CSIT DOI 10.1007/s40012-017-0158-8
ORIGINAL RESEARCH
An optimal path selection criterion in multi path routing using energy M. Kokilamani1 • E. Karthikeyan1
Received: 26 February 2015 / Accepted: 8 February 2017 Ó CSI Publications 2017
Abstract The properties of mobile ad hoc network created volume of applications and in the same time various limitations of mobile nodes creating immense opportunity for research. Among many constraints energy is the most important parameter to be considered when transmission happens. If we fail to monitor energy appropriately, there is a chance for node down and it leads to check alternate paths, sometime the whole network may be disturbed. To address this problem, in this paper a novel scheme is proposed that the nodes are selected for communication only when they have minimum energy to complete the transmission for which we introduce a minimum energy threshold value. The proposed approach EA-AOMDV is simulated using ns-2 and compared with AOMDV and LQBMR, it is found that the result obtained is significant with respect to average end-toend delay, energy required for packet transmission, packet delivery ratio, packet loss, routing overhead and throughput. Keywords Mobile ad hoc network Multipath routing AOMDV LQBMR EA-AOMDV Energy Factor Energy Cost for Transmission
1 Introduction In this digital era, communications over wireless medium stretch out auspicious opportunity for enormous applications in mobile ad hoc network (MANET) [1, 2]. MANET & M. Kokilamani
[email protected] E. Karthikeyan
[email protected] 1
Department of Computer Science, Government Arts College, Udumalpet, Tamil Nadu 642126, India
consists of mobile wireless nodes and independent of any infrastructure. During communication, nodes can move anywhere dynamically without any restriction. This nonrestricted mobility and easy deployment of nodes makes MANET very popular and more suitable for real-time application scenarios such as natural disaster, emergencies, conferencing, military applications etc. The nodes used in MANET are mainly constraint with energy power, speed, bandwidth and capacity. Among these constraints energy is a scare resource, so it plays a vital role and to be addressed primarily. Because energy cannot be replaced or making alternate is difficult while communication takes place. So it must be used as optimally as possible and energy conservation pay heeds attention in researchers today. Energy efficiency is a critical issue in MANETs [3–5]. The existing energy-efficient routing protocols often use residual energy, transmission power, or link distance as metrics to select an optimal path. In this paper, the focus is on energy efficiency in MANETs and the route selection policy with novel metric in order to increase path survivability of MANETs. The proposed novel metrics result in stable network connectivity and less additional route discovery operations. Moreover, the network has to operate for long period of time, but the nodes are battery powered, so the available energy resources limit their overall operation. The main design goal of routing protocol for MANETs is not only to transmit data from a source to a destination, but also to increase the lifetime of the network. This can be achieved by employing energy efficient routing protocols. Depending on the applications used, different architectures and designs have been applied in MANETs. The performance of a routing protocol depends on the architecture and design of the network, and this is a very important feature of MANETs. However, the operation of the protocol can affect the energy spent for the transmission of the data.
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There are some terms related to the energy efficiency on MANETs [6] that are used to evaluate the performance of the routing protocols and here are the most important ones. Energy per Packet This term is referred to the amount of the energy that is spent while sending a packet from a source to a destination. Energy and Reliability It refers to the way that a trade off between different application requirements is achieved. In some applications, emergency events may justify an increased energy cost to speed up the reporting of such events to increase the redundancy of the transmission by using several paths. Network Lifetime In MANETs, it is important to maximize the network lifetime, which means to increase the network survivability or to prolong the battery lifetime of nodes. Moreover, the lifetime of a node is effectively determined by its battery life. The main drainage of battery is due to transmitting and receiving data among nodes and the processing elements. Average Energy Dissipate This metric is related to the network lifetime and shows the average dissipation of energy per node over time in the network as it performs various functions such as transmitting, receiving, sensing and aggregation of data. The selection of the energy efficient protocols in MANET is a really critical issue and should be considered in all networks. The main objective of current research in MANET is to design energy-efficient nodes and protocols that could support various aspects of network operations. So, techniques and protocols that would consider energy efficiency and transmit packets through energy-efficient routing protocols and thus prolonging the lifetime of the network are required. The potential task of the protocols is not only to find the lowest energy path from a source to a destination, but also to find the most efficient way to extend the network’s lifetime. The continuous use of a low energy path frequently leads to energy depletion of the nodes along this path and in the worst it case may lead to network partition. Numerous energy aware routing protocols [7–12] have been proposed by considering issues such as transmission power adjustment, adaptive sleeping, topology control. But routing algorithms have not considered the problem like early exhaustion of nodes. Also these routing algorithms not considered other factors like node transmission power, remaining battery energy and reliability constraints. So it increases overall energy consumption of the node on the path. The amount of energy consumed by the nodes during transmission attains critical issue in routing. The energy depletion during transmission not only affects the nodes but also the whole network. Really routing algorithm only plays an important part to select a path that contain list of nodes participating with minimum energy. So that selecting energy aware nodes on the path will prolong lifetime of the communication and also avoid earliest exhaustion of nodes in network and network partitioning problem.
