Fill-ins are new nonzero elements in the summation of the upper and lower triangular factors generated during LU factorization. For large sparse matrices, they will increase the memory usage and computational time, and be reduced through proper row or column arrangement, namely matrix reordering. Finding a row or column permutation with the minimal fill-ins is NP-hard, and surrogate objectives are designed to derive fill-in reduction permutations or learn a reordering function. However, there is no theoretical guarantee between the golden criterion and these surrogate objectives. Here we propose to learn a reordering network by minimizing l1 norm of triangular factors of the reordered matrix to approximate the exact number of fill-ins. The reordering network utilizes a graph encoder to predict row or column node scores. For inference, it is easy and fast to derive the permutation from sorting algorithms for matrices. For gradient based optimization, there is a large gap between the predicted node scores and resultant triangular factors in the optimization objective. To bridge the gap, we first design two reparameterization techniques to obtain the permutation matrix from node scores. The matrix is reordered by multiplying the permutation matrix. Then we introduce the factorization process into the objective function to arrive at target triangular factors. The overall objective function is optimized with the alternating direction method of multipliers and proximal gradient descent. Experimental results on benchmark sparse matrix collection SuiteSparse show the fill-in and LU factorization time reduction of our proposed method is 0.2% and 17.8% compared with state-of-the-art baselines.

Recently, there has been an emerging radio frequency fingerprint identification (RFFI) technology that enhances fingerprint distinguishability by deliberately injecting an RF fingerprint into the device's Wi-Fi baseband signal. The current RF fingerprint injection methods are impractical, degrading the communication quality between Wi-Fi devices while offering limited improvements in distinguishability among a set of devices. To address these issues, we propose injecting I/Q imbalance into a short training field (STF) instead of the entire baseband signal. Our findings indicate that this method can effectively preserve the quality of the original wireless communication. Besides, a temperature-independent RF feature differential carrier frequency offset (DCFO) is proposed as an extended feature for the enhancement of fingerprint distinguishability. Building upon these, we introduce a fingerprinting scheme called PR-RFFI that generates distinguishable fingerprints for a set of devices by injecting appropriate I/Q imbalance and DCFO into the STF. Leveraging the short-term invariance of the channel, we design a practical I/Q imbalance extraction method based on the communication-quality preserving injection. Moreover, we design an optimal assignment method for I/Q imbalance and DCFO to maximize the distinguishability of RF fingerprints for all devices. Finally, we implement the PR-RFFI solution and conduct experiments in real-world and simulation scenarios. The experimental results demonstrate that PR-RFFI consistently maintains good communication quality, and achieves over 98% precision, recall, and F1-score.

In recent years, the Internet of Things (IoT) has rapidly advanced, with applications ranging from smart homes to industrial manufacturing, often involving densely deployed nodes such as temperature and humidity sensors. Since these nodes have limited computation and energy, the use of stuffed Wi-Fi management frames for data transmission has emerged as a promising way to avoid the association overhead of the traditional transmission mode. However, this unassociated data transmission mode continues to encounter significant channel contention in dense deployments. To this end, we propose ODGMAC, an on-demand grouping-based MAC solution that dynamically groups transmission-awaiting nodes and allocates time slots on a per-group basis, thereby enabling intra-group contention to improve transmission efficiency and reduce node energy consumption. Firstly, we present a fuzzy control-based algorithm at the access point (AP) to dynamically identify nodes with transmission demands in the current beacon period. On this basis, we then propose a hierarchical group-based time slot allocation methodology. Specifically, the nodes are initially clustered according to their per-packet airtime requirements. Within each cluster, we evenly partition nodes into multiple groups and assign each group to a unique time slot for channel contention, where the optimal slot count is determined by a renewal-theory-based analytical model with a discrete search over candidate counts. Finally, we implement the ODGMAC testbed with one AP and 100 IoT nodes, and conduct real-world experiments in a dense environment. The experimental results show that our solution outperforms existing methods in terms of both data delivery rate and node power consumption. Specifically, under severe channel collision conditions, our solution achieves an average increase of 14.77% in data delivery rate and an average reduction of 8.21% in node power consumption, while maintaining excellent fairness. Moreover, extended simulations show that our solution scales to 1000 nodes and maintains excellent performance under node mobility.

