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Channel-Adaptive Probabilistic Broadcast in Route Discovery Mechanism of MANETs

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Fig. 1.  Route discovery mechanism in AODV

Channel-Adaptive Probabilistic Broadcast in Route Discovery Mechanism of MANETs

Abstract

Mobile ad-hoc Networks (MANET) are self-managing wireless networks without requiring any central administration. Each MANT node can connect to its neighbors on ad-hoc basic and communicate with other nodes through its neighbors over multi-hop wireless links. The routes to destinations are discovered by broadcasting customized message which are re-broadcasted by the receiving nodes again and again until the message reaches the ultimate destination node. Continuous mobility of nodes and wireless nature of the communication links challenge to achieve efficient and smart broadcasting and hence efficient routing. A node receiving a broadcast message should be cautious on re-broadcasting to avoid BSP (Broadcast Storm Problem) while ensuring the reachability of the message to the final destination. This paper presents a distributed algorithm to decide the re-broadcasting for individual MANET nodes. The algorithm takes into account the neighboring node density and SINR (Signal to Interference plus Noise Ratio), and adapts accordingly. The proposed algorithm has been implemented in standard AODV using ns-2 simulator. The simulation results have shown that the proposed algorithm outperforms the standard AODV and two competitor schemes in terms of routing overhead, throughput, end-to-end delay and energy consumption significantly. This helps not only network performance but greener computing as well.

Index Terms— MANET; Routing AODV; Probabilistic Broadcast; green computing;

I.     INTRODUCTION

T

He proliferation of handheld devices (like electronic gadgets, laptops and smartphones) that are developed based on the IEEE 802.11 standard of wireless protocol have kept Mobile Ad-hoc Networks (MANETs) an active area of research over the past two decades. A MANET is a self-configuring, self-healing and infrastructure-less network of mobile nodes connected to each other over single-hop or multi-hop wireless links on ad-hoc basis [1].These characteristics of MANETs make them an ideal choice for a number of applications e.g., communications in battlefield, rescue operation in disaster areas or quick deployment of networks without requiring huge infrastructure.

MANET nodes can be located arbitrarily within an area and are free to move. The movement of MANET nodes changes the network topology dynamically. MANET nodes adapt to the changing topology by discovering new neighbours and establishing new routes to destination nodes [2]. A node may not communicate directly with a distant node due to limited transmission range, and may have to rely on other nodes to relay the message along the route to the final destination node. In this way, each node acts as a host node as well as a relay node to extend the reachability of other nodes.

When a node wants to send data to a remote node, first, it finds out a set of relay nodes between itself and the remote node. The process of finding the optimal set of relay nodes between the source node and the destination node is called route discovery. Node mobility, limited battery power and the error-prone nature of wireless links are the main challenges in designing an efficient rout discovery process in MANETs.

A number of routing protocols have been proposed in the literature [3][4]. These protocols generally fall into three categories namely table-driven (proactive), on-demand (reactive) and hybrid routing protocols. Table-driven routing protocols aim to maintain routes to all possible destinations in the network at all times. Examples of table-driven routing protocols include OLSR (Optimized Link State Routing) [5] and DSDV (Destination-Sequenced Distance-Vector) routing  [6]. In contrast to table-driven approach, on-demand routing protocols, e.g., AODV (Ad-hoc On-demand Distance Vector) routing [7], DSR (Dynamic Source Routing) [3], and ABR (Associativity-Based Routing) [8], discover a route only when it is needed. Hybrid routing protocols, e.g., ZRP (Zone Routing Protocol) [9] and CEDAR (Core-Extraction Distributed Ad-hoc Routing) [10] combine the features of both proactive and reactive routing protocols. Interested reader can find a survey in [11].

In on-demand routing protocols, the routing process consists of two phases namely route-discovery and route-maintenance. These protocols rely on broadcasting for route discovery. For example, in case of AODV routing protocol, a source node that needs to send data to a destination node triggers route discovery mechanism by broadcasting a special control packet, called Route Request (RREQ), to its neighbours who then rebroadcast the RREQ packet to their neighbours. The process continues until the RREQ packet arrives at the destination node. The destination node sends a control packet called Route Reply (RREP) that follows the path of RREQ in reverse direction and informs the source node that a route has been established. Since every node on receiving the RREQ for the first time rebroadcasts it, it requires T-2 rebroadcasts in a network of T nodes assuming the destination is reachable. This kind of broadcasting is called pure flooding and is depicted briefly in Figure 1 while details can be found in [7].

Pure flooding often results in substantial redundant transmissions because a node may receive the same packet from multiple other nodes. This phenomenon, commonly known as the broadcast storm problem (BSP) [12], causes frequent contention and packet collisions leading to increased communication overhead and serious performance complications in densely populated MANETs. BSP equally affects the route maintenance phase during which routes are refreshed by triggering new route discovery requests to replace the broken routes.

