A MIN-MAX SCHEDULING LOAD BALANCED APPROACH TO ENHANCE ENERGY EFFICIENCY AND PERFORMANCE OF MOBILE ADHOC NETWORKS

International Journal of Computer Networks & Communications (IJCNC) Vol.11, No.3, May 2019
DOI: 10.5121/ijcnc.2019.11306 85
A MIN-MAX SCHEDULING LOAD BALANCED
APPROACH TO ENHANCE ENERGY EFFICIENCY
AND PERFORMANCE OF MOBILE ADHOC
NETWORKS
K.Venkatachalapathy1
and D.Sundaranarayana2
1
Research Scholar, Department of Computer Science and Engineering,
Annamalai University, Chidambaram, India
2
Professors, Department of Computer and Information Science,
Annamalai University, Chidambaram, India
ABSTRACT
Energy efficiency and traffic management in Mobile Ad hoc Networks (MANETs) is a complex process due
to the self-organizing nature of the nodes. Quality of service (QoS) of the network is achieved by
addressing the issues concerned with load handling and energy conservation. This manuscript proposes a
min-max scheduling (M2S) algorithm for energy efficiency and load balancing (LB) in MANETs. The
algorithm operates in two phases: neighbor selection and load balancing. In state selection, the
transmission of the node is altered based on its energy and packet delivery factor. In the load balancing
phase, the selected nodes are induced by queuing and scheduling the process to improve the rate of load
dissemination. The different processes are intended to improve the packet delivery factor (PDF) by
selecting appropriate node transmission states. The transmission states of the nodes are classified through
periodic remaining energy update; the queuing and scheduling process is dynamically adjusted with energy
consideration. A weight-based normalized function eases neighbor selection by determining the most
precise neighbor that satisfies transmission and energy constraints. The results of the proposed M2SLB
(Min-Max Scheduling Load Balancing) proves the consistency of the proposed algorithm by improving the
network throughput, packet delivery ratio and minimizing delay and packet loss by retaining higher
remaining energy.
KEYWORDS
Energy Efficiency, Load Balancing, MANET, Packet Delivery Factor, Queuing and Scheduling.
1. INTRODUCTION
Mobile ad hoc network (MANET) is a collection of autonomous self-organizing terminal nodes
interconnected through wireless links. The terminal nodes exhibit two types of behavior: host and
router for sending, receiving and forwarding data. Data handling and transmitting is expedited by
selecting appropriate routing protocols. The nodes are mobile in nature, capable of moving
around the network with a varying speed; the network is volatile. This dynamic network does not
require any special infrastructure and is flexible for communication with any neighbor nodes. The
nodes communicate through radio units and they rely on their neighbors for transmitting data over
a long distance in a multi-hop fashion [1]. Routing protocols are designed to cope-up with the
intrinsic nature of the nodes to achieve better network performance. However, quality-of-service
(QoS) is still a demanding task that cannot be served by the conventional routing protocols. QoS
fulfillment varies with the demands of the end-users that are also controlled by the nature of the
nodes. Conventional routing protocols and algorithms designed so far concentrates in minimizing
network traffic or reducing delay due to multi-hops. This result in partial achievement of network
QoS, for which ongoing research process serves different solutions [2].