Unmanned aerial vehicles (UAVs) are being widely employed in wireless communication applications, e.g., collecting data from ground nodes (GNs). Minimizing UAV energy in these applications is crucial due to the limited energy supply onboard. Unlike previous studies that assume UAVs fly at a fixed altitude and simplify the energy consumption model of UAVs, we consider the impact of varying UAV altitudes on the ground-to-air communication and utilize a general communication model for GN. Furthermore, we conduct real-world flight tests and introduce a practical speed-related flight energy consumption model of UAVs. This paper focuses on the UAV altitude-speed scheduling and GN transmission switching (UASS-GTS) problem, specifically in scenarios where the UAV flies straight for monitoring applications such as power transmission lines, roads, and water/oil/gas pipes. However, minimizing energy consumption presents challenges due to the tight coupling of altitude scheduling and speed scheduling. To tackle this, first, we develop the looking before crossing algorithm for speed scheduling. We then extend this algorithm by integrating altitude scheduling to propose the Altitude-Speed Scheduling of UAV for Minimizing Energy (ASSUME) algorithm, using a dynamic programming method. The ASSUME algorithm is theoretically proven to be optimal. Additionally, based on ASSUME, we propose an offline-inspired online heuristic algorithm to handle agnostic situations where GN information is not available unless flies close. Simulations indicate that the ASSUME algorithm saves an average of 26.1%–62.7% energy compared to the baseline methods, and the performance gap between the online algorithm and the offline optimal algorithm ASSUME is 22.8%.

With metaverse attracting increasing attention from both academic and industry, the application of virtual reality (VR) has extended beyond 3D immersive viewing/gaming to a broader range of areas, such as banking, shopping, tourism, education, and so on, which involves a growing amount of sensitive and private user data into VR systems. However, with current password-based user authentication schemes in mainstream VR devices, studies demonstrate that side-channel attacks can pose a severe threat to VR user privacy. To mitigate the threat, we propose a novel panoramic-image-based VR user authentication system, i.e., Pivot , to defend against such attacks, yet maintain high usability. Specifically, in Pivot , we design an image-random-pivoting-based user interaction mechanism to assist users in quickly and securely selecting memorable points of interest in a panoramic image. Then an image region segmentation algorithm is designed to automatically scatter the points to regions to form the customized graphic password for the user, which could ensure a sufficiently large password space and also reduce the near-region point misclicks. Afterward, the region indexes are used to generate the hashed password for authentication. Both theoretical security analysis and extensive user studies demonstrate that Pivot is secure and user-friendly in practice.

In many image-guided clinical approaches, tumor segmentation is a fundamental and critical step for locating tumor involvement. However, the scarcity of annotated data and the low contrast of medical imaging techniques make it challenging to accurately segment tumors from surrounding tissues using supervised learning methods. To address these issues, we propose a patch-based contrastive semi-supervised learning framework with wavelet guidance and adaptive data augmentation (PaCaS-WAA). Specifically, we apply patch-based contrast to maintain high-quality segmentation results with limited labels. Moreover, to exploit the discriminative information about subtle boundaries, we use the wavelet domain guides UNet for more edge details. Besides, to increase the diversity of unlabelled data, we propose an adaptive data augmentation strategy to augment the unlabelled data according to its Challenging Grade. Experimental results on two publicly available datasets of different modalities demonstrate that our method consistently outperform the state-of-the-art semi-supervised segmentation methods.

Nowadays, LoRa radio frequency fingerprinting has gained widespread attention due to its lightweight nature and difficulty in being forged. The existing fingerprint extraction methods are mainly divided into two categories, i.e., deep learning-based methods and feature engineering-based methods. Deep learning-based methods have poor robustness and require significant resource costs for model training. Although feature engineering-based methods can overcome these drawbacks, the features it commonly uses, such as carrier frequency offset (CFO) and phase noise, lack sufficient discriminative power. Therefore, it is very challenging to design a radio frequency fingerprinting solution with high-accuracy and stable identification performance. Fortunately, we find that the differential phase noise of adjacent dual frames possesses excellent discriminative power and stability. Then, we design the corresponding radio frequency fingerprinting solution called Diff-ADF, which utilizes a classifier with differential phase noise as the primary feature, complemented by the use of CFO as an auxiliary feature. Finally, we implement the Diff-ADF and conduct experiments in real environments. Experimental results demonstrate that our proposed solution achieves an accuracy of over 90% on training and test data collected from different days, which is significantly superior to deep learning-based methods. Even in non-line-of-sight environments, our identification accuracy can still reach close to 85%.

