Quantum computing users increasingly access quantum hardware through cloud platforms and are charged based on usage. Because these resources are scarce and expensive, users often request specific devices to ensure performance, while providers may reassign jobs to other devices to maximize throughput, potentially compromising user expectations. Thus, we present QuanGuard, an efficient fingerprinting framework for verifying whether the allocated quantum resources match user selections. QuanGuard constructs dynamic fingerprints from the noisy execution results of a probing circuit, exploiting device-specific error evolution patterns to identify hardware uniquely. We further develop an error evolution algorithm that generates user-side fingerprints for lightweight matching against assigned resources. The method requires only a single probing circuit per detection, making it highly practical for current quantum cloud platforms. Experiments on seven IBM quantum computers show that QuanGuard achieves accurate and reliable device verification with minimal overhead.

Noisy Intermediate-Scale Quantum (NISQ) devices, while accessible via cloud platforms, face challenges due to limited availability and suboptimal quality. These challenges raise the risk of cloud providers offering fraudulent services. This emphasizes the need for users to detect such fraud to protect their investments and ensure computational integrity. This study introduces a novel dynamic fingerprinting method for detecting fraudulent service provision on quantum cloud platforms, specifically targeting machine substitution and profile fabrication attacks. The dynamic fingerprint is constructed using a single probing circuit to capture the unique error characteristics of quantum devices, making this approach practical because of its trivial computational costs. When the user examines the service, the execution results of the probing circuit act as the device-side fingerprint of the quantum device providing the service. The user then generates the user-side fingerprint by estimating the expected execution result, assuming the correct device is in use. We propose an algorithm for users to construct the user-side fingerprint with linear complexity. By comparing the device-side and user-side fingerprints, users can effectively detect fraudulent services. Our experiments on the IBM Quantum platform, involving seven devices with varying capabilities, confirm the method's effectiveness.

Quantum networks encounter unavoidable channel noises and erasure errors, presenting a huge obstacle in designing protocols that attain both high reliability and efficiency. Typically, quantum networks fall into two categories: those utilize quantum entanglements for quantum teleportation, and those directly transfer the actual quantum messages. In this paper, we present SurfNet, a quantum network that inherits the main advantages from both categories. It employs surface codes as logical qubits for encoding messages, and utilizes two parallel communication channels to fault-tolerantly transfer each surface code in a modular manner. Our approach of using surface codes can timely correct both operational and photon loss errors within the network, and the integration of the two channels within the network can greatly improve network throughput. For the implementation of SurfNet, we propose a novel network architecture, designed to better integrate surface codes into quantum networks. We also propose a novel error correction decoder, designed to fully utilize the modular characteristic of surface codes within our network. Simulation results demonstrate that SurfNet with its decoder significantly enhances the communication fidelity within quantum networks.

The sweep coverage problem is one of the important issues in spatial crowdsourcing, which requires task participants to monitor a series of Points of Interest (PoIs) periodically. In this paper, we study the Cooperative Sweep Coverage (CSC) problem with the objective of minimizing the maximum sweep period. We propose an iterative clustering algorithm based on spherical projection, called SP-Cycle. The algorithm firstly projects the points in the 2D space to a spherical surface in the 3D space. It then utilizes a new balancing clustering algorithm and uses an iterative coordinate updating method in the 3D spherical space based on the gradient descent, the aim of which is to use the simple minimum spanning tree length computation in the spherical surface to replace the complex Traveling Salesman Problem (TSP) cycle computation in the original 2D space, which improves the performance while keeping a low computational complexity. After we get the clusters, we can compute the lengths of TSP cycles and enter a new iteration. Experimental results based on synthetic and real-world datasets demonstrate the effectiveness of our proposed algorithm. The code is available at https://github.com/GaoYucen/CSC.

Model publishing and deployment are essential for artificial intelligence applications. A major challenge in model publishing is efficiently distributing the models in a scalable way without violating the privacy of sensitive data. With the wide adoption of machine learning techniques, the privacy concern has also drawn much attraction. Differential privacy has become an important notion for privacy protection and is popular in private learning. However, it may bring much accuracy loss to fulfill data privacy. In addition, the private models are also hard to train in terms of convergence, which makes the existing approaches not scalable for private model publishing. This paper proposes a model publishing framework that provides a novel way to train privacy-preserving machine learning models with fast convergence and a lower privacy budget. By incorporating the concept of iterative machine teaching and the techniques in differential privacy, we have explored a way to privately select more suitable examples in the training process for achieving good accuracy with fewer iterations. Our analysis shows the privacy and convergence performance of the proposed method, and extensive experiments have been performed on real-world datasets to demonstrate its effectiveness.

In recent years, deep neural networks have shown extraordinary power in various practical learning tasks, especially in object detection, classification, natural language processing. However, deploying such large models on resource-constrained devices or embedded systems is challenging due to their high computational cost. Efforts such as model partition, pruning, or quantization have been used at the expense of accuracy loss. Knowledge distillation is a technique that transfers model knowledge from a well-trained model (teacher) to a smaller and shallow model (student). Instead of using a learning model on the cloud, we can deploy distilled models on various edge devices, significantly reducing the computational cost, memory usage and prolonging the battery lifetime. In this work, we propose a novel neuron manifold distillation (NMD) method, where the student models imitate the teacher's output distribution and learn the feature geometry of the teacher model. In addition, to further improve the cloud-based learning system reliability, we propose a confident prediction mechanism to calibrate the model predictions. We conduct experiments with different distillation configurations over multiple datasets. Our proposed method demonstrates a consistent improvement in accuracy-speed trade-offs for the distilled model.

Location-based services have significantly affected mobile users’ everyday life, and location privacy has become essential. Some applications (e.g., location-based recommendation, mobility analytics) do not need the raw location data, and the service providers adopt aggregation to protect users’ location traces. However, some works show that even these aggregation data may disclose users’ location privacy when additional prior knowledge is available to an adversary. We consider the location privacy problem in the presence ofLocation Uniqueness, a property by which some geographical locations can be re-identified based on the aggregated point-of-interest information. We first study whether existing protection mechanisms are adequate for defending against this type of attack. Then we present two practical attacks for inferring users’ actual locations based on the POI aggregates. A secure POI aggregate release mechanism is proposed for defending against this type of re-identification attack and achieving differential privacy at the same time. We conduct extensive experiments on real-world datasets. The results show that the existing protection mechanisms cannot provide sufficient protection against location re-identification attacks. The proposed attacks can significantly improve the inference performance, and the proposed protection mechanism achieves satisfactory performance.

Mobile edge computing (MEC) is proposed as an extension of cloud computing in the scenarios where the end devices desire better services in terms of response time. Because the edges are usually owned by individuals or small organizations with limited operation capabilities, the data on the edges are easily corrupted (due to external attacks or internal hardware failures). Therefore, it is essential to verify data integrity in the MEC. We propose two Integrity Checking protocols for the mobile Edge storage, called ICE-basic and ICE-batch. Our protocols allow a third-party verifier to check the data integrity on the edges without violating users data privacy and query pattern privacy. We rigorously prove the security and privacy guarantees of the protocols. In addition, we have investigated how to let the end devices cache some verification tags such that the communication cost between end devices and the cloud can be further reduced when a user connects to multiple edges in sequence. We have implemented a proof-of-concept system that runs ICE, and extensive experiments are conducted to evaluate the performance of the proposed protocols. The theoretical analysis and experimental results demonstrate the proposed protocols are efficient both in computation and communication.

The convolutional neural network (CNN) has become a general approach for image processing in machine learning tasks. Quantum CNN (QCNN) is an emerging method to implement CNN using quantum computing. Quantum computing utilizes the properties of quantum mechanics to perform efficient computing. However, current quantum machines do not support large-scale QCNNs due to a lack of qubits. As a consequence, QCNNs are limited in scale and cannot directly process high-dimensional images. These shortcomings result in suboptimal QCNN performance. Meanwhile, building quantum machines with enough qubits is technically difficult and costly. These obstacles motivate us to design a quantum edge computing (QEC) system capable of achieving the scalability of QCNNs. Quantum machines are organized hierarchically in the QEC system. The quantum machines closer to the users collaboratively load and extract quantum features from the high-dimensional input data. Subsequently, the quantum machine in the next layer collects the extracted features and performs further operations to produce the final results. Each quantum machine in the QEC system is equipped with a local small-scale QCNN to capture the data pattern of its input. The local QCNNs could be combined to form a large-scale QCNN capable of learning and processing high-dimensional data, overcoming hardware limitations and improving performance.

Federated learning, in contrast to traditional learning paradigms, has demonstrated its unique advantages in providing intelligence at the edge. However, existing federated learning approaches focus on the end-to-end classification tasks requiring a simple collaboration procedure where each participant can perform its local training independently. Unfortunately, there are still many tasks relying on learning the distinguishable feature metrics with respect to all the data, which is a different collaboration procedure across training participants. For example, the model for people identification has to ensure the feature representing a person is dissimilar to those representing others. To enable such federated learning for deep metrics (a.k.a federated deep metric learning) is challenging due to the data privacy and procedure robustness issues. With the consideration of these two challenges, this work proposes a novel computing framework for federated deep metric learning. This framework leverages the system-algorithm co-design to address privacy concerns via the Trusted Execution Environment (SGX enclave) and Differential Privacy mechanism. It also introduces a large-scale federated protocol which can robustly and efficiently deal with practical factors like the network fluctuation. We implement and evaluate our computing framework with two settings. One is a real-world implementation with a large number of mobile devices, while the other one is in our controllable environment for conducting experiments in various tasks. Our evaluation results show that our computing framework is able to train federated deep metric learning models with excellent scalability, data privacy preserving, and considerable accuracy even in exception conditions.

