In cloud computing scenario, efficiency of application execution not only depends on quality and quantity of the resources in the data center, but also on underlying resource allocation approaches. An efficient resource allocation technique is necessary for building an efficient system. The objective of this work is to propose an agent based Best-Fit resource allocation scheme which increases utilization of the resources, lowers the service cost and reduces the execution time. The work employs two types of agents: user's cloudlet agent and provider's resource agent. Cloudlet agent is located at the client system, which collects job requirement and offers various QoS options for its execution. Resource agent at the server, uses Best-Fit approach to allocate resources for jobs received from Cloudlet agent. The proposed work is simulated and the results are compared with other agent based resource allocation approaches using First-Come-First-Serve and Round-Robin. It is observed that Best-Fit approach performs better in terms of VMs allocation, job execution time, cost and resource utilization.

Resource allocation in cloud computing using agents

Cloud computing is widely used in businesses as it increases efficiency, helps improve cash flow and offers many services. Cloud services allow individuals and businesses to use software and hardware that are managed by third parties at remote locations. Intelligent agent is a smart software program that is capable of taking flexible actions in some dynamic environments. A Multi agent system has more than one agent interacting with each other to cooperate, coordinate and negotiate, for solving a particular problem. Utilizing Multi Agent technologies allow clouds to be more flexible and more autonomous. This paper discuss the cloud computing environment and multi agent concept. It attempts to classify research papers in this area showing how agents help cloud through cloud services life cycle. A brief analysis of some research papers in different categories paving the way for open research points in this area.

Agent-Based Cloud Computing: A Survey

The rapidly expanding discipline of data analysis has an important role to play in the medical industry. Using this knowledge, we can uncover previously concealed details that might aid in early illness prediction. Predicting cardiovascular illness is one of the most pressing issues of our day. The medical community views heart disease prediction as a challenging endeavour. Machine learning is important for the medical field's massive data training and testing needs. Creating and assessing a heart disease prediction system is crucial for early detection and treatment of the condition. This research uses a variety of machine learning methods to predict the possibility of heart illness and diagnose the patient with heart disease or not. These methods include the Decision Tree, the K - Nearest Neighbour classifier, and the Support Vector Machine. Finally, this study provides a cardiac result, and trials comparing the suggested technique to others have shown that it may be used to provide the forecast to the patient. 

Implementation of Machine Learning Approach for Detecting Cardiovascular Diseases

The scientific and business communities are showing considerable interest in wireless sensor networks (WSN). The availability of low-cost, small-scale components like CPUs, radios, and sensors, which are often combined into a single chip, is crucial. Parallel to the evolution of WSNs, the concepts of the IoT have been evolving in recent years. Wireless communication technologies may play a significant role in the implementation of IoT, despite the fact that IoT does not need or require any particular technology for communication. WSN assisted IoT networks can drive several applications in many industries. The proposed research explores the possibility of enhancing energy efficiency in WSN-assisted IoTN by balancing various challenging sensor network performance metrics. The base station's current placement inside the sensing field is predetermined by the preexisting routing algorithms. Our study examines the impact of base station placement outside and within the prescribed sensing domains on energy consumption and network longevity. In addition, methods for transferring data from the distributed source sensor to the base station while minimizing energy consumption are investigated. In this preliminary study, we focus on developing an algorithm for WSN-Assisted IoTN that can balance network factors such as hop count, communication distance, and residual energy. To further optimize the routing route between local cluster heads and the base station, a novel network architecture is built based on the Ant-optimization model, which uses centroid routing to balance energy consumption among local clusters. An open-source Network Simulator (NS-3) is used to model the behaviour of the proposed routing protocols and compare them to comparable existing network protocols. All of the suggested protocols have the same fundamentals for creating networks, however they vary in terms of routing, optimization, and performance depending on the development effort under consideration.

/pdf/performance-analysis-of-energy-efficiency-and-security-126le2nw.pdf

Performance Analysis of Energy Efficiency and Security Solutions of Internet of Things Protocols

The pressure on today's factories to boost output is becoming steadily stronger. Automation of mundane tasks that wear people out is necessary to reach the new productivity heights. Self-driving mobile robots have been shown to increase output in a variety of material-handling, transportation, manipulation, and packaging tasks. When many mobile robots are used for these purposes, the question of how to divide up the work becomes more complicated. System efficiency might be enhanced by effectively assigning the robots to the job. The term "Multi-Robot Task Allocation" (MRTA) refers to the challenge of figuring out how to distribute work among several robots. Multiple robots must work together to complete the task under consideration in this study. Optimal utilisation of the robots is crucial to the success of the job completion. When multiple goals must be met at once, optimising the tasks assigned to these robots is essential. This study discovers how to optimise the distribution of work among several robots while satisfying multiple objectives at once. 