Recently, there has been an emerging radio frequency fingerprint identification (RFFI) technology that enhances fingerprint distinguishability by deliberately injecting I/Q imbalance into the device’s Wi-Fi baseband signal. Due to the additional injection of I/Q imbalance, this approach inevitably impacts the communication quality between devices, as it reduces the accuracy of channel estimation. To address this issue, we propose injecting the I/Q imbalance into a short training field (STF) instead of the entire baseband signal. Our findings indicate that this method can effectively preserve the quality of the original wireless communication. Building upon this, we introduce a fingerprinting scheme called CQP-RFFI that generates distinguishable fingerprints for a set of devices by injecting appropriate I/Q imbalance into the STF. Leveraging the short-term invariance of the channel, we design a practical I/Q imbalance extraction method based on the communication-quality preserving injection. Moreover, we design an optimal assignment method for I/Q imbalance to maximize the distinguishability of RF fingerprints for all devices. Finally, we implement the CQP-RFFI solution and conduct experiments in real-world scenarios. The experimental results demonstrate that CQP-RFFI achieves 96% precision, recall, and F1-score, and can consistently maintain good communication quality.

Radio frequency fingerprint identification (RFFI) is a promising technique for smartphone identification. However, we find that the temperature of the RF front end in smartphones can significantly impact the RF features, including the carrier frequency offset (CFO) and statistical RF features. The unstable RF features caused by temperature changes can negatively affect the performance of state-of-the-art RFFI approaches. To this end, we propose the RF-TESI solution for smartphone identification under temperature variation. First, we construct a dataset by extracting temperature and RF features. In the dataset, the extracted temperature values constitute a set of temperature values and each registered temperature value corresponds to a group of RF features. Next, we evaluate the distinctiveness of RF features across smartphones to select the most suitable RF fingerprint. Then, we train multiple random forest models, each tagged with a registered temperature. In addition, because there are still many temperatures out of the temperature set, we design an RF fingerprint estimation method to estimate RF fingerprints at unregistered temperatures. Finally, the experiments show RF-TESI demonstrates satisfactory performance under different scenarios, taking into account variations in temperature, time and position. Besides, our proposed approach is better than all state-of-the-art approaches in smartphone identification.

MAC address randomization is being adopted by an ever-increasing number of Wi-Fi-enabled devices, which aims to prevent users from being tracked by embedding random MAC addresses in probe request frames. Through measurement and analysis, researchers have found that its specific implementation mechanism differs for devices with different operating systems and obtained some analysis results. However, since the MAC address constantly changes, it is challenging to efficiently capture probe request frames of multiple target devices in an environment that typically has a large number of devices. Additionally, new devices are constantly emerging, making the results of previous analysis likely to be outdated. To address these issues, we propose a novel solution to measure and analyze Wi-Fi MAC address randomization with a sniffer array, called MASA. Firstly, we utilize a sniffer array to simultaneously capture probe request frames by multiple sniffers in the environment. Then, we propose a timeout bloom filter-based frame aggregation and processing method to aggregate the frames from the sniffer array, exclude frames of non-target devices, and split frames into their own set for the target device in real time. On this basis, we analyze the MAC address randomization mechanism in terms of address changing pattern, frame transmission frequency, and frame field/Information Element (IE) variation, considering devices’ association state, display status, and power-saving mode. Finally, we conduct corresponding experiments in real environments and make some new findings in the analysis. For example, all the latest iOS devices implement randomization strategies not only in the MAC address field but also in the Sequence Number field, while the MAC address randomization mechanism of some Android and HarmonyOS devices exhibits significantly different behaviors in the unassociated state and associated state.

