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A Survey on the Role of Wireless Sensor
Networks and IoT in Disaster Management
Ahsan Adeel, Mandar Gogate, Saadullah Farooq, Cosimo Ieracitano, Kia
Dashtipour, Hadi Larijani, Amir Hussain
Abstract Extreme events and disasters resulting from climate change or other eco-
logical factors are difficult to predict and manage. Current limitations of state-of-
the-art approaches to disaster prediction and management could be addressed by
adopting new unorthodox risk assessment and management strategies. The next gen-
eration Internet of Things (IoT), Wireless Sensor Networks (WSNs), 5G wireless
communication, and big data analytics technologies are the key enablers for future
effective disaster management infrastructures. In this chapter, we commissioned a
survey on emerging wireless communication technologies with potential for enhanc-
ing disaster prediction, monitoring, and management systems. Challenges, opportu-
nities, and future research trends are highlighted to provide some insight on the
potential future work for researchers in this field.
1 Introduction
Albert Einstein once said: The true sign of intelligence is not knowledge but imag-
ination. Imagining how technology could be exploited to address the challenging
real-world problems is necessary, especially when development of new device or
system is considered. Disaster management is one of the challenging real-world
problems that sought out emerging technologies. Natural disasters have been visit-
ing every part of the globe and the world has become increasingly vulnerable. Nearly
3 million people worldwide have been killed in past 20 years due to natural disasters,
including earthquakes, landslides, floods, cyclones, snow avalanches etc. Intelligent
infrastructures to enhance disaster management, community resilience and public
safety have become inevitably important, with the aim to save lives, reduce risk
Ahsan Adeel, Mandar Gogate, Amir Hussain, Cosimo Ieracitano and Kia Dashtipour are with the
Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of
Stirling. Hadi Larijani is with the School of Engineering and Built Environment, Glasgow Caledo-
nian University. e-mail: ahsan.adeel@stir.ac.uk
1
arXiv:1909.10353v1 [cs.NI] 23 Sep 2019
2 A. Adeel, M. Gogate, S. Farooq, C. Ieracitano, K. Dashtipour, H. Larijani, A. Hussain
and disaster impacts, permitting efficient use of material and social resources, and
protect quality of life and economic stability across entire regions. At the leading
edge of disaster management initiatives is the collection, integration, management
and analysis of an increasingly complex web of multi-modal data and digital in-
formation originating from mobile, fixed, and embedded sources. A national pro-
gram for investment in intelligent infrastructure can achieve dramatic economies of
scale and reduce long-term national debt. For example, United Sates spends billions
of dollars annually to suppress catastrophic wildfires, which consume millions of
acres each year. A single major hurricane or tornado can claim hundreds of lives
and cause billions of dollars damage. Intelligent infrastructure technologies such as
computer models taught through machine learning and calibrated on big data, in-
cluding ground-based sensors, streaming video from unmanned aerial vehicles, and
satellite imagery, could significantly reduce the social and economic costs of such
disasters. Priority should be placed on the areas such as sensing and data collec-
tion, communication and coordination, big data modelling frameworks (including
analytics and tools for disaster prediction and management), and social computing.
There is a great deal of interest in developing disasters management systems ca-
pable of saving lives, properties, and minimise the costly economic investments. In
order to develop an effective monitoring infrastructure, the information has to be
gathered from different sources. In this context, IoT technology is reporting consid-
erable success [1]. In last few years, innovative real-time monitoring and disasters
warning systems are based on the emerging IoT paradigm, in which things (i.e. sen-
sors) are globally interconnected. WSNs as part of IoT have been employed widely
for monitoring natural disasters in remote and inaccessible areas [2],[3],[4]. WSNs
use autonomous low-energy sensor nodes capable of measuring and recording sur-
rounding environmental conditions. Each sensor node typically consists of a power
supply, a micro-controller, a wireless radio transmission and a set of environmen-
tal sensors (i.e. humidity, pressure, temperature). WSNs and IoT together with the
recent advances in the Information and Communication Technology (ICT) could de-
velop ever more intelligent and connected infrastructures, where a huge amount of
data could be gathered and processed [5]. The combination of these heterogeneous
resources (gathered from digital infrastructures) and the latest artificial intelligence
technology could be used to develop next-generation of disaster management sys-
tems.
The rest of the chapter is organised as follows: Section 2 first presents an
overview of state-of-the-art WSNs driven disaster monitoring and management sys-
tems, including emerging technologies such as 5G, Device to Device communica-
tion, Fourth Generation (4G)/LTE, and software defined radio. Section 3 presents
an overview of existing IoT standards (LoRa/4G LTE), their limitations, and future
research directions. Finally, Section 4 concludes this chapter.
