that directly updates blendshapes without predefined expressions. Its aim is to preserve semantics based on the emotion information extracted from an arbitrary facial motion sequence. In addition, we have designed corresponding algorithms to efficiently update blendshapes with large- and middle-scale face shapes and fine-scale facial details, such as wrinkles, in a real-time face tracking system. The experimental results indicate that using a commodity RGBD sensor, we can achieve real-time online blendshape updates with well-preserved semantics and user-specific facial features and details. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) is an emerging paradigm that leverages pervasive smart terminals equipped with various embedded sensors to collect sensory data for wide applications. As the sensing scale increases in MCS, the design of efficient task allocation becomes crucial. However, many prior task allocation schemes, which ignore the time for task-performing, are not applicable to the scenario where mobile users with limited time budgets are able to undertake multiple sensing tasks. In this article, we focus on the task allocation in time dependent crowdsensing systems and formulate the time dependent task allocation problem, in which both the sensing duration and the user's sensing capacity are considered. We prove that the task allocation problem is NP-hard and propose an efficient task allocation algorithm called optimized allocation scheme of time-dependent tasks (OPAT), which can maximize the sensing capacity of each mobile user. The extensive simulations are conducted to demonstrate the effectiveness of the proposed OPAT scheme. [ABSTRACT FROM AUTHOR]

Exploiting the sparse user activity induced by sporadic transmission, compressed sensing (CS) has been widely applied in multiple-input multiple-output (MIMO) enabled non-orthogonal multiple access (NOMA) for efficient multiuser detection. However, most of the existing detection schemes in MIMO enabled NOMA systems focus mainly on the spatial structure in user activity induced by multiantenna reception, while the temporal correlation in user activity has rarely been incorporated for further performance improvement. To address this issue, we propose a novel Multiuser Detection framework in MIMO enabled NOMA (MDMN), which explicitly integrates the temporal correlation in user activity into the detection process to improve the user detection accuracy. Specifically, we first formulate multiuser detection with multiantenna reception in MDMN as a block CS problem, by exploiting the spatial correlation in user activity. Furthermore, through incorporating the temporal correlation in active user sets into block CS, an adaptive CS algorithm based on subspace pursuit is developed for MDMN, named spatial-temporal correlation enhanced adaptive subspace pursuit (STASP). In particular, STASP does not require any prior knowledge of the user sparsity, as the cross validation designed in STASP can properly terminate the algorithm. The superior performance of the proposed MDMN framework and the corresponding algorithms is corroborated by extensive simulation results. [ABSTRACT FROM AUTHOR]

Truth discovery is an effective paradigm which could reveal the truth from crowdsouced data with conflicts, enabling data-driven decision-making systems to make quick and smart decisions. The increasing privacy concern promotes users to perturb or encrypt their private data before outsourcing, which poses significant challenges for truth discovery. Although several privacy-preserving truth discovery mechanisms have been proposed, none of them take personal privacy expectation into consideration. In this work, we propose a novel personalized privacy-preserving truth discovery (PPPTD) framework over crowdsourced data streams to achieve timely and accurate truth discovery while guaranteeing the protection of individual privacy. The key challenges of PPPTD lie in improving the accuracy of truth estimation from the perturbed streaming data with personalized protection level. To address these challenges, we first develop a personalized budget initialization mechanism to quantify each user’s privacy protection requirement, and allocate personalized privacy budgets to users according to their privacy requirements. Then we propose a deviation-aware weighted aggregation method to improve the accuracy of truth discovery from streaming data with varying degrees of perturbation. In order to achieve privacy-utility tradeoff, we further propose an influence-aware adaptive budget adjustment mechanism that adaptively re-allocates privacy budgets to users based on the evolution of their influence in the weighted aggregation. We prove that PPPTD can achieve $\epsilon $ -differential privacy over the whole data generated by users and satisfy individual personalized privacy requirements. Extensive experiments on two real-world datasets demonstrate the effectiveness of PPPTD. [ABSTRACT FROM AUTHOR]

Radio frequency identification (RFID) systems have achieved wide applications in various scenarios, such as warehouse management, logistic tracking, smart transportation, etc. Despite the enormous benefits from the RFID systems, the security issues are still of great concern, such as the cloning attacks. In this article, we focus on the detection of probabilistic cloning attacks for the anonymous RFID systems, in which each cloned genuine tag suffers attacks from its clone tags with a certain probability. We propose an efficient detection protocol against the probabilistic cloning attacks in anonymous RFID systems named DAP, which can detect the probabilistic cloning attacks with the required detection reliability $\alpha$ if at least one tag is attacked with the probability no less than the threshold $P_T$. The proposed DAP protocol fully utilizes the inconsistency and unreconcilable collision caused by the probabilistic cloning attacks to effectively detect the probabilistic cloning attacks.The parameters are theoretically analyzed to maximize the detection efficiency. The extensive simulations are conducted and the results demonstrate the effectiveness of the proposed DAP protocol. [ABSTRACT FROM AUTHOR]