Time-series waveform data widely exist in various industrial fields, such as equipment monitoring and fault diagnosis. The current time series representation methods have limitations when dealing with industrial abnormal time-series waveforms, such as limited applicability, semantic ambiguity, and time distortion. This work proposes a novel shape reference-based representation method for industrial abnormal time-series waveform (ShapeRef), which takes the shape of the standard waveform as a reference to represent the anomaly deviation. Specifically, ShapeRef first establishes a time-series shape reference frame, then proposes the minimum shape difference-based mapping method to describe the mapping process of coordinates, and finally reduces multi-intersection points in the mapping process to achieve uniform mapping of the abnormal time-series waveform. Experimental results show that ShapeRef can effectively represent abnormal time-series waveforms and outperforms several baseline methods in the clustering task of a real industrial equipment waveform dataset. This work enhances the accuracy and reliability of industrial equipment monitoring and fault diagnosis, which could have significant practical implications.

IFTTT is a programming framework for Applets (i.e., user customized policies with a "trigger-action" syntax), and is the most popular Home Internet-of-Things (H-IoT) platform. The execution of an Applet prompted by a device operation suffers from a long delay, since IFTTT has to periodically reads the states of the device to determine whether the trigger is satisfied, with an interval of up to 5min for professionals and 60min for normal users. Although IFTTT sets up a flexible polling interval based on the past several times an Applet has run, the delay is still around 2min even for frequently executed Applets. This paper proposes a novel trigger notification mechanism "RTX-IFTTT" to implement real-time execution of Applets. The mechanism does not require any changes to the current IFTTT framework or the H-IoT devices, but only requires an H-IoT edge node (e.g., router) to identify the device events (e.g., turning on/off) and notify IFTTT to perform the action of an Applet when an identified event is the trigger of that Applet. The experimental results show that the averaged Applet execution delay for RTX-IFTTT is only about 2sec.

Website fingerprinting (WF) attacks allow an attacker to eavesdrop on the encrypted network traffic between a victim and an anonymous communication system so as to infer the real destination websites visited by a victim. Recently, the deep learning (DL) based WF attacks are proposed to extract high level features by DL algorithms to achieve better performance than that of the traditional WF attacks and defeat the existing defense techniques. To mitigate this issue, we propose a-genetic-programming-based variant cover traffic search technique to generate defense strategies for effectively injecting dummy Tor cells into the raw Tor traffic. We randomly perform mutation operations on labeled original traffic traces by injecting dummy Tor cells into the traces to derive variant cover traffic. A high level feature distance based fitness function is designed to improve the mutation rate to discover successful variant traffic traces that can fool the DL-based WF classifiers. Then the dummy Tor cell injection patterns in the successful variant traces are extracted as defense strategies that can be applied to the Tor traffic. Extensive experiments demonstrate that we can introduce 8.1% of bandwidth overhead to significantly decrease the accuracy rate below 0.4% in the realistic open-world setting.

Tor is the largest anonymous communication system, providing anonymous communication services to approximately 2.8 million users and 170,000 hidden services per day. The Tor hidden service mechanism can protect a server from exposing its real identity during the communication. However, due to a design flaw of the Tor hidden service mechanism, adversaries can deploy malicious Tor hidden service directories (HSDirs) to covertly collect all onion addresses of hidden services and further probe the hidden services. To mitigate this issue, we design customized honeypot hidden services based on one-to-one and many-to-one HSDir monitoring approaches to luring and identifying the malicious HSDirs conducting the rapid and delayed probing attacks, respectively. By analyzing the probing behaviors and payloads, we investigate a novel semantic-based probing pattern clustering approach to classify the adversaries so as to shed light on the purposes of the malicious HSDirs. Moreover, we perform theoretical analysis of the capability and accuracy of our approaches. Large-scale experiments are conducted in the real-world Tor network by deploying hundreds of thousands of honeypots during a monitoring period of more than three months. Finally, we identify 8 groups of 32 malicious HSDirs, discover 25 probing pattern clusters and reveal 3 major probing purposes.