86
The nodes are powered by built-in batteries that drain with time. The nodes exploit battery power
for sending and receiving data. The neighboring nodes that forward data also drain energy other
than the source and destination. This increases the energy expenses of MANET that needs to be
controlled for achieving efficiency. There are multiple factors affecting energy efficiency, the
resource constraint nature of the nodes is the prominent factor. Implementing an efficient energy
conservation policy for the mobile nodes will improve the network lifetime by confining the rate
of energy exploitation. The nodes are deployed in hostile environments where replacing and
recharging of the battery is practically impossible. Contrarily, the node has to dispatch all of its
communication within the dead state to ensure network reliability. The aforementioned efficiency
in the network is achieved by overseeing the protocols that are energy proficient which is
typically designed for this purpose. Similarly, complex optimization methods increase the
communication cost of the network by imposing a task load on the nodes. The design goal of
energy efficient routing algorithms and protocol is to improve network lifetime by minimizing
unnecessary energy exploitation and to prevent earlier energy drain of the nodes [3, 4].
Traffic management is another issue in the mobile network to handle congestion. QoS of the
network solely relies on the incoming the network traffic that is to be rationalized to prevent
congestion at nodes and communication links. Load balancing is the conventional solution to
handle network traffic and to prevent information overloading. Load balancing utilizes all the
resources in the network in accordance with the routing protocols to deliver information to
appropriate destinations. Along with energy efficiency, routing protocols are intended to improve
load dissemination through the available intermediate nodes. As the nodes possess limited
storage, data overloading cannot be prevented, resulting in bottlenecks. Appropriate load
balancing schemes are tested with wireless networks to minimize the rate of congestion through
uniform and on-demand load distribution. Avoiding unnecessary energy exploitation and earlier
energy drain are some of the joint benefits of load balancing [5-7].
2. RELATED WORKS
Farooq Aftab et al. [8] proposed a self-organizing clustering scheme for improving network
scalability and endurance. The authors address network congestion issues by varying cluster sizes
dynamically. Further improvements in the network such as energy efficiency, cluster head
changes are optimized with the consideration of node energy and mobility.
Constructive-Relay-based cooperative routing (CRCPR) protocol is introduced by Jingwen Bai et
al. [9] to improve the communication rates of MANET nodes. This protocol considers the energy
utilization and conservation rates of the nodes and link failure probability to determine the
optimal routing paths to the destination. This protocol improves network throughput with
prolonged network life span.
Ad hoc on demand multipath distance vector with fitness function (FF-AOMDV) [10] is an
approach for improving the performance and the efficiency of energy usage in the mobile ad-hoc
network environment. The fitness of the route is evaluated by considering the energy and distance
of the neighboring nodes. Conventional multipath routing is deviated from utilizing its distance
estimation to prolong the network lifetime.
Dynamic hop-aware buffering (DHAB) propose by Al-Mahdi and Kalil [11] is designed for
minimizing packet loss and delay in MANETs. Based on the packet’s priority, the buffer of the
nodes is virtually allocated for transmission over long hops. The buffering technique is designed
to dynamically adjust with the arriving traffic using a Markov chain model.

87
Magán-Carrión et al. [12] introduced a dynamic relay placement solution for improving the
operational performance of MANETs. A combination of particle swarm optimization (PSO) and
model predictive control (MPC) is used for the above- mentioned placement and selection. This
bi-objective optimization improves network throughput and link connectivity jointly.
Energy efficient lifetime aware multicast (EELAM) [13] is exclusively designed for MANETs for
improving its route discovery efficiency. EELAM is a multicast routing process that employs a
genetic algorithm for selecting nodes based on residual energy and energy exploitation. The
fitness function of the genetic process is intended to improve the energy efficiency of the
network.
Dynamic multi-stage tandem queue modeling-based congestion adaptive routing (DTQCAR) [14]
is introduced to improve the packet delivery ratio of the network besides unplanned network
traffic. This adaptive routing identifies congestion, alerts the neighbors for handling network
traffic for minimizing packet loss. This congestion control routing identifies congestion-free
neighbors for disseminating load.
The Power and load aware (PLA) multipath routing scheme are proposed by Ali et al. [15] for
prolonging MANET lifetime despite varying network load. This multipath routing is constructed
based on dynamic source routing protocol (DSR) and the protocol identifies neighbors using cost
function. With this cost function, the source identifies multiple disjoint optimal paths to the
destinations.
Learning automata (LA) based route selection process is introduced in [16] for improving the
service quality of MANETs. The feedback mechanism of LA helps the source node to discover
optimal routes satisfying energy, link stability, and delay factors. This method is more apt for a
network with varying node density and velocity.
3. PROPOSED METHODOLOGY
The proposed Min-Max Scheduling load balancing algorithm (M2SLB) is designed to improve
energy efficiency and load balancing in mobile nodes. Different from the existing approaches,
this algorithm considers two definitions with min-max constraints along with the energy of the
nodes before scheduling. The scheduling process is dynamic to cope-up with the mobility of the
nodes where a distinct set of evaluation criterion is considered. The min-max is formulated for
two different states of the nodes: energy retained state and half drain state. Link expiration time
(LET) and packet delivery factor (PDF) is the other two duos for the states of the nodes to
improve load handling. The different combinations of node factors aid to improve network
performance by improving throughput, packet delivery ratio, with lesser delay and energy
exploitation.
3.1 Network Model
We represent a MANET in the form of a graph where, N is the set of mobile nodes
that are connected through a set of vertices V. Consider two nodes and , the nodes are said to
be direct neighbors if distance , where R is the communication range of the
mobile node.