Incentive mechanisms are essential for stimulating adequate worker participation to achieve good truth discovery performance in mobile crowdsensing (MCS) systems. However, most of existing incentive mechanisms only consider compensating workers’ sensing cost, while the cost incurred by potential privacy leakage has been largely neglected. Moreover, none of existing privacy-preserving incentive mechanisms has incorporated workers’ different privacy preferences to provide personalized payments for them. In this paper, we propose a contract-based personalized privacy-preserving incentive mechanism for truth discovery in MCS systems, named Paris-TD, which provides personalized payments for workers as a compensation for privacy cost while achieving accurate truth discovery. The basic idea is that the platform offers a set of different contracts to workers with different privacy preferences, and each worker chooses to sign a contract which specifies a privacy-preserving degree (PPD) and the corresponding payment the worker will receive if she submits perturbed data with that PPD. Specifically, we respectively design a set of optimal contracts analytically under both full and incomplete information models, which maximize the truth discovery accuracy under a given budget, while satisfying the individual rationality and incentive compatibility properties. The feasibility and effectiveness of Paris-TD are validated through experiments on both synthetic and real-world datasets. [ABSTRACT FROM AUTHOR]

Mechanisms built upon geo-indistinguishability render location privacy, where a user can submit obfuscated locations to Location-Based Service providers but still be able to correctly utilize services. However, these mechanisms are vulnerable under inference attacks. Particularly, with background knowledge of a user’s obfuscated locations, an attacker can infer actual locations by carrying out long-term observation attacks. Unfortunately, how to defend long-term observation attacks in the field of differential location privacy remains open. In this paper, we first demonstrate the vulnerabilities of existing mechanisms under long-term observation attacks. In light of these vulnerabilities, we devise a novel mechanism, referred to as Eclipse, which bridges the gap between location protection and usability of services. Specifically, we harness geo-indistinguishability and $k$ k -anonymity to obfuscate locations and hide each location based on an anonymity set. As a result, our mechanism effectively perturbs the distribution of locations and suppresses leakage under long-term observation attacks. Moreover, the set of possible outputs is utilized to minimize the impacts to usability and correctness. We formally define and rigorously prove the security of the proposed mechanism by leveraging differential privacy. Moreover, we implement the proposed mechanism and conduct a series of experiments on real-world datasets to demonstrate its efficacy and efficiency. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a privacy-preserving distributed machine learning framework, which involves training statistical models over a number of mobile users (i.e., workers) while keeping data localized. However, recent works have demonstrated that workers engaged in FL are still susceptible to advanced inference attacks when sharing model updates or gradients, which would discourage them from participating. Most of the existing incentive mechanisms for FL mainly account for workers’ resource cost, while the cost incurred by potential privacy leakage resulting from inference attacks has rarely been incorporated. To address these issues, in this paper, we propose a contract-based personalized privacy-preserving incentive for FL, named Pain-FL, to provide customized payments for workers with different privacy preferences as compensation for privacy leakage cost while ensuring satisfactory convergence performance of FL models. The core idea of Pain-FL is that each worker agrees on a customized contract, which specifies a kind of privacy-preserving level (PPL) and the corresponding payment, with the server in each round of FL. Then, the worker perturbs her calculated stochastic gradients to be uploaded with that PPL in exchange for that payment. In particular, we respectively derive a set of optimal contracts analytically under both complete and incomplete information models, which could optimize the convergence performance of the finally learned global model, while bearing some desired economic properties, i.e., budget feasibility, individual rationality, and incentive compatibility. An exhaustive experimental evaluation of Pain-FL is conducted, and the results corroborate its practicability and effectiveness. [ABSTRACT FROM AUTHOR]