Increasingly, many security and privacy sensitive applications (apps for short) are running in the mobile platforms. However, as the mobile operating systems are becoming increasingly sophisticated, they are vulnerable to various attacks. In addressing the need of running high assurance mobile apps in a secure environment even though the operating systems are untrusted, this paper presents VTRUST, a new mobile app trusted execution environment, which offloads the general execution and storage of a mobile app to a trusted remote server (e.g., a VM running in a cloud) and secures the I/O between the server and the mobile device with the aid of a trusted hypervisor on the mobile device. Specifically, VTRUST establishes an encrypted I/O channel between the local hypervisor and the remote server, such that any sensitive data flowing through the mobile OS, which is hosted by the hypervisor, is encrypted from the perspective of the local mobile OS. To enhance the performance of VTRUST, we have also designed multiple optimizations, such as output data compression and selective sensor data transmission. We have implemented VTRUST and our evaluation shows that it has limited impact on both user experience and the app performance.

Training large-scale machine learning models usually demands a distributed approach to process the huge amount of training data efficiently. However, the high network communication cost introduced by parallel stochastic gradient descent (SGD) algorithms is a well-known bottleneck. To this end, we propose CE-SGD, a communication-efficient distributed machine learning algorithm that aggressively reduces the amount of gradient data exchanged among the training workers. CE-SGD belongs to the family of gradient sparsification schemes. CE-SGD adaptively adjusts the gradient sparsity according to the model's feedback. It also selectively transmits the gradients based on their degree of participation in the backpropagation. We mathematically prove the convergence of CE-SGD for both convex and non-convex cases and conduct a series of experiments on our CE-SGD implementation. Our experiments reveal that CE-SGD can achieve fast convergence, desirable gradient compression ratio, and high accuracy with low network bandwidth cost compared to state-of-the-art algorithms.

With the fast development of quantum computing and deep learning, quantum neural networks have attracted great attention recently. By leveraging the power of quantum computing, deep neural networks can potentially overcome computational power limitations in classic machine learning. However, when multiple quantum machines wish to train a global model using the local data on each machine, it may be very difficult to copy the data into one machine and train the model. Therefore, a collaborative quantum neural network framework is necessary. In this article, we borrow the core idea of federated learning to propose QuantumFed, a quantum federated learning framework to have multiple quantum nodes with local quantum data train a mode together. Our experiments show the feasibility and robustness of our framework.

Location-based services have significantly affected mobile users' everyday life, and location privacy is also an essential issue in these services. In some applications (e.g., location-based recommendation, mobility analytic), the raw data is not required, and the service providers adopt aggregation to protect users' location traces. However, some works show that even these aggregation data may disclose users' location privacy when other prior knowledge is available to an adversary. We consider the location privacy problem in the presence of Location Uniqueness, which is a property that some geographical locations can be re-identified based on the aggregated point-of-interest (POI) information. We first study whether previous protection mechanisms are effective for defending against this novel type of attack. Then we present two practical attacks for inferring users' actual locations based on the POI aggregates. Furthermore, we propose a secure POI aggregate release mechanism that can defend against this type of re-identification attack and achieve differential privacy at the same time. We conduct extensive experiments on real-world datasets. The results show that the existing protection mechanisms cannot provide sufficient protection. The proposed enhanced attacks can significantly improve the inference performance, and the proposed protection mechanism achieves satisfactory performance.

Although deep neural networks show their extraordinary power in various object detection tasks, it is very challenging for them to be deployed on resource constrained devices or embedded systems due to their high computational cost. Efforts such as model partition, pruning or quantization have been used at an expense of accuracy loss. Recently proposed knowledge distillation (KD) aims at transferring model knowledge from a well-trained model (teacher) to a smaller and faster model (student), which can significantly reduce the computational cost, memory usage, and prolong the battery lifetime. In this work, we propose a novel neuron manifold distillation (NMD), where the student models not only imitate teacher’s output activations, but also learn the feature geometry structure of the teacher. Our approach produces a high-quality, compact, and lightweight student model. We conduct comprehensive experiments with different distillation configurations over multiple datasets, and the proposed method demonstrates a consistent improvement in accuracy-speed trade-offs for the distilled model.

A large volume of data is generated by ubiquitous Internet-of-Things (IoT) devices and utilized to train machine learning models by IoT manufacturers to provide users with better services. Many deep learning systems for IoT data are required to perform all computation locally on small devices, which is not suitable for these resource-constrained devices. The devices can also send all the collected data to a server for costly model training by ignoring privacy concerns. To design an efficient and secure deep learning model training system, in this paper, we propose a federated learning system on the edge using the differential privacy mechanism to protect sensitive information and offload computation work from edge devices to edge servers, with consideration of communication reduction. In our system, a large-scale deep learning model is partitioned onto edge devices and edge servers, and trained in a distributed manner, in which all untrusted components are prevented from retrieving protected information from the training and inference process. We evaluate the proposed approach with respect to computation, communication, and privacy protection. The experiment results show that the proposed approach can preserve users’ privacy while significantly reducing computation and communication costs.

By combining the advantages of both quantum computing and deep learning, quantum neural networks have become popular in recent research. In order to collaborate multiple quantum machines with local training data to train a global model, quantum federated learning is proposed. However, similar to classic federated learning, when communicating with multiple machines, quantum federated learning also faces the threats of Byzantine attacks. The byzantine attack is a kind of attack in a distributed system when some machines upload malicious information instead of the honest computational results to the server. In this article, we compare the differences of Byzantine problems between classic distributed learning and quantum federated learning, and modify the previously proposed four kinds of Byzantine tolerant algorithms to the quantum version. We conduct simulated experiments to show a similar performance of the quantum version with the classic version.

Federated Learning is a machine learning scheme in which a shared prediction model can be collaboratively learned by a number of distributed nodes using their locally stored data. It can provide better data privacy because training data are not transmitted to a central server. Federated learning is well suited for edge computing applications and can leverage the the computation power of edge servers and the data collected on widely dispersed edge devices. To build such an edge federated learning system, we need to tackle a number of technical challenges. In this survey, we provide a new perspective on the applications, development tools, communication efficiency, security & privacy, migration and scheduling in edge federated learning.

Deep neural networks (DNNs) have brought significant performance improvements to various real-life applications. However, a DNN training task commonly requires intensive computing resources and a huge data collection, which makes it hard for personal devices to carry out the entire training, especially for mobile devices. The federated learning concept has eased this situation. However, it is still an open problem for individuals to train their own DNN models at an affordable price. In this article, we propose an alternative DNN training strategy for resource-limited users. With the help of an untrusted server, end users can offload their DNN training tasks to the server in a privacy-preserving manner. To this end, we study the possibility of the separation of a DNN. Then we design a differentially private activation algorithm for end users to ensure the privacy of the offloading after model separation. Furthermore, to meet the rising demand for federated learning, we extend the offloading solution to parallel DNN models training with a secure model weights aggregation scheme for the privacy concern. Experimental results prove the feasibility of computation offloading solutions for DNN models in both solo and parallel modes.

Over the last few years, there has been a growing interest in developing chatbots that can converse intelligently with humans. For example, consider Microsoft's Xiaoice. It is a highly intelligent dialogue system that serves as both a social companion and a virtual assistant. Targeted towards Chinese users, Xiaoice is connected to 660 million online users and 450 million IoT devices. Because of the deep learning revolution, the field is moving quickly, so this survey aims to introduce newcomers to the most fundamental research questions for next generation neural dialogue systems. In particular, our analysis of the state of the art reveals the following 4 key research challenges: 1) knowledge grounding, 2) persona consistency, 3) emotional intelligence, and 4) evaluation. Knowledge grounding endows the chatbot with external knowledge to generate more informative replies. Persona consistency grants dialogue systems consistent personalities. We divide each fundamental research challenge into several smaller and more concrete research questions. For each fine grained research challenge, we examine state of the art approaches and propose future research directions.

The rise of personal assistants serves as a testament to the growing popularity of chatbots. However, as the field advances, it is important for the conversational AI community to keep in mind any potential vulnerabilities in existing architectures and how attackers could take advantantage of them. Towards this end, we present a survey of existing dialogue system vulnerabilities in security and privacy. We define chatbot security and give some background regarding the state of the art in the field. This analysis features a comprehensive description of potential attacks of each module in a typical chatbot architecture: the client module, communication module, response generation module, and database module.

Mobile edge computing (MEC) is proposed as an extension of cloud computing in the scenarios where the end devices desire better services in terms of response time. Edge nodes are deployed at the proximity of the end devices, and it can pre-download parts of data stored in the cloud so that the end devices can access these data with low latency. However, because the edges are usually owned by individuals and small organizations, which have limited operation capacities for maintaining the machines, the data on the edges are easily corrupted (due to external attacks or internal hardware failures). Therefore, it is essential to verify data integrity in the MEC. We propose two Integrity Checking protocols for mobile Edge computing, called ICE-basic and ICE-batch, which are designed for the cases where the user wants to check data integrity on a single edge or multiple edges, respectively. Based on the concept of provable data possession and the technique of private information retrieval, our protocols allow a third-party verifier to check the data integrity on the edges without violating users' data privacy and query pattern privacy. We rigorously prove the security and privacy guarantees of the protocols. Furthermore, we have implemented a proof-of-concept system that runs ICE, and extensive experiments are conducted. The theoretical analysis and experimental results demonstrate the proposed protocols are efficient both in computation and communication.

