HAL Id: hal-01095749
https://hal.inria.fr/hal-01095749
Submitted on 16 Dec 2014
HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entic research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diusion de documents
scientiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.
Survey of Deployment Algorithms in Wireless Sensor
Networks: Coverage and Connectivity Issues and
Challenges
Ines Khou, Pascale Minet, Anis Laouiti, Saoucene Mahfoudh
To cite this version:
Ines Khou, Pascale Minet, Anis Laouiti, Saoucene Mahfoudh. Survey of Deployment Algorithms in
Wireless Sensor Networks: Coverage and Connectivity Issues and Challenges. International journal of
autonomous and adaptive communications systems, Inderscience Publishers, 2017, 10 (4), pp.341-390.
�10.1504/IJAACS.2017.10009671�. �hal-01095749�
Int. J. Autonomous and Adaptive Communications Systems, Vol. x, No. x, 20xx 1
Survey of Deployment Algorithms in Wireless Sensor
Networks: Coverage and Connectivity Issues and
Challenges
Ines Khoufi
INRIA, Rocquencourt,
78153 Le Chesnay Cedex, France,
Email: ines.khoufi@inria.fr
Pascale Minet
INRIA, Rocquencourt,
78153 Le Chesnay Cedex, France,
pascale.minet@inria.fr
Anis Laouiti
TELECOM SudParis,
CNRS Samovar UMR 5157,
91011 Evry Cedex, France,
Email: anis.laouiti@telecom-sudparis.eu
Saoucene Mahfoudh
INRIA, Rocquencourt,
78153 Le Chesnay Cedex, France,
saoucene.mahfoudh@inria.fr
Abstract: Wireless Sensor Networks (WSNs) have many fields of application, including industrial,
environmental, military, health and home domains. Monitoring a given zone is one of the main
goals of this technology. This consists in deploying sensor nodes in order to detect any event
occurring in the zone of interest considered and report this event to the sink. The monitoring task
can vary depending on the application domain concerned. In the industrial domain, the fast and
easy deployment of wireless sensor nodes allows a better monitoring of the area of interest in
temporary worksites. This deployment must be able to cope with obstacles and be energy efficient
in order to maximize the network lifetime. If the deployment is made after a disaster, it will operate
in an unfriendly environment that is discovered dynamically. We present a survey that focuses on
two major issues in WSNs: coverage and connectivity. We motivate our study by giving different
use cases corresponding to different coverage, connectivity, latency and robustness requirements of
the applications considered. We present a general and detailed analysis of deployment problems,
while highlighting the impacting factors, the common assumptions and models adopted in the
literature, as well as performance criteria for evaluation purposes. Different deployment algorithms
for area, barrier, and points of interest are studied and classified according to their characteristics and
properties. Several recapitulative tables illustrate and summarize our study. The designer in charge of
setting up such a network will find some useful recommendations, as well as some pitfalls to avoid.
Before concluding, we look at current trends and discuss some open issues.
Keywords: area coverage, barrier coverage, coverage, deployment algorithms, full connectivity, grid,
intermittent connectivity, node activity scheduling, point of interest coverage, sensor deployment,
virtual forces, wireless sensors, WSN
Biographical notes: Ines Khoufi received her Computer Science Engineering and Master degrees
from the National College of Computer Science (ENSI) in 2010 and 2011 respectively. Currently,
she is a Ph.D. student in HIPERCOM2 research team at Inria. Her current research interest is on
wireless sensor networks deployment and progressive discovery of an unfriendly environment.
Pascale MINET works at the Inria research center of Rocquencourt, near Versailles. She is head of the
HIPERCOM (High Performance Communication) team. She got her qualification in advising PhD
students in 1998 from the University of Versailles. Previously she got her PhD diploma in Computer
Science, the University of Toulouse and her Engineer diploma in Computer Science in 1980 from
Copyright © 20xx Inderscience Enterprises Ltd.
2 I. Khoufi, P. Minet, A. Laouiti and S. Mahfoudh
ENSEEIHT (Engineering school of Toulouse). Her research topics relate to wireless sensor networks
and mobile ad hoc networks and more particularly energy efficiency, routing, node activity
scheduling, multichannel communication, redeployment and quality of service in these networks.