This article proposes unsupervised truth-finding algorithms that combine consideration of multi-modal content features with analysis of propagation patterns to evaluate the veracity of observations in social sensing applications. A key social sensing challenge is to develop effective algorithms for estimating both the reliability of sources and the veracity of their observations without prior knowledge. In contrast to prior solutions that use labeled examples to learn content features that are correlated with veracity, our approach is entirely unsupervised. Hence, given no prior training data, we jointly learn the importance of different content features together with the veracity of observations using propagation patterns as an indicator of perceived content reliability. A novel penalized expectation maximization (PEM) algorithm is proposed to improve the quality of estimation results for observations bolstered by multiple features. In addition, we develop a constrained expectation maximum likelihood with multiple features (CEM-MultiF) that introduces a novel constraint to boost the probability of correctness of some claims. Finally, we evaluate the performance of the proposed algorithms, called EM-Multi, CEM-Multi and PEM-MultiF, respectively, on real-world data sets collected from Twitter. The evaluation results demonstrate that the proposed algorithms outperform the existing fact-finding approaches, and offer tunable knobs for controlling robustness/performance trade-offs in the presence of malicious sources. [ABSTRACT FROM AUTHOR]

Radio-Frequency Identification (RFID) is an emerging technology which has been widely applied in various scenarios, such as tracking, object monitoring, and social networks, etc. Cloning attacks can severely disturb the RFID systems, such as missed detection for the missing tags. Although there are some techniques with physical architecture design or complicated encryption and cryptography proposed to prevent the tags from being cloned, it is difficult to definitely avoid the cloning attack. Therefore, cloning attack detection and identification are critical for the RFID systems. Prior works rely on that each clone tag will reply to the reader when its corresponding genuine tag is queried. In this article, we consider a more general attack model, in which each clone tag replies to the reader’s query with a predefined probability, i.e., attack probability. We concentrate on identifying the tags being attacked with the probability no less than a threshold $P_{t}$ with the required identification reliability $\alpha $. We first propose a basic protocol to Identify the Probabilistic Cloning Attacks with required identification reliability for the large-scale RFID systems called IPCA. Then we propose two enhanced protocols called MS-IPCA and S-IPCA respectively to improve the identification efficiency. We theoretically analyze the parameters of the proposed IPCA, MS-IPCA and S-IPCA protocols to maximize the identification efficiency. Finally we conduct extensive simulations to validate the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

Cell selection is a critical issue in sparse mobile crowdsensing (MCS) systems. However, the sensing cost heterogeneity among different cells (subareas) has long been ignored by existing works. Moreover, the data provided by participants are not always trustworthy, and some malicious participants may intend to launch data positioning attacks, which raises a new challenge for cell selection. In this paper, to address these issues, we propose a trustworthy and cost-effective cell selection (TCECS) framework that takes cell heterogeneity and malicious participants into consideration simultaneously. To this end, we first offer to utilize an iterative statistical spatial interpolation technique to identify trustworthy participants with the help of a small portion of dedicated sensors. Furthermore, we employ the regularized mutual coherence (RMC) in compressive sensing (CS) theory to characterize the contribution to inference accuracy of measurements submitted by different trustworthy participants. Finally, the cell selection strategy, which consumes the least sensing cost while satisfying a given sensing quality, is determined via an RMC-constrained optimization problem. Extensive experiments on a real-world taxi GPS dataset demonstrate that the proposed approach can mitigate the adverse effects of malicious participants and outperforms the baselines with less sensing cost for the same required sensing quality. [ABSTRACT FROM AUTHOR]

Worker recruitment is an important problem in mobile crowdsourcing (MCS), which aims to find sufficient and suitable participants to perform tasks. However, existing worker recruitment approaches mainly focus on how to select the most suitable workers for tasks from a large worker pool, while the recruitment problem under insufficient workers (e.g., a new MCS system) has not been well addressed. In this paper, we focus on the insufficient participation problem of MCS systems with limited number of workers, and propose to leverage social network to recruit workers for task completion as well as expanding the worker pool. To this end, we propose a dynamic incentive mechanism, called SocialRecruiter, to encourage workers on the MCS platform to propagate tasks through social networks, so that inviting friends to join in the MCS platform to further propagate and complete tasks. Motivated by the SIR epidemic model, we propose a novel task-specific epidemic model to characterize the status change of users for task propagation and completion through social networks. In order to encourage task completion and propagation, the propagating reward and completing reward are provided according to workers’ actions. In particular, in order to maximize the task completion within the financial budget, the propagating and completing rewards are dynamically updated at each cycle according to real-time worker recruitment progress. The extensive experimental results on two real-world datasets demonstrate that SocialRecruiter outperforms the state-of-the-art approaches in terms of worker recruitment and task completion. [ABSTRACT FROM AUTHOR]

