IoT Data Delivery: Sensor Networking...

Networking Laboratory

Sungkyunkwan University

Copyright 2000-2019 Networking Laboratory

IoT Data Delivery:

Sensor Networking Perspective

Tien-Dzung Nguyen, Hyunseung Choo

College of Computing, Sungkyunkwan University

October 2019

Mobile Computing 2019 Networking Laboratory 2/59

Contents

1. Overview

2. Data Collection in Wireless Sensor Networks

3. Delay-Efficient Data Aggregation

4. Recent Solutions

5. Future Trends


1. OverviewIoT Architecture

https://www.scnsoft.com/blog/iot-architecture-in-a-nutshell-and-how-it-works

https://www.scnsoft.com/blog/iot-architecture-in-a-nutshell-and-how-it-works


1. OverviewPreliminaries (1/2)

*Ref: Wireless Sensor Network Survey, Computer Networks Journal, I. Akilydiz , et al.

Sensor node:

► A device capable of physical sensing of environmental phenomena or events,

processing sensed data, and reporting the measurements


Actuator: action command generator based on data

► Receives data from sensors and process it

► Generates an action command based on the result

► Action command is converted to an analog Signal

1. Overview Preliminaries (2/2)

*Ref: Wireless Sensor Network Survey, Computer Networks Journal, I. Akilydiz, et al.

Sensor node Integrated with actuator


MicaZ 2004250kbps

2.4GHz ISM802.15.4/

Zigbee

1. Overview Sensor Hardware Platform


1. Overview Features

Sensor nodes are often with constrained capacity

► Limited processing power, storage, bandwidth, and battery.

► Replacing these batteries requires network redeployment, which can be a

very expensive process

High density and often in large quantities and support sensing, data

processing, embedded computing and connectivity

Radio communication is the major source of energy consumption


1. OverviewWireless Sensor Networks (WSNs)

Composed of one ore multiple base stations (gateways/sinks) and

(thousands of) sensor nodes deployed in an area called sensing field.

The sensor node

extracts (senses) the

data from the

environment.

Designed to gather

data from the network

to the sink(s) using

hop-by-hop wireless

communication.


1. Overview Traffic Patterns (1/3)

Routing

► Data are often routed to a collection point, called sink node or base station

Al-Karaki, Jamal N., and Ahmed E. Kamal. "Routing techniques in wireless sensor networks: a survey." IEEE wireless communications 11, no. 6 (2004): 6-28.

b) Multiple sources modela) Event-radius model

Base station Source


Broadcasting: An indispensable operation in WSNs for providing control

or routing functionalities

► A base station disseminates a message to the whole network

► Upon receiving a broadcasting message, the node re-broadcasts it to all

other nodes in its transmission range



Collection/Aggregation: all/subset of nodes send data to the base

station

► For applications that rely on collected data (monitoring, tracking etc.)

► Intermediate nodes forward raw/aggregated data toward the base station



1. Overview Challenges in Communications (1/2)

Due to the wireless communication, collecting data from an WSN faces

many challenging issues

Challenges- half duplex communication

Bagaa, Miloud, et al. "Data aggregation scheduling algorithms in wireless sensor networks: Solutions and challenges." IEEE Communications Surveys &

Tutorials 16.3 (2014): 1339-1368.


1. Overview Challenges in Communications (2/2)

Due to the wireless communication, collecting data from a WSN faces

many challenging issues

Challenges- radio propagation

Bagaa, Miloud, et al. "Data aggregation scheduling algorithms in wireless sensor networks: Solutions and challenges." IEEE Communications Surveys &

Tutorials 16.3 (2014): 1339-1368.