88
3.1.1 Energy Model
Mobile nodes utilize energy for transmitting and receiving packets. Each node is fitted with a
battery source with initial energy . The energy utilization of a node is the summation of
transmitting and receiving energies that is represented in equation (1)
(1)
With the knowledge of energy utilization, the remaining energy of the node is estimated
as
(2)
3.2 Min-Max Scheduling for Energy Efficient Load Balancing (M2SLB) Algorithm
The methodology is modeled to handle two different situations before scheduling; before half
drain and after half drain of the node. The half drain of a node is defined as the point at
which its remaining energy reaches exactly half of its initial energy. We notify the fore and after
drain states as active transmission and light transmission correspondingly. In active and light
transmission states, the neighbor selection is prioritized based on energy for ensuring the energy
efficiency of the network. The entire process of M2SLB is divided as neighbor selection and load
distribution. The Neighbor selection phase is responsible for conserving energy and load
distribution improves the transmission rate of the network.
Figure 1. Process of M2SLB
We consider a normalization function to ease neighbor selection, from which energy
retaining nodes are selected for transmission. The distinct metrics are balanced by assigning
appropriate weights for the considered metrics depending upon the state classification. Figure 1
illustrates the process of M2SLB with their properties and functionality considerations in each
stage.
3.2.1 State Definition based neighbor selection
The state definition of a node is classified as active or light transmission. This classification is
based on the remaining energy of the node. If a node is less than its half drain then, the
node is in light transmission state otherwise it is inactive transmission state. During the active
transmission period, the neighbor selection is facilitated by evaluating and link expiry time
(LET). LET indicates the lifetime of the vertex , i.e. the link between two nodes. The LET
of a vertex between and is estimated using equation (3).

89
(3)
Where , , ,
The other variables used are and ( represents the coordinates of the nodes
and moving with a velocity of and respectively. The nodes are mobile with a mobility
deviation of and correspondingly with the estimation of LET, the normalization function
is estimated with the weight and as given in the below equation (4)
(4)
Here, must be minimum and LET is maximum, which is an ideal condition that is satisfied at
random time. There are two cases wherein the node attributes affects the normalization function:
Case 1: The nodes possess higher mobility (i.e.) a neighboring node selected is induced under
variable velocity. In this case, the weight of the LET metric is given a higher choice
where . This condition is valid until
Case 2: The remaining energy of the node ceases more abruptly, in this case, and the
node moves to light transmission state.
When a node moves to a light transmission state, the remaining energy and PDF are considered
selection; the min-max is violated. The PDF at the destination is estimated using equation (5)
(5)
Where the packets lost in the transmission, n represents the sender/ transmitting node and h
is the hop distance between source and destination. Different from the above cases, weights are
assigned with balanced values (i.e.) the weights are equally considered. Obviously, the link
transmission rate in the specific vertex is reduced to cope- up with the available energy of the
node. From either of the cases, is selected as the next neighbor for
transmitting packets. The process of state definition based neighbor selection is illustrated in the
below Figure 2.
Figure 2. State Definition Process Illustrations

