International Journal of Emerging Engineering Research and Technology
Volume 8, Issue 1, 2020, PP 11-22
ISSN 2349-4395 (Print) & ISSN 2349-4409 (Online)

Wireless Sensor Networks for Building Robotic Paths - A Surveyof Problems and Restrictions
José P. A. Amaro1, João M. L. P. Caldeira2*, Vasco N. G. J. Soares3 and Pedro D. Gaspar4
1

Instituto Politécnico de Castelo Branco, Portugal
Instituto Politécnico de Castelo Branco, Instituto de Telecomunicações, Portugal
3
Instituto Politécnico de Castelo Branco, Instituto de Telecomunicações, Portugal
4
Universidade de Beira Interior, Portugal
2*Corresponding Author: João M. L. P. Caldeira, Instituto Politécnico de Castelo Branco, Institutode Telecomunicações, Portugal.
ABSTRACTThe conjugation of small nodes with sensing, communication and processing capabilities allows for thecreation of wireless sensor networks (WSNs). These networks can be deployed to measure a very wide
range of environmental phenomena and send data from remote locations back to users. They offer new andexciting possibilities for applications and research. This paper presents the background of WSNs by firstly
exploring the different fields applications, with examples for each of these fields, then the challenges facedby these networks in areas such as energy-efficiency, node localization, node deployment, limited storage
and routing. It aims at explaining each issue and giving solutions that have been proposed in the research
literature. Finally, the paper proposes a practical scenario of deploying a WSN by autonomous robot path
construction. The requirements for such a scenario and the open issues that can be tackled by it are
exposed, namely the issues of associated with measuring RSSI, the degree of autonomy of the robot andconnectivity restoration.
Keywords: Wireless sensor network (WSN); Coverage; Connectivity; Limited energy supply; Routing;Node deployment; Localization of nodes; Connectivity Restoration; Autonomous robot path construction

INTRODUCTION
A wireless sensor network (WSN) is a group ofsmall sensor nodes, that are distributed in aspace and wirelessly connected to each other,
forming a network. Such a network allows
measuring environmental points of interest
using the sensors equipped in the nodes. Eachnode is a small device that can be thought of asa tiny computer being equipped with a wirelessradio, one or more sensors, its own processor

and a power supply. The processor of a sensor
node makes up a processing unit which
generally has a small storage unit. The sensors
make up a sensing unit generally equipped withan analog-to-digital converter (ADC), which is
responsible for converting the analog signals
from the observed phenomenon to digital ones
[1].
Figure is a representation of the general sensor
node architecture.

Figure1. Sensor Node Architecture

Such a sensor node architecture guarantees thateach node has communication, sensing andcomputing capabilities [2]. The nodes may bemobile, with unpredictable or deterministic

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Wireless Sensor Networks for Building Robotic Paths - A Survey of Problems and Restrictions

movement patterns, or stationary [3]. Developments
in micro-electrical-mechanical systems technology
and digital electronics allowed for the creationof these small nodes, that have the multiple
functions described. They can be acquired for
low-cost, have low energetic requirements andcan communicate effectively in short distances.
These are the conditions that allowed for the
emergence of wireless sensor networks [1][4].
The fact that each node has its own processor
also means that sensor nodes can cooperate incollecting data and process the raw data in order
to send only the required information, diminishing
the computing requirements at the receiving
ends [1].
A sink node is the node that receives data fromthe WSN and routes it to a computer, reaching
the end user directly, or to the Internet and fromthere to the end user. Figure 2 represents thisprocess, in which sensors scattered in a sensor
field route data to a sink node that in turn sends
it to an end device.

