Deep learning-based multi-view stereo (MVS) methods enable dense point cloud reconstruction in texture-rich areas. However, existing methods incur significant computational costs to capture pixel dependencies for complete reconstruction in low-texture regions. Additionally, discrete depth layers in occluded environments hinder the cost volume’s ability to model object information effectively. To address these issues, we propose a spatial-aware multi-view stereo network with attention cost volume, termed SA-MVSNet. The network introduces the pixel-driven spatial interaction (PDSI) module, which integrates the hierarchical spatial location enhancement mechanism (HSLE) and the spatial context aggregation mechanism (SCA). Leveraging an efficient parallel architecture, the PDSI module captures pixel-level spatial dependencies with the HSLE and strengthens global contextual information through the SCA. This design improves the network’s ability to represent features in low-texture regions while maintaining high inference efficiency. Furthermore, SA-MVSNet incorporates an attention weight generation branch that refines the cost volume by aggregating multi-scale depth cues, effectively mitigating the impact of occlusion. Experiments on the DTU dataset and the Tanks and Temples dataset show that our method outperforms other learning-based methods, achieving superior performance and strong generalization ability.

Vehicular edge computing (VEC) emerges as a promising paradigm for processing computing-intensive parallel vehicular tasks, where vehicular tasks can be offloaded to the edge nodes [e.g., roadside units (RSUs)] to seek less computing delay. Considering the impact of computation services on offloading efficiency, there are several works that jointly study the decision making of task offloading and service caching. However, the existing works fail to consider the time-varying service requests and ignore the time-slots correlation of the computation services. To bridge the gap, this work designs a service-aware parallel task offloading approach, which is the first work to jointly explore time-varying computation services and task offloading based on real-world vehicular trajectory data in VEC networks. Specifically, we first propose a computation service prediction algorithm using the real-world vehicular trajectory data. Guided by this, RSUs flexibly precache computation services. Then, we propose a learning-based parallel task offloading algorithm, which allows vehicles to make offloading decisions based on the history of the edge selections. Furthermore, we conduct simulations to validate the proposed algorithm. The results demonstrate that the proposed algorithm reduces task delay by 45%, 58%, and 55% compared to the algorithms without service-aware computation offloading under various CPU cycles, task numbers, and time slots.

With the commercialization of fifth-generation (5G) mobile communication technology and the rapid proliferation of mobile devices (MDs), demand for data computation is surging. This growth increases the reliance of MDs on low latency and high throughput. For this purpose, Mobile Edge Computing (MEC) enhances the user's data processing capability by offloading computation tasks to servers at the network edge. However, achieving high efficiency in task offloading is challenging due to factors such as decision complexity, network dynamics, and user data privacy protection. Additionally, energy causal constraints and the coupling between offloading proportions and resource distribution cannot be ignored. In this paper, we first establish a dynamic task offloading problem to optimize the long-term throughput of the system. Using perturbed Lyapunov optimization, we transform MD delay and energy threshold constraints into the stability control of corresponding virtual queues. Then, we propose the Lyapunov-guided federated deep reinforcement learning (DRL) online task offloading algorithm called LyFOTO, which combines a federated learning (FL) framework and an Actor-Critic (AC) model. Under favorable communication conditions, the LyFOTO algorithm adaptively boosts system throughput; under poorer conditions, it properly delays task offloading, without violating queue backlog constraints. Through mathematical analysis, we discuss the performance of the LyFOTO algorithm. Simulation experiments validate that LyFOTO effectively balances system throughput and device battery energy. Finally, Comparative results show that LyFOTO outperforms other benchmark algorithms in maximizing system throughput while ensuring task backlog and energy threshold constraints.

