Slides for IoT summer school

1. Emergency Communications, Floating Content
(and IoT)
Gianluca Rizzo
University of Applied Sciences of Western Switzerland – HES-SO Valais
Universita’ di Foggia – Italy
gianluca.rizzo@hevs.ch
gianluca.rizzo@unifg.it
May 21st, 2021
Summer school UNI-INTERNATIONAL SCHOOL ON IOT – Universita’ di Salerno

2. Goals
• Be able to recognize the various dimension of a
(natural) disaster, and their implications for the
provisioning of content, computing, and
communication
• Be able to recognize the specificities of demand for
content, computing and communications in the
aftermath of a disaster
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3. What is a disaster?
1. A serious problem
2. Causing human, material,
economic or environmental
loss
3. Exceeding the ability of the
affected community to cope
using its own resources.
Q1: are all of these three elements
necessary?
Ruins from the 1906 San Francisco earthquake(Wikipedia)
Source: WHO, IFRC

4. Disasters take a heavy toll on world
economy and people’s lives
Source: UNISDR – Human and Economic Impact of Disaster in last 10 years

5. Disasters Prone regions are common
in every continent
Source: UNISDR – 2015 Disasters in numbers *Epidemics and Infections not included

6. Number of disasters by type (2012-2018)
The vast majority of natural disasters are tightly coupled to
local environmental and geological features

7. A Disaster Manangement strategy is the
combination of risk management and
crisis management
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8. What is the role of ICT in Disaster
management?
• Ensuring an adequate diffusion of key information is
vital for both crisis management and risk management
• Both risk and crisis management have a near-term
(immediate aftermath) and medium-long term
component
• The most challenging (and largely open) issue is how
ICT can cater for crisis management in the immediate
aftermath of a disaster
• Key for saving lives and mitigating overall impact of disaster
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9. Earthquakes
• Area covered: a city or a small region
• Impact on population: migration (partially
definitive), disruption of almost all human
activities
• Difficult to predict (sometimes, a few
seconds ahead)
• However, seismic risk level is well known and
mapped in detail.
• Importance of early support (first
hours/days)
• Entrapped people: Life expectancy drops to zero
after 3 days
• Hazard reduction: gas leaks, floods, fires, electricity

10. A forecasted disaster:
Hurricane Dorian
• The most intense tropical cyclone on
record to strike the Bahamas
• worst natural disaster in the country's
history
• Peak on Abaco Island on Sept 1st 2019, over
by Sept 3rd.
• Damages:
• badly damaged roofing
• power lines cut off
• roads impassable
• fatalities: 63 direct, 7 indirect, 280 dispersed
• damage ≥ 8.28 billion USD
• Local community required massive
interventions from non-local actors
• Huge deployment of external rescue teams,
humanitarian organizations

11. If entirely predictable, how was
Dorian a disaster?
• Forecasted well in advance, in a
region usually struck by hurricanes
• However:
• big differences in amount of damage
between the area directly covered
and neighboring areas
• hard to predict exact path more than
1-2 days in advance
• serious damage is relatively rare:
• Last hurricane which made
comparable damages in the same
areas struck 50 years before
• Uncertainty made perceived risk
too low to take drastic preventive
measures

12. Key
properties
of a
disaster
Disruption is limited in time (and space)
• Short or long period
• Permanent disaster is not a disaster
Directly related to the choice of:
• A model of the environment, of
operational conditions
• A risk management strategy (Implicit or
Explicit)
• A tradeoff between amount of tolerated
risk and costs of
• Ordinary operations
• Disruptions due to disasters
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13. Elements of a
disaster
management
strategy
Goal: Minimize consequences of
disasters, given:
a. A model of the environment
b. Budget constraints
c. Cost of (ordinary) operations
d. Cost of disaster management
• before and after the disaster
e. Available technologies
13

14. Elements of a
disaster
management
strategy
Goal: Minimize consequences of
disasters, given:
a. A model of the environment
b. Budget constraints
c. Cost of (ordinary) operations
d. Cost of disaster management
• before and after the disaster
e. Available technologies
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15. How can an inadequate model of the
environment affect system fragility?
15
Two factors (among many):
Cognitive biases
Networking effects
Things are getting worse as technology
progresses…