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The remaining of the paper organised as follows. Followed by the simple introduction, Sect. 2 briefs the related work done in this field and Sect. 3 illustrates the proposed scheme. The results obtained from the proposed scheme are discussed in Sect. 4. Section 5 concludes the paper.
2 Related works In this section the literature survey of energy efficient routing techniques are discussed to extend the lifetime of the network. The Minimum Battery Cost Routing (MBCR) [13] takes into account the remaining power of nodes to prolong network lifetime by selecting one path with the maximum remaining power from all available paths. To find the path with the maximum remaining power, MBCR calculates the sum of the remaining power of each node in a path, using f i ðt Þ ¼
1 Ci ðtÞ
Bð r d Þ ¼
d X
fi ðtÞ
i¼0
Bðro Þ ¼ min ðBðrd ÞÞ rd¤r
where Ci(t) is the remaining power of node i at time t and B(rd) is the sum of the inverse of the remaining power of nodes in path d. MBCR uses to select from set r* of all paths the path B(ro) with the maximum remaining power. Although MBCR uses the inverse of the remaining power of the nodes in a path to select the desired path, the selected path may have a node with low remaining power. This may cause path breakage during data transmission. Meng Li et al. [14] proposed a protocol Energy-aware Multipath routing Protocol (EMPR) for MANET. In this scheme by sharing information among physical layer, MAC sub-layer and network layer, EMPR efficiently utilized network resources. EMRP calculates weight (w) of each node along the path to makes a decision to select that path. EMPR sorts all available routes in an ascending order of W and takes the top N sets of routes as primary paths to transmit data and take next N sets of routes as backup paths. Simultaneously transmitting packets along these routes need more energy and lifetime of node is very low. The Min–Max Battery Capacity Routing (MMBCR) [15] selects the path in which the minimum remaining power of nodes in this path is greater than the maximum remaining power in other paths, using 1 PMMBCR ¼ min max R2S n2R BCn where S is the set of all paths, R is a path, and BCn is the remaining power of node n. In MMBCR, a routing path
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that contains a node with low remaining power can be avoided. However, MMBCR does not take transmission power into account. In [16], Liu et al. proposed the Multipath Routing Protocol for Network Lifetime Maximization (MRNLM), a protocol defines a threshold to optimize the energy usage at data transmission. Thus, MRNLM use single path from the multiple paths during data transmission based the energy cost function. From the simulation result it consumes less energy but delay and overheads are increased. Multipath Energy-Efficient Routing Protocol (MEERP) is proposed in [17]. The protocol selects energy-efficient and node disjoint paths based on the residual energy and successful transmission rate. In this protocol only a single path is used at data transmission from the multiple paths. Simulation result of the protocol shown that the protocol increases network lifetime but packet delivery ratio is less. Omar Smail et al. [18], propose a new algorithm called Ad Hoc On-Demand Multipath Routing with Lifetime Maximization (AOMR-LM), which preserves the residual energy of nodes and balances the consumed energy to increase network lifetime. The authors introduce threshold and co-efficient factors for selecting homogenous paths in terms of energy. However, reliability of the link and transmission energy cost are not taken into account for path selection, they leads to network partitioning and re-route discovery. Minimum