Route Stability in MANET Based on AODV
Mohamed Er-rouidi1(B), Houda Moudni1, Hassan Faouzi1, Hicham Mouncif2, and Abdelkrim Merbouha1
1 Faculty of Sciences and Technology,
Sultan Moulay Slimane University, Beni Mellal, Morocco {m.errouidi,h.moudni,h.faouzi,merbouha}@usms.ma
2 Faculty Polydisciplinary, Sultan Moulay Slimane University Beni Mellal, Beni Mellal, Morocco
hmouncif@yahoo.fr
Abstract. In recent years, mobile ad hoc network (MANET) is becom- ing more and more useful in many domains. While MANETs still suffer from several problems. Among these problems, the energy conservation.
Where the energy presents one of the greatest restriction, and has a mas- sive effect on others metrics like packet delivery ratio, overhead, end-to- end delay and the lifetime of the network. As most of mobile ad hoc stations based on a limited battery in their mission. For these reasons, we propose in this paper a fuzzy logic system (FLS) to enhance the per- formance of one of the reactive routing protocols Ad hoc On-demand Distance Vector (AODV) by avoiding nodes with low amount of energy and select the more stable path. Our fuzzy system uses three input para- meters that have a large impact on the stability of the links: energy drain rate, mobility of the node and the distance between two communi- cating nodes. Simulation results show that our protocol gives good result by reducing significantly the energy dissipation, also certain parameters affected by the energy issue.
Keywords: MANET
·
AODV·
Routing protocol·
Fuzzy logic system·
Energy
1 Introduction
With the increase of using wireless terminals, mobile ad-hoc networks receive significant attention in recent years as a technique to offer the communications between these terminals without the existing of any fixed infrastructure or cen- tralized administration. Each node in this network operates as a host and also as a router, by forwarding packets of other nodes whose destinations are not in their direct transmission range. Based on a routing protocol nodes can select the next node and forward the packets. Various routing protocols have been submit- ted to the Internet Engineering Task Force Mobile Ad Hoc Networking group [1], based on different assumptions, such as AODV [2], Dynamic Source Routing
c Springer International Publishing AG 2017
A. El Abbadi and B. Garbinato (Eds.): NETYS 2017, LNCS 10299, pp. 40–48, 2017.
DOI: 10.1007/978-3-319-59647-1 4
(DSR) [3], Destination Sequenced Distance-Vector (DSDV) [4] and Temporally Ordered Routing Algorithm (TORA) [5]. Most of these protocols take the short- est path as the main metric in building routes. While this selection method presents several effects on the network. Among this effects traffic, concentration on certain part of nodes, which results in the consumption of large amount of resource of selected nodes. Energy is one of valuable resource in mobile ad-hoc networks, since most nodes in such network are powered by battery which cannot be recharged in most cases. In order to keep the network functional as long as possible, energy-efficient routing algorithms should be developed. In this paper, we propose an enhancement of the routing protocol AODV by introducing a fuzzy logic system that use as inputs parameters three important metrics that have a large impact on the stability of the routs which are the average energy of the route, mobility of the node and the distance between two communicating nodes in order to select routs with more stability. The rest of the paper is organized as follows. In Sect.2, we address the related work Sect.3gives a brief description of AODV routing protocol and the fuzzy logic theory. Section4 describe the pro- posed solution. The performance of the proposed protocol evaluated in Sect.5.
Finally, Sect.6 concludes the paper.
2 Related Work
To face these problems, many improvements to these protocols are proposed. In [6] the authors propose a fuzzy inference system as an adaptive computational approach to compute a node’s trust value based on the residual energy level and speed of node. Also, introduce an efficient routing scheme by selecting the most trustworthy nodes to establish a stable route. In order to decreases the probability of route breaks during the data relay period. During this process, intermediate node initiates a timer if the RREQ packet has not been previously received, in purpose of waiting another RREQ from node with best trust value.
But this technique leads to a higher latency. In other hand authors of [7] propose the same technique. However, only the destination node who apply the fuzzy logic system and wait for the best route. As well, authors in [8] Propose a dynamic fuzzy energy state based AODV (DFES-AODV) routing protocol for MANETs, based on fuzzy logic and reinforcement learning [9]. In route discovery phase of this protocol, each node uses a Mamdani [10] fuzzy logic system (FLS) and use like inputs the residual battery level and energy drain rate of mobile node to decide its Route REQuests (RREQs) forwarding probability.
