Showing papers in "IEEE Transactions on Mobile Computing in 2019"
TL;DR: This study paves the way for operators of smart environments to monitor their IoT assets for presence, functionality, and cyber-security without requiring any specialized devices or protocols.
Abstract: The Internet of Things (IoT) is being hailed as the next wave revolutionizing our society, and smart homes, enterprises, and cities are increasingly being equipped with a plethora of IoT devices. Yet, operators of such smart environments may not even be fully aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. In this paper, we address this challenge by developing a robust framework for IoT device classification using traffic characteristics obtained at the network level. Our contributions are fourfold. First, we instrument a smart environment with 28 different IoT devices spanning cameras, lights, plugs, motion sensors, appliances, and health-monitors. We collect and synthesize traffic traces from this infrastructure for a period of six months, a subset of which we release as open data for the community to use. Second, we present insights into the underlying network traffic characteristics using statistical attributes such as activity cycles, port numbers, signalling patterns, and cipher suites. Third, we develop a multi-stage machine learning based classification algorithm and demonstrate its ability to identify specific IoT devices with over 99 percent accuracy based on their network activity. Finally, we discuss the trade-offs between cost, speed, and performance involved in deploying the classification framework in real-time. Our study paves the way for operators of smart environments to monitor their IoT assets for presence, functionality, and cyber-security without requiring any specialized devices or protocols.
452 citations
TL;DR: Splicer, a software-based system that derives high-resolution power delay profiles by splicing the CSI measurements from multiple WiFi frequency bands is presented and a set of key techniques to separate the mixed hardware errors from the collected CSI measurements are proposed.
Abstract: Power delay profiles characterize multipath channel features, which are widely used in motion- or localization-based applications. The performance of power delay profile obtained using commodity Wi-Fi devices is limited by two dominating factors. The resolution of the derived power delay profile is determined by the channel bandwidth, which is however limited on commodity WiFi. The collected CSI reflects the signal distortions due to both the channel attenuation and the hardware imperfection. A direct derivation of power delay profiles using raw CSI measures, as has been done in the literature, results in significant inaccuracy. In this paper, we present Splicer, a software-based system that derives high-resolution power delay profiles by splicing the CSI measurements from multiple WiFi frequency bands. We propose a set of key techniques to separate the mixed hardware errors from the collected CSI measurements. Splicer adapts its computations within stringent channel coherence time and thus can perform well in the presence of mobility. Our experiments with commodity WiFi NICs show that Splicer substantially improves the accuracy in profiling multipath characteristics, reducing the errors of multipath distance estimation to be less than $2\;\mathrm{m}$2m. Splicer can immediately benefit upper-layer applications. Our case study with recent single-AP localization achieves a median localization error of $0.95\;\mathrm{m}$0.95m.
274 citations
TL;DR: This paper proposes a new deep learning based approach, i.e., attention based bi-directional long short-term memory (ABLSTM) for passive human activity recognition using WiFi CSI signals, employed to learn representative features in two directions from raw sequential CSI measurements.
Abstract: Human activity recognition can benefit various applications including healthcare services and context awareness. Since human actions will influence WiFi signals, which can be captured by the channel state information (CSI) of WiFi, WiFi CSI based human activity recognition has gained more and more attention. Due to the complex relationship between human activities and WiFi CSI measurements, the accuracies of current recognition systems are far from satisfactory. In this paper, we propose a new deep learning based approach, i.e., attention based bi-directional long short-term memory (ABLSTM), for passive human activity recognition using WiFi CSI signals. The BLSTM is employed to learn representative features in two directions from raw sequential CSI measurements. Since the learned features may have different contributions for final activity recognition, we leverage on an attention mechanism to assign different weights for all the learned features. Real experiments have been carried out to evaluate the performance of the proposed ABLSTM for human activity recognition. The experimental results show that our proposed ABLSTM is able to achieve the best recognition performance for all activities when compared with some benchmark approaches.
264 citations
TL;DR: An energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time is provided and the eDors algorithm can effectively reduce EEC by optimally adjusting CPU clock frequency of SMDs in local computing, and adapting the transmission power for wireless channel conditions in cloud computing.
Abstract: Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy for smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto resource-rich cloud. However, how to achieve energy-efficient computation offloading under hard constraint for application completion time remains a challenge. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into an energy-efficiency cost (EEC) minimization problem while satisfying task-dependency requirement and completion time deadline constraint. We then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control, and transmission power allocation. Next, we show that computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, we provide experimental results in a real testbed and demonstrate that the eDors algorithm can effectively reduce EEC by optimally adjusting CPU clock frequency of SMDs in local computing, and adapting the transmission power for wireless channel conditions in cloud computing.