Transmission Power Consumption Routing protocol (MTPCR) [19], takes into account high transmission bandwidth as a path selection parameter. MTPCR considers power consumption, distance and transmission bandwidth for discovers the desired routing path that has reduced power consumption during data transmission. Also path maintenance mechanism to maintain good path bandwidth and efficiently reduces number of path breakages. Thus, little additional overhead is required for the computation of the transmission bandwidth in the route discovery process. Jinhua Zhu and Xin Wang [20] presented a new link cost model for selecting energy-efficient path called Progressive Energy Efficient Routing (PEER). This protocol uses Distributed Co-ordination Function (DCF) for calculating link cost and transmission power of each node, so path setup and maintenance are efficiently handled. PEER protocol can reduce up to 2/3 path discovery overhead, delay and 50% transmission energy consumption. However, remaining energy level is no t fairly shared among nodes in the network. Localized Energy-Aware Restricted Neighborhood routing (LEARN) [21], consider critical transmission radius and energy mileage to guarantee energy- efficiency in route selection. This protocol is based on geographical localized routing, so the routing decision only uses local
information of distance and energy consumption. Thus, mobility and link cost is not taken into account for optimal path selection, this leads to path failures and congestion. Sungoh kwon et al. [22] presented a novel Energy-efficient Unified Routing (EURo) algorithm that adapts to varying wireless environments. This algorithm takes into account four key wireless system elements such as transmission power, interference, residual energy and energy replenishment in great manner. Based on the above key factors the algorithm calculates weight vector for energy replenishment and link scheduling. The interference level also considered as main factor for classifying the nodes on the path, But, this algorithm doesn’t capable for large scale networks in terms of topology changes and energy conservation. Javad Vazifehdan et al. [23] propose two novel energy aware routing algorithms called Reliable Minimum Energy Cost Routing (RMECR) and Reliable Minimum Energy Routing (RMER). RMECR select paths based on its remaining battery energy, energy consumption and quality of links. RMER finds routes minimizing the total energy required for end-to-end packet traversal. Both schemes considers minute details such as energy consumption of processing elements, Limited number of retransmissions, packet sizes, Link weight, expected energy cost into detail. This leads to delay in finding path and doesn’t support bandwidth constraints. Young-Min Kim presented Ant Colony Optimization based Energy Saving Routing (A-ESR) [24] to overcome the energy-consumption minimized network (EMN) problem. The A-ESR scheme introduces the traffic centrality concept to measure traffic volumes along the nodes on the route. Based on the above factor every node on the path determines delay information and chooses lightly-loaded links for transmission. It balances the traffic load efficiency, but fails to support minimizing energy consumption and remaining battery energy. Bin Li et al. [25] propose new metric for energy efficient node selection in multi-hop random wireless sensor networks. Forwarding area radius and angle are taken as an important factor during route discovery process. Also this metric calculates fading level of wireless channel and circuit energy consumption for relay node selection. This approach reduces energy consumption by selecting the appropriate forwarding radius and angle, but doesn’t consider the remaining battery energy.
