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Internet of Things (IoT), Middleware Architecture, Based on Smart-Home: Survey

By A'aeshah Alhakamy

Department of Computer and Information Science Indiana University – Purdue University Indianapolis (IUPUI)

Advanced Mobility and Cloud Computing Dr. Arjan Durresi

8 May 2016

Abstract Recently, the internet of things (IoT) and home energy administration framework get to be noticeable subjects, electronic appliances acknowledgment innovation can offer clients some assistance with identifying the electronic machines being employ and assist enhancing power consumption practice. Nevertheless, as well known by the power consumption practice for home clients, it is conceivable to all the while switch on and off electronic machines. In this manner, this survey presents a review of smart home and appliances among the Internet of thing (IoT) concept following three section of outlines: First section we discuss several features and characteristics that are desired in producing a practical architecture of IoT; Second section we show one conceivable architecture that mirrors the configuration standards sketched out in the previous section; Third section we present some of current applicable cloud architecture for smart homes and take a closer look at what each design could provide and how it might solve some of the issues that could encounter the system. Keywords: Smart-Appliances, Intelligent-Appliances, smart home, Cloud, Home Energy Management System, and Network architecture. Table of Content:

1. Introduction 2. Design Models 3. Practical IoT Architecture 4. Quick View Of Other Architecture Examples 5. Conclusion (Summery) 6. References 7. List of Acronyms

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1. Introduction Recently, as distributed computing and the Internet of Things (IoT) add to, the advancement of the intelligent home obviously enters another stage, in this way, the commercial enterprises and the scholarly world of different nations have concentrated on creating Smart Grids, Cloud Computing Services, and Green Buildings [4]. The ability of IoT to force a manageable ordinary life tasks is more than desirable. This is clearly confirmed throughout its present application domains, for instance, agriculture, energy preservation at home or in manufacturing settings and the pollution and transportation control inside an urban area [7]. One case of such potential is the Google's Nest Thermostat, maybe the most popular IoT gadget in 2014. The expanding demand for insightful and customized services drive to implement context-aware systems in smart homes which known as “a general class of mobile systems that can sense their physical environment, and adapt their behavior accordingly” [8]. These context-aware systems have progressed by offering versatile service forecast as for the request pattern and the user activity. As indicated by the user activities and prerequisites, these frameworks can reason the adaptive services by examining incidents and maintaining regulations [1]. The rising of green IT and Smart Grid technologies have modified the electricity utilization to be more proficient. These innovations grant both power system administrators and consumers to enhance energy efficiency and diminish home pollution by advancing energy distribution and management [9]. Although the standard systems must assemble and store valuable contexts for pattern generation purposes, these frameworks have inordinate resource utilization and long-term pattern design investigation during context coalition. Moreover, current home energy management system employed several embedded sensors, e.g. smart meters or monitoring sensors, to sustain multiple applications and services. The extensive use of embedded sensors will prompt incredible ascent of machine-to-machine (M2M) interchanges over wired and remote connections, which additionally requires massive computing resources [1]. Future smart green home (see figure. 1) which creates and provides internal electricity without the outer supply of power by boosting energy performance, could be recognized by the context-awareness innovation and renewable energy framework. In any case, the current frameworks incorporated at home have the accompanying issues:

1. Disagreement of the request and supply: the amount of day by day energy consumption is shifting continuously based on the daily fluctuation of climate conditions. Hence, distinctive frequency and voltage are vital issues when coordinating renewable energy frameworks into the standard power network.

2. Expensive establishment of the system: for the renewable energy distribution and management service, high-cost battery, energy management system (EMS), power sensors, and solar panels are required. Note that these high-cost frameworks introduced at home are not entirely utilized. To solve this problem, the recommended strategy is to extend the cloud computing innovation to the energy resources which is being used for productive use of database or computing resources.