261 citations
TL;DR: This paper investigates the problem of multi-user computation offloading for mobile cloud computing under dynamic environment, wherein mobile users become active or inactive dynamically, and the wireless channels for mobile users to offload computation vary randomly.
Abstract: Driven by the growing popularity of mobile applications, mobile cloud computing has been envisioned as a promising approach to enhance computation capability of mobile devices and reduce the energy consumptions. In this paper, we investigate the problem of multi-user computation offloading for mobile cloud computing under dynamic environment, wherein mobile users become active or inactive dynamically, and the wireless channels for mobile users to offload computation vary randomly. As mobile users are self-interested and selfish in offloading computation tasks to the mobile cloud, we formulate the mobile users’ offloading decision process under dynamic environment as a stochastic game. We prove that the formulated stochastic game is equivalent to a weighted potential game which has at least one Nash Equilibrium (NE). We quantify the efficiency of the NE, and further propose a multi-agent stochastic learning algorithm to reach the NE with a guaranteed convergence rate (which is also analytically derived). Finally, we conduct simulations to validate the effectiveness of the proposed algorithm and evaluate its performance under dynamic environment.
204 citations
TL;DR: This paper proposes a personalized privacy-preserving task allocation framework for mobile crowdsensing that can allocate tasks effectively while providing personalized location privacy protection, and proposes a Vickrey Payment Determination Mechanism to determine the appropriate payment to each winner by considering its movement cost and privacy level.
Abstract: Location information of workers are usually required for optimal task allocation in mobile crowdsensing, which however raises severe concerns of location privacy leakage. Although many approaches have been proposed to protect the locations of users, the location protection for task allocation in mobile crowdsensing has not been well explored. In addition, to the best of our knowledge, none of existing privacy-preserving task allocation mechanisms can provide personalized location protection considering different protection demands of workers. In this paper, we propose a personalized privacy-preserving task allocation framework for mobile crowdsensing that can allocate tasks effectively while providing personalized location privacy protection. The basic idea is that each worker uploads the obfuscated distances and personal privacy level to the server instead of its true locations or distances to tasks. In particular, we propose a Probabilistic Winner Selection Mechanism (PWSM) to minimize the total travel distance with the obfuscated information from workers, by allocating each task to the worker who has the largest probability of being closest to it. Moreover, we propose a Vickrey Payment Determination Mechanism (VPDM) to determine the appropriate payment to each winner by considering its movement cost and privacy level, which satisfies the truthfulness, profitability, and probabilistic individual rationality. Extensive experiments on the real-world datasets demonstrate the effectiveness of the proposed mechanisms.
170 citations
TL;DR: In this article, the authors studied two fast UAV deployment problems: one is to minimize the maximum deployment delay among all UAVs (min-max) for fairness consideration, and the other is minimizing the total deployment delay (minsum) for efficiency consideration.
Abstract: Unmanned Aerial Vehicle (UAV) networks have emerged as a promising technique to rapidly provide wireless coverage to a geographical area, where a flying UAV can be fast deployed to serve as cell site. Existing work on UAV-enabled wireless networks overlook the fast UAV deployment for wireless coverage, and such deployment problems have only been studied recently in sensor networks. Unlike sensors, UAVs should be deployed to the air and they are generally different in flying speed, operating altitude and wireless coverage radius. By considering such UAV heterogeneity to cover the whole target area, this paper studies two fast UAV deployment problems: one is to minimize the maximum deployment delay among all UAVs (min-max) for fairness consideration, and the other is to minimize the total deployment delay (min-sum) for efficiency consideration. We prove both min-max and min-sum problems are NP-complete in general. When dispatching UAVs from the same location, we present an optimal algorithm of low computational complexity $O(n^2)$ for the min-max problem. When UAVs are dispatched from different locations, we propose to preserve their location order during deployment and successfully design a fully polynomial time approximation scheme (FPTAS) of computation complexity $O(n^2 \log \frac{1}{\epsilon })$ to arbitrarily approach the global optimum with relative error $\epsilon$ . The min-sum problem is more challenging. When UAVs are dispatched from the same initial location, we present an approximation algorithm of linear time. As for the general case, we further reformulate it as a dynamic program and propose a pseudo polynomial-time algorithm to solve it optimally.
150 citations
TL;DR: This article proposes a joint collaborative caching and processing framework that supports Adaptive Bitrate (ABR)-video streaming in MEC networks and proposes practically efficient solutions, including a novel heuristic ABR-aware proactive cache placement algorithm when video popularity is available.
