I am Rashed Hossain Miraj, master student at the University of Stuttgart, Germany. I covered the topic as part of my degree requirement.

1. Seminar
Internet of Things: Energy Efficient Public Sensing
Rashed Hossain Miraj
Examiner: Dr. Adnan Tariq
Supervisor: Zohaib Riaz
WS2015
Universität Stuttgart
Institut für Parallele und Verteilte Systeme
(IPVS)
Universitätsstraße 38D-70569 Stuttgart
1

2. Outline
Internet-Of-Things(IoT):
Introduction
Application Example
IoT : Public Sensing
Introduction
Opportunities
Challenges
PSense:
Naive Sensing Approach
Motivation
Introduction
Adaptive Positioning
Query Adaptation
Ad-Hoc Information exchange
Ad-Hoc Query Adaption
Evaluation
Opportunistic Position Update
Protocols:
Introduction
Energy Characteristics
Opportunistic Position Update
Scenarios
Markov Decision Process
Cost Model
Time-, Distance Based Position Update
Protocols.
Dead Reckoning Position Update
Protocols
Evaluation
DrOPS: Model Driven Optimization for
Public Sensing :
Introduction
System Model
Model Driven Approach
Model Driven Sensing Algorithm
Model Management: MOCHA
Model Management: OLA
Evaluation
Summary
2

3. Internet of Things(IoT)
What is IoT?
 Connecting everyday objects with
the internet to make them smarter.
 439 million smartphones added in
2014.
 By 2020, there will be 50 billion!
things connected.
 Huge impact on our everyday life.
3

4. Internet of Things(IoT)
Example: Industry 4.0
 A German innovation concept.
 The term was first used in 2011 at
the Hannover Fair.
 Connects all parts of the
production process: machines,
products, systems, and people.
4

5. IoT: Public Sensing
 Collecting sensor data from mobile
devices.
 Real time collection of sensor data(e.g.
noise, temperature)
 Number of smartphone users
worldwide will surpass 2 billion in
2016.
www.nilmedia.com
5

6. Public Sensing: Opportunities
Compared to traditional sensor networks, Public Sensing brings
two major advantages:
1. No sensors have to be deployed since most of the nowadays
mobile devices already come with a set of integrated
sensors.
2. Wide spatial range can be covered by the mobile sensors.
6

7. Public Sensing: Challenges
 Sensing has to be performed in an opportunistic manner.
 Maintaining certain quality of service (QoS) requirements.
 Must not influence the normal operation of a mobile device.
Things to consider:
1. Battery life of a mobile device.
2. Performance of the normal operation mode of the device.
7

8. Public Sensing: Energy Efficient
Sensing Approaches
 PSense.
 Opportunistic Position Update Protocols.
 DrOPS.
8

9. Naive Sensing Approach
 Consists of one server and arbitrary number
of mobile devices.
 Server accepts sensing queries 𝑄 that
contain set of desired locations.
 Each sensing location specified by : spatial
coordinates (𝑥𝑙, 𝑦𝑙) and a particular sensor
type 𝑡𝑦𝑝𝑒(𝑙).
 The server assigns sensing tasks to mobile
devices that move within the vicinity of the
queried sensing locations
 Furthermore, a device can read a sensing
location l at time t if the following conditions
are satisfied:
1. l is compatible with m by sensor s
2. dist((𝑥𝑙, 𝑦𝑙) , (𝑥 𝑚(𝑡), 𝑦 𝑚(𝑡)) ) ≤ range(s)
www.comnsense.de
9

10. Naive Sensing Approach
 The server distributes L to all mobile devices that are located in
the queried area.
 After receiving L , the mobile device starts periodically updating
its position every 𝑡 𝑝𝑜𝑠 seconds.
 After each position fix the device checks whether the conditions
for reading sensing locations in L are fulfilled.
 If that is the case for l, the device reads l and uploads the data
to the server.
 The server delivers it to the user and removes l from L.
 The server notifies the devices every time L is changed.
10

11. Naive Sensing Approach: Pros
and Cons
Pros:
 Provides a naive way for providing requested sensor data to the
user.
 Ensures that no mobile device passes a sensing location without
noticing it (at least if 𝑡 𝑝𝑜𝑠 is chosen sufficiently small).
Cons:
High energy consumption due to continuous fixing of positions.
11

12. PSense: Why?
 Existing approaches assumes that mobile device are always aware
of their position.
 Position fix is a very energy consuming operation.
PSense:
 Avoiding unnecessary position fixes reduces energy consumption
of mobile devices by 70%.
12