Password, fingerprint and face recognition are the most popular authentication schemes on smartphones. However, these user authentication schemes are threatened by shoulder surfing attacks and spoof attacks. In response to these challenges, eye movements have been utilized to secure user authentication since their concealment and dynamics can reduce the risk of suffering those attacks. However, existing approaches based on eye movements often rely on additional hardware (such as high-resolution eye trackers) or involve a time-consuming authentication process, limiting their practicality for smartphones. This paper presents DEyeAuth, a novel dual-authentication system that overcomes these limitations by integrating eyelid patterns with eye gestures for secure and convenient user authentication on smartphones. DEyeAuth first leverages the unique characteristics of eyelid patterns extracted from the upper eyelid margins or creases to distinguish different users and then utilizes four eye gestures (i.e., looking up, down, left, and right) whose dynamism and randomness can counter threats from image and video spoofing to enhance system security. To the best of our knowledge, we are among the first to discover and prove that the upper eyelid margins and creases can be used as potential biometrics for user authentication. We have implemented the prototype of DEyeAuth on Android platforms and comprehensively evaluated its performance by recruiting 50 volunteers. The experimental results indicate that DEyeAuth achieves a high authentication accuracy of 99.38% with a relatively short authentication time of 6.2 seconds, and is effective in resisting image presentation, video replaying, and mimic attacks.

Modern smartphones hold massive amounts of private and potentially sensitive user data (e.g., identity and messages). User authentication is the key measure to protect such sensitive data from adversaries. In this article, we explore a novel authentication mechanism, LipAuth, leveraging the unique spatial-temporal features (i.e., both static physiological and dynamic behavioral characteristics) of human lips biometrics for secure and convenient user authentication, without requiring any special sensors on smartphones. The key principle behind LipAuth is that the geometric structure of lips is unique across different users while consistent and stable for the same user, which is dependent on three types of static features, i.e., lip width, thicknesses, and the joint characteristic of the former two, and the dynamic features in smiling process, i.e., the bending processes of the boundary lines between the upper and lower lips. On that basis, LipAuth can accurately identify legal users by actively extracting the spatial-temporal features on the lips’ profile changes, while also remaining fast and easy to use. We have implemented the prototype of LipAuth on Android platforms and comprehensively evaluated its performance by recruiting 50 volunteers. The experimental results show that LipAuth can achieve an overall 99.24% accuracy for user authentication and can resist potential intrusion from video replaying and mimic attacks.

To balance venue safety with public photography rights, this article presents CamShield—a novel system for selective defense against unauthorized photography. Amid dense electromagnetic environments, CamShield reliably identifies cameras by analyzing their unintended electromagnetic emissions. By tracing frequency drift patterns and harmonic spectral movements unique to each device, CamShield can accurately detect cameras despite environmental noise or model similarities. An integrated antenna amplitude ratio module and Kalman filter further localize threats through resilient positioning. Directional ultrasonic beams then focus tuned acoustic interference toward devices, temporarily disrupting visualization in restricted locations while preserving ambient imaging freedoms. Comprehensive evaluations across three state-of-the-art object detectors quantify real-world reliability. With 30 intruding cameras, CamShield exhibited obstruction latencies below 346 ms. Furthermore, CamShield achieves three times the coverage using the same power as traditional Omnidirectional transmission. Together, the breakthroughs in pervasive camera sensing and context-aware actuation contribute toward advancing policy-centric access controls at the edge of cyber-physical convergence. CamShield sets an important precedent on enforcing venue custom protections in bounded secure zones without undermining positive public photography assumptions elsewhere.

The increasing misuse of commercial drones for illicit activities poses significant challenges in their detection and identification. Existing methods, such as acoustic-based, radio frequency-based, and computer vision approaches, face limitations due to factors like miniaturization, stealth, and background noise. In this paper, we propose E-Argus, a system that leverages the electromagnetic radiation (EMR) emitted by the memory of drones. It is a basic fact that, with all types of drones, the implementation of arbitrary behavior must be digested in the built-in memory, and electromagnetic radiation is thus generated. Specifically, the memory clock drives the switching regulator causing current fluctuations that generate EMR signals at the clock frequency. E-Argus combines the relationship between the flight pattern of the drone and the memory EMR signal, analyzes the unique side-channel signatures, and utilizes advanced neural network-based identification; E-Argus can accurately detect and identify various types of illegal drones. We designed a system prototype based on USRP B210 and conducted experiments in a wide range of scenarios. The evaluation shows that E-Argus has low latency, high accuracy, and robustness in real environments.