Abstract: Mobile-Edge Computing (MEC) is a promising paradigm that provides storage and computation resources at the network edge in order to support low-latency and computation-intensive mobile applications. In this article, we propose a joint collaborative caching and processing framework that supports Adaptive Bitrate (ABR)-video streaming in MEC networks. We formulate an Integer Linear Program (ILP) that determines the placement of video variants in the caches and the scheduling of video requests to the cache servers so as to minimize the expected delay cost of video retrieval. The considered problem is challenging due to its NP-completeness and to the lack of a-priori knowledge about video request arrivals. Our approach decomposes the original problem into a cache placement problem and a video request scheduling problem while preserving the interplay between the two. We then propose practically efficient solutions, including: (i) a novel heuristic ABR-aware proactive cache placement algorithm when video popularity is available, and (ii) an online low-complexity video request scheduling algorithm that performs very closely to the optimal solution. Simulation results show that our proposed solutions achieve significant increase in terms of cache hit ratio and decrease in backhaul traffic and content access delay compared to the traditional approaches.
144 citations
TL;DR: A new holistic vision-based mobile assistive navigation system to help blind and visually impaired people with indoor independent travel and an efficient obstacle detection and avoidance approach based on a time-stamped map Kalman filter (TSM-KF) algorithm are presented.
Abstract: This paper presents a new holistic vision-based mobile assistive navigation system to help blind and visually impaired people with indoor independent travel. The system detects dynamic obstacles and adjusts path planning in real-time to improve navigation safety. First, we develop an indoor map editor to parse geometric information from architectural models and generate a semantic map consisting of a global 2D traversable grid map layer and context-aware layers. By leveraging the visual positioning service (VPS) within the Google Tango device, we design a map alignment algorithm to bridge the visual area description file (ADF) and semantic map to achieve semantic localization. Using the on-board RGB-D camera, we develop an efficient obstacle detection and avoidance approach based on a time-stamped map Kalman filter (TSM-KF) algorithm. A multi-modal human-machine interface (HMI) is designed with speech-audio interaction and robust haptic interaction through an electronic SmartCane. Finally, field experiments by blindfolded and blind subjects demonstrate that the proposed system provides an effective tool to help blind individuals with indoor navigation and wayfinding.
132 citations
TL;DR: By leveraging the implicit spatiotemporal correlations among heterogeneous tasks, this work proposes a two-stage HMTA problem-solving approach to effectively handle multiple concurrent tasks in a shared resource pool and evaluates the approach extensively using two large-scale real-world data sets.
Abstract: Mobile crowdsensing (MCS) is a new paradigm to collect sensing data and infer useful knowledge over a vast area for numerous monitoring applications. In urban environments, as more and more applications need to utilize multi-source sensing information, it is almost indispensable to develop a generic mechanism supporting multiple concurrent MCS task assignment. However, most existing multi-task assignment methods focus on homogeneous tasks. Due to the diverse spatiotemporal task requirements and sensing contexts, MCS tasks often differ from each other in many aspects (e.g., spatial coverage, temporal interval). To this end, in the paper, we present and formalize an important Heterogeneous Multi-Task Assignment (HMTA) problem in mobile crowdsensing systems, and try to maximize data quality and minimize total incentive budget. By leveraging the implicit spatiotemporal correlations among heterogeneous tasks, we propose a two-stage HMTA problem-solving approach to effectively handle multiple concurrent tasks in a shared resource pool. Finally, in order to improve the assignment search efficiency, a decomposition-and-combination framework is devised to accommodate large-scale problem scenario. We evaluate our approach extensively using two large-scale real-world data sets. The experimental results validate the effectiveness and efficiency of our proposed approach.
109 citations
TL;DR: A cooperative V2V-aided transmission scheme based on a coalitional game (CVCG) is proposed that allows the OBUs to cooperate with their neighbors to provide the missing popular content.
Abstract: As one of the key services for non-safety applications in Vehicular Ad-hoc Networks (VANETs), the Popular Content Distribution (PCD) has become a hot issue in recent years. In popular content distribution, the On-Board Units (OBUs) passing the Area of Interest (AoI) receive popular content broadcast by the RoadSide Units (RSUs). However, due to the high speed of OBUs, limited bandwidth, and unstable wireless connections, only a portion of the popular content can be received by OBUs. To address this issue, in this paper, a cooperative V2V-aided transmission scheme based on a coalitional game (CVCG) is proposed. The scheme allows the OBUs to cooperate with their neighbors to provide the missing popular content. In addition, a coalition graph game algorithm is designed for optimizing the cooperative behaviors among OBUs. The performance of our CVCG scheme is evaluated by different metrics compared to other three content distribution schemes. The numerical results show that the proposed CVCG scheme could outperform the three schemes in terms of the number of iterations for 99 percent finished PCD, the average content completion percentage, and the number of completed OBUs.