Figure2. Sensor Node Field Architecture

There are other important concepts which havebeen widely studied in the context of WSNs,
such as coverage and connectivity. The concept
of coverage is related to the sensors of eachnode and their sensing range. It is said that atarget area is covered if all the points of the area
are inside the sensing range of at least one of thesensors. Whereas connectivity is related to thecommunication between nodes and is affectedby the communication range of each node. It issaid that an area has connectivity if every single
node is connected to the sink node either
directly or through a multi-hop path [5].
The main goal of this paper is to offer acomprehensive understanding of WSNs andintroduce a practical scenario that may help toanswer challenges associated with these networks.
It means to achieve this by firstly exploring thebackground of WSNs: the wide areas ofapplication of WSNs, providing examples in
each, and the challenges faced by WSNs,
explaining each challenge and giving examples
of proposed solutions founded in the literature.
After picturing the background of WSNs it
presents our proposed scenario of autonomous
robot path construction. It mentions both therequirements and the open issues that can begiven answer through this scenario. The rest of
the paper is organized as follows: Section 2
12presents the background of WSNs, Section 3
focus on our scenario of autonomous robot path
construction, Section 4 concludes the paper.

WSNS BACKGROUND
Throughout recent years these networks havebeen used in many different fields in various
applications. Due to their characteristics they
can be very useful and adaptable. We explore
the fields in which they are used and give
examples of specific applications for each ofthese fields. Due to their specific limitations andwide range of applications WSNs also face
many different challenges. We mean to expose
these challenges in categories and present solutions
for these problems found in the literature.
Applications of WSN
Wireless sensor networks allow us to monitor
environmental conditions with a higher degreeof precision than ever before. Examples ofconditions that can be measured aretemperature, sound, humidity, wind, vehiculartraffic, among many others. There are many andgrowing fields of applications for these types of
networks, such as military, health, home, disaster
relief, machinery monitoring, environment
monitoring, volcanic monitoring, vehicle tracking,weather monitoring and network monitoring[2][1][5]. Figure 3 shows the general areas of
WSNs applications.

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Wireless Sensor Networks for Building Robotic Paths - A Survey of Problems and Restrictions

Figure3. Applications of WSNs

Military applications were the first that motivatedresearch and progress in the field of WSNs [6].
They take advantage of certain properties ofthese networks like the fact they can be deployed
rapidly, they can have self-organization capabilities
and the redundancy of nodes creates several
routing paths which provides tolerance to failureof individual nodes. This allows them to be used
for monitoring friendly forces, monitoring
equipment and ammunition, communications, battle
field surveillance, reconnaissance, targetingsystems, battle damage assessment or chemical
attack detection [1][4].
An example of health applications of WSN isrecording and transmission of biosignals,
namely the electrocardiogram, pulse wave andbody weight, which are important parameters
for cardiovascular monitoring [7]. Anotherexample is an in-vehicle WSN platform capableof monitoring health risk factors due to longtime sitting and driving [8]. It is also possible tomonitor Electromyogram sensors in a WirelessBody Area Network so that the results can be
determined and analysed by a physician orexpert, despite the distance apart from patients[9]. A good use in hospital premises is the tracking
of doctors and patients activity and health, throughstationary and wearable sensors [10].
Examples of environmental applications of WSNs
are animal tracking such as monitoring wildlife
with the goal of preserving endangered species[11], and also monitoring endangered animals
that live in areas where human access is difficult,like polar regions [12]. Other environmental

applications are the use of WSN to monitor
conditions for irrigation and for precision
agriculture, resulting in reducing water
consumption and increasing the yield of crops by
giving them uniform water [13][14][15]. WSNs
are also used for chemical detection, such as
heavy metal monitoring [16] and monitoring
toxic gases in the environment [17].
Detection of forest fires is another important use
of WSNs, both through flame sensor modules,
that aim at early detection of fire [18], andthrough analysing the noise power spectrum offorest fires, which can determine the type offorest fire and help in fighting it [19]. Flooddetection is another important environmental
application of WSNs, as they are used to build
flood prediction systems [20] and flood warning
systems [21]. Finally WSNs are also used tomonitor water [22] and air pollution [23][24] [25].
Home applications are based on home automationand smart automation, such as monitoring andcontrolling environmental, safety and electricalparameters of the house [26], with uses such as
fire detection, gas leakage detection anddetermination of whether any door is closed oropen [27].
Challenges in WSNs
WSNs have specific challenges presented in
Figure 4. Such as limited energy supply,
localization of nodes, node deployment, limited
storage and routing [5]. These and other researchchallenges are discussed in the next subsections.

