Proposal of a Context-aware Task Scheduling Algorithm for the Fog Paradigm

According to the author's knowledge task scheduling in fog paradigm is highly complex and in the literature there are still few studies on it. In the cloud architecture, it is widely studied and in many researches, it is approached from the perspective of service providers. Trying to bring innovative contributions in these areas, in this paper, we propose a solution to the context-aware task-scheduling problem for fog paradigm. In our proposal, different context parameters are normalized through Min Max normalization, requisition priorities are defined through the application of the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Non-Linear Programming optimization (MONLIP) technique.

With the rapid development of in modern technologies has led to a novel generation of cyberspace equipment and secure analysis methods such as cyber‐physical system (CPS). The CPS system refers to an intelligent dependable system of embedded smart devices used to monitor and control the cyberspace conditions in a fog computing environment. The fog computing environment connects resources even human beings together to enhance the quality of life through performing of CPS applications using computation resources at the network edge. The optimization of task scheduling for the CSP applications as a challenging issue has been considered as the NP‐hard problem in fog computing environment which is not trivial. On the one hand, reasonable tasks scheduling can increase resource utilization, which can avoid less idle resource. This paper presents a task scheduling algorithm based on moth‐flame optimization algorithm to assign an optimal set of tasks to fog nodes to meet the satisfaction of quality of service requirements of CPS applications in such a way that the total execution time of tasks is minimized. The minimization of task execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed solution is carried out in iFogSim toolkit. As a result of the simulation based on proposed algorithm found, the optimal solution for the scheduling of tasks and equal distribution of tasks to fog nodes has been provided, and less total execution time consumption has been achieved compared with other algorithms.

In this paper Principal Components (PC) and Multiple Linear Regression (MLR) Technique were used for development of pre-harvest model for rice yield in the Navsari district of south Gujarat. The weather indices were developed and utilized for development of pre-harvest forecast models. The data of rice yield and weather parameters from 1990 to 2012 were utilized. The cross validation of the developed forecast model were confirmed using data of the years 2013 to 2016. It was observed that value of Adj. R2 varied from 89 to 96. The appropriate forecast model was selected based on high value of Adj. R2. Based on the outcomes in Navsari district, MLR techniques found to be better than PCA for pre harvest forecasting of rice crop yield. The Model-2 found competent to forecast rice yield in Navsari district before eight weeks of actual harvest of crop (37th SMW) i.e during reproductive stage of the crop growth period.

In this work, multiple linear regression technique is employed to study the implicit relation between fundamental categories of a academic topic to the performance of the student. Such a problem is important in the development of personalised learning system. Initially a matrix representation of test questions with its fundamental categories is discussed. Subsequently the responses of the test questions are related to the fundamental categories using a system of linear equations. Multiple Linear Regression (MLR) technique is employed to study the effect of each fundamental categories on the test scores. A synthesised data-set of known probability distributions is used during evaluations. It has been found that the MLR using F-Statistics can identify the complexity of fundamental categories and useful for recommending questions in a personalised learning system.KeywordsMultiple linear regressionPersonalised learning systemF-statistic

Soil nutrients are the important parameter which contributes a major role in healthy plant growth. In soil, the presence of three macro nutrients namely Nitrogen (N), Phosphorus (P), and Potassium (K) are essential for proper crop growth. Without having adequate knowledge about nutrient levels present in soil, farmers apply large quantity of fertilizers in their field. This leads to depletion or enhancement of nutrient content in the soil and it degrades the soil fertility. Laboratory soil test is time consuming and it involves addition of many chemical reagents. Hence in this paper, the soil macronutrients are predicted using Multiple Linear Regression (MLR) technique. It is a statistical method where several independent or explanatory variables are used to predict the dependent or response variable. MLR technique is formulated to determine a mathematical relationship among several parameters. It shows the relationship between dependent and independent variables. In this technique, soil parameters like nitrogen, phosphorus, potassium, pH and electrical conductivity are used to sketch the relationship among these parameters and predict the values of NPK. The predicted NPK data shows an accuracy of approximately 80% when compared with the actual dataset. These results improve the decision-making capabilities of farmers in applying right quantity of fertilizers and increase crop production.