TL;DR: It is proved that DADP can provide real-time crowd-sourced statistical data publishing with strong privacy protection under an untrusted server and a distributed budget allocation mechanism and an agent-based dynamic grouping mechanism to realize global $w-event $\epsilon$ε-differential privacy in a distributed way.
Abstract: The continuous publication of aggregate statistics over crowd-sourced data to the public has enabled many data mining applications (e.g., real-time traffic analysis). Existing systems usually rely on a trusted server to aggregate the spatio-temporal crowd-sourced data and then apply differential privacy mechanism to perturb the aggregate statistics before publishing to provide strong privacy guarantee. However, the privacy of users will be exposed once the server is hacked or cannot be trusted. In this paper, we study the problem of real-time crowd-sourced statistical data publishing with strong privacy protection under an untrusted server. We propose a novel distributed agent-based privacy-preserving framework, called DADP, that introduces a new level of multiple agents between the users and the untrusted server. Instead of directly uploading the check-in information to the untrusted server, a user can randomly select one agent and upload the check-in information to it with the anonymous connection technology. Each agent aggregates the received crowd-sourced data and perturbs the aggregated statistics locally with Laplace mechanism. The perturbed statistics from all the agents are further combined together to form the entire perturbed statistics for publication. In particular, we propose a distributed budget allocation mechanism and an agent-based dynamic grouping mechanism to realize global $w$w-event $\epsilon$e-differential privacy in a distributed way. We prove that DADP can provide $w$w-event $\epsilon$e-differential privacy for real-time crowd-sourced statistical data publishing under the untrusted server. Extensive experiments on real-world datasets demonstrate the effectiveness of DADP.
TL;DR: 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 is proposed.
Abstract: 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.
TL;DR: This work proposes a novel decomposition of in-cell and inter-cell data traffic, and applies a graph-based deep learning approach to accurate cellular traffic prediction, and reveals intensive spatio-temporal dependency even among distant cell towers, which is largely overlooked in previous works.
Abstract: Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers, and city authorities Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviors and frequent user mobility citywide In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 15 million users and 5,929 cell towers in a major city of China We reveal intensive spatio-temporal dependency even among distant cell towers, which is largely overlooked in previous works To explicitly characterize and effectively model the spatio-temporal dependency of urban cellular traffic, we propose a novel decomposition of in-cell and inter-cell data traffic, and apply a graph-based deep learning approach to accurate cellular traffic prediction Experimental results demonstrate that our method consistently outperforms the state-of-the-art time-series based approaches and we also show through an example study how the decomposition of cellular traffic can be used for event inference
TL;DR: This paper proposes source-based and destination-based multipath cooperative routing algorithms, which deliver different parts of a data flow along multiple link-disjoint paths dynamically and cooperatively, and designs an efficient No-Stop-Wait ACK mechanism for the NCMCR protocol to accelerate the data transmission.
Abstract: Multipath routing can significantly improve the network throughput and end-to-end (e2e) delay. Network coding based multipath routing removes the complicated coordination among multiple paths so that it further enhances data transmission efficiency. Traditional network coding based multipath routing protocols, however, are inefficient for Low Earth Orbit (LEO) satellite networks with the long link delay and regular network topology . Considering these characteristics, in this paper, we first formulate the multipath cooperative routing problem, then propose a Network Coding based Multipath Cooperative Routing (NCMCR) protocol for LEO satellite networks to improve the throughput. We propose source-based and destination-based multipath cooperative routing algorithms, which deliver different parts of a data flow along multiple link-disjoint paths dynamically and cooperatively. Furthermore, we design an efficient No-Stop-Wait ACK mechanism for our NCMCR protocol to accelerate the data transmission, where a source node continuously sends subsequent batches before it receives ACK messages for the batches sent previously. Under the proposed acknowledgement mechanism, we theoretically analyze the number of coded packets that should be sent and the transmission times of each batch for successfully decoding a batch. NS2-based simulation results demonstrate that our NCMCR outperforms the most related protocols.
TL;DR: WiGest is a system that leverages changes in WiFi signal strength to sense in-air hand gestures around the user's mobile device, and is robust to the presence of other interfering humans, highlighting WiGest's ability to enable future ubiquitous hands-free gesture-based interaction with mobile devices.