Figure4. Challenges in WSNs
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Wireless Sensor Networks for Building Robotic Paths - A Survey of Problems and Restrictions

Limited Energy Supply
Many times the area where the network is to be
deployed does not have infrastructure to supply
energy to the sensor nodes [2]. Hence arises the
problem of the limited energy supply of eachnode. The fact each sensor node needs to beequipped with a battery makes them susceptible
to failure if the battery is depleted. Changing
batteries may not be an option in situations likemonitoring volcanoes, battlefields or earthquakes.
In the case of node failure, due to depletion of
its energy supply, the network may suffer fromcoverage holes and data may not reach the sink
node, breaking network connectivity [5]. It isalso important to guarantee that network lifetime isenough to accomplish what is required by the
network application in question [28]. These
problems bring our attention to the need toconserve energy, so that we can prolong network
lifetime and avoid problems of coverage andconnectivity.
In order to present possible solutions, it is firstimportant to understand how energy is spent in asensor node. First the communication unit has amuch higher consumption than the computing
unit. The radio transceiver spends about thesame in reception, transmission or while in idle
mode, but a lot less when in sleep mode. The
sensing unit may also spend a considerable
amount of energy, but this heavily depends on
the specific application of the network [28].
Attempts at reducing energy consumption canbe categorized in three main techniques: duty
cycling, data-driven approaches and mobility[28]. Duty cycling is based on turning off theradio transceiver or changing it to sleep mode
when it is not required [29]. Data-driven
approaches focus on avoiding sending redundantdata, for example samples with high spatial andtemporal correlations, and using special nodes
called cluster heads to perform aggregation
techniques that minimize data size and forward
it to the sink node [30]. Data-driven approaches
also propose reducing the consumption of thesensing unit, by limiting the amount of sampled
data, while trying to maintain enough sensing
accuracy for the purpose of the network
application [31]. Mobility approaches focus on
having the mobile nodes collect the data fromstationary nodes, creating a communication inproximity and not through a multi-hop path tothe sink node, thus reducing energy spent incommunication [32]. If the energy spent inmaking nodes mobile is too high, than sensor
nodes can be attached to mobile entities that14

move around the sensing filed, such as vehicles
or animals [28].
Localization of Nodes
The challenge of localization in WSNs meansthat many times it is necessary to know thelocation of each node after it’s been deployed.
This assumes the nodes are not manuallydeployed and their localization is not previously
known. Such is the case in situations like natural
disasters or military reconnaissance. The reason
for the necessity of the location of each node isthat without location information the sensing
information given by a node might be imprecise
or even useless. The location information is also
the basis of many applications of WSNs likenavigation, tracking or rescue operations [3][33][34].
There are many algorithms to deal with thelocalization problem, they can be classified inbroad categories such as being centralized or
distributed, anchor based or anchor less, range
based or range free [35].
Centralized algorithms have a central basestation. A base station is a special node thatroutes information from the network to a PC.
This base station is also responsible for computing
the localization of the nodes. The main problem
with these algorithms is the computing overhead
and increase in system cost [36][37][38].
In distributed algorithms the computing is doneby each node. Each is responsible for figuring
out their own location and the nodes communicate
with one another to find their place in the WSN[39].
In anchor based algorithms there are several
nodes whose locations are previously known,these are called anchor or beacon nodes. Eachanchor node acts as a point of reference. Thus,
more anchor nodes lead to a lesser average of
localization errors. The problem with thisapproach is that the cost of the system increases
because of having these special nodes with morecapabilities [37][40][41][42].
The anchorless algorithms don’t use special
nodes but instead focus on creating a map of thenodes by measuring the distance between nodes[43][44][45].
Range-based algorithms uses range based
techniques with the help of sensors and othernode capabilities to estimate the absolute positionof the unknown or non-anchor node. They havea good precision but might require additional
sensors, increasing the cost of the system, mightInternational Journal of Emerging Engineering Research and Technology V8 ● I1 ● 2020