Many times, training a large scale deep learning neural network on a single machine becomes more and more difficult for a complex network model. Distributed training provides an efficient solution, but Byzantine attacks may occur on participating workers. They may be compromised or suffer from hardware failures. If they upload poisonous gradients, the training will become unstable or even converge to a saddle point. In this paper, we propose FABA, a Fast Aggregation algorithm against Byzantine Attacks, which removes the outliers in the uploaded gradients and obtains gradients that are close to the true gradients. We show the convergence of our algorithm. The experiments demonstrate that our algorithm can achieve similar performance to non-Byzantine case and higher efficiency as compared to previous algorithms.

The rise of edge computing gives birth to a spectrum of delay-sensitive applications. Many of these applications build their services atop the functionality that the edge nodes quickly negotiate a unique order on the events received from a massive number of client devices, even under very high event rates. To this end, we propose a protocol, called Nomad, for achieving fast event ordering in edge computing environments. Nomad is designed as a consensus protocol that employs a lease-based approach to take advantage of the locality of the unbalanced workload across the system. It also dynamically adjusts the leadership distribution on the edge nodes based on the recent running history, and relies on a cloud-based arbitrator to resolve contentions. Experiments demonstrate that Nomad outperforms the existing solutions, such as Multi-Paxos, Mencius and E-Paxos, in achieving fast event ordering for large-scale, delay-sensitive edge-cloud applications.

Sharing small pieces of information, such as URLs, Internet of Things (IoT) commands, or encryption keys is an extremely common use case in IoT applications. These are examples of transient, spontaneous proximity networking, in which both the sender and receiver are physically co-located. In this paper, we aim to provide a mechanism for proximity networking based on very high-frequency sound waves emitted and captured by the speaker and microphone found on commodity smartphones. Our approach has several benefits over existing solutions including easy deployment, lower cost for manufacturers, and intuitive security guarantees based on the physical characteristics of ultrasound signals. We implement a software-based modem called “Hush,” which we provide in an open source library for use in Android applications. It is practically inaudible and fast, achieving an effective transmission rate of 4900 bits per second at an ideal distance of 5-20 cm.

Many edge computing systems rely on virtual machines (VMs) to deliver their services. It is challenging, however, to deploy the virtualization mechanisms on edge computing hardware infrastructures. In this paper, we introduce the engineering and research trends of achieving efficient VM management in edge computing. We elaborate on: 1) the virtualization frameworks for edge computing developed in both the industry and the academia; 2) the virtualization techniques tailored for edge computing; 3) the placement and scheduling algorithms optimized for edge computing; and 4) the research problems in security related to virtualization of edge computing.

Mobile users across edge networks require seamless migration of offloading services. Edge computing platforms must smoothly support these service transfers and keep pace with user movements around the network. However, live migration of offloading services in the wide area network poses significant service handoff challenges in the edge computing environment. In this paper, we propose an edge computing platform architecture which supports seamless migration of offloading services while also keeping the moving mobile user “in service” with its nearest edge server. We identify a critical problem in the state-of-the-art tool for Docker container migration. Based on our systematic study of the Docker container storage system, we propose to leverage the layered nature of the storage system to reduce file system synchronization overhead, without dependence on the distributed file system. In contrast to the state-of-the-art service handoff method in the edge environment, our system yields a 80 percent (56 percent) reduction in handoff time under 5 Mbps (20 Mbps) network bandwidth conditions.

The papers in this special section focus on fog computing and services. The emerging Internet of Things (IoT) and rich cloud services have helped create the need for fog computing (also known as edge computing), in which data processing occurs in part at the network edge or anywhere along the cloud-to-endpoint continuum that can best meet user requirements, rather than completely in a relatively small number of massive clouds. Fog computing could address latency concerns, devices’ limited processing and storage capabilities and battery life, network bandwidth constraints and costs, and many security and privacy concerns that arise from the emerging IoT.

Today's enterprises are exposed to sophisticated attacks, such as Advanced Persistent Threats~(APT) attacks, which usually consist of stealthy multiple steps. To counter these attacks, enterprises often rely on causality analysis on the system activity data collected from a ubiquitous system monitoring to discover the initial penetration point, and from there identify previously unknown attack steps. However, one major challenge for causality analysis is that the ubiquitous system monitoring generates a colossal amount of data and hosting such a huge amount of data is prohibitively expensive. Thus, there is a strong demand for techniques that reduce the storage of data for causality analysis and yet preserve the quality of the causality analysis. To address this problem, in this paper, we propose NodeMerge, a template based data reduction system for online system event storage. Specifically, our approach can directly work on the stream of system dependency data and achieve data reduction on the read-only file events based on their access patterns. It can either reduce the storage cost or improve the performance of causality analysis under the same budget. Only with a reasonable amount of resource for online data reduction, it nearly completely preserves the accuracy for causality analysis. The reduced form of data can be used directly with little overhead. To evaluate our approach, we conducted a set of comprehensive evaluations, which show that for different categories of workloads, our system can reduce the storage capacity of raw system dependency data by as high as 75.7 times, and the storage capacity of the state-of-the-art approach by as high as 32.6 times. Furthermore, the results also demonstrate that our approach keeps all the causality analysis information and has a reasonably small overhead in memory and hard disk.

The great potential of mobile crowdsourcing has started to attract attention of both industries and the research community. However, current commercial mobile crowdsourcing marketplaces are unsatisfactory because of the limited worker base and functionality. In this paper, we first revisit the foundation of performing mobile crowdsourcing on location-based social networks (LBSNs) through specially designed survey studies and comparison experiments involving hundreds of users. Our results reveal that active check-ins are good indicators of picking a right user to perform tasks, and LBSN could be an ideal platform for mobile crowdsourcing given proper services provided. We then propose both the centralized and decentralized design of MobiCrowd, a mobile crowdsourcing service built on LBSNs. Our evaluation, through trace-driven simulation and real-world experiments, demonstrates that the proposed schemes can effectively find workers for mobile crowdsourcing tasks associated with different venues by analyzing their location check-in histories.

Because of the smaller size of mobile devices, text entry with on-screen keyboards becomes inefficient. Therefore, we present CamK, a camera-based text-entry method, which can use a panel (e.g., a piece of paper) with a keyboard layout to input text into small devices. With the built-in camera of the mobile device, CamK captures images during the typing process and utilizes image processing techniques to recognize the typing behavior, i.e., extract the keys, track the user's fingertips, detect, and locate keystrokes. To achieve high accuracy of keystroke localization and low false positive rate of keystroke detection, CamK introduces the initial training and online calibration. To reduce the time latency, CamK optimizes computation-intensive modules by changing image sizes, focusing on target areas, introducing multiple threads, removing the operations of writing or reading images. Finally, we implement CamK on mobile devices running Android. Our experimental results show that CamK can achieve above 95 percent accuracy in keystroke localization, with only a 4.8 percent false positive rate. When compared with on-screen keyboards, CamK can achieve a 1.25X typing speedup for regular text input and 2.5X for random character input. In addition, we introduce word prediction to further improve the input speed for regular text by 13.4 percent.

We present LAVEA, a system built for edge computing, which offloads computation tasks between clients and edge nodes, collaborates nearby edge nodes, to provide low-latency video analytics at places closer to the users. We have utilized an edge-first design to minimize the response time, and compared various task placement schemes tailed for inter-edge collaboration. Our results reveal that the client-edge configuration has task speedup against local or client-cloud configurations.

Smartphone lock screens are implemented to reduce the risk of data loss or compromise given the fact that increasing amount of person data are accessible on smartphones nowadays. Unfortunately, many smartphone users abandon lock screens due to the inconvenience of unlocking their phones many times a day. With the wide adoption of wearables, token-based approaches have gained popularity in simplifying unlocking and retaining security at the same time. To this end, we propose to take advantage of the smartwatch for easy smartphone unlocking. In this paper, we have designed WearLock, a system that uses acoustic tones as tokens to automate the unlocking securely. We build a sub-channel selection and an adaptive modulation in the acoustic modem to maximize unlocking success rate against ambient noise only when those two devices are nearby. We leverage the motion sensor on the smartwatch to reduce the unlock frequency. We offload smartwatch tasks to the smartphone to speed up computation and save energy. We have implemented the WearLock prototype and conducted extensive evaluations. Results achieved a low average bit error rate (BER) as 8% in various experiments. Compared to traditional manual personal identification numbers (PINs) entry, WearLock achieves at least 18% unlock speedup without any manual effort.

CPU is one of the most significant sources of power consumption on smartphones. Power modeling is a key technique and important tool for power estimation and management, both of which are critical for providing good QoS for smartphones. However, we find that existing CPU power models for smartphones are ill-suited for modern multicore CPUs: they can give high estimation errors (up to 34%) and high estimation accuracy variation (more than 30%) for different types of workloads on mainstream multicore smartphones. The cause is that the existing approaches do not appropriately consider the effects of CPU idle power states on smartphones CPU power modeling. Based on our extensive measurement experiments, we develop a new CPU power modeling approach that carefully considers the effects of CPU idle power states. We present the detailed design of our power modeling approach, and a prototype CPU power estimation system on commercial multicore smartphones. Evaluation results show that our approach consistently achieves higher power estimation accuracy and stability for various benchmarks programs and real apps than the existing approaches.