16. Factors affecting
system fragility:
Procrustean Bed
Blindness to sources of
risk which:
• we do not understand
• we are not able to
model
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17. Rare
events
matter!
• Extrapolating from data: The Turkey
• A nonzero probability of ruin
• The era of AI: Are we all getting turkeys?
• The more we know, the more we get
confident on our risk model
• Gaussian vs fat tail domains: «Rare» events
17

18. Complex systems,
interconnectedness
and fragility
Interconnectedness among its parts makes
a system «complex» and more fragile
• Directly: e.g. power distribution network
and telecommunication network
• Other examples: Financial
markets…
• Indirectly: Harder to model
• Interrelationships make collapses and
cascading failures more dangerous
18 A graph visualisation of the topology of network connections of the core of the Internet, December 11 2000. (Source: Bill Cheswick, http:// www.lumeta.com)

19. Elements of a
disaster
management
strategy
Goal: Minimize consequences of
disasters, given:
a. A model of the environment
b. Budget constraints
c. Cost of (ordinary) operations
d. Cost of disaster management
• before and after the disaster
e. Available technologies
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20. In many disasters, disruptions in network
operations are a consequence of network
design choices
• Before: Reassure, minimize.
• Use media to minimize impact of disaster (and of lack of investments)
on their image
• After: be unresponsive, hide issues from the news, disclose
nothing.
• Hurricane Dorian post disaster communication disruptions (relatively hard to find):
• By September 11, cellular services had resumed in most affected areas except for eastern Grand Bahama and northern parts of Abaco,
including Cooper’s Town and Treasure Cay. Aliv, one of the country’s telecommunications providers, continues to conduct repairs and
estimates that up to 80 percent of the network in Abaco will be restored within one week; re-establishing cellular service in eastern
Grand Bahama is anticipated to take longer. As telecommunications services improve, Télécoms Sans Frontières (TSF) noted plans to
reduce the quantity of free humanitarian calling operations for isolated communities beginning September 11; TSF continues to provide
satellite connection to facilitate communication for humanitarian operations in Abaco and Nassau.
• Electricity infrastructure in Abaco, particularly in Marsh Harbour and across the island’s northern areas, remains extensively damaged,
leaving affected communities reliant on generators for power and increasing the demand for fuel, NEMA reports. Equipment and
personnel from the Bahamas Power and Light Company (BPLC) arrived in Abaco on September 11 and 12 to begin repairs, which will
initially focus on restoring power in southern Abaco where damage is less severe. NEMA is coordinating with relief partners to facilitate
the acquisition and transport of additional equipment—including high-capacity generators—to supplement BPLC efforts.

21. Pre-disaster measures aimed mainly at manipulating
user perception of incoming communication issues

22. What is a
reasonable
goal for
research on
post-disaster
technologies?
Goal: Minimize consequences of
disasters, given:
a. A model of the environment
b. Budget constraints
c. Cost of (ordinary) operations
d. Cost of disaster management
• before and after the disaster
e. Available technologies
22

23. What is a
reasonable
goal for
research in
post-disaster
technologies?
• Lowering the cost (and the amount)
of pre/post disaster interventions
• Increasing their effectiveness
Information diffusion is a key enabler
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24. Why are disasters a
problem for a
telecommunication
networks?
Disaster: A set of events which
• disrupts the services provided by a communication
network
• changes their operating conditions, and
• not taken into account in network design phase.
• in a way which the network is not able to satisfy
demand
• in part, at least
Fallen telephone poles resulting from Hurricane Katrina. Photo by USACE.