3 Proposed work From the review studied in the previous section, it is understood that the importance of energy and how far it is essential while communication take place between nodes
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among MANET. The nodes participating in data communication are too constraint devices. If we fail to consider battery energy before start transmission, some time the nodes with low energy will create trouble and need to find alternate path. So considering the energy aware node along with the path selection will not trouble at any point. So we compare our scheme to existing AOMDV and LQBMR protocol. 3.1 Ad Hoc Ondemand Multipath Distance Vector Routing Protocol AOMDV protocol is an extension to the AODV protocol for computing multiple loop-free and link disjoint paths [26]. The routing entries for each destination contain a list of the next-hops along with the corresponding hop counts where next hops have the same sequence number and helps in keeping path of a route. For each destination, a node maintains the advertised hop count, which is defined as the maximum hop count for all the paths, which is used for sending route advertisements of the destination. Each duplicate route advertisement received by a node may define an alternate path to the destination. Loop freedom is assured for a node by accepting all alternating paths to destination if it has a less hop count as compare to advertised hop count for that destination. As the maximum hop count is used, the advertised hop count does not change for the same sequence number. When a route advertisement is received for a destination with a greater sequence number, the next-hop list and the advertised hop count are reinitialized. AOMDV find node-disjoint or link-disjoint routes. For finding node-disjoint routes, each node does not immediately reject duplicate RREQs and each RREQs arriving via a different neighbor of the source defines a node-disjoint path. This is mainly due to lake of broadcast of duplicate RREQs by node, and any two RREQs arriving at an intermediate node via a different neighbor of the source could not have traversed the same node. The prime advantage of AOMDV is that it allows intermediate nodes to reply to RREQs, while still selecting disjoint paths. However, it has more overheads during route discovery due to increased flooding and since it is a multipath routing protocol, the replies are also in longer overhead. 3.2 Link Quality Based Multipath Routing protocol In [27], authors proposed a novel link quality based multipath routing protocol called Link Quality Based Multipath Routing (LQBMR) protocol which is the extension of a well known AOMDV routing protocol. The proposed protocol finds multiple link reliable paths using Path-Link Quality Estimator (P-LQE) such as Cumulative Expected
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Transmission Count (CETX). The LQBMR protocol uses only CETX instead of hop count as path metric for determining more link reliable paths between any source and destination pair for data transmission. Expected Transmission Count (ETX) is a qualitative link based routing metric for estimating the number of transmissions and retransmissions needed to send a data packet over a link, called link ETX. Cumulative Expected Transmission Count (CETX) is the summation of the ETX of all participating links of the route, called path ETX. RSSI is determined initially by RREQ or RREP during route discovery and then by HELLO packets during route selection and maintenance. Since the RREQ or RREP packets are used to determine the stability of links between nodes during route discovery, they are used to calculate both ETX and CETX in this protocol. The ETX of a link between nodes along the forward path is computed using RREP packets as well as the ETX of a link between nodes along the reverse path is computed using RREQ packets. However, it consumes more energy for finding reliable paths, more delay for transmitting packets and Routing Overheads. 3.3 Energy aware Ad Hoc On-demand Multipath Distance Vector Routing protocol By considering the energy level of the nodes, we propose a new scheme called Energy Aware Ad hoc On-demand Multipath Distance Vector Routing Protocol (EAAOMDV). The main objective of this work is to maintain prolong node energy to increase lifetime of the nodes and as well as whole network. The basic design of the proposed scheme is to select a best path based on energy level of the node and energy transmission cost. We consider the following condition before path selection, refer here under: 1. 2.
In every path, nodes are selected depending on the energy threshold value. Each RREQ and RREP packets are to be forwarded or discarded depending on the threshold value.
