Adda, Mo

Lomas, C.

Peart, Amanda

English

Portsmouth University Research Portal (Pure)

Ant replication: Saving power expenditure in MANETs 
Mo Adda, Cheryl Lomas, Amanda Peart 
School of Computing, University of Portsmouth  
Buckingham Building, Lion Terrace, Portsmouth PO1 3HE, UK 
{mo.adda, Amanda.peart} @ port.ac.uk, cherylannelomas@yahoo.co.uk 
 
 
Abstract 
 
Mobile ad hoc networks (MANETs) require no fixed infrastructure, thus allowing dynamic networks to 
be created whenever and wherever required.  This advantage is not only of huge benefit for 
military/rescue operations but would also be similarly advantageous in the industrial and educational 
sectors.  However, MANETs are not without their drawbacks.  Routes may frequently be broken 
without notice, due to nodes moving out-of-range, or from the mobile device containing the node being 
switched off or if power expires.  Due to the need for economies in power expenditure, reactive routing 
protocols are favoured over those that are proactive as nodes are only activated when a route is 
required.  Proactive protocols that keep routes continuously updated will in turn exhaust the batteries 
of the mobile devices.  This paper proposes that power saving can be achieved at the route discovery 
phase in relation to the routing hybrid protocols involving the use of ants.  We will demonstrate that by 
splitting ants and recovering their history we will achieve an efficient power saving as oppose to global 
ants routing. 
 
Keywords 
Mobile ad-hoc network, ant replication, splits, energy-efficiency, power saving, route discovery 
 
 
1.  INTRODUCTION  
 
With the advent of wireless networks, as their popularity increases, not only in industry but also for 
educational and home use, interest is now turning to mobile ad hoc networks (MANETs).  Unlike 
wireless networks, MANETs do not require infrastructure support and the mobile nodes, which act, as 
both host and router, are autonomous.  These nodes may join and leave the network at will which make 
MANETs especially advantageous in disaster zones (earthquakes, floods) where networks 
infrastructures have been destroyed or for rescue operations (sea, mountain) where there is no 
infrastructure. 
 
The nodes are embedded into devices such as laptops, mobile phones, PDAs and may even be 
integrated into clothing (an idea which is not far from fruition) therefore indicating that the majority of 
nodes forming the MANET are battery operated.  Although these batteries are re-chargeable, the 
environments in which such networks are formed means that the energy supply required for re-
charging is not constantly available.  Hence it follows that MANETs have a finite power supply, thus 
energy conservation is of paramount importance.  Due to the nature of MANETs where nodes may be 
leaving due to travelling out-of-range or being switched off, it is essential to the network’s success that 
as few nodes as possible are lost because of power failure.  A way of achieving this, and an area in 
which much research is being undertaken, is to ensure routing protocols are as energy efficient as 
possible. 
 
Toh’s [?] CMMBCR (Conditional Min-Max Battery Cost Routing) has combined the features of two 
energy-efficient protocols namely MMBCR and MTPR, to provide a hybrid protocol, which uses two 
conditions in order to select the most energy-efficient route to take.  Briefly, MMBCR and MTPR 
function in the following ways: 
 
MMBCR – determines its condition on the residual power levels of each individual node within sets of 
routes.  Attention is given to the weakest node of each route, that is, the node with the least amount of 
power left.  The route to be used is the one whose weakest link is the strongest when compared to all 
the other routes’ weakest nodes.  Basically, by avoiding the very weakest nodes unless they are 
critically needed increases the network’s lifetime.  Where MMBCR considers individual node power 
capacities in order to ascertain the route to take, MTPR studies the power requirements of routes as a 
whole.  The routes using the least amount of power are the ones with short hops between nodes, as 
opposed to the longer hop routes.  However, the route(s) using short hops may incur a greater number 
of intermediary nodes between source and destination and although they may be favourable in terms of 
energy cost, the probability of nodes with low residual power capacity being used, increases.  This 
could inevitably result in weaker nodes being frequently used, possibly to the point of power 
exhaustion and resulting in their departure from the MANET causing damaging breaks. 
 