Accurate perception of intelligent vehicles is critical for the safe operation of autonomous vehicles. However, current perception methods often struggle to effectively detect intelligent vehicles when obstacles block their field of view. Collaborative perception, although attracting considerable attention, presents challenges in terms of privacy and data trust. In this study, we present a novel design for Enhancing Intelligent Vehicle (), a cost-effective and comprehensive perception system for intelligent vehicles. We discovered that during the process of memory caching raw sensing data in the intelligent vehicle’s system-on-chip (SOC), continuous fluctuating currents inside the memory result in the emission of Electromagnetic Radiation (EMR). As a result, intelligent vehicles actively expose themselves on the electromagnetic spectrum. is based on a set of specially designed antenna arrays that scan the spectrum and utilize a joint Kalman filtering algorithm to enhance EMR signals. The micro-Doppler signature of each EMR signal is then analyzed to identify signals from intelligent vehicles and construct a vehicle database. A multi-antenna joint estimation algorithm is also designed to further estimate the position, distance, and direction of the target vehicle. Our experiments demonstrate that offers advantages in terms of timeliness, robustness, and accuracy.

In this article, we explore a new design paradigm of duty-cycling mechanism that supports low-power devices to fully turn channel contention into transmission opportunities. To achieve this goal, we propose Concurrent Low-power Listening (CLPL) to enable contention-tolerant and concurrent media access control (MAC) for widely deployed low-power devices. The fundamental principle behind CLPL is that frequency modulated receiver can reliably demodulate the strongest signal even if cochannel interference and noise exist. By using CLPL, a sender inserts a series of tailor-made signals (namely, wake-up signal) between adjacent data frames to awaken appointed receiver, making it capable to receive the next data frame. According to system-defined maximum transmission power level, CLPL adopts an adaptive algorithm to adjust the transmission power of wake-up signals so that its signal strength is above receiver sensitivity and will not interfere with the other data frames in transit. By exploiting the spatial-temporal correlation, we further develop a light-weight wake-up signal detection method to enable a waiting sender to accurately identify the current channel condition. Then, it schedules the sender’s data frame transmissions by overlapping with those wake-up signals, without conflicting with existing data frame transmissions. We have implemented the prototype of CLPL and conducted extensive experiments on a real testbed. In comparison with the state-of-the-art low-power MAC schemes, such as ContikiMAC, A-MAC, BoX-MAC, and opportunistic scheme ORW, CLPL can improve the throughput by 2–6 times and halve the end-to-end transmission delay.

In recent years, unmanned aerial vehicles (UAVs) have been widely used in various industries (e.g., search and rescue, express delivery, etc.) due to their high flexibility. In addition, the deployment of UAVs equipped with mobile-edge computing (MEC) servers to provide computing services at the edges of networks has become an emerging method. Under complex and limited resource constraints, increasing the total number of computation bits in the system becomes a challenging problem. Motivated by this, in this article, we propose an optimization framework to maximize the computation bits of the whole system by jointly optimizing the bandwidth allocation, the task offloading time allocation, and the trajectory of the UAV under the energy constraints of ground devices (GDs) and the maximal battery energy of the UAV. The formulated problem is a nonconvex and nonconcave problem that is very difficult to solve. To this end, we decompose the objective function into three suboptimization problems and adopt successive convex optimization techniques to solve them. Then, we utilize the block coordinate descent (BCD) algorithm to address the overall optimization problem. By doing so, the bandwidth allocation of GDs, task offloading time and local computing time allocation in each time slot, and the trajectory of the UAV are optimized alternately during each iteration. We conduct extensive simulations, and the results verify that the proposed solution achieves a better performance than those of other benchmark schemes.

