The Fog Computing (FC) paradigm is rapidly becoming an appropriate framework for the infrastructure related to the Internet of Things (IoT). FC can be a good framework for mobile applications in the IoT. This architecture is referred to as the Mobile Fog Computing (MFC). Modules in the applications can be sent to the Fog or Cloud layer in the event of the lack of resources or increased runtime on the mobile. This increases the efficiency of the whole system. As data is entered sequentially, and the input is given to the modules, the number of executable modules increases. So, this research was conducted to find the best place in order to run the modules that can be on the mobile, Fog, or Cloud. According to the proposed method, first, the Fog Devices (FDs) were locally evaluated using a greedy technique; namely, the sibling nodes followed by the parent and in the second step, a Deep Reinforcement Learning (DRL) algorithm found the best destination to execute the module so as to create a compromise between the power consumption and execution time of the modules. The evaluation results obtained regarding the parameters of the power consumption, execution cost, delay, and network resource usage showed that the proposed method on average is better than the local execution, First-Fit (FF), and standard DRL by 18, 6, and 2%, respectively.

Autonomous computation offloading and auto-scaling the in the mobile fog computing: a deep reinforcement learning-based approach

Routing and hotspot are considered among the key issues in Wireless Sensor Networks (WSNs). Several routing protocols have been proposed; yet they suffer from the lack of fault tolerance, the uneven load balancing and the local optimal solution issues. To fix these issues, we propose in this paper an algorithm named Energy-efficient Cluster-based Routing Protocol using Unequal Clustering and improved Ant Colony Optimization (ACO) techniques (ECRP-UCA). ECRP-UCA divides the network into unequal clusters based on residual energy, distance to the sink, number of neighbor nodes, and a new parameter named number of backward relay nodes in previous round to properly balance the load among Cluster Heads (CHs). We propose also a batch-based clustering method that allows the network to function several rounds without requiring control overhead for its configuration. Additionally, we devise an improved ACO based routing technique for efficient and reliable inter-cluster routing from CHs to the sink. In this algorithm, the heuristic function is formulated considering the energy of next hop sensor node, distance from the current sensor node to the next sensor node and the latter to the destination, and the new parameter Number of Preferred Probable Relay Nodes (NPPRN). By using NPPRN, the ants have the probability to follow other paths, which improves the algorithm’s ability to obtain the optimal global solution and protect the algorithm from converging quickly into the optimal local solution. Moreover, the ant searching direction is improved. The proposed routing protocol is intensively experimented and compared with recent and relevant existing protocols. The simulation results show that the proposed ECRP-UCA outperforms these protocols in terms of various interesting metrics.

An energy-efficient cluster-based routing protocol using unequal clustering and improved ACO techniques for WSNs

Partial offloading with stable equilibrium in fog-cloud environments using replicator dynamics of evolutionary game theory

One of the most critical concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the virtual machine placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of Internet of Things (IoT) tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the sub-problems of energy consumption minimization and scheduling fall into NP-hard issues. This study proposes a multi-objective virtual machine placement to jointly minimize energy costs and scheduling. After presenting a modified Memetic algorithm, we compare its performance with baseline methods and state-of-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, service-level agreement violations, and the total response time of IoT requests. 

https://www.researchsquare.com/article/rs-1233833/latest.pdf

Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods

Today, there exists a growing demand for Internet of Things (IoT) services
 in the form of vehicle networks, smart cities, augmented reality, virtual reality, positioning systems, and so on. Due to the considerable distance between the IoT devices and the central cloud, using this option may no longer be a suitable solution for delay-constraint tasks. To overcome these drawbacks, a complementary solution called fog computing, also known as the cloud at the edge is used. In this solution, nodes at the edge of the network provide resources for IoT applications. Although offloading tasks on the fog nodes save energy on IoT devices, it increases task response time. Therefore, making a trade-off between energy consumption and latency is crucial for IoT devices. Because offloading falls into the category of NP-hard knapsack problems, metaheuristic methods have been widely used in recent years. In this paper, we formulate the problem of joint optimization of energy consumption and latency in the form of a multi-objective problem and solve it using the non-dominant sorting genetic algorithm (NSGA-II) and Bees algorithm (BA). Also, to improve the quality of solutions, we combine each of these methods with a robust type of differential evolution approach called minimax differential evolution (MMDE). This combination moves the solutions to better areas and increases the convergence speed. The simulation results show that NSGA-based methods have remarkable robustness compared to BA-based methods in terms of significant criteria such as energy consumption, time delay, and so on. Our statistical analysis shows that both NSGA-based and BA-based metaheuristic methods not only do not significantly increase energy consumption but also drastically reduce response time.