Heterogeneous platform (1700+ nodes)

Mobility in experimentation

Remote application development and experimentation

1. Overview Large-scale IoT Testbed: Fit-IoT testbed


2. Data Collection in WSNsTypes of Data Collection

One-shot data collection: in event-driven applications, it is very critical

to deliver alarms about serious events in a timely manner so that an

appropriate action can be taken in response

► Example: detecting oil/gas leak or structural damage

Continuous data collection, require periodic and fast data delivery over

long periods of time [*]

► Example: surveillance, monitoring

Ozlem Durmaz Incel, Amitabha Ghosh, Bhaskar Krishnamachari, and Krishna Chintalapudi. "Fast data collection in tree-based wireless sensor

networks." IEEE Transactions on Mobile computing 11, no. 1 (2011): 86-99.


2. Data Collection in WSNs Design Goals

Energy efficiency

Minimum latency

Data accuracy

Aggregation freshness

Collisions avoidance


2. Data Collection in WSNs Design Consideration

Aggregation function (How?)

► Specifying the raw data collection or aggregated data collection

Routing scheme (Where?)

► Where to send data to

Aggregation scheduling (When?)

► At when the transmission occurs


2. Data Collection in WSNs Collection Features

Three components:

► Aggregation function

► Routing scheme

► Aggregation schedule

M. Bagaa, Y. Challal, A. Ksentini, A. Derhab, & N. Badache (2014). Data aggregation scheduling algorithms in wireless sensor networks: Solutions and

challenges. IEEE Communications Surveys & Tutorials, 16(3), 1339-1368.

Collection features


2. Data Collection in WSNs Routing Scheme: Spanning Tree

Forming a spanning tree rooted at the sink node

Every node sends data through a multi-hop path toward the sink

Intermediate node relays data for its descendants

Well-known variants: Shortest Path Trees (SPTs)- Prim [4], Breath First

Search

a

s

b

c d

e

b) A Shortest Path Treea) Network topology

Neighbor

Tree link


2. Data Collection in WSNsRouting Scheme: Clustering

The network is into a set of areas called clusters

► Each cluster is composed of a set of nodes: 1 cluster head and the rest are

cluster members

Cluster members send data to the

cluster head

Cluster head forward data to base

station directly (one hop) or through

a gateway (multi-hop)

Jun Yuea, Weiming Zhang, Weidong Xiao, Daquan Tang, and Jiuyang Tang.

"Energy efficient and balanced cluster-based data aggregation algorithm for

wireless sensor networks." Procedia Engineering 29 (2012): 2009-2015.

Base station


2. Data Collection in WSNs Aggregation Function (1/3)

Data can be perfectly or partially aggregated in network

► Perfect aggregation: MIN, MAX, COUNT, SUM

► Partial aggregation: AVERAGE (combination of SUM and COUNT)



Non-aggregated traffic

► Node 𝑎 has to send data to 𝑠 2 times:

One is to forward data from 𝑐 to 𝑠

One is to send data of itself

► Similarly, 𝑑 has to send data 2 times to 𝑏,

𝑏 has to send data 3 times to 𝑠

► Total: 9 packets

Aggregated traffic:

► All the nodes send data once each

► Total: 5 packetsa,3

s

b,4

c,2 d,2

e,1

a) Non-aggregated scheme

b) Aggregated scheme



Data aggregation helps reduce:

► Number of transmitted packets

► Medium access contention

► Data collection delay

► Energy consumptiona,3

s

b,4

c,2 d,2

e,1

An aggregated scheme


2. Data Collection in WSNs Scheduling

Why scheduling?

► Given two nodes 𝑢, 𝑣 having data to transmit to their parents

► Nodes 𝑢 and 𝑣 cannot transmit data at the same time

Scheduling is to identify who (sender/child) should send data to whom

(receiver/parent) at which time? – without collisions


2. Data Collection in WSNs Scheduling: Slotted Protocol

The MAC protocol to be used is TDMA (contention-free)

Each sender transmits data within its assigned time slot

Example:

► Transmission 𝑒 − 𝑑: time slot 1

► Transmission 𝑐 − 𝑎: time slot 2

Why 𝑐 − 𝑎 and 𝑒 − 𝑑 cannot

happen at the same time?

