Dynamic Quality of Service Stability Based Multicast Routing Protocol for MANETs (DQSMRP)

David C. Wyld et al. (Eds) : CSITA, ISPR, ARIN, DMAP, CCSIT, AISC, SIPP, PDCTA, SOEN - 2017
pp. 159– 173, 2017. © CS & IT-CSCP 2017 DOI : 10.5121/csit.2017.70116
DYNAMIC QUALITY OF SERVICE
STABILITY BASED MULTICAST ROUTING
PROTOCOL FOR MANETS (DQSMRP)
M.Vijayalakshmi1
, and Dr.D.SreenivasaRao2
1
Department of Electronics and Communication Engineering, GNITS, India
mvlakshmi_gnits@yahoo.co.in
2
Department of Electronics and Communication Engineering, JNTUH, India
dsraoece@gmail.com
ABSTRACT
Mobile ad hoc networking does not possess any fixed infrastructure and hence, stable routing is
the major problem. The mobility nature of MANET’s node facilitates rediscovery of a new path
to organizing a routing. In order to intensify the Quality of Service and routing stability in
MANET, we propose a Dynamic Quality of service Stability based Multicast Routing Protocol
by modifying the Cuckoo Search Algorithm through a modernizing mechanism which is derived
from the differential evolution algorithm. Tuned CSA is a combined feature of CSA and DE
algorithms. Periodically, each node in the network creates neighbour stability and QoS
database at every node by calculating the parameters like node and link stability factor,
bandwidth availability, and delays. Finally, multicast path constructs route request and route
reply packets, stability information and performing route maintenance.
KEYWORDS
Manets, QoS, Tuned CSA, DQSMRP
1. INTRODUCTION
Mobile Ad hoc Networks (MANETs) are foremost in class kind of wireless communication
networks in which mobile nodes link on an on-demand basis. MANETs are having the ability to
configure on their own and easing peer-level communications among mobile nodes independent
by having no fixed infrastructure. When the route traffic occurs, constructing a MANET with
each node’s to continuously retain the information remains a major challenge. Normally,
MANETs are used for group communications where multicast protocols are efficiently compared
to unicast protocols since they improve the efficiency of the wireless links in MANETs. The
multicast protocols are also used as application demands for transmitting copies from either
single or multiple sources to the receivers.
Multicasting reduces the communication costs by sending the single copy of the data to multiple
recipients through which it minimizes link bandwidth, processing and transmission delay.
Previous researchers have developed different algorithms to improvise the ability for selecting the

160 Computer Science & Information Technology (CS & IT)
routing path(s) and thereby, effectively met the desired Quality of Service (QoS) [1]. This
infrastructure results in a highly dynamic topology causing a challenging task for QoS Routing .
When compared with well-established networks such as Wi-Fi, Global System for Mobile
communication (GSM) and Code Division Multiple Access (CDMA), MANET was not reliable
to meet QoS performances. The MANET performance is determined by the QoS through the
basic parameters like bandwidth or delay or loss. QoS routing addresses both the issues of
deciding a path from source to destination and fulfilling the QoS constraints like bandwidth,
delay and loss. Route selection method is used to fulfill QoS requirements. The goal of QoS
routing strategy is to maximize the network resources in order to reduce the constraints in
MANET’s mobile nodes.
In real time applications, QoS routing protocol works based on multi-hop mobile networks [3, 4].
Particularly, QoS requirement depends on constraints such as link constraints, path constraints
and tree constraints. End to End delay is usually affected by path constraint whereas, delay-jitter
[5] by tree constraint. In order to meet all the constraints, robust techniques are required to solve
the QoS requirements. Heuristic techniques are better to solve these constraints rather than
deterministic methods. Researchers have studied various improvements based on heuristic
methods [5, 6] and at the same time devised various solutions to solve QoS routing problems
using heuristic methods [7].
Ad hoc mobile routing (AM Routing) was studied through which a specific unicast routing
protocol was identified as more feasible than other unicast routing protocols (8). In QoS routing,
network topology was constructed in which bandwidth link works in destination point [9]. A new
protocol was proposed for ad hoc networks which are used to establish and maintain a shared
mesh for each multicast group called Policy-based Unified Multi-radio, Architecture for Agile
Mesh Networking (PUMA) [10]. A new single mixed metrics have been delineated for multi-
constraint QoS routing [11]. Previous literaturehas found that Agent Based Multicast Routing
Scheme (ABMRS) on MANET’s could be used as a set of static and mobile agents for QoS
routing [12, 13]. Optimized link state routing protocol was launched and scrutinized in different
research papers [14].
Besides that, several heuristic methods are examined to study the multi-constraint QoS routing. In
[15], Simulated Annealing (SA) was introduced to solve the QoS routing problems. Reference
[16] discussed Bees Life Algorithm (BLA) to solve the QoS multicast routing problem and
constraints such as delay, allowed jitter, and requested bandwidth have been studied. Similarly, a
Tabu search [17] based method concentrates on two important constraints in QoS such as
bandwidth and end-to-end delay. Ant algorithm optimization was globally accepted for the
effective and systematic handling of QoS routing [18, 19]. Harmony search (HS) algorithm, a
unique algorithm was used to find the path of the MANET and it was discussed based on
bandwidth-delay-constraints in multicast routing problem [20].
Hybrid methods have been proposed to address the delay of premature convergence [21]. A PSO-
GA algorithm was evolved using a combination of both particle swarm optimization (PSO)
method and genetic algorithm (GA) which provides an effective and efficient search for the
solution. In [22], the new fuzzy genetic algorithm is also introduced for QoS multicast routing is
also discussed.