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Figure 1: Example of smart green home [K-ENERGY LTD]

Some other strategies that could be taken into consideration are: First, assigns dynamic priority to a smart appliance according to the type of appliance and their current status to maximize the benefits of distributed renewable energy source. Second, employs cloud computing methods to provide energy management services to automatically and dynamically deploy and manage virtual infrastructure in the cloud. Third, optimally control household appliance based on patterns, such as resident’s behavior, resident’s profiles, energy consumption, and so on [1]. To such extend, this survey presents a review of smart home among the internet of thing (IoT) concept using three cloud network architectures. Before the exploration of these designs we reveals general information about the concept and some applicable approaches to cope with the dynamic nature of available resources enabling the evolution and adaptation of current created applications. Then, we would extract the common features and patterns and separate the new strategies to solve the previous problems. 2. Design Models The four key parts of the IoT ecosystem are humans, data, communication, and devices. Based on the issues mentioned in the previous section, we can synthesize several features that are desired in a practical architecture of IoT. First: User- centered instead of Thing- centered

Due to the operational importance in the IoT system, the current architectures are device or network oriented. Nonetheless, two key angles that are regularly disregarded are the users who are a piece of this ecosystem and the context of interaction between people and “Things”. The user-centered model of IoT, known also as Internet of Things and Humans (IoTH), resets the center from what is permitted by networked devices and sensors to explicitly what benefit can be accumulated for people who are a piece of, affected by and impact the network[11][2]. There will be an immense attention on neighborhood and locality by the way people comprehend, decipher and utilize their own space. Here, the information richness will be more significant at close proximity to

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"users", yet will drain and turn out to be less engaging and helpful as the proximity zone spread. For such systems, every single related technology and service must be people centric like networking, communication, decision making, quality-of-service, and so on, for making an IoT experience that profoundly engages with users. Such a methodology has some results. For instance, the detecting and activation of physical infrastructure can actually stretch out to individuals themselves, with people acting as data sources (e.g., tweets, health monitoring), and similarly reacting to controls (e.g., notifications to deliberately turn down the air-conditioning to spare energy). This offers the plausibility of providing uniform control/service and data plane abstractions to both humans (virtual sensors/actuators) and "Things". Moreover, the interface to the physical world should be straightforward and effortlessly reasonable for dealing with the scale, huge number and heterogeneity of devices [4]. Second: Virtual and Physical Expansion

A great part of the IoT discussion is about the physical infrastructure and its enhancement. Acquiring people and social components (with their virtual online avatars like in social media) serves expand the digital and physical universes, besides coordinate crosswise over users and infrastructure. Reaching proximity and interaction between users and "Things" (H2H, H2M, M2M), both in the physical and virtual universes, is important for actionable knowledge. Third: Big Data or “Little” Data!

Analysis conducted on information from assorted sources inside of the IoT architecture supports with data-driven decision making. There are two classes of such data, first, transient sensor and individual information constantly gathered from humans or physical gadgets, refer to as "Little" data. Second, determined knowledge-bases and documents that have vast domains and accessible in central repositories or Clouds are known as "big" data. The meaningful analysis requires both Big and Little information to be consolidated, and in the real-time mostly [11] [5]. The Analytic services themselves could either be conveyed on-interest, on-devices or Clouds, or be confined to central data storages. Therefore, neither a completely unified nor a totally incorporated data storage model will scale or manage. An asymmetric storage and service model that adjusts to the social and service context is fundamental. There would be many entities in this mix:

Table 1: Some of IoT Entities

Entity characteristic Example

Data generators With real-time, cached or archived data. From sensors Stored at the source site. Mobile phone

Data owners Release the data outside the source site with or without identity, in return for payment or rewards.

Knowledge-bases open or exclusive corpus of information that are stored at archive sites. Public Clouds

Integration services Use data from generators and knowledge-based to offer homogenized information on which analytics can be performed.

• Single site: Mobile Phone, Private Cloud.

• Multiple Sites: P2P network

Analytic services Consume the integrated data and generated operational intelligence. They are willing to pay for the integrated data they consume, and may charge consumers of the analytics.

• Centrally: on a phone/public Cloud • Distributed: across a P2P network

Data brokers Discover the data and pay for data owners for consumed data, and negotiate to move the data.

Service brokers Discover the data analytics or integrate services with the payment methods, and negotiate deployment/movement service.

Analytics consumers Responsible for the analytics service payments with the data privacy and ownership start becoming concern about the data movement.