Prediction of greenhouse tomato yield using artificial neural networks combined with sensitivity analysis

Mobile edge computing is an emerging technology to offer resource-intensive yet delay-sensitive applications from the edge of mobile networks, where a major challenge is to allocate limited edge resources to competing demands. While prior works often make a simplifying assumption that resources assigned to different users are non-sharable, this assumption does not hold for storage resources, where users interested in services (e.g., data analytics) based on the same set of data/code can share storage resource. Meanwhile, serving each user request also consumes non-sharable resources (e.g., CPU cycles, bandwidth). We study the optimal provisioning of edge services with non-trivial demands of both sharable (storage) and non-sharable (communication, computation) resources via joint service placement and request scheduling. In the homogeneous case, we show that while the problem is polynomial-time solvable without storage constraints, it is NP-hard even if each edge cloud has unlimited communication or computation resources. We further show that the hardness is caused by the service placement subproblem, while the request scheduling subproblem is polynomial-time solvable via maximum-flow algorithms. In the general case, both subproblems are NP-hard. We develop a constant-factor approximation algorithm for the homogeneous case and efficient heuristics for the general case. Our trace-driven simulations show that the proposed algorithms, especially the approximation algorithm, can achieve near-optimal performance, serving 2–3 times more requests than a baseline solution that optimizes service placement and request scheduling separately.

Internet of Vehicles (IoV) represents a complex and dynamic environment of collaboration and data exchange between vehicles, infrastructure devices, pedestrians, cloud nodes, and other components. Establishing a trust relationship between such objects has proven to be unavoidable for allowing secure access to services such as routing, road safety consigns, and traffic data accessibility. However, security and privacy issues become more complex in such a context characterized by the large volume of data and the geographical dispersion and the dynamicity of involved objects. In this work, we present security and privacy issues encountered in an IoV environment and recently proposed solutions. Moreover, we propose an approach to improve IoV security and privacy while respecting latest advancements in IoV architectures which are based on edge, fog, and cloud layers. Practically, we consider the fog paradigm, which has recently emerged, to handle authentication and authorization of IoV devices at the edge level. Thus, a trust authority is deployed at every fog node along with an interfog authority allowing the handover and collaboration between fog nodes. This approach improves the manageability of low-level devices, thus allowing detecting threats at the fog node level, as well as allows the isolation of threats protecting the overall system from infections.

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

Modeling of soil shear strength using multiple linear regression (MLR) at Penang, Malaysia

<span lang="EN-US">Using mobile and Internet of Things (IoT) applications is becoming very popular and obtained researchers’ interest and commercial investment, in order to fulfill future vision and the requirements for smart cities. These applications have common demands such as fast response, distributed nature, and awareness of service location. However, these requirements’ nature cannot be satisfied by central systems services that reside in the clouds. Therefore, edge computing paradigm has emerged to satisfy such demands, by providing an extension for cloud resources at the network edge, and consequently, they become closer to end-user devices. In this paper, exploiting edge resources is studied; therefore, a cooperative-hierarchical approach for executing the pre-partitioned applications’ modules between edges resources is proposed, in order to reduce traffic between the network core and the cloud, where this proposed approach has a polynomial-time complexity. Furthermore, edge computing increases the efficiency of providing services, and improves end-user experience. To validate our proposed cooperative-hierarchical approach for modules placement between edge nodes’ resources, iFogSim toolkit is used. The obtained simulation results show that the proposed approach reduces network’s load and the total delay compared to a baseline approach for modules’ placement, moreover, it increases the network’s overall throughput.</span>