Worker recruitment in mobile crowdsensing systems aims to recruit the most suitable users to perform tasks with high quality and in real-time. Many worker recruitment or task matching mechanisms have been proposed, especially for crowdsourcing platforms, where content information of tasks from the implicit feedback of workers’ attendance is extensively exploited to help workers find preferred tasks efficiently. Different from traditional crowdsourcing systems, tasks in mobile crowdsensing systems are usually time-sensitive and location-dependent which also play a crucial role in worker recruitment. However, these context information have not been effectively explored for user recruitment in mobile crowdsensing systems. In this paper, we propose a novel personalized task-oriented worker recruitment mechanism for mobile crowdsensing systems based on a careful characterization of workers’ preference. In particular, we fully exploit the content information (e.g., task category, task description) together with the context information (e.g., task time, task location) from the implicit feedback of workers’ attendance to accurately model workers’ preference on tasks. Moreover, we regard the task-worker fitness prediction as a binary classification problem and utilize the Logit model to integrate the heterogeneous factors into a single framework to predict the matching probability of each task-worker pair. Finally, the workers with the highest matching probability are recruited proactively for each new task. Extensive experiments on real-world datasets demonstrate that the proposed mechanism achieves better performance than the benchmarks. [ABSTRACT FROM AUTHOR]

Pattern lock is an essential authentication method on mobile devices. Recent works on cracking pattern locks either require additional network facilities (e.g., WiFi hotspots) or suffer from strict constraints (e.g., physical closeness to the victim and good lighting). Being too susceptible to environment settings, these attacks are less effective in practice and cannot scale to a large number of users. To address these concerns, in this paper, we propose PatternListener+, a practical attack on pattern locks using the speakers and microphones on mobile devices. The speaker plays inaudible acoustic signals, which are reflected by the fingertip when the victim is drawing the pattern, and then recorded by the microphone. The recorded acoustic signals contain rich information of the fingertip motion that can be leveraged to infer the pattern. We carefully design a series of algorithms to eliminate the dynamic and static interferences, segment acoustic signals into fragments corresponding to all pattern lines, and recover each line composed of the pattern according to the signals. Finally, we recover the candidate pattern by mapping all line candidates into grid patterns with a tree structure. We implement a PatternListener+ prototype using off-the-shelf smartphones, and extensive experiments confirm the effectiveness and robustness of PatternListener+. The attack success rate is over 90 percent on 120 patterns in five attempts. [ABSTRACT FROM AUTHOR]

With the unprecedented convenience brought by Apps on mobile devices, we are facing severe security attacks and privacy leakage caused by them since they may stealthily access unclaimed or unneeded permissions for some purposes. Many works strive to discover these malicious apps using program analysis techniques, however, they fail to tell users why an app needs to request the permission from users’ perspective. In this paper, we leverage the power of the crowdsourced user reviews to understand why an app requests a permission. We propose a framework, called SmartPI, that automatically identifies functionality-relevant user reviews and infers the permission implication of them, bridging the gap between the functionalities and the actual behaviors of an app. In particular, we extract features from the platform documents to identify functionality-relevant user reviews from noisy crowdsourced user reviews with Natural Language Processing (NLP) techniques. The topic model is further adopted to infer the permission implications of apps from the functionality-relevant user reviews. More than 20,000 apps, 2,653,159 users, and 4,247,769 user reviews are crawled from Google Play as a real-world dataset to evaluate the performance of SmartPI. The experiments results show that the permission usage of apps can be better reflected by user reviews than the claimed descriptions of apps. [ABSTRACT FROM AUTHOR]

It is important to transmit data reliably, and efficiently in vehicular networks. Existing works usually study routing strategies, and cooperative scheduling to improve the efficiency of transmission. However, the data transmission remains inefficient because of the lack of full use of communication resources. The transmission is unreliable because information cannot be completely transmitted to the destination vehicles. Moreover, the increasing number of connected vehicles, and the limitation of available communication resources make task scheduling challenging in vehicular networks. In this work, we propose Cluster-enabled Cooperative Scheduling based on Reinforcement Learning (CCSRL) to improve the communication efficiency, and reliability of vehicular networks, with the goal of maximizing the information capacity. In particular, we leverage the stability to select a cluster head vehicle to enhance data transmission efficiency, and a reinforcement learning-based auxiliary transmission is further designed to guarantee the reliable communication among vehicles. The experimental results demonstrate that the performance of the proposed scheduling algorithm, especially the performance of the packet delivery ratio, and node packet loss ratio, is better than that of the state-of-the-art algorithm. [ABSTRACT FROM AUTHOR]