CMMBCR has encompassed the benefits from both these protocols.  Initially, CMMBCR has a pre-
determined level of acceptable power capacity and if the nodes within the routes are greater than or 
equal to this level, the MTPRs condition is applied.  However should nodes within all the routes fall 
short of CMMBCRs level, using a weak node is unavoidable and it is therefore advantageous to the 
network to use the route with the strongest of these nodes.  Hence, the protocol MMBCR is applied. 
 
These protocols are concerned with energy efficiency in the transmission of packets across the 
network.  This paper proposes power saving during the route discovery phase at node level.  Generally 
it is felt that reactive protocols should be favoured over proactive ones as power expenditure is only 
used when a route is required as opposed to using valuable power to determine routes, which may not 
be used.  However, in the case of MANETs, table-driven (proactive) protocols are advantageous in 
keeping routes up-to-date and correct; taking in changes brought about by the nodes mobility.  Hence, 
various hybrid routing protocols have been developed where a proactive protocol is used alongside a 
reactive one.  A number of these use ants (proactive) for example, Ant-AODV [1] and AntNet [?].  It is 
within this context that considerable power saving can be achieved.   
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 1. Example of an ant splitting at node 3  
 
 
2.  ANALYSIS OF POWER CONSUMPTION 
 
We will show in this paper that power consumption can be considerably reduced by having the route-
discovering ants split once their node history has reached a pre-determined count.  This second ant will 
be a replica of its ‘parent’, hence each will possess identical node histories, see figure 1.  Thus at this 
point, their node histories will have been acquired using only half the power consumption usually 
required.  Post-split, the ants will work independently and power expenditure returns to the level of the 
currently proposed ant routing protocols.  Using the ratio of ants to nodes as 1:2, it is assumed that the 
majority of nodes will be in active mode, so power required to activate a sleeping node is not being 
taken into consideration. In the following subsections we will compare the power consumption 
between splitting and non-splitting ants in wireless network.  We will denote the powers in each case as 
Ps and Pns respectively.   Throughout this paper we will be interested in the percentage of the power 
saving between the two techniques by computing Pr% = (1 - Ps/ Pns) * 100. 
 
The following notations are used throughout the analysis of the power consumption. 
Aij = Ptij + Prij + Ppij is the power consumed by every node visited by an ant, where Ptij, Prij and Ppij 
represent the transmitting, the receiving and the processing powers respectively.  Depending on the 
history of an ant, the consumed power of each node in the network may be different.   
Vi denotes the number of visited nodes before two consecutive ant splits. 
Uij denotes the number of visited nodes by an ant after which the ant vanishes due to insufficient power 
in the transmitter or the receiver has moved out of range, where i is the level of spilt and j is the path 
taken in that level by an ant. 
1 
6 
2 
3 
4 
5 
1
  
1 2  
1 2 3  
1 2 3  
1 2 3 5  
1 2 3 4  
  
 
 
 
 
 
 
 
 
 
 
 
Fig 2. Ants splitting on a node history count of 3 
 
In general, using figure 2, one can deduce the powers consumed with and without ant splitting as 
 
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where n is the number of splits or simply called in this paper the number of levels.  Between each level 
an ant may visit Uij number of nodes after which it will vanish or Vi number of nodes after which it 
splits to generate the next level.  In general for a network with multiple sources the above equations are 
summed up to cover all the paths visited by the ant in the network. 
 
2.1  Ant splitting and reaching maximum node history 
   
Although initially the concept of having ants split at a certain point would help to make ant protocols 
more energy efficient, it also appears to provide a possible solution to the loss of the ant population as 
noted by Marwaha et al [1].  An expansion of the notion of ants splitting at a certain node history count 
would be to have the ants split on a regular count, for example, on history of three, see figure 2 as an 
example.  The following calculation considers the power saving (Pr) percentage achieved in terms of 
power consumption for the reception and transmission of the ants when splitting, refer to figure 2.  On 
transmission of the second ant, the ant will select a random hop different from the one it has originated 
from.  By setting Uij =Vi in equation 1 and 2, and computing the gain in power consumption for ant 
splitting with maximum history, we can deduce the following equation. 
 
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There are several cases related to the number of visited nodes and the amount of consumed power per 
nodes that can be studied from this equation.  In the following cases we will describe, possibly, the 
most common ones. 
  
