Presented at the FDP  Emerging Trends in IOT Enabled Wireless Communication , 8TH – 12TH, August, 2022 (ONLINE MODE) Organized By  Department of Electronics Communication Engineering In association with IQAC,  Haldia Institute of Technology Haldia-721657, West Bengal.

1. Presented at the FDP
Emerging Trends in IOT Enabled Wireless Communication , 8TH – 12TH, August,
2022 (ONLINE MODE) Organized By Department of Electronics Communication
Engineering In association with IQAC, Haldia Institute of Technology Haldia-721657,
West Bengal.

2. Presented by
Dr. Sanjay Dhar Roy
Associate Professor
Department of Electronics and Communication Engineering
National Institute of Technology Durgapur, India
IoT Enabled Wireless Communication Systems

3. Acknowledgement
Thanks to Alok and Sayanti for their help

4. Contents
• Introduction
• Existing works
• Our works
• References.

5. Introduction: IoT…
“IoT refers to the interconnection via the Internet of computing devices embedded in
everyday objects, enabling them to send and receive data”
“Internet of Things, 2nd Edition”
Shriram K Vasudevan, Abhishek S Nagarajan, & RMD Sundaram
Copyright  2020 Wiley India Pvt. Ltd.
IEEE has a definition to enhance the understanding. IEEE defines IoT as “A network of
items—each embedded with sensors—which are connected to the Internet.”

6. Introduction: XG WLCS
 Exponential increase in the no. of wireless devices gives rise to massive flow
of data. [3]
The data requirement of all upcoming devices differ from each other.
Some devices need high data rate, while others need reliable connection with
sparse flow of data.
5G: IoT enabling technology: uses FR1 - below 6 GHz, and FR2- above
24 GHz (24–54 GHz). However, the speed and latency in early FR1
deployments, using 5G NR software on 4G hardware (non-standalone), are
only slightly better than 4G systems.
https://en.wikipedia.org/wiki/5G

7. Introduction: 5G and IoT
@ NR : a new air interface that brings new features for 5G which are not backward compatible to 4G.
@Device connectivity in 5G: Standalone mode- NR is connected to 5G core; non-standalone- LTE/NR
based on LTE anchored dual connectivity with NR in user plane.
@ IoT devices can connect to 5G core through enterprise LAN as a fog service.
But, 5G proposes data processing and control capabilities at the operator's core network by introducing
multi access edge computing (MEC) for quick data analysis and decision making, and to eliminate
disadvantages of high response time of cloud.
@5G enables sliced network where within one coverage area, different services, or solutions occupy
their own slice with different speeds and QoS.
For e.g. one slice can support IoT based transport system and another may support IoT based home
automation system.
#Internet of Things concept and applications, Editor K. N. Raja Rao, Wiley, 2021

8. Introduction Contd.
Fig. 3 – Commercial use of bands in USA
(Source: United States GAO)
Bands recommended to use for IoT Applications

9. Fig. 3 Sub 3 GHz radio spectrum
(Source: USA Frequency Allocation (FCC)
 Sub 3GHz band is heavily crowded--> Cognitive Radio/ spectrum sharing
Introduction Contd.

10. Cellular IoT: OMA vs. NOMA
Disadvantages of orthogonal multiple access (OMA)
Dilemma to realize a better trade-off between throughput and user
fairness, illustrated in the following example: [5]
 A user with a poor connection to the base station (BS) is served by using OMA.
-->Spectral efficiency is low since this user cannot utilize the allocated
bandwidth efficiently.
 Since OMA is used, the bandwidth resources occupied by this user cannot be
shared by the others.
Difficult to support massive connectivity
 Three key requirements for 5G are to support.:
-High throughput, low latency and massive connectivity

11. Non-orthogonal multiple access (NOMA)
Main idea: Spectrum sharing [7]
NOMA is advancing rapidly for the next generation wireless networks
 In 5G, Power-domain (PD) NOMA [8]
 Included in the forthcoming digital TV standard (ATSC 3.0)
https://www.intechopen.com/chapters/52822

12. NOMA:
DL NOMA: Let us consider the farthest user first. The signal it
decodes first will be its own signal since it is allocated the most power
. The signals for other users will be seen as
interference. Therefore, the signal-to-noise ratio (SNR) for UEk
And for UE1, last signal it decodes will be its own signal:
UL NOMA: Decode the signal of the nearest user first.
In the uplink, the received signal by the BS that includes all the user
signals is written as:
At the receiver, the BS implements SIC. The first signal it decodes will
be the signal from the nearest user. The SNR for the signal for the UE:
*R. C. Kizilirmak, "Non-Orthogonal Multiple Access (NOMA) for 5G Networks", in Towards 5G Wireless Networks - A Physical Layer Perspective. London, United Kingdom: IntechOpen, 2016 [Online].
Available: https://www.intechopen.com/chapters/52822 doi: 10.5772/66048