--collision at node 𝑑

► Transmission 𝑑 − 𝑏: time slot 2

► Transmission 𝑎 − 𝑠 and 𝑏 − 𝑠:

time slots 3 and 4

Transmitting time slot of a parent is

bigger than its children

An example of a slotted protocol

4 Time slot when a data reception occurs

a,1 Node a transmits at time slot 1


2. Data Collection in WSNs Scheduling: Impact of Routing Scheme

Shortest path-based

Delay-optimal routing

b)

a)


2. Data Collection in WSNsCommon Terminologies (1/2)

Neighbor: a pair of nodes within each other’s transmission range are

called neighboring nodes

Sender/Receiver: A node which sends/receives data packet

Transmission link (𝑢, 𝑣): a directed edge pointed from a sender 𝑢 to a

corresponding receiver 𝑣

Link conflict: two transmission links conflict with each other if the

simultaneous transmission through them leads to a collision.


2. Data Collection in WSNsCommon Terminologies (2/2)

Example

► Neighbors of node 𝑎: {𝑠, 𝑐, 𝑑}

► Sender/Receiver: sender 𝑎, receiver 𝑠

► Transmission link: (𝑎, 𝑠)

► Link conflict: (𝑐, 𝑎) and (𝑒, 𝑑)

a

s

b

c d

e

Network topology

Neighbor

Tree link


3. Delay-Efficient Data AggregationMotivation

In many industrial applications, sensory data need to be collected at the

base station as fast as possible

Failure in timely data delivery would result in system error or at least

performance degradation

Smart applications also need fresh data to make timely decisions

In delay-efficient data aggregation schemes, the aggregation time- the

time needed to collect all data from the sensor nodes- is a minimization

objective


3. Delay-Efficient Data Aggregation General Idea (1/2)

Routing structure construction: Identifying who (sender/child) should

send data to whom (receiver/parent)?

Scheduling: among the identified sender-receiver pairs, which pair

sends data at what time?

► A scheduling algorithm finds maximum number of concurrent transmissions

in every time slot, starting from time slot 1, 2, …➔ the aggregation time is

minimized

Base station Sensor node


3. Delay-Efficient Data Aggregation General Idea (2/2)

The scheduling algorithm begins with finding the transmissions from the

leaf nodes, then gradually go up to the sink

► Start with time slot 1, begin with leaf nodes {𝑐, 𝑑}

► Can nodes 𝑐 and 𝑑 send data at the same time to their parents?

No, because of collision at node 𝑎

Neighbor

Tree link


3. Delay-Efficient Data Aggregation Impact of Collisions

Collision prevents the intended receiver from getting data

Example:

► Nodes 𝑐 and 𝑑 canNOT send data at the same time to their parents

► Nodes 𝑎 and 𝑑 can send data at the same time to 𝑠 and 𝑏.

A 5-node topo and a schedule of length 3

Neighbor

Tree link


3. Delay-Efficient Data Aggregation Impact of Density

More collisions (denser networks) result in longer aggregation time

Example:

► Every two (sender, receiver) pairs cannot transmit at the same time:

𝑐, 𝑎 , 𝑑, 𝑏 , 𝑎, 𝑠 , (𝑏, 𝑠)

A 5-node topo and a schedule of length 4

Neighbor

Tree link


3. Delay-Efficient Data Aggregation Prioritization Metric (1/2)

In time slot 1: among 3 leaf nodes {𝑑, 𝑒, 𝑓}, schedule which one first to have more

number of concurrent transmissions?