Abstract: We present WiGest: a system that leverages changes in WiFi signal strength to sense in-air hand gestures around the user's mobile device. WiGest uses standard WiFi equipment, with no modifications, and requires no training for gesture recognition. The system identifies different RSS change primitives, from which we construct mutually-independent gesture families. These families can be mapped to distinguishable application actions. More fine-grained features can also be recognized for the detected primitives using CSI. WiGest addresses various challenges including cleaning the noisy signals, gesture type and attribute detection, reducing false positives due to interfering humans, and adapting to changing signal polarity. We implement a proof-of-concept prototype using off-the-shelf devices and extensively evaluate the system in two different environments. Our results show that WiGest detects the basic primitives with an accuracy of 87.5 percent using one AP, including through-the-wall non-line-of-sight scenarios, which increases to 96 percent using three overheard APs. Additionally, when evaluating the system using a multi-media player application, we achieve an accuracy of 96 percent. This accuracy is robust to the presence of other interfering humans, highlighting WiGest's ability to enable future ubiquitous hands-free gesture-based interaction with mobile devices.
TL;DR: This paper considers selfish mobile devices in a dense wireless network, in which individual mobile devices can offload computations through multiple access points or through the base station to a mobile cloud so as to minimize their computation costs.
Abstract: Offloading computation to a mobile cloud is a promising solution to augment the computation capabilities of mobile devices. In this paper, we consider selfish mobile devices in a dense wireless network, in which individual mobile devices can offload computations through multiple access points or through the base station to a mobile cloud so as to minimize their computation costs. We provide a game theoretical analysis of the problem, prove the existence of pure strategy Nash equilibria, and provide an efficient decentralized algorithm for computing an equilibrium. For the case when the cloud computing resources scale with the number of mobile devices, we show that all improvement paths are finite. Furthermore, we provide an upper bound on the price of anarchy of the game, which serves as an upper bound on the approximation ratio of the proposed decentralized algorithms. We use simulations to evaluate the time complexity of computing Nash equilibria and to provide insights into the price of anarchy of the game under realistic scenarios. Our results show that the equilibrium cost may be close to optimal, and the convergence time is almost linear in the number of mobile devices.
TL;DR: An optimized solution for network assisted adaptation specifically targeted to mobile streaming in multi-access edge computing (MEC) environments is presented, designed a heuristic-based algorithm with minimum need for parameter tuning and having relatively low complexity.
Abstract: Nearly all bitrate adaptive video content delivered today is streamed using protocols that run a purely client based adaptation logic. The resulting lack of coordination may lead to suboptimal user experience and resource utilization. As a response, approaches that include the network and servers in the adaptation process are emerging. In this article, we present an optimized solution for network assisted adaptation specifically targeted to mobile streaming in multi-access edge computing (MEC) environments. Due to NP-Hardness of the problem, we have designed a heuristic-based algorithm with minimum need for parameter tuning and having relatively low complexity. We then study the performance of this solution against two popular client-based solutions, namely Buffer-Based Adaptation (BBA) and Rate-Based Adaptation (RBA), as well as to another network assisted solution. Our objective is two fold: First, we want to demonstrate the efficiency of our solution and second to quantify the benefits of network-assisted adaptation over the client-based approaches in mobile edge computing scenarios. The results from our simulations reveal that the network assisted adaptation clearly outperforms the purely client-based DASH heuristics in some of the metrics, not all of them, particularly, in situations when the achievable throughput is moderately high or the link quality of the mobile clients does not differ from each other substantially.
TL;DR: An evolutionary self-cooperative trust (ESCT) scheme that imitates human cognitive process and relies on trust-level information to prevent various routing disruption attacks is proposed and evaluated.
Abstract: How to achieve reliable routing has always been a major issue in the design of communication networks, among which mobile ad hoc networks (MANETs) possess the most adversarial networking environment due to the absence of fixed infrastructure, the nature of open transmission media and the dynamic network topology. These characteristics also make the design of routing protocols in MANETs become even more challenging. In this paper, we propose an evolutionary self-cooperative trust (ESCT) scheme that imitates human cognitive process and relies on trust-level information to prevent various routing disruption attacks. In this scheme, mobile nodes will exchange trust information and analyze received trust information based on their own cognitive judgment. Eventually, each node dynamically evolves its cognition to exclude malicious entities. The most attractive feature of ESCT is that they cannot compromise the system even if the internal attackers know how the security mechanism works. In this paper, we evaluate the performance of ESCT scheme under various routing disruption attack situations. Simulation results affirm that ESCT scheme promotes network scalability and ensures the routing effectiveness in the presence of routing disruption attackers in MANETs.
TL;DR: This paper attempts to utilize channel state information (CSI) derived from wireless signals to realize the device-free air-write recognition called Wri-Fi, and uses the Hidden Markov model for character modeling and classification.