13. Two ways of Communication (HD and FD)
Full Duplex (FD)
mode
Half Duplex (HD)
mode
 Bi-directional data transmission (Simultaneously).
 Increases Spectrum efficiency and doubles link
throughput.
 FD is different than TDD/ FDD (used till 4G). In FD case,
simultaneous TX and RX at same time and on same
frequency.
 Bi-directional data
transmission (One at a time)

14. D2D-IoT
 Other use cases of
communication systems
based on IoT and D2D
-V2V
-Remote healthcare
-Disaster relief
-Ubiquitous computing
- Proximity services
-Yaacoub, Elias, and Osama Kubbar. "Energy-efficient device-to-device communications in LTE public
safety networks." In 2012 IEEE Globecom Workshops, pp. 391-395. IEEE, 2012.

15. Case studies on IoT based WLC:5G-D2D-IoT
 Long range MTC over resource constrained devices requires low power wide area
(LPWA) technology, for e.g., NB-IoT (3 km for urban; 15 km for rural).
 D2D provides mechanism to transmit the NB-IoT UE acquired data to the BS using
nearby cellular devices, which act as relaying nodes to support D2D communication.
 Deterministic D2D (2D2D) for delay sensitive
applications such as monitoring the vital sign of a heart
,regulating heavy traffic in smart city, the amount
of packet drop and delay is not permissible.
 -The MBS selects a group of UEs by analysing
their channel gains and the residual energy levels to
help D2D communication.
- Using AI based ranks and the current signal to
handle the packet, the MBS allocates a relaying
UE for the NB-IoT UE.
- Ali Nauman, M.A. Jamshed, R. Ali, Y. B. Zikria, S. W. Kim, “An intelligent deterministic D2D
communication in NB-IoT,” 2019 15th International Wireless Communications & Mobile Computing
Conference (IWCMC), Tangier, Morocco, 2019, pp. 2111-2115, doi: 10.1109/IWCMC.2019.8766786. 15

16. Case studies on IoT based WLC: Smart farming
• Leaf area index of crops assessed
• Two types of PAR sensors used
-ground level (G) and above ground (R)
-The PAR (Photosynthetically Active Radiation)
Sensors (G & R) report the Photosynthetic Photon Flux
Density (PPFD)
* J. Bauer and N. Aschenbruck, "Design and implementation of an agricultural monitoring system for smart farming," 2018 IoT Vertical and Topical Summit on Agriculture - Tuscany (IOT
Tuscany), 2018, pp. 1-6, doi: 10.1109/IOT-TUSCANY.2018.8373022.
* Misra, S., Mukherjee, A., & Roy, A. (2021). Introduction to IoT. Cambridge: Cambridge University Press. doi:10.1017/9781108913560
- Clustered ground-level sensors (G) and above reference
motes (R) are connected to Raspberry Pis (RPi).
-Via PLMN communication, they are further integrated in
an MQTT-based IoT architecture, making in-situ crop
information instantaneously available.

17. Case studies on IoT based WLC: Vehicular IoT
• Internal sensors placed within vehicle for sensing parameters directly associated with the vehicle.
For e.g. GPS, proximity, accelerometer, pressure and temperature.
• External sensors: to sense vacant parking lots, on road cameras for taking still images and videos in
smart traffic system, for e.g. images for toll collection.
• Satellites images for on road congestion and road blockages.
• Wireless connectivity, for e.g. Wi-Fi, Bluetooth, GSM: for transmitting sensed data to RSU, RSUs to
cloud
• RSU : static entity equipped with sensors, communication units and fog devices; collaborates
with internal and external sensors.
• Cloud and fog computing: Fog -> the location and extent of short on road congestion from a
certain location; Cloud ->for regular on-road congestion and predictions based on historical data.
• Analytics: Strong data analytics required to predict on road traffic conditions that may occur after
an hour.
* Misra, S., Mukherjee, A., & Roy, A. (2021). Introduction to IoT. Cambridge: Cambridge University Press. doi:10.1017/9781108913560

18. Case studies on IoT based WLC: Healthcare IoT
* A. Roy, C. Roy, S. Misra, Y. Rahulamathavan and M. Rajarajan, "CARE: Criticality-Aware Data Transmission in CPS-Based Healthcare Systems," 2018 IEEE
International Conference on Communications Workshops (ICC Workshops), 2018, pp. 1-6, doi: 10.1109/ICCW.2018.8403540.
* Misra, S., Mukherjee, A., & Roy, A. (2021). Introduction to IoT. Cambridge: Cambridge University Press. doi:10.1017/9781108913560
* CARE system: decides whether to transmit the
physiological sensor data to the fog aggregation node
or the cloud, based on the patient’s criticality.
* Based on an index, which measures the health
criticality of a patient, based upon which the
physiological parameters are transmitted to the cloud
or the fog.
*Further, a Nash Bargaining process formulation done,
where the LPUs act as players and bargain among
themselves to transmit the data to a cloud or fog node.