While the Internet of Things (IoT) is making our lives much easier, managing the IoT becomes a big issue due to the huge number of connections, and the lack of protections for devices. Recent work shows that software-defined networking (SDN) has a great capability in automatically and dynamically managing network flows. Besides, switches in SDNs are usually powerful machines, which can be used as fog nodes simultaneously. Therefore, SDN seems a good choice for IoT-Fog networks. However, before deploying to IoT-Fog networks, the security of the OpenFlow channel between the controller and its switches need to be addressed. Since all the controller commands are sent through this channel, once compromised, the network will be completely controlled by an attacker. This is a disaster for both the network service providers and their customers. Previous works on SDN security either protect controllers themselves or make a strong assumption that the OpenFlow channel is already secured. Using TLS to encrypt the channel is not a “silver-bullet” solution due to the known TLS vulnerabilities. In this paper, we specifically investigate the potential threats of man-in-the-middle attacks on the OpenFlow control channel. We first introduce a feasible attack model in an IoT-Fog architecture, and then we implement attack demonstrations to show the severe consequences of such attacks. Additionally, we propose a lightweight countermeasure using Bloom filters. We implement a prototype for this method to monitor stealthy packet modifications. The result of our evaluation shows that our Bloom filter monitoring system is efficient and consumes few resources.

Securing a wireless channel between any two vehicles is a crucial component of vehicular networks security. This can be done by using a secret key to encrypt the messages. We propose a scheme to allow two cars to extract a shared secret from RSSI (Received Signal Strength Indicator) values in such a way that nearby cars cannot obtain the same key. The key is information-theoretically secure, i.e., it is secure against an adversary with unlimited computing power. Although there are existing solutions of key extraction in the indoor or low-speed environments, the unique channel conditions make them inapplicable to vehicular environments. Our scheme effectively and efficiently handles the high noise and mismatch features of the measured samples so that it can be executed in the noisy vehicular environment. We also propose an online parameter learning mechanism to adapt to different channel conditions. Extensive real-world experiments are conducted to validate our solution.

The record-and-replay approach for software testing is important and valuable for developers in designing mobile applications. However, the existing solutions for recording and replaying Android applications are far from perfect. When considering the richness of mobile phones' input capabilities including touch screen, sensors, GPS, etc., existing approaches either fall short of covering all these different input types, or require elevated privileges that are not easily attained and can be dangerous. In this paper, we present a novel system, called MobiPlay, which aims to improve record-and-replay testing. By collaborating between a mobile phone and a server, we are the first to capture all possible inputs by doing so at the application layer, instead of at the Android framework layer or the Linux kernel layer, which would be infeasible without a server. MobiPlay runs the to-be-tested application on the server under exactly the same environment as the mobile phone, and displays the GUI of the application in real time on a thin client application installed on the mobile phone. From the perspective of the mobile phone user, the application appears to be local. We have implemented our system and evaluated it with tens of popular mobile applications showing that MobiPlay is efficient, flexible, and comprehensive. It can record all input data, including all sensor data, all touchscreen gestures, and GPS. It is able to record and replay on both the mobile phone and the server. Furthermore, it is suitable for both white-box and black-box testing.

Smartphone users are often grouped to exchange files or perform collaborative tasks when meeting together. We argue that the location information of group members is critical to many mobile applications. Existing localization solutions mostly rely on anchor nodes or infrastructures to perform ranging and positioning. These approaches are inefficient for ad hoc scenarios. In this paper, we propose AMIL, an Acoustic Mobility-Induced TDoA (Time-Difference-of-Arrival)-based Localization scheme for smartphones. In AMIL, a smartphone user can use simple gestures (e.g., hold the phone and draw a triangle in the air) to quickly obtain the relative coordinates of neighboring mobile devices. We have implemented and evaluated AMIL on off-the-shelf smartphones. The field tests have shown that our scheme can achieve less than three degree orientation errors and can successfully build a simple map of 12 people in an office room with average error of 50cm.

We have seen an emerging trend towards wearables nowadays. In this paper, we focus on smart glasses, whose current interfaces are difficult to use, error-prone, and provide no or insecure user authentication. We thus present GlassGesture, a system that improves Google Glass through a gesture-based user interface, which provides efficient gesture recognition and robust authentication. First, our gesture recognition enables the use of simple head gestures as input. It is accurate in various wearer activities regardless of noise. Particularly, we improve the recognition efficiency significantly by employing a novel similarity search scheme. Second, our gesture-based authentication can identify owner through features extracted from head movements. We improve the authentication performance by proposing new features based on peak analyses, and employing an ensemble method. Last, we implement GlassGesture and present extensive evaluations. GlassGesture achieves a gesture recognition accuracy near 96%. For authentication, GlassGesture can accept authorized users in near 92% of trials, and reject attackers in near 99% of trials. We also show that in 100 trials imitators cannot successfully masquerade as the authorized user even once.

Due to the smaller size of mobile devices, on-screen keyboards become inefficient for text entry. In this paper, we present CamK, a camera-based text-entry method, which uses an arbitrary panel (e.g., a piece of paper) with a keyboard layout to input text into small devices. CamK captures the images during the typing process and uses the image processing technique to recognize the typing behavior. The principle of CamK is to extract the keys, track the user's fingertips, detect and localize the keystroke. To achieve high accuracy of keystroke localization and low false positive rate of keystroke detection, CamK introduces the initial training and online calibration. Additionally, CamK optimizes computation-intensive modules to reduce the time latency. We implement CamK on a mobile device running Android. Our experiment results show that CamK can achieve above 95% accuracy of keystroke localization, with only 4.8% false positive keystrokes. When compared to on-screen keyboards, CamK can achieve 1.25X typing speedup for regular text input and 2.5X for random character input.

In recent years mobile smart devices such as tablets and smartphones have exploded in popularity. We are now in a world of ubiquitous smart devices that people rely on daily and carry everywhere. This is a fundamental shift for computing in two ways. Firstly, users increasingly place unprecedented amounts of sensitive information on these devices, which paints a precarious picture. Secondly, these devices commonly carry many physical world interfaces. In this paper, we propose information leakage malware, specifically designed for mobile devices, which uses covert channels over physical "real-world" media, such as sound or light. This malware is stealthy; able to circumvent current, and even state-of-the-art defenses to enable attacks including privilege escalation, and information leakage. We go on to present a defense mechanism, which balances security with usability to stop these attacks.

Mobile devices are now ubiquitous in the modern world. In this paper, we propose a novel and practical mobile-cloud platform for smart mobile devices. Our platform allows users to run the entire mobile device operating system and arbitrary applications on a cloud-based virtual machine. It has two design fundamentals. First, applications can freely migrate between the user's mobile device and a backend cloud server. We design a file system extension to enable this feature, so users can freely choose to run their applications either in the cloud (for high security guarantees), or on their local mobile device (for better user experience). Second, in order to protect user data on the smart mobile device, we leverage hardware virtualization technology, which isolates the data from the local mobile device operating system. We have implemented a prototype of our platform using off-the-shelf hardware, and performed an extensive evaluation of it. We show that our platform is efficient, practical, and secure.

In this paper, we show the first comprehensive experimental study on mobile RFID reading performance based on a relatively large number of tags. By making a number of observations regarding the tag reading performance, we build a model to depict how various parameters affect the reading performance. Through our model, we have designed very efficient algorithms to maximize the time-efficiency and energy-efficiency by adjusting the reader's power and moving speed. Our experiments show that our algorithms can reduce the total scanning time by 50 percent and the total energy consumption by 83 percent compared to the prior solutions.

Collecting histograms over RFID tags is an essential premise for effective aggregate queries and analysis in large-scale RFID-based applications. In this paper we consider an efficient collection of histograms from the massive number of RFID tags, without the need to read all tag data. In order to achieve time efficiency, we propose a novel, ensemble sampling-based method to simultaneously estimate the tag size for a number of categories. We first consider the problem of basic histogram collection, and propose an efficient algorithm based on the idea of ensemble sampling. We further consider the problems of advanced histogram collection, respectively, with an iceberg query and a top-k query. Efficient algorithms are proposed to tackle the above problems such that the qualified/unqualified categories can be quickly identified. This ensemble sampling-based framework is very flexible and compatible to current tag-counting estimators, which can be efficiently leveraged to estimate the tag size for each category. Experiment results indicate that our ensemble sampling-based solutions can achieve a much better performance than the basic estimation/identification schemes.

As the ubiquity of smartphones increases we see an increase in the popularity of location based services. Specifically, online social networks provide services such as alerting the user of friend co-location, and finding a user's k nearest neighbors. Location information is sensitive, which makes privacy a strong concern for location based systems like these. We have built one such service that allows two parties to share location information privately and securely. Our system allows every user to maintain and enforce their own policy. When one party, (Alice), queries the location of another party, (Bob), our system uses homomorphic encryption to test if Alice is within Bob's policy. If she is, Bob's location is shared with Alice only. If she is not, no user location information is shared with anyone. Due to the importance and sensitivity of location information, and the easily deployable design of our system, we offer a useful, practical, and important system to users. Our main contribution is a flexible, practical protocol for private proximity testing, a useful and efficient technique for representing location values, and a working implementation of the system we design in this paper. It is implemented as an Android application with the Facebook online social network used for communication between users.

