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The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey

January 2015
IEEE Transactions on Emerging Topics in Computing 3(4)

DOI:10.1109/TETC.2015.2390034

Authors:

Charith Perera

Cardiff University

Chi Harold Liu

Beijing Institute of Technology

Srimal Jayawardena

The Commonwealth Scientific and Industrial Research Organisation

The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as RFIDs, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organizations making their way into the market. In this paper, we survey over 100 IoT smart solutions in the marketplace and examine them closely in order to identify their applications and the technologies they use. More importantly, we identify the trends, opportunities, and open challenges in the industry-based IoT solutions. Based on the application domain, we classify and discuss these solutions under five different categories: 1) smart wearable; 2) smart home; 3) smart city; 4) smart environment; and 5) smart enterprise. This survey is intended to serve as a guideline and a conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions. 14 INDEX TERMS Internet of Things, industry solutions, IoT marketplace.

Common Communication Patterns in IoT Applications. There are mainly three types of common patterns

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 1

The Emerging Internet of Things Marketplace From

an Industrial Perspective: A Survey

Charith Perera, Member, IEEE, Chi Harold Liu Member, IEEE, Srimal Jayawardena, Member, IEEE,

Abstract—The Internet of Things (IoT) is a dynamic global

information network consisting of internet-connected objects,

such as Radio-frequency identiﬁcation (RFIDs), sensors, actu-

ators, as well as other instruments and smart appliances that

are becoming an integral component of the future internet. Over

the last decade, we have seen a large number of the IoT solutions

developed by start-ups, small and medium enterprises, large cor-

porations, academic research institutes (such as universities), and

private and public research organisations making their way into

the market. In this paper, we survey over one hundred IoT smart

solutions in the marketplace and examine them closely in order

to identify the technologies used, functionalities, and applications.

More importantly, we identify the trends, opportunities and open

challenges in the industry-based the IoT solutions. Based on the

application domain, we classify and discuss these solutions under

ﬁve different categories: smart wearable, smart home, smart,

city, smart environment, and smart enterprise. This survey is

intended to serve as a guideline and conceptual framework for

future research in the IoT and to motivate and inspire further

developments. It also provides a systematic exploration of existing

research and suggests a number of potentially signiﬁcant research

directions.

Index Terms—Internet of things, industry solutions, smart

wearable, smart home, smart city, smart environment, smart

enterprise, IoT marketplace, IoT products

I. INTRODUCTION

The Internet of Things (IoT) is a network of

networks where, typically, a massive number of

objects/things/sensors/devices are connected through

communications and information infrastructure to provide

value-added services. The term was ﬁrst coined in 1998 and

later deﬁned as “The Internet of Things allows people and

things to be connected Anytime, Anyplace, with Anything and

Anyone, ideally using Any path/ network and Any service”

[1]. As highlighted in the deﬁnition, connectivity among the

devices is a critical functionality that is required to fulﬁl the

vision of the IoT. The main reasons behind such interest are

the capabilities and sophistication that the IoT will bring to

society [2]. It promises to create a world where all the objects

around us are connected to the Internet and communicate with

each other with minimal human intervention. The ultimate

goal is to create “a better world for human beings”, where

objects around us know what we like, what we want, and

what we need, and hence act accordingly without explicit

instructions [3].

This work is sponsored in part by National Natural Science Foundation of

China (Grant No.: 61300179).

Charith Perera, and Srimal Jayawardena, are with the Research School of

Computer Science, The Australian National University, Canberra, ACT 0200,

Australia. (e-mail: ﬁrstname.lastname@ieee.org)

Chi Harold Liu is with Beijing Institute of Technology, China. (e-mail:

chiliu@bit.edu.cn)

Manuscript received xxx xx, xxxx; revised xxx xx, xxxx.

There have been a number of surveys conducted in the IoT

domain. The area of the IoT has been broadly surveyed by

Atzori et al. in [2]. Bandyopadhyay et al. have surveys of

the IoT middleware solutions in [4]. Layered architecture in

industrial IoT are discussed in [5]. A similar survey focusing

on data mining techniques for the IoT are discussed in [6].

Edge mining in IoT paradigm is discussed in [7]. In contrast

to the traditional data mining, edge mining takes place on the

wireless, battery-powered, and smart sensing devices that sit at

the edge points of the IoT. The challenges in self organizing

in IoT are discussed in [8]. Atzori et al [9] have discussed

how smart objects can be transformed in to social objects.

Such transformation will allow the network to enhance the

level of trust between objects that are ‘friends’ with each

other. IoT technologies and solutions towards Smart Cities are

reviewed in [10]. Communication protocols and technologies

play a signiﬁcant role in IoT. Sheng et al. [11] have survey

a protocol stack developed speciﬁcally for IoT domain by

Internet Engineering Task Force (IETF).

Internet of things: vision, applications and research chal-

lenges are discussed from a research perspective in [12],

[13]. Further, the IoT has been surveyed in a context-aware

perspective by Perera et al. [14]. A survey on facilitating

experimentally IoT research is presented by [15]. Palattella

et al. [16] have introduced a communications protocol stack

to support and standardise IoT communication. Security chal-

lenges such as general system security, network security, and

application security in the IoT are discussed in [17]. The

security issues in perception layer, network layer and appli-

cation layer in architectures have discussed in [18]. Hardware

devices, specially nano sensors and technologies, used in IoT

are surveyed in [19]. Another similar survey has been done

by Hodges et al. [20]. This paper discusses a open-source

hardware platform called .NET Gadgeteer, a rapid prototyping

platform for small electronic gadgets and embedded hardware

devices..NET Gadgeteer is coming from an industrial setting

similar to Arduino [21].

Besides the above articles, there are a number of surveys

and reviews that have been conducted by researchers around

the world in the IoT domain, from which we have hand picked

some to represent the existing body of knowledge.

As far as we know, however, no survey has focused on

IoT industry solutions. All the above-mentioned surveys have

reviewed the solutions proposed by the academic and research

community and refer to scholarly publications. In the present

paper, we review the IoT solutions that have been proposed,

designed, developed, and brought to market by industrial

organisations. These organisations range from start-ups and

small and medium enterprises to large corporations. Because

of their industrial and market-driven nature, most of the IoT

solutions in the market are not published as academic works.

arXiv:1502.00134v1 [cs.CY] 31 Jan 2015

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2

Therefore, we collected information about the solutions from

their respective web-sites, demo videos, technical speciﬁca-

tions, and consumer reviews. Understanding how technologies

are used in the IoT solutions in the industry’s marketplace is

vital for academics, researchers, and industrialists so they can

identify trends, opportunities, industry requirements, demands,

and open research challenges. It is also critical for understand-

ing trends and open research gaps so future research directions

can be guided by them.