In recent years, radio frequency (RF) fingerprinting has attracted more and more attention. Many different types of RF fingerprints have been proposed, such as carrier frequency offset (CFO), sampling frequency offset and error vector magnitude. Among them, the CFO fingerprint is recognized as a promising RF fingerprint. However, for commonly used smartphones, we find that its CFO fingerprint is unstable, because the temperature of crystal oscillator varies greatly and large fluctuations of temperature significantly affect its CFO fingerprint. Therefore, the solutions of CFO-based fingerprinting will no longer be effective for smartphones if the temperature of crystal oscillator is not involved. To this end, we propose a more reliable and applicable CFO-based fingerprinting approach called temperature-aware radio frequency fingerprinting (TeRFF). First, we construct a dataset by extracting crystal oscillator's temperature and the corresponding CFO value on multiple smartphones over a period. In the dataset, the extracted temperature values constitute a set of temperature values, and each registered temperature value corresponds to a group of CFO samples. On this basis, we train multiple Naive Bayes models, each tagged with a registered temperature value. Moreover, since there are many temperature values which are not in the temperature set, we design a CFO estimation method to estimate the CFO fingerprint at the unregistered temperature. Finally, the experimental results demonstrate that our proposed solution TeRFF makes the CFO fingerprinting still effective for smartphone identification, and its performance is better than other existing RF fingerprinting schemes.

In Wi-Fi networks, devices can be identified by physical features or MAC layer features, and the solutions of device identification can be used to enhance device authentication. Since 802.11ac Standard has been widely applied in Wi-Fi devices in recent years, the traditional identification methods designed for 802.11b/g/n devices will be no longer applicable. Therefore, it is necessary to design the corresponding 802.11ac device identification method. Compared with the physical feature-based method, the MAC layer-based method has advantages of low cost and easy deployment, so it has attracted more and more researchers' attention. In this paper, we use the fields from 802.11ac MAC frame as fingerprints. Through the analysis of 802.11ac MAC frame, a preprocessing method of the frame is proposed to mask strong and easy-to-modified identifiers. Then to overcome the difficulties caused by random changes in field values, we propose a device identification method based on the deep learning to select features automatically. Compared with the previous one using the transmitting rate as a feature, our method does not spend much time capturing packets in the device identification stage and has better performance whose average precision and recall exceed 99%.

Unmanned aerial vehicles (UAVs) are being widely used in wireless communication, e.g., collecting data from ground nodes (GNs), where energy is critical. Existing works combine speed scheduling, i.e., the controlling of speed, with trajectory design for UAVs, making it complicated to solve while loses focus on the fundamental nature of speed scheduling. We focus on speed scheduling by considering straight line flights, with applications in monitoring power transmission lines, roads, water/oil/gas pipes and rivers/coasts. By real-world flight tests, we disclose a speed-related flight energy consumption model, distinct from typical distance-related or duration-related models. Based on such a practical energy model, we develop the looking before crossing (virtual rooms) algorithm, where virtual rooms on the time-distance diagram represent the spatio-temporal constraint of GNs in wireless transmission. This algorithm is proved to be optimal in solving the offline problem, where all information is known before scheduling. For the online problem, i.e., GN information is not unavailable unless flies close, we propose an offline-inspired online heuristic. Simulation shows its performance is near the offline optimal. Our study on the practical flight energy model and speed scheduling sheds light on a new research direction on UAV-aided wireless communication.

Unmanned aerial vehicle (UAV) assisted data collection is a promising technology, where a base station (BS) is mounted on a UAV to collect data from ground sensors (GSs). However, it is very challenging to save the energy of UAV while completing the tasks of data collection. In this work, a novel energy consumption model of UAV is adopted, where the UAV flies at a proper speed is the most energy efficient, i.e., the UAV will cost more energy when it flies faster or slower. According to this model, we investigate the Energy-efficient Trajectory Planning and Speed Scheduling (ETPSS) problem, aiming at minimizing the total energy consumption of UAV by determining flight trajectory and speed of UAV while completing the task of data collection for each GS. To solve this problem, we decompose it into two sub-problems, i.e., trajectory design and speed scheduling, and propose a three-step scheme named Energy-efficient Trajectory and Speed optimization (ETSO). Moreover, the second step of ETSO optimally solves the speed scheduling sub-problem. Finally, we conduct simulation experiments, and the results demonstrate that the ETSO performs well on energy efficiency.