Federated learning has emerged as an advanced privacy-preserving learning technique for mobile edge computing, where the model is trained in a decentralized manner by the clients, preventing the server from directly accessing those private data from the clients. This learning mechanism significantly challenges the attack from the server side. Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i.e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client. To analyze the privacy leakage of federated learning, this paper gives the first attempt to explore user-level privacy leakage by the attack from a malicious server. We propose a framework incorporating GAN with a multi-task discriminator, called multi-task GAN – Auxiliary Identification (mGAN-AI), which simultaneously discriminates category, reality, and client identity of input samples. The novel discrimination on client identity enables the generator to recover user specified private data. Unlike existing works interfering the federated learning process, the proposed method works “invisibly” on the server side. Furthermore, considering the anonymization strategy for mitigating mGAN-AI, we propose a beforehand linkability attack which re-identifies the anonymized updates by associating the client representatives. A novel siamese network fusing the identification and verification models is developed for measuring the similarity of representatives. The experimental results demonstrate the effectiveness of the proposed approaches and the superior to the state-of-the-art. [ABSTRACT FROM AUTHOR]

Incentive mechanisms have been commonly proposed to encourage people to participate in mobile crowdsensing (MCS). However, most of them set unchangeable rewards for sensing tasks, while the inherent inequality and on-demand feature of sensing tasks have been long ignored, especially for location-dependent sensing tasks (LDSTs). In this paper, we focus on location-dependent MCS systems and propose a demand-driven dynamic incentive mechanism that dynamically changes the rewards of sensing tasks at each sensing round in an on-demand way to balance their popularity. A demand indicator is introduced to characterize the demand of each sensing task by considering its deadline, completing progress, and number of potential participants. At each sensing round, we use the Analytic Hierarchy Process (AHP) to calculate the relative demands of all sensing tasks and then determine their rewards accordingly. Moreover, we consider two task selection problem with participatory users and opportunistic users, respectively, and prove that both of them are NP-hard. We propose an optimal dynamic programming based solution for participatory scenario and an optimal backtracking based solution for opportunistic scenario to help each user select tasks while maximizing its profit. Extensive experiments show that the demand-driven dynamic incentive mechanism outperforms existing incentive mechanisms. [ABSTRACT FROM AUTHOR]

Complex networks have become an active interdisciplinary field of research inspired by the empirical study of various networks. A subway network is a real-world example of complex networks in the transportation domain, which has attracted growing attention in network analysis recently. Analyzing human mobility patterns, specifically in ranking subway stations closely bounded by urban subway planning and individuals’ travel experience, is still an open issue. In this paper, we propose a novel ranking method of station importance (SIRank) by utilizing human mobility patterns and improved PageRank algorithm. Specifically, by analyzing human mobility patterns of the subway system in Shanghai, we demonstrate both static and dynamic characteristics using two network models (Shanghai subway static network and Shanghai subway passenger network). In particular, the SIRank focuses on bi-directional passenger flow analysis between origins and destinations to iteratively generate the importance value for each station. We implement a range of the experiments to illustrate the effectiveness of SIRank using the real-world subway transaction datasets. The results demonstrate that the hit ratio in SIRank reaches 60% in the top five stations, which is much higher than that of ranking by a weighted mixed index (WMIRank) and ranking by node degree (NDRank) approaches. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) has now become an effective paradigm to collect massive data for various sensing applications. However, the interactions between mobile users and the platform, and the data release to third parties, pose severe challenges of privacy leakage for MCS systems, such as the leakage of users' identities and locations. Although several works on MCS have explored the privacy issues in task allocation, incentive, and data reporting, there is still a lack of a comprehensive privacy preserving framework for MCS to protect the privacy of users throughout users' involvement in crowdsensing tasks. In this article, we divide the life cycle of each crowdsensing task in MCS into four phases: task allocation, incentive, data collection, and data publishing, and design a privacy-preserving framework for MCS to protect users' privacy in the whole life cycle of MCS. [ABSTRACT FROM AUTHOR]

In this letter, we demonstrate an integrated negative capacitance oxide thin-film transistor (NC-OTFT) based on ferroelectric Hf0.5Zr0.5O2 (HfZrO) film. The buried-gated TiN/HfZrO/Al2O3 structure is developed with indium zinc oxide as its channel. The transfer characteristics are measured with 1.2-V supply voltage. The NC-OTFT 15-nm HfZrO/6-nm Al2O3 shows $2.5\times 10^{\textsf {7}}$ ON/OFF current ratio and 120-mV anticlockwise hysteresis. The subthreshold swing (SS) values of this NC-OTFT are lower than those of the control devices without HfZrO. The reverse SS shows sub-60 mV/decade about 2 orders of drain current. The minimal reverse and forward SS values are 52.8 and 74.1 mV/decade, respectively. The NC-OTFT with 8-nm HfZrO/6-nm Al2O3 is inspected with considerably narrower hysteresis of 50 mV. Its reverse and forward SS values reach as low as 63.6 and 69.8 mV/decade, respectively. The NC effect is observed in the gate-stack capacitance of the NC-OTFTs. This NC-OTFT is promising for low operating voltage and low power consumption applications. [ABSTRACT FROM AUTHOR]

