With the proliferation of smartphone application services, the pattern lock remains widely used for authentication. Notably, the risks associated with password entry in public spaces have attracted significant attention from researchers. Various attacks have been explored to steal passwords, but each comes with limitations, such as requiring good lighting conditions, close proximity, pre-deployed devices, or system intrusion. To address these challenges, we propose an attack method called BEyes, which utilizes beamforming feedback information (BFI) to eavesdrop on pattern passwords drawn on smartphone screens. Since BFI is transmitted in clear text and describes the downlink channel state information (CSI), any Wi-Fi 5-enabled device can capture it out of the victim’s view, reducing the likelihood of the attack being detected. To avoid missing critical pattern drawing information, we propose a traffic generation mechanism based on traffic competition, which ensures stable BFI. To mitigate the effects of frequency-selective fading and noise, we apply subcarrier alignment and principal component analysis (PCA) to improve efficiency. Additionally, we introduce a motion-based joint inference model, enabling BEyes to generalize its inference from a few known pattern passwords to unknown ones. Extensive experiments demonstrate that BEyes achieves an accuracy of 89.2% in inferring a 3-line pattern password within the Top-10 attempts.

Deep learning-based approaches have demonstrated significant advancements in time series forecasting. Despite these ongoing developments, the complex dynamics of time series make it challenging to establish the rule of thumb for designing the golden model architecture. In this study, we argue that refining existing advanced models through a universal calibrating strategy can deliver substantial benefits with minimal resource costs, as opposed to elaborating and training a new model from scratch. We first identify a multi-target learning conflict in the calibrating process, which arises when optimizing variables across time steps, leading to the underutilization of the model's learning capabilities. To address this issue, we propose an innovative calibrating strategy called Socket+Plug (SoP). This approach retains an exclusive optimizer and early-stopping monitor for each predicted target within each Plug while keeping the fully trained Socket backbone frozen. The model-agnostic nature of SoP allows it to directly calibrate the performance of any trained deep forecasting models, regardless of their specific architectures. Extensive experiments on various time series benchmarks and a spatio-temporal meteorological ERA5 dataset demonstrate the effectiveness of SoP, achieving up to a 22% improvement even when employing a simple MLP as the Plug (highlighted in Figure 1).

Short-term prediction of fishing effort distributions will guide fishery management in a dynamic way. However, it meets two unique challenges: the randomness of fishers’ behaviors and the diversity of marine meteorology such as sea storms in the short period. This study proposes short-term prediction system of fishing effort distribution by mining a new kind of knowledge: fishing chronology among trawlers. We first define, quantify, and dig out chronological fishing relations among trawlers based on the VMS dataset. Then the system extracts the optimal early bird set from chronological fishing relations, whose current fishing behaviors can serve as indicators of future fishing effort distributions. Based on the knowledge of fishing chronology, we further design a Convolution Neural Network (CNN) to predict the short-term fishing effort distribution, only taking the current fishing behaviors of early birds as input. We evaluate the system performance on the VMS dataset of 1589 trawlers in the East China Sea from October 2015 to April 2017. The system uses the VMS traces in the first half period to calculate fishing chronology among trawlers, to extract early birds, and to train the CNN model. The traces in the last half period is used to evaluate the prediction accuracy. The results confirm a low prediction error ratio of 6.95% across all the weeks only by tracking 19 early birds. More importantly, our prediction system keeps its accuracy during the week of a sea storm in Feb. 8th to 10th, 2017. The application of our system for fishery management is encouraging: tracking only 1% trawlers suffices to predict short-term fishing effort distributions in the near future.

