Indoor localization determines target locations by analyzing wireless signal characteristics and is widely used in indoor emergent rescue, mobile healthcare, and intelligent warehousing. Fingerprint-based localization methods mostly adopt received signal strength (RSS), amplitude, or phase. However, in non-line-of-sight (NLOS) scenarios, these classic features are insufficient to accurately distinguish the locations of closely adjacent devices, resulting in limited localization accuracy. Therefore, we propose a heterogeneous spatiotemporal feature fusion (HSFF) and dual-channel convolutional broad learning networks (DC-BLN) for indoor localization. The method first couples phase difference (PD) and power delay profile (PDP) extracted from channel state information (CSI). These fused features are encoded into a three-channel image that preserves both domain and spatial characteristics. A lightweight DC-BLN is then designed to decouple deep spatiotemporal features and perform incremental broad expansion for fast online updates. A large number of tests are carried out in typical laboratory and meeting room, and the experimental results show that the proposed method achieves mean errors of 2.07 m (laboratory) and 1.42 m (meeting room), with corresponding standard deviations of 1.60 m and 0.94 m respectively. These results significantly outperform six existing baseline methods in localization accuracy and robustness.

Abstract We investigate temporal-clique subgraph pattern matching, where edges must both form a specific topological sub-structure and temporally overlap within a specified window. This problem has widespread applications across domains including social networks, life sciences, smart cities, and telecommunications. However, existing subgraph matching techniques are inefficient at processing such queries that combine both temporal and structural constraints. We propose a novel approach that effectively leverages both topological and temporal selectivities of the query to significantly improve processing performance. Our solution introduces key innovations across the query processing pipeline, including a specialized multi-way join operator, an optimized query planner, and an accurate cardinality estimator. Through additional optimizations, we further enhance the efficiency of our approach. Extensive experiments demonstrate that our method substantially outperforms state-of-the-art techniques while requiring minimal additional storage overhead.

Node localization technology is increasingly receiving extensive attention from academia and industry due to its strong concealment and high fault tolerance in wireless sensor networks (WSNs). Mobile anchor nodes (MANs) assisted localization is often used in existing WSNs. However, assisted localization based on MANs is still a challenging problem. On one hand, it is difficult to determine the number of anchor nodes (ANs) to support the energy required for the entire movement trajectory. On the other hand, unknown nodes at the boundary region are difficult to obtain sufficient beacon information for localization. A localization algorithm based on the relationship between trapezoidal trajectory and energy consumption of MANs is proposed to solve this challenging problem in the current research. In the proposed algorithm, we design a trapezoidal trajectory based localization algorithm for MANs (TTLMA) to optimize the movement trajectory of ANs. At the same time, determine the number of ANs by analyzing the relationship between the initial energy of ANs and the energy required by the localization algorithm. Select an appropriate algorithm to locate unknown nodes (UNs) according to the number of beacon information received by them. We conducted multiple simulations to evaluate the proposed algorithm’s performance. The experimental results indicate that compared with five existing typical localization algorithms, the proposed algorithms have positive advantages in terms of localization error and coverage, with average localization error reduced by 0.15m-1.16m, average localization coverage improved by 8%-38%. Moreover, the energy consumption of the proposed algorithm is relatively low, requiring only one anchor node to traverse the designed trapezoidal trajectory.

IP scanning is crucial for network management and security. However, the brute-force scanning is infeasible in IPv6 networks due to the vast address space. Consequently, target generation algorithms (TGAs) have become necessary to address this issue. Nevertheless, existing algorithms often struggle with low hit rates due to coarse-grained pattern mining. To address this problem, we propose 6Subpattern, a target generation algorithm based on subpattern analysis for Internet-wide IPv6 scanning. 6Subpattern first clusters seeds into high-density regions according to the seed structure information. Subsequently, pattern mining and subpattern analysis are carried out in these regions. Different from previous works, 6Subpattern can obtain all fine-grained patterns while automatically avoiding the influence of outlier addresses and the quandary of setting heuristic thresholds through subpattern analysis. Moreover, pattern refining is conducted based on the distribution of nibbles in address regions to further narrow the scanning space. Finally, targets are effectively generated according to the density of the patterns. Experimental results on real-world networks demonstrate that the address patterns discovered by 6Subpattern provide a superior scanning space than existing algorithms. Further results of hit rates on nine candidate seed sets reveal that 6Subpattern can achieve a 53%-315% improvement over the static TGAs on all seed sets and achieve a 15%-25% improvement on all randomly sampled seed sets compared with dynamic TGAs in Internet-wide IPv6 scanning.

The geographical locations of smart devices can help in providing authentication information between multimedia content providers and users in 5G networks. The IP geolocation methods can help in estimating the geographical location of these smart devices. The two key assumptions of existing IP geolocation methods are as follows: (1) the smallest relative delay comes from the nearest host; (2) the distance between hosts which share the closest common routers is smaller than others. However, the two assumptions are not always true in weakly connected networks, which may affect accuracy. We propose a novel street-level IP geolocation algorithm (Corr-SLG), which is based on the delay-distance correlation and multilayered common routers. The first key idea of Corr-SLG is to divide landmarks into different groups based on relative-delay-distance correlation. Different from previous methods, Corr-SLG geolocates the host based on the largest relative delay for the strongly negatively correlated groups. The second key idea is to introduce the landmarks which share multilayered common routers into the geolocation process, instead of only relying on the closest common routers. Besides, to increase the number of landmarks, a new street-level landmark collection method called WiFi landmark is also presented in this paper. The experiments in one province capital city of China, Zhengzhou, show that Corr-SLG can improve the geolocation accuracy remarkably in a real-world network.

The notion of socioeconomic status (SES) of a person or family reflects the corresponding entity's social and economic rank in society. Such information may help applications like bank loaning decisions and provide measurable inputs for related studies like social stratification, social welfare and business planning. Traditionally, estimating SES for a large population is performed by national statistical institutes through a large number of household interviews, which is highly expensive and time-consuming. Recently researchers try to estimate SES from data sources like mobile phone call records and online social network platforms, which is much cheaper and faster. Instead of relying on these data about users' cyberspace behaviors, various alternative data sources on real-world users' behavior such as mobility may offer new insights for SES estimation. In this paper, we leverage Smart Card Data (SCD) for public transport systems which records the temporal and spatial mobility behavior of a large population of users. More specifically, we develop S2S, a deep learning based approach for estimating people's SES based on their SCD. Essentially, S2S models two types of SES-related features, namely the temporal-sequential feature and general statistical feature, and leverages deep learning for SES estimation. We evaluate our approach in an actual dataset, Shanghai SCD, which involves millions of users. The proposed model clearly outperforms several state-of-art methods in terms of various evaluation metrics.