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Forth: Investigation from the device to the Cloud

Identified with Big-Little data is performing appropriated investigation and decision making. The present model of pushing all data to a central Cloud for investigation won't scale, is wasteful, and raises security concerns. Given the improved abilities of edge devices like advanced cells combined with discontinuous network associations, decisions on whether a part of the “Big” data and decision diagnostic ought to be pushed to the telephone, or the “Little” data and systematic collected in the Cloud must be automated. These are educated by the device capacity, security needs, energy and network expenses, and application Quality of Service (QoS) [6] [11]. Fifth: Bring the “Network to the Sensor”

As moderate price IoT devices multiply, they will be much more compelled in energy and communication abilities. As opposed to depending on the enormous organization of custom sensor networks and new standards, there is quality in carrying on existing, broadly embraced guidelines and reusing infrastructure to accomplish the system scale and densities at affordable expenses. For example, affordable as P2P data mule for last mile network to sensors joined with profoundly practical portals and Clouds for coordination proposes an asymmetric architecture. Given the expansive assorted qualities of “Things", it will be far-fetched that they all will share a single kind of communication infrastructure. It arrives that the current Internet Protocol(IP) will give the fundamental interoperability to sticking existing networks that keep running on different sorts of correspondence connections. IPv4 has been the universal stack for the Internet, and along these lines, gives a roughly tested system to flexibility, scalability and configuration management. IPv4 will without a doubt stay in operation for a considerable length of time to come; however, given the huge scale expansion of “Things", its capacity to handle the address space could get examined. Hence, an early reception of the following adaptation of IP (i.e., IPv6) might be a conceivable aspect. Since the profoundly asymmetric architecture, it might be a pointless excess to provision for IP in the cheap sensing platforms; yet would be a decent move to constrain it to the exceptionally practical portals [3] [11]. Sixth: What price you can afford?

Technology infiltration has not been uniform crosswise over countries, areas, or, so far as that is concerned, commercial enterprises. This divergence is an impression of the distinction in infrastructure, expense of access, telecom networks and services, and policies among various economies. Subsequently, the cost and Technology behind the sensing, devices, networking and logical solutions for the IoT ought to be affordable and scale to billions of clients. This requires reuse of commodity hardware and sensors, and existing infrastructure in novel courses as opposed to custom solutions with advanced abilities, or canned solutions produced for cutting edge economies. The expense to-advantage tradeoffs get to be basic [11]. Seventh: Data ownership transparency

The devices intersection, communication, data and people inside IoT offers intriguing motivating force and plans of action. A key achievement of the WWW is the capacity for organizations to monetize clients' data, for example, Google Ads utilizing client's web data pays with the expectation of complimentary search and email services. Devices with IoT will be much closer to people and mix into our surroundings. Guaranteeing there is clarity in data ownership, sharing, and use is critical. Further, there is extension for data brokering that energizes open data offering by clients to business consequently for clear compensates, be they fiscal, peer recognition, or for more prominent benefit.

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Eight: When is it "adequate"?

IoT is usually an assorted environment with unreliability and vulnerabilities as the following points on its availability:

• Modest sensors mean uncertain data quality. • People are changeable to demonstrate. • Physical frameworks are complex. • Distribute "Things" and irregular communication are a given. • Data privacy has limits.

Therefore, analytic and decision making must be probabilistic; and the framework and application must be aware of what is "adequate" and not come up short without immaculate behavior. Night: Context determines the Action

Given the instabilities of the system and people being focal entities, a significant part of the decision making inside of the IoT infrastructure and applications must be relevant. Context ties individuals and "Things" to a typical extension, and consequently, will ease mining of related information. There must be semantic learning that catches framework and social response, some specified while others are studied utilizing models. Intelligent agents will regularly follow up for the benefit of people. They might know about individual preferences such as, Apple's Siri, Microsoft's Cortana, and Google Now), and these will associate with digital operators of service suppliers, utilities and vendors. Semantic context will need to supplement web guidelines for basic language structure to permit such M2M interaction to be compelling [1]. Tenth: Business Plan

In the event that the IoT is to yield productive business models, we first need to perceive that IoT is not a new product or market. What IoT brings is an extra arrangement of innovations, lower power, more computation and storage, less expensive devices, better remote availability, a great deal more granular control and observation abilities. What it allows is scaling in both directions - top and bottom, and the ability of seeing ourselves and the world in a remarkable level of point of interest. IoT business models fall into two general categories to supply an end user with a value proposition:

1. Horizontals concerned with enabling components and technology a. The improvement of particular sensors and actuators that permit the generation of new, or

more price effective observations. b. The necessities of building out to the extent of the data gathering, data storage and data

curation and data brokering requirements of IoT-based frameworks. c. The requirements for an arrangement of expository procedures to change over the data

assembled into actionable information. While the initial two have been the center of IoT's antecedent advances, IoT's scale is driving dynamic improvement no matter how you look at it.