With the rapid development of various applications in the Internet of Things (IoT), we have witnessed much progress with very wide differences in characteristics and requirements. In this article, we propose a novel dynamic channel assembling (DChA) strategy for channel access of heterogeneous secondary user (SU) flows in IoT-oriented cognitive radio networks (CRNs), making use of the priority queues based on fine-grained flow classification. Specifically, three categories of SU flows are considered, so-called the real-time SU (RSU) flows, the elastic large SU flows, and the elastic small SU flows. On top of this, channel access opportunities are distributed to the SU flows in three specially designed queues performing the channel access algorithm. The highlight of our main idea is that the RSU flows with higher priority are only supposed to assemble as few channels as possible, so long as their minimum requirements are fulfilled, thereby minimizing the impact on elastic SU (ESU) traffic. For the sake of performance evaluation, we utilize the continuous-time Markov chain to model our proposed strategy and conduct theoretical analyses. With the detailed theoretical analyses and extensive simulations, the proposed DChA strategy is demonstrated to be able to fulfill the deadline of SU flows, while significantly reducing the blocking probability as well as the completion time of the ESU flows.

Long-term evolution advanced (LTE-A) heterogeneous networks have been observed to offer reliable and service-differentiated communication, thereby enabling numerous mobile applications such as smart meters, remote sensors, and vehicular applications. This fact envisions the trend of Internet of Things (IoT) underlaying heterogeneous small cell networks. On this basis, this paper proposes an energy-efficient framework for such a scenario, where multitier heterogeneous small cell networks provide wireless connection and seamless coverage for mobile users and IoT nodes. In our proposed framework, an elastic cell-zooming algorithm based on the quality of service and traffic loads of end-users is performed by adaptively adjusting the transmission power of small cells in order to reduce energy consumption. In addition, aiming at the high energy efficiency of IoT underlaying small cell networks, a clustering-based IoT structure is used, where a SWIPT-CH selection algorithm is proposed to maximize the average residual energy of IoT nodes and to mitigate resource competition between IoT nodes and mobile users. Extensive simulations demonstrate that our proposed framework can significantly enhance the energy efficiency for IoT underlaying small cell networks with guaranteed outage probability.

The number of vehicles has increased year by year, especially individual vehicles. In addition to meeting basic transportation needs, vehicles are expected to serve varied location-based services and applications for humans. However, it can constitute severe risks for privacy. In this article, we concentrate on one of the most sensitive information, namely, social relationships, that can be inferred from the vehicle mobility data. We propose a social relationship inference model, which provides a new perspective for privacy preservation in human mobility data. In particular, we extract discriminative features from both the spatial and temporal dimensions. Then, the heterogeneous features are being merged with a fusion model to improve the performance of inference. Extensive experiments on the real-world data set validate the effectiveness of the extracted features in estimating social connections and demonstrate that our method significantly outperforms the baseline models.

Mobile-edge computing (MEC) has been witnessed as a promising solution for the vehicular task offloading. Due to the limited computing resource of individual MEC servers, it faces challenges when higher requirements are put forward for timely task processing of a large amount of computations in the emerging vehicular applications. In this article, we strive to realize the efficient vehicular task offloading via heat-aware MEC cooperation from the game theory perspective. Here, the heat indicates the vehicle density and is tightly related to the requests of vehicle users when they drive through the hot zones. Specifically, a deep learning-based prediction method is proposed, capturing the dynamic time-varying heat value of the hot zones based on the analysis of the real-world private car trajectory data. To identify the role of MEC in the cooperation, we take the time-delay constraint into consideration for the task offloading. To realize MEC grouping for task offloading in MEC cooperation, we formulate the MEC grouping as a utility maximization problem via designing a noncooperative game-theoretic strategy selection based on regret-matching. Furthermore, we derive the correlated equilibrium and prove that the fast convergence can be achieved. Extensive simulation results validate the effectiveness of the proposed vehicular task offloading approach under various system parameters, such as computation workload, time slots, and MEC servers number. The proposed method outperforms the existing methods, which is able to significantly reduce the task complete delay, and in the meantime enhance the MEC energy efficiency with end users' quality-of-experience guaranteed.

