Game theoretic and auction-based algorithms towards opportunistic communications in LPWA LoRa networks

Low Power Wide Area (LPWA) networks have been the enabling technology for large-scale sensor and actuator networks. Low cost, energy-efficiency and longevity of such networks make them perfect candidates for smart city applications. LoRa is a new LPWA standard based on spread spectrum technology, which is suitable for sensor nodes enabling long battery life and bi-directional communication but with low data rates. In this paper, we will demonstrate a use-case inspired model in which, end-nodes with multiple radio transceivers (LoRa/WiFi/BLE) have the option to interconnect via multiple networks to improve communications resilience under the diverse conditions of a smart city of a billion devices. To facilitate this, each node has the ability to switch radio communications opportunistically and adaptively, and this is based on the application requirements and dynamic radio parameters

Fusion of Remote Sensing Images Using Improved ICA Mergers Based on Wavelet Decomposition

AbstractSpectral distortion is one of the most significant problems in the field of remote sensing image fusion. In former studies, we found the fusion method based on independent component analysis (ICA) could solve this problem effectively, and attain a better balance between spectral and spatial information of fused image. However, this method may lead to spectral distort in a few local regions unavoidably. In this paper, an improved ICA fusion method is proposed. Improvement mainly includes two aspects. Firstly, a convenient way which uses negentropy to measure the nongaussianity of IC is presented to select main body independent component (MBIC); secondly, in order to avoid too much spatial information caused by replacing MBIC with panchromatic (PAN) image directly, a wavelet decomposition is applied to extract the detail information of PAN image. The results show that the proposed method can have a better trade-off between spectral and spatial information. Moreover, compared with ICA fusion method, it can not only improve the spatial resolution of fused image, but also eliminate the drawback of spectral distortion of ICA fusion method in some local regions

Optimization between heating load and entropy-production rate for endoreversible absorption heat-transformers

For an endoreversible four-heat-reservoir absorption heat-transformer cycle, for which a linear (Newtonian) heat-transfer law applies, an ecological optimization criterion is proposed for the best mode of operation of the cycle. This involves maximizing a function representing the compromise between the heating load and the entropy-production rate. The optimal relation between the ecological criterion and the COP (coefficient of performance), the maximum ecological criterion and the corresponding COP, heating load and entropy production rate, as well as the ecological criterion and entropy-production rate at the maximum heating load are derived using finite-time thermodynamics. Moreover, compared with the heating-load criterion, the effects of the cycle parameters on the ecological performance are studied by numerical examples. These show that achieving the maximum ecological criterion makes the entropy-production rate decrease by 77.0% and the COP increase by 55.4% with only 27.3% heating-load losses compared with the maximum heating-load objective. The results reflect that the ecological criterion has long-term significance for optimal design of absorption heat-transformers.Four-heat-reservoir absorption heat-transformer Ecological criterion COP Heating load Entropy-production rate

An absorption heat-transformer and its optimal performance

On the basis of an endoreversible absorption heat-transformer cycle, a generalized irreversible four-heat-reservoir heat-transformer cycle model has been established by taking account of the heat resistances, heat leaks and irreversibilities due to the internal dissipation of the working substance. The heat transfer between the heat reservoir and the working substance is assumed to obey the linear (Newtonian) heat-transfer law, and the overall heat transfer surface area of the four heat-exchangers is assumed to be constant. The fundamental optimal relations between the coefficient of performance (COP) and the heating load, the maximum coefficient of performance and the corresponding heating load, the maximum heating load and the corresponding coefficient of performance, as well as the optimal temperatures of the working substance and the optimal heat-transfer surface areas of the four heat exchangers are derived using finite-time thermodynamics. Moreover, the effects of the cycle parameters on the characteristics of the cycle are studied by numerical examples.Finite-time thermodynamics Absorption heat-transformer cycle Heat resistances Heat leak Internal irreversibilities

Irreversible absorption heat-pump and its optimal performance

On the basis of an endoreversible absorption heat-pump cycle, a generalized irreversible four-heat-reservoir absorption heat-pump cycle model is established by taking account of the heat resistances, heat leak and irreversibilities due to the internal dissipation of the working substance. The heat transfer between the heat reservoir and the working substance is assumed to obey the linear (Newtonian) heat-transfer law, and the overall heat-transfer surface area of the four heat-exchangers is assumed to be constant. The fundamental optimal relations between the coefficient of performance (COP) and the heating-load, the maximum COP and the corresponding heating-load, the maximum heating load and the corresponding COP, as well as the optimal temperatures of the working substance and the optimal heat-transfer surface areas of the four heat-exchangers are derived by using finite-time thermodynamics. Moreover, the effects of the cycle parameters on the characteristics of the cycle are studied by numerical examples.Finite-time thermodynamics Absorption heat-pump Heat resistances Heat leak Internal irreversibilities