13. PSense: System Model
 Each device m comes with a set of
specific sensors 𝑆 𝑚.
 Each sensor 𝑠 ∈ 𝑆 𝑚 has limited
reading range 𝑟𝑎𝑛𝑔𝑒 𝑠 and type
𝑡𝑦𝑝𝑒 𝑠 which specifies type of
data that can be sensed.
 Mobile device’s maximum speed
𝑣 𝑚𝑎𝑥.
 Cellular mobile network for the
communication between server
and mobile devices.
13

14. PSense: Optimization Algorithms
 Adaptive Positioning:
1. Adaptive Positioning Interval.
2. Query Adaption.
 Ad-Hoc Information Exchange
1. Message Exchange.
2. Ad-Hoc Query Adaption.
14

15. Adaptive Positioning
 Finds an adaptive positioning interval of length 𝑡 𝑎 that determines the
time until the next GPS fix.
𝑑 𝑚𝑖𝑛 = distance to its closest compatible sensing location l.
𝑣 𝑚𝑎𝑥 = mobile device’s maximum speed.𝑡 𝑎
=
𝑑 𝑚𝑖𝑛
𝑣 𝑚𝑎𝑥
 If l is within range, reads the data immediately and uploads to server.
 If the device cannot sense l, it uses the provided 𝑑 𝑚𝑖𝑛 to calculate 𝑡 𝑎 and
schedules its next position fix to time 𝑡 𝑎.
 The decrease in the number of position fixes outweighs the energy spent for
sending positioning queries.
15

16. Query Adaption
Why?
 Adaptive Positioning does not proactively send the set of current
sensing locations to the mobile devices.
 Therefore, the individual positioning interval of some mobile
devices may no longer be appropriate when a new sensing query
arrives.
Example:
 A user issues a query for data at time t from a sensing location l
that is next to a mobile device m.
 If l is compatible with m, but m has already scheduled its next
position fix to some time 𝑡′> t, it is possible that m misses to read
l.
 To avoid this, the server has to recalculate the distance to the
closest compatible sensing location by taking into account the
newly queried sensing locations
16

17. Query Adaption
Fig : Arrival of new sensing locations
 Upon receiving a new query
Q the server checks for every
device m if there are sensing
locations l ∈ Q that are
compatible with m.
 If 𝑑 𝑚𝑖𝑛
′
< 𝑑 𝑚𝑖𝑛, the server
queries device m for its
position.
 Knowing this position the
server returns a fresh sensing
update.
17

18. Ad-Hoc information Exchange
Why?
Adaptive Positioning decreases the amount of GPS fixes but
increases the number of messages exchanged with server.
 If a mobile device receives a sensing update from the server it
can locally forward this information via its ad-hoc interface.
 Nearby mobile devices can now use this information to adapt their
respective positioning interval without communicating with the
server.
To implement this concept, the adaptive positioning algorithm is
augmented by two extensions:
First: Message Exchange
Second: Ad-hoc Query Adaptation
18

19. Message Exchange
𝑚 𝑠 𝑚 𝑟
d(l)
range(ah)
dist
Ad-hoc range
range(s)
𝑚 𝑠 sends ad-hoc broadcast. Each
receiving device 𝑚 𝑟 does the following:
1. Check if l is compatible with 𝑚 𝑟. If
not, stop.
2. Check if 𝑆𝑟⊆ 𝑆𝑆. If not, stop.
3. Compute 𝑡 𝑎
′ according to the
following equation:
𝑡 𝑎
′
=
𝑑 𝑙 − 𝑟𝑎𝑛𝑔𝑒(𝑠)
𝑣 𝑚𝑎𝑥
=
𝑑𝑖𝑠𝑡
𝑣 𝑚𝑎𝑥
4. If 𝑡 𝑎
′ is bigger than the remaining
time until the next position fix is
performed, then reschedule the next
position fix to 𝑡 𝑎
′
. If not, stop.
5. Send an ACK-message back to 𝑚 𝑟
19

20. Ad-Hoc Query Adaption
Why?
The server does not take notice of the ad-hoc messages that the
devices exchange. As a result, it cannot determine which devices
need an update if a query Q arrives.
To avoid this, the query adaption is extended as follows:
 𝑚 𝑠 remembers the set of other devices 𝑀0 that returned ACK-
meesage .
 If the server now receives a new sensing location which causes
an update for 𝑚 𝑠 device 𝑚 𝑠 returns the set 𝑀0 to the server.
 The server subsequently queries each device m ∈ 𝑀0for its
position and returns a fresh sensing update.
 Based on this update each of theses devices adapts now its
positioning interval.
20

21. Evaluation
Figure: Total Energy Consumption Figure: Positioning Operations Figure: GPRS Communication
Figure: Sensing Effectiveness
Operation Energy[mJ]
GPS Position Fix 75
GPRS Send (1000 Bit)
GPRS Receive (1000Bit)
80
40
802.11b Send (1000Bit)
802.11b Receive (1000Bit)
2
1
Table: Energy Model
21