The present paper is organised into sections as follows:

In Section II, we evaluate and examine the functionalities

provided by each solution under the ﬁve categories identiﬁed

in the earlier section. At the end of that section, we summarise

the functionalities and highlight the major domains that are

commonly targeted by the solutions. Then, we examine the

IoT solutions from a technology and business perspective.

Hardware platforms, software platforms, additional equipment,

communication protocols, and the energy sources used by each

solution are examined in Section III. At the end of that section,

we summarise the technologies and business models used by

the IoT solutions so trends and opportunities can be identiﬁed.

In Section IV, we identify such trends using the evaluations

we conducted in the previous sections. Later, opportunities

for research and development will be assessed in Section V.

Concluding remarks will be presented in Section VI.

II. FUNCTIONALITY REVIEW OF IOT SOLUTIONS

In this section, we focus on the functionalities of the IoT

solutions. The next section discusses the technologies used by

these solutions under common themes. In both sections, our

intention is not to describe each IoT solution in detail, but to

organise them into common themes so we can identify trends

and opportunities. However, readers can use citation numbers

to track a given IoT solution throughout the paper, if desired.

Such an option allows consolidating the knowledge we have

put separately in two sections, to better understand a single

IoT solution. In Section IV, we will analyse the trends from

both the functional and the technological point of views.

A. Smart Wearable

Wearable solutions are diverse in terms of functionality.

They are designed for a variety of purposes as well as for

wear on a variety of parts of the body, such as the head, eyes,

wrist, waist, hands, ﬁngers, legs, or embedded into different

elements of attire. In Table I, we summarise popular wearable

IoT solutions. This table includes a brief description of each

solution, context information gathered, similar solutions, and

the context-aware functionality provided by the solution. The

IoT solutions are categorised by the body part on which

the solution must be worn, as illustrated in Figure 1. In

addition to the industry IoT solutions, academic solutions

in the wearable computing area are discussed in [26], [27].

Challenges and opportunities in developing smart wearable

solutions are presented in [28].

B. Smart Home

Solutions in this category make the experience of living at

home more convenient and pleasant for the occupants. Some

smart home [54] solutions also focus on assisting elderly

Hand

(Gloves)

Finger

(Rings)

Wrist

(Watch/

Bands)

Eyes

(Glasses)

Legs

(Socks)

Foot (Shoes)

Head

(Helmet)

Body

(Cloth)

Waist

(Band)

Chest

(Band)

Fig. 1. Different body parts popularly targeted by wearable IoT solutions in

the industry market-place.

people in their daily activities and on health care monitoring

[55]. Due to the large market potential, more and more

smart home solutions are making their way into the market.

From the academic point of view, smart energy and resource

management [56], [57], human–system interaction [58], and

activity management [59], have been some of the major foci.

Platforms: Smartthings [60] is a generic platform that con-

sists of hardware devices, sensors, and software applications.

Context information is collected through sensors and injected

into applications where reasoning and action are performed

accordingly. For example, the sprinkler installed in the user’s

garden can detect rain and turn itself off to save energy. Nin-

jablocks [61] and Twine [62] provide similar functionalities.

These solutions were mainly developed to support smart home

and building domains, but they can be customised to other

domains. HomeOS [63] is a platform that supports home au-

tomation. Instead of custom hardware (e.g. a smartthings hub),

HomeOS is a software platform which can be installed on a

normal PC. As with the smartthings platform, applications can

be installed to support different context-aware functionalities

(e.g. capturing an image from a door camera and sending it

to the user when someone rings the doorbell). Lab-of-things

[64] is a platform built for experimental research. It allows

the user to easily connect hardware sensors to the software

platform and enables the collection of data and the sharing of

data, codes, and participants.

Virtual Assistance: Ubi [65] supports residents by acting as

a voice-activated computer. It can perform tasks such as audio

calendar, feed reader, podcast, voice memos, make lighting-

based notiﬁcations to indicate the occurrence of certain events,

weather, stock, email, and so on. Ubi has a microphone and

speakers. It also has sensors to monitor the environment,

such as monitoring the temperature, humidity, air pressure,

and ambient light. Netatmo [66] is an air quality monitoring

solution for smart homes. In order to determine air quality,

it collects context information from sensors such as temper-

ature, humidity, and CO2. The solution monitors the home

environment and sends an alert when the residents’ attention

is required. Meethue [67] is a bulb which can be controlled

from mobile devices. The bulb reacts to the context and can

change its colour and brightness according to user preferences,

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 3

TABLE I

SUMMARY OF WEARABLE IOT SOLUTIONS

Functionalities Provided by Different wearable IoT solutions

Cloth

•Monitor respiration, body position, activity level, skin temperature, and audio of a baby using pressure, stretch, noise, and

temperature sensors, and provide notiﬁcation through a smart phone regarding any situation that parents need to attend to (Baby

Monitor: RestDevice / Mimobaby [29]).

•A sleep-tracking device that uses a thin-ﬁlm sensor strip placed on a mattress in combination with smart phone to help to create

a nightly rest proﬁle. It helps to improve user’s sleep over time (Sleep Tracking: Beddit [30]).

•Jacket relieves anxiety and stress from those diagnosed with autism spectrum disorder (ASD) or attention-deﬁcit/hyperactivity

disorder. Built-in motion sensors and pressure sensors track the frustration and activity levels of the child throughout the day

and generate custom notiﬁcation alerts based on that information (Medical Assistant :MyTJacket [31]).

Waist / Chest

•Tracks posture and daily activities in real time. It provides advice on posture issues so users can improve their posture (Daily

Activity and Fitness Monitor / Medical: Lumoback [32]).

•A device that updates Twitter when a baby in the womb kicks its mother (Medical Assistant: kickbee [33]).

•A chest band that tracks heart rate, speed, distance, stress level, calories, and activity level. It allows recommended working out

within certain heart rate zones to achieve goals such as weight loss or cardiovascular improvement. (Personal Sports Assistant:

BioHarness [34]).

Wrist

•A wrist band that tracks steps taken, stairs climbed, calories burned, and hours slept, distance travelled, and quality of sleep and

provides recommendation for a healthier lifestyle (Daily Activity and Fitness Monitor: MyBasis [35], BodyMedia [36], Lark

[37]).