Case 1:  
In this scenario we assume that all the nodes between two consecutive splits or within a level consume 
the same amount of power, Aij = A, with equal number of visited nodes between the splits, Vi = V.  
Applying this assumption to equation 3 and after some mathematical manipulations the result is 
 
3 
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12/3 12/3 
… 
… 
… 
6 nodes history 
12 nodes history 
3 nodes history 
9 nodes history 
15 nodes history 
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Case 2: 
In this scenario we assume that the power in each node increases by a factor of A every time an ant 
visits it, due to the accumulation of the ant history.  This can be proved when the number of visited 
nodes between splits is fixed, the consumed power for each level is  
 
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where A is the consumed power of the source node that originated the ant, and V is the constant 
distance in term of nodes between two consecutive splits.  By using equation 5 in equation 3, and after 
some mathematical manipulations, we can deduce the gain in power consumption as  
 
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2.2  Ants splitting possibly without reaching maximum node history. 
 
The above equations give the theoretical power saving, as it assumes that all the ants ‘live’ to their full 
node history before being deleted from the network.  However, taking into consideration Marwaha et 
al’s [1] observations of ants diminishing at a considerable rate from the network, thus implying full 
node histories are not collected by all the ants, as deduced in equations 1 and 2 for the general cases. 
 
Case 3 
In this scenario we assume that there is a single split, n=1 and the power consumption per node 
increases by a factor of A, refer to equation 5.  Figure 4 in the results section will show the power 
saving for this case. 
 
 
4.  RESULTS 
 
As figure 3 shows, with case 1 the resultant power saving would be 72% with up to 6 splits.  In this 
case, the power saving is independent of the number of nodes visited within each level, and almost 
increases linearly with the number of splits.  However, in case 2 where the power consumption 
increases each time an ant traverses the network, the gain in power consumption is slightly depending 
on the number of visited nodes in each level.  As such negligible differences in power saving are 
achieved by varying the number of nodes within the levels, it would seem to be more advantageous to 
have a lower number of nodes per level, as this would facilitate a higher degree of individuality within 
an ants node history.   For case 3, we assume that the network contains 100 nodes with a maximum 
number of 50 sources, the maximum node history is set to 15 after which the ants are deleted, with the 
split occurring at node count 5.  Therefore the maximum number of nodes visited per source would be 
25.  On average, as shown in figure 4, a gain of 13% could be achieved.  For larger numbers of splits 
one would expect a greater saving in power consumption, to be around 50% for 6 splits, as shown in 
figure 3.   
010
20
30
40
50
60
70
80
0 1 2 3 4 5 6
Number of ant splits (levels)
%
 p
o
w
e
r
 s
a
v
e
d
Equation 4 where n=6
Equation 6 where n=6, V= 2
Equation 6 where n=6, V= 3
Equation 6 where n=6, V=4
Equation 6 where n=6, V=5
 
Fig. 3.  Power savings achieved at each level 
 
10
10.5
11
11.5
12
12.5
13
13.5
14
14.5
15
0 10 20 30 40 50
Number of source nodes
%
 P
o
w
er
 s
a
v
in
g
 
Fig. 4.  Power savings achieved when ants split once only 
 
 
5.  CONCLUSION 
 
This paper has shown that an energy saving can be achieved at the route discovery stage when using 
routing protocols involving ants.  Although proactive protocols are useful for MANETs, as they can 
keep up-to-date with the network topology, they are less favourable in terms of power expenditure 
when compared to reactive protocols.  Ant replication produces results, which could make the hybrid 
proactive protocols a competitive option.  It can be seen from the foregoing results that the higher the 
level an ant reaches within its lifetime, the greater the power saving.  Ants, which split only once, 
produced the lowest percentages of power saving indicating that multiple splits are preferable.  Within 
these splits, it can be seen that the lower the number of nodes involved, the better the yield of 
individual routes obtained.  Additionally, a healthy ant population is more likely to be maintained.  
 
References 
 
[1] 
 
[2] 
 
[3] 
 
[4] 
 
[5] 


Ant replication: saving power expenditure in MANETs

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Ant replication: saving power expenditure in MANETs

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