Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

[ACM Press the 2nd ACM Workshop - Zurich, Switzerland (2010.11.02-2010.11.02)] Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys \'10 - Occupancy-driven energy management for smart building automation

In machine learning, a computer first learns to perform a task by studying a training set of examples. The computer then performs the same task with data it hasn't encountered before. This article presents a brief overview of machine-learning technologies, with a concrete case study from code analysis.

http://ieeexplore.ieee.org/iel7/52/7548893/07548905.pdf

Machine Learning

Internet of things (IoT) is one of key pillars in fifth generation (5G) and beyond 5G (B5G) networks. It is estimated to have 42 billion IoT devices by the year 2025. Currently, carbon emissions and electronic waste (e-waste) are significant challenges in the information & communication technologies (ICT) sector. The aim of this article is to provide insights on green IoT (GIoT) applications, practices, awareness, and challenges to a generalist of wireless communications. We garner various efficient enablers, architectures, environmental impacts, technologies, energy models, and strategies, so that a reader can find a wider range of GIoT knowledge. In this article, various energy efficient hardware design principles, data-centers, and software based data traffic management techniques are discussed as enablers of GIoTs. Energy models of IoT devices are presented in terms of data communication, actuation process, static power dissipation and generated power by harvesting techniques for optimal power budgeting. In addition, this article presents various effective behavioral change models and strategies to create awareness about energy conservation among users and service providers of IoTs. Fog/Edge computing offers a platform that extends cloud services at the edge of network and hence reduces latency, alleviates power consumption, offers improved mobility, bandwidth, data privacy, and security. Therefore, we present the energy consumption model of a fog-based service under various scenarios. Problems related to ever increasing data in IoT networks can be solved by integrating artificial intelligence (AI) along with machine learning (ML) models in IoT networks. Therefore, this article provides insights on role of the ML in the GIoT. We also present how legislative policies support adoption of recycling process by various stakeholders. In addition, this article is presenting future research goals towards energy efficient hardware design principles and a need of coordination between policy makers, IoT devices manufacturers along with service providers.

/pdf/green-internet-of-things-giot-applications-practices-2e4u68yjwx.pdf

Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges

We live in complex times in the health, social, political, and energy spheres, and we must be aware of and implement new trends in intelligent social health systems powered by the Internet of Things (IoT). Sustainable development, energy efficiency, and public health are interrelated parameters that can transform a system or an environment for the benefit of people and the planet. The integration of sensors and smart devices should promote energy efficiency and ensure that sustainable development goals are met. This work is carried out according to a mixed approach, with a literature review and an analysis of the impact of the Sustainable Development Goals on the applications of the Internet of Things and smart systems. In the analysis of results, the following questions are answered about these systems and applications: (a) Are IoT applications key to the improvement of people's health and the environment? (b) Are there research and case studies implemented in cities or territories that demonstrate the effectiveness of IoT applications and their benefits to public health?

https://www.mdpi.com/1424-8220/21/7/2330/pdf

Application of IoT in Healthcare: Keys to Implementation of the Sustainable Development Goals.

Distributed Denial of Service (DDoS) attacks have emerged as a popular means of causing mass targeted service disruptions, often for extended periods of time. The relative ease and low costs of launching such attacks, supplemented by the current inadequate sate of any viable defense mechanism, have made them one of the top threats to the Internet community today. Since the increasing popularity of web-based applications has led to several critical services being provided over the Internet, it is imperative to monitor the network traffic so as to prevent malicious attackers from depleting the resources of the network and denying services to legitimate users. This paper first presents a brief discussion on some of the important types of DDoS attacks that currently exist and some existing mechanisms to combat these attacks. It then points out the major drawbacks of the currently existing defense mechanisms and proposes a new mechanism for protecting a web-server against a DDoS attack. In the proposed mechanism, incoming traffic to the server is continuously monitored and any abnormal rise in the inbound traffic is immediately detected. The detection algorithm is based on a statistical analysis of the inbound traffic on the server and a robust hypothesis testing framework. Simulations carried out on the proposed mechanism have produced results that demonstrate effectiveness of the proposed defense mechanism against DDoS attacks.