She is co-author of the OLSR routing protocol standardized at IETF.
Anis Laouiti is an associate professor at Telecom SudParis since 2006. Before, he did his Phd
research work and worked as a research engineer within Hipercom team at Inria-Rocquencourt
where he participated to the OLSR routing protocol design (RFC3626). His research covers different
aspects in wireless ad hoc and mesh networks including protocol design, performance evaluation
and implementation testbed.
Saoucene MAHFOUDH received her Computer Science Engineering degree from ENSI in 2005
and her Master degree from the University of Paris 6 in 2006. She obtained her Ph D diploma in
2010 from the University of Paris 6. Her research topics deal with energy efficiency, cross-layering,
routing and redeployment in wireless sensor networks. After a post-doctoral fellow at Inria, she has
been working at King AbdelAziz University since 2013.
1 Introduction
With the emergence of the Internet of Things (IoT),
several billion electronic devices and machines will be
able to be connected to one another via the Internet.
The devices will be able to communicate without the
intervention of humans. Such communication is called
Machine-to-Machine communication, denoted M2M, and
mobile wireless technology is an ideal technology to support
M2M communication. These emerging technologies based
on wireless communication can be autonomous and cope
with changes without any human help. As an example, the
fast and easy deployment of mobile wireless sensors in a
temporary worksite is very useful for maintenance purposes
and quality insurance. As these wireless sensor nodes are
battery equipped, the deployment should be energy efficient
in order to maximize network lifetime. Sensors can also be
used for damage assessment after a disaster, when the network
infrastructure has been damaged. In this case, the Wireless
Sensor Network (WSN) operates in an unfriendly environment
that can only be discovered progressively. However, some
major challenges need to be tackled, such as:
• How to deal with the large volume of data produced.
Collecting and exploiting these large data flows is
commonly known as big data transfer and processing.
• How to organize communication. Communicating
objects will exist everywhere. They are generally used
to monitor a phenomenon. This phenomenon can be
monitored by a single object (e.g. the temperature of
coffee in a mug) or require collaboration between several
objects, which are usually deployed in a predefined area.
In the latter case, these objects need to communicate and
organize themselves in the form of a network. That is the
focus of this paper.
Depending on the size of the entity (area, barrier or point
of interest) monitored, a multi-hop network may need to be
deployed to enable the monitoring of this area as well as
the delivery of the collected data. To meet the application
requirements, the deployment of these communicating objects
(e.g. sensor nodes) must ensure coverage and connectivity
properties. Roughly speaking, coverage refers to the ability
to detect events occurring in the entity monitored (e.g. area,
barrier, point of interest) whereas connectivity refers to the
ability to report this event to the sink.
1.1 Motivation
There are several types of coverage and connectivity problems
in WSNs.
With regard to the coverage of the entity monitored
we distinguish between area, barrier and point of interest
coverage. Furthermore, the coverage can be full (i.e. any
point of the entity is covered) or partial, depending on the
application requirements. If the coverage is full, any point can
be monitored by a single sensor node (i.e. simple coverage) or
by several sensor nodes (i.e. multiple coverage), depending on
the degree of robustness required. If the application requires
short delays to detect an event, any point must be permanently
covered. In other cases, any point is temporarily covered.
With regard to connectivity, if the application requires
short delays to report the event detected to the sink, permanent
connectivity is needed. Otherwise, intermittent connectivity
is sufficient. An example is given by a mobile robot collecting
data from disconnected islands of sensor nodes and, more
generally, delay tolerant networks taking advantage of the
mobility of data mules such as robots or people, etc.. If the
application requires a high degree of robustness, multiple
paths toward the sink are needed. Otherwise, a single path is
sufficient.
As concerns the quality of data gathered, the application
specifies its requirements for the time and space consistency
of the data gathered. If both are required, a regular and
uniform deployment is needed.
Coverage and Connectivity Issues and Challenges in WSN 3
Usually, an initial deployment is provided. It can be
random (e.g. sensor nodes are dropped from a helicopter), all
sensor nodes can be grouped together at an entry point, or they
can form disconnected groups, each group consisting of a set
of connected sensor nodes, etc. However, such a deployment
usually fails to ensure the coverage and connectivity properties
required by the application. For instance, some regions can
be highly covered whereas others are poorly covered and
may contain some coverage holes that are not monitored.