A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management 3
2 WSN Driven Disaster Monitoring and Management Systems
2.1 Applications of Sensor Networks in Disasters Management
The application of sensor networks for monitoring natural hazards (such as floods
[6], wildfires [7] or sandstorms [8]) has become a special research topic for many
researchers and engineers. In this context, a lot of work has been focusing on us-
ing WSNs for monitoring landslides (e.g. downfalls of a large mass of ground, rock
fragments and debris especially in unstable areas where intense rainfalls, floods or
earthquakes occur and might cause loss of lives, damage buildings and influence
the economy [9]). Kotta et al. [10] proposed a WSNs system based on accelerom-
eters for vibrations detection triggered by landslides. Experimental results showed
that accelerometers values above 1g (gravity) indicated intense mass sliding and
hazardous conditions. Ramesh et al. [11] installed a sensors distributed monitoring
system based on 50 geological sensors and 20 wireless sensor nodes to monitor a
local zone highly at risk from landslides in India (Idukki, Kerala State). The pro-
posed system was able to provide three level alerts (low, intermediate, high) and its
effectiveness was tested during the monsoon season. Terzis et al. [12] proposed a
sensor columns based network to detect the slip surface location and the trigger of
a landslide. Lee et al. [13] presented a slope movement monitoring WSNs system
capable of reducing the power consumption during the standby mode (0.05 mA at
3.6 V). Rossi et al. [14] reported the development of a landslides monitoring sys-
tem installed in the Apennines (North Italy). Similarly, Giorgettiet al. [15] deployed
a network of 15 wireless sensors on a landslide in Torgio vannetto (Italy) observ-
ing high level of robustness in term of self-organisation, node failures, and energy
consumption.
2.2 5G and Device to Device Communication
The upcoming 5G systems are envisioned to have the crucial capabilities such as
network flexibility, (re)configuration and resilience and therefore, expected to play
a key role in improving disaster situation communications. Furthermore, in 5G,
network will provide media independent handover (IEEE 802.21 support) allow-
ing seamless hand-off between various available networks thereby enabling disaster
communication without any disruption. 5G networks are not only expected to at-
tain much faster transmission throughput, but also support the emerging use-cases
related to the IoT, Machine Type Communications (MTC), broadcast-like services
and lifeline communications during natural disasters. 5G will fulfill these demands
by adopting new technologies like proximity services, through which devices com-
municate with each other directly instead of relying on base stations (eNodeB) of
network operators [16].
4 A. Adeel, M. Gogate, S. Farooq, C. Ieracitano, K. Dashtipour, H. Larijani, A. Hussain
Device-to-Device (D2D) communication has also been used in disaster scenar-
ios (e.g. for public safety and warning messages) to manage the radio spectrum
and energy consumption for providing high Quality of Experience (QoE) and better
Quality of Service (QoS). In disaster, the effective use of the radio resources is of
extreme importance with the goal of serving a large number of affected people to
collect information from different nodes in the disaster zone. In this context, D2D
communication will be an effective solution allowing an efficient spectrum alloca-
tion without adding any further delay in content uploading for the User Equipments
(UEs) [16].
2.3 Software Defined Radio
While LTE provides a solution to address the lack of broadband connectivity in
disaster network, Software Defined Radio (SDR) technology provides a solution to
address the lack of interoperability in a wireless communication scenario e.g., in
military applications. SDR enables a platform to interface and communicate with
different communication technologies. SDR technology could be used to support
various wireless communications technologies on the same radio platform. It is also
essential to define a common waveform to support the wireless backbone network.
Though SDR is a promising technology, its potential application in the disaster man-
agement requires addressing various issues, for example: (1) Military oriented so-
lutions for SDR equipment are rather costly for disaster applications, (2) Waveform
processing in SDR need significant energy and computing resources that is a prob-
lem for handheld terminals.
2.4 Cognitive Radio (CR)
Public safety agencies are increasingly using wireless communication technologies
to monitor disaster conditions using video surveillance cameras and sensors. The
increasing use has led to congestion [17] in radio frequency channels allocated to the
agency. In order to address the aforementioned issue of optimum resource allocation
during emergency response, CR technology could be exploited to replace the current
state-of-the-art channel allocation protocols with an adaptive CE [18].
