On Localization Issues of Mobile Devices

Mobile devices, such as sensor nodes, smartphones and smartwatches, are now widely used in many applications. Localization is a highly important topic in wireless networks as well as in many Internet of Things applications. In this thesis, four novel localization schemes of mobile devices are introduced to improve the localization performance in three different areas, like the outdoor, indoor and underwater environments. Firstly, in the outdoor environment, many current localization algorithms are based on the Sequential Monte MCL, the accuracy of which is bounded by the radio range. High computational complexity in the sampling step is another issue of these approaches. Tri-MCL is presented, which significantly improves on the accuracy of the Monte Carlo Localization algorithm. To do this, three different distance measurement algorithms based on range-free approaches are leveraged. Using these, the distances between unknown nodes and anchor nodes are estimated to perform more fine-grained filtering of the particles as well as for weighting the particles in the final estimation step of the algorithm. Simulation results illustrate that the proposed algorithm achieves better accuracy than the MCL and SA-MCL algorithms. Furthermore, it also exhibits high efficiency in the sampling step. Then, in the GPS-denied indoor environment, Twi-Adaboost is proposed, which is a collaborative indoor localization algorithm with the fusion of internal sensors such as the accelerometer, gyroscope and magnetometer from multiple devices. Specifically, the datasets are collected firstly by one person wearing two devices simultaneously: a smartphone and a smartwatch, each collecting multivariate data represented by their internal parameters in a real environment. Then, the datasets from these two devices are evaluated for their strengths and weaknesses in recognizing the indoor position. Based on that, the Twi-AdaBoost algorithm, an interactive ensemble learning method, is proposed to improve the indoor localization accuracy by fusing the co-occurrence information. The performance of the proposed algorithm is assessed on a real-world dataset. The experiment results demonstrate that Twi-AdaBoost achieves a localization error about 0.39 m on average with a low deployment cost, which outperforms the state-of-the-art indoor localization algorithms. Lastly, the characteristics of mobile UWSNs, such as low communication bandwidth, large propagation delay, and sparse deployment, pose challenging issues for successful localization of sensor nodes. In addition, sensor nodes in UWSNs are usually powered by batteries whose replacements introduces high cost and complexity. Thus, the critical problem in UWSNs is to enable each sensor node to find enough anchor nodes in order to localize itself, with minimum energy costs. An Energy-Efficient Localization Algorithm (EELA) is proposed to analyze the decentralized interactions among sensor nodes and anchor nodes. A Single-Leader-Multi-Follower Stackelberg game is utilized to formulate the topology control problem of sensor nodes and anchor nodes by exploiting their available communication opportunities. In this game, the sensor node acts as a leader taking into account factors such as `two-hop' anchor nodes and energy consumption, while anchor nodes act as multiple followers, considering their ability to localize sensor nodes and their energy consumption. I prove that both players select best responses and reach a socially optimal Stackelberg Nash Equilibrium. Simulation results demonstrate that the proposed EELA improves the performance of localization in UWSNs significantly, and in particular the energy cost of sensor nodes. Compared to the baseline schemes, the energy consumption per node is about 48% lower in EELA, while providing a desirable localization coverage, under reasonable error and delay. Based on the EELA scheme, an Adaptive Energy Efficient Localization Algorithm using the Fuzzy game theoretic method (Adaptive EELA) is proposed to solve the environment adaptation problem of EELA. The adaptive neuro-fuzzy method is used as the utility function of the Single-Leader-Multi-Follower Stackelberg game to model the dynamical changes in UWSNs. The proposed Adaptive EELA scheme is able to automatically learn in the offline phase, which is required only once. Then, in the online phase, it can adapt to the environmental changes, such as the densities of nodes or topologies of nodes. Extensive numerical evaluations are conducted under different network topologies and different network node densities. The simulation results demonstrate that the proposed Adaptive EELA scheme achieves about 35% and 66% energy reduction per node on average comparing the state-of-the-art approaches, such as EELA and OLTC, while providing a desirable localization coverage, localization error and localization delay

Network Intrusion Detection with Edge-Directed Graph Multi-Head Attention Networks