Wireless and Solar-Powered Multipurpose Robot for Industrial Automation: A Sustainable Solution

Cloud computing is one of the booming Technology. Cloud computing systems provide large-scale infrastructures for high-performance computing that can adapt to user and

/pdf/multi-agent-based-cloud-services-vzldcnb7qb.pdf

Multi-Agent based Cloud Services

A significant shift towards autonomous and connected cars is currently expected in the automotive sector. This creates new risks in terms of privacy and the complexity of electrical and electronic systems. Although safety risk management is still in its infancy, it is encouraging that the auto industry has well-known and regulated safety risk management practices. They propose a showed improved risk assessment framework and show its validity with an actual use-case to make it easier to derive safety procedures & safety solutions for automotive integrated devices. The adequacy of functional safety to existing methods and accessibility for non-safety professionals are some of the important aspects. Risk analysis is an essential element of the methodology used to assess properties and capacity risks. The level of hazard and the level of effect are estimated in the next stage of the risk management process. This step uses several existing guidelines and requirements to make amendments as necessary. To create high-level security objectives, a level of security has finally been determined. This architecture should be well adapted to the requirements of the automobile industry due to its excellent convergence with current principles and specifications.

A Safety Assessment Model for Automotive Embedded Systems Networks

The primary goal of accident detection implementation is to reduce traffic accidents that result in the loss of priceless human life and other valuable items. Accident detection systems that use GPS and GSM save lives by shortening the time it takes for emergency personnel to reach the scene of an accident. We made the decision to recognise an automobile collision and notify the emergency personnel as well as the driver's main contacts. The product's main goal was to increase security for consumers and their families. GPS (Global Positioning System) and GSM are its foundations (Global system of Mobile communication).

Accident Detection System Using GPS and GSM by IOT

Most of the accidents that happens that happens nationwide is due to the drowsiness. Drowsy driving causes 21% of all fatal collisions. About 168 million adult drivers, or 60% of all drivers, admitted to driving while fatigued in the previous year. Drowsiness is an inattentive state. Drowsiness makes the individual to loss of their control during their driving state, thus leading to devastating accidents. To avoid such catastrophic accidents, a sleep detection system is proposed by which the model embeds an EEG sensor, to detect sleep onset while driving and a CNN-based hybrid model is developed which is used to track the progress of the drivers’ mental and physiological conditions. The activities of brain is evaluated using EEG signal. The EEG signals identifies the fatigue which remains to be the primary reasons for accidents. The model aims to attain the terminology of detecting the driver drowsiness by EEG signals with the high accuracy and efficiency. All the signals are transmitted to the mobile of the driver thereby the CNN algorithm monitors the state of driver. Whenever the driver is in sleepy or fatigue state, then these results are sent to the Car system and the car gives an alert to the driver in the form of an alarm. The combination of CNN based model and EEG sensor will give accurate and optimized results with respect to the driver’s condition. This was the scenario that were often used in the previous works which seems to be a tedious task to wear a hand band and drive for the drivers. So, we are proposing a model that only focuses on the eyes of the driver to detect the onset of the fatigue and drowsiness.

An Efficient Driver Drowsiness Detection Using Deep Learning

With the development of image handling technology, computerized technology, and the theory of image preparation, it has become clear that image processing is a crucial area of computer application. It is frequently used in many logical and designing applications, such as remote detection, medicine, meteorology, exchanges, and so on. However, with the swift development of picture preparation technology, it is becoming more and more important to precisely and successfully evaluate the quality of a picture. Recently, image quality evaluation has grown in importance as a study area in the field of developing picture data, which has attracted a lot of attention from academics. The importance of picture quality primarily takes into account two aspects: picture loyalty and picture coherence. picture quality directly depends on depending on the optical characteristics of the imaging equipment, image contrast, instrument clamor, and other factors. It may provide checking intentions to depict gaining, handling, and various connections through quality assessment. The evaluation of image quality assessment has become one of the essential breakthroughs of picture data designing to create a meaningful assessment of all components of picture preparation. People have needed to learn picture loyalty and the understandability of the quantitative estimation strategy using the picture a lot framework plan as the assessment premise for a very long time, but one of the people on the human visual characteristics is still not fully understood, in particular the description methods of psychological characteristics in human vision is also difficult to learn the quantitative evaluation of image quality, so, extensive investigation is required.

Effective Drive an Autonomous Vehicle, The Environment Characteristics Are Extracted Via Intelligent Image Processing

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