2.5 Indoor Position Technologies
In disaster scenarios, Global Navigation Satellite Systems (GNSS) based position-
ing is used to enhance the coordination of the rescue teams. However, due to the lack
of GNSS coverage in indoor environments (such as tunnels and buildings), indoor
A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management 5
navigation is required for providing the location services to first time responders. In
order to make indoor positioning a potential technology for disaster scenario, some
of the issues that need to be addressed are: (1) indoor-positioning devices should
not be cumbersome to enable their easy deployment, (2) designing energy efficient
algorithms for indoor positioning to maximize the battery lifetime of the mobile
nodes in a localization system for disaster scenario [19].
2.6 Disaster Situation Aware Protocols for Mobile Devices
The integration of context-aware computing with mobile devices enable them to
adapt and react to dynamic changes in the environment. This concept is used in [20]
to design a context-aware ad hoc network for effective crowd disaster mitigation by
issuing an alert to prevent a stampede in the crowded area. The authors designed
Disaster Aware Protocols (DAP) taking into account the disaster situation, allowing
mobile devices to be effective in a disaster scenario. In the absence (or partial pres-
ence) of an infrastructure, a mobile device should be able to operate in a disaster
mode serving as a lifeline for the common people on the ground. DAP for mobile
devices should feature communication mode switching scheme in which informa-
tion such as amount of remaining battery, mobility (mobile phones movement), and
number of neighboring mobile devices could used by the mobile phone to decide
the apt communication mode [21].
2.7 Mobile Phone Disaster Mode
Mobile phone is a potential device in the event of a disaster scenario to be able to
help us connect with family and friends, locate resources, navigate to a safer location
and help others. In addition, smartphones can use their integrated sensors to help
allocate scarce resources to the most affected people by collecting data to enable
disaster relief teams to comprehend the unraveling situation in the disaster zone.
However, due to the challenges of energy-management and connectivity, currently
available smart phones are not well equipped to operate efficiently during disaster.
Often, disaster victims are left helpless with poor or no connectivity.
3 Existing IoT Standards and Future Research Directions
According to ITU, IoT is defined as: A global infrastructure for the information so-
ciety enabling advanced services by interconnecting things based on, existing and
evolving, interoperable information and communication technologies.’ [22]. Max-
imizing the communication of hardware objects and converting the harvested data
6 A. Adeel, M. Gogate, S. Farooq, C. Ieracitano, K. Dashtipour, H. Larijani, A. Hussain
into a meaningful information without any human involvement, are the two major
objectives of IoT. IoT is the combination of three basic elements: hardware, mid-
dleware, and presentation [23]. Hardware is further divided into embedded sensors,
actuators, and communication systems. The embedded sensors collect data from the
monitoring area and send it to the middleware element. Middleware element pro-
cesses a huge amount of received data and extracts interpretable information with
the help of different data analysis tools. Visualization of processed data in an easily
readable form gets transformed through the presentation element. Presentation ele-
ment also processes user queries to the middleware element for necessary actions.
Fig. 1 shows the block diagram of an IoT system, where different communication
standards have been used (in the literature) to communicate between blocks. We
will discuss two major standards: LoRa and 4G LTE.
Fig. 1: IoT’s three main elements and their communication
3.1 LoRa
In 2012, Semtech acquired a spread spectrum technique named LoRa. LoRa can be
formed by taking the derivative of Chirp Spread Spectrum (CSS). Any MAC layer
could be used with LoRa physical layer. However, the currently proposed MAC is
Low Range Wide Area Network(LoRaWAN) which works on the principle of sim-
ple star topology. LoRa supports a star topology; therefore, it can transmit over a
very long distance. Gateway, which is connected to a backbone infrastructure, is
directly connected with the nodes. These gateways are powerful devices capable of
receiving and decoding a number of concurrent transmissions (up to 50) through
powerful radios. Node devices are classified into three classes: (1) Class A end
devices: Transmission from node to the gateway only occurs when needed. After
transmission receive window is activated to obtain the queued messages through
gateway (2) Class B end-devices with scheduled receive slots: Class B operates on
a similar principle as Class A node, but with additional receive window (3) Class
C end-devices with maximal receives lots: These nodes are not suitable for battery
powered operations due to continuous listening.