With the ubiquitous demand for wireless communications, researchers have studied heterogeneous networks (HetNets) for years. Often the HetNets include a macrocell base station (MBS), several sets of macrocell users (MUs), a large number of femtocell base stations (FBSs), and femtocell users (secondary users), where the femtocells help the macrocell system relay the uplink or downlink traffic between the MUs and the MBS. In this article, we propose a novel joint information and energy cooperation method, with the aim of enhancing the spectrum and energy efficiency (EE) for cognitive HetNets. Specifically, the MUs and the femtocells harvest wireless energy from the radio frequency signal transmitted by MBS. By using the harvested energy, femtocells obtain the transmission opportunity to forward the signals of their serving users. We theoretically derive the theoretical expressions of the outage probabilities of the primary link as well as the secondary link. Then, we focus on investigating how to maximize EE by jointly considering time allocation and power control. Furthermore, we formulate the EE maximization problem, which contains the fractional form objective function and the linear inequality constraints and hence is nonconvex. To resolve this, we integrate the Dinkelbach method with convex optimization to derive the tractable and optimal solution. The numerical results demonstrate the simulations well match our theoretical analysis. Moreover, the results validate the feasibility of the proposed method for high-quality transmission without incurring extra energy consumption.

In traditional ground cellular systems, ground terminals (GTs) in marginal areas are often faced with performance bottlenecks due to they are too far from the macro base station (MBS). For some temporary and unexpected communication service requirements, it is uneconomical to deploy femtocell access points (FAPs) on the edge of cell. In this paper, we investigate a cellular system that utilizes a unmanned aerial vehicles (UAVs) with base station module unit as an airborne mobile base station to provide communication channel for the cell-edge users offloaded by the MBS. Compared with FAPs, UAV is cheaper and flexible which can provide better communication quality for the cell-edge GTs with better channel condition. Based on the theoretical model, we proposed plausible optimal algorithm to maximize the energy-efficiency (EE) of the UAV by jointly optimizing the spectrum allocation, flying speed and user partition between the UAV and MBS. Numerical results indicate that our design could achieve relatively higher energy-efficiency by exploiting optimal flight strategy and spectrum allocation strategy.

The communication infrastructures are likely to fail, in the case of disasters like earthquakes and debris flow, resulting in blind areas and the inconvenience of residents' communication. In this paper, we propose a novel scheme connecting these infrastructure failure areas, namely, an energy-aware device-to-device communication scheme (NEED). Our proposed scheme, taking advantage of clustering technology, connects users within the infrastructure failure areas that often have no direct access to the cellular network. Compared with the clustering used in traditional cases, we add the process of determining candidate cluster heads (CHs) before determining final CHs. Based on location and residual energy, the final CHs are selected in the candidate CHs, and dual CHs in the cluster run alternately to share the communication cost. Besides, a modified ant colony algorithm (MACA) is developed to increase routing efficiency. The simulation results show the effectiveness of our proposed NEED scheme in terms of energy consumption and energy balance, and demonstrate that the scheme significantly extends the lifetime of the whole network.