•Open wearable sensor platform, a wrist band that comprises number of different sensors such as pulse, blood ﬂow sounds, blood

oxygen saturation, blood ﬂow waveform, pulse, acceleration, type of activity, calories burned and number of steps taken, skin

temperature (Open Platform: AngelSensor [38]).

•EMBRACE+, a wrist band that connects to the user’s smartphone via Bluetooth and displays any notiﬁcations user may receive

as ambient light notiﬁcations (Personal Sports Assistant: EmbracePlus [39]).

•Electrocardiogram technology (ECG), Bluetooth connectivity and a suite of sensors are used to recognize users’ heart rhythm

uniquely and securely and continuously log into users’ nearby devices (Secure Authentication: nymi [40]).

•A watch that helps athletes to keep track of their training. Context information such as mapping, distance, speed, heart rate, and

light are collected and fused to generate athletes’ training proﬁle (Personal Sports Assistant: Leikr [41]).

Eyes

•Sports-speciﬁc (skiing) goggles that monitor jump analytics, speed, navigation, trip recording, and peer tracking (Personal Sports

Assistant: Oakley Goggles [42]).

•A pair of glasses that consist of camera, projector, and sensors to support functionalities such as navigation calendar notiﬁcation,

navigation, voice activated, voice translation, communication and so on. It also acts as an open platform where different context-

ware functionalities can be built using provided sensors and processing capabilities (Open Platform: Google Glass [43]).

Head

•Sports-speciﬁc (American football) helmet that determines when to take a player off the ﬁeld and seek medical advice through

impact detection and analysis (Personal Sports Assistant: TheShockBox [44]).

•A bicycle helmet that detects a crash. If the user’s head hits the pavement (or anything hard (ice, snow, dirt)), a signal will be

sent to the smartphone automatically to generate a call for help (Emergency Accident monitor: ICEdot [45]).

Hands

•Monitor, analyze and improve golf swing through motion sensors embedded in gloves (Personal Sports Assistant: Zepp [46] )

•A ring that monitors and keeps track of the user’s heart rate (Medical Assistant: ElectricFoxy [47]).

Legs / Foot

•A sock that combines an accelerometer with textile sensors to measure steps, altitude and calories burnt. It helps runners to avoid

potentially dangerous techniques: heel striking or excessive forefoot running that could lead to back pain or Achilles ten-don

injuries. (Daily Activity and Fitness Monitor / Medical: Heapsylon [48])

•A pair of shoes that provides feedback through vibrations in an intuitive and non-obstructive way. The shoes suggest the right

direction and detect obstacles (Disability Assistance: LeChal [49])

Internal

•A small patch worn on the body working together with 1mm sensor-enabled pills and a back-end cloud service to collect and

process real-time information (e.g. heart rate, temperature, activity and rest patterns throughout the day) on the user’s medication

adherence (Medical: Proteus Digital Health [50]).

Multi

•A device that can be worn on multiple body parts tracks steps taken, stairs climbed, calories burned, and hours slept, distance

travelled, quality of sleep (Daily Activity and Fitness Monitor: Fitbit [51]).

•An ultra-small GPS unit and ﬁve in-built sensors are used to collect data and fused to tell the camera exactly the right moment

to take photos (Leisure: Autographer [52]).

•Remote monitoring system that collects data through devices that can be worn on different body parts on a patient’s physiological

conditions to support physicians (Health Monitoring: Preventice BodyGuardian [53]).

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 4

time / day / season, and activity (e.g. resident enters home)

and is also sensitive to changes in the weather during the day.

Smart Objects: WeMo [68] is a Wi-Fi enabled switch that

can be used to turn electronic devices on or off from anywhere.

Context-aware schedules are also supported, where turning on

or off is performed automatically according to the time of day,

sunrise, or sunset. Tado [69] is an intelligent heating control

that uses a smartphone. It offers context-aware functionalities

such as turning down the heating when the last person leaves

the house, turning the heating back up before someone gets

home, and heats the house less when the sun is shining.

Nest [70] is a thermostat that learns what temperatures users

like and builds a context-aware personalised schedule. The

thermostat automatically turns to an energy-efﬁcient ‘away

temperature’ when occupants leave the home. If it senses

activity, such as a friend’s coming over to water the plants,

Nest could start warming up the house. The thermostat can

be activated remotely through the Nest mobile app. Lockitron

[71] is a door lock that can be opened and closed by a phone

over the Internet. Residents can authorise family and friends

to open a given door by providing authorisation over the

Internet, so that others can use their smartphones to unlock

doors. Bluﬁtbottle [72] is a water bottle that records drinking

habits while keeping the users healthy and hydrated. If the user

starts to fall behind with hydration, the bottle has customisable

sounds and lights to alert them.

Digital Relationships: Wheredial [73] offers a way to

make a personal connection with family members or friends.

It retrieves a person’s location from Foursquare, Google Lat-

itude, and a variety of other services. Then it rotates the

dial (like a clock) to show where the person is at a given

moment. Goodnightlamp [74] is a family of connected lamps

that let the user remotely communicate the act of coming back

home to their loved ones easily and in an ambient way by

fusing location-aware sensing. The objective of Wheredial and

Goodnightlamp is the same: helping to build and maintain

family relationships and further strengthen friendships by

mitigating the fact that the users are apart from each other.

Such solutions are extremely important in terms of social,

psychological, and mental well-being.

C. Smart City

Towns and cities accommodate one-half of the world’s

population, creating tremendous pressure on every aspect of

urban living. Cities have large concentrations of resources and

facilities [75]. The enormous pressure towards efﬁcient city

management has triggered various Smart City initiatives by

both government and private sector businesses to invest in in-

formation and communication technologies to ﬁnd sustainable

solutions to the growing problems [14]. Smart grid is one of

the domains in which academia, industry, and governments are

interested and invested signiﬁcantly [76], [77].

Smart Trafﬁc ParkSight [78] is a parking management

technology designed for cities. Context information is retrieved

through sensors (magnetometers) embedded in parking slots.

Application support is provided via location and map services

to guide drivers to convenient parking based on real-time

context analysis. Uber [79] allows users to request a ride at

any time. The company in a particular place sends a cab.

In contrast to transitional taxi services, no phone call or

pick-up location is required. A mobile application shows the

cabs close to the users and their movement in real time. A

cab can be requested by means of a single smartphone tap.