19. Work 1
*Microcellular LTE environment having
coverage area of radius R_1=530m
*Good coverage up to radius R_2=500m
*IoT Devices (IoTDs) and CUEs are
randomly distributed inside the cell.
*IoT Gateway (IoT-GW) inside the poor
coverage region.
*Cellular mode communication: CUEs and
IoTDs inside good coverage region.
*D2D mode communication: IoTDs inside
poor coverage region.

20. •IoTDs to IoT-GW links in D2D mode.
•OFDMA for sharing physical resource block
(PRB).
•Strong fiber optic communication between
IoT-GW and eNodeB.
•For D2D and cellular communication: micro-
cellular channel, characterized by path loss,
shadowing and multi-path fading.
*SINR value at IoT-GW due
to IoTD found considering interference from
other IoTDs
*Next outage probability and sum
rate found.

21. Fig.: Outage Probability of D2D Communication link versus SINR Threshold for different
Transmit Power of IoTD

22. Work 2
 IoT devices integrated into a
Cellular Network.
 Independent cellular users and
IoT devices sharing resource
blocks transmit their message
signals over power domain (PD)
NOMA.
Further, relay with some power
helps in transmission of its signal
to destination.
Relay selection strategy plays a
role for signal transmission.
Fig. 6

23. The combined signal received at relay:
The signal of K-th user left at relay Y:
is due to imperfect SIC of (K-1) users.
After successfully decoding each users signals at relay, finally signals retransmitted to destination
users.
are the power coefficients allocated to transmission of the users'
signals.

24. After successfully decoding each users signals at relay as well as at destination, we have two SINR of
each users.
denotes the minimum of two hops SINR of user 1, 2, 3, and 4.
denotes the achievable data rate of each corresponding user.
The outage probability of the system can be expressed as:

25. Work 3
 Two types of network are there.
primary Network (PN) and
secondary Network (SN)
 In first phase of transmission
(Underlay mode), xp a primary
signal transmitted by Primary
transmitter (PT) to Secondary
transmitter (ST) and Primary
destination (PD), and xs a
secondary signal transmitted by
ST to Secondary destination
(SD).

26. The received signal at PD, ST, and SD in 1st transmission phase is given as:
The received signal at PD, and SD in 2nd transmission phase:
After applying MRC at each user, and calculating capacity of each user. The Outage probability of
each users is given as:

27. Work 4: D2D-IoT Relay Hopper
-In the first step, the IoT devices satisfying
the QoS constraint are selected and matched
with appropriate reuse candidates, that is, the
cellular user equipments (CUEs) by an optimum
resource allocation scheme.
-Next, link reliability of these links are
computed and weak links are discarded.
-Finally, the disconnected IoT devices are rerouted to the IoT
gateway via IoT devices possessing strong links following a
relay hopper model.
-A. Pradhan, S. Basu, S. Sarkar, S. Mitra and S. D. Roy, "Implementation of relay hopper model for reliable communication of IoT devices in LTE
environment through D2D link," 2018 10th International Conference on Communication Systems & Networks (COMSNETS), Bengaluru, 2018, pp. 569-572,
doi: 10.1109/COMSNETS.2018.8328275.

28. Work 5: UAV-IoT-D2D
28
 An unmanned aerial vehicle (UAV) can be used as a flying base station (BS)
to provide emergency assistance and cover a part of the disaster area
 Many D2D users in multi-hop mode assist the UAV for relaying the
information and extend the large disaster area
 In an NFA, multiple communication services such as satellite-assisted
communication, walkie-talkie communication, etc, can exist.
Liu, Xiaonan, Zan Li, Nan Zhao, Weixiao Meng, Guan Gui, Yunfei Chen, and Fumiyuki Adachi. "Transceiver design and multi-hop
D2D for UAV IoT coverage in disasters." IEEE Internet of Things Journal 6, no. 2: 1803-1815, 2018.
 A network model with inner and outer region involving Temporary
BS (T-BS), UAV, IoT users, cellular users (CUs), and D2D users is
proposed
 The outage probability of IoT and D2D users in the inner and outer
regions are derived analytically