Summary Dual‐hop 60 GHz wireless networks which support relay‐assisted dual‐hop transmission have been widely adopted in the recent years, aiming to prolong communication distance and bypass obstacles in 60 GHz band. However, it is very challenging to perform link scheduling in such dual‐hop architecture while considering several factors, i.e., reducing network power consumption, avoiding overloaded APs/relays and adapting to network dynamics. To this end, we investigate the problem of energy‐efficient link scheduling with load constraints (ELL), and propose solutions to deal with network dynamics. First, we present a fine‐grained energy model for dual‐hop 60 GHz networks, and formulate the ELL problem as an integer linear programming model that aims to minimize the network power consumption, while satisfying AP/relay load constraints. Then, we propose a polynomial‐time global scheduling algorithm that obtains a near‐optimal link scheduling solution via iterative relaxation, and the load constraint at each AP/relay can only be violated by at most an additive constant of two. Moreover, we present a local adjustment algorithm to adjust link‐scheduling solutions efficiently, such as client arrival/departure and link blockage, and design a hybrid algorithm that combines global scheduling and local adjustment. Finally, we conduct simulation experiments that validate our algorithms' effectiveness and efficiency.

Cybercrime is significantly growing as the development of internet technology. To mitigate this issue, the law enforcement adopts network surveillance technology to track a suspect and derive the online profile. However, the traditional network surveillance using the single-device tracking method can only acquire part of a suspect’s online activities. With the emergence of different types of devices (e.g., personal computers, mobile phones, and smart wearable devices) in the mobile edge computing (MEC) environment, one suspect can employ multiple devices to launch a cybercrime. In this paper, we investigate a novel cross-device tracking approach which is able to correlate one suspect’s different devices so as to help the law enforcement monitor a suspect’s online activities more comprehensively. Our approach is based on the network traffic analysis of instant messaging (IM) applications, which are typical commercial service providers (CSPs) in the MEC environment. We notice a new habit of using IM applications, that is, one individual logs in the same account on multiple devices. This habit brings about devices’ receiving sync messages, which can be utilized to correlate devices. We choose five popular apps (i.e., WhatsApp, Facebook Messenger, WeChat, QQ, and Skype) to prove our approach’s effectiveness. The experimental results show that our approach can identify IM messages with high F1 -scores (e.g., QQ’s PC message is 0.966, and QQ’s phone message is 0.924) and achieve an average correlating accuracy of 89.58% of five apps in an 8-people experiment, with the fastest correlation speed achieved in 100 s.

In recent years, 802.11ac wireless local area networks (WLANs) have been popular in campus and enterprise environments, and a large number of access points (APs) are densely deployed to meet the rapidly increasing clients' demand. In such networks, it is challenging to promote the performance of AP-client association since numerous clients need to find their respective optimal APs under the conditions of multiple capability-limited APs and a small number of available channels. Thus, the conventional association mechanism that only utilizes local information on the client side, such as received signal strength indicator (RSSI), may lead to poor network performance. In this context, we propose and implement an airtime-aware centralized association control system that deals with client requests in a centralized manner and makes AP-client association decisions according to the global information, that is, AP airtime utilization and client requests' RSSI. In particular, we design an AP airtime measurement method to obtain AP airtime utilization from the ath10k driver, and present an airtime-aware AP selection algorithm to implement AP selection policy. Furthermore, we develop an experimental testbed, and conduct the experiments to evaluate the performance of our system. The results demonstrate that our system can significantly improve network throughput and effectively guarantee clients' fairness.