Alltrafﬁcsolutions [80] collects trafﬁc data through sensors and

visualises it on maps in order to provide drivers with trafﬁc

updates. Further, it provides remote equipment management

support related to trafﬁc control (e.g. changes in digital road

signs, speed limit boards, variable message signs (e.g. ‘event

parking’) to drivers, and changes in the brightness of digital

signs based on the context information). Streetbump [81] is

a crowd-sourcing project that helps residents to improve their

neighbourhood streets. Volunteers use the Streetbump mobile

application to collect road condition data while they drive. The

data are visualised on a map to alert residents regarding real-

time road conditions. The collected data provide governments

with real-time information with which to ﬁx problems and

plan long-term investments.

Platforms Libelium [25] provides a platform of low-level

sensors that is capable of collecting a large amount of

context information to support different application domains

[9]. Thingworx [82] and Xively [83] are cloud-based on-

line platforms that process, analyse, and manage sensor data

retrieved through a variety of different protocols.

Resource Management SmartBelly [84] is a smart waste

management solution. It provides a sensor-embedded trash can

that is capable of real-time context analysis and alerting the

authorities when it is full and needs to be emptied. Loca-

tion information is used to plan efﬁcient garbage collection.

Echelon [85] has developed a smart street lighting solution

transforming street-lights into intelligent, energy-efﬁcient, re-

motely managed networks. It schedules lights to be turned

on or off and sets the dimming levels of individual lights or

groups of lights so a city can intelligently provide the right

level of lighting needed by analysing the context such as time

of day, season, or weather conditions.

Activity Monitoring Livehoods [86] offers a new way to

conceptualise the dynamics, structure, and character of a city

by analysing the social media its residents generate. This

is achieved through collecting context information such as

check-in patterns. Livehoods shows how citizens use the urban

landscape and other resources. Scenetap [87] shows real-

time info about the city’s best places. It shows the context

information of a given location such as how many people are

there, the male to female ratio, and the average age of everyone

inside. This helps users to ﬁnd the best places to hang out (e.g.

cinema, bar, restaurant) at a given time and gives information

such as availability.

D. Smart Environment

Air Quality Monitoring Airqualityegg [88] is a community-

led sensor system that allows anyone to collect context in-

formation such as the carbon monoxide (CO) and nitrogen

dioxide (NO2) gas concentrations outside their home. Such

data are related to urban air pollution. Communitysensing [89]

is also an air quality monitoring system which provides both

hand-held devices and a platform to be ﬁxed into municipal

vehicles such as street sweepers. Aircasting [90] is a platform

for recording, mapping, and sharing health and environmental

data using smartphones and custom monitoring devices. Con-

text information includes sound levels, temperature, humidity,

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 5

carbon monoxide (CO) and nitrogen dioxide (NO2) gas con-

centrations, heart and breathing rate, activity level, and peak

acceleration.

Water Quality Monitoring Floating Sensor Network [91]

collects real-time, high-resolution data on waterways via a

series of mobile sensing ‘drifters’ that are placed in the water.

It collects context information such as water quality, water

ﬂow movement, and speed, temperature and water pollution.

Intelligentriver [92] is also an observation system that sup-

ports research and provides real-time monitoring, analysis and

management of water resources. A similar solution has been

developed by Roboshoal [93]. The difference is that their

station is a mobile ﬁsh-shaped robotic device whose movement

is controllable. Dontﬂush [94] is designed to enable residents

to understand when overﬂows happen and reduce their waste-

water production before and during an overﬂow event. Con-

text information is processed in order to determine real-time

sewage levels and advise users regarding safe ﬂushing through

a context-aware light bulb and SMS.

Natural Disaster Monitoring AmritaWNA [95] is a wire-

less landslide detection system that is capable of releasing

alerts about possible landslides caused by torrential rain in

the region. Context information is collected by sensors such

as strain gauge piezometers, vibrating wire piezometers, di-

electric moisture sensors, tilt meters, and geophones. This is

a station-based solution. Insightrobotics [96] is a solution that

detects forest ﬁres by fusing context information collected

through various kinds of sensors (i.e. temperature, wind, and

so on) and networked cameras.

Smart Farming Microstrain [97] has developed a wire-

less environmental sensing system to monitor key conditions

during the growing season in vineyards. Context information

such as current temperature and soil moisture conditions, leaf

wetness, and solar radiation is collected and fused in order to

monitor vineyards remotely and alert farmers regarding critical

situations. The collected data are used to support both real-

time context-aware functionalities and historic data analysis.

Bumblebee [98] monitors the lives of bumblebees by collect-

ing and processing context information such as visual, audio,

temperature, sunlight, and weather. It automatically tweets the

current situation of the colony and well-being of the bees.

Hydropoint [99] retrieves context information through 40,000

weather stations and automatically schedules irrigation based

on individual landscape needs and local weather conditions,

resulting in lower water bills and energy savings.

E. Smart Enterprise

In general, enterprise IoT solutions are designed to support

infrastructure and more general purpose functionalities in

industrial places, such as management and connectivity.

Transportation and Logistics Senseaware [100] is a

solution developed to support real-time shipment tracking.

The context information such as location, temperature, light,

relative humidity and biometric pressure is collected and

processed in order to enhance the visibility of the supply

chain. HiKoB [101] collects real-time measurements such

as temperature gradients within the road, current outdoor

temperatures, moisture, dew and frost points from sensors

deployed in roads and provides trafﬁc management, real-time

information on trafﬁc conditions, and services for freight and

logistics. Cantaloupesys [102] allows the user to keep track

of stocks in vending machines remotely. Timely and optimal

replenishment strategies (i.e. the elimination of unnecessary

truck travel and smaller loads per truck) are determined from

context information related to usage patterns.

Infrastructure and Safety SmartStructures [103] collects

data from sensors embedded within concrete piles in founda-

tions which enables post-construction long-term load and event

monitoring. Yanzi [104] is a solution that enables the user to

monitor, maintain, and manage lifts, elevators, heating sys-

tems, energy consumption, motion detection, and surveillance.

Context information is retrieved through sensors such as video,

temperature, motion, and light. Engaugeinc [105] is a remote

ﬁre extinguisher monitoring system. Multiple sensors are used

to collect context information that allows the user to determine

when a ﬁre extinguisher is blocked, when it is missing from

its designated location, or when its pressure falls below safe

operating levels. Alerts are sent out via email, phone, pager,

and a software-based control panel.

Energy and Production Wattics [106] is a smart metering

solution that manages energy consumption at the individual

appliance and machine level. Context information is used

to understand usage pattern recognitions of each appliance

through software algorithms which predict and load balance

to reduce the energy cost. Sightmachine [107] continuously

processes context data gathered from sensors, lasers, and

network cameras, makes assessments in real time, and allows

the user to stop problems before they happen with regard to

industrial manufacturing machines and equipment.

