Due to the intricate chaotic environments encountered in distributed sensor applications, such as sea monitoring, machinery fault diagnosis, and EEG weak signal detection, neural networks often face insufficient data to effectively carry out detection tasks. In contrast to traditional machine learning models, a statistical approach employing multidimensional nonlinear correlation (MNC) exhibits an unparalleled signal pattern prediction capability and possesses a streamlined yet robust framework for signal processing. However, the direct application of MNC to weak pulse signal detection remains constrained. To surmount these challenges and achieve high‐precision signal detection, we explore a novel MNC approach, integrating phase reconstruction and manifold broad learning, specifically tailored for distributed sensor fusion detection amidst chaotic noise. Initially, the distributed observational data undergoes phase space reconstruction, transforming it into fixed‐size arrays. These reconstructed tuples are then processed through the high‐dimensional sequence of manifold broad learning, serving as inputs for the nonlinear correlation module to extract spatiotemporal features. Subsequently, a MNC system augmented with a QRS detector layer is devised to predict and classify the presence of a weak pulse signal. This integrated MNC approach, combining phase reconstruction and broad learning, operates within an enhanced feature space of the source domain, realizing detection fusion across distributed sensors through a majority voting principle. Simulation studies and experiments conducted on sea clutter datasets demonstrate the efficacy and robustness of the proposed MNC method, leveraging phase reconstruction and manifold broad learning strategies, for distributed sensor weak pulse signal fusion detection within chaotic backgrounds.

Providing encrypted communications among power grid components is expected to be a basic requirement of smart grid systems in the future. Here, we propose a key management architecture and associated protocols tailored to support encrypted smart grid communications. The architecture consists of two levels structured around the grid control system hierarchy. At the top level, which consist of control centers and regional coordinators, a bottom-up key structure is adopted using hash chaining and a logical key hierarchy. The lower level of the architecture consists of the regional coordinators (i.e., substations and distribution systems) and remote ends (e.g., meters and pole-top sensors) and utilizes a top-down key management approach built on an inverse element method. The proposed key management schema supports the hierarchical structure of the smart grid control mechanisms, and it takes the resource and electronic/physical security differences of the control levels into account. We define a set of protocols utilizing the architecture to provide secure unicast, multicast, and broadcast communications. Furthermore, we illustrate how the architecture is flexible enough to easily handle power grid nodes joining and leaving the system at the different levels. Lastly, we compare the proposed schema with existing ones and show that our architecture can achieve efficient key management to provide secure communications. Copyright © 2016 John Wiley & Sons, Ltd.

Location-Based Social Networks (LBSNs), (also called as Geo-Social Networks), has been attracting more and more users by providing services that integrate social activities with location information. LBSN systems usually provide support for indicating various Points of Interest (POIs) but there is no straightforward rating mechanism for POIs in most LBSNs [1]. POI recommendations in LBSNs, thus, is an important and challenging research topic. In this paper, we first investigate the dataset crawled from Foursquare to explore the features that attract and influence users to check in at various POIs. Based on the analysis results, we propose a HITS (Hypertext Induced Topic Search)-based POI recommendation algorithm to recommend POIs to LBSN users that can also incorporate the impact of the social relationships on recommendations. We evaluate our proposed model on Foursquare dataset and compare our results with the latest POI recommendation algorithm. The experimental results show that our approach performs better.

Recently, social media has become an increasingly important part of business and marketing. More and more businesses use social media as part of their marketing platforms. Moreover, the fast development of the 4th generation mobile network and the ubiquity of the advanced mobile devices in which GP

Understanding users' behavior in Location-based Social Networks (LBSNs) is becoming an interesting research topic. In LBSNs, users can explore the places of interest around their current locations, check in at these locations and share such check-ins with their friends or the public. Therefore, the check-ins are valuable information for studying user behavior. Many services would benefit from the research of user behavior. For example, it can help the urban design and development based on the user mobility patterns and it could also improve the location recommendations to help users to find their places of interests. Intrinsically, traveler's activities in LBSN are distinctive, especially when compared with the a local user's activities. Therefore, a study of travelers' activities in LBSNs can help understand traveler behavior and then help LBSNs provider to improve their services, e.g. location recommendation service to new visitors. The location recommendation is especially important for a new visitor to a city. However, in the literature, there is little work specially focusing on the research of travelers' behavior in LBSNs. In this paper, we take the first step towards understanding such user behavior in LBSNs. Our research is based on the travelers' check-in information created in the greater Pittsburgh area in Foursquare. At first, we empirically study the venues and the check-ins created on such venues based on venue category information. After that, we investigate the temporal features of travelers' check-ins, and examine the evolution of check-ins created at the venues related to four categories using spatio-temporal information. Besides the empirical study, we employ the notion of user entropy to investigate the diversity of the travelers' check-ins. Through the research of the user entropy as a function of the user's check-ins, we find that the majority travelers usually exhibit higher diversity in their activities. Moreover, we also use the Latent Dirichlet Allocation (LDA) to generate travelers' mobility patterns. These human centric latent topics cannot only help to cluster the venues but also address the hot spots in a city based on the crowd level.

The location based social networking services (LBSNSs) are becoming very popular today. In LBSNSs, such as Foursquare, users can explore their places of interests around their current locations, check in at these places to share their locations with their friends, etc. These check-ins contain rich information and imply human mobility patterns; thus, they can greatly facilitate mining and analysis of local geographic topics driven by users' trajectories. The local geographic topics indicate the potential and intrinsic relations among the locations in accordance with users' trajectories. These relations are useful for users in both location and friend recommendations. In this paper, we focus on exploring the local geographic topics through check-ins in Pittsburgh area in Foursquare. We use the Latent Dirichlet Allocation (LDA) model to discover the local geographic topics from the checkins. We also compare the local geographic topics on weekdays with those at weekends. Our results show that LDA works well in finding the related places of interests.

Universal Mobile Telecommunications System (UMTS) can support IP Multimedia services by including the IP Multimedia Subsystem (IMS) as part of its core network. To use IMS services, a user equipment needs to first authenticate itself with the UMTS and then with the IMS. However, these two authentication protocols share many similar operations. Recent research efforts have highlighted this issue and hence researchers have proposed one-pass authentication protocols to reduce the number of such overlapping steps and to address security vulnerabilities in the original IMS protocol. In this paper, we propose an enhanced one-pass authentication protocol that addresses the weaknesses of the two previously proposed one-pass authentication protocols. We also provide comparative analysis of our proposed work with the existing approaches in terms of performance, security and compatibility.

3GPP has standardized IP multimedia subsystem (IMS) as the solution for multimedia service delivery based on Internet for future next-generation networks (NGNs) and it aims to merge Internet and wireless cellular networks together in order to enable rich multimedia services. The home subscriber server (HSS) is a crucial entity in IMS, which provides the functions such as access authentication and authorization as well as service data management in GPRS and CS domain. However, it still cannot provide the automatic service selection to help the users access application servers according to their demands and contexts. This paper proposes a novel context-aware service selection approach which helps perform service selection according to individual user's contexts such as the preferences and habits. This approach includes two main components which are set in HSS. One is context management and the other is selection engine, which employs the dynamic fuzzy analysis hierarchy model (DFAHM) to conduct service selection according to the contexts. Based on the quantified dynamic and fuzzy evaluation, the DFAHM helps select the most suitable services for the users. And, the model evaluation is presented to verify the feasibility of DFAHM. The results show that the DFAHM helps achieve service selection according to the users' contexts.