The Internet of Things (IoT) has gained wide popularity both in academic and industrial contexts. Nowadays, such systems exhibit many important challenges across many dimensions. In this work, we propose ThingsJS, a rich Javascript-based middleware platform and runtime environment that abstracts the inherent complexity of such systems by providing a high-level framework for IoT system developers, built over Javascript. ThingsJS abstracts several large-scale distributed systems considerations, such as scheduling, monitoring and self-adaptation, by means of a rich constraint model, a multi-dimensional resource prediction approach and a SMT-based scheduler to properly schedule and manage the execution of high-level, large-scale distributed applications on heterogeneous physical IoT devices. ThingsJS also provides a rich inter-device communication framework built on top of the widely-used publish/subscribe/MQTT paradigm. Finally, ThingsJS also proposes a rich inter-device Javascript-based code migration framework to support the transparent migration of live IoT components between heterogeneous devices.

The recent trend in the Internet of Things (IoT) is bringing innovations in almost every field of science. IoT is mainly focused on the connectivity of things via the Internet. IoT’s integration tools are developed based on the Do It Yourself (DIY) approach, as the general public lacks technical skills. This paper presents a thermal comfort system based on tasks allocation mechanism in smart homes. This paper designs and implements the tasks allocation mechanism based on virtual objects composition for IoT applications. We provide user-friendly drag and drops panels for the new IoT users to visualize both task composition and device virtualization. This paper also designs tasks generation from microservices, tasks mapping, task scheduling, and tasks allocation for thermal comfort applications in smart home. Microservices are functional units of services in an IoT environment. Physical devices are registered, and their corresponding virtual objects are initialized. Tasks are generated from the microservices and connected with the relevant virtual objects. Afterward, they are scheduled and finally allocated on the physical IoT device. The task composition toolbox is deployed on the cloud for users to access the application remotely. The performance of the proposed architecture is evaluated using both real-time and simulated scenarios. Round trip time (RTT), response time, task dropping and latency are used as the performance metrics. Results indicate that even for worst-case scenarios, values of these metrics are negligible, which makes our architecture significant, better and ideal for task allocation in IoT network.

We provide a lightweight decentralized publish-subscribe framework for supporting large-scale actor communication on edge networks. Our framework, called Loquat, does not depend on any reliable central nodes (e.g., data centers), provides reliability in the face of massive node failures and network partitioning, and provides scalability as the number of nodes increases. We consider that high reliability, i.e., that send operations reach close to 100% of live destination nodes, is a critical property for communication frameworks on edge networks. But reliability is difficult to achieve in a scalable way on edge networks because of the network's dynamicity, i.e., frequent node failures and partitioning. For example, both Internet of Things networks and mobile phone networks consist of devices that are often offline. To achieve reliability, our framework is based on two hybrid gossip algorithms, namely HyParView and Plumtree. Hybrid gossip algorithms combine gossip with other distributed algorithms to achieve both efficiency and high resilience. Our current implementation is written in Erlang and has demonstrated scalability up to 1024 nodes in Amazon's cloud computing environment.

Accurate estimation of evaporation from agricultural fields and water bodies is needed for the efficient utilisation and management of water resources at the watershed and regional scale. In this study, multiple linear regression (MLR) and artificial neural network (ANN) techniques are used for the estimation of monthly pan evaporation. The modelling approach includes the various combination of six measured climate parameters consisting of maximum and minimum air temperature, maximum and minimum relative humidity, sunshine hours and wind speed of two stations, namely Gangtok in Sikkim and Imphal in the Manipur states of the northeast hill region of India. Average monthly evaporation varies from 0.62 to 2.68 mm/day for Gangtok, whereas it varies from 1.4 to 4.3 mm/day for Imphal during January and June, respectively. Performance of the developed MLR and ANN models was compared using statistical indices such as coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) with measured pan evaporation values. Correlation analysis revealed that temperature, wind speed and sunshine hour had positive correlation, whereas relative humidity had a negative correlation with pan evaporation. Results showed a slightly better performance of the ANN models over the MLR models for the prediction of monthly pan evaporation in the study area.