Wireless Sensor Networks for Building Robotic Paths - A Survey of Problems and Restrictions

have a high complexity, increasing the computing
overhead, and also have large communication
overhead between nodes, which strains theenergy limitation of each node [34][42][46][47].
Range-free algorithms do not measure absolute
distance or orientation of the nodes but instead
estimate the distance between two nodes using
messages exchanged between them. Suchalgorithms have a lower cost, not requiring specifichardware, and low computation requirements,
which is appropriate for larger WSNs. But if
enough messages aren’t exchanged between
nodes the localization accuracy of thesealgorithms drops a lot [34][48][49][50].
Node Deployment
The problem of node deployment is very importantbecause it affects critical network areas such as
coverage, connectivity and network lifetime.
The problem of optimal sensor deployment canbe defined in two forms. The first is: given asensor node with a certain sensing range to findthe smaller number of nodes required to cover
the entire area to be monitored. The second is:given a fixed number of sensor nodes to find theminimum sensing range that guarantees the area
will be fully covered [51]. Deployment of thenodes can be done either in a deterministic way,in which the location of the nodes is previously
known, or in a random way, in which the nodes
possess self-configuration capabilities andshould locate themselves properly, without any
user assistance, in order to guarantee the desired
network characteristics, such as coverage andconnectivity. The latter is used in applications
such as battlefield monitoring, disaster areas orunderwater. The problem with random
deployment is that it does not guarantee coverageof the whole area of interest or network
connectivity. Nodes out of communication range
will also reduce the possible routing paths,
increasing energy consumption and decreasing
the data throughput [52][53].
Many algorithms have been proposed to deal
with the node deployment problem. They areusually specific to the application of the networks
and the characteristics of the sensors. Examples
of algorithms to deal with this problem are theartificial bee colony, particle swarm optimization,
genetic algorithm and artificial immune system[54].
The artificial bee colony (ABC) algorithm isinspired by the behaviour of bees while foraging
for food. Three types of bees are considered:

employed bees, onlookers and scout bees.
Employed bees exploit food resources and giveinformation about food sources to onlookers
when they return to the hive. Scout bees always
look for new food sources. If the scout bees finda good quality food source, they do a dance
when they return to the hive which informs theother bees about it, this dance is called thewaggle dance. For the context of the algorithm
each of the food sources is considered a solution
to the problem and the quality of the food sourceis the fitness value of the solution. Firstly,
employed bees are associated to random foodsources or solutions, and in each iteration thebees evaluate food sources near its current foodsource and evaluate its quality or fitness value.
And so, the food sources with more quality will
have more onlookers. If quality does not
improve over a certain number of iterations thebee becomes a scout [51]. The ABC algorithm isused to find optimal locations for sensor nodes,
such as in [55], where the two main concerns
are coverage of a target area and network
connectivity.
Another algorithm used to deal with the node
deployment problem is the particle swarm
optimization. This approach consists of imitating
the behaviour of a flock of birds. The idea is thata swarm of particles explores a spatial dimension
looking for a solution to a certain problem, much
like birds that regulate their direction by theirneighbors and their own experience. The swarm
intelligence approach used by this algorithm isvery useful to find solutions to optimize realworld problems that have a degree of uncertainty,
for example optimizing the coverage area of the
network [54]. It is also used to move the sensor
nodes from an initial position, obtained fromrandom deployment, to new positions obtainedby the optimization algorithm, such as in [56],where new positions intend to maximize
network lifetime.
The genetic algorithms are also used to deal
with the node deployment problem. The basis of
this algorithm is Darwin’s theory of theevolution of species, in which genetic advantages
are carried from generation to generation
assuring the survival of the species that better
adapt to the environment. It consists of several
steps such as: selecting an initial population,
representing the chromosomes, applying afitness function, selecting chromosomes, applying
crossover and mutation and eventually reachinga termination point. The genetic algorithm isused to deal with deployment problems such asin [57], in which the sensor nodes are randomlyInternational Journal of Emerging Engineering Research and Technology V8 ● I1 ● 202015