Vehicle state including location and motion information plays an important role in various applications such as Internet of Vehicles (IoV), autonomous cars, and driving safety monitoring. Achieving accurate vehicle state is a challenging task in those applications due to the noise disturbances. Recent studies suggest that noise is not generally Gaussian distributed and many physical environments can be handled more accurately as non-Gaussian rather than Gaussian model. Inspired by this observation, we strive to improve the vehicle state estimation by investigating the effects of that assumption when process and measurement noises are non-Gaussian distributed. Here, process noise represents the noise during the state information processing. To that end, we exploit the generalized error distribution (GED) to compute the non-Gaussian probability density during the vehicle state estimation. We then derive extensive theoretical analysis targeting to estimate the parameters such as the mean and the variance (or covariance matrix) related to both process and measurement noises and reduce the computational burden of the distribution. Further, we propose a non-Gaussian particle filter for vehicle state estimation (nGPF-VSE) algorithm wherein we utilize the genetic operator resampling (GOR) technique to enhance the efficiency of particle filter (PF) relying on the selection of the importance sampling distribution. To evaluate the performance of the proposed approach, we conduct numerical simulations on the popular system of state-space equations and a real experiment for estimating the vehicle state. The results from the numerical simulations, experimental data and the statistical evaluation confirm that nGPF-VSE outperforms existing methods in terms of vehicle state accuracy.

Private cars, a class of small motor vehicles usually registered by an individual for personal use, constitute the vast majority of city automobiles and hence significantly affect urban traffic. In particular, private cars tend to stop-and-wait (SAW) in specific regions during daily driving. This SAW behavior produces a spatiotemporal aggregation effect, which facilitates the formation of urban hot zones. In this paper, we investigate the SAW behavior and aggregation effect based on large-scale private car trajectory data. Specifically, motivated by the first law of geography, we leverage the kernel density estimation (KDE) method and extend it to three dimensions to capture the density distribution of the SAW data. Furthermore, according to the inherent relationship between the present SAW density and future SAW aggregation, we propose a 3D-KDE-based prediction model to characterize the dynamic spatiotemporal aggregation effect. In addition, we design a modified inertia weight particle swarm optimization (MIW-PSO) algorithm to determine the optimal weight coefficients and to avoid local optima during SAW prediction. Extensive experiments based on real-world private car SAW data validate the effectiveness of our method for discovering dynamic aggregation effects, therein outperforming the current methods in terms of the Kullback-Leibler (KL) divergence, mean absolute error (MAE), and root mean square error (RMSE). To the best of the authors’ knowledge, our work is the first to utilize private car trajectory data to study the aggregation effect in urban environments, thereby being able to provide new insight into the study of traffic management and the evolution of urban traffic.

Due to the popularity of mobile computing and mobile sensing, users' traces can now be readily collected to enhance applications' performance. However, users' location privacy may be disclosed to the untrusted data aggregator that collects users' traces. Cloaking users' traces with synthetic traces is a prevalent technique to protect location privacy. But the existing work that synthesizes traces suffers from the social relationship based de-anonymization attacks. To this end, we propose W3-tess that synthesizes privacy-preserving traces via enhancing the plausibility of synthetic traces with social networks. The main idea of W3-tess is to credibly imitate the temporal, spatial, and social behavior of users' mobility, sample the traces that exhibit similar three-dimension mobility behavior, and synthesize traces using the sampled locations. By doing so, W3-tess can provide “differential privacy” on location privacy preservation. In addition, compared to the existing work, W3-tess offers several salient features. First, both location privacy preservation and data utility guarantees are theoretically provable. Second, it is applicable to most geo-data analysis tasks performed by the data aggregator. Experiments on two real-world datasets, loc-Gwalla and loc-Brightkite, have demonstrated the effectiveness and efficiency of W3-tess.

As cognitive radio (CR) technologies developed, cooperative CR networks (CCRNs) have become a potential way to increase the spectrum efficiency. Moreover, as the authors face the problem of energy shortage nowadays, energy harvesting (EH) is considered to be an effective method to alleviate this problem. However, with fitful and random energy arrivals, the energy harvested from natural energy source cannot guarantee satisfactory quality of service in EH networks. Hybrid energy supplies system has emerged as a promising way to solve unstable power supply problems, which mean that the communication terminals in these networks are powered by both fixed power supply and harvested energy. In this study, they propose a novel utility‐based cooperative spectrum leasing scheme for CCRNs with hybrid energy supplies. The secondary user (SU) is a cooperative relay powered largely by harvested energy extracted from the radio‐frequency signal of the primary user (PU). In this way, SU can shorten the primary data transmission period, and then win the access opportunities for secondary data transmission. They formulate this model as a Stackelberg game. In such a scenario, the PU intends to enhance its revenue and save energy by cooperating with the SU, while the SU tries to improve throughput and minimise the fixed energy consumption. The Stackelberg equilibrium for the proposed scheme is analysed. The experimental results show that the utility of both PU and SU can be improved under this scheme.