Abstract: Recently, handwriting recognition approaches has been widely applied to Human-Computer Interface (HCI) applications. The emergence of the novel mobile terminals urges a more man-machine friendly interface mode. The previous air-writing recognition approaches have been accomplished by virtue of cameras and sensors. However, the vision based approaches are susceptible to the light condition and sensor based methods have disadvantages in deployment and highcost. The latest researches have demonstrated that the pervasive wireless signals can be used to identify different gestures. In this paper, we attempt to utilize channel state information (CSI) derived from wireless signals to realize the device-free air-write recognition called Wri-Fi . Compared to the gesture recognition, the increased diversity and complexity of characters of the alphabet make it challenging. The Principle Component Analysis (PCA) is used for denoising effectively and the energy indicator derived from the Fast Fourier Transform (FFT) is to detect action continuously. The unique CSI waveform caused by unique writing patterns of 26 letters serve as feature space. Finally, the Hidden Markov model (HMM) is used for character modeling and classification. We conduct experiments in our laboratory and get the average accuracy of the Wri-Fi are 86.75 and 88.74 percent in two writing areas, respectively.
TL;DR: Experimental results demonstrate that the proposed algorithms are more effective and efficient than baselines, fulfilling the food delivery service using a smaller number of taxis within the given time.
Abstract: This paper builds a Food Delivery Network (FooDNet in short) using spatial crowdsourcing (SC). It investigates the participation of urban taxis to support on demand take-out food delivery. Unlike existing SC-enabled service sharing systems (e.g., ridesharing), the delivery of food in FooDNet is more time-sensitive and the optimization problem is more complex regarding high-efficiency, huge-number of delivery needs. In particular, two on demand food delivery problems under different situations are studied in our work: (1) for O-OTOD, the food is opportunistically delivered by taxis when carrying passengers, and the optimization goal is to minimize the number of selected taxis to maintain a relatively high incentive to the participated drivers; (2) for D-OTOD, taxis dedicatedly deliver food without taking passengers, and the aim is to minimize the number of selected taxis (i.e., to raise the reward for each participant) and the total traveling distance to reduce the cost. A two-stage approach, including the construction algorithm and the Adaptive Large Neighborhood Search (ALNS) algorithm based on simulated annealing, is proposed to solve the problem. We have conducted extensive experiments based on the real-world datasets, including city-wide restaurant data, cell tower data, and the large-scale taxi trajectory data with 10,000 taxis in the city of Chengdu, China. Experimental results demonstrate that our proposed algorithms are more effective and efficient than baselines, fulfilling the food delivery service using a smaller number of taxis within the given time.
TL;DR: DP-Star as discussed by the authors is a methodical framework for publishing trajectory data with differential privacy guarantee as well as high utility preservation, which relies on a novel combination of several components, such as minimum description length (MDL), density-aware grid, and private Markov mobility model.
Abstract: The universal popularity of GPS-enabled mobile devices and traffic navigation services has fueled the growth of trajectory data, as evidenced by Uber Movement and NYC taxi data release. Although trajectory data can generate valuable insights and value-added services for many, publishing this data while respecting mobile users’ privacy has been a long-standing challenge. In this paper, we present DP-Star, a methodical framework for publishing trajectory data with differential privacy guarantee as well as high utility preservation. DP-Star relies on a novel combination of several components. First, DP-Star's normalization algorithm uses the Minimum Description Length metric to summarize raw trajectories using their representative points, thereby achieving a desirable trade-off between the preciseness and conciseness of their information content. Second, DP-Star constructs a density-aware grid which ensures spatial densities can be preserved despite the noise added to satisfy differential privacy. Third, DP-Star preserves the correlations between trajectories’ end points through a private trip distribution, and intermediate points through a private Markov mobility model. Finally, DP-Star estimates users’ trip lengths using a median length estimation method, and generates synthetic trajectories that preserve both differential privacy and high utility. Our experimental comparison shows that DP-Star significantly outperforms existing approaches in terms of trajectory utility and accuracy.
TL;DR: A point of interest (PoI) based mobility prediction model is presented to obtain the probabilities that tasks would be completed by users and a greedy offline algorithm to select a set of users under a participant number constraint is proposed.