Computer Science & Information Technology (CS & IT) 161
Thus in this paper, we propose Dynamic Quality of Service Stability based multicast routing
protocol (DQSMRP) for the multi-constraint QoS routing problem. Recently, Cuckoo Search
(CS) is derived from reproduction strategies [23] which are widely used in heuristic based
algorithms. [24, 25].It is more exhaustive and provides quick search to obtain better solutions to
routing in more reliable and robust manner.
2. PROBLEM DEFINITION, RESEARCH METHODOLOGY
The optimization problem is used to solve the QoS routing problem which is the primary
objective to find a multicast tree constraints based on the cost function in order to reduce the
practical constraints. Four different constraints are considered for formulating the current study
such as delay, packet loss, bandwidth and jitter. The following equation provides formulation data
for computation [5] QoS routing function is formulated as:
Reasons to merge both cost optimization and multi-constrained routing are as follows:
Formulations are based on multiple constraints and the multicast structure in order to spot
an attainable path for each node having source and destination.
To shrink the network resource utilization by users.
In progressively,
The Delay Constraint is defined as the allowed delay limit that exists in any branch of the
network. When there is no link provided between any two nodes, a penalty occurs.
The Bandwidth Constraint is the requirement of average bandwidth among all tree branches in the
network.
Specifically are the bandwidth, delay, delay-jitter and packet-loss for the
corresponding penalty constants. The objective of the initiated solution is to solve the multicast
QoS routing problem as described by numerical experiments in the coming section.

2.1 Tuned Cuckoo Search Multicast Routing Protocol
A technique that produces simple implementation and quality solutions is the best solution
provider. This paper provides better results over the existing algorithms such as meta-heuristic
techniques. Before analyzing the proposed method, the cuckoo algorithm is discussed as follows.
2.1.1. An Overview of Cuckoo Search Algorithm
By analogy of reproduction strategy, CS algorithm is a well known prominent heuristic search
algorithms which follow the principle of cuckoos (chosen nests of other birds for laying eggs)
[26, 27]. Generally, the host bird would not permit the cuckoo eggs to easily differentiate and
vice-versa. The important optimization procedure is as follows:
Initial Solution: Cuckoo egg symbolizes a set of solutions and its value of dimensions
was extracted from various nests randomly;
Next Generation: The best eggs with common solutions only able to proceed to the next
generation.
• Acceptance Rule: New egg will succeed in the nest if eggs are unknown
Important step of CS are explained as follows:
Step 1-Randomly set“Pop” host nests, a group of the population. (e.g.,ܲ = ∑ ܲ௜
௣௢௣
௜ୀଵ )
Step 2-Evaluate the fitness of all nests‫ܨ‬ = ∑ ݂(ܲ௜
௣௢௣
௜ୀଵ )
Step 3- Do Iterations < Max.Iterations
Step 4- Produce a cuckoo egg ܲ௜
by applying a Lévy flight from a random nest
Step 5- Compute the fitness of ݂(ܲ௜)
Step 6- Check for existence: choose again a random nest J
Step 7- if ݂൫‫݌‬௜
ଵ
൯ < ݂(ܲ௜) then
Step 8- ܲ௝ୀ ‫݌‬௜
ଵ
Step 9- end if
Step 10- Remove a fraction of the worst nests at the rate of
Step 11- Randomly, Building new nests at new locations using Levy flights replacements
Step 12- End While