Resources Management Onfarmsystems [108] is an IoT

solution designed to facilitate smart farming through accom-

modating increasingly complex and interconnected farming

equipment. Context information such as energy, pesticide,

mapping/ location, soil moisture, telemetry, weather, and mon-

itoring are used to support efﬁcient real-time decision-making.

HeatWatch [109] is a cattle monitoring solution that records

the activities of each animal. Recorded context information

includes such information as movement, time of day of the

mount, and duration of the mount. Such information enables

farmers to breed more cows and heifers earlier, obtain better

results (more pregnancies), use less semen, spend much less

time, and be more efﬁcient. Motionloft [110] is a solution

that monitors pedestrian and vehicle movements in real-time

by collecting activity data. It enables boutique retailers, large

chains, restaurants, and bars to understand the impact which

vehicle and pedestrian trafﬁc has on their revenue.

III. TECHNOLOGY REVIEW OF IOT SOLUTIONS

In this section, we summarise, from the technology point

of view, the results surveyed so far. Our review criteria are

explained in detail in Table II. The results are presented in

Table III. Speciﬁcally, our objective is not to discuss the

technologies in details but to survey and compare the usage

of the technologies (i.e. column (7), (8), (9) in Table II)

and solution models employed by different IoT solutions (i.e.

column (10), (11), (12) in Table II). Over the last few years,

IoT has been surveys in many different perspective. Leading

surveys that discuss different aspects of IoT technologies are

presented in Section I. In Section IV, we will analyse trends

and lessons learned from the survey in detail.

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 6

TABLE II

SUMMARY OF THE TAXONOMY USED IN TAB LE III

Taxonomy Description

1 Project The name of the project, product or solution sorted by ‘Category’ and then by ‘Project Name’ within each category

in ascending order

3 Year Last known active year of the project.

4 Category

Category that the solution belongs to. Each category is denoted by a different colour: red (smart city), yellow

(smart environment), blue (smart enterprise), green (smart wearable), and purple (smart home). Some

solutions belongs to multiple categories.

5 Availability The ability to obtain (free or purchase) each IoT solution. Available solutions are denoted by (X). .

6 Price

The price of the IoT solution. It can be unit price or service prise or both. All the prices are denoted in US

dollars. Superscript number denotes the original currency where the IoT solution has been sold: USD (1), AUD

(2), EURO (3), and GBP (4). Further, if it is a service, additional superscripts are used to denote payment period:

monthly (m) and yearly (y). Currency conversion has been performed on 2013-December-11 using online service

provided by www.xe.com.

7 Hardware and / or Software This indicate whether the IoT solution consists of hardware (H) unit, software (S) service or both

8 Wireless Technology

Different types of wireless technologies used in each of the IoT solutions are denoted as follows: (Mobile) Ad-hoc

Network using Ultrasonic communication (A) , WiFi (W), Bluetooth (B), USB (U), Celluar Radio / GSM (C),

ZigBee (Z), RF (R), GPS (G)

9 Platform IoT solutions have utilized different platforms. Some solutions support multiple platforms as follows: Android

(A), Blackberry (K), IOS (I), Web based service (B), Mac OSX (M), Windows (W), and Linux (L)

10 License

The IoT solutions are covered by different licenses as follows: Commercial (C), Open-source (O), Research &

Development (R), and Free (F). No speciﬁc license information were available for cases denoted by (R) which

were carried out as research initiatives and possibly available for collaborative research work with permission for

non-commercial work. (F) denotes solutions which are available for free without any governing licenses.

11 Unit and / or subscription The IoT solution that are sold as a unit are denoted by (U) and others cases where the solution need to be

purchased as a subscription are denoted by (S)

12 Product and / or Service IoT solutions that marketed as a product are denoted by (P) and the solutions marketed as services are denoted

by (S). Some IoT solutions have both product and service components.

Note: Cases where sufﬁcient information were not available are denoted by (-)

IV. TRE ND S AN D LES SO NS LE AR NE D

In this section, we highlight and discuss some of the trends

in the IoT solutions in the marketplace. The trends can be

categorised 1) based on domains, functionalities, and value,

and 2) based on technology.

1) Domains, Functionalities, and Value: Most of the IoT

solutions are narrowly focused on providing one functionality.

However, we have seen that a number of generic platforms are

being developed (e.g. Ninja Blocks [61], SmartThings [60],

and Twine [62]) to support applications in the domains of

the smart home and the smart city. In general, more solutions

are focused on the wearable and the smart home domains.

One reason for this concentration is the market potential.

These solution providers can earn a signiﬁcant ﬁnancial return

for their solutions due to the larger consumer market. In

addition, it has been revealed that the IoT solutions in the

smart home and wearable domains are comparatively easy to

develop and therefore low in price. Building smart enterprise,

smart environment, and smart city solutions takes much effort

and time due to the complexity and unique challenges in

comparison to other domains. Some of the unique challenges

are the sustainability of the hardware devices in harsh outdoor

environments; the availability of energy sources for sensing,

processing and communication in remote and outdoor loca-

tions; and the maintenance and repair of the hardware. These

challenges justify the low interest in these domain areas,

specially on the part of start-ups and small companies.

2) Technology: Hardware and Software Platforms: Ac-

cording to the survey results presented in Table III, it is

evident that most of the IoT solutions include both custom

hardware and software. It is also to be noted that some of

the solutions are not available for immediate purchase but

are on the way to the market (e.g. pre-order). In terms of

communication, WiFi and Bluetooth are the most commonly

used protocols. Additionally, an increasing number of the IoT

solutions support more than one platform (e.g. Android, iOS,

browser-based, Windows, Linux, and Mac). Mostly, they are

built around the Android and iOS platforms. Most of the

solutions are protected under a commercial license and both

software and hardware are closed-source. The majority of the

IoT solutions are sold as units. Though solutions may have

both software and hardware components, the price is mainly

for the hardware and the accompanying software is free. The

only exceptions are solutions that are completely based on the

cloud, where they charge for subscription.

In most of the wearable solutions, smartphones are used

as an interface for human–system interaction. Smart wearable

solutions generally have two or three components. Custom

designed wearable devices are used to capture the context and

sense the phenomena. Then, either processed or raw data is

sent to a processing device, which is usually a smart phone (or

a device with a similar computational capability). The smart-

phone then visualises and presents the outcome (e.g. alerts and

notiﬁcations) to the users. One such example is Lumoback

[32], which tracks posture and daily activities in real time.