Green wireless local area networks (WLANs) have captured the interests of academia and industry recently, because they save energy by scheduling an access point (AP) on/off according to traffic demands. However, it is very challenging to determine user association in a green WLAN while simultaneously considering several other factors, such as avoiding AP congestion and user migration constraints. Here, we study the energy‐efficient user association with congestion avoidance and migration constraint (EACM). First, we formulate the EACM problem as an integer linear programming (ILP) model, to minimize APs’ overall energy consumption within a time interval while satisfying the following constraints: traffic demand, AP utilization threshold, and maximum number of demand node (DN) migrations allowed. Then, we propose an efficient migration‐constrained user reassociation algorithm, consisting of two steps. The first step removeskAP‐DN associations to eliminate AP congestion and turn off as many idle APs as possible. The second step reassociates thesekDNs according to an energy efficiency strategy. Finally, we perform simulation experiments that validate our algorithm’s effectiveness and efficiency.

We investigate the problem of joint AP coverage adjustment and user association optimization for load balancing in multi-rate WLANs in this paper. We first divide the problem into two sub-problems and then formulate them as mixed integer linear programming models, which aim to minimize the AP utilization of the most congested AP while satisfying users' traffic demands. Then we design two corresponding heuristic algorithms that are performed in sequence to address the problem. Finally, we conduct extensive simulations to evaluate the performance of the proposed algorithms. The results not only show that the algorithms can balance the loads among APs effectively and efficiently, but also demonstrate that the solutions of joint AP coverage adjustment and user association optimization outperform that of AP coverage adjustment with low overhead.

The development of the Internet of Things has accelerated research in the indoor location fingerprinting technique, which provides value-added localization services for existing WLAN infrastructures without the need for any specialized hardware. The deployment of a fingerprinting based localization system requires an extremely large amount of measurements on received signal strength information to generate a location fingerprint database. Nonetheless, this requirement can rarely be satisfied in most indoor environments. In this paper, we target one but common situation when the collected measurements on received signal strength information are insufficient, and show limitations of existing location fingerprinting methods in dealing with inadequate location fingerprints. We also introduce a novel method to reduce noise in measuring the received signal strength based on the maximum likelihood estimation, and compute locations from inadequate location fingerprints by using the stochastic gradient descent algorithm. Our experiment results show that our proposed method can achieve better localization performance even when only a small quantity of RSS measurements is available. Especially when the number of observations at each location is small, our proposed method has evident superiority in localization accuracy.

Android lacks fine-grained permission control for the external storage. Under the current coarse-grained mechanism, any application is able to access all the data on the external storage very easily. At the same time, many applications store sensitive data into the external storage, and some of these data are highly concerned with user privacy, which could bring severe security problems. In this paper, we propose a fine-grained permission control mechanism for external storage of Android. The mechanism is based on Filesystem in Userspace (FUSE) and offers the following features: protecting user private media files such as photos and videos; isolating the data of each application; providing access control settings for user. We implement this mechanism on the latest Android version, by introducing a new type of GID (ESDS-GID), extending the functionality of the emulated filesystem as well as the system services. The results of functional verification and performance benchmark show that with a reasonable performance overhead, this mechanism brings considerable enhancement for Android system security.

Multi-radio WLAN mesh networks (WMNs) are intended to provide a wireless access infrastructure with high throughput and reliable transmission. With the increasing demand for ubiquitous Internet access, access networks tend to be large-scale, complex and dense, and energy consumption has become a critical concern. Since the networks are designed to support peak traffic demand but does not serve peak traffic demand all the time, which leads to significant energy wastage in off-peak conditions. This means that energy consumption can be effectively reduced through scheduling nodes on/off according to the current traffic demand. In multi-radio WMNs, each node is equipped with multiple radios, and radios can also be switched on/off for further energy saving. Therefore, we investigate the problem of joint node scheduling and radio switching for energy efficiency in this paper, which is proven to be NP-hard. We first formulate the problem as an integer linear programming model, which aims to minimize the power consumption of the network while satisfying node-radio-link, traffic demand and routing constraints. Then, we propose an efficient heuristic algorithm, which iteratively finds routing paths for all mesh access points (MAPs) while satisfying their traffic demand. In each iteration, exact one MAP is selected, and the nodes on its routing path are scheduled on and the corresponding radios are switched on. Finally, extensive simulations are conducted to evaluate the performance of the proposed algorithm in terms of the number of active nodes and radios. The results not only show that the algorithm can achieve energy saving efficiently and effectively, but also demonstrate that the joint optimization of node scheduling and radio switching can obtain better performance on energy efficiency.

