Wireless Mesh Networks Based on MBPSO Algorithm to Improvement Throughput

International Journal of Electrical and Computer Engineering (IJECE)
Vol.8, No.6, December 2018, pp. 4374~4381
ISSN: 2088-8708, DOI: 10.11591/ijece.v8i6.pp4374-4381  4374
Journal homepage: http://iaescore.com/journals/index.php/IJECE
Wireless Mesh Networks Based on MBPSO Algorithm to
Improvement Throughput
Shivan Qasim Ameen1
, Firas Layth Khaleel2
1
Universiti Kebangsaan Malaysia, Faculty of Information Science and Technology, Softam Department, Malaysia
2
Tikrit University, Faculty of Computer Science Compuer Science Department, Salah Din, Iraq
Article Info ABSTRACT
Article history:
Received Dec 24, 2017
Revised Mar 10, 2018
Accepted Mar 24, 2018
Wireless Mesh Networks can be regarded as a type of communication
technology in mesh topology in which wireless nodes interconnect with one
another. Wireless Mesh Networks depending on the semi-static configuration
in different paths among nodes such as PDR, E2E delay and throughput. This
study summarized different types of previous heuristic algorithms in order to
adapt with proper algorithm that could solve the issue. Therefore, the main
objective of this study is to determine the proper methods, approaches or
algorithms that should be adapted to improve the throughput. A Modified
Binary Particle Swarm Optimization (MBPSO) approach was adapted to
improvements the throughput. Finally, the finding shows that throughput
increased by 5.79% from the previous study.
Keyword:
Heuristic algorithm
MBPSO
Minimize cost of distance
Routers
Wireless mesh networks Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Firas Layth Khaleel,
Tikrit University - Faculty of Computer Science,
Computer Science Department, Salah Din, Iraq.
Email: Firas_Layth@tu.edu.iq, Firas_Layth@yahoo.com
1. INTRODUCTION
A mesh topology in which radio nodes are arranged, making up a communications network as
shown in Figure 1, is known as a Wireless Mesh Network (WMN) [1]. This network also takes the form of an
ad-hoc wireless network [2]. WMNs usually involve mesh clients, mesh routers, and gateways. The mesh
clients usually comprise a variety of wireless appliances including PCs, handsets, and the like. On the other
hand, the mesh routers help in traffic forwarding to and from gateways, which may not have Internet
connection. The radio node coverage area that functions as one network is at times referred to as a mesh
cloud and accessing this mesh cloud depends wholly on the radio nodes functioning in accord with one other
to produce a radio network. A mesh network offers redundancy and is reliable [1]. Whenever a particular
node stops functioning, the other nodes continue communicating with one another directly, via one
intermediate node or more. Wireless Mesh Networks can self-heal and self-form [1]. Wireless Mesh
Networks can be executed via several wireless technologies comprising 802.16, 802.15, and 802.11 cellular
technologies and require no restriction to any protocol or technology.
The importance of Wireless mesh network leads to be used in several domains such as [1-10]. Also
wireless mesh architecture is an initial phase towards offering high dynamic-bandwidth and cost-efficient
networks for a particular coverage area. Excluding the cabling between nodes, a network of routers makes up
the wireless mesh infrastructure. This comprises peer radio appliances that require no wiring to a cabled port,
unlike traditional access points (AP) in WLAN. By separating the distances into a succession of small hops,
the mesh infrastructure can convey data through large distances. Intermediate nodes help boost the signal and
also cooperate in transmitting data from a particular point to another point (e.g., Point A to Point B) by
making decisions for forwarding based on their understanding of the network, i.e. through implementing

Int J Elec& Comp Eng ISSN: 2088-8708 
Wireless Mesh Networks Based on MBPSO Algorithm to … (Shivan Qasim Ameen)
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routing. This type of architecture might, with cautious design, offer an economic advantage, spectral
efficiency, and high bandwidth all throughout the coverage area.
Wireless Mesh Networks have a comparatively steady topology apart from the rare malfunctioning
of their nodes or added-on nodes. There are infrequent changes happening to the traffic path, as these results
from the aggregation of a huge number of end users. Virtually all infrastructure mesh network traffic is either
forwarded to the gateway or from it, while in client mesh networks or ad-hoc networks the flow of traffic
occurs amid arbitrary node pairs [46].