Our scheme Energy Aware Ad hoc On-demand Multipath Distance Vector Routing (EA-AOMDV) is incorporated at route discovery phase for selecting energy aware paths for sending packets and RREP phase for calculating energy cost for transmitting a packet from source to destination. 3.3.1 Network model We consider a wireless network with a graph G(N,E) where N and E are set of nodes and links. Each node is assigned a unique identifier number between 1 and
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M = |N|. All the nodes are battery equipped and the initial battery energy is denoted as IBEs for all nodes is set to be 100 Jules and its Remaining Battery Energy is RBE, where s € N. When RBEs of any node falls below the Energy Factor threshold (EFth,) then the node is not considered during the forward path selection. Every node on the path is examined whether its RBEs is greater than EFth, for a node s € N, the condition is EFth \ RBEs EFth ¼ IBEs ECTðs;uÞ =5 To transmit a packet from node s to u, where s,u € N. The total energy needed for transmitting a packet called Energy Cost for Transmission (ECT) is calculated by ECTðs;uÞ ¼ Tðs;uÞ þ Rðs;uÞ where T(s,u) is transmission energy needed to transmit a packet from node s to u and R(s,u) receiving energy needed to receive a packet from node s to u. The proposed concepts are introduced in the existing well known multipath routing protocol called AOMDV and the same is given in Algorithm 1
3.3.2 Route selection process In general when source node have packets for destination, it initiates Route Discovery Process and also it assigns EFth (Energy Factor Threshold) value to select energy aware paths. First a source node makes a search of its route cache for intended destination. If no routes are available on the route cache, it floods or broadcasts RREQ packet with assigned values over the network. RREQ packet contains an ID, Threshold value for evaluation, source and destination address along with request ID, which uniquely identifies the current route discovery. When intermediate node receives RREQ packet, it checks whether it is the specified destination or not. If yes, it will send a RREP packet to the destination. Before re-broadcasting, the intermediate node itself makes a decision whether it is qualified one for communication based on the following condition EFth RBEs When the above mentioned condition becomes fails on any intermediate node and then the node will be not considered. By doing so, the overloaded nodes are excluded from the path and a node with minimum energy is prolonged. When RREQ packet reaches the specified destination, it creates RREP packet to the source. The RREP packet calculates transmission cost of packet from node i to j. Every node on the path must calculate its Remaining Battery Energy (RBE) value and it can be added in the RREP packet. Calculating RBE by the node is as follows: ECTðs;uÞ ¼ Tðs;uÞ þ Rðs;uÞ By calculating the Energy Cost for Transmission the accurate value of Remaining Battery Energy (RBE) is obtained and stored node’s routing table for further path selection process. 3.3.3 Illustration of proposed scheme To illustrate the proposed scheme, we consider a network model as shown in Fig. 1 with 19 wireless nodes. Let us consider that the nodes S, R and B are source nodes and D, Z and Y are their corresponding destinations and they use multipath routing scheme. The possible paths for each pair are: 1. 2.
(S-D) - {(S-A-N-D), (S-P-M–N-D), (S-A-K-X-D), (SP-Y–Z-D), (S-A-K-L-X-D)} (R-Z) - {(R-A-M-Z), (R-A-M-Y–Z), (R-A-K-N-Z), (RA-P-Y–Z), (R-S-P-Y–Z)}
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3.
(B-Y) - {(B-A-P-Y), (B-A-M-Y), (B-A-S-P-Y), (B-RS-P-Y)}
In the above diagram, let us consider node A whose energy level at time T1 is 78 Jules. The node A accepts paths from nodes (S, R, B) respectively. At the same time the node A also transmits the received packets to other nodes through available paths mentioned above. So the energy level of node A gets reduced due to the transmission of data. From Fig. 2 at time T2 based on the routing information, node P sends its RREQ (P) to node A for transmission of packet and node A compare its RBE value with EF threshold value. Node A already has RBE value is 18 Jules. By comparing EF (i.e. IBEs-ECT(s,u)/5) and RBE values, node A drops the RREQ (P) and not involves the new communication at any node. Also sending of packet from node A to node N is rejected because energy level is low and will not be able to withstand till the complete packet transmission.
consumption, Packet delivery fraction, Packet loss Ratio, Routing Overheads and Throughput are taken into account. The considered simulation parameters are given in Table 1. 4.1 Results based on different pause time: 4.1.1 Performance metrics Performance metrics are qualitative measures used to evaluate any MANET routing protocol in terms of Quality of Service (QoS). We have evaluated the following five different performance metrics: 1.
4 Simulation of the proposed scheme We consider the AOMDV [26] and LQBMR [27] protocol to compare with the proposed EA-AOMDV and NS2 is used to simulate the results. The performance metrics such as Average End-to-End Delay, Minimum Energy
Average End-to-End delay (in ms)—the average time of the data packet to be successfully transmitted across a MANET from source to destination. It includes all possible delays such as buffering during the route discovery latency, queuing at the interface queue, retransmission delay at the MAC, the propagation and the transfer time and is calculated as follows: Pn RI SI Average E 2 E Delay ¼ i¼1n where n is the number of data packets successfully transmitted over the MANET, ‘ i ‘ is the unique packet identifier, Ri is the time at which a packet with unique identifier ‘ i ‘ is received and Si is the time at which a packet with unique identifier ‘ i ‘ is sent.
