Introduction - IoT as a paradigm
Methodology
Inclusion criteria
Exclusion criteria
Resulting sample
2015 | 2016 | Publisher’s total | |
---|---|---|---|
ACM | 5 | 4 | 9 |
IEEE | 4 | 14 | 18 |
Springer | 7 | 14 | 21 |
Year’s total | 16 | 32 | 48 |
Sample’s analysis
Analysis
Scenarios’ variables
Analyzed variables
Variable | Description | Quantifier |
---|---|---|
Density (DEN) | Density represents the quantity of sensors placed in one area. | When the area has a single sensor, it is classified as “0.” If the area has up to five sensors, it is defined as “1”; over 5 and under 15 sensors, a “2” is allocated, and the density is “3” when there are more than 15 sensors in an area. |
Mobility (MOB) | This is the thing’s ability to change location in an area. | “0” is attributed for a static sensor. If the sensor can change its location in a predefined route, it is classified as “1.” When the sensor has the ability to move across areas, it is given a “2.” And in the case of large changes, as in a city scale, it is represented as “3.” |
Intensity (INT) | This reflects the sensor’s data refresh rate. A higher refresh rate makes the data register more critical. | Data refresh rate over 5 min are classified as,“0”; between 1 and 5 min as “1”; each minute as “2,” and real-time “3.” |
Heterogeneity (HET) | This denotes for the variety of data types and sensors in the context. | Just one sensor with one data type is represented by “0.” A sensor with various data type is classified as “1”; various sensors with one data type are labeled “2,” and various sensors with various data type are classified as “3.” |
Area (A) | This is the geographical displacement of the things. | A single room is represented as “0”; a single store building as “1”; more than one store building as “2”; “3” represents a smart city. |
Human interface (HI) | This variable registers the degree of interaction of the thing with a human being. | Ranges from “ 0—not related” when the device is independent of human input, “1—weakly related” when the device/thing is used or held by a human, “2—moderately related” when the sensor is a wearable device, or “3—totally related” when it is a direct input interface (neurotransmitter) or implanted chip. |
Actuability (ACT) | This represents the capability of a thing in the system to act. | Ranges from “sensor only” (0), “self-acting/self-reconfiguring” (1), “acts in the environment” (2) to “acts on an external thing or human being” (3). |
Sensors’ analysis
Category | Description |
---|---|
Ambient | This refers to sensors that gather data from the environment or the space around them. |
Motion | This is used to perceive motion of people or things in a context (as in accelerometers and gyroscopes). |
Electric | This category holds the sensors that are applied to electricity grids. |
Biosensor | The biosensors are worn by humans or animals. They return vital signs and/or biological information about one subject. |
Identification | This represents a semantic or identity of another thing to the IoT system. The most common items in this classification are RFID and NFC tags and their readers. |
Position | This is related to identifying an object’s position in a global scale (as with GPS) or in a local scale (as in small beacon position). |
Presence | This captures the presence of a person, an animal, or object in a space and registers it in the system. The most common solution is the PIR sensor. |
Machine vision | This family of sensors captures images that will be processed by a computer to produce information. |
Interaction | These types of sensors are devices that are human-activated to trigger an event, such as a button or a lever. |
Acoustic | Such sensors are activated by soundwaves, producing data from the ambient sound change. |
Force/load | The force/load sensors are activated by external forces, capturing the deformation or the intensity of those forces to the system. |