A network intrusion usually involves a number of network locations. Data flow (including the data generated by intrusion behaviors) among these locations (usually represented by IP addresses) naturally forms a graph. Thus, graph neural networks (GNNs) have been used in the construction of intrusion detection models in recent years since they have an excellent ability to capture graph topological features of intrusion data flow. However, existing GNN models treat node mean aggregation equally in node information aggregation. In reality, the correlations of nodes and their neighbors as well as the linked edges are different. Assigning higher weights to nodes and edges with high similarity can highlight the correlation among them, which will enhance the accuracy and expressiveness of the model. To this end, this paper proposes novel Edge-Directed Graph Multi-Head Attention Networks (EDGMAT) for network intrusion detection. The proposed EDGMAT model introduces a multi-head attention mechanism into the intrusion detection model. Additional weight learning is realized through the combination of a multi-head attention mechanism and edge features. Weighted aggregation makes better use of the relationship between different network traffic data. Experimental results on four recent NIDS benchmark datasets show that the performance of EDGMAT in terms of weighted F1-Score is significantly better than that of four state-of-the-art models in multi-class detection tasks

CAF: Cluster Algorithm and A-Star with Fuzzy Approach for Lifetime Enhancement in Wireless Sensor Networks

Energy is a major factor in designing wireless sensor networks (WSNs). In particular, in the real world, battery energy is limited; thus the effective improvement of the energy becomes the key of the routing protocols. Besides, the sensor nodes are always deployed far away from the base station and the transmission energy consumption is index times increasing with the increase of distance as well. This paper proposes a new routing method for WSNs to extend the network lifetime using a combination of a clustering algorithm, a fuzzy approach, and an A-star method. The proposal is divided into two steps. Firstly, WSNs are separated into clusters using the Stable Election Protocol (SEP) method. Secondly, the combined methods of fuzzy inference and A-star algorithm are adopted, taking into account the factors such as the remaining power, the minimum hops, and the traffic numbers of nodes. Simulation results demonstrate that the proposed method has significant effectiveness in terms of balancing energy consumption as well as maximizing the network lifetime by comparing the performance of the A-star and fuzzy (AF) approach, cluster and fuzzy (CF)method, cluster and A-star (CA)method, A-star method, and SEP algorithm under the same routing criteria

Development of SCAR Marker Related to Summer Stress Tolerance in Tall Fescue (Festuca arundinacea)

Summer stress tolerance (SST) is one of the most important breeding objectives in tall fescue (Festuca arundinacea), an important perennial cool-season grass. However, breeding for better SST is generally complicated by the many environmental factors involved during the growing season. Utilizing the bulked segregant analysis (BSA), we were able to identify one marker related to SST from 100 inter-simple sequence repeat (ISSR) markers and 800 random amplified polymorphic DNA (RAPD) markers, and successfully developed a dominant sequence characterized amplified region (SCAR) marker T_SC856 from the UBC856 sequence. Furthermore, the SCAR marker was tested in different clones of new populations, which were identified under complex summer stress (high temperature and humidity, Pythium blight, and brown patch), and it exhibited relatively high consistency (77%) with the phenotype. We believe that with more markers obtained in the future, better efficiency is likely to be achieved in breeding for improved SST in tall fescue and possibly other species as well. Further studies that analyze the factors relating to the SCAR marker are needed

What we know about grief intervention: a bibliometric analysis

BackgroundGrief is a natural and individualized response to different losses, but if grief persists or becomes pathological, professional interventions are required. Grief and corresponding interventions have received increasing attention, as the related concepts have been incorporated into the DSM-5 and ICD-11. Therefore, we conducted a bibliometric analysis to explore the developments in the field of grief intervention research.MethodsArticles on grief interventions were systematically searched and screened from the Web of Science Core Collection. The retrieved data were analyzed and visualized using VOSviewer and Bibliometrix software for journals, authors, institutions, countries, references, and keywords.ResultsA total of 9,754 articles were included. The number of articles on grief interventions has increased significantly each year since 1990. Death Studies was the journal that published the most articles in this field. We identified 25,140 authors contributed to this research area and these authors were from 123 countries and 6,630 institutions. Boelen PA secured the first position in article production, Columbia University emerged as the most productive affiliation and the United States was the foremost leading in grief intervention research. The prevalent keywords utilized in this field comprised bereavement, grief, death, depression, and palliative care.ConclusionThe quantity of publications regarding grief interventions is increasing. Although most prior studies have focused on mortality, grief, and health, emerging themes such as COVID-19, grief among workers, and disfranchised grief have drawn increasing attention in recent years. Future studies may focus on investigating the complexities and challenges of grief, including its underlying mechanisms and impact on mental well-being

Analysis of Genetic Diversity in 73 Kentucky Bluegrass Materials by SSR and SRAP Markers