Wireless Sensor Networks for Building Robotic Paths - A Survey of Problems and Restrictions

deployed at first and then re-positioned using thegenetic algorithm to achieve maximum network
coverage, removing the overlap between nodes.
Artificial immune system algorithms copy thefunctions of the biological immune system
which recognizes and attacks pathogens. Thesealgorithms typically use two main entities,
antibodies and antigens, and go through several
stages such as the clustering phase, thepresentation phase, the activation phase, theclonation-mutation phase and the suppression
phase. The cloning, mutation and selection is
responsible for the long term adaptation, while
short term adaptation is accomplished by the
antibodies covering the zone where antigens are[58]. It is used in WSNs with mobile nodes as away to redeploy nodes after initial random
deployment, such as in [59], where it intends tomaximize network coverage while minimizing
the total distance of movement by sensor nodes.
Routing
One of the most important challenges in thecontext of WSNs is that of routing. A routing
protocol aims to discover the best way between
a source node and a destination node. It is what
guarantees the communication between nodes
and between nodes and a base station. Due tothe specific characteristics of WSNs many
problems arise when considering routing
protocols. The goal would be to maximize thelifetime of the network, preventing degradationof connectivity. The challenges to this goal are
the limitations in power supply of the nodes,
transmission bandwidth and processing capability.
Other challenges related to routing are that ofnode
deployment,
scalability,
coverage,connectivity and security. Good routing protocols
are required to deal with these challenges, being
responsible for discovering and maintaining
routes that are energy efficient and reliable[60][6][61].
Routing protocols can be categorized bydifferent criteria. If we consider the structure of
the network, we can distinguish three types ofrouting protocols, namely flat-based routing[62], in which all the nodes have the samefunctions, hierarchical-based routing [63], inwhich different nodes have different functions,and location-based routing [64], in which thelocations of the nodes plays an important role in
how to route the data. If we consider the protocol
operation instead, we can classify routing protocols
as multipath-based, query-based, negotiationbased, QoS-based or coherent-based [61].
16

Multipath-based routing protocols use multiple
paths with the goal of improving network
performance. The fact that more paths exist
between a source and a destination can be usedin the case that one path fails, because it
increases the reliability of the network. The
price to pay is that maintaining more paths
increases energy consumption and thus reduces
the lifetime of the network, in the case of eachnode having finite non-replaceable batteries, andincreases network traffic, because the paths aremaintained by periodic messages between nodes[65][66].
In query-based routing, the destination nodes
emit a query for data to the whole network, thenode that has the data will send it to the nodethat emitted the query [67][68][69].
Negotiation-based routing uses data descriptors
that are used to eliminate redundant communications. The decisions of communication arethus negotiated between the nodes and thesedecisions are also based on available resources[70][71].
In Quality of Service (QoS) based routing the
network is forced to satisfy certain QoS metrics,
such as delay, energy or bandwidth, whenrouting data to a base station [67][68].
In coherent routing, raw data is only minimallyprocessed by local nodes. This processing
usually means being time stamped and the
suppression of duplicate data. Then, the data issent to other nodes, called aggregators that will
be responsible for the rest of the data processing
tasks. It is a routing protocol that aims at
improving the energy efficiency of the networkby focusing heavy data processing task on
special nodes [72][73][74].

WSN FOR AUTONOMOUS ROBOT PATHCONSTRUCTION
After an overview of WSNs uses and challenges
we focus on one specific scenario. We intend to
deploy this scenario in the real world and use it
in an attempt at testing solutions to challenges
that have been identified for WSNs.
Our scenario is to have a robot that moves frompoint A to point B, given that both points areindoor and be connected by a straight line. The
robot achieves this goal by deploying a wirelesssensor network. This robot, besides having
movement capabilities, is also equipped with anode that has communication capabilities, isconnected to a wireless network and can measurea received signal strength indication (RSSI).