Similarly, disconnected groups of sensors may fail to report
the event detected to the sink. In both cases, the quality of
data gathered is inappropriate, making, new a deployment
necessary.
To save energy and maximize network lifetime, it is
necessary after the final deployment to schedule node activity
to make nodes sleep (e.g. redundant nodes for full coverage,
useless nodes for partial coverage) while meeting the
application requirements. Notice that node activity scheduling
differs from sensor node deployment, because existing sensor
nodes are only switched on or off but are not moved.
This paper is organized as follows. This section ends with a
description of representative use cases and the positioning
of our contribution with regard to other surveys. Section 2
defines the coverage and connectivity issues encountered
in wireless sensor networks, (WSNs). Section 3 deals
with analysis criteria for deployment algorithms, and more
particularly presents factors impacting the deployment,
common assumptions and models adopted, as well as
performance evaluation criteria. In Sections 4, 5 and 6, we
focus on deployment algorithms ensuring coverage of area,
barrier and point of interest respectively. Section 7, explores
some energy-efficient optimization of a deployment, based on
node activity scheduling. In Section 8, we provide guidelines
to help the designer to select the deployment algorithm
suitable for the application requirements. Finally, we discuss
some trends and open issues for deployment algorithms in
Section 9 before concluding in Section 10.
1.2 Representative use cases
Depending on the application requirements, we can
distinguish the following use cases (UC) dealing with coverage
and connectivity, and representative of most applications:
UC1 monitoring of a temporary industrial worksite requires
full area coverage, permanent network connectivity and
a uniform deployment of sensor nodes to reduce data
gathering delays and provide a better balancing of node
energy.
UC2 forest fire detection requires full area coverage in dry
seasons and only 80% in rainy seasons. Permanent
connectivity is required in both cases to alert the
firefighters.
UC3 detecting and tracking of intruders in restricted areas.
Such applications require full area coverage; furthermore,
the most critical zones should be covered by more than
one sensor node (i.e. multiple coverage). Permanent
connectivity is also required.
UC4 monitoring of endangered wild species at some water
points: the idea is to compute statistics about the number
of individuals of this species from the number of
individuals visiting the water point. A full or partial belt
of sensor nodes is built along the water point depending
on its size. Intermittent connectivity is usually sufficient.
UC5 detection of intruders crossing a barrier (e.g. the border
of a country, a door or windows in an apartment). Such
applications require a barrier coverage with a permanent
connectivity. Depending on the application requirements,
one or several barriers are needed, the latter case being
called multiple barrier coverage.
UC6 air pollution monitoring in a smart city. Partial area
coverage is sufficient and intermittent connectivity can
be compliant with the application requirements.
UC7 instantaneous snapshot of measures taken at locations
predefined by the application. In precision agriculture,
the goal is to detect the appearance of diseases in the
crops. In a smart city, the goal is to track an air pollutant.
Such applications require the coverage of static points
of interest. Permanent connectivity may be not needed.
Intermittent connectivity can be provided by mobile
robots (e.g. tractors for precision agriculture).
UC8 tracking of wild animals or a truck fleet with embedded
sensors. In such a case, different technologies can
be used to track these mobile points of interest (e.g.
Argos beacons for animals, 3G/4G systems for trucks).
Depending on the application requirements, connectivity
may be intermittent (e.g. animals) or permanent (e.g a
truck fleet).
UC9 health monitoring of isolated workers, disabled people
or elderly. They are considered as mobile Points of
interest that must be permanently covered. Permanent
connectivity is required.
All these uses cases will enable us to classify the coverage
and connectivity problems encountered in the literature (see
Table 1), according to the criteria defined more precisely in
Section 2.
With the emergence of smart cities, different use cases can
coexist simultaneously. For instance, air pollution monitoring,
surveillance of parking lots, public lighting control, and
pollutant tracking are examples of sensor deployments that
will be very common in our cities in the near future.