Effective truth discovery and fair reward distribution for mobile crowdsensing

By leveraging the sensing capabilities of consumer mobile devices, mobile crowdsensing (MCS) systems enable a number of new applications for Internet of Things (IoT), such as traffic management, environmental monitoring, and localisation. However, the sensing data collected from the crowd workers are of various qualities, making it difficult to discover the ground truth and maintain the fairness of incentivisation schemes. In this paper, we propose a truth discovery algorithm based on a two-stage Maximum Likelihood Estimator (MLE), which explicitly characterises the heterogeneous sensing capabilities of the crowd and is able to estimate ground truth accurately using only a small amount of data from IoT infrastructures. Moreover, based on the truth discovery algorithm, two reward distribution schemes, LRDS and MRDS, are proposed to ensure fairness of rewarding the crowd according to their effort levels. We evaluate the estimation accuracy of the truth discovery algorithm and the fairness of the reward distribution schemes using both simulations and real-world MCS campaigns. The evaluation results indicate that the proposed methods achieve superior performance compared with state-of-the-art methods in terms of estimation accuracy and fairness of reward distribution. © 2018 Elsevier B.V

Thermo-economic optimization of an endoreversible four-heat-reservoir absorption-refrigerator

Based on an endoreversible four-heat-reservoir absorption-refrigeration-cycle model, the optimal thermo-economic performance of an absorption-refrigerator is analyzed and optimized assuming a linear (Newtonian) heat-transfer law applies. The optimal relation between the thermo-economic criterion and the coefficient of performance (COP), the maximum thermo-economic criterion, and the COP and specific cooling load for the maximum thermo-economic criterion of the cycle are derived using finite-time thermodynamics. Moreover, the effects of the cycle parameters on the thermo-economic performance of the cycle are studied by numerical examples.Absorption-refrigeration-cycle Finite-time thermodynamics Thermo-economic performance Optimization

The Sustainable Rural Industrial Development under Entrepreneurship and Deep Learning from Digital Empowerment

This paper aims to realize the planning of resource utilization and development of rural industries endowed by digitalization under entrepreneurship. First, the global classic practical experience of digitizing rural industries is studied, and the development model of existing rural industries is captured from the perspective of entrepreneurship. Second, the influencing factors of rural industrial development are extracted, the structure of resource development is analyzed, and a Neural Network (NN) model of industrial development aiming at expected per capita annual income is established. In addition, a Genetic Algorithm (GA) is introduced to learn the weights of influencing factors in the model. The structure of the NN is determined through extensive experiments. Finally, conclusions are drawn through the simulation and experiment of NN and GA. Tourism, infrastructure, and transportation planning have weights of 7.79, 5.6, and 6.4, respectively, and these three sectors should be vigorously developed. In the future, the weight values of these factors can be used for reference, and the development of various aspects can be refined. This paper clarifies the core of industrial development in rural revitalization based on the perspective of entrepreneurship. The problem of how to realize the optimal utilization of resources is solved scientifically and rationally through the mathematical model. The introduction of deep learning algorithm models provides data support for resource allocation and industrial planning in the process of digital empowerment of traditional rural industries, which is of great value and significance for exploring digital models for rural industry development

Frequency-dependent performance of an endoreversible Carnot engine with a linear phenomenological heat-transfer law

On the basis of an endoreversible Carnot heat-engine model, the frequency-dependent performance of the engine is analyzed when the heat transfers between the working fluid and the heat reservoirs obey a linear phenomenological heat-transfer law, i.e., Q [is proportional to] ([Delta]T-1). The relations among average power-output, efficiency, available temperature-drop, cycle frequency and ratio of the heat-transfer times are derived. They are different from those obtained with Newton's law. The results can provide guidance for selecting the appropriate working points of heat engines.Finite-time thermodynamics Heat engine Cycle frequency Heat-transfer law

Decoding Optical Responses of Contact-Printed Arrays of Thermotropic Liquid Crystals Using Machine Learning: Detection and Reporting of Aqueous Amphiphiles with Enhanced Sensitivity and Selectivity

Surfactants and other amphiphilic molecules are used extensively in household products, industrial processes, and biological applications, and are also common environmental contaminants; as such, methods that can detect, sense, or quantify them are of great practical relevance. Aqueous emulsions of thermotropic liquid crystals (LCs) can exhibit distinctive optical responses in the presence of surfactants and have thus emerged as sensitive, rapid, and inexpensive sensors or reporters of environmental amphiphiles. However, many existing LC-in-water emulsions require the use of complicated or expensive instrumentation for quantitative characterization, owing to variations in optical responses among individual LC droplets. In many cases, the responses of LC droplets are also analyzed by human inspection, which can miss subtle color or topological changes encoded in LC birefringence patterns. Here, we report an LC-based surfactant sensing platform that takes a step toward addressing several of these issues and can reliably predict concentrations and types of model surfactants in aqueous solutions. Our approach uses surface-immobilized, microcontact printed arrays of micrometer-scale droplets of thermotropic LCs and hierarchical convolutional neural networks (CNNs) to automatically extract and decode rich information about topological defects and color patterns available in optical micrographs of LC droplets to classify and quantify adsorbed surfactants. In addition, we report computational capabilities to determine relevant optical features extracted by the CNN from LC micrographs, which can provide insights on surfactant adsorption phenomena at LC-water interfaces. Overall, the combination of microcontact-printed LC arrays and machine learning provides a convenient and robust platform that could prove useful for developing high-throughput sensors for on-site testing of environmentally or biologically relevant amphiphiles