► Example:

Fig. a) Choose 𝑒 first ➔ transmission 𝑒 − 𝑏blocks two others 𝑑 − 𝑎 and 𝑓 − 𝑐

Fig. b) Choose 𝑑 first ➔ transmission 𝑒 − 𝑏 is

blocked but 𝑓 − 𝑐 can happen

Prioritizing the transmissions is important to

achieve a short aggregation time

► By using prioritization metrics, for example:

number of non-leaf neighbors

a) Select 𝑒➔ 1 transmission in ts#1

b) Select 𝑑, 𝑓➔ 2 transmissions in ts#1

Neighbor

Tree link


3. Delay-Efficient Data Aggregation Prioritization Metric (2/2)

Using number of non-leaf neighbors

► A node with a higher number of non-leaf

neighbors is a bottleneck node, hence it

should be scheduled with higher priority

Example:

► Number of non-leaf neighbors of:

Node 𝑑: 2

Node 𝑒: 1

Node 𝑓: 2

a) A tree topology

b) Select 𝑑 first

Neighbor

Tree linkc) Select 𝑓 second


4. Recent SolutionsPopular Approaches

Prioritization metrics:

► Node degree

A node with more neighbors will affect more number of other transmissions

► Link conflict degree

A link that collides with many other links will lower the number of concurrent

transmissions

Scheduling approaches:

► Bottom-up [2][4]

► Top-down [3][5]


4. Recent Solutions Scheme 1: Weighted Incremental Ranking for convErgecast

with aggregation Scheduling (WIRES)

Build the schedule from the leaf nodes, up to the sink

Prioritization metric: number of non-leaf

neighbors of a node

Start with time slot 1, repeat:

► Pick a node with highest

metric among the leaves, remove

the conflicted transmissions, then pick

the next highest-metric node, … until no

leaf node left

► Assign current time slot to all the picked nodes & remove them from the tree

► Increase time slot

a,1 Node a scheduled at time slot 1Candidate sender with 3 non-leaf neighbors

3Unscheduled node


4. Recent Solutions Scheme 1: Time slot 1 (1/2)

Candidate senders: {𝑎, 𝑑, 𝑒, 𝑓}

Select the highest non-leaf degree {a}

Because of the transmission 𝑎 − 𝑠, the

transmissions 𝑒 − 𝑐 and 𝑑 − 𝑏 are blocked

Node Non-leaf neighbors

a 2: {s,c}

d 2: {s,b}

e 1: {c}

f 1: {b}


3Unscheduled node


4. Recent Solutions Scheme 1: Time slot 1 (2/2)

Candidate senders: {𝑎, 𝑑, 𝑒, 𝑓}

Transmission 𝑓 − 𝑏 can also happen at

time slot 1


a 2: {s,c}

d 2: {s,b}

e 1: {c}

f 1: {b}


3Unscheduled node


4. Recent Solutions Scheme 1: Time slot 2

Candidate senders: 𝑑, 𝑒

Now, the two transmissions

𝑑 − 𝑏 and 𝑒 − 𝑐 are not collided, so both

can take time slot 2


3Unscheduled node


d 2: {s,b}

e 1: {c}



Candidate senders: 𝑏, 𝑐

Only either 𝑏 or 𝑐 is allowed to transmit

data to 𝑠 in time slot 3

Assign time slot 3 to the transmission 𝑏 − 𝑠


3Unscheduled node


b 1: {s}

c 1: {s}



Candidate senders: 𝑐

Assign time slot 4 to the transmission 𝑐 − 𝑠


3Unscheduled node


c 1: {s}


4. Recent Solutions Scheme 1: Final result

Final result: Aggregation time is 4 (time slots)


3Unscheduled node


4. Recent SolutionsScheme 2: Minimum Interference Network Topology

Grow the aggregation tree starting from the sink node simultaneously

with assigning a schedule

Find a set of concurrent transmissions that can happen simultaneously,

time slot by time slot

Reverse the schedule to obtain a normal schedule

s

2

Node s

Tree link

Neighbor

Transmission order

a

s

b

c d

f

1

2 3

3 4

a

s

b

c d

f

4

3 2

2 1

(a) Normal order (b) Reverse order


4. Recent Solutions Scheme 2: Blockcount

Blockcount of a link represents the number of other transmissions that

are blocked by the selected link (considering all collisions)

Example:

► Node 𝑎 is scheduled to send data to node 𝑠 at time slot 1.