Figure 1. Wireless mesh network diagram [2]
This kind of infrastructure may be centrally handled (using a central server) or decentralized
(without any central server) [47], [11] These types are comparatively low-cost and may be very resilient and
reliable, as the individual node requires only the transmittance to the degree of the next node. Nodes function
as routers for the transmission of data ranging from nodes that are close by to far away peers, which cannot
be reached in just one hop, leading to a network that may span longer distances. A mesh network topology is
also dependable, as every node is coupled to a few other nodes. Once a node falls out of the network, as a
result of the failure of the hardware or any other cause, its neighbors will swiftly determine an alternative
route via a routing protocol.
2. RESEARCH BACKGROUND
Wireless Mesh Networks can be regarded as a type of communication technology in mesh topology
in which wireless nodes interconnect with one another [12], [13]. Mesh network communication tools are
commonly grouped as routers, gateways, and clients. In these networks, every gateway might directly offer a
type of service, and the data, which flows amid gateways and subscribers, are relayed via routers. IEEE
802.11, IEEE 802.15, and IEEE 802.16 are some of the technologies in which the networking of wireless
mesh finds its application and are presumed to be the provider carrier for the problem design. When

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considering the success recorded by Wi-Fi technology (IEEE 802.11) in the substitution of wired network
computers from offices and households, the main objective of this research and industry is to harness
resources towards eradicating the cost of setting up and maintaining cable use in metropolitan broadband
network areas. This research resulted in the enhancement of IEEE 802.16, which now performs the function
of a backhaul for broadband wireless access for (WMAN) metropolitan wireless network areas [14]. The
interoperable application of the IEEE 802.16 wireless family is popularly known as WiMAX [15]. The IEEE
Standards Board initiated a functioning group in 1999 to formulate standards for the WMAN broadband. The
founded group made available their IEEE 802.16 initial draft in February 2004. In this phase, the (SS)
subscriber stations and (BS) base stations ought to be immobile and in line-of-sight, respectively [12], [16].
Another substantial improvement occurred with the IEEE 802.16e-2005 introduction, which deals
with communication and mobility that are non-line-of-sight for the subscribers between BS and SS,
utilization of Scalable Orthogonal Frequency Division Multiple Access, enhanced service quality support,
and much more [16]. Practical problems still exist even with the level of development recorded such as the
requirement for access of uneven traffic distribution in densely populated areas, the signal-to-noise ratio
occurring at the edge of the cell, and coverage holes that emerge as a result of non-light-of-sight networks
and shadowing, etc. The WiMAX protocols have to guarantee dependability, address coverage holes, and
support utmost mobility to compete with wired broadband providers and 3G. Each challenge is in contrast to
the other. Reliability decreases as rate of data increases. However, coverage area (i.e. the size of the cell)
reduces as the reliability of service increases. Reduction of the size of the cell would result in an increase in
the quantity of BSs for a specific area coverage, which will result in the rise of network costs [12], [16].
Relay station (RS) insertion between the BSs and SSs serves as the best solution at this point, which
will route data in between the stations. The relay is utilized for the extension of network coverage range and
capacity, which also connects the coverage holes e.g., shadows of buildings, therefore, enhancing end-to-end
communication quality [17]. IEEE 802.16j is a modified version of IEEE 802.16e. It projects that data amid a
SS and BS can be relayed through a RS via MMR (mobile multi-hop relay network), which utilizes the
strengths of wireless multi-hop connectivity [18]. The range of coverage and quantity of Wimax is expanded
with the introduction of IEEE 802.16j, addressing the problem of building coverage holes, and thus
simplifying the extension of coverage temporarily to areas with a high-density population. The architecture
of the network presents several complications within the previously challenged radio access networks that
provide support for mobility [19], [20] e.g., the scheduling of channel access regarding frequency reuse, RS
and BS placement, resource (time and frequency) allocation, frequency and time, etc. [12], [16]. The
subsequent subsections provide samples of various researches carried out on the networks of IEEE 802.16j to
develop a greater level of information regarding networks and problems encountered in wireless network
design, which share some similarity to the problem highlighted in this study.
