GTDM: A DTN Routing on Noncooperative Game Theory in a City Environment

The performance of delay tolerant networks (DTNs) can be influenced by movement model in different application environments. The existing routing algorithms of DTNs do not meet the current city environments due to the large differences in node densities, social characteristics, and limited energy. The key indicators of DTNs such as success delivery ratio, average delivery latency, network lifetime, and network overhead ratio can influence the performances of civil DTNs applications. Aiming to improve the key indicators of DTNs in city environments, this paper presents a fixed sink station based structure and a more proper routing algorithm named Game Theory Based Decision Making (GTDM). GTDM shows decision-making process for neighborhood selection and packet delivering strategy which is based on the noncooperative game theory method and city environment characteristics. GTDM performance is evaluated using numerical simulations under Working Day Movement (WDM) model and the results suggested that GTDM outperforms other traditional DTNs routing approaches, such as Epidemic and Prophet algorithms

A New Variant of Game Theory Based Decision Making (GTDM) Algorithm Routing Protocols to Improve Energy Efficiency on Vehicular Delay Tolerant Network (VDTN)

These days, the application of Delay Tolerant Networks (DTN) have been expanded into various scenarios of communications field. Vehicular Ad hoc Networks (VANETs) as a communication scenario which treat its subject to disruption and disconnection with frequent partitioning and high latency. Therefore, Vehicular Delay Tolerant Network (VDTN) is introduced as a new research paradigm due to several characteristics match according to specific prerequisites. DTNs is proposed in Vehicular Network because its mechanisms which is using store-carry-forward, can be implemented to deliver the packets, without end-to-end connection, to the destination. One of challenging research of DTN in routing protocol is to meet prerequisites of many applications, especially in vehicular network (VDTN).  This paper presents a new variant of Game Theory based on Decision Making (GTDM) that can deliver packet to static node due to improve the energy efficiency of DTNs in city environments. Hence, its destination node (Receiver Node) needs to go to the static node to take their packet under Working Day Movement (WDM), because relay node will be passing by the static node with continuously move to its track to deliver packet. In this paper author will analyze the new variant of GTDM (NVGTDM) which can be more useful than original GTDM for application in city environment with using transportation movement. We conclude that modification of GTDM routing algorithm (NVGTDM) improves energy efficiency as much as 10.38% than the original GTDM. Hence, it can be ensured to compare either to Epidemic or PRoPHET routing algorithm with 55.44% and 68.75% in rates of energy efficiency respectively

The Sabatier principle for Battery Anodes: Chemical Kinetics and Reversible Electrodeposition at Heterointerfaces

How surface chemistry influences reactions occurring thereupon has been a long-standing question of broad scientific and technological interest for centuries. Recently, it has re-emerged as a critical question in a subdiscipline of chemistry - electrochemistry at heterointerphases, where the answers have implications for both how, and in what forms, humanity stores the rising quantities of renewable electric power generated from solar and wind installations world-wide. Here we consider the relation between the surface chemistry at such interphases and the reversibility of electrochemical transformations at a rechargeable battery electrode. Conventional wisdom holds that stronger chemical interaction between the metal deposits and electrode promotes reversibility. We report instead that a moderate strength of chemical interaction between the deposit and the substrate, neither too weak nor too strong, enables highest reversibility and stability of the plating/stripping redox processes at a battery anode. Analogous to the empirical Sabatier principle for chemical heterogeneous catalysis, our finding arises from the confluence of competing processes - one driven by electrochemistry and the other by chemical alloying. Based on experimental evaluation of metal plating/stripping systems in battery anodes of contemporary interest, we show that such knowledge provides a powerful tool for designing key materials in highly reversible electrochemical energy storage technologies based on earth-abundant, low-cost metals.Comment: 64 pages. Initially submitted on March 16th, 2021; revised version submitted on November 14th, 2021 to the same Journa