25. Consequences of a disaster on
communications
• Some services /QoS levels cannot be guaranteed
• Service demand changes
• Disaster changes (sometimes drastically) the
composition and distribution of the population
• It introduces new communication needs, which have to
be satisfied (rapidly)
• It introduces new needs for data/content, and for
computing resources
• The outcome is the emergence of new demand
patterns, which cannot be satisfied adequately by the
network

26. An agenda for post-disaster
communications research
Before a disaster strikes: Disaster preparedness
• Make technical solutions for network resilience more convenient
• Prepare for post-disaster service delivery
After a disaster: Mitigate the consequences on communications
• Providing (alternative) strategies for information diffusion which
are
• Efficient
• Effective
• Timely
• Fit for the service demand in a post disaster scenario
• Challenges: hostile environment, lack of infrastructure (power),
difficulty in movements, rapidly evolving conditions and needs,
heterogeneity of conditions and needs

27. Impact on population distribution
• Displacements of a large
fracton of the population,
and for several tens of km
• People start moving back
to Abaco only two weeks
after
• Impact on communication
demand distribution in
space and on capacity
provisioning

28. Communications in the aftermath of
Dorian
• On the Abaco Island, cellular communications and landlines have been completely
down for a week, and intermittently working for several days more.
• Communication services were missing
when and where they were needed the most
9/2 – Andrew Schroeder, Director of Research and Analysis at Direct Relief:
“The main takeaway in the Bahamas is that we can’t say much about dynamics in the affected area due to network outage. It’s wiped out in
the affected area.”

29. The main impact of earthquakes on communication
infrastructure is through disruption of power supply
• Disruptions of telecom infrastructure:
• Destruction of antenna masts/BS equipments (seldom), disruptions of cables
(seldom)
• Power failures leading to power outages (very frequent, often as a precaution)
• Scope: ~10 square km and up
• Typically, cellular and fixed communications remain available until telecom
equipment runs out of power (a few hours).
• Restoration activities
• Antenna masts and BSs: deployment of truck mounted devices for temporary
provisioning (a few days after).
• Power distribution network: gradual reactivation and repairing of damages
• Duration: from a few days after the earthquake, to several weeks after.
• Earthquakes tend to change permanently the population distribution in the
area, their requirements and traffic demands.
• Ex. L’Aquila (Italy): “New towns”, fully new districts built in safer areas

30. Rate of population change over different sampling periods in Fukushima, Miyagi, and
Iwate prefectures in Japan (A-D) where the size of circles denotes the number of
observed evacuations (larger circles indicate more observed evacuations) and the color
of circles indicates the rate of population change in a specific area (ranging from -1 to 1
(-100% to 100%)).
Long- term changes in population distribution
modify traffic demand patterns
Tōhoku earthquake and tsunami (March 11, 2011) – cell network call records
Song, X., Zhang, Q., Sekimoto, Y., Horanont, T., Ueyama, S., & Shibasaki, R. (2013, August). Modeling and probabilistic reasoning of population evacuation during large-
scale disaster. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1231-1239). ACM.

31. • Characterizing how they change in response to an emergency is
key to post-disaster communications design
• Immediate aftermath (first hours/first days) is critical for e.g.
people in entrapment after earthquake
• Barabasi et al.: characterized real time changes in mobility and
communication patterns in the vicinity of eight different
emergencies
• N.W.: only some of them are disasters
• Compared to eight planned, non-emergency events
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Communication patterns after a disaster may
radically differ from normal conditions
Bagrow, J. P., Wang, D., & Barabasi, A. L. (2011). Collective response of human populations to large-scale emergencies. PloS one, 6(3), e17680.

32. 32
Jet scare refers to a sonic boom interpreted by the local population and initial media reports as
an explosion

33. 33
Impact of emergencies on traffic demand
patterns over time

34. Patterns of information diffusion in space depend on
the spatial coverage of the emergency
G0: users directly affected by the event
G1: users that receive calls from G0 but are not near the event
G2: users contacted by G1 but not in G1 or G0, etc.
• Great heterogeneity in peak, location and duration
• Some disasters cause a communication cascade, with far-reaching effects
(socially and geographically)
Bagrow, J. P., Wang, D., & Barabasi, A. L. (2011). Collective response of human populations to large-scale emergencies. PloS one, 6(3), e17680.
Changes in call volume over time since event

35. Onset speed of anomalous call activity
• lower fmid indicates a faster onset
Spatial extent of the events
• non-emergency events are far more centrally
localized
Likelihood of calling an acquaintance on
the onset of a disaster.
Patterns of communications during disasters differ
drastically according to the type of disaster
Bagrow, J. P., Wang, D., & Barabasi, A. L. (2011). Collective response of human populations to large-scale emergencies. PloS one, 6(3), e17680.