Kentucky bluegrass (Poa pratensisL.) (KBG) is a commonly used grass that possesses excellent quality, as well as a complex genetic background and reproductive patterns. In this study, a total of 73 KBG germplasms were collected, of which 49 were imported varieties, 5 were Chinese breeding varieties, and 19 were wild materials. A total of 70 simple sequence repeat (SSR) and 75 sequence-related amplification polymorphism (SRAP) markers were selected to use for genetic diversity analysis. From these studies, high levels of polymorphisms were observed in SRAPs (91.8%) and SSRs (94.5%), respectively. Three dendrograms that were generated from SRAP, SSR, and SRAP+SSR combined data revealed a general similarity for the positioning of the majority of materials. However, certain materials, including Z65, Z25, and Z27, were found to be located in diverse clusters among different dendrograms. Further analysis demonstrated no significant association between geographical origin and molecular marker clusters in the wild materials. Combined with the seedling phenotype identification carried out in our prior study, it seems as though there is no significant relationship between agronomic characterization and marker-based clustering in these materials, except for in the case of leaf color. These studies provided an increased understanding of genetic diversity among KBG materials, which will be beneficial for genetic improvement and germplasm conservation in the future

A Cross-Scale Neutral Theory Approach to the Influence of Obesity on Community Assembly of Human Gut Microbiome

Background: The implications of gut microbiome to obesity have been extensively investigated in recent years although the exact mechanism is still unclear. The question whether or not obesity influences gut microbiome assembly has not been addressed. The question is significant because it is fundamental for investigating the diversity maintenance and stability of gut microbiome, and the latter should hold a key for understanding the etiological implications of gut microbiome to obesity.Methods: In this study, we adopt a dual neutral theory modeling strategy to address this question from both species and community perspectives, with both discrete and continuous neutral theory models. The first neutral theory model we apply is Hubbell's neutral theory of biodiversity that has been extensively tested in macro-ecology of plants and animals, and the second we apply is Sloan's neutral theory model that was developed particularly for microbial communities based on metagenomic sequencing data. Both the neutral models are complementary to each other and integrated together offering a comprehensive approach to more accurately revealing the possible influence of obesity on gut microbiome assembly. This is not only because the focus of both neutral theory models is different (community vs. species), but also because they adopted two different modeling strategies (discrete vs. continuous).Results: We test both the neutral theory models with datasets from Turnbaugh et al. (2009). Our tests showed that the species abundance distributions of more than ½ species (59–69%) in gut microbiome satisfied the prediction of Sloan's neutral theory, although at the community level, the number of communities satisfied the Hubbell's neutral theory was negligible (2 out of 278).Conclusion: The apparently contradictory findings above suggest that both stochastic neutral effects and deterministic environmental (host) factors play important roles in shaping the assembly and diversity of gut microbiome. Furthermore, obesity may just be one of the host factors, but its influence may not be strong enough to tip the balance between stochastic and deterministic forces that shape the community assembly. Finally, the apparent contradiction from both the neutral theories should not be surprising given that there are still near 30–40% species that do not obey the neutral law

Development of a SCAR Marker for Rapid Identification of New Kentucky Bluegrass Breeding Lines

As a commonly used turfgrass, Kentucky bluegrass (Poa pratensis L.) (KBG) has many commercially available cultivars for production. After several years of screening, two new lines were obtained (‘KBG03’ and ‘KBG04’), which have high tolerance to summer. The study showed that the two lines revealed similar morphological characteristics, with light green leaf color, narrow leaf blade, high plant height and light 1,000-grain weight. A total of 400 random amplified polymorphic DNA (RAPD) primers and 256 sequence-related amplified polymorphism (SRAP) primer combinations were screened among the two lines and other 4 imported commercial cultivars. The percentages of polymorphic sites were 65.5% (RAPD) and 22.6% (SRAP) respectively. By cluster analysis of RAPD and SRAP data, the dendrogram at a similarity of 0.29 gave two main clusters, of which one group had 4 commercial cultivars, and the other had the two new breeding lines. Furthermore, one specific band of ‘KBG04’ was successfully converted into a dominant sequence characterized amplified region marker (SCAR196). Then the SCAR marker was verified by 39 KBG DNA samples, including imported varieties, domestic varieties and self-breeding lines of our laboratory, and it exhibited high consistency with the original RAPD polymorphic amplification. The results showed that the SCAR marker can be used to distinguish the new line ‘KBG04’ from numerous KBG germplasms, which would be useful for cultivar identification and property rights protection in the future