22. Opportunistic Position Update
Protocols for Mobile devices
 Location based applications (LBAs) enjoying great popularity.
 The mobile object updates its position on the remote Location
Server(LS) instead of sending its position to each LBA individually.
 Energy requirement by a device for sending its positions to the LS
is a big concern.
 Opportunistic Position Update Protocols Improves energy
efficiency of these protocols by taking into account the energy
characteristics of mobile network interface.
22

23. Energy Characteristics
IDLE
DCHFACH
DCH
FACH
IDLE
t
W
Tail Time
Data
Transfer
Figure: Energy Characteristics
A cellular network interface has :
1. An ideal state (IDLE)
2. Two operation states (DCH and FACH)
23

24. Opportunistic Position Update
Scenarios
Scenario 1
ut1t
1T
1t ut
Tail Time
1T
Figure: Forced Update Figure: Opportunistic Update
ut2t 3t1t
1T 2T 3T
?
Scenario 2
24

25. Opportunistic Update Protocols
25

26. Markov Decision Process(MDP)
Why?
To decide whether an opportunistic position update should be sent with
the current transmission or not.
Figure: Markov Decision Process
 A generic state 𝑆 𝑑 represents
the starting point of our
decision.
 Possible actions from state
𝑆 𝑑 are 𝐴 = {𝑠𝑒𝑛𝑑, 𝑠𝑒𝑛𝑑}
 Three possible states 𝑆0,
𝑆1, 𝑆2
 c values defines the
respective state transition
cost.
26

27. Markov Decision Process(MDP)
 State 𝑆0 :
Action send is taken and an
opportunistic position update
is sent right after the current
transmission.
 State 𝑆1 :
Action send is taken and no other
transmission occurs before
the quality constraint will be violated.
 State 𝑆2 :
utt
1T ut
ut1t
ut
2t1t
2T
?
State 𝑆0 State 𝑆1
State 𝑆2
Figure: States of MDP
Action 𝑠𝑒𝑛𝑑 is taken and at least one
other transmission occurs on the
device before 𝑡 𝑢.
27

28. Cost Model
 Relate the transition costs of MDP to energy cost as our goal is to
minimize energy consumption.
𝑐1 = 𝐸 𝑢
𝑐2 = 0
𝑐0 = 𝐸 𝑢.
𝑡 𝑢 − 𝑡
𝑡 𝑢 − 𝑡 𝑢−1
28

29. Deciding an Action
P( 𝑋𝑡 ≤ 𝑡 𝑢 |𝑋𝑡−1 = 𝜏 𝑡−1 ) <
𝑡−𝑡 𝑢−1
𝑡 𝑢−𝑡 𝑢−1
…………(1)
 If this inequality is fulfilled, the expected energy costs for sending
an opportunistic position update are less than the cost of not
sending an opportunistic update.
 In this case the opportunistic position update is sent.
 Solving Equation (1) requires knowledge about the due time of
position updates 𝑡 𝑢 .
 Estimation of 𝑡 𝑢 for the time-, distance-based and dead reckoning
position update protocols follows.
?
29

30. Time-based Update Protocol
Time-based update protocol forces a position update to the LS in a
predefined time interval δ 𝑇
𝑡 − 𝑡 𝑢−1 ≥ δ 𝑇
 𝑡 𝑢−1 denotes the time when the last position update was sent.
 For this protocol, all the parameters to evaluate Equation (1) are
known, since time 𝑡 𝑢 can be easily calculated based on the time of
the last update.
30

31. Distance-based Update Protocol
 The update condition is defined by the Euclidean Distance
between the current position of the mobile device 𝑃𝑑(𝑡) and the
current position 𝑃𝑠(𝑡) stored on the LS.
 When this distance at time t is bigger than accuracy threshold δ 𝐷, a
position update is sent:
𝑑𝑖𝑠𝑡(𝑃𝑑 𝑡 , 𝑃𝑆(𝑡)) ≥ δ 𝐷
 𝑡 𝑢 is not known at decision time, but is needed to calculate eqn. 1
Figure: Prediction of 𝑡 𝑢
 The time until the next update can be
predicted by the value of Δ𝑇 for which
the distance between 𝑃𝑑 𝑡 + Δ𝑡. 𝑣
and 𝑃𝑆(𝑡) is equal to the threshold δ 𝐷.
31