3 Applied Methods and Routing Protocol
3.1 Fuzzy Logic
We use Fuzzy Logic theory [11] to combine some metrics in order to make good routing decisions. In general, Fuzzy Logic can be seen as a generalization of classical set theory. By introducing the notion of degree in the verification of
a condition, thus enabling a condition to be in a real value in [0,1] other than true or false. Let U be a nonempty set and x an element in U, A is a set in U characterized by the membership function μA, In classical set theory, the membership function ofx in Ais evaluated by 1 or 0 (1). But in fuzzy set, the membership function of x in A will be a real value in [0,1] (2). Fuzziness is a language concept; its main strength is its valuable flexibility for reasoning, which makes it possible to take into account inaccuracies and uncertainties.
∀x∈U, μA(x) =
1 x∈A
0 x /∈A (1)
μA(x) :U →[0,1] (2)
3.2 Ad Hoc On-Demand Distance Vector (AODV) Routing Protocol
AODV [2] routing protocol is an adaptation of the Destination Sequenced Distance-Vector (DSDV) [4] and Dynamic Source Routing (DSR) [3] algorithms.
It is belonging to on-demand protocol family: only the node that requires a route toward a given destination launch the route discovery process, if it has no fresher route in its routing table. During the construction of the routes AODV protocol take the shortest path as the main metric, and does not take into consideration the capabilities of intermediate nodes, which play an important role in achieving the quality of service.
4 Fuzzy AODV
To deal with the problem of route selection in AODV protocol, in our pro- posed solution we are introduce three new parameters in the selection criteria of AODV. These parameters have a large impact on the stability of the links, which are residual energy, the mobility of the node and the distance between two communicating nodes. In [12,13] authors show that the distance between two communicating nodes and their mobility can affect the link stability between these nodes. The packet transmission error rate becomes higher if the distance of the link is longer, as it approaches the transmission range of mobile nodes. In this case, a small movement of one of the involved nodes can result in packet loss due to a link failure. As the link has a high probability of being broken, if one of the intermediate nodes have a low amount of energy [14]. Furthermore, packets are more likely to be lost due to external environmental factors like white noise and wireless interference if the signal strength is not very strong. For this reason, in our approach we try to investigate these parameters to enhance the perfor- mance of the network. We propose a system that contains two fuzzy logic system.
The first FLS1 has three inputs: harmonic mean of the energy of the traversed nodes by the route request message, the distance between the two communicat- ing nodes and the variation of the distance between nodes. This FLS1 executed
at each intermediate node, does not have the route to the destination. And it is executed when the intermediate node receives the route request message. FLS1 calculates and makes a suitable adaptation decisions of a stability values that measure the quality of the links between source and the intermediate node. The second FLS2 take two inputs parameters: stability (the output of FLS1) and the hope count number of the route. FLS2 executed at each intermediate node knows the route to the destination node or the destination node itself. The out- put of FLS2 give us the weight of the route, based on the stability and the length of the route. According to this value intermediate and destination node, select the right route between available routes (Fig.1). The process of the fuzzy logic system is composed of three parts (Fig.2): firstly, the crisp set of input data are gathered and converted to a fuzzy set using fuzzy linguistic variables and membership functions as shown in (Fig.3); this part is known asFuzzification;
afterwards, an inference engine is made based on a set of IF-THEN rules as shown in Table1; finally, the fuzzy output is mapped to a crisp output using the membership functions, in the Defuzzification part (Fig.3). The main advan- tages of using the fuzzy logic system are ease to model our reasoning, the ability to deal with uncertainty and non-linearity, the ease of implementation, the use of linguistic variables and it requiring less computing power [15,16].
The estimated remaining energy is computed periodically as follows in each node:
REi(t) = max
CEt−Σj=1j=Nbr−pktsEt(j),0
(3) whereECtis the current energy value of the node. For more accurate estimation of this residual energy, we reduce the value of the power that will be consumed to transmit the remaining packets in the buffer noted by Nbr-pkts. The parameter ECt(j) represents the energy needed for transmitting the packet numberj. Our fuzzy system take as input the harmonic mean of the energy of traversed nodes by the route request message and is computed as follows:
Hmean= N br of Hops ΣNbr Hopsi=1 1
REi
(4)
FIS 1 Executed in each
intermediate route FIS 2
Executed in the destination node or intermediate
node has the route to the
destination Stability
Hop Count number Energy
Distance
Mobility
Weight of the route
Fig. 1.Proposed fuzzy logic system
Fuzzification Module
Knowledge Base Inference Engine
Defuzzification Module Input Fuzzy Sets Output Fuzzy Sets
Output Membership
Function Input
Membership Function
Input Parameters Output
Fig. 2.Module of Fuzzy logic system
0 0.2 0.4 0.6 0.8 1 1.2
0 0.2 0.4 0.8 0.9 1
Membership Degree
Distance
Near Medium Far
0 0.5 1 1.5
0 25 50 75 100
Membership Degree
Energy
Low Medium High
0 0.5 1 1.5
0 0.3 0.7 1
Membership Degree
Mobility
Low High
0 0.5 1 1.5
0 10 20 30 40 55 70 85 100
Membership Degree
Stability
Vlow Low Medium High
(a) (c)
Fig. 3.Fuzzy membership sets of the input and output variables of FLS1. (a) Mem- bership function of the energy input. (b) Membership function of mobility input. (c) Membership function of the distance input. (d) Output membership function of Sta- bility
The distance between two nodes can be predicted by using signal strength para- meter during route discovery process. This value is calculated using two ray ground model defined in MAC layer of ns-2.35.