90
The process of energy dependent neighbor selection is described in Algorithm 1.
Algorithm 1. Energy based Neighbor Selection
//initial data transmission path
//verify shortest distance
//estimate link expiry time
//estimate remaining energy
//neighbor selection
3.2.2 Scheduling and Transmission for Load Balancing
Figure 3 portrays the general representation of scheduling and transmission process.
Figure 3. Scheduling and Transmission Process
In the scheduling process, first –in – first out method is followed to distribute load across the
network. Before the scheduling process, the selected nodes are utilized to store and forward
incoming traffic packets. The Queuing process is temporary to prevent packets being lost in
congestion. The rate of queuing relies on the number of packets being transmits. The packets
transmitted is then estimated as

91
(6)
Where and are the data rate and transmitting time observed for a vertex V.
Figure 4. Queuing and Packet Dissemination
The queue of the selected neighbor is filled in the order of received. The accepted packets are
forwarded with a small waiting time. The further neighbor set is selected by estimating PDF at
the destination through that neighbor. For energy retained node,
is selected for further transmission. The above
condition is optimal for case 1 discussed earlier. For nodes entering light transmission state, is
less and hence wait time is less but PDF through the node must satisfy maximum delivery or
maximum loss. Those nodes are prevented from being overloaded by avoiding queue overflow
achievement. The process of PDF scheduling and packet queuing is illustrated in figure 4.
Algorithm 2 describes the process of scheduling and transmission of neighbor selection.

92
4. RESULTS AND DISCUSSION
The proposed min-max scheduling for energy efficient load balancing has been simulated in the
Network simulator – 2 simulation tool and its performance under different parameters are
examined. A comparative analysis of the proposed method with FF-AOMDV [10] and DTQCAR
[14] takes place for the metrics: throughput, packet delivery ratio, delay, remaining energy and
packet loss. Table 1 summarizes the simulation parameters and their values.
Table 1. Simulation Parameters and Values
Parameter Value
Network Dimension 1000m x 1000m
Mobile Nodes 50
MAC 802.11
Range of the Node 250m
Application Type Constant Bit Rate
20 J
Pause Time 2 ms
Queuing Model FIFO
4.1 Throughput Analysis
Figure 5. Throughput Comparisons
Figure 5 illustrates the comparisons of throughput observed in the proposed and existing methods.
In the proposed M2SLB, the incoming network traffic is disseminated through dynamic queuing
and adjustable link rates. The number of stagnant packets in the node queue is less, as the further
neighbors and transmission rates are decided upon the PDF and factors. With the varying
data rate, node transmission is modeled to support both load handling and energy awareness. This
feature improves the throughput, where the throughput of the existing DTQCAR and FF-
AOMDV is 31.11 % and 48.48 % lower when compared to the proposed M2SLB (at the number
of nodes =50).

93
4.2 Packet Delivery Ratio Analysis
Figure 6. Packet Delivery Ratio (PDR) Comparisons
The diminishing factors in the network due to network load and energy are addressed by selecting
neighbors that achieve higher PDF. In both energy efficient neighbor selection and queuing
process, the transmission is preceded by selecting neighbors ensuring maximum PDF. This factor
is achieved for varying data rates; the energy of the nodes is also accounted for ensuring seamless
transmission. In both active and light transmission, the neighbors satisfy to achieve
higher delivery ratio (Figure 6). The proposed Packet delivery ratio has been improved, where the
existing DTQCAR and FF- AOMDV has 2.25 % and 3.07 % (at data rate= 300Kb) lower PDR
than M2SLB.
4.3 Delay Analysis
The comparisons of delays between the existing and proposed methods are illustrated in Figure 7.
The number of packet loss in the proposed M2SLB is less for which the retransmission required
is less. The waiting time of the packets is less as the neighbor in both states is instantly identified
to forward packets to the destination. Therefore, along with the round trip time of the packet, less
waiting time is accounted.
Figure 7. Comparisons of Delays