In multi-radio wireless networks, it is vital to efficiently utilize the resource of non-overlapping channels, and thus the problem of channel assignment has been widely studied. Most proposed channel assignment approaches focus on reducing interference or maximizing throughput, assuming that all the radios on each node are keeping active. However, it is a significant waste of energy when network traffic is low, since the network is designed for peak traffic demands but does not serve peak traffic demands all the time. Even if the number of active radios is dynamically adaptive to time-varying network traffic, some radios will inevitably switch their channels, which significantly increase network delay. Therefore, we investigate the energy efficient channel assignment problem with switching optimization in this paper. To address this problem, we first define the optimization objective to minimize the number of active radios and channel switchings, and then propose several constraints, such as channel switching, radio amount, transmission existence, link rate, link interference, traffic demand and routing constraints. On this basis, we present an MILP model called EECA-SO, and evaluate its performance through numerical analysis. The results show that the proposed EECA-SO model can not only achieve energy saving effectively, but also obtain the trade off between energy efficiency and switching optimization.

In multi-channel wireless mesh networks (WMNs), it is significantly important to achieve efficient channel utilization. The channel assignment problem, which investigates how to seek a proper mapping between available channels and network interface cards (NICs) on mesh routers (MRs), is attracting more and more attention from the research community. However, most proposed channel assignment approaches assume that NICs are evenly placed on the MRs, and its amount is also pre-determined. Due to the non-uniform distribution of WMN traffic, the equal interface placement will inevitably cause that the bottleneck MRs suffer from NIC shortage, and MRs with less load only have a low utilization of NICs. Hence, in order to improve network performance and reduce network cost, interface placement should be considered carefully in the process of planning a WMN. In this paper, we investigate the joint interface placement and channel assignment problem, i.e., how to appropriately place NICs on MRs and assign channels to them. We first formulate the problem as a mixed integer linear programming (MILP) model, which aims to minimize the total number of NICs while satisfying logical link, link bandwidth, link interference and traffic demand constraints. Then, we propose an MILP-based heuristic algorithm, using iterated local search to obtain sub-optimal solutions efficiently. Finally, we conduct simulation experiments to compare the heuristic solutions with the optimal solutions, and the results show that our proposed heuristic algorithm can achieve good sub-optimal solutions. Moreover, the simulation results also demonstrate that the algorithm can be well applied in large-scale WMNs where the optimal solutions cannot be obtained, and can perform well under different traffic demands.

Wireless mesh networks (WMNs), as a promising technology to provide broadband Internet access, is attracting more and more attention from research community. In the research on WMN design, gateway placement is one of the most important and challenging aspects, that is, finding the optimal number and locations of gateways. Although several gateway placement approaches have been proposed, few of them consider the effect of link interference and gateway failure on network performance. In this paper, we address this issue to further optimize network performance. First of all, a tri-state interference model is defined, and on that basis, a new metric for gateway interference is proposed. Next, the gateway placement problem, which involves reducing link interference and assuring fault tolerance, is formulated as a multi-objective integer linear program issue. Then, an interference-aware and K-coverage gateway placement algorithm (IKGPA) is proposed, and a distributed fault tolerance routing mechanism is presented. Finally, the performance of our algorithm IKGPA is evaluated. Simulation results not only show the effectiveness of our algorithm, but also demonstrate that our algorithm achieves fault tolerance assurance with placing only a few additional gateways.

Wireless mesh networks (WMNs) have recently evoked much research attention as a novel technology for last-mile broadband Internet access. When designing a WMN, gateway placement is significant for it determines the total network throughput. To address this problem, a novel gateway placement approach is proposed in this paper, in which three objectives are optimized, i.e. the number of gateways, the average MR (mesh router)-GW (gateway) hop count and the variance of gateway load. The gateway placement problem is modeled as a multiple objective linear program first, and then a two-stage load balanced gateway placement algorithm is proposed. The first stage is weight-based greedy gateway selection, and the second stage is load balanced MR attachment. Simulation results show that the algorithm behaves similarly compared with existing approaches in both number of gateways and average MR-GW hop count, while achieving better load balance.