29. System Model
29
Fig. 2 System model
FA
BS
Millimeter-wave BS
Directional antenna
Temporary BS
IoT users
IoT gateway (IoT GW)
Cellular users
D2D users
UAV
NFA

30. System Model
30
 Due to longer distance some D2D users (placed in the outer circle) cannot
communicate to T-BS in large disaster area
 Assume the UAV to be stationary to maintain the energy consumption and
required connectivity as per the network

31. System Model
31
r
R
Signal
Interference link
Fig. 2(a) Working model for analytical evaluation of Fig. 2
 Uplink Signal transmission
o Inner circle

32. System Model
32
 The received signal at the IoT GW can be expressed as:
 The received signal at the T-BS can be expressed as:
 The received signal to interference noise ratio (SINR) are represented as:

33. 33
System Model
 Outage Probability Analysis
 The outage probability of an IoT user to the IoT GW can be expressed as:

34. 34
System Model
 The conditional outage probability of a user located in the outer circle
and it can be expressed as:

35. 35
System Model
 The overall outage probability of a user anywhere in the NFA is found as:

36. 36
Results and Discussions
Fig. 3 Overall outage probability vs. SINR
threshold in dB

37. Conclusions for Work 5
37
 A temporary BS and a UAV-based D2D network is investigated in an
uplink scenario
 The temporary BS and the UAV provide assistance in establishing
communication in a disaster-affected region
 The UAV cannot cover the whole NFA or disaster area due to its limited
battery power
 Assumed a stationary UAV, which is cost-effective than a rotating UAV
 The analytical expression of outage probability for both inner region and
outer region users has been developed
 The overall outage probability is observed with the variation of SINR
threshold, distance from IoT users to the IoT GW, distance from D2D users
to a UAV etc.

38. 1. Internet of Things at a glance, 2020. [Online]. Available: http://www.cisco.com/c/dam/en/us/products/collateral/se/internet-of-things/at-a-
glance-c45-731471.
2. L. Chettri and R. Bera, "A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems," in IEEE Internet of Things
Journal, vol. 7, no. 1, pp. 16-32, Jan. 2020, doi: 10.1109/JIOT.2019.2948888.
3. Jie Lin, Wei Yu, Nan Zhang, Xinyu Yang, Hanlin Zhang, and Wei Zhao, “A Survey on Internet of Things: Architecture, Enabling Technologies,
Security, privacy, and Applications”, IEEE internet of things journal, vol. 4, no.5 5, October 2017.
4. Y. Endo, Y. Kishiyama, and H. Higuchi, ``Uplink non-orthogonal access with MMSE-SIC in the presence of inter-cell interference,'' in Proc. Int. Symp. Wireless
Commun Syst, 2011,pp. 261_265.
5. Z. Ding and R. Schober, “Recent Progress in Non-Orthogonal Multiple Access”, IEEE ICC Tutorial, 2017.
6. N. Zhang, J. Wang, G. Kang and Y. Liu, "Uplink Nonorthogonal Multiple Access in 5G Systems," in IEEE Communications Letters, vol. 20, no. 3, pp. 458-461,
March 2016, doi: 10.1109/LCOMM.2016.2521374.
7. L. Dai, B. Wang, Z. Ding, Z. Wang, S. Chen and L. Hanzo, "A Survey of Non-Orthogonal Multiple Access for 5G," in IEEE Communications Surveys & Tutorials,
vol. 20, no. 3, pp. 2294-2323, thirdquarter 2018, doi: 10.1109/COMST.2018.2835558.
8. Y. Saito, A. Benjebbour, Y. Kishiyama, and T. Nakamura, “System level performance evaluation of downlink non-orthogonal multiple access (NOMA)”, IEEE
PIMRC, 2013.
9. Z. Ding, Z. Yang, P. Fan and H. V. Poor, “On the Performance of Non-Orthogonal Multiple Access in 5G Systems with Randomly Deployed Users”, IEEE SPL,
2014.
10. L. R. Varshney, "Transporting information and energy simultaneously," 2008 IEEE International Symposium on Information Theory, Toronto, ON, Canada, 2008,
pp. 1612-1616, doi: 10.1109/ISIT.2008.4595260.
11. Derrick Wing Kwan Ng; Trung Q. Duong; Caijun Zhong; Robert Schober, "The Era of Wireless Information and Power Transfer," in Wireless Information and
Power Transfer: Theory and Practice , Wiley, 2019, pp.1-16, doi: 10.1002/9781119476863.ch1.
12. A. Rauniyar, P. Engelstad and O. N. Østerb, "RF Energy Harvesting and Information Transmission Based on Power Splitting and NOMA for IoT Relay
Systems," 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, 2018, pp. 1-8, doi:
10.1109/NCA.2018.8548068.
References

39. 39
Thank You….