Freehand exercises help improve physical fitness without any requirements on devices, or places (e.g., gyms). Existing fitness assistant systems require wearing smart devices or exercising at specific positions, which compromises the ubiquitous availability of freehand exercises. This work proposes MobiFit, a contactless freehand exercise assistant using just one cellular signal receiver. MobiFit monitors the ubiquitous cellular signals sent by the base station and provides accurate repetition counting, exercise type recognition, and workout quality assessment without any attachments to the human body. To design MobiFit, we first analyze the characteristics of the received cellular signal sequence during freehand exercises through experimental studies. Based on the observation, we construct the analytic model of the received signals. Guided by the analytic model, MobiFit segments out every repetition and rest interval from one exercise session through spectrogram analysis, and extracts low-frequency features from each repetition for type recognition. We have implemented the prototype of MobiFit and collected 22,960 exercise repetitions performed by ten volunteers over six months. The results confirm that MobiFit achieves high counting accuracy of 98.6%, high recognition accuracy of 94.1%, and low repetition duration estimation error within 0.3s. Besides, the experiments show that MobiFit works both indoor and outdoor, and supports multiple users exercising together.

Communication in the ocean vessel wireless ad hoc networks which currently rely on shore-based cellular stations, is expensive and can only be used near the coast. It is a big challenge for ocean vessels to achieve economic and large-scope communications, due to the rare encounter opportunities and highly dynamic vessel mobilities. Previous works on routing algorithms for vessel wireless ad hoc networks did not take into account the social characteristics of ocean vessel networks. According to our empirical study on real trajectories of more than 7,000 ocean vessel recorded by the Vessel Monitoring System (VMS), we have made innovative observations. Firstly, there exist stable social connections among those vessels frequently encounter with each other. Moreover, stable social communities can be discovered for those ocean vessels with long-term connections. Inspired by the significant observations, two unicast routing approaches based on social society characteristics are proposed for wireless ocean vessel ad hoc networks, i.e., familiarity based routing (FBR) and community based routing (CBR). The key metric for message forwarding decision making of FBR is the expected multi-hop encounter probability of the potential forwarder with the message receiver. The expected multi-hop encounter probability is derived from the raised definition of familiarity, which measures the connection strengths between vessels. The proposed CBR forwards messages based on the metric of inter-community betweenness centrality of a potential forwarder, which describes the bridge capability between two communities. Extensive trace-driven simulations based on real ocean vessel trajectories are performed and the evaluation results demonstrate the significant performance achieved by the proposed approaches. The transmission cost has been largely reduced with comparable delivery ratio and delay.

We present WFID, a passive device-free indoor human identification system with one pair of WiFi signal transmitter and receiver. WFID design is motivated by the observation that PHY layer Channel State Information (CSI) is capable of capturing the frequency diversity of wideband channel, such that the human body curve may be uniquely identified by learning the feature pattern of CSI. Different from many CSI-based techniques focusing on phase shift, we propose a novel feature of subcarrier-amplitude frequency (SAF). Based on this feature, WFID realizes human identification through a linear-kernel SVM. We have implemented a prototype of WFID with a commercial AP and a computer equipped with one Intel 5300 NIC. WFID is evaluated in two typical indoor scenarios. The results confirm that WFID achieves high classification accuracy which is permanent over several days under two typical indoor scenarios, with low computation cost. This reveals the potential for WFID to realize real-time indoor human identification.

Ocean sensor networks have many applications ranging from oceanographic data collection, pollution monitoring, disasters prevention to tactical surveillance. However, due to the harsh ocean environment, it is difficult to deploy three-dimensional sensor networks underwater for sustainable monitoring tasks. In this paper, we propose a novel floating three-dimensional sensor networks for ocean monitoring and surveillance applications, which leverage nodes' restricted movement to enlarge the monitoring area. The networks are deployed on both the surface of the ocean and underwater, with many chains of nodes attached to mooring lines. Compared with deployments requiring self-adjusted nodes, our scheme is simpler and more cost-efficient, and the sustainable monitoring cost is low. We design deployment strategies, and provide in-depth mathematical analysis for network coverage. Then we derive the minimum number of sensors to achieve full surface coverage. Evaluated by simulations of a few underwater scenarios, the effectiveness and efficiency of the proposed scheme is proved.

This paper combines keystroke dynamics features with recently proposed keystroke acoustic features for user identification and authentication. Traditional keystroke dynamics uses temporal features. This paper explores the discriminative capability of the fusion of temporal features and acoustic features. This paper also explores the influence of filtering keystroke sound which turns out to be harmful for the performance of keystroke acoustic features. We collected a total of 824 samples from 7 subjects for experiments. 46 features including 38 acoustic features are extracted for the proposed user identification and authentication system. C-Support Vector Classification (C-SVC) and one-class Support Vector Machine (1-SVM) are applied for user identification and authentication respectively. Extensive experiments are carried out to verify the proposed system. Our work achieves 92.8% accuracy for user identification. The FRR (False Rejection Rate) and FAR (False Acceptance Rate) are only 12% and 11% for user authentication.