In wireless communication, interference may arise due to sharing of communication medium within
the stations. To solve this problem, network resources [21] suggest a scheduling algorithm, which will
support spatial reuse gains from the two hops in a network that is relay-enabled. Ibrahim et. al. and Ge et. al.
discovered that during their study of an analytical model to examine the capacity of a cell with the extension
of a two-hop coverage, that spatial reuse could reduce losses in capacity [22], [23]. Moreover, many
researches have focused on wireless network locational design. Also, a good amount of research has been
done involving various wireless network providers and the topological and architectural planning for the
networks. [24], [25] used an integer programming model including many algorithms based on Tabu Search
and Greedy [26] to decide better positions to cover various traffic concentrations using BSs [24], [25]. The
major challenge faced by the mobile industry is the migration from 2G to 3G networks in a way that
satisfaction of the customer is achieved with a low cost of operation, and the number of cells that is used is
also reduced to the minimum. Kaur et. al. proposed the determination of cell sites using a heuristic algorithm,
which works by ranking cells from the generated simulated data and cell removal from the periodically
simulated model [27].
A preliminary study was conducted by Sinha et. al. and Doppler et. al. in regard to the IEEE 802.16j
design structure [28], [29]. One previous work designed a programming model for integers on IEEE 802.16d
networks and suggested solving the issues of creating the lowest cost backhaul wireless network using
heuristic algorithms (with BSs) to fulfill the SS requirements with no effect on the capacity limits in BSs
[30]. A number of case studies have been presented to determine relay optimal location and to push the
system output to the max within the BS-RS cell coverage in 802.16j networks [22]. However, these works
fail to talk about the issues associated with multiple-relay planning.
Another study developed heuristic-based RSs to address the issue of network deployment and reuse
of radio resouce when IEEE 802.16j MMR networks are involved [31]. Yet another study assessed the ability
of a network for IEEE 802.16j via cooperative diversity for uplink transmissions [32]. The result from the

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research can be employed in analyzing the pros and cons of capacity improvement relay and deployment
cost.
Vasishta et. al. discussed a programming formulation for integers in positioning BS and RS with the
aim of reducing the cost of establishment under the limitation of user traffic demand [33]. Bound techniques
and a standard branch were applied in solving this problem. However, unlike the case of small instances,
when it comes to solving metropolitan-scale large instances, this approach is limited. In one study, a
clustering approach was used on a similar problem [33]. To achieve the least amount of RSs in an MMR
network, Chang et. al. recommended a heuristic algorithm [34] .
A model for relay-centric hierarchal optimization, which can be used for both optimization of
network planning for RSs and radio resource MMR networks management, was also proposed [35]. The aims
of the research are to make the most of the utilization of the RSs and achieve optimum reserved bandwidth.
In a new algorithm formulation of the optimization issue, the problems of assignment constrained by chance
are focused on, so as to achieve the ideal decisions on relay positioning and base station selection.
Shim et. al. produced a paper on the use of IEEE 802.16j technology for the enhancement of
infrastructure communication in vehicular wireless networks [36]. They presumed the vehicular SSs location
as known, and used the detail obtained for the ideal placement of RSs in a way that the end-to-end capability
was taken full advantage of. The study combined a model of highway mobility and a nonlinear optimization
model for the problem. The model solution guarantees ultimate end-to-end capabilities for SSs.
3. THE ADVANTAGES AND DISADVANTAGES OF PREVIOUS ALGORITHMS
In Table 1, a summary of different algorithms based on the advantage and disadvantage of each one
is presented. Finally, the previous section summarizes the algorithms, methods and approaches in this field as
shown in Table 1. At the same time, ideal learning environments can be created. In other words, the Heuristic
algorithm for the Wireless Mesh Network has to adapt the PSO approach because this approach is more
comprehensive than other algorithm functionalities. Also, a wide range of continuous optimization problems
can be addressed via the successful application of PSO.