36. What can we learn?
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Technology and preparedness will not free us from disasters
• We need post disaster communication strategies to mitigate consequences
of a disaster
Information exchange after a disaster:
• Increases very quickly and drastically
• Creates waves and cascading effects, even well beyond the
perimeters of the disaster area
• Evolves over time in a disaster-specific manner, and
radically differently than ordinary conditions
Bottom line: Need for disaster-specific
• preparedness strategies, and
• post-disaster communication technologies

37. Voice communications for coordination of first
responders (and not only) are the first service to be
restored
Amatrice earthquake: providing wifi, voice services, and
phone charging station
Dorian Hurricane: satellite phones for voice communications:
first responders and local population
Enable efficient coordination:
• between rescue team members
• between on-field teams and other first-
responder team members
• One of the first services currently
provided after a disaster
• Point-to-point or (small) group
communications
• Slow to be deployed
• Hard to collect and spread information
on local status beyond first responders

38. The smart-everything paradigm is offering
new opportunities for disaster response
and mitigation
• Disaster scenarios are getting information rich, too
• Smart city, smart buildings, IoT paradigms, 5G,…
• Power distribution is still centralized and vulnerable
• Prosumer trend and microgrid, smart grid paradigm are
changing that too
• New disaster response approaches require massive
data, computing and communication resources
• E.g.: Telehealth, post-disaster assessment, etc

39. Situation Awareness in the age of
social media
• When based on organized rescue groups and teams:
• It is slow and partial
• Mainly based on voice communications
• In the last years, social media have been integrated into SA solutions
• As a source of data, and
• As a medium for information diffusion
• Problem: create a live picture of the status of the crisis zone, of
resources and of the operations
• Key for disaster response coordination
• Its effectiveness has a strong impact on quality of life of affected population

40. Impact of social media on Situation Awareness:
The Hurricane Irma case
• Social media (Facebook, Twitter)
Initially: discouraged
• Concerns of robbery, misinformation, and
widespread panic
• Rely on 911
BUT: Official channels were quickly
overwhelmed
• People were routed from 911 to social
media channels (Zello, etc)
• Facebook, Twitter used to monitor people
movements and identify critical issues
Source: https://psmag.com/social-justice/what-harvey-and-irma-taught-about-using-social-media-in-emergency-response

41. How to deliver situation awareness when
the cellular network fails?
 Opportunistic communications offer an ideal instrument to
overcame failure of infrastructure-based communications
 Exploit pervasive resources and sources of data: UE, IoT devices
 Partial failures: Offload cellular network
 Coverage holes: Coverage extension through ad hoc networking.
 Restoring p2p communications might not be always feasible
 Not in the immediate aftermath of a disaster, at least
 They take a large toll on network resources
 We focus on non-real time diffusion of information of interest to a
(sub)set of the on site actors
 Priority to information of common interest
 Key idea: Empowering both first responders and people on site to take
informed decisions
 Collaborative approach, combining infrastructure, IoT devices, and UE

42. 1) A node generates content
Floating Content: A scheme for collaborative, probabilistic
information storage based on opportunistic communications

43. 2) Content is replicated opportunistically
Floating Content: A scheme for collaborative, probabilistic
information storage based on opportunistic communications

44. III: Content starts to float
3) Content “floats” within the Anchor Zone (AZ)
Nodes delete content
upon going outside AZ
Floating Content: A scheme for collaborative, probabilistic
information storage based on opportunistic communications

45. III: Content starts to float
Zone of Interest (ZOI)
Success Ratio: Fraction of users entering the ZOI with the content
Floating Content: A scheme for collaborative, probabilistic
information storage based on opportunistic communications

46. Floating Content implements a probabilistic,
infrastructure-less spatial information storage
• Typical applications:
• Proactive caching at the user
• Events notifications (e.g. road accidents)
• Proximity marketing
• Social networking (chats, situated introductions)
• Infrastructure may take a coordinating role
• Orchestrates content diffusion and delivery
• Initiates it
• Monitors and optimizes resources utilization (both MNO’s and
user’s)
• Takes over when distributed implementation fails (and/or when
MNO resources are available)
• Nowadays, (partially) available even in disaster locations