A Robust Mechanism for Defending Distributed Denial OF Service Attacks on Web Servers

In the present time deaths because of heart disease has become a significant issue roughly one individual kicks the bucket every moment because of heart disease. Machine learning includes man-made brainpower, and it is utilized in taking care of numerous issues in information science. One normal utilization of Machine learning is the expectation of a result dependent on existing information. The machine takes in designs from the current dataset, and afterward applies them to an obscure dataset so as to anticipate the result. Characterization is an amazing Machine learning strategy that is regularly utilized for forecast. Some order calculations anticipate with acceptable precision, while others show a constrained exactness. This paper explores a technique named outfit characterization, which is utilized for improving the exactness of frail calculations by consolidating different classifiers. Investigations with this apparatus were performed utilizing a heart disease dataset. The focal point of this paper isn’t just on expanding the exactness of frail order calculations, yet in addition on the execution of the calculation with a restorative dataset, to demonstrate its utility to anticipate infection at a beginning period. The consequences of the investigation show that group strategies, for example, stowing and boosting, are viable in improving the expectation precision of feeble classifiers, and display palatable execution in distinguishing danger of heart disease.

Improving the Accuracy in Prediction of Heart Disease using Machine Learning Algorithms

Many big data systems are in the rise to handle the high volume of data that need to be stored and processed in the current world scenarios. One of the key features of the big data system is an efficient job scheduling using a robust scheduling algorithm. This paper will do a survey of some of the big data scheduling algorithms in terms of fairness, speed of processing, efficiency etc. This paper describes briefly about how a big data system works and then go deep in to some of the scheduling algorithms used in the big data systems.

A Feature Based Comparison Study of Big Data Scheduling Algorithms

/pdf/visualizing-road-damage-by-monitoring-system-in-cloud-2mwn48a7y3.pdf

Visualizing road damage by monitoring system in cloud

Currently, bogus voting in elections is remaining as a serious issue. The question of casting a ballot as far as health and protection are concerned is still important. The undertaking is primarily planned for giving a most verified and easy to understand Online Election System. An online political decision system is a voting system. In Traditional political race condition casting a ballot procedure now and again become very badly designed because of the reluctances of specific voters to visit a surveying stall to cast casts a ballot other than including tremendous social and HR. This paper manages the development and progress of an electronic voting system using distributed computing and aadhar card to provide the voting system with a superior with high security. Additionally, web innovation is used to make the voting system reasonable. The Internet surveying system is the system that empowers the client to cast a ballot on the web. In particular, the Voting system comprised of server administrations which are each connected with a database for putting away persevering information. Voters can likewise utilize the administrations to sign into the electronic voting system site. The field official will send the voter data and their voting's and that data will be kept up by creator card check. Administrators will keep up all data concerning voter and tallies consequently their deciding in favor offs their chosen gatherings.

Aadhar Card Verification Base Online Polling

Most of the internet based companies are now relying on the capabilities of recommendation models to increase their product sales. By applying efficient recommendation models businesses can track their customer preferences and effectively recommend products to users thereby increasing their sales. This paper describes the prototype implementation of two recommendations models using machine learning algorithms. The first prototype system is a banking service recommendation system and the second one is a movie recommendation system. These prototype implementations are evidence of how effectively machine learning algorithms can be applied for designing recommendation models.

Suja Cherukullapurath Mana

Papers

Improving the Accuracy in Prediction of Heart Disease using Machine Learning Algorithms

A Feature Based Comparison Study of Big Data Scheduling Algorithms

Visualizing road damage by monitoring system in cloud

Aadhar Card Verification Base Online Polling

A Machine Learning Based Implementation of Product and Service Recommendation Models