Cognitive radio plays an important role in improving spectrum utilization in wireless services. In the cognitive radio paradigm, secondary users (SUs) are allowed to utilize licensed spectrum opportunistically without interfering with primary users (PUs). To motivate PU to share licensed spectrum with SU, it is reasonable for SU to pay PU a fee whenever the former is utilizing the latter's licensed spectrum. SU's detailed usage information, such as when and how long the licensed spectrum is utilized, is needed for PU to calculate payment. Providing usage information to PU, however, may compromise SU's privacy. To solve this dilemma, we are the first to propose a novel privacy-preserving mechanism for cognitive radio transactions through commitment scheme and zero-knowledge proof. This mechanism, on one hand, only allows PU to know the total payment to SU for a billing period, plus a little portion of SU's usage information. On the other hand, it guarantees PU that the payment is correctly calculated. We have implemented our mechanism and evaluated its performance.

The soaring adoption of location-based social networks (LBSNs) makes it possible to analyze human socio-spatial behaviors based on large-scale realistic data, which is important to both the research community and the design of new location-based social applications. However, performing direct measurements on LBSNs is impractical, because of the security mechanisms of existing LBSNs, and high time and resource costs. The problem is exacerbated by the scarcity of available LBSN datasets, which is mainly due to the privacy concerns and the hardness of distributing large-volume data. As a result, only a very few number of LBSN datasets are publicly released. In this paper, we extract and study the universal statistical features of three LBSN datasets, and propose LBSNSim, a trace-driven model for generating synthetic LBSN datasets capturing the properties of the original datasets. Our evaluation shows that LBSNSim provides an accurate representation of target LBSNs.

Collecting histograms over RFID tags is an essential premise for effective aggregate queries and analysis in large-scale RFID-based applications. In this paper we consider efficient collection of histograms from the massive number of RFID tags without the need to read all tag data. We first consider the problem of basic histogram collection and propose an efficient algorithm based on the idea of ensemble sampling. We further consider the problems of advanced histogram collection, respectively, with an iceberg query and a top-k query. Efficient algorithms are proposed to tackle the above problems such that the qualified/unqualified categories can be quickly identified. Experiment results indicate that our ensemble sampling-based solutions can achieve a much better performance than the basic estimation/identification schemes.

We present HoWiES, a system that saves energy consumed by WiFi interfaces in mobile devices with the assistance of ZigBee radios. The core component of HoWiES is a WiFi-ZigBee message delivery scheme that enables WiFi radios to convey different messages to ZigBee radios in mobile devices. Based on the WiFi-ZigBee message delivery scheme, we design three protocols that target three WiFi energy saving opportunities in scanning, standby and wakeup respectively. We have implemented the HoWiES system with two mobile devices platforms and two AP platforms. Our real-world experimental evaluation shows that our system can convey thousands of different messages from WiFi radios to ZigBee radios with an accuracy over 98 percent, and our energy saving protocols, while maintaining the comparable wakeup delay to that of the standard 802.11 power save mode, save 88 and 85 percent of energy consumed in scanning state and standby state respectively.

Wi-Fi tethering (i.e., sharing the Internet connection of a mobile phone via its Wi-Fi interface) is a useful functionality and is widely supported on commercial smartphones. Yet, existing Wi-Fi tethering schemes consume excessive power: they keep the Wi-Fi interface in a high power state regardless if there is ongoing traffic or not. In this paper, we propose DozyAP to improve the power efficiency of Wi-Fi tethering. Based on measurements in typical applications, we identify many opportunities that a tethering phone could sleep to save power. We design a simple yet reliable sleep protocol to coordinate the sleep schedule of the tethering phone with its clients without requiring tight time synchronization. Furthermore, we develop a two-stage, sleep interval adaptation algorithm to automatically adapt the sleep intervals to ongoing traffic patterns of various applications. DozyAP does not require any changes to the 802.11 protocol and is incrementally deployable through software updates. We have implemented DozyAP on commercial smartphones. Experimental results show that, while retaining comparable user experiences, our implementation can allow the Wi-Fi interface to sleep for up to 88% of the total time in several different applications and reduce the system power consumption by up to 33% under the restricted programmability of current Wi-Fi hardware.

Cognitive radio (CR) networks have an ample but dynamic amount of spectrum for communications. Communication rendezvous in CR networks is the process of establishing a control channel between radios before they can communicate. Designing a communication rendezvous protocol that can take advantage of all the available spectrum at the same time is of great importance, because it alleviates load on control channels, and thus further reduces probability of collisions. In this paper, we present ETCH, efficient channel-hopping-based MAC-layer protocols for communication rendezvous in CR networks. Compared to the existing solutions, ETCH fully exploits spectrum diversity in communication rendezvous by allowing all the rendezvous channels to be utilized at the same time. We propose two protocols, SYNC-ETCH, which is a synchronous protocol assuming CR nodes can synchronize their channel hopping processes, and ASYNC-ETCH, which is an asynchronous protocol not relying on global clock synchronization. Our theoretical analysis and ns-2-based evaluation show that ETCH achieves better performances of time-to-rendezvous and throughput than the existing work.

Efficient web caching in mobile apps eliminates unnecessary network traffic, reduces web accessing latency, and improves smartphone battery life. However, recent research has indicated that current mobile apps suffer from poor implementations of web caching. In this work, we first conducted a comprehensive survey of over 1000 Android apps to identify how different types of mobile apps perform in web caching. Based on our analysis, we designed CacheKeeper, an OS web caching service transparent to mobile apps for smartphones. CacheKeeper can not only effectively reduce overhead caused by poor web caching of mobile apps, but also utilizes cross-app caching opportunities in smartphones. Furthermore, CacheKeeper is backward compatible, meaning that existing apps can take advantage of CacheKeeper without any modifications. We have implemented a prototype of CacheKeeper in Linux kernel. Evaluation on 10 top ranked Android apps shows that our CacheKeeper prototype can save 42% networks traffic with real user browsing behaviors and increase web accessing speed by 2x under real 3G settings. Experiments also show that our prototype incurs negligible overhead in most aspects on cache misses.

Mencius is a protocol for general state machine replication that tolerates crash failures. It has high performance in wide-area networks. However, the commit latency of Mencius is limited by the slowest replica. This paper presents Fast Mencius, a crash fault-tolerant state machine replication protocol, which enhances Mencius with Active Revoke and Multi-instance Propose. Active Revoke allows the non-slow replicas to proceed without being delayed by the slowest replica, while Multi-instance Propose enables the slow replicas to have their proposals chosen by the replicated state machine. Our evaluation shows that in presence of slow replicas, Fast Mencius's commit latency is significantly lower than that of Mencius, and it also achieves high throughput.

We propose HoWiES, a system that saves energy consumed by WiFi interfaces in mobile devices with the assistance of ZigBee radios. The core component of HoWiES is a WiFiZigBee message delivery scheme that enables WiFi radios to convey different messages to ZigBee radios in mobile devices. Based on the WiFi-ZigBee message delivery scheme, we design three protocols that target at three WiFi energy saving opportunities in scanning, standby and wakeup respectively. We have implemented the HoWiES system with two mobile devices platforms and two AP platforms. Our real-world experimental evaluation shows that our system can convey thousands of different messages from WiFi radios to ZigBee radios with an accuracy over 98%, and our energy saving protocols, while maintaining the comparable wakeup delay to that of the standard 802.11 power save mode, save 88% and 85% of energy consumed in scanning state and standby state respectively.

This paper presents APT, a localization system for outdoor pedestrians with smartphones. APT performs better than the built-in GPS module of the smartphone in terms of accuracy. This is achieved by introducing a robust dead reckoning algorithm and an error-tolerant algorithm for map matching. When the user is walking with the smartphone, the dead reckoning algorithm monitors steps and walking direction in real time. It then reports new steps and turns to the map-matching algorithm. Based on updated information, this algorithm adjusts the user's location on a map in an error-tolerant manner. If location ambiguity among several routes occurs after adjustments, the GPS module is queried to help eliminate this ambiguity. Evaluations in practice show that the error of our system is less than 1/2 that of GPS.

A crucial component of vehicular network security is to establish a secure wireless channel between any two vehicles. In this paper, we propose a scheme to allow two cars to extract a secret key from RSSI (Received Signal Strength Indicator) values in such a way that nearby cars cannot obtain the same secret. Our solution can be executed in noisy, outdoor vehicular environments. We also propose an online parameter learning mechanism to adapt to different channel conditions. We conduct extensive realworld experiments to validate our solution.

In this paper, we show the first comprehensive experimental study on mobile RFID reading performance based on a relatively large number of tags. By making a number of observations regarding the tag reading performance, we build a model to depict how various parameters affect the reading performance. Through our model, we have designed very efficient algorithms to maximize the time-efficiency and energy-efficiency by adjusting the reader's power and moving speed. Our experiments show that our algorithms can reduce the total scanning time by 50\% and the total energy consumption by 83\% compared to the prior solutions.