85.9
L
W
75.8 20
T 86.8 7
B
K
70.9
R
78.9
X
20.9
85.9
J
65.5 Energy problem of node A N
78
A
71.6 7
D
89.7
S
84.8
M
56.8
Z 95.9
P C
82.9
F 25.9
35.7
O 45.9
Y Fig. 1 Energy problem of node A
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65.9
L
W
75.8 10
T 66.8 7
B
K
40.9
R 18
A
58.9
X N
x
J
65.5 71.6 7
D
39.7
S
20.9
75.9
x
64.8
M
36.8
Z 93.9
P
F
82.9 20.9
15.7
C
O 25.9
Y Fig. 2 Our proposed scheme to alleviate energy problem of node A
2.
Energy consumed for packet transmission (in Joules)— the summation of the energy consumed by all nodes in the simulation environment. The Total energy consumption is calculated as follows: n P Total Energy Consumed ¼ ðInitial Energyi i¼1
3.
Residual Energyi Þ Packet delivery ratio (%)—the ratio of data packets delivered to the destination to those generated by the sources and is calculated as follows: Packet Delivery Ratio ¼
4.
Number of Data Packets Received 100 Number of Data Packets Sent
Packet Loss Ratio (%)—the ratio of data packets not delivered to the destination to those generated by the sources calculated by Packet Loss Ratio No of Data Packets Sent No of Data PacketsReceived 100 ¼ No of Data Packets Sent
5.
Routing Overhead (Pkts)—the total number of control or routing packets generated by routing protocol during simulation and is obtained as follows:
Routing Overhead ¼ Number od RTR packets 6.
Throughput (in Kbps)—is the number of bytes received successfully and is calculated by Throughput ¼
Number of Bytes Received 8 kbps Simulation Time 1000
4.2 Average End-to-End Delay analysis with normal AOMDV, LQBMR and energy with EA-OMDV Average End-to-End Delay is represented by how much time it takes for successful packet transmission. Here the average end-to-end delay for tested AOMDV and LQBMR protocol increases when increasing the network size with various pause times, but in EA-AOMDV delay is decreases with significant value in Table 2. The proposed scheme show significant improvements with mobility and pause time increases. In AOMDV routing the performance of network degrades but proposed EA- AOMDV scheme gives better performance than previous approach. The Table 2 and Fig. 3 illustrate an average delay time by each protocol.
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CSIT Table 1 Simulation Parameters for EA-AOMDV Parameter
Value
Simulator
NS-2.34
Simulation time
100 s
Simulation area
1520 9 1520 m2
Transmission range
250 m
Packet size
512 bytes
Traffic and mobility model
CBR/TCP
Traffic rate
10 packets/s
Simulation model
Random way point
Pass time
5s
Number of nodes
100
MAC type Channel type
802.11 DCF Wireless channel
Routing protocols
AOMDV, LQBMR, EA-AOMDV
Antenna model
Omni
Network load
4 packets/s
Number of connections
1, 5, 10, 20, 30, 40
Radio propagation model
Two way ground
Idle power
0.0001 W
Transmission power
1.0 W
Receiving power
1.0 W
Sleep
Power 0.0001 W
Transition power
0.002 W
Transition time
0.005 s
Initial energy
100 Joules
Interface queue length
50
Interface queue type
DropTail/PriQueue
Speed Frequency
5 m/s 2.4 GHz
Data rate
11.4 Mbps
Carrier sensing range
500 m
Carrier receiving range
250 m
4.3 Minimum energy consumption analysis with normal AOMDV, LQBMR and energy with EA-OMDV In this analysis the energy consumption for packet transmission (transmitting and receiving) is taking into account. Here we consider residual node energy compared with energy factor for route selection. Thus EA-AOMDV balances the energy among all the nodes and prolongs the individual node lifetime and hence the entire network lifetime. With out considering the energy constraint the AOMDV and LQBMR required more energy for packet transmission during communication shown in Table 3. Our scheme finds energy aware path and compare energy of node in AOMDV, LQBMR and EA-AOMDV in Table 3 with Fig. 4:
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4.4 Packet delivery ratio delay analysis with normal AOMDV, LQBMR and energy with EA-OMDV This is the performance analysis depending on the ratio of packets in case of previous AOMDV, LQBMR and proposed EA-AOMDV scheme. Here the PDF performance of proposed scheme is about 78% but in case of previous normal AOMDV and LQBMR the PDF is about 75% and 74% that is lesser than previous. In case of normal AOMDV and LQBMR the energy concept are not added it means the nodes notify maximum packet drops, delay and degrade the performance of the network. But in case of proposed scheme each node that handles energy constraint in good way and minimize energy consumption for packet transmission, routing overhead and Delay. The Table 4 and Fig. 5 shows packet delivery fraction of PE-AOMDV and AOMDV. 4.5 Packet Loss Ratio delay analysis with normal AOMDV, LQBMR and energy with EA-OMDV The reasons for packet drops can be incorrect routing information, mobility & power management. AOMDV cannot maintain precise routes and drops, when nodes move often. The usage of state routes from its caches is the major reason for AOMDV packet drops. In this graph the packet loss analysis has been done in both the cases, normal AOMDV, LQBMR and energy enhanced EAAOMDV schenme. Here the packet loss is more in case of normal AOMDV and LQBMR, it means only the concept of multipath routing does not provide the reliable packet delivery but if we enhance the performance of AOMDV and LQBMR by including the concept of energy efficiency, it extends network lifetime. In this technique the packet loss has minimized. We noticed only 20% packets drop in network that is much better than previous because in previous about 24% of AOMDV and 25% of LQBMR packets were dropped in network. It means that there is a significant difference in packet loss between normal AOMDV,LQBMR and EA-AOMDV technique. The Table 5 and Fig. 6 illustrate that the number of packets dropped by each protocol. 4.6 Routing Overheads Delay analysis with normal AOMDV, LQBMR and energy with EA-OMDV The routing packets in network are required to establish connection between sender and receiver and the less number of routing packets shows the better network performance. In this graph the performance of proposed EAAOMDV protocol is better as compared to previous normal AOMDV, LQBMR routing protocol. Here in case of proposed scheme about only 93 routing packets are delivered
CSIT Table 2 Average End-to-End Delay
Pause Time
AOMDV (in ms)
LQBMR (in ms)
EA-AOMDV (in ms)
0
2.219
3.659
2.261
5
1.927
3.873
2.211
10
2.560
2.894
2.039
15
3.077
4.058
2.439
20
2.414
3.016
2.481
25
2.537
3.739
2.224
Energy Required to packet transmission for 3 schemes
Average End-to-End Dealy for 3 schemes 4.2
3500
4.0 3.8
3000
Energy Required (in Jules)
Delay in seconds
3.6 3.4 3.2
AOMDV LQBMR EA-AOMDV
3.0 2.8 2.6 2.4 2.2
2500
2000
AOMDV LQBMR EA-AOMDV
1500
1000
2.0 500
1.8 3
0
3
5
8
10
13
15
18
20
23
25
28 0
Pause Time (in seconds)
5
10
15
20
25
Pause Time (in seconds)
Fig. 3 Average End-to-End Delay of EA-AOMDV with AOMDV Fig. 4 Energy consumption of EA-AOMDV with AOMDV
in network but in case of previous normal routing about 105 and 102 packets are delivered in network. In AOMDV and LQBMR Routing Overheads are increased, due to the earliest exhaustion of node and path life time. AOMDV path selection doesn’t care of battery energy for path selection. So it causes more processing power and route rediscovery packets. The Table 6 and Fig. 7 compares the routing overhead of AOMDV, LQBMR and EA-AOMDV. EA-AOMDV reduces Routing Overhead in the way of selecting energy aware paths at the time of Route Discovery.