Abstract: Mobile CrowdSensing is a new paradigm in which requesters launch tasks to the mobile users who provide the sensing services. The tasks, in practice, are usually heterogeneous (have diverse spatial-temporal requirements), which make it hard to select an efficient subset of users to perform the tasks. In this paper, we present a point of interest (PoI) based mobility prediction model to obtain the probabilities that tasks would be completed by users. Based on it, we propose a greedy offline algorithm to select a set of users under a participant number constraint. Furthermore, we extend the user selection problem to a more realistic online setting where users come in real time and we decide to select or not immediately. We formulate the problem as a submodular $k$k-secretaries problem and propose an online algorithm. Finally, we design a distributed user selection framework Crowd UserS and implement an Android prototype system as proof of the concept. Extensive simulations have been conducted on three real-life mobile traces and the results prove the efficiency of our proposed framework.
TL;DR: This paper proposes a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs.
Abstract: Vehicular Ad Hoc Networks (VANETs) bring many benefits and conveniences to road safety and drive comfort in future transportation systems. However, VANETs suffer from almost all security issues as same as wireless networks. Sybil attack is one of the most risky threats since it violates the fundamental assumption of VANETs-based applications that all received information are correct and trusted. Sybil attacker can generate multiple fake identities to disseminate false messages. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. Unlike most of previous RSSI-based methods that compute the absolute position or relative distance according to RSSI values, or make statistic testing based on RSSI distributions, Voiceprint adopts RSSI time series as the vehicular speech and compares the similarity among all received series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without support of the centralized node. Moreover, we improve Voiceprint by allowing it to conduct detection on Service Channel (SCH) to shorten observation time. Furthermore, we extend Voiceprint with change-points detection to identify those illegitimate nodes performing power control. Extensive simulations and real-world experiments demonstrate that Voiceprint is an effective method considering the cost, complexity, and performance.
TL;DR: ViNav is proposed, a scalable and cost-efficient system that implements indoor mapping, localization, and navigation based on visual and inertial sensor data collected from smartphones, and achieves competitive performance compared with traditional approaches.
Abstract: Smartphone-based indoor navigation services are desperately needed in indoor environments. However, the adoption of them has been relatively slow, due to the lack of fine-grained and up-to-date indoor maps, or the potentially high deployment and maintenance cost of infrastructure-based indoor localization solutions. This work proposes ViNav, a scalable and cost-efficient system that implements indoor mapping, localization, and navigation based on visual and inertial sensor data collected from smartphones. ViNav applies structure-from-motion (SfM) techniques to reconstruct 3D models of indoor environments from crowdsourced images, locates points of interest (POI) in 3D models, and compiles navigation meshes for path finding. ViNav implements image-based localization that identifies users’ positions and facing directions, and leverages this feature to calibrate dead-reckoning-based user trajectories and sensor fingerprints collected along the trajectories. The calibrated information is utilized for building more informative and accurate indoor maps, and lowering the response delay of localization requests. According to our experimental results in a university building and a supermarket, the system works properly and our indoor localization achieves competitive performance compared with traditional approaches: in a supermarket, ViNav locates users within 2 seconds, with a distance error less than 1 meter and a facing direction error less than 6 degrees.
TL;DR: A simpler heuristic algorithm is introduced that essentially serves as a form of lightweight control over recommendations so that they are both appealing to end-users and friendly to network resources.
Abstract: Caching decisions typically seek to cache content that satisfies the maximum possible demand aggregated over all users. Recommendation systems, on the contrary, focus on individual users and recommend to them appealing content in order to elicit further content consumption. In our paper, we explore how these, phenomenally conflicting, objectives can be jointly addressed. First, we formulate an optimization problem for the joint caching and recommendation decisions, aiming to maximize the cache hit ratio under minimal controllable distortion of the inherent user content preferences by the issued recommendations. Then, we prove that the problem is NP-complete and that its objective function lacks those monotonicity and submodularity properties that would guarantee its approximability. Hence, we proceed to introduce a simpler heuristic algorithm that essentially serves as a form of lightweight control over recommendations so that they are both appealing to end-users and friendly to network resources. Finally, we draw on both analysis and simulations with real and synthetic datasets to evaluate the performance of the algorithm. We point out its fundamental properties, provide bounds for the achieved cache hit ratio, and study its sensitivity to its own as well as system-level parameters.
TL;DR: Experimental results demonstrate that the HuMAn system can detect 21 complex at-home activities with high degree of accuracy, and a novel two-level structured classification algorithm that improves accuracy by leveraging sensors in multiple body positions.