Email is a key application used on smartphones. Even when the phone is in stand-by mode, users expect the phone to continue syncing with an email server to receive new mes-sages. Each such sync operation wakes up the smartphone for data reception and processing. In this paper, we show that this "cost of email sync" in stand-by mode constitutes a significant source of energy consumption, and thus reduces battery life. We quantify the power performance of different existing email clients on two smartphone platforms, An-droid and Windows Phone, and study the impact of system parameters such as email size, inbox size, and pull vs. push. Our results show that existing email clients do not handle email sync in an energy efficient way. This is because the underlying protocols and architectures are not designed for the specific needs of operating in stand-by mode. Based on our findings, we derive general design principles for energy-efficient event handling on smartphones, and apply these principles to the case of email sync and implement our techniques on commercial smartphones. Experimental results show that our techniques are able to significantly reduce energy cost of email sync by 49.9% on average with our experiment settings.

The electric power grid is a crucial infrastructure in our society and is always a target of malicious users and attackers. In this paper, we first introduce the concept of unidentifiable attack, in which the control center cannot identify the attack even though it detects its presence. Thus, the control center cannot obtain deterministic state estimates, since there may have several feasible cases and the control center cannot simply favor one over the others. Given an unidentifiable attack, we present algorithms to enumerate all feasible cases, and propose an optimization strategy from the perspective of the control center to deal with an unidentifiable attack. Furthermore, we propose a heuristic algorithm from the view of an attacker to find good attack regions such that the number of meters required to compromise is as few as possible. We also formulate the problem that how to distinguish all feasible cases if the control center has some limited resources to verify some meters, and solve it with standard algorithms. Finally, we briefly evaluate and validate our enumerating algorithms and optimization strategy.

Distributed systems without trusted identities are particularly vulnerable to sybil attacks, where an adversary creates multiple bogus identities to compromise the running of the system. This paper presents SybilDefender, a sybil defense mechanism that leverages the network topologies to defend against sybil attacks in social networks. Based on performing a limited number of random walks within the social graphs, SybilDefender is efficient and scalable to large social networks. Our experiments on two 3,000,000 node real-world social topologies show that SybilDefender outperforms the state of the art by more than 10 times in both accuracy and running time. SybilDefender can effectively identify the sybil nodes and detect the sybil community around a sybil node, even when the number of sybil nodes introduced by each attack edge is close to the theoretically detectable lower bound. Besides, we propose two approaches to limiting the number of attack edges in online social networks. The survey results of our Facebook application show that the assumption made by previous work that all the relationships in social networks are trusted does not apply to online social networks, and it is feasible to limit the number of attack edges in online social networks by relationship rating.

As the first step of the communication procedure in 802.11, an unwise selection of the access point (AP) hurts one client's throughput. This performance downgrade is usually hard to be offset by other methods, such as efficient rate adaptations. In this paper, we study this AP selection problem in a decentralized manner, with the objective of maximizing the minimum throughput among all clients. We reveal through theoretical analysis that the selfish strategy, which commonly applies in decentralized systems, cannot effectively achieve this objective. Accordingly, we propose an online AP association strategy that not only achieves a minimum throughput (among all clients) that is provably close to the optimum, but also works effectively in practice with reasonable computation and transmission overhead. The association protocol applying this strategy is implemented on the commercial hardware and compatible with legacy APs without any modification. We demonstrate its feasibility and performance through real experiments and intensive simulations.

Accurate spectrum sensing is important in cognitive radio networks. False sensing results in either waste of spectrum or harmful interference to primary users. To improve accuracy, cooperative spectrum sensing, in which a set of secondary users cooperatively sense the presence of the primary user, has emerged. This technique, however, opens a window for malicious users and attackers, who may remotely or physically capture the sensors and manipulate the sensing reports. In this paper, we consider the attack model whereby the attacker injects self-consistent false data simultaneously, and propose a modified COI (combinatorial optimization identification) algorithm to defend against such attacks. We also provide a theorem that detection uncertainty may exist in cooperative spectrum sensing. We intensively evaluate our algorithm with simulations, and the results show that our algorithm is a good technique to complement an existing algorithm, called IRIS.

Location sharing is a fundamental component of mobile online social networks (mOSNs), which also raises significant privacy concerns. The mOSNs collect a large amount of location information over time, and the users' location privacy is compromised if their location information is abused by adversaries controlling the mOSNs. In this paper, we present MobiShare, a system that provides flexible privacy-preserving location sharing in mOSNs. MobiShare is flexible to support a variety of location-based applications, in that it enables location sharing between both trusted social relations and untrusted strangers, and it supports range query and user-defined access control. In MobiShare, neither the social network server nor the location server has a complete knowledge of the users' identities and locations. The users' location privacy is protected even if either of the entities colludes with malicious users.

Distributed systems without trusted identities are particularly vulnerable to sybil attacks, where an adversary creates multiple bogus identities to compromise the running of the system. This paper presents SybilDefender, a sybil defense mechanism that leverages the network topologies to defend against sybil attacks in social networks. Based on performing a limited number of random walks within the social graphs, SybilDefender is efficient and scalable to large social networks. Our experiments on two 3,000,000 node real-world social topologies show that SybilDefender outperforms the state of the art by one to two orders of magnitude in both accuracy and running time. SybilDefender can effectively identify the sybil nodes and detect the sybil community around a sybil node, even when the number of sybil nodes introduced by each attack edge is close to the theoretically detectable lower bound. Besides, we propose two approaches to limiting the number of attack edges in online social networks. The survey results of our Facebook application show that the assumption made by previous work that all the relationships in social networks are trusted does not apply to online social networks, and it is feasible to limit the number of attack edges in online social networks by relationship rating.

Wi-Fi tethering (i.e., sharing the Internet connection of a mobile phone via its Wi-Fi interface) is a useful functionality and is widely supported on commercial smartphones. Yet existing Wi-Fi tethering schemes consume excessive power: they keep the Wi-Fi interface in a high power state regardless if there is ongoing traffic or not. In this paper we propose DozyAP to improve the power efficiency of Wi-Fi tethering. Based on measurements in typical applications, we identify many opportunities that a tethering phone could sleep to save power. We design a simple yet reliable sleep protocol to coordinate the sleep schedule of the tethering phone with its clients without requiring tight time synchronization. Furthermore, we develop a two-stage, sleep interval adaptation algorithm to automatically adapt the sleep intervals to ongoing traffic patterns of various applications. DozyAP does not require any changes to the 802.11 protocol and is incrementally deployable through software updates. We have implemented DozyAP on commercial smartphones. Experimental results show that, while retaining comparable user experiences, our implementation can allow the Wi-Fi interface to sleep for up to 88% of the total time in several different applications, and reduce the system power consumption by up to 33% under the restricted programmability of current Wi-Fi hardware.

This paper considers vehicular rogue access points (APs) that rogue APs are set up in moving vehicles to mimic legitimate roadside APs to lure users to associate to them. Due to its mobility, a vehicular rogue AP is able to maintain a long connection with users. Thus, the adversary has more time to launch various attacks to steal users' private information. We propose a practical detection scheme based on the comparison of Receive Signal Strength (RSS) to prevent users from connecting to rogue APs. The basic idea of our solution is to force APs (both legitimate and fake) to report their GPS locations and transmission powers in beacons. Based on such information, users can validate whether the measured RSS matches the value estimated from the AP's location, transmission power, and its own GPS location. Furthermore, we consider the impact of path loss and shadowing and propose a method based on rate adaption to deal with advanced rogue APs. We implemented our detection technique on commercial off-the-shelf devices including wireless cards, antennas, and GPS modules to evaluate the efficacy of our scheme.

Recent studies have revealed security vulnerabilities in implantable medical devices (IMDs). Security design for IMDs is complicated by the requirement that IMDs remain operable in an emergency when appropriate security credentials may be unavailable. In this paper, we introduce IMDGuard, a comprehensive security scheme for heart-related IMDs to fulfill this requirement. IMDGuard incorporates two techniques tailored to provide desirable protections for IMDs. One is an ECG based key establishment without prior shared secrets, and the other is an access control mechanism resilient to adversary spoofing attacks. The security and performance of IMDGuard are evaluated on our prototype implementation.

In a dynamic spectrum access (DSA) network, communication rendezvous is the first step for two secondary users to be able to communicate with each other. In this step, the pair of secondary users meet on the same channel, over which they negotiate on the communication parameters, to establish the communication link. This paper presents ETCH, Efficient Channel Hopping based MAC-layer protocols for communication rendezvous in DSA networks. We propose two protocols, SYNC-ETCH and ASYNC-ETCH. Both protocols achieve better time-to-rendezvous and throughput compared to previous work.

We consider a hybrid two-tiered sensor network consisting of regular sensors and special sensors with large storage capacity, called storage nodes. In this structure, regular sensors "push” their raw data to nearby storage nodes and the sink diffuses queries only to storage nodes and "pull” the reply from them. We investigate security and privacy threats when the sensor network is deployed in an untrusted or hostile environment. The major concern is that storage nodes might easily become the target for the adversary to compromise due to their important role. A compromised storage node may leak the data stored there to the adversary breaching the data privacy. Also, it may send wrong information as the reply to a query breaking the data integrity. This paper focuses on range query, a fundamental operation in a sensor network. The solution framework includes a privacy-preserving storage scheme which utilizes a bucketing technique to mix the data in a certain range, and a verifiable query protocol which employs encoding numbers to enable the sink to validate the reply. We further study the performance of event detection, an application implemented by range query. Our simulation results illustrate that our schemes are efficient for communication and effective for privacy and security protection.