Pr=Pt∗Gt∗Gr λ2
(4∗π∗d)2 (5)
Table 1.The Fuzzy inference rules of FLS1.
Energy Distance Mobility Stability Energy Distance Mobility Stability
Low Near Low VLow Medium Medium High Medium
Low Near High VLow Medium Far Low High
Low Medium Low VLow Medium Far High VLow
Low Medium High VLow High Near Low Medium
Low Far Low Low High Near High Low
Low Far High VLow High Medium Low High
Medium Near Low Medium High Medium High Low
Medium Near High Low High Far Low High
Medium Medium Low Medium High Far High VLow
where Pr = received power, Pt = transmitted power, Gt = antenna gain of the transmitter, Gr = antenna gain of the receiver, λ= wavelength, and d = distance. For the third input parameter of our fuzzy system. We measure the variation of the distance between nodes over time in order to estimate the relative mobility of two nodes. To calculate this value, we compute the difference of the distance at time t and the distance at timet−1. Relative mobility at node X with respect to nodeY at t is calculated as follows:
RMXY =DtXY −DXYt−1 (6) Then the variation of the distance is defined as the changes of estimated distances between node. Each node in the network has a series of estimated distance values from its neighbors, measured at certain time interval for n times where n≤10 [17].
60 80 100
5 10 15 20 25
PDR
Number of connecƟon
AODV Fuzzy AODV Our AODV
Fig. 4.Packet Delivery Ratio vs number of connection
5 Simulation and Results
The performance of our proposed protocol is evaluated and compared with the basic AODV and the Fuzzy AODV protocol proposed in [6]. Simulator NS-2 was used during these simulations. In these simulations, we consider 100 mobile nodes move within a square field of 1000 m×1000 m in size. Nodes max moving is 10.0 m/s and the pause time between movements is 5 s with 200 s of simulated time. Every plot is taken as the average of twenty different runs. Each run is executed with a random sources and destinations pairs, and a random destina- tion mobility. Our protocol is evaluated using four metrics Packet delivery ratio, Normalized routing load, End-to-End delay and energy consumption. Figure4 present the variation of packet delivery ratio with the modification of the con- nection number. As we can see our approach perform better especially when the number of connection increases. Also, the normalized routing load that represent the ratio of control message per the packet received. We observe that the NRL generated by the Enhanced AODV is less than Fuzzy-AODV by 1% and 4%
than AODV protocol, and that with 25 connections. These improvements are due to the decrease in the number of retransmissions of control packets (RREP
0 0.1 0.2 0.3 0.4 0.5 0.6
5 10 15 20 25
EED
Number of connecƟon
AODV Fuzzy AODV Our AODV
Fig. 5.Average end to end delay vs number of connection
5 6 7 8 9 10 11 12 13
5 10 15 20 25
NRL
Number of connecƟon
AODV Fuzzy AODV Our AODV
Fig. 6.Normalized Routing Load Vs Number of connection
30 40 50
5 10 15 20 25
consomed energy
Number of connecƟon
AODV Fuzzy AODV Our AODV
Fig. 7.Energy consumption vs number of connection
and RREQ) to construct a new route after link breakage, which engendered by the bad selection of intermediate nodes that have a low remaining energy Fig.5.
Figure6 depicts the average end-to-end delay. All protocols have higher end- to-end delay with high number of connections. Mostly because frequent route breaks due to the dead of intermediate nodes and mobility. Our protocol reduces this problem and still perform better than AODV and Fuzzy-AODV, Even if the traffic load increases. This is due to our protocol decrease the number of link failure, as the time lost during the reconstruction of the route after link failure are eliminated. The average of energy consumption of the three protocols are presented in Fig.7. Energy consumption increases respectively with the increase of the number of connection. However, our protocol performs better than others with more 15 connections. This is because the our modified AODV tends to avoid intermediate nodes with low remaining energy in its construction of the route.