A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management 7
3.1.1 Limitations of LoRaWAN
The two main pillars of the IoT growth are transportation and logistics. Efficiency
of multiple applications is targeted in areas such as disaster management, public and
goods transportation. However, some applications are resistant to swing, delay and
fluctuations, while others are not. Delay constraints are diverse for different applica-
tions but LoRaWAN being a low power wide area network (LPWAN) solution, is not
well-suited for these applications. Contrarily, LoRaWAN supports solutions such as
fleet management and control. Similarly, for video surveillance, MJPEG, MPEG-4,
and H.264 are the most commonly used digital video formats for IP-based video
systems. The data rate recommended for IP surveillance cameras ranges from 130
kb/s with low-quality MJPEG coding to 4 Mb/s for 1920x1080 resolution and 30
fps MPEG-4/H.264 coding [24]. The data rate of LoRaWAN ranges from 0.3 to 50
kb/s per channel, thus it is not well-suited for these applications.
3.2 4G LTE
4G LTE is ideal for IoT application not only for its flat all-inclusive nature of IP
architecture but also because it has built-in security along with robust and scal-
able traffic management capabilities. The spectral-efficiency of LTE is greater than
second generation (2G) and third generation (3G) networks; therefore, data trans-
mission could be done at a much lower rate. In this regard, data transmission is 2-3
times less costly than 3G while 20 times less than 2G. IoT friendly LTE chipsets
are the foundation for the new wave of LTE device development, which are flexible,
efficient, and low cost. Numerous LTE chipsets and modules are available today.
These innovative solutions provide all required features to build a robust and long-
life LTE devices for numerous applications at a low cost. Features includes a small
footprint and ultra-low power consumption.
3.2.1 Limitations of 4G LTE
4G systems are mainly designed to deal with Human-type Communication (HTC)
traffic. Consequently, when considering 5G systems, IoT dictate to simultaneously
handle the presence of HTC and Machine-type Communication (MTC) traffic, while
meeting the requirements of these traffic types. As a further step, the disruptive tech-
nologies are aiming at introducing flexibility, customization and re-congurability of
the network in both radio and core segments, in order to enable the provisioning
of enhanced IoT services. Indeed, a natural evolution of connecting devices to the
Internet is to remotely control these devices through the Internet. However, for large
number of users, 4G LTE suffers from high delay and high packet loss. The work
presented in [25] revealed that the efficiency of 4G LTE decreases dramatically as
the amount of traffic increases.
8 A. Adeel, M. Gogate, S. Farooq, C. Ieracitano, K. Dashtipour, H. Larijani, A. Hussain
3.3 Research recommendations/future directions
Indeed, the next generation IoT, WSNs, 5G, and big data analytics stands as a major
enabler to realise future intelligent infrastructures for enhanced disaster manage-
ment. The widespread demand for data and the emergence of new services are in-
evitably leading to the so-called Resource Crisis. Hence, the evolution of the current
centralised model of networked systems to new paradigms such as low power high
data rate cognitive networks present a suitable path to counteract this crisis.
The existing LoRa provides transmission parameters such as transmission power,
coding rate, spreading factor, and bandwidth, resulting in over 936 possible combi-
nations. These configuration parameters could be optimally tuned to acquire op-
timized bit-rate, airtime, and energy consumption, taking into account the local
electromagnetic environment, constraints, and objectives. For example, increasing
spreading factor to improve link reliability nearly halves the datarate and doubles
the energy consumption. Similarly, increasing bandwidth doubles the datarate and
halves the energy consumption and airtime, reducing link reliability due to addi-
tional unwanted noise.
Recent research on LoRa/LoRaWAN has mainly focused on LoRa performance
evaluation in terms of coverage, capacity, scalability and lifetime [26][27][28].
Furthermore, recent work has also proposed adaptive approaches to allocate op-
timal transmission parameters [29]. However, most of these methods are based
on state-of-the-art mathematical/statistical models and suffer from limited mod-
elling assumptions, limited learning, inability to deal with non-linear complex be-
haviours, poor scalability, and no time-series/temporal data exploitation. Future re-
search should focus on developing robust fair data rate allocation and power con-
trol methods to address existing LoRa limitations and acquire optimised airtime,
datarate, and energy consumption.
4 Conclusion
The emergence of new wireless communication services and demand for Big Data
processing in real-time poses new architectural and radio resource management
challenges. This chapter surveys research on emerging wireless communication
technologies for effective disaster monitoring and management systems. WSN and
IoT stands as a major enabler for enhanced disaster monitoring and management
systems. In this chapter, limitations of two major IoT standards (LoRA and 4G
LTE) are presented with some future research recommendations. It is concluded that
future research should focus on developing artificial intelligence/machine learning
driven more robust radio resource management strategies to enable optimised oper-
ations in real-time.
A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management 9
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