This paper considers a category of rogue access points (APs) that pretend to be legitimate APs to lure users to connect to them. We propose a practical timing-based technique that allows the user to avoid connecting to rogue APs. Our detection scheme is a client-centric approach that employs the round trip time between the user and the DNS server to independently determine whether an AP is a rogue AP without assistance from the WLAN operator. We implemented our detection technique on commercially available wireless cards to evaluate their performance. Extensive experiments have demonstrated the accuracy, effectiveness, and robustness of our approach. The algorithm achieves close to 100 percent accuracy in distinguishing rogue APs from legitimate APs in lightly loaded traffic conditions, and larger than 60 percent accuracy in heavy traffic conditions. At the same time, the detection only requires less than 1 second for lightly-loaded traffic conditions and tens of seconds for heavy traffic conditions.

Deploying road-side WiFi access points has made possible internet access in a vehicle, nevertheless it is challenging to maintain client performance at vehicular speed especially when multiple mobile users exist. This paper considers the association control problem for vehicular WiFi access in the Drive-thru Internet scenario. In particular, we aim to improve the efficiency and fairness for all users. We design efficient algorithms to achieve these objectives through several techniques including approximation. Our simulation results demonstrate that our algorithms can achieve significantly better performance than conventional approaches.

Radio Frequency IDentification (RFID) technology has attracted much attention due to its variety of applications, e.g., inventory control and object tracking. One important problem in RFID systems is how to quickly estimate the number of distinct tags without reading each tag individually. This problem plays a crucial role in many real-time monitoring and privacy-preserving applications. In this paper, we present an efficient and anonymous scheme for tag population estimation. This scheme leverages the position of the first reply from a group of tags in a frame. Results from mathematical analysis and extensive simulation demonstrate that our scheme outperforms other protocols proposed in the previous work.

RFID is an emerging technology with many potential applications such as inventory management for supply chain. In practice, these applications often need a series of continuous scanning operations to accomplish a task. For example, if one wants to scan all the products with RFID tags in a large warehouse, given a limited reading range of an RFID reader, multiple scanning operations have to be launched at different locations to cover the whole warehouse. Usually, this series of scanning operations are not completely independent as some RFID tags can be read by multiple processes. Simply scanning all the tags in the reading range during each process is inefficient because it collects a lot of redundant data and consumes a long time. In this paper, we develop efficient schemes for continuous scanning operations defined in both spatial and temporal domains. Our basic idea is to fully utilize the information gathered in the previous scanning operations to reduce the scanning time of the succeeding ones. We illustrate in the evaluation that our algorithms dramatically reduce the total scanning time when compared with other solutions.

In this paper we consider how to efficiently identify tags on the moving conveyor. Considering conditions like the path loss and multi-path effect in realistic settings, we first propose a probabilistic model for RFID tag identification. Based on this model, we propose efficient solutions to identify moving RFID tags, according to the fixed-path mobility on the conveyor. A dynamic program based solution and an adaptive solution are proposed to select optimized frame sizes during the query cycles. Simulation results indicate that by leveraging the probabilistic model our solutions can achieve much better performance than using parameters for the ideal propagation situations.

In a Wireless Local Area Network (WLAN), the Access Point (AP) selection of a client heavily influences the performance of its own and others. Through theoretical analysis, we reveal that previously proposed association protocols are not effective in maximizing the minimal throughput among all clients. Accordingly, we propose an online AP association strategy that not only achieves a minimal throughput (among all clients) that is provably close to the optimum, but also works effectively in practice with a reasonable computational overhead. The association protocol applying this strategy is implemented on the commercial hardware and compatible with legacy APs without any modification. We demonstrate its feasibility and performance through real experiments.

In this article, we present Microsearch, a search system suitable for embedded devices used in ubiquitous computing environments. Akin to a desktop search engine, Microsearch indexes the information inside a small device, and accurately resolves a user's queries. Given the limited hardware, conventional search engine design and algorithms cannot be used. We adopt Information Retrieval (IR) techniques for query resolution, and proposed a new space-efficient top- k query resolution algorithm. A theoretical model of Microsearch is given to better understand the trade-offs in design parameters. Evaluation is done via actual implementation on off-the-shelf hardware.

Data storage has become an important issue in sensor networks as a large amount of collected data need to be archived for future information retrieval. Storage nodes are introduced in this paper to store the data collected from the sensors in their proximities. The storage nodes alleviate the heavy load of transmitting all data to a central place for archiving and reduce the communication cost induced by the network query. The objective of this paper is to address the storage node placement problem aiming to minimize the total energy cost for gathering data to the storage nodes and replying queries. We examine deterministic placement of storage nodes and present optimal algorithms based on dynamic programming. Further, we give stochastic analysis for random deployment and conduct simulation evaluation for both deterministic and random placements of storage nodes.

Embedding small devices into everyday objects like toasters and coffee mugs creates a wireless network of objects. These embedded devices can contain a description of the underlying objects, or other user defined information. In this paper, we present Snoogle, a search engine for such a network. A user can query Snoogle to find a particular mobile object, or a list of objects that fit the description. Snoogle uses information retrieval techniques to index information and process user queries, and Bloom filters to reduce communication overhead. Security and privacy protections are also engineered into Snoogle to protect sensitive information. We have implemented a prototype of Snoogle using off-the-shelf sensor motes, and conducted extensive experiments to evaluate the system performance.

As RFID tags become more widespread, new approaches for managing larger numbers of RFID tags will be needed. In this paper, we consider the problem of how to accurately and efficiently monitor a set of RFID tags for missing tags. Our approach accurately monitors a set of tags without collecting IDs from them. It differs from traditional research which focuses on faster ways for collecting IDs from every tag. We present two monitoring protocols, one designed for a trusted reader and the other for an untrusted reader.

Deploying city-wide 802.11 access points has made possible internet access in a vehicle, nevertheless it is challenging to maintain client performance at vehicular speed especially when multiple mobile users exist. This paper considers the association control problem for vehicular networks in drive-thru Internet scenarios. In particular, we aim to improve the overall throughput and fairness for all users. We design efficient algorithms to achieve the objectives through several techniques including approximation. Our simulation results confirm the performance of our algorithms.

This paper considers a category of rogue access points (APs) that pretend to be legitimate APs to lure users to connect to them. We propose a practical timing based technique that allows the user to avoid connecting to rogue APs. Our method employs the round trip time between the user and the DNS server to independently determine whether an AP is legitimate or not without assistance from the WLAN operator. We implemented our detection technique on commercially available wireless cards to evaluate their performance.

A body sensor network (BSN) is a network of sensors deployed on a person's body for health care monitoring. Since the sensors collect personal medical data, security and privacy are important components in a BSN. In this paper, we developed IBE-Lite, a lightweight identity-based encryption suitable for sensors in a BSN. We present protocols based on IBE-Lite that balance security and privacy with accessibility and perform evaluation using experiments conducted on commercially available sensors.

As RFID tags become more widespread, new approaches for managing larger numbers of RFID tags will be needed. In this paper, we consider the problem of how to accurately and efficiently monitor a set of RFID tags for missing tags. Our approach accurately monitors a set of tags without collecting IDs from them. It differs from traditional research which focuses on faster ways for collecting IDs from every tag. We present two monitoring protocols, one designed for a trusted reader and another for an untrusted reader.

While symmetric-key schemes are efficient in processing time for sensor networks, they generally require complicated key management, which may introduce large memory and communication overhead. On the contrary, public-key based schemes have simple and clean key management, but cost more computational time. The recent progress of elliptic curve cryptography (ECC) implementation on sensors motivates us to design a public-key scheme and compare its performance with the symmetric-key counterparts. This paper builds the user access control on commercial off-the-shelf sensor devices as a case study to show that the public-key scheme can be more advantageous in terms of the memory usage, message complexity, and security resilience. Meanwhile, our work also provides insights in integrating and designing public-key based security protocols for sensor networks.

We consider a sensor network that is not fully trusted and ask the question how we preserve privacy for the collected data and how we verify the data reply from the network. We explore the problem in the context of a network augmented with storage nodes and target at range query. We use bucketing scheme to mix the data for a range, use message encryption for data integrity, and employ encoding numbers to prevent the storage nodes from dropping data.

Hardware advances will allow us to embed small devices into everyday objects such as toasters and coffee mugs, thus naturally form a wireless object network that connects the object with each other. This paper presents Snoogle, a search engine for such a network. Snoogle uses information retrieval techniques to index information and process user queries, and compression schemes such as Bloom filters to reduce communication overhead. Snoogle also considers security and privacy protections for sensitive data. We have implemented the system prototype on off-the-shelf sensor motes, and conducted extensive experiments to evaluate the system performance.

As RFID tags are increasingly attached to everyday items, it quickly becomes impractical to collect data from every tag in order to extract useful information. In this paper, we consider the problem of identifying popular categories of RFID tags out of a large collection of tags, without reading all the tag data. We propose two algorithms based on the idea of group testing, which allows us to efficiently derive popular categories of tags. We evaluate our solutions using both theoretical analysis and simulation.

A body sensor network (BSN), is a network of sensors deployed on a person's body, usually for health care monitoring. Since the sensors collect personal medical data, security and privacy are important components in a body sensor network. At the same time, the collected data has to readily available in the event of an emergency. In this paper, we present IBE-Lite, a lightweight identity-based encryption suitable for sensors, and developed protocols based on IBE-Lite for a BSN.