Table 1. Particle Swarm Optimization Parameters and Chosen Values
No. Algorithm Advantages Disadvantages
1
Heuristic
Algorithm [37,
38]
The performance of the proposed scheme is not
only close to the optimal multi-path solution, it
also outperforms existing multi-path routing
schemes.
It does not follow a standard mathematical model.
Lower capability for generalization.
2
Genetic
Algorithm [39,
40]
Every routing session concurrency transmission
is effectively maximized through elimination of
interference between wireless mesh routers,
using this algorithm.
To solve the problem of mesh router node
placement, Tabu Search, an example of a local
search method, and Genetic algorithms, which are
population-based methods, must be hybridized.
3
Adaptive Mixed
Bias (AMB)
Algorithm [41]
Better performance is observed with the use of
the proposed approach concerning Adaptive
Mixed Bias compared to both existing mixed
bias approaches and IEEE 802.11 MAC.
Varied packet rates, diverse network topologies,
and numerous sources through experiments must
be used to achieve the changes in performance and
in enhancing robust solutions
It is vital to study the parameters of Tabu Search
for more information to enhance the Adaptive
Mixed Bias method performance.
4 Greedy Algorithm
Greedy Algorithms mostly (but not always) fail
to find globally optimal solutions, because they
usually do not operate exhaustively on all the
data. This method can result in the prevention of
arriving at the best overall solution in the future,
as this method can commit too early to certain
choices.
For instance, the Greedy Algorithms, which are
considered Greedy typically, fail to discover the
problem of graph coloring and that of the globally
optimum, but other NP-complete problems
provide a solution. Nevertheless, they function
exhaustively because they are swift in reaching
optimum approximations.
5
Local search
algorithm
Generally, all local search algorithms yield
results that are better than the Greedy
Algorithms.
However, the drawback of this improvement is a
longer running time.
6
Variable
Neighborhood
Search
Algorithms
Local search procedure, in determining the
solutions to different optimization problems, is
very effective.
However, it can get stuck in a local minima.
7
Particle Swarm
Optimization
Wide-ranging problems of optimization that are
continuous can apply PSO and obtain successful
results.
Rarely in solving the issues, do the mesh router
nodes face placement problems.

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4. RESEARCH DESIGN
The current research design has four phases of methodology in relation to this study the Analysis
phase, Compilation phase, Innovation phase, and Validation phase are shown. At each phase, there are steps
to be concluded before the succeeding phase can proceed. For example, the Analysis phase entails the stages
of analysis in this study while the Innovation phase entails the enhancement and design phases, and finally,
the Validation phase entails the evaluation and application stages. The following section describes in detail
each of these stages.
4.1. Analysis Phase
This method uses the results from previous research to assess the current problem. Different
research approaches, and the result quality is acquired to provide a solution to: 1) the problem of Unsplittable
Flow; 2) the problem of Bin Packing; 3) the problem of Capacitated Set Covering; and 4) the Problem of Set
Covering.
4.2. Comparison Phase
This phase involves a comparative study to determine the best method to be applied in this work.
This is done through initiating a comparative study between the previous algorithms and the current methods
used in this study.
4.3. Innovation Phase
The innovation phase considers the process of the proposed MBPSO algorithm, as follows:
a. Particle Swarm Optimization (PSO)
b. Binary Particle Swarm Optimization (BPSO)
c. Modified Binary Particle Swarm Optimization (MBPSO)
4.4. Evaluation Phase
To ensure that the algorithm works correctly, the validation phase goes through three metrics after
the Heuristic algorithm is completed. These metrics are throughput, End-to-End Delay (E2E DELAY), and
Packet Delivery Ratio (PDR). Finally, the result is compared with the nearest technical study [42] in order to
measure the results improvement from the original one.
5. PERFORMANCE METRICS
Two basic performance metrics such as the ones that run via E2E delay and packet delivery fraction
have been proposed in numerous works [43], [44]. Additionally, simulation is considered with the mobility
pattern of nodes. To achieve delay and packet drop, Mirjalili et. al. propose the use of a random waypoint
mobility model [45].