Hydraulic | These are applied in the water system to measure and control the flow. |
Chemical | Chemical sensors are capable of detecting chemical substance(s) in the air or water. |
Object information | This specific category includes sensors with similar functions to the previous categories. They differ in that their application is confined to a specific object. The object information is the result of a small context application of a sensor. For instance, a temperature sensor used inside a machine provides object information which is different from an ambient temperature sensor. |
Application analysis
Results
Application results
Application | Papers count |
---|---|
Smart home | 11 |
Smart healthcare | 9 |
Smart city | 8 |
Smart agriculture | 4 |
Smart building | 3 |
Energy monitoring | 2 |
Playful furniture | 2 |
Robot movement | 2 |
Water management | 1 |
Learning device | 1 |
Disaster management | 1 |
Smart vehicle | 1 |
Smart security | 1 |
Personal security | 1 |
Personal information | 1 |
Total | 48 |
Scenarios patterns
Variable and scenario potentiality analysis
DEN | MOB | INT | HET | A | HI | ACT | |
---|---|---|---|---|---|---|---|
Average | 1.17 | 0.85 | 2.10 | 1.44 | 1.00 | 0.83 | 1.00 |
% of the max score | 38.10% | 27.89% | 68.71% | 46.94% | 28.57% | 27.21% | 32.65% |
Standard deviation | 1.17 | 1.15 | 1.17 | 0.97 | 1.00 | 1.02 | 1.38 |
Potential | Quantity |
---|---|
Under 20% | 10 |
Between 20% and 40% | 10 |
Between 40% and 60% | 23 |
Between 60% and 80% | 4 |
Over 80% | 1 |
Total | 48 |
Scenarios’ variables correlation
DEN | MOB | INT | HET | A | HI | ACT | ||
---|---|---|---|---|---|---|---|---|
DEN | Pearson’s r | – | − 0.140 | 0.250 | 0.592*** | 0.434** | − 0.137 | − 0.052 |
p value | – | 0.344 | 0.086 | < 0.001 | 0.002 | 0.354 | 0.723 | |
MOB | Pearson’s r | – | 0.059 | 0.116 | 0.409** | 0.416** | − 0.268 | |
p value | – | 0.690 | 0.431 | 0.004 | 0.003 | 0.066 | ||
INT | Pearson’s r | – | 0.241 | 0.011 | 0.086 | − 0.092 | ||
p value | – | 0.099 | 0.939 | 0.560 | 0.534 | |||
HET | Pearson’s r | – | 0.299* | 0.011 | − 0.207 | |||
p value | – | 0.039 | 0.942 | 0.158 | ||||
A | Pearson’s r | – | − 0.229 | − 0.061 | ||||
p value | – | 0.117 | 0.679 | |||||
HI | Pearson’s r | – | 0.030 | |||||
p value | – | 0.838 | ||||||
ACT | Pearson’s r | – | ||||||
p value | – | |||||||
*p <.05, | **p <.01, | ***p <.001 |
Sensors’ analysis results
Sensor | Quantity |
---|---|
Temperature | 19 |
Accelerometer | 12 |
Humidity | 10 |
Light sensor | 10 |
RFID | 7 |
PIR | 6 |
Acoustic | 5 |
Camcoder | 5 |
AC analyzer | 3 |
Button | 3 |
ECG | 3 |
Energy consumption | 3 |
Gyroscope | 3 |
Position | 3 |
Chemical detector | 2 |
Current | 2 |
GPS | 2 |
Heartbeat | 2 |
Magnetometer | 2 |
pH | 2 |
Pressure | 2 |
Temperature (object) | 2 |
Tension | 2 |
Various unique sensors4 | 32 |
Total | 142 |
Sensor type | Quantity |
---|---|
Ambient | 47 |
Motion | 18 |
Electric | 13 |
Biosensor | 10 |
Chemical | 9 |
Position | 9 |
Machine vision | 8 |
Identification | 8 |
Presence | 6 |
Acoustic | 5 |
Interaction | 3 |
Hydraulic | 2 |
Force/load | 2 |
Object information | 2 |
Total | 142 |
Application | Sensors |
---|---|
Smart healthcare | 28 |
Smart home | 28 |
Smart city | 23 |
Smart agriculture | 13 |
Smart building | 9 |
Smart security | 8 |
Personal information | 7 |
Playful furniture | 5 |
Smart vehicle | 4 |
Water management | 4 |
Robot movement | 4 |
Personal security | 3 |
Energy monitoring | 2 |
Learning device | 2 |
Disaster management | 2 |
Total | 142 |
Applications, scenario, and sensor relationship
Potentiality | ||
---|---|---|
Sensor’s variety | Pearson’s r | 0.361 |
p value | 0.011 |
Application | Scenarios |
---|---|
Bottom sample (1 sensors) | |
Smart city | 3 |
Smart healthcare | 3 |
Energy monitoring | 2 |
Smart home | 2 |
Playful furniture | 1 |
Robot movement | 1 |
Smart agriculture | 1 |