With the increased popularity of RFID applications, different authentication schemes have been proposed to provide security and privacy protection for users. Most recent RFID protocols use a central database to store the RFID tag data. The RFID reader first queries the RFID tag and returns the reply to the database. After authentication, the database returns the tag data to the reader. In this paper, we propose a more flexible authentication protocol that provides comparable protection without the need for a central database. We also suggest a protocol for secure search for RFID tags. We believe that as RFID applications become widespread, the ability to securely search for RFID tags will be increasingly useful.

Outlier detection has many important applications in sensor networks, e.g., abnormal event detection, animal behavior change, etc. It is a difficult problem since global information about data distributions must be known to identify outliers. In this paper, we use a histogram-based method for outlier detection to reduce communication cost. Rather than collecting all the data in one location for centralized processing, we propose collecting hints (in the form of a histogram) about the data distribution, and using the hints to filter out unnecessary data and identify potential outliers. We show that this method can be used for detecting outliers in terms of two different definitions. Our simulation results show that the histogram method can dramatically reduce the communication cost.

With the increasing popularity of RFID applications, different authentication schemes have been proposed to provide security and privacy protection to users. Most recent RFID protocols use a central database to store the RFID tag data. An RFID reader first queries the RFID tag and returns the reply to the database. After authentication, the database returns the tag data to the reader. In this paper, we proposed a more flexible authentication protocol that provides comparable protection without the need for a central database. We also suggest a protocol for secure search for RFID tags. We believe that as RFID applications become widespread, the ability to search for RFID tags will be increasingly useful

As a family of wireless local area network (WLAN) protocols between physical layer and higher layer protocols, IEEE 802.11 has to accommodate the features and requirements of both ends. However, current practice has addressed the problems of these two layers separately and is far from satisfactory. On one end, due to varying channel conditions, WLANs have to provide multiple physical channel rates to support various signal qualities. A low channel rate station not only suffers low throughput, but also significantly degrades the throughput of other stations. On the other end, the power saving mechanism of 802.11 is ineffective in TCP-based communications, in which the wireless network interface (WNI) has to stay awake to quickly acknowledge senders, and hence, the energy is wasted on channel listening during idle awake time. In this paper, considering the needs of both ends, we utilize the idle communication power of the WNI to provide a Cooperative Relay Service (CRS) for WLANs with multiple channel rates. We characterize energy efficiency as energy per bit, instead of energy per second. In CRS, a high channel rate station relays data frames as a proxy between its neighboring stations with low channel rates and the Access Point, improving their throughput and energy efficiency. Different from traditional relaying approaches, CRS compensates a proxy for the energy consumed in data forwarding. The proxy obtains additional channel access time from its clients, leading to the increase of its own throughput without compromising its energy efficiency. Extensive experiments are conducted through a prototype implementation and ns-2 simulations to evaluate our proposed CRS. The experimental results show that CRS achieves significant performance improvements for both low and high channel rate stations

Object detection quality and network lifetime are two conflicting aspects of a sensor network, but both are critical to many sensor applications such as military surveillance. Partial coverage, where a sensing field is partially sensed by active sensors at any time, is an appropriate approach to balancing the two conflicting design requirements of monitoring applications. Under partial coverage, we develop an analytical framework for object detection in sensor networks, and mathematically analyze average-case object detection quality in random and synchronized sensing scheduling protocols. Our analytical framework facilitates performance evaluation of a sensing schedule, network deployment, and sensing scheduling protocol design. Furthermore, we propose three wave sensing scheduling protocols to achieve bounded worst-case object detection quality. We justify the correctness of our analyses through rigorous proof, and validate the effectiveness of the proposed protocols through extensive simulation experiments

Abstract — As a family of wireless local area network (WLAN) protocols between physical layer and higher-layer protocols, IEEE 802.11 has to accommodate the features and requirements of both ends. However, current practice has addressed the problems separately and is far from being satisfactory. On the one end, due to varying channel conditions, WLANs have to provide multiple data channel rates to support various bit error rates. A low channel rate station not only suffers low throughput itself, but also significantly degrades the throughput of other stations. On the other end, TCP is not energy efficient running on 802.11. This is because a wireless network interface (WNI) has to stay awake to generate timely acknowledgments during a TCP session, and hence, the energy consumed during idle awake time is wasted for channel listening. In this paper, considering the needs of both ends, we utilize the idle communication power of the WNI to improve the throughput and energy efficiency of stations in WLANs supporting multiple channel rates. We characterize the energy efficiency as energy per bit, instead of energy per second. Based on modeling and analysis, we propose a data forwarding mechanism and an energy-aware channel allocation mechanism. In such a system, a high channel rate station relays data frames between its neighboring stations with low channel rates and Access Point, improving their throughput and energy efficiency. Different from traditional relaying approaches, our scheme compensates for the energy consumption for data forwarding. The forwarding station gets additional channel access time from its beneficiaries, leading to the increase of its own throughput without compromising its energy efficiency. We implement a prototype of our proposed system and evaluate it through extensive experiments. Our results show significant performance improvements for both low and high channel rate stations. I.

Data storage has become an important issue in sensor networks as a large amount of collected data need to be archived for future information retrieval. This paper introduces storage nodes to store the data collected from the sensors in their proximities. The storage nodes alleviate the heavy load of transmitting all the data to a central place for archiving and reduce the communication cost induced by the network query. This paper considers the storage node placement problem aiming to minimize the total energy cost for gathering data to the storage nodes and replying queries. We examine deterministic placement of storage nodes and present optimal algorithms based on dynamic programming. Further, we give stochastic analysis for random deployment and conduct simulation evaluation for both deterministic and random placements of storage nodes.

Global synchronization is important for many sensor network applications that require precise mapping of collected sensor data with the time of the events, for example, in tracking and surveillance. It also plays an important role in energy conservation in MAC layer protocols. This paper describes four methods to achieve global synchronization in a sensor network: a node-based approach, a hierarchical cluster-based method, a diffusion-based method, and a fault-tolerant diffusion-based method. The diffusion-based protocol is fully localized. We present two implementations of the diffusion-based protocol for synchronous and asynchronous systems and prove its convergence. Finally, we show that, by imposing some constraints on the sensor network, global clock synchronization can be achieved in the presence of malicious nodes that exhibit Byzantine failures.

We develop distributed algorithms for adaptive sensor networks that respond to directing a target through a region of space. We model this problem as an online distributed motion planning problem. Each sensor node senses values in its perception space and has the ability to trigger exceptions events we call “danger” and model as “obstacles”. The danger/obstacle landscape changes over time. We present algorithms for computing distributed maps in perception space and for using these maps to compute adaptive paths for a mobile node that can interact with the sensor network. We give the analysis to the protocol and report on hardware experiments using a physical sensor network consisting of Mote sensors. We also show how to reduce searching space and communication cost using Voronoi diagram.

Global synchronization is crucial to many sensor network applications that require precise mapping of the collected sensor data with the time of the events, for example in tracking and surveillance. It also plays an important role in energy conservation in MAC layer protocols. This paper discusses three methods to achieve global synchronization in a sensor network: a node-based approach, a hierarchical cluster-based method, and a fully localized diffusion-based method. We also give the synchronous and asynchronous implementations of the diffusion-based protocols.

We develop distributed algorithms for self-organizing sensor networks that respond to directing a target through a region. The sensor network models the danger levels sensed across its area and has the ability to adapt to changes. It represents the dangerous areas as obstacles. A protocol that combines the artificial potential field of the sensors with the goal location for the moving object guides the object incrementally across the network to the goal, while maintaining the safest distance to the danger areas. We give the analysis to the protocol and report on hardware experiments using a physical sensor network consisting of Mote sensors.

Abstract This paper discusses online power‐aware routing in large wireless ad hoc networks (especially sensor networks) for applications in which the message sequence is not known. We seek to optimize the lifetime of the network. We show that online power‐aware routing does not have a constant competitive ratio to the off‐line optimal algorithm. We develop an approximation algorithm calledmax–minzPminthat has a good empirical competitive ratio. To ensure scalability, we introduce a second online algorithm for power‐aware routing. This hierarchical algorithm is called zone‐based routing. Our experiments show that its performance is quite good. Finally, we describe a distributed version of this algorithm that does not depend on any centralization. Copyright © 2003 John Wiley & Sons, Ltd.

This paper discusses online power-aware routing in large wireless ad-hoc networks for applications where the message sequence is not known. We seek to optimize the lifetime of the network. We show that online power-aware routing does not have a constant competitive ratio to the off-line optimal algorithm. We develop an approximation algorithm called max-min zPmin that has a good empirical competitive ratio. To ensure scalability, we introduce a second online algorithm for power-aware routing. This hierarchical algorithm is called zone-based routing. Our experiments show that its performance is quite good.

An ad-hoc network is formed by a group of mobile hosts upon a wireless network interface. Previous research in this area has concentrated on routing algorithms which are designed for fully connected networks. The usual way to deal with a disconnected ad-hoc network is to let the mobile computer wait for network reconnection passively, which may lead to unacceptable transmission delays. In this paper, we propose an approach that guarantees message transmission in minimal time. In this approach, mobile hosts actively modify their trajectories to transmit messages. We develop algorithms that minimize the trajectory modifications under two different assumptions: (a) the movements of all the nodes in the system are known and (b) the movements of the hosts in the system are not known.