As our protocol leads to decrease the number of link failure, the energy lost during the broadcast of the route request packet are minimized. Consequently, the lifetime is significantly improved.
6 Conclusion
Given the problems that face mobile ad-hoc network, especially that use reac- tive routing protocols. As the stability of the route is very important. In this paper, we are proposed an enhancement protocol of the reactive routing pro- tocol AODV. In our solution, we added three parameters among the selection criteria of AODV. These parameters have an important impact on the stability of the route, which are energy, the mobility of the node and distance between two communicating nodes. We are used fuzzy logic theory that combines these parameters, in order to produce a value that represent the stability of the route.
Our enhanced protocol take this value in consideration with the number of hope during the selection of the route, to select a route with more stability. Our proto- col show significant performance improvements in terms of packet delivery ratio, normalized routing load, end-to-end delay and average of energy consumption
compared with AODV and fuzzy AODV, especially in a network with more con- nections. Taking into account the benefit of the solution proposed in this paper, in future work we will try to expand the solution by proposing fuzzy system with dynamic function membership for more accuracy.
References
1. Abolhasan, M., Wysocki, T., Dutkiewicz, E.: A review of routing protocols for mobile ad hoc networks. Ad hoc Netw.2(1), 1–22 (2004)
2. Perkins, C., Belding-Royer, E., Das, S.: Ad hoc on-demand distance vector (aodv) routing. Technical report (2003)
3. Johnson, D.B., Maltz, D.A.: Dynamic source routing in ad hoc wireless networks.
In: Imielinski, T., Korth, H.F. (eds.) Mobile computing, pp. 153–181. Springer, New York (1996)
4. Perkins, C.E., Bhagwat, P.: Highly dynamic destination-sequenced distance-vector routing (dsdv) for mobile computers. In: ACM SIGCOMM Computer Communi- cation Review, vol. 24, pp. 234–244. ACM (1994)
5. Park, V., Corson, M.S.: Temporally-ordered routing algorithm (tora) ver- sion 1 functional specification. Technical report, Internet-Draft (1997).
draft-ietf-manet-tora-spec-00.txt
6. Abbas, N.I., Ilkan, M., Ozen, E.: Fuzzy approach to improving route stability of the aodv routing protocol. EURASIP J. Wirel. Commun. Netw.2015(1), 235 (2015) 7. Torshiz, M.N., Amintoosi, H., Movaghar, A.: A fuzzy energy-based extension to
aodv routing. In: International Symposium on Telecommunications 2008, IST 2008, pp. 371–375. IEEE (2008)
8. Chettibi, S., Chikhi, S.: Dynamic fuzzy logic and reinforcement learning for adap- tive energy efficient routing in mobile ad-hoc networks. Appl. Soft Comput. 38, 321–328 (2016)
9. Al-Rawi, H.A.A., Ng, M.A., Alvin Yau, K.-L.: Application of reinforcement learn- ing to routing in distributed wireless networks: a review. Artif. Intell. Rev.43(3), 381–416 (2015)
10. Mamdani, E.H.: Application of fuzzy logic to approximate reasoning using linguis- tic synthesis. In: Proceedings of the Sixth International Symposium on Multiple- Valued Logic, pp. 196–202. IEEE Computer Society Press (1976)
11. Zadeh, L.A.: Fuzzy sets. Inf. Control8(3), 338–353 (1965)
12. Sarma, N., Nandi, S.: Route stability based qos routing in mobile ad hoc networks.
Wirel. Pers. Commun.54(1), 203–224 (2010)
13. Youssef, M., Ibrahim, M., Latif, M.A., Chen, L., Vasilakos, A.V.: Routing metrics of cognitive radio networks: a survey. IEEE Commun. Surv. Tutorials 16(1), 92–
109 (2014)
14. Fotino, M., De Rango, F.: Energy issues and energy aware routing in wireless ad hoc networks. INTECH Open Access Publisher (2011)
15. De Reus, N.M.: Assessment of benefits and drawbacks of using fuzzy logic, espe- cially in fire control systems. Technical report, DTIC Document (1994)
16. Driankov, D., Saffiotti, A.: Fuzzy logic techniques for autonomous vehicle naviga- tion. Physica61, 392 (2013)
17. Er, I.I., Seah, W.K.G.: Mobility-based d-hop clustering algorithm for mobile ad hoc networks. In: Wireless Communications and Networking Conference 2004, WCNC 2004, vol. 4, pp. 2359–2364. IEEE (2004)
