Modelling and planning reliable wireless sensor
networks based on multi-objective optimization genetic
algorithm with changeable length
Danping He • Gabriel Mujica • Jorge Portilla •
Teresa Riesgo
Abstract Wireless sensor networks (WSN) have shown their potentials in various
applications, which bring a lot of benefits to users from different working areas. How-
ever, due to the diversity of the deployed environments and resource constraints, it
is difficult to predict the performance of a topology. Besides the connectivity, cov-
erage, cost, network longevity and service quality should all be considered during
the planning procedure. Therefore, efficiently planning a reliable WSN is a challeng-
ing task, which requires designers coping with comprehensive and interdisciplinary
knowledge. A WSN planning method is proposed in this work to tackle the above
mentioned challenges and efficiently deploying reliable WSNs. First of all, the above
mentioned metrics are modeled more comprehensively and practically compared with
other works. Especially 3D ray tracing method is used to model the radio link and
sensing signal, which are sensitive to the obstruction of obstacles; network routing is
constructed by using AODV protocol; the network longevity, packet delay and packet
drop rate are obtained via simulating practical events in WSNet simulator, which to
the best of our knowledge, is the first time that network simulator is involved in a
planning algorithm. Moreover, a multi-objective optimization algorithm is developed
to cater for the characteristics of WSNs. Network size is changeable during evolu-
tion, meanwhile the crossovers and mutations are limited by certain constraints to
eliminate invalid modifications and improve the computation efficiency. The capa-
bility of providing multiple optimized solutions simultaneously allows users making
their own decisions, and the results are more comprehensive optimized compared
with other state-of-the-art algorithms. Practical WSN deployments are also realized
for both indoor and outdoor environments and the measurements coincident well with
the generated optimized topologies, which prove the efficiency and reliability of the
proposed algorithm.
Keywords Efficient planning method
•
Measurement of WSN
•
Modeling of WSN
•
Multi-objective optimization
•
NSGA-II
1 Introduction
Recent years
have
witnessed
an
increased interest
in
the use of wireless sensor networks
(WSNs) in various applications such as environmental monitoring, space exploration,
factory automation, habitat tracking, secure surveillance, and battlefield surveillance.
This technology has brought
a
lot of benefits to the users from both research and indus-
trial areas. In WSNs, miniaturized sensor nodes are deployed to operate autonomously
in different types of environments. Sensor networks may consist of different types of
sensors such as seismic, visual, infrared, acoustic and so on. In addition to the ability to
probe its surroundings, each sensor node has an onboard radio to communicate with
other nodes through wireless communication protocols such as ZigBee
rM
(2007),
Bluetooth™ (1998) and Ultra-wideband (UWB) (2009) among others. The sensed
data are collected and sent to a base station directly or via multiple hops depending
on the network topology and routing protocols. For many setups, it is envisioned that
WSNs will consist of tens to hundreds of nodes that operate on small batteries. A
sensor stops working when it runs out of energy and results in a damage in the WSN
structure.
The major challenge in designing WSNs is the support of various application
requirements while coping with the computation, energy, communication, sensing
and cost constraints. Careful node placement can be a very effective optimization
means for achieving the desired design goals. However, optimal node placement has
been proven to be NP-Hard for most of the formulations of node deployment (Efrat
et al. 2004; Cheng et al. 2008; Poduri et al. 2006). Several works are developed to
tackle the efficiency of planning algorithms: Misra et al. (2010) concern the relay
node placement problem for WSNs in the aim of placing a minimum number of relay
nodes into a WSN to meet certain connectivity or survivability requirements. Relay
nodes can only be placed at a set of candidate locations and they discuss the computa-
tional complexity and present a framework of polynomial time C(l)-approximation
algorithms with small approximation ratios. Numerical results show that their approx-
imation algorithms can produce solutions very close to optimal solutions. Shams et al.
(2008) propose an approximation algorithm that runs in
O
(n
2
) time complexity, it tar-
gets to find a feasible solution for minimizing number of relay nodes in such a fashion
that each sensor node must have at least one relay node within its one hop distance.
Lee and Lee (2013) presents
a
polynomial-time relay node placement algorithms using
Minimum Steiner tree on convex hull.
Beyond the complexity control, most planning methods focus on modeling impor-
tant parameters that have strong impacts on the network performance, and on optimiz-
ing topology based on the modeled metrics. In this work, only those methods with 3D
calculation ability are investigated.
The 3D indoor planning heuristic (LowCost) proposed by Kouakou et al. (2010), to
the best of our knowledge, is the first indoor 3D WSN deploy heuristic that considers
impacts of obstacles. It consists of two steps: Provided a 3D indoor environment
model with furniture and obstacles recorded, the first step calculates the coverage
to deployment cost ratio for all the candidate points in the deployable area. Sensor
nodes are iteratively put at the point with the maximum coverage to deployment cost
ratio,
so that the target region is covered with the minimum sensor node cost after
this step. Then the connectivity of the deployed nodes is checked in the second step.