A number of probabilistic broadcasting schemes have been proposed in the literature to address BSP. However, the performance of these schemes can be argued for real MANETs because these schemes either ignored thermal noise and interference at all [13] [14] [15]or they used the noise drawn from a distribution rather than measuring it at lower layers [16]. Real life MANETs are noisy and the communication is not error free. A number of channel impairments like noise, co-channel interference, signal attenuation, fading and user mobility affect the transmission and should be taken into account. This paper presents a novel Channel Adaptive Probabilistic Broadcasting (CAPB) scheme that adapts the probability of rebroadcasting RREQ packets dynamically according to the thermal noise, co-channel interference and node density in the neighbourhood. The proposed scheme is implemented in the network simulator ns-2 and its performance has been compared with SoA schemes in terms of routing overhead, throughput, end-to-end delay and energy consumption. Simulation results showed that the proposed scheme outperforms the SoA broadcast schemes significantly. The proposed scheme is light and does not require any extra information to be exchanged among the neighbours. This paper is an extension of our work presented in 25th International Conference on Software, Telecommunications and Computer Networks SoftCOM 2017 [17].

The rest of the paper is organized as follows: Section II presents the related work, Section III presents the proposed efficient broadcast scheme, and Section IV presents simulation results and analysis followed by conclusions in Section V.

II.   Related work

To elevate the damaging impact of pure flooding, a number of improved broadcasting techniques have been proposed in the literature, [6]. These schemes generally fall in two categories namely deterministic and probabilistic broadcasting. Deterministic schemes (e.g., MPR [18] and Self Pruning Scheme [19]) exploit network information to make more informed decisions. However, these schemes carry extra overhead to exchange location and neighborhood information among the nodes. On the other hand, the probabilistic schemes e.g., Fixed Probabilistic [20], and Counter Based Scheme [14] take local decisions to broadcast or not to broadcast a RREQ packet according to a predetermined probability.

A fixed probabilistic scheme is similar to pure flooding except that nodes rebroadcast with a predetermined probability. Cartigny and Simplot [15] presented an improved probabilistic scheme combination where the rebroadcast probability is calculated from the number of neighbors which are considering retransmission. This scheme was shown to achieve significant reduction in the number of rebroadcasts. However, this scheme did not consider thermal noise and co-channel interference.

Zhang and Zhou  [13] proposed an algorithm of load balancing based on history information. In their algorithm mobile nodes use history load information and judge route access by probability. They combined the algorithm with DSR, and they named HPDSR (Historical Probability Dynamic Source Routing). Their computer simulation results showed that HPDSR protocol improves network throughput and reduces the end-to- end delay effectively without extra route overhead. However, they did not consider noise effect at all.

Ali, et al [21] proposed a routing protocol, called  neighbor-based Dynamic Connectivity Factor routing Protocol (DCFP), that dynamically probe the status of the underlying network without the intervention of a system administrator, while reducing the RREQ overhead using a new connectivity factor. Their results revealed that the suggested protocol showed better performance than AODV in terms of end-to-end delay, normalized routing overhead, MAC collision, energy consumption, network connectivity, and packet delivery ratio.

Zhang and Agrawal [22] suggested a probabilistic scheme that dynamically modifies the rebroadcasting probability based on the node distribution and the node movement by considering local information but without needing any distance measurements or exact location determination devices. Their results showed an improvement in performance when compared to both pure flooding and static probabilistic schemes. However, the effects of noise and interference were ignored. The same authors (in another work [23]) suggested a levelled probabilistic routing scheme for MANETs. In this scheme, mobile hosts are divided into four groups and different rebroadcast probabilities are assigned to each group. The results showed gains in throughput.

Mohammed et al. [14] suggested a probabilistic counter-based scheme that reduces the retransmission of RREQ packets during the route discovery phase. The results revealed an enhancement in the performance of AODV in terms of routing overhead, MAC collisions, and end-to-end delay while still achieving a good throughput. However, this approach did not consider thermal noise plus interference.

Fig. 2. (a) Simple flooding in noiseless MANETs, (b) Fixed Probabilistic scheme in noiseless MANETs, and (c) Fixed Probabilistic scheme in noisy MANETs

Al-Bahadili and Sabri [16] proposed a probabilistic algorithm for route discovery based on  the noise-level called Dynamic Noise-Dependent Probabilistic (DNDP) scheme. In this scheme the noise-level value is drawn from a distribution rather than measuring it at lower layers. The simulation results showed that the suggested algorithm presented higher network reachability than the dynamic probabilistic algorithm with a reasonable increase in the number of retransmissions for a wide range of noise-levels.

Linfoot, et al.[22] studied the effects of physical and virtual carrier sensing on the AODV routing protocol and showed that the route discovery mechanism is affected by the interference when the number of nodes increases.

In wireless networks, physical layer characteristics affect the higher layer protocols. This shows a great potential of exploiting cross layer optimization approaches. Takai et al. [24] studied the role of physical layer modelling in evaluating the performance of higher layer protocols and showed that the physical layer modelling plays a key role even though the higher layer protocols do not interact with the physical layer directly.