5.1. Packet Delivery Ratio (PDR)
The number of delivered packets is divided by the destination to give PDR. To calculate PDR and to
determine the loss rate of the packet, the number of packets given by the application layer of the source is
used to divide the number of packets received by the destination. In this way, the maximum network
throughput becomes limited. In the routing protocol, an imperative factor to be accomplished is PDR, as
there is no margin for error in a real-life environment like flooding and earthquakes.
5.2. End-to-End Delay (E2E DELAY)
The data packet will arrive at the endpoint within the time that is averaged out. For the metric
calculation, the arrival time of the first data packet is used to subtract the time at which the first packet was
transmitted.
5.3. Throughput
The average ratio of the total simulation time duration to the successful data packets is the average
throughput metric. The unit of Kilobits per second (Kbytes/sec) is used to measure average throughput,
where the efficiency and effectiveness of the routing protocol in receiving data packets by destination is
measured.

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6. EVALUATION RESULT
Cost optimization is an area of research in WMNs. Our research is based upon improving the
solution proposed by [42] Khaled and Shah Mostafa [42]. The author considered cost optimization without
taking the distance between nodes into consideration. Our research therefore updated the optimization
function to take into consideration the distances between the different nodes, using the Modified Binary
Particle Swarm Optimization (MBPSO) approach. The results are positive and our approach shows
noticeable improvement compared to the benchmark study. The PDR shows an approximate increase of
22.47%, whereas the E2E delay saw an approximate decrease of 21.14%, and finally the throughput
increased by 5.79% from the previous work as shown in Figure 2.
Figure 2. Throughput comparisons between the original and modified objective function
7. DISCUSSION AND CONCLUSION
The main contribution of this study is the proposed proper MBPSO algorithms used in this study to
solve the research problem. This approach is adapted from previous work, so the main point of this study is
to compare the result of this study with previous work. This work assumes the same coverage area for all
nodes. Further development could be done by considering different coverage areas according to node energy
and node priority. In addition, testing the performance on a range of node speeds would be a more practical
scenario, with considering the speed as an influence in the standard function. Furthermore, some nodes in the
network might not be trusted to transport the packet.
These nodes have a nature of selfishness, so there should be a criterion to detect and avoid them.
Future works should cover the aspects of different ranges of coverage zone, speeds, and confidence for the
nodes of the network. BPSO is simple and highly robust. The BPSO can solve multidimensional and
multimodal optimization problems because this algorithm uses control parameters that are simple.
Multidimensional functional optimization problems can also be addressed with the use of the BPSO
algorithm. However, this issue is beyond the scope of the current study.
There are several directions in future work that could be implemented to enhance the performance
metrics used in this study. For instance, it is possible to consider other network metrics such as packet size,
number of nodes, and simulation time. This can increase the practicality and efficiency of the algorithm for
functioning in the real world. On the other hand, future works on the MBPSO algorithm should probably
concentrate on a particular part of the MBPSO algorithm, which could be expected to provide good cost
minimization among node connections.
The research problem is addressed using heuristics that are based on different variations of heuristic
algorithms. This study tested the algorithms based on 500-5000-sized nodes. Finally, this study compared the
algorithm results obtained with previous work. The finding shows that PDR shows an approximate increase
of 22.47%, whereas the E2E delay saw an approximate decrease of 21.14%, and finally the throughput
increased by 5.79% from the benchmark study.
8. CONCLUSION
Cost optimization is an area of research with regard to WMNs. This research was based upon
improving the solution proposed by Nleya and Sindiso M. [15] in this regard. They have considered cost
optimization without taking distance between nodes into consideration. This research considered the problem

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using a Modified Binary Particle Swarm Optimization (MBPSO) approach by updating the optimization
function to take into consideration the distances between the different nodes. The results were positive and
this approach showed noticeable improvement compared to the benchmark. The PDR showed an
approximate increase of 17.83% whereas the E2E delay saw an approximate decrease of 8.33%, and finally
the throughput increased by 5.79% from the benchmark study.
ACKNOWLEDGEMENT
This work is supported by Ministry of Education of Malaysia, Grant no:
FRGS/1/2015/ICT04/UKM/02/3.
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