Table 3 Minimum energy consumption
Pause time
4.7 Throughput delay analysis with normal AOMDV, LQBMR and energy with EA-OMDV Throughput is obtained by calculating how many packets are received at the destination from the source at a specified time interval (kbps). The Table 7 and Fig. 8 show throughput of each protocol in packet delivery fraction. EA-AOMDV protocol throughput becomes high when nodes scalability is increased. But AOMDV and LQBMR protocol throughput becomes less when nodes scalability increased.
AOMDV (in Jules)
LQBMR (in Jules)
EA-AOMDV (in Jules)
0
422.315
2845.54
488.491
5
293.479
2883.74
461.834
10
441.518
3172.70
307.970
15
670.804
2155.58
525.940
20
479.165
3086.86
638.916
25
470.198
3073.49
441.435
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CSIT Table 4 Packet delivery ratio
Pause time
AOMDV (in %)
LQBMR (in %)
EA-AOMDV (in %)
0
81.418
74.651
77.797
5
87.661
73.415
85.731
10
74.434
82.864
85.681
15
71.947
73.691
79.795
20
78.580
77.073
84.092
25
75.684
74.061
78.754
Packet Delivery Ratio for 3 schemes
Packet Loss Ratio for 3 schemes
30 28
AOMDV LQBMR EA-AOMDV
86
Percentage of droed packets
Percentage of delivered packets
88
84 82 80 78 76 74 72
26 24 22 20 18 16 AOMDV LQBMR EA-AOMDV
14 12
70
3
3
0
3
5
8
10
13
15
18
20
23
25
0
3
28
5
8
10
13
15
18
20
23
25
28
Pause Time (in seconds)
Pause Time (in seconds) Fig. 6 Packet losses of EA-AOMDV with AOMDV Fig. 5 Packet delivery ratio of EA-AOMDV with AOMDV
Table 5 Packet loss ratio
Table 6 Routing overheads
Pause time (in sec)
AOMDV (in %)
LQBMR (in %)
EA-AOMDV (in %)
Pause time
0
18.582
25.349
22.203
0
50
12.339
26.585
14.269
5
89
85
77
10
25.566
17.136
14.319
10
84
82
87
15
28.053
26.309
20.205
15
83
81
79
20
21.420
22.927
15.908
20
99
95
81
25
24.310
25.909
20.246
25
105
102
93
5 Conclusion Even though many factors are to be considered to improve the QoS aspect of MANET, routing is standing front of all. Among the routing protocols, recently energy efficient multipath routing protocols attained very big attention among the research communities. In this paper we proposed a novel scheme called EA-AOMDV to improve the
123
AOMDV (no of packets) 77
LQBMR (no of packets) 79
EA-AOMDV (no of packets) 80
performance of the AOMDV and LQBMR routing protocol. By introducing the new threshold variable, the nodes lifetime is extended. From the simulated result it is found that the proposed scheme give a better result than the existing AOMDV and LQBMR with respect to Average End-to-End Delay, Minimum Energy consumption, Packet delivery fraction, Packet loss Ratio, Routing Overheads and Throughput.
CSIT Acknowledgements This work is supported by University Grants Commission (UGC-MRP (F.No:41-614/(2012) SR) under the Major Research Project Scheme.
Routing Overheads for 3 Schemes
108 105
AOMDV
103
LQBMR
Routing Overhead Packets
100
EA-AOMDV
References
98 95 93 90 88 85 83 80 78 75 73
3
0
3
5
8
10
13
15
18
20
23
25
28
Pause Time (in seconds)
Fig. 7 Routing Overheads of EA-AOMDV with AOMDV Table 7 Throughput Pause time 0
AOMDV (in %)
LQBMR (in %)
EA-AOMDV (in %)
114.337
106.789
108.517
5
122.815
124.336
120.145
10
103.632
104.960
119.359
15
100.688
108.321
111.626
20
108.341
112.542
118.014
25
105.343
115.654
123.076
Throughput for 3 schemes
Percentage of throughput
125
120
115
110
105 AOMDV LQBMR EA-AOMDV
100
3
0
3
5
8
10
13
15
18
20
Pause Time (in seconds)
23
25
28
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