The authors consider two options to satisfy the connectivity of
WSN,
the prior one is
realized by moving the unconnected node towards the closest connected node without
influencing the sensing coverage of the first
step;
otherwise, if
the
preferential option is
not applicable, extra sensor nodes will be added along
the line
between
the
unconnected
node and the closest connected node. Note that despite this approach manages to cover
the sensing area with the "minimum cost", the connectivity of the WSN is ensured by
simply moving or placing extra nodes without carefully selecting optimal positions to
decrease the hardware cost, improve the link quality or prolong the network lifetime.
Moreover, although the modelling of the sensing signal considers obstacles, the radio
propagation model is too simple because the communication links are established only
between line-of-sight
nodes,
which is obviously not true in the real-world propagation.
The MOGA algorithm (Jourdan and
de
Week
2004)
employs multi-objective genetic
algorithm, which is proved to be efficient in solving NP-hard problem, to evolve the
decision. Topology solution for the same network varies at different runs, which pro-
vides more options than the deterministic approach of LowCost. However, it focuses
on maximizing the sensing coverage and prolonging the network lifetime with a pre-
determined number of nodes, as a result the hardware cost can not be optimized.
Moreover, the modeling of radio signal and sensing signal are based on ideal disc
model thus it is not environmental sensitive.
The authors of Shams et al. (2008) propose an approximation algorithm to And a
feasible solution for relay node placement to deploy a minimum set of relay nodes in
such a fashion that each sensor node must have at least one relay node within its one
hop distance and all deployed relay nodes eventually form
a
connected network among
themselves including one or more base-stations. The work reveals an approximation
algorithm that runs in
O
(n
2
) time complexity, to And a feasible solution for the above
challenge.
The work in Kim et al. (2007) proposes multiple-objective metric for base station
placement in WSNs to fairly increase various properties. It considers four different
metrics for base station placement in WSNs. First, the ratio of sensor nodes which
can communicate with a base station via either single-hop or multi-hop represents the
coverage of sensor
nodes.
Second, the average ratio of connected sensor nodes after the
failure of base stations represents the fault tolerance of a network. Third, the average
distance between sensor nodes and their nearest base station represents the energy
consumption of
a
network. However, as discussed before, not only the distance but also
the obstacles lead to attenuation of the received signal strength (RSS). Moreover, more
energy is consumed at nodes with larger degree, as a result the energy consumption
is not practically modelled by this work. Fourth, the standard deviation of the degree
of base stations represents the average delay of a network due to congestions. The
limitation of this algorithm is that sensor nodes should be pre-located by designers,
which neither guarantees the sensing coverage without expert experience nor allows
optimizing the hardware cost for WSN.
The coverage problem of WSNs for the rolling terrains is studied in Liu and Ma
(2012) to derive the general expression of the expected coverage ratio for regular
terrains and irregular terrains.
Huang et al. (2008) develop a tool that integrates a 3D indoor deployment heuristic
together with NS-2 simulator to assist designers deploying and analyzing the perfor-
mance of networks. They propose
a
heuristic that minimizes hardware cost while satis-
fying requirements on coverage and connectivity. The network topology is constraint
to the type of cluster tree and three different devices are provided: the coordinator,
router and sensor. Sensors can only communicate with routers and coordinator. The
heuristic considers radiation pattern of antenna as well as the effects of obstacles by
using an accurate ray-tracing algorithm. Once the topology is generated, the integrated
NS-2 simulator is driven to simulate the packet drop rate and latency, and the results
are demonstrated to users. The merit of this method is the integration of an authorized
network simulator to evaluate the performance of generated topologies, which pro-
vides a much more practical implication on packet delivery performance to designers.
However, as the evaluation from NS-2 has no contribution on improving the generated
topology, the proposed deployment heuristic should be run several times so that by a
certain chance, designers can observe a satisfied solution with low cost, low drop rate
and latency. The user interface allows users to prosecute many configurations includ-
ing map, node properties, topology constraints and environment types. The generated
topology can be shown and results evaluated by NS-2 are reported on the interface.
A method for deploying relay node and sink node for indoor environment is pro-
posed by Guinard et al. (2011) and McGibney et al. (2011), the tool allows users
defining the node demand zones, power source, sensing interval and transmission
delay. By encapsulating those metrics into a complete requirements model, the tool
optimizes the infrastructure of WSN and maximizes the utility function, which pro-
vides a normalized equation that observes the coverage, link quality, lifetime and
infrastructure cost. The lifetime (L) of sensor node is considered in that work and is
modelled by (2), The electric charge of a sensor node EC, expressed in mAh, is cal-
culated according to (1) where I
a
and I
s
are the power consumption in active state and
sleep state respectively. t
a
and t
s
represents their time durations in
a
node interval. The
current capacity of the battery CC is expressed in mAh. Figures
1
and 2 are examples
of generated solutions for single-hop and multihop topologies respectively. The model
of lifetime only represents a coarse estimation and only on sensor nodes, moreover
the authors did not consider the modeling of packet delivery ability to ensure a more
reliable WSN.
Fig. 1 Demonstration single-hop solution of work in Guinard et al. (2011)
Fig. 2 Demonstration multi-hop solution of work in Guinard et al. (2011)
3600
EC = ——— X (t
a
X I
a
+ t
s
X I
s
)
ta + ts
cc
L =
EC
(1)
(2)