Abstract: Current state-of-the-art systems in the literature using wearables are not capable of distinguishing a large number of fine-grained and/or complex human activities, which may appear similar but with vital differences in context, such as lying on floor versus lying on bed versus lying on sofa. This paper fills the gap by proposing a novel system, called HuMAn , that recognizes and classifies complex at-home activities of humans with wearable sensing. Specifically, HuMAn makes such classifications feasible by leveraging selective multi-modal sensor suites from wearable devices, and enhances the richness of sensed information for activity classification by carefully leveraging placement of the wearable devices across multiple positions on the human body. The HuMAn system consists of the following components: (a) a practical feature set extraction method from selected multi-modal sensor suites; and (b) a novel two-level structured classification algorithm that improves accuracy by leveraging sensors in multiple body positions; and (c) improved refinement in classification of complex activities with minimal external infrastructure support (e.g., only a few Bluetooth beacons used for location context). The proposed system is evaluated with 10 users in real home environments. Experimental results demonstrate that the HuMAn system can detect 21 complex at-home activities with high degree of accuracy. For same-user evaluation strategy, the average activity classification accuracy is as high as 95 percent over all of the 21 activities. For the case of 10-fold cross-validation evaluation strategy, the average classification accuracy is 92 percent, and for the case of leave-one-out cross-validation strategy, the average classification accuracy is 75 percent.
TL;DR: Wang et al. as mentioned in this paper leveraged the influence propagation on the social network to assist the mobile crowd sensing (MCS) worker recruitment, and the ultimate goal is to maximize the coverage.
Abstract: Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influence propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and Fast-Selector, respectively. Basic-Selector adopts an iterative greedy process based on the predicted mobility, which has good performance but suffers from inefficiency concerns. To accelerate the selection, Fast-Selector is proposed, which is based on the interdependency of geographical positions among friends. Empirical studies on two real-world datasets verify that Fast-Selector achieves higher coverage than baseline methods under various settings, meanwhile, it is much more efficient than Basic-Selector while only sacrificing a slight fraction of the coverage.
TL;DR: Through an extensive performance evaluation study and simulation of large-scale scenarios, the results demonstrated that the protocol achieved better performance compared to the state-of-the-art solutions in terms of network lifetime, energy consumption, routing efficiency, sender waiting time, and duplicate packets.
Abstract: Opportunistic Routing (OR) is adapted to improve the performance of low Duty-cycled Wireless Sensor Networks by exploiting its broadcast nature. In contrast to traditional routing, where packets are transmitted along pre-determined paths, OR uses a prioritization metric to select a set of candidates as potential forwarders. This solves the sender's waiting time problem. However, too many candidates may simultaneously wake-up, generating more duplicate packets, occupying the restricted resources and hinder the packet delivery performance. Consciously, to restrict the number of candidates and to counterbalance between the waiting time problem and the duplicate packets problem, this paper proposed a new protocol that combines two main parts. First, each node defines a Candidates Zone (CZ) by a regular geometric shape of four corners. The packets generated by the node will be routed via any path within the CZ. Expressly, the nodes within the CZ are allowed to be selected as candidates. The size of CZ is controlled by the network density. Second, the candidates within the CZ are prioritized based on the OR metric, which is defined as the multiplication of four-distributions: direction distribution, transmission-distance distribution, perpendicular-distance distribution, and residual energy distribution. Through an extensive performance evaluation study and simulation of large-scale scenarios, the results demonstrated that our protocol achieved better performance compared to the state-of-the-art solutions in terms of network lifetime, energy consumption, routing efficiency, sender waiting time, and duplicate packets.
TL;DR: It is shown that the path planning problem is NP-complete and an efficient path planning for reliable data gathering (EARTH) algorithm is proposed by relaxing these impractical assumptions to find short traveling paths for the MS to collect sensing data without packet loss.
Abstract: Wireless sensor networks are vulnerable to energy holes, where sensors close to a static sink are fast drained of their energy. Using a mobile sink (MS) can conquer this predicament and extend sensor lifetime. How to schedule a traveling path for the MS to efficiently gather data from sensors is critical in performance. Some studies select a subset of sensors as rendezvous points (RPs). Non-RP sensors send data to the nearest RPs and the MS visits RPs to retrieve data. However, these studies assume that sensors produce data with the same speed and have no limitation on buffer size. When the two assumptions are invalid, they may encounter serious packet loss due to buffer overflow at RPs. In the paper, we show that the path planning problem is NP-complete and propose an efficient path planning for reliable data gathering (EARTH) algorithm by relaxing these impractical assumptions. It forms a spanning tree to connect all sensors and then selects each RP based on hop count and distance in the tree and the amount of forwarding data from other sensors. An enhanced EARTH (eEARTH) algorithm is also developed to further reduce path length. Both EARTH and eEARTH incur less computational overhead and can flexibly recompute new paths when sensors change sensing rates. Simulation results verify that they can find short traveling paths for the MS to collect sensing data without packet loss, as compared with existing methods.