Owing to its unique capability to sustain computation progress over power outages, a nonvolatile processor (NVP) is promising for energy-harvesting-powered Internet-of-Things devices. However, the widespread application of NVP is continually blocked by the system integration issues and the configuration overheads of peripheral devices. This paper presents a nonvolatile system-on-chip (NVSoC) with improved integration level, power management flexibility, and system wake-up speed. An on-chip power management subsystem is designed to minimize the number of external components while supporting versatile power policies. And a direct peripheral restore architecture is outlined, which enables a fast and parallel re-configuration of peripheral devices after the resumption of power supply. A test chip is fabricated in a 130-nm ferroelectric-CMOS process with 22.09-mm2 area. Measurement results show 6 $\times $ higher data throughput as compared with a conventional NVP when facing power failures. [ABSTRACT FROM AUTHOR]

With the ubiquitous usage of mobile devices, we are witnessing the emergence of commercial crowdsourcing applications that leverage the power of the crowd (workers) to collect massive data. However, the participation unbalance problem commonly occurs in existing location-dependent mobile crowdsourcing applications as workers tend to select nearby tasks while far away tasks are ignored. In this article, we propose a novel task bundling based incentive mechanism that dynamically bundles tasks with different popularity together to solve the participation unbalance problem. We consider the continuous sensing scenarios and categorize tasks into high-popularity (hot) tasks and low-popularity (cold) tasks at each round according to the real-time participation situation of tasks at the last round. We then formulate the task bundling problem as a multi-objective optimization problem, and propose a dynamic task bundling algorithm that dynamically bundles cold tasks with hot tasks at each round. The experimental results demonstrate that the bundling incentive mechanism has a more balanced participation for location-dependent tasks in mobile crowdsourcing systems. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN) has become very popular in image classification tasks. With the increasing demand on intelligent classification on battery-powered devices, energy-efficient ASICs for CNN are badly needed. While previous CNN ASIC processors support operations of different kernel sizes, they sacrifice efficiency to support flexible convolution operations. In fact, convolution operations with a certain kernel size are dominating in many real-case CNNs. This brief proposes a kernel-optimized architecture for $3\,{\times }\,3$ kernels (KOP3), which are dominating operations in mainstream image classification CNNs. Although KOP3 aims at $3\,{\times }\,3$ kernel operations, it also provides programmability to support arbitrary kernel sizes. KOP3 achieves average energy efficiency of 3.77TOPS/W, which is $4.01{ \times }$ better than the best state-of-the-art CNN ASIC processor. [ABSTRACT FROM AUTHOR]

Barcode-based visible light communication (VLC) over screen-camera links has attracted great research interest recently due to its many desirable properties, including being free of charge, free of complex network configurations, and its well-controlled communication security. However, existing VLC systems over screen-camera links suffer from low-communication capacity and reliability, and a lack of transmission feedback from the receiver. In this paper, we design RainBar+, an online robust high-goodput color barcode-based VLC system with realtime feedback, to fully guarantee the communication reliability and exploit the communication capacity under a different link quality. To the best of our knowledge, this is the first system that provides a feasible feedback mechanism to enable the sender to retransmit the lost frames and optimize the barcode configurations. To this end, RainBar+ is designed from three orthogonal perspectives: optimizing the encoding capacity of each frame, fully utilizing the transmission capacity of the sender by solving the frame synchronization problem and adaptively adjusting the barcode configurations, and enabling the selective retransmission via realtime feedback with the speaker-microphone link. Extensive experiments with two commercial off-the-shelf smartphones show that RainBar+ outperforms existing systems and can support realtime high-goodput data communication in dynamic environments. [ABSTRACT FROM AUTHOR]