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Wireless Sensor Networks for Building Robotic Paths - A Survey of Problems and Restrictions

There is one sink or master node, connected to acomputer, through this node an end user cansend movement commands to the robot andreceive messages from the robot, for example
getting the RSSI value. The robot will know when
to deploy a new relay node based on the RSSI.
If the RSSI value is below a predeterminedthreshold then the robot stops its movement andsends a message to the master node, informing it
another stationary relay node needs to be deployed.
The result is that the robot will construct a path of
sensor nodes, that will allow him to move frompoint A to B, maintaining connectivity with themaster node, as shown in Figure 5.

Figure5. Autonomous Robot Path Construction

This scenario fits into the definition of a Robotic
Wireless Sensor Network (RWSN) given by[75]. Here RWSN is defined as a wirelessnetwork with mobile robotic nodes and sensor
nodes, and assumes that all nodes have thecapabilities to communicate wirelessly. A RWSN
inherits challenges from both fields, robotics
and WSN, but also has new specific problems,such as RSSI estimations when considering
present and future locations of a robot [75].

Light Routing Protocols

We also find similar proposals such as [76], inwhich a WSN is deployed autonomously by amobile robot, that can also restore the networkby deploying additional sensor nodes. In thisexperiment a deployment and retrieval mechanism
were mounted on the mobile robot. The mobile
robot monitors the RSSI value to ensurecommunication between sensor nodes anddeploys additional sensor nodes if a connectivity
failure is detected.

Each sensor node should be energy efficient asthey are powered by a finite battery. Replacing
or recharging batteries may not be an option.
This means every node operation, such as
routing, sensing or computing, must be sensible
to energy constraints.

Scenario Requirements
Following the above proposed scenario andassumptions, the requirements are discussed next.
Network Scalability

The system requires an efficient way to route
packets through the sensor nodes that form thepath back to the master node. Efficiency ofrouting is important for network applications
and to guarantee that each sensor node does notwaste too much energy routing packets,
extending its life-time and thus the network
life-time.
Energy Constraints

Connectivity Assurance
It is critical to maintain network connectivity
because there is one routing path for packets toreach the master node. A single failure inconnectivity compromises the entire network
because any data beyond the point of failure will
not be able to reach the master node. It is very
important to build methods to detect failures andto restore connectivity in case of failure.

The system should support the deployment of alarge number of sensors nodes so that it can
extend a path long enough to meet the necessities
of different applications.

Quality of Service

Network Coverage

Fault Tolerance

The sensing unit of each sensor node shouldhave the capabilityto cover a target area requiredby different applications.

The system should be tolerant to failure insensor nodes. It should incorporate a way todetect failure in nodes, due to depletion of

The system should guarantee performance
metrics that assure it can be used for different
applications and in different environments.

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Wireless Sensor Networks for Building Robotic Paths - A Survey of Problems and Restrictions

battery, physical destruction or others. It should
also have a way to restore the network once
failure in a sensor node occurs.
Programmability
The sensor nodes and the robot node should be
programmable in order to accommodate different
applications, changes in scenario parameters or in
the environment. Programmability augments
flexibility of the system and widens the range ofpossible applications.
Open Issues
The value of RSSI is very important in our
scenario because the robot uses it to decide
when to deploy another stationary node, which
affects how the path is built. Measuring RSSI
faces problems because the signal is affected bycharacteristics of the physical environment suchas obstacles and the medium of propagation,
which may cause fading, shadowing orinterference [75]. Future research should focus
on building a generic model for interference. It
would also be very useful to perform experiments
and collect real world data that can help us
understand radio frequency properties in different
environments, such as mines, undergrounds orfirefighting environments [75].
Another issue related to our scenario is thedegree of autonomy of the robot. The main
purpose of the robot is to autonomously deploy
a network. But at times it may be useful for the
robot to be commanded by a human user, that
sends the movement commands, or even othercommands via the master node connected acomputer. The problem here is switching therobot from fully-autonomous mode to humancontrolled mode and vice versa, how that switch
is to be achieved and the consequences it may
have on network connectivity, QoS and task
allocation [77].
The problem of connectivity is a sine qua non of
application functionality in WSNs. There aremany reasons that may cause node failure,especially considering that in a lot of WSN
applications the nodes are deployed in harshenvironments, such as military applications inhostile areas. We can expect node failure fromboth internal limitations, such as limited battery
life or mechanical flaws, and external forces,
such as weather or intentional sabotage [78].
The biggest problem here is when node failurecauses the network to be separated into disjointed
segments, thus breaking network connectivity,and causing a part of the nodes to not be able to18