Time synchronization is the foundation for collaboration among sensor nodes. Acoustic communication and restricted mobility of sensor nodes are two unique characteristics of underwater sensor networks (UWSNs), negating the effect of terrestrial synchronization schemes. Existing UWSN time synchronization protocols focus on point to point synchronization, incompatible for large-scale networks. Moreover, some of them require special hardwares and deployment conditions. In this paper, we propose MulSync, a scalable synchronization protocol for multi-hop UWSNs. MulSync includes the synchronization communication scheme to exploit acoustic communication nature of broadcast. The skew and offset are estimated by performing linear regression three times over a set of time stamp pairs gathered through message exchange. Three linear regressions are exploited to make full use of the time reference information delivered from multi-source beacons. Simulation results demonstrate that MulSync achieves high accuracy at low message overhead and time cost.

High accurate indoor localization and tracking of smart phones is critical to pervasive applications. Most radio-based solutions either exploit some error prone power-distance models or require some labor-intensive process of site survey to construct RSS fingerprint database. This study offers a new perspective to exploit RSS readings by their contrast relationship rather than absolute values, leading to three observations and functions called turn verifying, room distinguishing and entrance discovering. On this basis, we design WaP (WiFi-Assisted Particle filter), an indoor localization and tracking system exploiting particle filters to combine dead reckoning, RSS-based analyzing and knowledge of floor plan together. All the prerequisites of WaP are the floor plan and the coarse locations on which room the APs reside. WaP prototype is realized on off-the-shelf smartphones with limited particle number typically 400, and validated in a college building covering 1362m2. Experiment results show that WaP can achieve average localization error of 0.71m for 100 trajectories by 8 pedestrians.

Data aggregation is a crucial technique for energy constrained sensor networks. Previous researches on data aggregation are featured as query-oriented, which only provide partial information of the event happened in the deployment area at the base station. In this paper, we propose an event-oriented data aggregation approach, called EDA. EDA presents the distributed algorithm by exploiting Cloud Membership model of fuzzy logic to aggregate the information of events in the sensor networks. It also presents a distributed algorithm to collect and aggregate the event information. The base station will restore the whole event information when receiving the aggregated packets of event features. EDA can balance the tradeoff between delay, traffic savings, and precision of the restored events. The performance has been evaluated through both theoretical analysis and simulations. We also confirm the performance with the traces of our offshore sensor network testbed (OceanSense).

This paper presents a distributed and robust time slot scheduling algorithm, which is suitable for underwater acoustic sensor network (UASN). The information of nodes' 2-hop neighbors is needed to be collected and then be used to calculate nodes' initial time slot by a distributed algorithm. A maximal independent set is formed by the nodes which were assigned with the same initial slot. Some theorems were proved to reveal that in an interference graph the size of this maximal independent set is at least of the size of the maximum independent set of the nodes. The simulation compares UD-TDMA with other three MAC protocols. The results show that the proposed protocol is effective in the UASN with random deployment, especially in high-density underwater acoustic sensor network.

Duty cycling is an important method for energy constrained sensor networks to prolong its lifetime. Current researches on duty cycling are often based on such assumption that all the sensory coverage should be maintained while some nodes are sleeping. For the applications of target monitoring, however, it is not necessary anymore to keep the whole sensory coverage of the sensor networks. It only needs to make sure that such kinds of nodes are active which can perceive the activities of the monitored targets. This observation brings new challenges to the design of duty cycling for the sensor networks. This paper proposes the novel duty cycling design for the sensor networks of target monitoring, which includes two algorithms of the sleep scheduling algorithm and the gradient query algorithm based on sleep periods. Under the proposed design, most of the sensor nodes can be in sleep, while still keep the functions of target monitoring and target query in the sensor networks. The performance of our design has been evaluated through both theoretical analysis and simulations, which prove the functionality of the proposed design on the reduction of energy consumption.