2. Verticals, which integrate these technologies. It will pull solutions and services across these horizontals to convey final end client esteem. The emphasis here will be on the vital space and framework integration skill and the capacity to fabricate the important joint efforts crosswise over customers and suppliers [11] [8].

3. Practical IoT Architecture IoT has proved its existence in a large application space and is driven by a wide several of use cases. It would be helpful to have a modular and scalable architecture that promote adding and removing abilities

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based on the functional requirements. In this section, we show one conceivable architecture that mirrors the configuration standards sketched out in the previous section. Figure 2. Outlines the four layers architecture stack that we think is more suitable for the general idea.

Figure 2: a High-level architecture for IoT. [Many references] Redrawn by me

3.1. Physical and Virtual Layer

The first layer is an accumulation of sensing components, or data generators and human that give context information. The sense of this information is not just from the physical space of hardware-level sensors, additionally from delicate sensors that exist in the virtual space. Physical platforms comprise of an interface to the physical world. There exists a vast number of sensor products, from several manufacturers, to gauge different physical parameters, for example, temperature, pressure, humidity, illumination, acoustics, motion, location, touch, and so on.,. These devices range in heterogeneity and multifaceted nature, from an embedded 8-bit SoC unit with a solitary sensor/actuator to 32/64 bit figuring stage with numerous transducers; and interface with different restrictive communication technology (direct Ethernet, WiFi, BLE, NFC, Zigbee, 6LowPAN, UART on the other hand serial lines, SPI or I2C wired transports) over a wide assortment of protocols [12]. The virtual sensing range is likewise very diverse. It includes sensing entities that can be human, (e.g.,

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crowdsourced data, collaborative projects, blogs, content communities, social networking sites, virtual game worlds, virtual social worlds, electronic calendars, a travel-booking systems), their virtual operators (e.g., Siri, Cortana), or advanced applications and services that complete and offer higher request detecting (e.g., midpoints from various physical sensors).

Figure 3: Edward Hall's proxemic zones mapped to "Things"[11]. Redrawn by me

3.1. Sensor and Network as a Service

The Sensor/Network as Service (SNaaS) layer comprises of data and control planes. The data plane will grant channel models (e.g., event-driven, sample, hold, and so forth,) as a widespread deliberation for input and output to the physical and virtual space of the sensing layer. The control plane will be accountable for dealing with the sensor and network and delivering a regulated life-cycle to the revelation, configuration and utilization of the channel models. The blend permits the formation of the plug-n-play framework (crosswise over platforms from diverse vendors) vital for interoperability and practical deployment of immense scale system. The empowering highlight for plug-n-play would be a transducer electronic data sheet (TEDS) that will be utilized to portray the physical components, for example, transducer identification, calibration, correction data, measurement range, and producer related data, and so on. This information will either be shown by the new sensing entity to the infrastructure components, or forcefully pulled by the infrastructure itself from a TEDS vault on receipt of the sensor identifier. Delivering a mechanism to depict the transducers will permit the application (client, service, and agent) to record its properties and semantic portrayal (semantic metadata). The control plane will open approaches to turn on/off sensors or specific properties, change their inspecting interval/transmission interval, the kind of quality controls done such as linear interpolation, etc. Taking after the configuration of the "Thing", the data plane will distribute the perceptions of every sensor as single data streams to the layer above. The communication between the SNaas and upper layers will take after a stateless and self-describing interface utilizing REST-based protocols [13] [12] [11].

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The mixture of semantic capacity and data push/pull with a publish/subscribe model will permit "Things" to be assembled into a semantically connected data stream diagram. The SNaaS layer will, in this manner, go about as a registry for semantic revealing and linkage of "Things", and for virtualizing this present reality. 3.1. Data Management