communicate with a sink node. The inability ofnodes to make data reach end users causes
negative effects to network applications andmay result in incalculable loss, in applications
such as rescue operations. Connectivity also
affects the robustness and maximum throughput
of network communication [79]. If we consider
single path deployments, in which there is only
one route to the sink node, then the network iseven more susceptible to path failure and loss ofconnectivity, seeing as any one node failure may
cause it [5]. Guaranteeing better connectivity inthe network is usually attempted by increasing
the communication range of each node orotherwise increasing the number of nodes in thearea [79]. Other attempts are focused on
restoring network connectivity after a node
failure compromises it [80].
Many approaches have been tried in order torestore network connectivity and two maincategories of approaches can be mentioned. The
first is to deploy additional nodes to the locationof the network failure. The second is to takeadvantage of the capabilities of a mobile nodeand move it to restore network connectivity, thisobviously requires at least one node withmoving capabilities in the network [81]. Thisapproach is based on implementing a connectivity
restoration algorithm in a mobile node for it torestore connectivity [78]. It is also important tolocate the network discontinuity or network cut
to be able to restore it [82]. Different methods
based on mobile nodes to restore network
connectivity have been proposed.

CONCLUSION
The emergence of WSNs allows for many new
possibilities that are still being explored. Thesepossibilities make use of the specificcharacteristics of WSNs, such as the low cost of
sensor nodes, self-deployment, self-organizing
capabilities and the ability to monitor ourenvironment with a higher degree of precision
than ever before. This not only helps research
and monitoring efforts but also allows for
informed decision making, as well as prevention
and alarm systems. For these reasons WSNs
have been used in innumerable fields ofapplication, such as military, health, home,disaster relief, among others. WSN have thus
become an intricate part of almost every aspectof modern life and will play an important role inthe development of future smart cities.
Despite the large number of applications of
WSNs, or maybe because of it, they still face
many challenges. Nodes deployed in places

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Wireless Sensor Networks for Building Robotic Paths - A Survey of Problems and Restrictions

without an energy infrastructure face theproblem of limited energy supply. This andharsh environment conditions, in applications
such as disaster relief or military applications,
may both cause node failure, which in turn can
result in coverage and connectivity problems forthe network. Issues such as node deployment,
how to know the location of nodes in the
network and routing are also critical in WSNs.
All these challenges motivate many research
efforts, as this paper has tried to show, whilesummarizing different approaches that havebeen attempted to resolve each issue.
In order to give an experimental answer to someof the challenges described a practical scenario
was proposed, in which a robot node deploys awireless sensor network. Within this proposal
some open issues were identified such asmeasuring RSSI, seeing as the signal is affectedby the physical environment research to build amodel for interference and collection of realworld data in different environments would bevery helpful. Also, the problem of degree of
autonomy of the robot, which should answer
how to switch from fully autonomous to humancontrolled and vice versa and deal with its
consequences to the network. Finally, the issueof network connectivity was identified, due toits critical importance to any application of
WSNs. In fact, without the ability to make datacollected by the network reach an end user the
network becomes pointless. The area ofconnectivity restoration seems of interest in thisregard, namely through efforts of implementing
connectivity restoration algorithms in mobile
nodes, used to heal the network.

ACKNOWLEDGMENTS
The authors would like to acknowledge thecompany Inspiring Sci, Lda for the interest andvaluable contribution to the successful developmentof this work.

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Citation: José P. A. Amaro, João M. L. P. Caldeira, Vasco N. G. J. Soares and Pedro D. Gaspar, “WirelessSensor Networks for Building Robotic Paths - A Survey of Problems and Restrictions”, International Journalof Emerging Engineering Research and Technology, 8(1), 2020, pp. 11-22.

Copyright: © 2020 João M. L. P. Caldeira. This is an open-access article distributed under the terms of theCreative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any
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