32. Dead Reckoning Update Protocol
 Defines a threshold on the deviation between the position on the
device and the LS.
 But the position on the LS is not statically set to the position that
was contained in the last position update rather evolves over time.
 LS estimates the device position 𝑃𝑒𝑠𝑡 𝑡 at time t as follows:
𝑃𝑒𝑠𝑡 𝑡 = 𝑃𝑢−1 + 𝑣(𝑡 − 𝑡 𝑢−1)
Position update is triggered at time t if
𝑑𝑖𝑠𝑡(𝑃𝑑 𝑡 , 𝑃𝑒𝑠𝑡(𝑡)) ≥ δ 𝐷𝑅
Figure: Prediction of 𝑡 𝑢
 𝑡 𝑢 needs to be predicted.
 The time until the next position
update can be predicted by predicted
by Δ𝑇 for which the distance between
𝑃𝑑 𝑡 + Δ𝑡. 𝑣 𝑑and 𝑃𝑒𝑠𝑆 𝑡 + Δ𝑡. 𝑣 is
equal to the threshold δ 𝐷𝑟.
32

33. Evaluation – Energy Consumption
Figure: Energy Consumption of Update Protocols
33

34. Evaluation: Update Messages
Figure: Sent Position updates
 The basic approach sends lowest number of position updates.
 Non-predictive approach sends the most position updates.
 Opportunistic approach can be considerer as tradeoff between these two
extremes. Although number of position updates increases compared to
the basic approach, a large fraction of messages are opportunistically
sent in an energy efficient manner.
34

35. Evaluation – Position Accuracy
Figure: Distance Based Protocol Figure: Dead Reckoning Protocol
 The position of the mobile device is updated more often on the LS and
the deviation between device position and LS position decreases on
average.
 Thus opportunistic extensions not only decrease the energy consumption
of the basic update protocols but also increase the position accuracy on
the LS.
35

36. DrOPS: Model-Driven Optimization of
Public Sensing Systems Motivation
Motivation:
 Due to the mobility of participants, capturing sensor data at certain
points of interests (POI) is more challenging than for fixed sensor
networks where a sensor could simply be installed at each POI.
 Existing model driven sensing approaches takes a long time to
obtain the model.
 No data is available if there is no sensor in the vicinity of a POI.
36

37. Model-Driven Approach: Introduction
 Sensors placed on walls facing the sun will report similar values.
 Sensors placed on walls facing away from the sun will report similar
values.
 Sensors in each group show a high correlation.
37

38. DrOPS: Introduction
 DrOPS utilizes a model-driven
approach, where readings from mobile
smartphones is reduced by inferring
readings from the model.
 Virtual Sensors (v-sensors) are placed
at each POI as a mobility transparent
abstraction of PS System.
 PS system is responsible for selecting
suitable mobile devices in the vicinity
of the v-sensor to capture the data
associated with this v-sensor.
 Model is used to infer readings of
unavailable v-sensors.
38

39. System Model and Architecture
 Consists of gateway server
and mobile devices.
 DrOPS accepts queries
Q=(V,P,QoS) issued by clients
V= set of v-sensors
P= sampling period
QoS= set of quality parameters
set by client.
 At the end of each sampling
period a result set 𝑅 𝑄 is sent
to client.
 𝑅 𝑄 contains all effective
readings as well as inferred
readings computed by the
gateway for virtual sensors.
Figure: Sensing Task execution
39

40. Model-Driven Approach
 Initially, DrOPS uses the basic sensing algorithm to execute the
query and learns the model in parallel.
 Once a model is created, DrOPS switches to an optimized operation
phase.
 A validity check algorithm continuously monitors model accuracy and
causes an update of the model if necessary.
 The inference process INFER(MGD,P) infers readings of unavailable
v-sensors using model.
 Identifies strongly correlated v-sensors to infer other sensors’ values.
40

41. Model Driven Sensing Algorithm
Selects 𝑉𝑒𝑓𝑓
} Task execution
Inference
41

42. Model Management: MOCHA
42
𝑅 𝑐 = Mean value of inferred readings
𝐶𝑐 = Mean value of effective control readings

43. Model Management: OLA
43

44. Model Management: OLA
https://notesonml.wordpress.com
44

45. Evaluation
Figure: Cumulated relative energy consumption
45

46. Summary
 PSense:
 Significantly reduces the number of GPS fixes.
 Adaptive sensing approach increase amount of cellular messages.
 Ad-Hoc exchange mechanism replaces energy intensive cellular
messages.
 Opportunistic Position Update Protocols:
 Energy characteristics of cellular network interface is taken into account.
 Position updates are sent opportunistically together with other messages.
 Markov Decision Process optimizes the cost with respect to energy.
 DrOPS:
 Reduces set of mobile nodes that are queried to reduce energy
consumption.
 Missing readings are compensated.
Extra
46

49. Evaluation: Testbed Evaluation
Figure: Public Sensing Testbed Figure: Output of the testbed evaluation. 15
s sampling period

50. Multivariate Gaussian Distribution:
Changing Covariene

51. Multivariate Gaussian Distribution:
Changing Covariene
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52. Euclidean Distance
http://gamesetmap.com
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