S. N. Akshay Uttama Nambi

DIAT: A Scalable Distributed Architecture for IoT

The advent of Internet of Things (IoT) has boosted the growth in number of devices around us and kindled the possibility of umpteen number of applications. One of the major challenges in the realization of IoT applications is interoperability among various IoT devices and deployments. Thus, the need for a new architecture-comprising smart control and actuation-has been identified by many researchers. In this paper, we propose a Distributed Internet-like Architecture for Things (DIAT), which will overcome most of the obstacles in the process of large-scale expansion of IoT. It specifically addresses heterogeneity of IoT devices, and enables seamless addition of new devices across applications. In addition, we propose an usage control policy model to support security and privacy in a distributed environment. We propose a layered architecture that provides various levels of abstraction to tackle the issues such as scalability, heterogeneity, security, and interoperability. The proposed architecture is coupled with cognitive capabilities that helps in intelligent decision-making and enables automated service creation. Using a comprehensive use-case, comprising elements from multiple-application domains, we illustrate the usability of the proposed architecture.

A scalable distributed architecture towards unifying IoT applications

The advent of Internet of Things (IoT) has kindled the possibility of umpteen number of applications. One of the major challenges in the realization of IoT applications is interoperability among various IoT entities. Thus, the need for a new architecture - comprising of smart control and actuation - has been identified by many researchers. In this article, we propose a distributed, interoperable architecture for IoT, which will overcome most of the obstacles in the process of large scale expansion of IoT. It specifically addresses heterogeneity of IoT devices, and enables seamless addition of new devices across applications. We propose a layered architecture that provides various levels of abstraction to tackle the issues such as, scalability, heterogeneity and interoperability. Using a comprehensive study of a use-cases, comprising elements from multiple-application domains, we illustrate the usability of the proposed architecture.

A unified semantic knowledge base for IoT

In the Internet of Things (IoT), interoperability among heterogeneous entities is an important issue. Semantic modeling is a key catalyst to support interoperability. In this work, we present a unified semantic knowledge base for IoT that uses ontologies as the building blocks. Most of the current ontologies for IoT mainly focus on resources, services and location information. We build upon the current state-of-the-art ontologies to provide contextual information and set of policies to execute services. Our knowledge base consists of several ontologies viz, resource, location, context & domain, policy and service ontologies. This helps in building a unified knowledge representation for IoT entities. In our knowledge base, we specifically model dynamic environments in which IoT entities operate. Our knowledge base also facilitates service-composition, discovery and modeling for IoT in dynamic environments.

A distributed smart application for solar powered WSNs

Energy harvesting (EH) is a major step in solving the critical issue of availability of energy for sensor nodes. However, it throws many challenges. The applications built on the sensor networks powered by EH need to adapt their operations yet serve the purpose. We propose a distributed smart application for a multihop sensor network and in general in the future Internet of Things (IoT) where a network node executes an optimal number of policies to minimize the difference between available energy and consumed energy (called residual energy) for the execution of an application policy . We formulate this as a multi-criteria optimization problem and solve it using linear programming Parametric Analysis. We demonstrate our approach on a testbed with solar panels. We also use a realistic solar energy trace with a three year database including seasonality. The smart application is capable of adapting itself to its current energy level as well as that of the network. Our analytical results show a close match with the measurements conducted over testbed.

Toward the Development of a Techno-Social Smart Grid

Advancement in communication and computing technology is driving the next-generation electrical smart grid, SG. SG envisions developing user-centric distributed systems to offer cost-effective and reliable power supply. Its effectiveness depends highly on consumer awareness and engagement. SG deployments and programs have been found to be lacking in active consumer participation. We address this by proposing a techno-social framework for SG, TSSG, wherein technologies related to energy infrastructure interface with social activities of consumers. Social interactions play a crucial role in preferences of consumers and the decisions they make. The rapid evolution of social networks now enables us to model and capture these interactions. The proposed framework combines social networks with energy networks to understand individual and collective behavior of consumers in order to change the energy demand patterns. We describe several mechanisms to enable harnessing useful data from such a framework, including its applicability to various SG applications. Specifically, we illustrate the benefits of collective modeling of techno-social aspects by developing goal-oriented virtual communities. This is one of the first articles to consider both energy consumption information and characteristics of consumers to determine such communities. We employed data from a real-world SG deployment with more than 4000 households along with their preferences, opinions, and interests to evaluate our proposal.