Lumoback collects data through a wearable waist belt and

pushes the data directly to the smartphone. Human Computer

Interaction (HCI) plays a signiﬁcant roes in the success of IoT

products and solutions. When combining different interaction

mechanisms, IoT product designers will need to select the right

combination of methods based on number of different factors

such as data processing and communication capability, energy,

hardware cost, target user knowledge, criticality of the product

and so on. Commonly available options are gesture, voice,

touch. Further, IoT products can use smart phones, tablets and

wearable devices to enable user interactions.

Alternatively, sensors may send data to custom gateway

devices and then push to the cloud over GSM or WiFi. In such

situations, cloud services push the outcome to a mobile device

to update the user on the real-time activities. For example,

Mimobaby [29] is a baby movement monitoring wearable

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 7

TABLE III

EVALUATI ON OF SU RVE YED RESEARCH PRO TOTY PE S, SY ST EMS ,AND APP ROACH ES

Project Name

Citations

Year

Category

Availability

Price

Hardware

Software

Wireless

Technology

Platform

License

Unit

Subscription

Product

Service

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

SIGMO [133] 2013 X- H,S B - C U P

TJacket [31] 2013 X499.001H,S - I,A C U P

Withings Wireless Scales [134] 2013 X137.48-206.263H,S B,W A,I R U P

BeClose Senior Safety System [135] 2013 X399.001- H,S W - C U,S P

Smart pill bottles [136] 2013 - - - C - O,R U P

Whistle [137] - X99.951H,S B,W A,I C U P

AirBoxLab [138] 2013 X- H W A,I C U P

BiKN [139] 2013 X129.991H,S W I C U P

BrewBit [140] 2012 X199.001H,S W A,I O U P

Canary [141] 2013 - - H,S W A,I C U P

Fliwer [142] 2013 X- H,S R,W B C U P

Good Night Lamp [74] 2013 X122.42-206.333H W B C U P

Hintsights [143] 2013 - - H,S - - O - S

iDoorCam [144] 2013 X164.951H,S W A,I C U P

Iris [145] 2013 X299.001H W B C U,S P,S

Koubachi [146] 2013 X122.41-273.723H W A,I C U P

Lernstift [147] 2013 - - H W L C U P

Lockitron [71] 2009 X179.001H B,W A,I C U P

Nest Thermostat [70] 2013 X249.001H,S W I,A C U P

Netatmo [66] 2013 X129.00-199.001H - A,I C U P

Ninja Blocks [61] 2013 X199.00-250.001H,S W,B,Z A,B,I C,O U P,S

OpenSprinkler [148] 2013 X114.15-206.313H,S W A,I O U P

PetzillaConnect [149] 2013 X94.91-3H,S W A,I C U P

Philips Hue Connected Bulb [150] 2013 X199.951H,S W I C U P

Pintofeed [151] 2013 X179.001H,S W A,I,W C U P

Sensr.net [152] 2013 X94.91-3H W B C U,S P,S

SmartThings [60] 2013 X199.00-299.001H,S - I,A C U P

TADO [69] 2013 X- H,S W I,A C U P

Twine [62] 2013 X124.95-199.951H W B C U P

Ubi [65] 2013 X219.001H,S W A O U P

WeMo Switch [68] 2013 X75.22-78.991H,S W A,I C U P

WhereDial [73] 2013 X162.77-179.204H W B O U P

solution. Mimobaby collects data from sensors attached to

the baby’s clothes. Then, it transfers the data to a nearby

custom gateway which uses home WiFi connectivity to push

the data to the cloud. Then, the cloud services alert the

parents’ smartphone in real-time. Figure 2 illustrates some of

the most common communication patterns used in the IoT

solutions. Data collected by the IoT solutions may be sent to

the cloud for further processing, historical archiving, or pattern

recognition. Mobile devices allow users to immediately take

action or perform actuation tasks. In such circumstances, the

communication between the hardware and the mobile devices

is performed using short distance communication protocols,

such as Bluetooth, and long range communication tasks are

performed via WiFi or GSM.

Smart Objects

Smart Phone

Cloud Platform

Galway Device

Consumers

Fig. 2. Common Communication Patterns in IoT Applications. There are

mainly three types of common patterns

It is also evident that cloud IoT platforms are trying to build

their own ecosystems by facilitating and supporting third party

extensions (also called plugins) development and distribution

through app store. We have repeatedly seen such trends in both

PC market and smartphone markets. IoT platform developers

are increasingly support non-technical people to build IoT

solutions by providing easy ways to assemble the components

without programming knowledge [25], [153].

V. OP EN RESEARCH CHALLENGES

In this section, our objective is to discuss some of the major

challenges that need to be addressed in order to build the IoT.

These challenges are yet to be addressed, by either academia

or industry, in a comprehensive manner. The solutions for

these issues need to be come from technological, social, legal,

ﬁnancial, and business backgrounds in order to receive wide

acceptance by the IoT community.

1) Modularity and Layered Interoperability: Modularity is

a key to success in the IoT paradigm. The notion of modularity

goes hand in hand with interoperability. We can interpret

modularity in different ways. First, the hardware / physical

layer in the IoT needs to support modularity. This means,

ideally, consumers should be able to build a smart object

(or an Internet connected object) by putting different modules

(e.g. sensors or actuators) produced by different manufacturing

companies together without getting restricted to one vendor.

Such modularity reduces the entry barriers to the IoT mar-

ketplace. Further, interoperability will increase the level of

creativity and will reduce costs due to competition. Modularity

also allows organisations to focus on one component of the IoT

architecture and become experts on that, rather than having

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 9

to build end to end solutions, something which leads to re-

inventing the wheel. Furthermore, modularity provides more

choices and options to the consumers as to which modules to

use and when, based on factors such as reliability.

Modularity is vital in software / cloud services layer as well.

Especially in sensing as a service model [14], something which

provides an economical business model for the IoT, users

should have the right and ﬂexibility to choose and use cloud

services either in a standalone fashion or as a composition of

multiple services [154], [155] based on their own priorities

and preferences [156]. Modularity needs to be governed by

rigorous standardisation processes. Semantics technologies can

also be used to improve the interoperability through knowledge

reuse and knowledge mapping. In addition, interoperability

can be achieved through mediators and adapters. At the

hardware level, modularity has been introduced to some extent

by platforms such as Arduino [21] and Microsoft’s .NET

Gadgeteer [20]. However, cross platform compatibility is not

yet supported. In recent years, we have seen many different

IoT companies building cross layer partnerships with each

other to ensure comparability and interoperability. For exam-

ple, sensing hardware platform designers are partnering with

cloud IoT solution providers. However, standardisations and

collaboration with competitors is rarely seen within layers

(e.g. among different hardware vendors). The concept of an

app store for IoT solutions is currently supported by HomeOS

[63] and ThingWorx [82]. They have started to support mod-

ularity by allowing third parties to develop extensions to their

IoT middleware platforms. The integration of multiple cloud

platform service providers will enable more data sharing and

value creation [157].