94
This time is comparatively less as the retransmission is controlled. The proposed M2SLB
minimizes delay by 17.17 % and 35.43% compared to DTQCAR and FF-AOMDV respectively
(Rate= 300Kb).
4.4 Remaining Energy
Figure 8. Remaining Energy Analysis Comparisons
In the state definition process of M2SLB, the transmission is classified based on the remaining
energy of the nodes. On estimating the remaining energy, the transmission and queuing rates of
the nodes are dynamically adjusted to improve the flow of . Unlike the other methods, the
lesser enduring energy nodes are prevented from handling higher traffic to conserve their lifetime.
The nodes are prevented from draining all of its energy, by distributing the load among the
available neighbors. Both the energy and load handling features of the proposed method aids to
retain higher remaining energy when compared with previous DTQCAR and FF-AOMDV, where
they have 20.94% and 45.08% lower remaining energy respectively when compared with M2SLB
when the number of nodes =100.
4.5 Packet Loss Analysis
Figure 9. Packet Loss Factor Comparisons

95
Figure 9 shows the packet loss factor comparisons of the proposed M2SLB with the existing
methods. Here, as the comparison is verified a factor, the maximum value of the y-axis is 1.
Packet loss which is caused by energy and traffic congestion is resolved by selecting energy
efficient and delivery assisting nodes in the network. The Initial state segregation process and
later queuing and scheduling process improve the transmission rate with controlled delay.
Henceforth, the proposed M2SLB minimizes packet loss by 27.95 % and 30.20 % at the time of
congestion and queuing compared to the existing DTQCAR and FF-AOMDV when the rate is
300Kb. Table 2 presents the comparative values of the simulation for the above Parameters.
Table 2. Comparative tabulation of Results
Metric FF-AOMDV DTQCAR M2SLB
Throughput (Kbps) 198.25 265.09 384.82
PDR (%) 90.28 91.04 93.14
Delay (s) 1.27 0.99 0.82
Remaining Energy (J) 4.3 6.19 7.83
Packet Loss Probability 0.096 0.093 0.067
5. CONCLUSION
This manuscript introduces a min-max scheduling (M2S) algorithm for achieving energy
efficiency and load balancing in MANETs. This algorithm accounts the energy based link
stability for improving the energy efficiency of the network. Based on the energy, the nodes are
classified under two stages that aids load distribution ensuring higher packet delivery. In the load
balancing phase, the selected nodes are influenced by the queuing and scheduling process to
improve the rate of data handling irrespective of the type of traffic. From the experimental results,
it is seen that the proposed algorithm, on average, improves throughput and PDR by 39.82% and
2.66% correspondingly by retaining 33.01 % higher remaining energy, where minimizing delay
and packet loss by 26.3% and 29.07%. The experimental results prove the consistency of the
algorithm by improving network performance and energy efficiency in terms of throughput,
packet delivery, delay, packet loss and remaining energy.
REFERENCES
[1] T. Spyropoulos, R. N. B. Rais, T. Turletti, K. Obraczka, and A. Vasilakos, “Routing for disruption
tolerant networks: taxonomy and design,” Wireless Networks, vol. 16, no. 8, pp. 2349–2370, Oct.
2010.
[2] S. Batabyal and P. Bhaumik, “Mobility Models, Traces and Impact of Mobility on Opportunistic
Routing Algorithms: A Survey,” IEEE Communications Surveys & Tutorials, vol. 17, no. 3, pp.
1679–1707, 2015.
[3] K.Venkatachalapathy, D.Sundaranarayana, “A Survey on Energy Efficiency Issues and solutions in
Mobile ad-hoc Networks,” International Journal of advanced computational engineering and
networking, vol. 5, no. 9, pp. 38–44, 2017.
[4] D.Sundaranarayana, K.Venkatachalapathy, “An Effectual load distribution approach based on
transmission power and topology controlled clustered environment in mobile adhoc network,”
International Journal of Computer Applications, vol.179, no. 25, pp. 34–38, 2018.