The benefit of this heuristics is its simplicity. The single parameter of CS algorithm should be
considered enough when compared with PSO and Ant colony algorithms.
2.1.2 Node Stability
For efficient better packet delivery services in forwarding group, the mobile node is to be very
stable .The way of providing stability is that moving node around its current position. Node
stability metric have been used in previous work of authors is given in [28]. It is studied that
stable path nodes have been found from packets from the source to the multicast group. Self-
stability and neighbor nodes stability are two metrics to represent node stability.
Following steps to find stability of a node:
In MANET’s nodes, it establishes self-stability, i.e., node movement is relative to its
previous position.
MANET’s node gives the node stability factor from self-stability and neighbor stability.
Actually, stability is based on the distance between the movements of nodes positioning
within transmission range. Stable node is created when movement within transmission
range.
2.1.3 Neighbor node stability
It can be defined as how the node is being connected to its neighbour to obtain self-stability.
Whenever nodes are in transmission range, it can exchange messages with each other. Each node
consists of connectivity information, the signal stability of one-hopneighbors, and also maintains
a neighbor list. The degree of a node n is denoted as number of links (or nodes) which connected
to it and denoted as ND.
Where, α is the weighting factor (lies between 0 and 1) and is distributed between 0.6 and 0.7
since they yield better results in simulation.
2.1.4 Link Stability
Link stability between the nodes defines quality and existence of the connection. It is reckoned
based on two parameters:
Received Signal Strength and
A lifetime of the Link.
The algorithm 1 represents a pseudocode for updating link stability status between the
nodes. The different parameters used in the algorithm are as follows:

Lifetime – Duration of continuous connectivity between the nodes
Lifetime threshold – Indicates the maximum limit of link lifetime that decides link
stability
Link stability status – It is a Boolean variable that defines link stability between the
nodes.
Recent – Indicates most recent response received for a Hello packet from a neighbor
P – Represents Total Number of Hello packets
Received signal strength – Represents the strength of the signal received from a neighbor
Signal threshold – Is acceptable signal strength to be received from neighbors
2.1.5 Algorithm: Link stability status between the nodes
Step 1: P = No of Hello Packets;
Step 2: Lifetime = 0;
Step 3: Link stability status = 0;
Step 4: Recent = 0;
Step 5: Lifetime threshold = P × Hello Packet Interval;
Step 6: While P > 0 do
Step 7: If received signal strength ≥ signal threshold then
Step 8: Lifetime = lifetime + 1;
Step 9: Recent = 1;
Step 10: P = P−1;
Step 11: else
Step 12: Recent = 0;
Step 13: P = P−1;
Step 14: end if
Step 15: end while
Step 16: lifetime sec = lifetime × Hello Packet Interval;
Step 17: if (lifetime sec > lifetime threshold) and (Recent)
Then
Step 18: link stability status = 1;
Step 19: else
Step 20: link stability status = 0;

Step 21: end if
2.1.6 Delay Estimation
For delay estimation, an arbitrary node that contributes to traffic forwarding using the M/M/1
queuing system is modeled. This queue represents a single queuing station with a single server
[29]. The authors assume that the contributing nodes are served by a single server with first come
first serve queuing policy. Packets arrive according to a Poisson distribution rate (λ), and the
probability distribution of the service rate is exponential, denoted by µ. The maximum size of the
queue in every node is represented by K.
To satisfy delay requirements in multimedia real-time applications, packets must be received by
multicast receivers which satisfy the application delay constraints. When a packet is to be sent
either by a source node or forwarding group of nodes, it experiences three types of delays:
queuing, contention and transmission delay. The total delay considered over a link between two
nodes is given by
The queuing delay denoted by is the delay between the time the packet is assigned to a queue
and the time it starts transmission. The packet waits and gives other packets in transmission queue
are transmitted at this particular time. This is the amount of time a packet is spent in the
interfacing queue (T).
2.1.7 Bandwidth Estimation
Bandwidth is one of the key principles for assessing Quality of service (QoS). The authors
considered their previous work presented in [30], to calculate the available bandwidth based on
their channel status of the radio link to calculate the idle and busy periods of the shared wireless
media. Bandwidth is measured by the node activities and its surrounding neighbors using the
channel utility.
In IEEE 802.11 MANETs, once it is achieved to gain the channel access, a node can be able to
transmitting data packets. Hence, a node senses the channel and estimates bandwidth by using the
idle and busy times in a predefined interval. This is expressed by the following equation.
2.1.8 Route Discovery Process using Tuned Cuckoo Process
Multicast stable QoS path creation involves two phases: a request and a reply phase. Request
phase invokes route discovery process to find routes having a group of receivers using stable and
QoS intermediate nodes. It helps to reply phase for updating of RIC and also performs
confirmation process. It acts as intermediate nodes that help to create a multicast mesh to the
group of receivers.
RR packets are used in source node to find the route to its group of receivers. The sequences of
operations that occur are as follows:

Source node prepares an RR packet with an application of bandwidth and delay
requirements.
Selective transmission of RR packet to neighbors once satisfies stability criteria, i.e.,
SFBN greater than SFTH, and bandwidth requirement, i.e., estimated bandwidth greater
than twice the application requirements.
Once received, node discards RR packet (both sequence number and source address).
If RR packet is not a duplicate, check if the availability of Routing Information Cache
(RIC); if available, RP packet create and start reply propagation to the source.
If RR packet is a duplicate, then discard it and stop transmission of RR packet.
If not duplicate and no route available in RIC, transmit the RR packet by updating its
fields (route record, Stability Factor Between Nodes (SFBN ) record, bandwidth record,
delay record, time to live, and nexthop address) to its neighbors as in step 2.
Perform steps 3 to 6 (Till destination reached).
If the receiver is not reached within certain hops, send RE packet to the source node.
Figure 1: Route request paths from S to R1 and R2
3. RESULTS AND DISCUSSION
3.1 Simulations and Performance Evaluation
In this section, the performance of proposed protocol with DQSMRP and Fully Distributed
Multicast Routing Protocol (FDMRP) [31] is compared, through a considerable set of
simulations. These etiquettes have been taken for comparison because both are mesh based. These
protocols are collated in terms of packet delivery ratio, control overhead and average end-to-end
delay. Simulations considering the values of the production parameters are taken for several
iterations, and these are computing the mean. The values lying within 95% of the confidence

interval of the mean are used for computing the mean value, which is shown in the graphs in
result analysis section. The various network scenarios have been simulated. Simulation
environment consists of four models: Network, Channel, Mobility and Traffic. An ad hoc network
is created at an area of l × b square meters as network model; it is having N number of mobile
nodes spaced randomly. Limited bandwidths in coverage area around each node have shared to its
neighbors. It is assumed that the operating range of transmitted power and communication range
are constant.
3.1.1 Performance Parameters
Following metrics have been used to scrutinize the performance:
Packet Delivery Ratio (PDR) : The ratio of a number of average data packets received at
the multicast receivers to the number of data packets sent by the source.
Packet Over head : The ratio of control packets sent to the network to the total number
of average data packets delivered to the receivers.
Average end-to-end Delay: The average delay experienced by the successfully delivered
packets in reaching their receiver.
Table 1: Simulation scenario
No. of Nodes 50,100,150 and 200.
Routing Protocol DQSMRP
Area Size 1000 X 1000
Mac 802.11
Radio Range 250m
Simulation Time 10 sec
Traffic Source CBR
Packet Size 512
Receiving Power 0.395
Sending power 0.660
Idle Power 0.035
Initial Energy 10.0 J
Node Speed 5,10,10 and,20 ms
No of destinations 2,4,6 and 8

3.2 Result Analysis
The simulation results of DQSMRP and FDMRP present the comparison results by varying
number of multiple destinations and node speed. According to the simulation results in Fig 2 to
Fig 7, the performance of TCSMRP is more reliable than FDMRP. The execution of DQSMRP
packet delivery ratio is gathered more than 14.2% compare to FDMRP. Moreover, the rendering
of DQSMRP startsto end delay is less than FDMRP. The performance rate is almost 18% less and
the delay ratio is varied with respective of node speed and number of destinations. According to
the overhead performance scenario, the overhead rate is 22% less than FDMRP.
Figure 2: PDR vs Number of Destinations
Figure 3: PDR vs Node Speed

Figure 4: Overhead vs Node Speed
Figure 5: Overhead vs Number of destinations

Figure 6: End - End Delays vs Speed
Figure 7: End - End Delays vs Number of Destinations
4. CONCLUSIONS
This paper clarifies about a hybrid computational intelligent algorithm i.e., Tuned Cuckoo Search
Algorithm (TCSA), that includes the dominant attribute of two different heuristic techniques have
been used to solve QoS multicast routing. The QoS routing in a MANET is a complicated to
determine a prime routing. The general conclusion from presented simulation experiments reveals
that proposed routing protocol performs better than FDMRP in terms of packet delivery ratio,

packet overhead and average delay from start to end as a function of varying number of receivers,
sources and node speeds. In future works, the author’s aims to study further by comparing our
Tuned Cuckoo Search Multicast Routing Protocol with some more QoS based routing protocols
in MANETs. Numerical experiments have shown that the propounded hybrid TCSA algorithm
provides superior search multicast trees, particularly in both swift convergence and robustness,
when compared to other algorithms. This proposed algorithm shall be the lead for real-time
networks with suitable refinement based on when and where it utilized.
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AUTHORS
M. Vijayalakshmi Completed B.E and M.E in E.C.E from Osmania University. Presently pursuing Ph.D in
the area of Wireless Networks at Jawaharlal Nehru Technological University, Hyderabad,(JNTUH) Under
the guidance of Prof., Dr.D.Sreenivasarao, JNTUH. And working as an Assoc.Prof. in G.N.I.T.S.,
Hyderabad. Total teaching experience is of 16 years.

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