The previous layer awards a channel for streaming "little" data from distributed physical and virtual sensors. The data must be cataloged, curated and if essential, persisted and aggregated, so as to perform subsequent analytics. The data management layer supports obtain and maintain a Registry of data sources and their attributes, for example, periodicity, exuberance, and quality, and make them accessible for subsequent analytics. These might be from physical sensors that are conveyed as a feature of static infrastructure, mobile devices that may radiate data transiently, or virtual sensors that share posts in view of social conditions. The registry itself might be united utilizing a P2P model, i.e. the data management layer is itself circulated and not significantly a centralized unit hosted solely in the Cloud. Likewise, access to data might be as surges of occasions pushed utilizing a distribute subscribe model, pulled on-demand, or gathered at the device for neighborhood operations pushed to it, utilizing avaliable web and open criterions like REST, COAP, Atom and JMS. These might be controlled employing data privacy and distributed access control policies that are upheld by this layer.[2][11] This management layer ought to additionally incorporate with total and moderate evolving data "Big" that is accessible from authoritative sources or have amassed from sensor streams. Regularly, the Big data is facilitated at focal locations, globally or at nearby reserves, to evade expensive data movement and replication, and may have less restrictive protection requirements. These may incorporate institutional and crowd-sourced data. Coordinating this "big" data with the "Little" data requires standard vocabulary that builds the semantics. DBPedia and SWEET offer reusable semantic ontologies for general purpose and specific domains to automate matchmaking of data requirements, while manageable glossaries or scientific categorizations may be satisfactory in different cases. So also, data quality checks and validation can improve intuitiveness of data for specific needs. To help interface data consumers with data producers, the data brokering service is enabled utilizing these building blocks of data ownership, incentives to gather data, data description, data quality and get to control. The broker can help incentivize data accumulation and reuse through attribution, bargain, or even fiscal compensates. [13] [11] 4.4 Analytics and Decision Making

Internet of things applications Analytics frequently fall into classifications of data and pattern mining, predictive analytics and forecasting, event and pattern detection, and optimizations. While there might be domain/application specific logical, it is valuable to have key examination algorithms accessible that can be reconfigured to suit regular needs. Time arrangement and regression tree forecasting are helpful for anticipating future conditions, for example, power request, in view of past behavior of univariate or multivariate components. Pattern mining and clustering can support gather environmental conditions and clients who show comparative behavior with the goal that aggregate action can reach place, or extrapolation from one entity in the cluster to rest can happen, like recommendations.[3][11] Mining can likewise recognize causality between patterns of characteristic and an event of interest. Such patterns and expectations can encourage into real-time complex event pattern coordinating that recognize circumstances of interest and react to, or preferable, appropriate them. These additionally sustain into improvement algorithms that can control physical or virtual "Things" to guarantee dependability and effectiveness of the system.

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These analytics algorithms and platforms, as earlier, need to work in a distributed circumstances instead of assuming centralized accessibility of data or the capacity to suit in memory. Taking into account the security and communication constraints, and availability of distributed data and devices, this analytics will need to keep running crosswise over edge devices and the Cloud.[11][4] Since analytics and optimizations over streaming data may drive real-time decision making, latency is another key metric. Such an obliged situation in the nearness of possibly untrustworthy data and calculation capacity implies that analytics and decisions must be probabilistic. The cost of performing the decision, including data, network, computers and intellectual property, should be calculated in and exchanged off against the value from the decision.[1] [11] 4. Quick View of Other Architecture Examples Here we present some of the current applicable cloud architecture for smart appliances and take a closer look at what each design could provide and how it might solve some of the issues discussed above. 4.1. Intelligent cloud management server (iCMS)[1]

A normal for cloud computing is that the service should be reconfigured to designate extra or discharge unneeded resources and fittingly reorganize the conveyed software and hardware parts. As per the fundamental architecture of cloud computing, iCMS has three architectural layers: application layer, management layer, and cloud infrastructure layer. Figure. 4 shows the middleware architecture of iCMS.[1] The similarity overcomes the differences between the previous design and iCMS architecture. However, there are many points that we need to mention. We can see that the physical and virtual spaces are separated which could be more organize, but the physical space seems insignificant. This real example shows in details the parts clearly and what each of them responsible for which we discussed in general above.[1]

Figure 4: Middleware architecture of iCMS.[1]

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4.2. ZigBee-based intelligent self-adjusting sensor (ZiSAS) [12]

This system exhibits a situation-based self-adjusting scheme, an event-based self-adjusting sensor network, and hardware and middleware implementation. They also introduce some smart home services utilizing this system. They implemented the system in the real test bed and conducted an experiment. The experiment shows that they reduce the system’s energy consumption.[12]