96
[5] K.Venkatachalapathy, D.Sundaranarayana, “Energy Efficient modified butterfly optimization
based clustering algorithm for cluster head selection in mobile ad hoc network,” Journal of advanced
research in dynamical and control systems, vol.2, Special isssue, pp.1521–1531, 2018.
[6] Mohammed Abdul waheed, K.Karibasappa, “QOS Routing for Heterogeneous MANET’s,”
International journal of computer engineering sciences (IJCES), vol.2, Issue.3, pp.77-81, 2012.
[7] P. F. A. Selvi and M. M.s.k, “Ant Based Multipath Backbone Routing for Load Balancing in
MANET,” IET Communications, 2016.
[8] F. Aftab, Z. Zhang, and A. Ahmad, “Self-Organization Based Clustering in MANETs Using Zone
Based Group Mobility,” IEEE Access, vol. 5, pp. 27464–27476, 2017.
[9] J. Bai, Y. Sun, C. Phillips, and Y. Cao, “Toward Constructive Relay-Based Cooperative Routing in
MANETs,” IEEE Systems Journal, vol. 12, no. 2, pp. 1743–1754, 2018.
[10] A. Taha, R. Alsaqour, M. Uddin, M. Abdelhaq, and T. Saba, “Energy Efficient Multipath Routing
Protocol for Mobile Ad-Hoc Network Using the Fitness Function,” IEEE Access, vol. 5, pp. 10369–
10381, 2017.
[11] H. Al-Mahdi and M. A. Kalil, “A Dynamic Hop-Aware Buffer Management Scheme for Multi-hop
Ad Hoc Networks,” IEEE Wireless Communications Letters, pp. 1–1, 2016.
[12] R. Magán-Carrión, J. Camacho, P. García-Teodoro, E. F. Flushing, and G. A. D. Caro, “A Dynamical
Relay node placement solution for MANETs,” Computer Communications, vol. 114, pp. 36–50,
2017.
[13] N. Papanna, A. R. M. Reddy, and M. Seetha, “EELAM: Energy efficient lifetime aware multicast
route selection for mobile ad hoc networks,” Applied Computing and Informatics, 2017.
[14] A. Amuthan, N. Sreenath, P. Boobalan, and K. Muthuraj, “Dynamic multi-stage tandem queue
modeling-based congestion adaptive routing for MANET,” Alexandria Engineering Journal, vol. 57,
no. 3, pp. 1467–1473, 2018.
[15] H. A. Ali, M. F. Areed, and D. I. Elewely, “An on-demand power and load-aware multi-path node-
disjoint source routing scheme implementation using NS-2 for mobile ad-hoc networks,” Simulation
Modelling Practice and Theory, vol. 80, pp. 50–65, 2018.
[16] S. Hao, H. Zhang, and M. Song, “A Stable and Energy-Efficient Routing Algorithm Based on
Learning Automata Theory for MANET,” Journal of Communications and Information Networks,
vol. 3, no. 2, pp. 52–66, Dec. 2018.

A MIN-MAX SCHEDULING LOAD BALANCED APPROACH TO ENHANCE ENERGY EFFICIENCY AND PERFORMANCE OF MOBILE ADHOC NETWORKS

More Related Content

What's hot (19)

Similar to A MIN-MAX SCHEDULING LOAD BALANCED APPROACH TO ENHANCE ENERGY EFFICIENCY AND PERFORMANCE OF MOBILE ADHOC NETWORKS (20)

More from IJCNCJournal (20)

Recently uploaded (20)

A MIN-MAX SCHEDULING LOAD BALANCED APPROACH TO ENHANCE ENERGY EFFICIENCY AND PERFORMANCE OF MOBILE ADHOC NETWORKS