► What are the blockcounts of the candidate transmissions?

s

a,1 b

dc3

3 33

Node in growing tree

a,1 Node a scheduled at time slot 1

Candidate sender

Candidate transmission with block count 3

3



Initially, 𝑆 = {𝑠}

Candidate transmissions: { 𝑎, 𝑠 , 𝑏, 𝑠 , 𝑐, 𝑠 , (𝑑, 𝑠)}

➔ select randomly, e.g. (𝑎, 𝑠) to assign

time slot 1

All other candidates 𝑏, 𝑠 , 𝑐, 𝑠 , (𝑑, 𝑠) are

blocked ➔ no more link is scheduled in time slot 1

Link Blockcount

(𝑎, 𝑠) 3: {(𝑐, 𝑠), (𝑏, 𝑠), (𝑑, 𝑠)}

(𝑏, 𝑠) 3: {(𝑎, 𝑠), (𝑐, 𝑠), (𝑑, 𝑠)}

(𝑐, 𝑠) 3: {(𝑎, 𝑠), (𝑏, 𝑠), (𝑑, 𝑠)}

(𝑑, 𝑠) 3: {(𝑎, 𝑠), (𝑏, 𝑠), (𝑐, 𝑠)}



Candidate receivers: {𝑠, 𝑎}

Candidate transmissions: { 𝑏, 𝑠 , 𝑐, 𝑠 , 𝑑, 𝑠 , (𝑐, 𝑎)}

➔ select randomly, e.g. (𝑏, 𝑠) to assign

time slot 2

All the links 𝑐, 𝑠 , 𝑑, 𝑠 , (𝑐, 𝑎) are blocked

➔ no more link is scheduled in time slot 2

Link Blockcount

(𝑏, 𝑠) 3: {(𝑐, 𝑎), (𝑐, 𝑠), (𝑑, 𝑠)}

(𝑐, 𝑠) 3: {(𝑐, 𝑎), (𝑏, 𝑠), (𝑑, 𝑠)}

(𝑑, 𝑠) 3: {(𝑐, 𝑎), (𝑐, 𝑠), (𝑏, 𝑠)}

(𝑐, 𝑎) 3: {(𝑐, 𝑠), (𝑏, 𝑠), (𝑑, 𝑠)}


4. Recent SolutionsScheme 2: Time slot 3

Candidate receivers: {𝑠, 𝑎, 𝑏}

Candidate transmissions: { 𝑐, 𝑠 , 𝑐, 𝑎 , 𝑑, 𝑠 , (𝑓, 𝑏)}

➔ select one among (𝑐, 𝑎) and (𝑓, 𝑏)e.g. (𝑐, 𝑎) to assign time slot 3

Links { 𝑐, 𝑠 , 𝑐, 𝑎 , 𝑑, 𝑠 } are blocked by selecting (𝑐, 𝑎)➔ remains link (𝑓, 𝑏)