With the promising technology of far-field wireless power transfer, wearable devices are able to harvest radio frequency (RF) energy from RF-based chargers and thus operate continuously. Rational planning of the quantity and positions of the chargers provide an effective way to improve the recharging efficiency and save the charger deployment budget. Existing work on RF-based charger placement mainly considers the situation where the devices are static, or adopt very simple mobility model. In this paper, we consider wireless charging service provision for wearable devices worn by users in a two-dimensional area, with users having a specific stay–move behavior pattern characterized by the trajectories, stay points, and stay time distribution. Based on this mobility pattern, we formulate the problem as how to find a charger placement to minimize the charging service budget, subject to the power non-outage probability requirement of the wearable devices. We further transform this problem to several equivalent problems for easier tractability and prove that it is NP-complete. Both greedy heuristic and particle swarm optimization (PSO) based solutions are proposed to solve the problem with certain approximation ratio. Finally, performances of the proposed solutions are compared with exhaustive search and existing point provisioning algorithm through extensive simulations. Simulation results show that both greedy heuristic and PSO-based solutions outperform the baseline algorithms, while PSO-based solution requires least number of chargers and thus is the most cost-effective. [ABSTRACT FROM AUTHOR]

Recently radio frequency identification (RFID) is promoting the rapid development of Internet of Things and has been widely applied in numerous industrial applications such as inventory management, object tracking and smart logistics, etc. Tags in some RFID applications can be classified into multiple categories according to the types of objects they are attached to. One of the important functionalities for the multi-category RFID systems is the missing tag cardinality estimation of each category. This paper focuses on solving the missing tag iceberg query problem for multi-category RFID systems, i.e., to determine a set of categories, whose missing tags are more than a threshold, with a required reliability. We firstly propose two basic missing tag i ceberg query schemes called MAC-SZE and MAC-HZE, which adopt the singleton-zero estimator and homogeneous-zero estimator, respectively. To further improve the query efficiency, we then propose two segmented enhanced schemes called MAC-SSZE and MAC-SHZE, which eliminate the unnecessary slots in each frame. We conduct theoretical analysis to guarantee the required reliability is satisfied. The simulations are finally conducted and the results illustrate that the proposed missing tag iceberg query schemes greatly outperform other existing one. [ABSTRACT FROM AUTHOR]

Smart cities have been recognized as a promising research focus around the world. To realize smart cities, computation and utilization of big data are key factors. More specifically, exploring the patterns of human mobility based on large amounts of multi-source data plays an important role in analyzing the formation of social-economic phenomena in smart cities. However, our acquired knowledge is still very limited for smart cities. In this article, we propose an integrated computing method to rescale heterogeneous traffic trajectory data, which leverages MLE and BIC. Our analysis is based on two real datasets generated by subway smart card transactions and taxi GPS trajectories from Shanghai, China, which contain more than 451 million trading records by 14 subway lines and 34 billion GPS records by 13,695 taxis. Specifically, we quantitatively explore the patterns of human mobility on weekends and weekdays. Through logarithmic binning and data fitness, we calculate the Bayesian weights to select the best fitting distributions. In addition, we leverage three metrics to analyze the patterns of human mobility in two datasets: trip displacement, trip duration, and trip interval. We obtain several important human mobility patterns and discover quite a few interesting phenomena, which lay a solid foundation for future research. [ABSTRACT FROM AUTHOR]

In many radio-frequency identification (RFID) applications, one of the essential systematic functionalities is to quickly detect missing-tag event in case of misplacement or other incorrect operations. In this paper, we focus on probabilistically detecting the missing tags for the anonymous multicategory RFID systems without revealing the tag privacy. The main objective is to minimize the detection time while satisfying the required detection reliability of each category. First, we propose to use a multihash technique to sequentially detect the missing tags category-by-category, called segmented sequential detection approach, in which the frame segmentation is adopted to reduce the detection time. Then, we propose an enhanced segmented sequential detection approach to further improve the detection efficiency by deactivating the identified existing tags. We conduct extensive simulations to illustrate the effectiveness of our proposed two missing tag detection approaches. [ABSTRACT FROM AUTHOR]

With an ever-increasing demand for energy efficiency, processors with instant-on and zero leakage features are highly appreciated in energy harvesting as well as “normally off” applications. Recently, zero-standby power and fast switching nonvolatile processors (NVPs) have been proposed based on emerging nonvolatile memories (NVMs), such as ferroelectric RAM or spin-transfer-torque magnetic RAM. However, previous NVPs store all data to NVM upon every power interruption, resulting in high-energy consumption and degraded NVM endurance. This paper presents a 65-nm fully CMOS-logic-compatible ReRAM-based NVP supporting time-space domain adaption. It incorporates adaptive nonvolatile controller, nonvolatile flip-flops, and nonvolatile static random access memory (nvSRAM) with self-write termination. Data redundancy in both time and space domain is fully exploited to reduce store/restore time/energy and boost clock frequency. The NVP operates at >100 MHz and achieves 20 ns/0.45 nJ restore time/energy, realizing >6 $\times $ and >6000 $\times $ higher speed and energy efficiency of restore and >4 $\times $ faster operating frequency compared with that of state of the art. [ABSTRACT FROM AUTHOR]