Figure 5: Middleware architecture of the ZiSAS.[12]

4.3. The Gator Tech Smart House [13]

To generate the Gator Tech Smart House, They built up a generic reference architecture suitable to any pervasive computing space. As Figure 6 demonstrates, the middleware contains separate physical, sensor platform, service, knowledge, context management, and application layers. They have implemented most of the reference architecture though much work remains to be done at the knowledge layer.[13]

Figure 6: Smart Space middleware. This generic reference architecture applies to any pervasive computing environment [13]

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5. Conclusion (Summery) The abnormal state IoT vision of building an omnipresent society of individuals and "Things", aside from the arrangement of empowering innovations, requires a flexible, scalable architecture. The proceeded with increment in the expansion of "Things", there exists an extensive populace of physical entities that are currently recognizable, however, don't effortlessly associate meaningfully to individuals or each other at the envisioned scale. In this survey, we endeavored to disaggregate these core issues; and offered a direction with an arrangement of ideal outline models and a conceivable architecture for a new IoT ecosystem.

Citations (References)

[1] Byun, Jinsung, Insung Hong, and Sehyun Park. "Intelligent cloud home energy management system using household appliance priority based scheduling based on prediction of renewable energy capability." Consumer Electronics, IEEE Transactions on 58.4 (2012): 1194-1201. [2] Niyato, Dusit, Lu Xiao, and Ping Wang. "Machine-to-machine communications for home energy management system in smart grid."Communications Magazine, IEEE 49.4 (2011): 53-59. [3] Arling, Paul D., et al. "Home appliance control system and methods in a networked environment." U.S. Patent No. 7,136,709. 14 Nov. 2006. [4] Chen, Shih-Yeh, et al. "Intelligent home-appliance recognition over IoT cloud network." Wireless Communications and Mobile Computing Conference (IWCMC), 2013 9th International. IEEE, 2013. [5] Yamamoto, Seiichi, Shinichi Matsumoto, and Mitsutoshi Nakamura. "Using cloud technologies for large-scale house data in smart city." Cloud Computing Technology and Science (CloudCom), 2012 IEEE 4th International Conference on. IEEE, 2012. [6] Van der Meulen, Pieter Sierd. "Intelligent appliance home network." U.S. Patent No. 6,906,617. 14 Jun. 2005. [7] Ventura, Daniela, et al. "ARIIMA: a real IoT implementation of a machine-learning architecture for reducing energy consumption." Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services. Springer International Publishing, 2014. 444-451. [8] Robles, Rosslin John, and Tai-hoon Kim. "Review: context aware tools for smart home development." International Journal of Smart Home 4.1 (2010). [9] R. F. Arritt and R. C. Dugan, "Distribution System Analysis and the Future Smart Grid," IEEE Trans. on Industry Applications, vol. 47, no. 6, pp. 2343-2350, Nov.-Dec. 2011. [10] K-Energy Cyprus - Photovoltaic - Renewable Energy Audits by Karantonis - Smart Buildings. (n.d.). Retrieved March 14, 2016, from http://www.cypruspv.com/en/services/smart-buildings [11] Misra, Prasant, Yogesh Simmhan, and Jay Warrior. "Towards a Practical Architecture for the Next Generation Internet of Things." arXiv preprint arXiv:1502.00797 (2015). [12] Byun, Jinsung, et al. "An intelligent self-adjusting sensor for smart home services based on ZigBee communications." Consumer Electronics, IEEE Transactions on 58.3 (2012): 794-802. [13] Helal, Sumi, et al. "The gator tech smart house: A programmable pervasive space." Computer 38.3 (2005): 50-60.

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List of Acronyms

IoT Internet Of Things M2M Machine-To-Machine EMS IoTH QoS SoC BLE NFC

6LowPAN UART

SPI I2C

SNaaS TEDS REST

P2P COAP

JMS SWEET

Energy Management System Internet Of Things And Humans Quality Of Service System On A Chip Bluetooth Low Energy Near Field Communication Ipv6 Over Low Power Wireless Personal Area Networks Universal Asynchronous Receiver/Transmitter Serial Peripheral Interface Inter-Integrated Circuit Sensor/Network As Service Transducer Electronic Data Sheet Representational State Transfer Peer-to-peer Constrained Application Protocol Java Message Service Semantic Web for Earth and Environmental Terminology