Smart building | 1 |
Total | 14 |
Top sample (> 5 sensors) | |
Smart city | 2 |
Smart healthcare | 2 |
Personal information | 1 |
Smart agriculture | 1 |
Smart building | 1 |
Smart home | 1 |
Smart security | 1 |
Total | 7 |
Sensors and applications relationship
Conclusions and contributions of this work
Validity threats and restrictions
Appendix 1: List of surveyed papers and classifications
Paper | Application | Scenario | Paper | Application | Scenario |
---|---|---|---|---|---|
[18] | Smart agriculture | 0000000 | [19] | Smart home | 1032102A |
[20] | Smart home | 0000103 | [21] | Smart home | 1101133 |
[22] | Smart home | 0011010 | [23] | Smart home | 1132020 |
[24] | Smart building | 0020000 | [25] | Smart building | 1132110 |
[26] | Smart agriculture | 0020000A | [27] | Smart home | 1232120 |
[28] | Water management | 0021000 | [29] | Disaster management | 1301210 |
[30] | Smart home | 0021000A | [31] | Smart vehicle | 1332300 |
[32] | Energy monitoring | 0030000 | [33] | Playful furniture | 2012010 |
[34] | Energy monitoring | 0031000 | [35] | Smart agriculture | 2023200 |
[36] | Smart home | 0101003 | [37] | Smart city | 2030103 |
[38] | Playful furniture | 0101123 | [39] | Smart city | 2032013 |
[40] | Smart healthcare | 0130030 | [41] | Smart healthcare | 2033020 |
[42] | Smart healthcare | 0131113 | [43] | Robot movement | 2130033 |
[44] | Learning device | 0230023 | [45] | Smart healthcare | 2133030 |
[46] | Smart healthcare | 0231020 | [47] | Smart building | 3001200 |
[48] | Smart healthcare | 0312020 | [49] | Smart home | 3022103 |
[50] | Smart healthcare | 0321210 | [51] | Smart city | 3032100 |
[52] | Smart healthcare | 0322310 | [53] | Robot movement | 3032200 |
[54] | Smart healthcare | 0331020 | [55] | Smart security | 3032203 |
[56] | Smart home | 1002013 | [57] | Smart city | 3032213 |
[58] | Smart home | 1002100 | [59] | Smart city | 3033000 |
[60] | Smart city | 1020202 | [61] | Smart city | 3233200 |
[62] | Smart agriculture | 1032003 | [63] | Personal security | 3322300 |
[64] | Smart city | 1032102 | [65] | Personal information | 3333320 |
Appendix 2: List of sensors from the sample
Sensor | Type | Quantity | Sensor | Type | Quantity |
---|---|---|---|---|---|
Temperature | Ambient | 19 | Degree of cloudness | Ambient | 1 |
Accelerometer | Motion | 12 | Direction | Position | 1 |
Humidity | Ambient | 10 | Discomfort index | Biosensor | 1 |
Light sensor | Ambient | 10 | EEG | Biosensor | 1 |
RFID | Identification | 7 | Electrical conductivity | Electric | 1 |
PIR | Presence | 6 | Float sensor | Hydraulic | 1 |
Acoustic | Acoustic | 5 | Flow sensor | Hydraulic | 1 |
Camcoder | Machine vision | 5 | IR camcoder | Machine vision | 1 |
AC analyzer | Electric | 3 | Lane perception | Machine vision | 1 |
Button | Interaction | 3 | Laser scanner | Motion | 1 |
ECG | Biosensor | 3 | Load sensor | Force/load | 1 |
Energy consumption | Electric | 3 | NFC | Identification | 1 |
Gyroscope | Motion | 3 | ORP | Chemical | 1 |
Position sensor | Position | 3 | Proximity sensor | Motion | 1 |
Chemical detector | Chemical | 2 | Smoke sensor | Chemical | 1 |
Current | Electric | 2 | Soil conductivity | Electric | 1 |
GPS | Position | 2 | Soil humidity | Ambient | 1 |
Heartbeat | Biosensor | 2 | Soil microorganism | Ambient | 1 |
Magnetometer | Position | 2 | Soil temperature | Ambient | 1 |
pH | Chemical | 2 | Solar irradiation index | Ambient | 1 |
Pressure | Ambient | 2 | Speed | Force/load | 1 |
Temperature (object) | Object information | 2 | Swallow sensor | Biosensor | 1 |
Tension | Electric | 2 | Thermal camera | Machine vision | 1 |
Blood pressure | Biosensor | 1 | Tilt sensor | Motion | 1 |
Breath sensor | Biosensor | 1 | Ultrasonic sensor | Position | 1 |
Capacitance | Electric | 1 | Water oxygen | Chemical | 1 |
CO2 | Chemical | 1 | Water quality | Chemical | 1 |
Wind velocity | Force/load | 1 | |||
Total | 142 |