2) Uniﬁed Multi-Protocol Communication Support: De-

signing protocols and systems for wireless industrial com-

munications will have a signiﬁcant impact on the successful

adoption of the IoT [158]. IoT solutions use different types of

communication protocols, mostly through wireless channels

[159]. WiFi, Bluetooth, 3G, Zigbee, and z-wave are some of

them. Even though they seem few, incompatibility makes de-

veloping the IoT applications more challenging. Each protocol

has its own advantages and disadvantages [77]. Comparisons

of these protocols are presented in [159], [77]. Some protocols

are efﬁcient in long distance communication and others are

efﬁcient in short distance communication. It is important to

address the challenge of developing a high-level framework

that handles the difference of protocols behind the scenes

without bothering the developers or consumers. Therefore, an

ideal framework should allow the developers to focus on data

communications at a high level (e.g. what to send and when)

rather than dealing with low-level communication protocol

details (e.g. which protocol to use when, and implementation-

level differences). Such a high-level framework will increase

the efﬁciency and effectiveness of the IoT solutions and will

also save a signiﬁcant amount of development time. Intelligent

context-aware capabilities need to be integrated into the IoT

solutions so the communication tasks and related decisions

will be made and handled based on the capabilities and the

energy availability of the ICOs at a given situation. The

importance of standardising a protocol stack for the IoT is

highlighted and discussed in [16].

3) Sustainable Business Models: A sustainable business

model is essential for building a sustainable IoT paradigm.

Most of the IoT solutions we have reviewed are narrowly

focused on addressing one problem. They have missed the

bigger picture of the IoT and earnings potential. Sharing data

in open markets can add more value to the IoT solutions. One

such business model is presented in [14] in detail. Preliminary

work towards building such a model is currently conducted by

the project HAT (Hub of All Things) [160]. HAT is a platform

for a multi-sided market powered by the IoT which expects

to create opportunities for new economic & business models.

It aims to create a market platform for the home based on

the data generated by individuals’ consumption, behaviour,

and interactions. Such data exchange, in secure and privacy

preserved manner, would generate additional value that may

help to maintain the IoT infrastructure in the long-term.

For example, one institution (primary) may deploy and

maintain sensors in public infrastructures such as roads and

bridges with the intention of monitoring their structural health

and the public safety. Other institutions (secondary) who are

interested in such data can purchase them through an auction-

like marketplace. Secondary institutions may have different

intentions, such as local weather monitoring, environmental

pollution monitoring, and local trafﬁc condition monitoring.

The ﬁnancial support offered by secondary institutions mo-

tivates the primary institute to deploy and maintain sensors

over the long term. In this way, primary and secondary insti-

tutions will beneﬁt by the transaction’s creating a sustainable

economic model. The details of such markets are discussed

from the technology perspective in [14] and from the business

perspective in [160].

4) Ownership, Privacy and Security: One of the biggest

technical, social, and legal challenges is protecting privacy and

creating a secure environment for the IoT. Unfortunately, these

have been the challenges least addressed. Due to the limited

adoption of IoT, not many security and privacy challenges

have been identiﬁed. We can expect more challenges to be

identiﬁed over the coming years due to the growing adoption

of IoT solutions. The security issues have two aspects. One

aspect is data security. The other aspect is the security of

the IoT solutions (e.g. security related to sensing communi-

cation, iterations, authentication, and actuation). In the fully

automated and integrated IoT paradigm, security breaches can

be life threatening and can have devastating economic and

social impact. Especially the new business models that we

brieﬂy discussed in the above section may create additional

challenges regarding data ownership and privacy.

As we discussed in [14], anonymisation is a critical process

in the IoT data ﬂow. The data collected by households always

needs to be anonymised in such a way that no one will be

able to trace it back to its exact origin. Data may identiﬁed

and grouped broadly into certain geographical regions, but not

for individuals or households. Another aspect of this challenge

is ownership transfer. Technology should be intelligent enough

to identify its current owner and follow their commands

and preferences. The details of such ownership transfers are

discussed in [14]. In addition to the technology-based security

and privacy solutions, legal terms need to be developed in

order to protect the consumers and the data they own.

VI. CONCLUDING RE MA RK S

This paper presented a survey of the IoT solutions in

the emerging marketplace. We classiﬁed the solutions in the

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 10

market broadly into ﬁve categories: smart wearable, smart

home, smart city, smart environment, and smart enterprise.

Under each category, we discussed and summarised the func-

tionalities provided by each solution. We also examined the

contribution of each solution towards improving the efﬁciency

and effectiveness of consumers’ lifestyle as well as of society

in general. It is important to highlight the proliferation of

wearable solutions in the market. Despite the long existence

of wearable computing, those products did not reach the

consumer market until recently. It is clear that more and

more wearable solutions will make their way into the IoT

marketplace over the coming years. Further, we can see a

signiﬁcant investment and focus on indoor smart home and

ofﬁce domains, in comparison to environmental monitoring

solutions.

Moreover, we also see a substantial amount of investment

made in research and development towards supply chain

management. These solutions are aimed at large scale industry

players who are looking for novel methods to optimise their

supply chain processes, especially through real-time data col-

lecting, reasoning, and monitoring. Until household consumers

adopt IoT solutions, the majority of the value creation is

expected to occur with large scale industries. Finally, we

discussed the lessons learned and listed some of the major

research challenges and opportunities. We believe further

research that addresses these open challenges will help to

develop more interesting IoT solutions and strengthen the

existing solutions in this area in both the industrial and the

academic sectors.

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Charith Perera received his BSc (Hons) in Com-

puter Science in 2009 from Staffordshire Univer-

sity, Stoke-on-Trent, United Kingdom and MBA

in Business Administration in 2012 from Univer-

sity of Wales, Cardiff, United Kingdom and PhD

in Computer Science at The Australian National

University, Canberra, Australia. He is also worked

at Information Engineering Laboratory, ICT Centre,

CSIRO and involved in OpenIoT Project (FP7-ICT-

2011.1.3) which is co-funded by the European Com-

mission under seventh framework program. He has

also contributed into several projects including EPSRC funded HAT project

(EP/K039911/1) His research interests include Internet of Things, Smart

Cities, Mobile and Pervasive Computing, Context-awareness, Ubiquitous

Computing. He is a member of both IEEE and ACM.