Nonvolatile flip-flops (nvFFs) enable frequent-off processors to achieve fast power-off and wake-up time while maintaining critical local computing states through parallel data movement between volatile FFs and local nonvolatile memory (NVM) devices. However, current nvFFs face challenges in large store energy ( \textE\mathrm {S} ) and long voltage stress time on the device ( \textT\mathrm {STRESS} ), due to wide distribution in the write time of NVM device as well as unnecessary writes. Moreover, heavy parasitic load on the power rail cause long wake-up time for restoring data from NVM to FFs. This paper proposes the resistive RAM (ReRAM)-based nvFF with self-write termination (SWT) and reduced loading on power rail to: 1) reduce 93+% waste of \textE\mathrm {S} from fast switching or matched cells; 2) suppress endurance and reliability degradation resulted from overprogramming and long \textT\mathrm {STRESS} ; and 3) achieve reliable and 26+ times faster restore operation compared with previous nvFFs. We have fabricated a nonvolatile processor and a test chip with SWT-nvFFs using logic-process ReRAM in a 65-nm CMOS process. Measured results show sub-2-ns termination response time and sub-20-ns chip-level restore time. [ABSTRACT FROM PUBLISHER]

Delay-tolerant networks (DTNs) are an emergent communication paradigm characterized by intermittent connectivity. The nodes in DTNs can take advantage of their contact opportunities to forward messages. However, in noncooperative DTNs, the nodes may be selfish and reluctant to cooperate with each other in message forwarding. In such DTNs, stimulating cooperation among the nodes will be indispensable. Recently, many incentive mechanisms have been proposed to motivate nodes to cooperate in message forwarding. However, most of them cannot guarantee systematic security. To resolve the drawback of the previous incentive mechanisms, we first propose a credit-based rewarding scheme called the earliest path singular rewarding (EPSR) scheme to motivate the nodes to truthfully forward the messages during every contact opportunity. Then, we propose another credit-based rewarding scheme called the earliest path cumulative rewarding (EPCR) scheme by further considering that a node may get more contact information on others. We prove that both the EPSR and EPCR schemes are incentive compatible, and the payment for each delivered message is upper bounded. Furthermore, the proposed schemes can prevent selfish nodes having malicious behaviors. We have conducted real-trace-based simulations to illustrate the effectiveness of the proposed EPSR and EPCR schemes. [ABSTRACT FROM AUTHOR]

Using low duty-cycle is the most common technique to extend the system lifetime in WSNs. However, it also implies limited throughput and long delay and the penalty is even higher under variable traffic patterns. In this paper, we present iQueue-MAC, a hybrid CSMA/TDMA MAC that adapts to variable/bursty traffic. With light load, iQueue-MAC uses a contention-based CSMA mechanism that provides low delay with scattered transmissions. When traffic increases, detected by a forming backlog in the sender, iQueue-MAC changes to a contention-free TDMA mechanism allocating transmission slots. Thus, iQueue-MAC mitigates packet buffering and reduces packet delay, combining the best of TDMA and CSMA. In this paper we also show how iQueue-MAC can operate in both single and multi channel modes. We implemented it on SIM32W108 chips together with other reference WSN protocols for comparison. iQueue-MAC exhibits similar figures during light traffic. However, with bursty traffic its throughput can be five times that of CoSenS and Ri-MAC-MC and its delay 20 times lower. Finally, iQueue-MAC is able to effectively use multiple channels, duplicating its throughput when compared to single channel operation. [ABSTRACT FROM PUBLISHER]

Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a user’s preferences on friend selection in real life. In this paper, we present Friendbook, a novel semantic-based friend recommendation system for social networks, which recommends friends to users based on their life styles instead of social graphs. By taking advantage of sensor-rich smartphones, Friendbook discovers life styles of users from user-centric sensor data, measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. Inspired by text mining, we model a user’s daily life as life documents, from which his/her life styles are extracted by using the Latent Dirichlet Allocation algorithm. We further propose a similarity metric to measure the similarity of life styles between users, and calculate users’ impact in terms of life styles with a friend-matching graph . Upon receiving a request, Friendbook returns a list of people with highest recommendation scores to the query user. Finally, Friendbook integrates a feedback mechanism to further improve the recommendation accuracy. We have implemented Friendbook on the Android-based smartphones, and evaluated its performance on both small-scale experiments and large-scale simulations. The results show that the recommendations accurately reflect the preferences of users in choosing friends. [ABSTRACT FROM PUBLISHER]