47. Situation awareness with FC: Creating a shared
vision of a disaster area and of rescue operations
• Goal: Build collaboratively a common view,
accessible by everyone in the region
• Without infrastructure
• Many-to-many: no bottleneck
• Information sources: Smartphones, WSNs, and
any “smart thing”

49. FC for post-disaster communications: The Floater App

50. 50
Design of appropriate incentive schemes
for coopeation
• Common interest in contents (due to
context)
• Thus, willing to help its diffusion
• Storage: offer spatial redundancy
• Delivery: (selective) replication
• How to (optimally) manage redundancy?
• What about churn?
• Main limiting factor: Power supply
Open issues

51. 51
Ensuring only trustable information is
floated
• Solution taken in app:
• Confirmation
• «Rating» of contributors
Open issues

52. Challenges in implementing FC-
based content sharing
• FC requires a critical mass in order to work
• Typically, people/cars accumulate in the vicinity of an event
• Smart objects can be made part of the replication scheme, if allowed by
TX range
• Drones could be used to collect contributions from static IoT devices
• Seeding: A FC based app must be preloaded in some
nodes in the area, and spread opportunistically when
needed
• Current implementation: Smartphones/IoT devices as wifi APs + captive
portal
• How to configure IoT devices/gateways to join a FC scheme?
• Engineering a FC application may be a challenging task
• Balance QoS and resource efficiency
• Manage incentives to cooperation
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53. FC Integration with IoT
• Floating content finds a natural implementation in WSN
• Absence of a sink due to disaster: Information may be
aggregated and floated
• Duty cycling/intermittent failures
• Moving (IoT) nodes can relay aggregate information between
WSN and surviving access points
• Floating Content can boost proximity-based IoT
• Extend the interaction range of smart objects AND of users –
enriching situation awareness
• How to discover IoT resources when infrastructure fails? Need
for (some) infrastructure support
• How to enable P2P exchanges between devices and UE when
they are not designed for it?

54. Performance challenges tackled
• Derivation of application-level performance guarantees
• In general, a function of mobility patterns and specific app
• Appropriate mix of:
• Non-spatial modeling approach: Mean Field
• Spatial models (geometry and mobility dependent)
• Extension of the above to realistic settings
• Vehicular
• Office and campus
• Derived simple analytical models providing robust estimates
of success ratio
• Key step: Use of a (simple) abstraction for some key features
of mobility
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55. Performance challenges tackled
• Derivation of resource optimal configuration in heterogeneous
settings
• user density, speed, communication technology, storage, resource
costs, (among others)
• a discrete version of reaction-diffusion models, coupled with Mean
Field
• valid for stable settings and long lasting contents
• Extension to dynamic settings and short-lived contents
• No steady state solutions, no Mean Field
• Based on infrastructure support in:
• Collection of data about user mobility and request patterns
• Coordination of content replication and storage
• Content seeding
• Determination of the storage capacity of a probabilistic
caching system implemented via FC

56. • Each node plays two roles
(client and server)
• A single global
model or N
personalized models
• Merge-Update-Send cycle
Bringing computing and AI in post disaster scenarios:
Gossip Learning in dynamic settings
GL: decentralized learning without a central
server
Client3
Client2
Client1
Server
Federated Learning
Problem: enable users/devices to make sense of data in a disaster scenario

57. Conclusions
• FC for Situation Awareness is an active area of research
• Many applications under different names
• Infrastructure ubiquity in 5G/B5G and context-awareness
applications and services are key drivers
• Our work provides some tools for bringing FC closer to
adoption in disaster scenarios
• Application requirements, and QoS constraints
• Deal with (and take advantage of) node heterogeneity and
dynamicity
• Several issues are still open
• Support for distributed learning architectures
• Integration of IoT
• Adaptation to resource (energy) constrained devices
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58. Questions?
gianluca.rizzo@hevs.ch
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