With the increasing popularity of location-based services (LBSs), it is of paramount importance to preserve one's location privacy. The commonly used location privacy preserving approach, location k-anonymity, strives to aggregate the queries of k nearby users within a so-called cloaked region via a trusted third-party anonymizer. As such, the probability to identify the location of every user involved is no more than 1/k, thus offering privacy preservation for users. One inherent limitation of k-anonymity, however, is that all users involved are assumed to be trusted and report their real locations. When location injection attacks (LIAs) are conducted, where the untrusted users inject fake locations (along with fake queries) to the anonymizer, the probability of disclosing one's location privacy could be greatly more than 1/k, yielding a much higher risk of privacy leakage. To tackle this problem, in this paper we present ILLIA, the first work that enables k-anonymity-based privacy preservation against LIA in continuous LBS queries. Central to the ILLIA idea is to explore the pattern of the users' mobility in continuous LBS queries. With a thorough understanding of the users' mobility similarity, a credibility-based k-anonymity scheme is developed, such that ILLIA is able to defense against LIA without requiring in advance knowledge of how fake locations are manipulated while still maintaining high quality of services. Both the effectiveness and the efficiency of ILLIA are validated by extensive simulations on real world dataset loc-Gowalla.

For wireless networks in the Internet of Things (IoT), cognitive radio (CR) is a promising way to obtain the available spectrum for objects. Wide-band spectrum sensing plays an important role in building such CR networks of IoT. In this paper, we propose a novel dynamic compressive wide-band spectrum sensing method based on channel energy reconstruction. After a bank of wide-band random filters is employed to measure the channel energy, rather than to recover all the channel energy in the whole spectrum, only the channel energy with a changing occupancy status in consecutive time slots is recovered. Furthermore, it is unnecessary to use reconstruction algorithm unless there are two or more channels changing their occupancy status. Compared to the existing methods, our proposed schemes bear significant improvements in the probability of detection and reduction of probability of false alarms. Simulation results also show its fast speed and robustness to noise.

Indoor localization plays an important role as the basis for a variety of mobile applications, such as navigating, tracking, and monitoring in indoor environments. However, many such systems cause potential privacy leakage in data transmission between mobile users and the localization server (LS). Unfortunately, there has been little research done on privacy issue, and the existing privacy-preserving solutions are algorithm-driven, each designed for specific localization algorithms, which hinders their wide-scale adoption. Furthermore, they mainly focus on users' location privacy, while the LS's data privacy cannot be guaranteed. In this paper, we propose a Privacy-Preserving Paradigm-driven framework for indoor LOCalization (P3-LOC). P3-LOC takes the advantage that most indoor localization systems share a common two-stage localization paradigm: information measurement and location estimation. Based on this, P3-LOC carefully perturbs and cloaks the transmitted data in these two stages and employs specially designed “k -anonymity” and “differential privacy” techniques to achieve the provable privacy preservation. The key advantage is that P3-LOC does not rely on any prior knowledge of the underlying localization algorithms, and it guarantees both users' location privacy and the LS's data privacy. Our extensive experiments from the measured data have validated that P3-LOC provides privacy preservation for general indoor localization techniques. In addition, P3-LOC is comparable with the state-of-the-art algorithm-driven techniques in terms of localization error, computation, and communication overhead.