On systems generating context triggers through energy harvesting

Context awareness is one of the building blocks of smart applications that constitutes smart spaces. With the emergence of cyber physical systems, it is now possible to create spaces that are truly adaptable and smart. In these spaces, contextual parameters are captured by many wireless sensor nodes. This collected data is processed to understand the context in real time. Since a large number of sensors are deployed, processing all the data is a big task. Moreover, since the sensors are powered by batteries, they have a limited lifetime. Sensing only when there is a context event can save energy as well as reduce data processing. To make sensor nodes operate perpetually, ambient energy harvesters can be used. Typically, the energy harvesting source of a sensor is related to the physical parameters the sensor is measuring. We propose exploiting this property to develop a context-event triggering mechanism in this article. We also adapt the context-aware application framework for incorporating context-event triggers by harvesters. A Smart-M3-based architecture is also proposed. Through a real-world use case, we illustrate significant energy savings and reduced data processing in our proposed approach.

Near Field Communication – Applications and Performance Studies

Near Field Communication (NFC), is an integration of Radio Frequency Identification (RFID) technology with mobile devices. NFC offers a quick and convenient method of interaction between humans and NFC enabled devices. Current research concerning NFC appears to mainly focus on development of NFC enabled applications and services. In this paper, we study the performance of NFC devices by considering metrics such as achieved data rates and received power for several distances. Knowledge of these metrics may be useful for application developers to build applications efficiently. We have developed various applications on NFC enabled devices for public transport systems. We also describe the design of 13.56 MHz antenna which was used for measurements of the received power.

Decentralized Energy Demand Regulation in Smart Homes

Smart grids offer better energy management at consumer premises as well as energy companies side using bi- directional communication and control. Energy companies can balance energy supply and demand to a large extent, with the advent of smart homes. They can also nudge consumers to shift their demands to off-peak hours for load balancing and monetary benefits. We propose a decentralized demand scheduling algorithm that minimizes consumer discomfort and electricity cost of a household. Our algorithm utilizes only aggregated energy consumption of a household to derive optimal appliance level demand schedules. Furthermore, a low-complexity energy disaggregation algorithm is proposed to derive fine- grained appliance information and consumer preferences. We propose three important coefficients related to energy usage of consumers. We utilize them to derive optimal day- ahead demand schedules. The decentralized algorithm is empirically evaluated using real-world energy usage data from open datasets, which include our own deployment. Our proposed scheduling algorithm saves up to 30% energy cost. This work is one of the first to derive day-ahead schedules using real-world data from multiple households.

PEAT, how much am i burning?

Depletion of fossil fuel and the ever-increasing need for energy in residential and commercial buildings have triggered in-depth research on many energy saving and energy monitoring mechanisms. Currently, users are only aware of their overall energy consumption and its cost in a shared space. Due to the lack of information on individual energy consumption, users are not being able to fine-tune their energy usage. Further, even-splitting of energy cost in shared spaces does not help in creating awareness. With the advent of the Internet of Things (IoT) and wearable devices, apportioning of the total energy consumption of a household to individual occupants can be achieved to create awareness and consequently promoting sustainable energy usage. However, providing personalized energy consumption information in real-time is a challenging task due to the need for collection of fine-grained information at various levels. Particularly, identifying the user(s) utilizing an appliance in a shared space is a hard problem. The reason being, there are no comprehensive means of collecting accurate personalized energy consumption information. In this paper we present the Personalized Energy Apportioning Toolkit (PEAT) to accurately apportion total energy consumption to individual occupants in shared spaces. Apart from performing energy disaggregation, PEAT combines data from IoT devices such as smartphones and smartwatches of occupants to obtain fine-grained information, such as their location and activities. PEAT estimates energy footprint of individuals by modeling the association between the appliances and occupants in the household. We propose several accuracy metrics to study the performance of our toolkit. PEAT was exhaustively evaluated and validated in two multi-occupant households. PEAT achieves 90% energy apportioning accuracy using only the location information of the occupants. Furthermore, the energy apportioning accuracy is around 95% when both location and activity information is available.

Predicting Room-Level Occupancy Using Smart-Meter Data

Occupancyinformationinbuildingsisacrucialinformationtoenableautomatedloadcontrolling resultinginsignificantenergysavings.Unfortunately,currentmethodsobtainoccupancydataby usingadditionalinfrastructure,whichcanbeexpensiveandinefficient.Inthispaper,weproposea methodtopredictroom-leveloccupancybyutilizingonlysmart-meterdata.Severalclassifiersare usedtoestimateroom-leveloccupancyinformation.Weidentifythebestfeaturesetconsistingof appliancesenergydata,appliancesstate,andhouse-leveloccupancydata.Thefeaturesareobtained usingonlysmartmeterdataalongwithnon-intrusiveloadmonitoringandhouse-leveloccupancy prediction.We show that theproposedmethods canachieveup to90%accuracy for room-level occupancypredictionusingonlysmartmeterdata. KeywORDS Energy Disaggregation, Multilabel Classifier, Non-Intrusive Load Monitoring (NILM), Occupancy Prediction, Principal Component Analysis (PCA), Support Vector Machine (SVM)