Users upload their own WiFi localization request signals to match against the received signal strengths (RSSs) in an offline constructed fingerprint database, thereby obtaining their own geographic location estimates. However, the relationships between the measured signals of users are seldom explored for enhancing the localization accuracy. Furthermore, the tedious site surveys required for building an offline fingerprint database hinder the wider application of this technology. To address the above issues, this article proposes a collaboratively enhanced lightweight WiFi localization algorithm named CBWF+, capable of providing an accurate indoor position with low-overhead fingerprints and collaborative localization. Specifically, we first discretize the area of interest to produce virtual points (VPs), and leverage the concept of circular-boundary-based localization to select a few VPs that share similar signal characteristics with the request signals. Then, important users for collaborative localization are chosen by two new proposed indicators. Next, based on the selection results and the RSS relationships among users, the spatiotemporal characteristics of WiFi signals are used to further filter out VPs that contradict the relationship between RSS and physical location. Finally, the credible VPs are used to obtain a high-accuracy estimate, by a proposed weighting method. The results of the real-world experiments validate the effectiveness of our proposed CBWF+ algorithm, compared to other state-of-the-art approaches (e.g., NN, OCLoc, CBWF, etc.). Specifically, in a 40-m$\times 17$-m real scenario with only 20 reference points (RPs) and 11 access point (APs), our algorithm achieves an average localization accuracy of 2.49 m. Our codes are available at:https://github.com/dadadaray/CBWF2.0.

As industrial control systems become increasingly interconnected with information networks, attackers could exploit vulnerabilities across different system layers to create complex exploit chains to compromise field control elements. As such, security decision-making is of essential importance to maintain the operational security of critical industrial infrastructures. In this paper, we consider the problem of designing cost-effective defense strategies to minimize the risk of successful attack paths. To this end, we propose a non-Markovian security game framework on labeled attack graphs to simulate the attack-defense process in industrial control systems. Compared with existing methods, where the cost of exploiting a vulnerability is considered constant, we consider a more dynamic and realistic case where the exploitation cost is discounted with the number of exploitations. Moreover, a state-decomposition based multi-agent reinforcement learning algorithm is developed to obtain the Nash equilibrium of the proposed non-Markovian security game. A case study on a simulated industrial control system is presented to illustrate the feasibility of the proposed approach. The results demonstrate that the discounting exploitation cost could greatly alter the attack and subsequently the defense strategies. In comparison to traditional static intrusion response approaches, our non-Markovian approach offers a more realistic and adaptive framework to anticipate evolving attack paths and allocate defense resources.

Document-level relation extraction (RE) aims to determine the relations between entities scattered across different sentences through reading and reasoning. Existing methods use semantic segmentation to obtain global information among triples by analyzing entity-level matrices. However, complete document input may introduce certain interference, making it challenging to express the underlying relationships. To address this, we propose a novel approach introducing a low-entity redundancy feature map, achieved by removing certain entities. The proposed optimal path filtering (OPF) selects entity-related sentences using heuristic rules and formulates sentence selection as a set cover problem, solved via backtracking pruning. U-Net is then applied to obtain global features. Our experiment achieves state-of-the-art results on two common document-level RE datasets, Re-DocRED and CDR, outperforming previous methods.

As a promising indoor localization technology, WiFi fingerprint-based localization encounters many issues that need to be addressed urgently, such as high-overhead fingerprint map construction, device heterogeneity among either mobile devices or access points (APs), etc. In this article, we present CBWF: a lightweight circular boundary -based WiFi fingerprinting localization system that is able to provide low-overhead, device calibration-free accurate indoor localization. CBWF achieves this by dividing a localization area into multiple subregions, and then leveraging the relation between the received signal strength (RSS) vectors from two different APs as fingerprints for localization. The key idea behind CBWF is that a superior division mechanism is attained to divide the localization area. Specifically, we propose the circle boundary mechanism to better approximate the real boundary of subregions, compared with the widely used linear boundary mechanism, and then sufficiently exploit the theoretical characteristics behind this novel mechanism. Extensive simulation and real-world experiments show that our lightweight system outperforms state-of-the-art approaches. Specifically, in a 40 m$\times 17$m real scenario with only 20 reference points (RPs) and 11 APs, CBWF achieves an average localization accuracy of 2.95 and 4.15 m for two different mobile devices, respectively. Our codes are available at:https://github.com/dadadaray/circular-boundary.

WiFi Fingerprint-based Localization (WFL) has recently achieved promising results in the bloom of deep learning techniques. Unfortunately, current studies reveal the great risks of deep-learning models when facing adversarial attacks, raising broader concerns about Deep-learning-based WiFi Fingerprint Localization Models (DFLMs). However, real-world adversarial attacks targeting DFLMs are not fully investigated, making it unclear how to counter this potential threat. In this paper, we take the first step to introduce adversarial examples into the physical world against DFLMs. Specifically, we propose a general attack method named Phy-Adv, consisting of a physical attenuation loss and a differentiable simulation module, the generated adversarial noise could be feasibly produced in the real world and make effects on DFLMs, i.e., misleading the DFLMs from the signal source end. Furthermore, aiming at countering this typical adversarial threat, we propose a Relaxant Multiple Batch Normalization (RMBN) approach, which alleviates the weak robustness of DFLMs by the data-end adaptive training-set segmenting and model-end multiple batch normalization designing. To demonstrate the de facto effectiveness of the proposed physical adversarial examples and the adversarial defense strategy, we conducted extensive experiments on 2 datasets, i.e., BHD and TUT, and multiple deep models, e.g., AlexNet, VGG, and ResNet. The experimental results strongly support that our Phy-Adv shows satisfactory adversarial attacking ability in the physical world, meanwhile, the RMBN enjoys considerable defense ability against the adversarial attacks.

The problem of seeking Nash equilibrium (NE) based on aggregative games under quantization constraints is full of challenges. Although the NE seeking algorithm in continuous-time systems has been studied, this problem in discrete-time systems still needs to be solved urgently. To address this problem, three distributed algorithms are first proposed under three quantization cases, adaptive, random, and time-varying quantizations, based on doubly stochastic communication topology networks. Then, the actions of players would eventually converge to NE under the conditions of vanishing step size and strong monotonicity are proved. Moreover, the convergence rate of the three quantization cases are analyzed, respectively. Finally, numerical experiments are implemented on plug-in hybrid electric vehicles (PHEVs) to validate the effectiveness of the proposed distributed algorithms. Comparing the convergence rates of the three proposed algorithms, the convergence effect of the adaptive quantization is better than that of the other two quantization cases.

WiFi fingerprinting-based indoor positioning system is vulnerable to the dynamic environment, which makes the positioning accuracy decrease and the fingerprint map invalid. To address these issues, an accurate indoor positioning system (AIPS) with fingerprint map adaptation is proposed. For online positioning, it treats the received signal strength (RSS) from each access point (AP) individually and can be divided into two steps: 1) coarse and 2) fine positioning. In coarse positioning, a novel clustering algorithm based on${RSS}$attenuation is proposed. In fine positioning, signal noise is considered to construct AP ring, and the reference point (RP) contained by the largest number of rings is selected as nearest RP, and then the RPs with larger number are searched out by region growing algorithm to estimate the location of test point (TP). For the fingerprint map adaptation,$K$-means is adopted to divide APs into two types, based on the number of rings, and to find which APs’ information has been changed by the dynamic environment, and then update them through Gaussian process regression. The experimental results show that the positioning algorithm in AIPS can improve the positioning accuracy compared with other algorithms, and the fingerprint map adaptation scheme in AIPS can reduce the online running time while keeping accuracy.