Link Blockcount

(𝑐, 𝑠) 3: {(𝑐, 𝑎), (𝑑, 𝑏), (𝑑, 𝑠)}

(𝑐, 𝑎) 2: {(𝑐, 𝑠), (𝑑, 𝑠)}

(𝑑, 𝑠) 3: {(𝑐, 𝑎), (𝑐, 𝑠), (𝑑, 𝑏)}

(𝑑, 𝑏) 3: {(𝑐, 𝑠), (𝑑, 𝑠), (𝑓, 𝑏)}

(𝑓, 𝑏) 2: {(𝑑, 𝑏), (𝑑, 𝑠)}


4. Recent SolutionsScheme 2:Time slot 3 (cont’d)

(𝑓, 𝑏) can just be assigned time slot 3


4. Recent SolutionsScheme 2: Time slot 4

Candidate receivers: {𝑠, 𝑎, 𝑏, 𝑓}

Candidate transmissions: { 𝑒, 𝑐 , 𝑒, 𝑓 , 𝑑, 𝑠 , 𝑑, 𝑏 , (𝑑, 𝑓)}

Select 𝑒, 𝑐 with smallest blockcount to

assign time slot 4, remove conflict { 𝑒, 𝑓 , 𝑑, 𝑓 }

Remaining { 𝑑, 𝑠 , (𝑑, 𝑏)}

Link Blockcount

(𝑒, 𝑐) 2: {(𝑒, 𝑓), (𝑑, 𝑓)}

(𝑒, 𝑓) 4: {(𝑒, 𝑐), (𝑑, 𝑓), (𝑑, 𝑠), (𝑑, 𝑏)}

(𝑑, 𝑠) 4: {(𝑒, 𝑐), (𝑒, 𝑓), (𝑑, 𝑓), (𝑑, 𝑏)}

(𝑑, 𝑏) 4: {(𝑒, 𝑐), (𝑒, 𝑓), (𝑑, 𝑠), (𝑑, 𝑓)}

(𝑑, 𝑓) 4: {(𝑒, 𝑐), (𝑒, 𝑓), (𝑑, 𝑠), (𝑑, 𝑏)}


Candidate transmissions: 𝑑, 𝑠 , 𝑑, 𝑏

Select 𝑑, 𝑠

4. Recent SolutionsScheme 2: Time slot 4 (cont’d)

Link Blockcount

(𝑑, 𝑠) 1: {(𝑑, 𝑏)}

(𝑑, 𝑏) 1: {(𝑑, 𝑠)}


4. Recent Solutions Scheme 2: Result

Let 𝐷 be the last time slot, 𝑡′(𝑢) be the assigned time slot of node 𝑢

The actual transmitting time slot of 𝑢, denoted by 𝑡 𝑢 , in normal

schedule will be:𝑡 𝑢 = 𝐷 + 1 − 𝑡′(𝑢)


5. Future Trends

Multi-channel WSNs

Mobile sink

Multi-sink

Battery-free WSNs

Duty-cycled WSNs

Multi-sink

Unreliable environment

Power control

WSN-derived networks: underwater WSNs, mobile WSNs

Deadline-constrained data aggregation

AI-based data aggregation

…and more


5. Future TrendsMulti-channel

With multi-channel, more transmissions can be done in a time slot

s

a

b

c

e fd


5. Future TrendsMobile Sink

With mobility, the sink can collect data more efficiently.


References [1] Miloud Bagaa, Yacine Challal, Adlen Ksentini, Abdelouahid Derhab, and Nadjib

Badache. "Data aggregation scheduling algorithms in wireless sensor networks: Solutions

and challenges." IEEE Communications Surveys & Tutorials 16, no. 3 (2014): 1339-1368.

[2] Baljeet Malhotra, Ioanis Nikolaidis, and Mario A. Nascimento. "Aggregation

convergecast scheduling in wireless sensor networks." Wireless Networks 17, no. 2

(2011): 319-335.

[3] Matthias Jakob, and Ioanis Nikolaidis. "A top-down aggregation convergecast

schedule construction." In 2016 9th IFIP Wireless and Mobile Networking Conference

(WMNC), pp. 17-24. Ieee, 2016.

[4] Cheng Pan, and Hesheng Zhang. "A time efficient aggregation convergecast

scheduling algorithm for wireless sensor networks." Wireless Networks 22, no. 7 (2016):

2469-2483.

[5] Dung T. Nguyen, Duc-Tai Le, Moonseong Kim, and Hyunseung Choo. "Delay-Aware

Reverse Approach for Data Aggregation Scheduling in Wireless Sensor

Networks." Sensors 19, no. 20 (2019): 4511.

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