Resource discovery in the Internet of Things integrated with fog computing using Markov learning model

The Journal of Supercomputing - Due to high and unpredictable connection delays, privacy gaps, and traffic load of networks connecting cloud computing to end users in many of the Internet of Things...     

A latency-aware task scheduling algorithm for allocating virtual machines in a cost-effective and time-sensitive fog-cloud architecture

The Journal of Supercomputing - Recently, with the expansion of communications and generated data, the need for processing this high volume of data in minimum time and maximum speed has increased....     

A localization algorithm for large scale mobile wireless sensor networks: a learning approach

Localization is a crucial problem in wireless sensor networks and most of the localization algorithms given in the literature are non-adaptive and designed for fixed sensor networks. In this paper, we propose a learning based localization algorithm for mobile wireless sensor networks. By this technique, mobility in the network will be discovered by two crucial methods in the beacons: position and distance checks methods. These two methods help to have accurate localization and constrain communication just when it is necessary. The proposed method localizes the nodes based on connectivity information (hop count), which doesn’t need extra hardware and is cost efficient. The experimental results show that the proposed algorithm is scalable with a small set of beacons in large scale network with a high density of nodes. The given algorithm is fast and free from a pre-deployment requirement. The simulation results show the high performance of the proposed algorithm.     

A trust-based service suggestion system using human plausible reasoning

Nowadays, there is a growing need to manage trust in open systems as they may contain untrustworthy service providers. Agent Trust Management (ATM) tries to address the problem of finding a set of the most trusted agents in multi agent systems. This paper presents ScubAA, a novel generic ATM framework based on the theory of Human Plausible Reasoning (HPR). For each user’s request, ScubAA determines a ranked list of the most trusted service agents, within the context of the request, and forwards the request to those trusted services only. ScubAA determines an agent’s degree of trust in terms of a single personalized value derived from several types of evidences such as user’s feedback, history of user’s interactions, context of the submitted request, references from third party users as well as from third party service agents, and structure of the society of agents. ScubAA is able to utilize more trust evidences towards a more accurate value of trust. We also propose a function to figure out how similar two users are in a given context. We apply the proposed HPR-based ATM framework to the domain of Web search. The resulting ATM system recommends to the user a list of the most trusted search engines ranked according to the retrieval precision of documents returned in response to the user’s query as well as the degree of trust of the search engines have gained by interacting with other related users within the context of the query. In addition, we conduct a statistical analysis of ScubAA based on ANOVA and by using a data set of forty queries in different domains. This analysis clearly reveals that ScubAA is able to successfully assess the trustworthiness of service agents.     

Classroom furniture and anthropometric characteristics of Iranian high school students: Proposed dimensions based on anthropometric data

The study evaluated the potential mismatch between classroom furniture dimensions and anthropometric characteristics of 978 Iranian high school students (498 girls, 480 boys), aged 15-18 years. Nine anthropometric measurements (stature, sitting height, sitting shoulder height, popliteal height, hip breadth, elbow-seat height, buttock-popliteal length, buttock-knee length and thigh clearance) and five dimensions from the existing classroom furniture were measured and then compared together (using match criterion equations) to identify any potential mismatch between them. The results indicated a considerable mismatch between body dimensions of the students and the existing classroom furniture, with seat height (60.9%), seat width (54.7%) and desktop height (51.7%) being the furniture dimensions with a higher level of mismatch. The levels of mismatch varied between the high-school grade levels and between genders, indicating their special requirements and possible problems. The proposed dimensions of the classroom furniture more appropriate for the students were given. This additional information on students' anthropometry can be used by local furniture industries as a starting point for designing more appropriate furniture for school children, or used by schools to aid in furniture selection.     

Analytical versus numerical analysis of back grading in CIGS solar cells

Band gap grading is able to improve the performance of CIGS solar cells. In order to get a better understanding of the parameters influencing the effect of a grading, a closed form analytical expression of the current density is derived. The advantages and drawbacks of this analytical expression with respect to numerical simulation results are assessed. The analytical analysis has limited validity due to restrictions on the absorption characteristic and on the recombination in the space charge layer. Nevertheless it leads to good results, it has the advantage of being faster than numerical simulations and it clarifies the interrelation of the meaningful parameters.     

Morphology formation during the nonisothermal thermally-induced phase separation (TIPS) process

In high-viscosity polymer solutions and blends, temperature variations during the quench process of the thermally-induced phase separation (TIPS) influence the dynamics and thermodynamics of phase separation. Hence, this study aims to investigate the impact of temperature variations on the morphology formation during the TIPS process. First, the influence of temporal temperature variations on phase separation is investigated by coupling a transient heat conduction model and the Cahn–Hilliard equation, and the results are compared with the isothermal phase separation process. Next, the morphology formation during phase separation is inspected by applying quench from two opposite sides of the sample to the same and different temperatures through coupling the Fourier heat transfer equation and the Cahn–Hilliard equation. The influence of the enthalpy of demixing on the morphology formation and the competition between the heat and mass transfer is also evaluated. It is confirmed that temporal variations of temperature alone have a significant impact on the morphology formation during the TIPS process. In addition, quenching the system to the same and different temperatures both leads to anisotropic morphology formation, which is affected by the quench rate, quench temperature, solution viscosity, and enthalpy of demixing. Upon applying different quench temperatures from opposite sides, two different types of morphologies and droplet sizes were formed as a result of the difference in the cooling rates between the two sides. Employing the enthalpy of demixing during phase separation induced a shallow quench effect on the deep quench side due to the fact that the heat moved toward the lowest temperature in the system, which led to the formation of a distinctive structure.     

The effects of catalyst and deactivator species in atom transfer radical polymerization of n-butyl methacrylate

Iranian Polymer Journal - Atom transfer radical polymerization (ATRP) of n-butyl methacrylate (n-BMA) was performed using CuIBr/N,N,N′,N″,N″-pentamethyldiethylenetriamine (PMDETA)...