1.3 Related work
In this section, we position our work with regard to other
existing surveys and highlight our contribution. Existing
surveys (2; 3; 4; 5; 6; 7; 8) introduce basic concepts
related to coverage and connectivity. For instance, (2)
focuses on how to ensure area coverage and how to
deploy sensor nodes. (3) classifies coverage problems as
coverage based on exposure and coverage exploiting mobility.
Area coverage, point coverage and barrier coverage is
another classification proposed and detailed in (4) and (5).
4 I. Khoufi, P. Minet, A. Laouiti and S. Mahfoudh
Area coverage Barrier coverage PoI coverage
Full Partial Full Partial Static Mobile
Simple Multiple Simple Multiple
Connectivity
Permanent Simple or multiple UC1 UC3 UC2 UC5 UC4 UC8 UC9
Intermittent UC6 UC4 UC4 UC7 UC8
Table 1 Classification of use cases.
In (6), the authors distinguish two coverage problems:
static coverage and dynamic coverage. They also propose
a study of sleep scheduling mechanisms to reduce energy
consumption and analyze the relationship between coverage
and connectivity. An overview of existing centralized and
distributed deployment algorithms is given in (7). The authors
in (8) discuss the different deployment algorithm strategies
such as forces, computational geometry and pattern based
deployment. These surveys are good references to have an
overall view of coverage and connectivity issues in WSNs.
In this survey, we define the coverage and connectivity
problems separately to provide a better understanding. The
originality of our approach lies in a different viewing
angle. We provide comprehensive definitions of coverage and
connectivity with their possible variants. Indeed, these variants
depend on the latency and robustness requirements that differ
in the applications considered, leading to representative use
cases. For each use case, we list some deployment algorithms
found in the literature. We give a global analysis of the
deployment problem by discussing the impacting factors,
detailing the common assumptions and models adopted in the
literature. Moreover, we propose some performance criteria to
evaluate deployment algorithms. In order to help the designer
to choose the most suitable deployment algorithms, we
dedicate an entire section to questions and recommendations
regarding coverage and connectivity problems. We also
provide a section summarizing current trends and discuss open
issues for deployment algorithms.
2 Coverage and connectivity problems in WSNs
2.1 Coverage
An area is said to be covered if and only if each location
of this area is within the sensing range of at least one active
sensor node.
In our work, we distinguish three types of coverage problems
: Area coverage, Point coverage and Barrier coverage.
2.1.1 Area coverage
In the area coverage problem, the goal is to cover the whole
area. Depending on the application requirements, full or partial
coverage is required. However, if the number of sensors is
not sufficient, full coverage cannot be achieved and the goal
becomes maximizing the coverage rate.
• Full coverage
Applications such as battlefield monitoring require full area
coverage. In these applications, every location is covered by
at least one sensor node (1-coverage) or by k > 1 sensor
nodes (k-coverage). Deploying sensors over a large area while
ensuring full coverage and network connectivity may be
expensive. However, full coverage with connectivity provides
the best surveillance quality. In the following we detail one-
coverage defined as simple coverage and k-coverage defined
as multiple coverage, depending on the degree of robustness
required by the application.
− Simple coverage
In WSNs, it is necessary to ensure full coverage of the area
considered while deploying the minimum number of sensor
nodes. This can be satisfied by covering every location in the
field using at least one sensor node. Then information detected
in this location should be reported to the sink. Many studies
aim to minimize the number of nodes deployed while ensuring
coverage and connectivity. For instance, the triangular lattice
deployment provides full coverage, connectivity and uniform
deployment using the minimum number of sensor nodes.
− Multiple coverage
Multiple coverage is defined as an extension of simple
coverage and is denoted by k-coverage. It is specific to
applications such as distributed detection, mobility tracking,
monitoring in high security areas and military intelligence in a
battlefield. Since the failure of a single node may result in the
loss or corruption of important data, one degree of coverage
is not sufficient for these applications. Such applications
require highly accurate information in order to provide fault
tolerance and allow good decisions to be made. The k-
coverage deployment is defined as a sensor deployment pattern
where each point in the area is covered by at least k deployed
sensor nodes. Then, k-coverage tolerates at least k − 1 node
failures while maintaining coverage.
a Simple coverage. b Multiple coverage.
Figure 1: Full area coverage.
• Partial coverage