In this paper, we focused on how to get CRs collaborate effectively as well as maximize energy savings in cognitive radio sensor network. We have established a model of evolutionary game between a cognitive sensors. In order to let cognitive sensors sense spectrum effectively, we designed a contribution-punishment mechanism, which can stimulate high SNR sensors to participate in spectrum sensing. Additionally, in order to reduce the wasted energy of sensors in idle, we introduced periodic sleep-listen mechanism. Finally, we simulated behavior dynamics for a 6-player polymorphic population game and compared our proposed scheme with non-periodic sleep-listen mechanism scheme. Result shows that our proposed scheme can stimulate high SNR sensors to participate in spectrum sensing effectively and can make the system more energy efficient.

This paper addresses the problem of decentralized event detection in large-scale wireless sensor networks (WSNs). Compared with centralized or hierarchical solutions, decentralized algorithms are superior in terms of scalability and robustness. However, traditional decentralized optimization tools, such as consensus optimization, entail intensive information exchange of high-dimensional decision vectors and multipliers. This paper exploits the phenomenon of limited influence, namely, the influence of one event only affects its neighboring area. For this scenario, we let each sensor make decisions for its local area rather than for the entire network, and individual decisions seek to collaboratively reach the global optimum through iterative local communications at low network costs. An optimal solution based on the alternating direction method of multipliers (ADMM) is developed. To further reduce the network communication load, we also propose a heuristic decentralized linear programming (DLP) algorithm, which is shown to be efficient via simulations.

This paper develops a distributed compressed spectrum sensing approach for cooperative wideband multi-hop cognitive radio (CR) networks where both primary users and CR users are active during the sensing stage. Due to the multi-hop nature, each CR needs to sense its individual spectral map that consists of both common spectral components from primary users and individualized spectral innovations arising from emissions of other CRs or interference in its local one-hop region. These CR-dependent spectral innovation components complicate the task of user cooperation for primary user detection. To cope with these difficulties, we adopt the compressed sensing approach at local CRs to attain high-resolution signal recovery at lower-than-Nyquist sampling rates. Each CR alternatively estimates the spectral occupancy of primary and CR users, and exchanges proper information with neighboring CRs to reach global fusion and consensus on the estimated primary user spectrum. The spectral orthogonality between primary users and CR users is exploited to improve the spectral estimation accuracy. Using only one-hop local communications, the proposed distributed algorithm converges fast to the globally optimal solution at low communication and computation load scalable to the network size.

Spectrum detection technology is one of the key technologies in cognitive radio network (CRN), and its primary task is to identify the existence of spectrum holes and the appearance of authorized users. In order to meet the hard real-time and high reliable requirements of the spectrum detection in CRN, this paper presents a novel wide-band spectrum sensing algorithm, called clustering-based joint compressive sensing(C-JCS), which combines hierarchical data-fusion idea with jointly compressive reconstruction technology. To validate the efficiency and effectiveness, we compare the C-JCS with independent compressive sensing (ICS) and joint compressive sensing (JCS) in the detection probability, false-alarm probability and algorithm execution time under the circumstance of different SNR and compression ratio. The simulation results show that the C-JCS can sense the wide-band spectrum with high accuracy in time, so as to meet the requirements of the spectrum sensing in CRN.

Localization has many important applications in wireless sensor networks. A variety of wireless technologies, such as acoustic, infrared, and ultra-wide band (UWB) media have been applied for localization purposes. This paper consists of two parts. The first part presents new UWB-based communication protocols for received signal strength (RSS) information collection, namely, a robust UWB coding method called U-BOTH (UWB based on Orthogonal Variable Spreading Factor and Time Hopping), an ALOHA-type channel access method and a message exchange protocol to collect location information. The second part presents the localization algorithm, which is applied in coal mine environments. The localization algorithm first derives the corresponding UWB path loss model, then applies the maximum likelihood estimation (MLE) method to compute the distances to the reference sensors using the RSS information, and to estimate the coordinate of the moving sensor using least squares (LS) method. The performance of the system is validated using theoretic analysis and simulations. Results show that U-BOTH transmission technique can effectively reduce the bit error rate under the path loss model, and the corresponding ranging and localization algorithms can accurately compute object locations in coal mine environments.