Wireless sensor networks have been applied to monitor pipeline structural health. In these networks, expensive multi-sinks with energy harvesting modules are deployed along the linear pipeline, and battery powered sensor nodes are deployed between the sinks. One of the main problems in such networks is the unbalance of energy consumption of sensor nodes, which makes the whole monitoring system lose its functionality with only a small percentage of sensor nodes depleted of their energy. In this paper, we propose a distributed sensing data propagation algorithm based on graded residual energy (GRE) of the sensor nodes, in order to achieve balanced energy consumption among sensor nodes. The optimum number of energy grades of GRE has been calculated through theoretical analysis in terms of maximizing network lifetime. The simulation results have shown that GRE can achieve balanced energy consumption between the sensor nodes and at the same time prolong the lifetime of the whole monitoring system.

Existing localization approaches are divided into two groups: range-based and range-free. The range-free schemes often suffer from poor accuracy and low scalability, while the range-based localization approaches heavily depend on extra hardware capabilities or rely on the absolute RSSI (received signal strength indicator) values, far from practical. In this work, we propose a mobile-assisted localization scheme called perpendicular intersection (PI), setting a dedicate tradeoff between range-free and range-based approaches. Instead of directly mapping RSSI values into physical distances, by contrasting RSSI values from the mobile beacon to a sensor node, PI utilizes the geometric relationship of perpendicular intersection to compute node positions. We have implemented the prototype of PI with 100 TelosBmotes. Through comprehensive experiments, we show that PI achieves high accuracy and low overhead, significantly outperforming the existing range-based and the mobile-assisted localization schemes.

Network diagnosis, an essential research topic for traditional networking systems, has not received much attention for wireless sensor networks. Existing sensor debugging tools like sympathy or EmStar rely heavily on an add-in protocol that generates and reports a large amount of status information from individual sen-sor nodes, introducing network overhead to a resource constrained and usually traffic sensitive sensor network. We report in this study our initial attempt at providing a light-weight network diag-nosis mechanism for sensor networks. We propose PAD, a prob-abilistic diagnosis approach for inferring the root causes of ab-normal phenomena. PAD employs a packet marking algorithm for efficiently constructing and dynamically maintaining the inference model. Our approach does not incur additional traffic overhead for collecting desired information. Instead, we introduce a prob-abilistic inference model which encodes internal dependencies among different network elements, for online diagnosis of an operational sensor network system. Such a model is capable of additively reasoning root causes based on passively observed symptoms. We implement the PAD design in our sea monitoring sensor network test-bed and validate its effectiveness. We further evaluate the efficiency and scalability of this design through ex-tensive trace-driven simulations.

BitTorrent is a very popular P2P file sharing system. It has been successful at distributing large files quickly and efficiently. Embedded in BitTorrent is a set of incentive mechanisms to encourage sharing and contribute, and prevent systematic free-riding. In this paper, a fluid model with two classes of peers is used to capture the effect of free-riding in a BitTorrent system. With the model, we explore how does free-riding influenced the BitTorrent system. From results, it is shown that BitTorrent mechanism is successful to guard against free-riding. Finally, we discuss the dying process of a BitTorrent system and the probability of system dead that is induced by free-riding

The variance of user interest is a significant factor for designing peer-to-peer content-based retrieval system which leads to the birth of this paper. This paper gives the preliminary scheme to solve the problem of Web search of Internet scale. All the personalized peers form an interest-oriented Web server union (IOWSU) by employing structured P2P routing mechanism. After a query request from a peer is posted to the union, an optimized and deterministic result based on the search peer's interest is returned from the union. The result of our simulation demonstrates the method proposed in this paper is practicable and efficient

The variance of job size is a significant factor for designing peer-to-peer system which leads to the born of JSChord(Job Size based Chord). JSChord is a peerto- peer overlay constructed on Chord aimed to exploit the variance of job size. The fundamental idea of JSChord is to group several Chord rings together to distribute the jobs according to their size. JSChord has achieved better performance than Chord with its special routing algorithm as shown in the simulation.