Joint optimization of energy consumption and time delay in IoT-fog-cloud computing environments using NSGA-II metaheuristic algorithm

Introduction: With regard to the importance of the environment in sustainable development along with environmental literacy among citizens, the present study aimed to assess the levels of environmental literacy (i.e. awareness  , attitudes, and practices) among citizens residing in the city of Yazd, Iran, on the subject of environmental issues and challenges. \nMaterials and Methods: In this descriptive--analytical study, the sample size was determined by 410 individuals as citizens aged over 18 years living in the city of Yazd based on the Cochran formula. The data selection tool was also a researcher-made questionnaire comprised of items on demographic characteristics information as well as levels of awareness, attitudes, and practices concerning the environment such as air pollution, waste management, water resources scarcity, soil pollution, etc. in 2017. Face validity verified by 10 qualified individuals as the faculty members and Reliability was confirmed by Cronbach’s alpha coefficient of 0.93. The collected data was analyzed using the   SPSS 22 software through descriptive statistics along with Mann-Whitney U test, Kruskal-Wallis test, and Chi-Square test in addition to Spearman’s correlation coefficient (r). In this study, significance level was considered as 0.05. \nResults: awareness score was statistically significant in terms of age, level of education, and employment status (p-value<0.05). However, attitudes were only in a significant relationship with employment status (p-value=0.004). The relationship between practice score was also statistically significant with regard to age, marital status, and area of residence (p-value<0.05). A significant relationship was further observed between age and level of income and level of awareness, but there was no significant relationship considering attitudes (p-value>0.05). Moreover, a positive correlation was found between awareness, attitude, and practice scores (0.323≥ r ≥0.28, p-value=0.000). \nConclusion: Although awareness among Yazd citizens on some environmental issues was at low levels, in general, their awareness specifically regarding health issues stemming from air pollution was proper. In view of the importance of environmental factors, it is necessary to conduct various research studies to identify and control these factors and to carry out further intervention studies to find right and proper solutions before their implementation.

Assessment of Environmental Literacy: A Case Study of Yazd Citizens’ Awareness, Attitudes, and Practices in 2017

Background: Cardiovascular diseases (CVDs) are the leading cause of premature death in the 21st century. Dietary factors, such as high salt intake, are related to increased risk of CVDs{Akpolat, 2009 #21}. One of the main sources of dietary salt is bread. On the other hand, salt content is a quality indicator of bread. Therefore, this study was conducted to evaluate the salt content in a variety of consumed breads in Yazd city, Iran. Methods: This study was a cross sectional research conducted in Yazd. The list of the bakeries obtained from their industry office. Ten percent of about 600 bakeries in Yazd (62 bakeries; 2 samples in each bakery) were selected using simple random sampling based on sample frame of ID number of each bakery. Finally, 9 types of bread included in this study. Sodium content was measured using flame photometer method. Salt content in breads was reported in each 100 g bread. Results: It was found that Nan-Taftoon Tanuri is the most popular bread among traditional breads in Yazd (45.2%). It had significantly more salt than Nan-Fantezi and Nan-Sangak (P < 0.02). Also, Nan-Sangak had the least salt among traditional breads. However, the mean salt of traditional breads was more than the standard level (1g salt/100g bread (P < 0.0001). Conclusion: The current study showed that the mean salt content of traditional breads was significantly more than the standard level. Furthermore, Nan-TaftoonTanuri had significantly more salt than others, such as Nan-Fantezi and Nan-Sangak.

Salt content in traditional and nontraditional breads in Yazd city, Iran, 2015-2016.

Inelastic behavioral characteristics of knee-braced moment-resisting frames having 3, 8 or, 12 stories were investigated under near-source earthquake excitation using initially pinned frames that were subsequently substituted with rigid knee elements. These frames were designed so that the knee braces would yield and buckle under seismic loading. Inelastic time-history analysis was carried out to assess the structural performance of the buildings by evaluating the maximum axial forces of the columns, vertical displacement of internal beams, roof horizontal displacement, and maximum base shear of columns in the building members using PERFORM-3D software. The nonlinear behavior of the frames was investigated by comparing the results of pinned and rigid knee elements subjected to near-field earthquakes. The results indicate that rigid-to-pinned connections for knee elements can increase the axial forces of columns by nearly 15% for a 3-story building and about 7% for 8- and 12-story buildings. The vertical displacement of the beams was noticeable, especially for the three-story building. The horizontal displacement of the roof and base shear of columns using pinned connections for knee elements were generally greater than for the rigid connections.

Vahid Jafari

Papers

Joint optimization of energy consumption and time delay in IoT-fog-cloud computing environments using NSGA-II metaheuristic algorithm

Assessment of Environmental Literacy: A Case Study of Yazd Citizens’ Awareness, Attitudes, and Practices in 2017

Salt content in traditional and nontraditional breads in Yazd city, Iran, 2015-2016.

Effect of near-field earthquake excitation on seismic behavior of knee-braced moment frames