A sentiment analysis approach for travel-related Chinese online review content

Using technology for sentiment analysis in the travel industry can extract valuable insights from customer reviews. It can assist businesses in gaining a deeper understanding of their consumers’ emotional tendencies and enhance their services’ caliber. However, travel-related online reviews are rife with colloquialisms, sparse feature dimensions, metaphors, and sarcasm. As a result, traditional semantic representations of word vectors are inaccurate, and single neural network models do not take into account multiple associative features. To address the above issues, we introduce a dual-channel algorithm that integrates convolutional neural networks (CNN) and bi-directional long and short-term memory (BiLSTM) with an attention mechanism (DC-CBLA). First, the model utilizes the pre-trained BERT, a transformer-based model, to extract a dynamic vector representation for each word that corresponds to the current contextual representation. This process enhances the accuracy of the vector semantic representation. Then, BiLSTM is used to capture the global contextual sequence features of the travel text, while CNN is used to capture the richer local semantic information. A hybrid feature network combining CNN and BiLSTM can improve the model’s representation ability. Additionally, the BiLSTM output is feature-weighted using the attention mechanism to enhance the learning of its fundamental features and lessen the influence of noise features on the outcomes. Finally, the Softmax function is used to classify the dual-channel fused features. We conducted an experimental evaluation of two data sets: tourist attractions and tourist hotels. The accuracy of the DC-CBLA model is 95.23% and 89.46%, and that of the F1-score is 97.05% and 93.86%, respectively. The experimental results demonstrate that our proposed DC-CBLA model outperforms other baseline models

On the Optimization Strategy of EV Charging Station Localization and Charging Piles Density

The penetration rate of electronic vehicles (EVs) has been increasing rapidly in recent years, and the deployment of EV infrastructure has become an increasingly important topic in some solutions of the Internet of Things (IoT). A reasonable balance needs to be struck between the user experience and the deployment cost of charging stations and the number of charging piles. The deployment of EV’s charging station is a challenging problem due to the uneven distribution and mobility of EV. Fortunately, EVs move with a certain regularity in the urban environment. It makes the deployment strategy design of EV charging stations feasible. Therefore, we proposed a deployment strategy of EV charging station based on particle swarm optimization algorithm to determine the charging station localization and number of charging piles. This strategy is designed based on the nonuniform distribution of EV in a city scene map, at the same time, the distribution of EV at different times, which makes the strategy more reasonable. Extensive simulation results further demonstrated that the proposed strategy can significantly outperform the K-means algorithm in the urban environment

The Effective Sleep Scheduling in Wireless Opportunistic Networks

One of the purposes of Internet of Things (IoT) is to reach more deeper perception. For this purpose, the efficient energy consumption is necessary among intelligent devices that make up the part of Opportunistic Networks (ONs). It is irrational for an ONs without any sleep scheduling because of awful user experience. We explore a sleeping schedule which is based on duty cycling for mobile devices to reduce energy consumption of ONs. To see how schedule affects the performance of ONs, we took a series of simulations and the results indicated that the sleeping schedule is an efficient method for prolonging the network life time in ONs. The successful delivery ratio can increase two to three times when factor Tr equal 0.2. We also observed that the network matrices are acceptable, and the network survival time can be extended effectively in ONs

The Effective Sleep Scheduling in Wireless Opportunistic Networks

One of the purposes of Internet of Things (IoT) is to reach more deeper perception. For this purpose, the efficient energy consumption is necessary among intelligent devices that make up the part of Opportunistic Networks (ONs). It is irrational for an ONs without any sleep scheduling because of awful user experience. We explore a sleeping schedule which is based on duty cycling for mobile devices to reduce energy consumption of ONs. To see how schedule affects the performance of ONs, we took a series of simulations and the results indicated that the sleeping schedule is an efficient method for prolonging the network life time in ONs. The successful delivery ratio can increase two to three times when factor Tr equal 0.2. We also observed that the network matrices are acceptable, and the network survival time can be extended effectively in ONs