Tropical Cyclone Damage Assessment using a Projection Pursuit Dynamic Cluster Model

Using data from 62 tropical cyclones (TCs) that landed in Guangdong Province in China between 2000 and 2019, we calculated six indices—minimum central pressure, maximum wind speed, maximum rainstorm ratio, cumulative surface rainfall, cyclone track length and lifetime—and constructed a projection pursuit dynamic cluster (PPDC) model to assess TC damage risk. Although a single index may provide correct information on the intensity of certain types of damage, a comprehensive damage risk assessment cannot be obtained from individual indices alone. The PPDC model is a stable tool for TC damage risk assessment, especially in terms of economic loss, agricultural disaster area and disaster-affected population. Model validation improved the correlation of each of the indices. Output from the PPDC model for disaster-affected population and agricultural disaster-affected area also improved after model validation. We examined the limitations of the single indices using data from three TCs. Output from the PPDC model can closely reflect the intensity of the damage caused by the cyclones. Projection pursuit dynamic clustering is a new and objective method for typhoon damage risk assessment, and provides the scientific basis to support disaster prevention and mitigation.

Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks

WLAN mesh networks are one of the key technologies for upcoming smart city applications and are characterized by a flexible and low-cost deployment. The standard amendment IEEE 802.11s introduces low-level mesh interoperability at the WLAN MAC layer. However, scalability limitations imposed by management traffic overhead, routing delays, medium contention, and interference are common issues in wireless mesh networks and also apply to IEEE 802.11s networks. Possible solutions proposed in the literature recommend a divide-and-conquer scheme that partitions the network into clusters and forms smaller collision and broadcast domains by assigning orthogonal channels. We present CHaChA (Clustering Heuristic and Channel Assignment), a distributed cross-layer approach for cluster formation and channel assignment that directly integrates the default IEEE 802.11s mesh protocol information and operating modes, retaining unrestricted compliance to the WLAN standard. Our concept proposes further mechanisms for dynamic cluster adaptation, including subsequent cluster joining, isolation and fault detection, and node roaming for cluster balancing. The practical performance of CHaChA is demonstrated in a real-world 802.11s testbed. We first investigate clustering reproducibility, duration, and communication overhead in static network scenarios of different sizes. We then validate our concepts for dynamic cluster adaptation, considering topology changes that are likely to occur during long-term network operation and maintenance.

Improvement of Perception Layer Routing Protocol with Static Nodes in IoT-Based Microgrids

Aiming at the problem of uneven clustering and the unreasonable energy consumption of LEACH protocol in the perception layer of IoT-based microgrids of static nodes; in this paper, we propose a stationary-node energy-based routing protocol (SERP). First, we select a dynamic cluster radius for clustering to meet the actual needs of the network during clustering. Then, to solve the problem that the number of cluster heads is difficult to determine, a dynamic optimal cluster head ratio is adopted. The dynamic optimal cluster head ratio can be obtained by minimizing the total energy consumption of cluster formation and the stable transmission phase, which can improve the efficiency of network transmission. Finally, by setting the residual energy factor and distance factor to improve the calculation of the cluster head election threshold, the energy load of the network is more uniform, and the location of the cluster head is more reasonable. Compared with the LEACH protocol and the HEED protocol, the simulation results show that the SERP protocol can effectively prolong the lifetime of the whole network.

Lightweight and Efficient Dynamic Cluster Head Election Routing Protocol for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have gained great significance from researchers and industry due to their wide applications. Energy and resource conservation challenges are facing the WSNs. Nevertheless, clustering techniques offer many solutions to address the WSN issues, such as energy efficiency, service redundancy, routing delay, scalability, and making WSNs more efficient. Unfortunately, the WSNs are still immature, and suffering in several aspects. This paper aims to solve some of the downsides in existing routing protocols for WSNs; a Lightweight and Efficient Dynamic Cluster Head Election routing protocol (LEDCHE-WSN) is proposed. The proposed routing algorithm comprises two integrated methods, electing the optimum cluster head, and organizing the re-clustering process dynamically. Furthermore, the proposed protocol improves on others present in the literature by combining the random and periodic electing method in the same round, and the random method starts first at the beginning of each round/cycle. Moreover, both random and periodic electing methods are preceded by checking the remaining power to skip the dead nodes and continue in the same way periodically with the rest of the nodes in the round. Additionally, the proposed protocol is distinguished by deleting dead nodes from the network topology list during the re-clustering process to address the black holes and routing delay problems. Finally, the proposed algorithm’s mathematical modeling and analysis are introduced. The experimental results reveal the proposed protocol outperforms the LEACH protocol by approximately 32% and the FBCFP protocol by 8%, in terms of power consumption and network lifetime. In terms of Mean Package Delay, LEDCHE-WSN improves the LEACH protocol by 42% and the FBCFP protocol by 15%, and regarding Loss Ratio, it improves the LEACH protocol by approximately 46% and FBCFP protocol by 25%.

A Life-Course View on Ageing Consumers: Old-Age Trajectories and Gender Differences

This study presents a dynamic, model-based view of consumers’ ageing developments, focused on gender differences, to uncover the pathways and socioeconomic transitions that female and male consumers take through old age. The analysis of longitudinal survey data spanning 15 years uses a latent Markov dynamic cluster model with transitions over time. The resulting life courses allow an exploration of lifestyle-related changes in multiple consumer well-being variables beyond age 50. Substantial well-being differences appear in the ageing paths of men and women. In both cases, a dominant chronological sequence through old age is complemented by less common transitions, rarely associated with advanced age. Although the model does not use chronological age as an independent variable, it outperforms purely agebased, or age- cohort-, and period-based models in predicting old-age consumer wellbeing. These results highlight the importance of considering within-cohort diversity when modelling the accompaniments of old age: while some older consumers enjoy active lifestyles, others of similar age succumb to depression and loneliness, rendering age an insufficient predictor of well-being states. In the future, the presented model could be matched with other, even cross-sectional, consumer survey data to help predict various dynamics in the ageing consumer population.