Chi Harold Liu is a Full Professor at the School

of Software, Beijing Institute of Technology, China.

He is also the Deputy Director of IBM Mainframe

Excellence Center (Beijing), Director of IBM Big

Data Technology Center, and Director of National

Laboratory of Data Intelligence for China Light

Industry. He holds a Ph.D. degree from Imperial

College, UK, and a B.Eng. degree from Tsinghua

University, China. His current research interests in-

clude the Internet-of-Things (IoT), big data analyt-

ics, mobile computing, and wireless ad hoc, sensor,

and mesh networks. He served as the consultant to Bain & Company, and

KPMG, USA, and the peer reviewer for Qatar National Research Foundation,

and National Science Foundation, China. He is a member of IEEE and ACM.

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 13

Srimal Jayawardena received his BSc (Hons) in

Electrical Engineering from the University of Per-

adeniya and his Bachelors in IT from the University

of Colombo School of Computing, both with ﬁrst

class honours in 2004. He also obtained a Masters

in Business Administration from the University of

Moratuwa in 2009. He holds a PhD in Computer

Science from The Australian National University,

Canberra. He is currently working as post-doctoral

research fellow at Computer Vision Laboratory

(CI2CV),in CSIRO. His research interests include

augmented reality, object recognition for the Internet of Things, computer

vision, human computer interaction, and machine learning. He is a member

of the Institute of Electrical and Electronics Engineers (IEEE).

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Internet of Things (IoT) has been recognized as a part of future internet and ubiquitous computing. It creates a true ubiquitous or smart environment. It demands a complex distributed architecture with numerous diverse components, including the end devices and application and association with their context. This article provides the significance of middleware system for (IoT). The middleware for IoT acts as a bond joining the heterogeneous domains of applications communicating over heterogeneous interfaces. First, to enable the better understanding of the current gap and future directions in this field a comprehensive review of the existing middleware systems for IoT is provided here. Second, fundamental functional blocks are proposed for this middleware system, and based on that feature wise classification is performed on the existing IoT-middleware. Third, open issues are analyzed and our vision on the research scope in this area is presented. KEYWORDS Internet of Things, middleware, semantic model, context-awareness, ubiquitous computing.

Survey internet of things: Vision, applications and research challenges

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Jan 2012
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Jan 2014

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The JOYO Remote Monitoring System

Article

The evolution of the personal computer, related operating system and applications software and the Internet has brought drastic change and many benefits worldwide. Remote monitoring systems can benefit from computer network and other modem software technologies. The availability offast, inexpensive and secure communications enables new solutions for monitoring system applications. The JOYO Remote Monitoring System (RMS) utilizes computer network communications and modular software design to provide a distributed integrated solution for monitoring multiple storage locations. This presentation describes the remote monitoring system installed at the JOYO Fast Reactor. The system combines sensors, software, and computer network technologies to create a powerful data collection, storage and dissemination capability. The RMS provides a flexible, scaleable solution for a variety of applications. The RMS integrates a variety of state of the art technologies from several sources and serves as a test bed for cutting edge technologies that can be shared with outside users. This paper describes the system components and their operation and discusses system benefits. Current activities and future plans for the JOYO RMS will be discussed.

Data Mining for Internet of Things: A Survey

Article

Mar 2014

It sounds like mission impossible to connect everything on the Earth together via Internet, but Internet of Things (IoT) will dramatically change our life in the foreseeable future, by making many "impossibles" possible. To many, the massive data generated or captured by IoT are considered having highly useful and valuable information. Data mining will no doubt play a critical role in making this kind of system smart enough to provide more convenient services and environments. This paper begins with a discussion of the IoT. Then, a brief review of the features of "data from IoT" and "data mining for IoT' is given. Finally, changes, potentials, open issues, and future trends of this field are addressed.

An operating system for the home

Conference Paper

Apr 2012

Network devices for the home such as remotely controllable locks, lights, thermostats, cameras, and motion sensors are now readily available and inexpensive. In theory, this enables scenarios like remotely monitoring cameras from a smartphone or customizing climate control based on occupancy patterns. However, in practice today, such smarthome scenarios are limited to expert hobbyists and the rich because of the high overhead of managing and extending current technology. We present HomeOS, a platform that bridges this gap by presenting users and developers with a PC-like abstraction for technology in the home. It presents network devices as peripherals with abstract interfaces, enables cross-device tasks via applications written against these interfaces, and gives users a management interface designed for the home environment. HomeOS already has tens of applications and supports a wide range of devices. It has been running in 12 real homes for 4-8 months, and 42 students have built new applications and added support for additional devices independent of our efforts.

Lab of things: A platform for conducting studies with connected devices in multiple homes

Conference Paper

Sep 2013

Researchers who develop new home technologies using connected devices often want to conduct large-scale field studies in homes to evaluate their technology, but conducting such studies today is extremely challenging. Inspired by the success of PlanetLab, which enabled development and evaluation of global network services, we are developing a shared infrastructure for home environments, called Lab of Things. Our goal is to substantially lower the barrier to developing and evaluating new technologies for the home environment.

A Survey on the Internet of Things Security

Conference Paper

Dec 2013

The security issues of the Internet of Things (IoT) are directly related to the wide application of its system. Beginning with introducing the architecture and features of IoT security, this paper expounds several security issues of IoT that exist in the three-layer system structure, and comes up with solutions to the issues above coupled with key technologies involved. Among these safety measures concerned, the ones about perception layer are particularly elaborated, including key management and algorithm, security routing protocol, data fusion technology, as well as authentication and access control, etc.

Network self-organization in the Internet of Things

Conference Paper

Jun 2013

The Internet of Things is a paradigm that allows the interaction of ubiquitous devices through a network to achieve common goals. This paradigm like any man-made infrastructure is subject to disasters, outages and other adversarial conditions. Under these situations provisioned communications fail, rendering this paradigm with little or no use. Hence, network self-organization among these devices is needed to allow for communication resilience. This paper presents a survey of related work in the area of self-organization and discusses future research opportunities and challenges for self-organization in the Internet of Things. We begin this paper with a system perspective of the Internet of Things. We then identify and describe the key components of self-organization in the Internet of Things and discuss enabling technologies. Finally we discuss possible tailoring of prior work of other related applications to suit the needs of self-organization in the Internet of Things paradigm.