Alnajjar and Chen [25] stated a cross-layer mechanism wherein the routing protocols adapt to the current Signal to Noise Ratio (SNR). This approach was implemented in DSR protocol and was shown to enhance the performance.

To the best of the authors’ knowledge, no previous work on probabilistic broadcast in route discovery mechanism has considered the effects of thermal noise, co-channel interference, and node density in the neighbourhood simultaneously to address the BSP.

III. Proposed broadcast scheme

The proposed CAPB scheme adjusts the probability of rebroadcasting RREQ packets dynamically by taking into account two factors. The first factor is the measured co-channel interference plus thermal noise, and the second factor is the node density in the neighbourhood. These two factors affect the efficacy of disseminating RREQ packets as discussed below.

A.      Effect of Co-Channel Interference & Thermal Noise

Consider Figure 2 where node A broadcasts a RREQ message to find the route to node G. In Figure 2(a), using pure flooding in absence of co-channel interference and thermal noise, the destination node (G) receives the RREQ packet from node B as well as node C. The destination node (G) however, will only send one RREP packet to either node B or C whichever forwards the RREQ first. Using probabilistic broadcast, there are three possibilities (i) both B and C, (ii) either B or C and (iii) neither of the two nodes will rebroadcast the RREQ packet. As exemplified in Figure 2b, using probabilistic broadcast in absence of co-channel interference and thermal noise, only node B manages to rebroadcast the RREQ. By considering the effects of thermal noise and co-channel interference (Figure 2c), assuming that node A fails to deliver the RREQ packet to node B (because of thermal noise plus interference in the area), but is able to deliver the same packet to node C, the RREQ packet is therefore undelivered to node G. Node G will thus be declared unreachable.

Packet Error Rate (PER) is closely related to SINR (Signal to Interference plus Noise Ratio) and packet size as shown in Figure 3 [26]. In the proposed CAPB scheme, when a node receives a RREQ packet, it obtains the SINR value, as measured at the physical layer and infers the PER using the relationship shown in Figure 3. If the PER is higher, then the probability of receiving the same RREQ packet by the neighbouring nodes is low. In this case, naturally the lucky node that has received the RREQ should rebroadcast the RREQ with high probability to increase the dissemination of this particular RREQ packet. On the other hand, a low PER implies that many nodes in the neighbourhood have also received the RREQ packet with high probability, therefore the rebroadcast probability should be relatively low to avoid the BSP.

B.      Effect of Node Density in Neighbourhood

When a node receives a RREQ packet, the decision of rebroadcasting should take into account the number of neighbouring nodes and their geographic distribution to make a wise decision. In a densely populated area, not all nodes need to rebroadcast to avoid redundancy and the risk of increased collision leading to packet loss and energy wastage. On the other hand, in a sparsely populated area relatively more nodes should rebroadcast the RREQ packet to ensure dissemination of the RREQ packet. Here we consider only the number of nodes in the transmission range of the node receiving the RREQ packet to determine the rebroadcast probability.

Fig2.jpg

Fig. 3. Relationship between PER and SINR for different packet sizes [26].

Fig. 5. Node R receiving RREQ from node S.

C.     The Proposed CAPB Algorithm

  

Upon  receiving a RREQ packet  m at a node R

Event: Node R receives RREQ packet m

if Node R is the destination node for RREQ m

Send RREP

else

Calculate Nb

Obtain SINR and infer PER

Calculate Neff using eq. 4

Calculate Preb from eq. 6

Generate a random number  δ  between 0 and 1.0

          if δ < =

Preb then

Broadcast the RREQ message m

          else

Drop the RREQ message m

         end if

end if

End if

Fig. 4 Proposed CAPB scheme

The proposed CAPB scheme has been shown in Figure 4. When node R receives a RREQ packet, for which R is not the destination node, it rebroadcasts the RREQ packet with probability

Preb. To determine the value of

Preb, node R determines the value of

Neffwhich denotes the number of effective nodes within its transmission range r which have received the same RREQ packet. This is done as follows. Assume

Nis the total number of nodes within the transmission range of node R. We use Hello Packets to infer the value of

N. The number of nodes

Nbwhich are located within the transmission range of both nodes R and S can be calculated from the overlapped area A of the two circles as shown in Figure 5. Using geometry, the overlapped area A can be given by

A=(θ× π/180-sinθ)×r2          (1)

Here θ is the angle of the circular segment in degrees. Note that θ=120o when node R is at the edge of the transmission range r of node S, and θ=180o when node S is very close to node R. Node R estimates its distance from node S from the signal strength of the received RREQ packet and calculates the value of θ using simple trigonometric relations. To keep our scheme simple, we assume that nodes are uniformly distributed. With this assumption, the value of

Nbcan be given by

Nb=N×A/πr2                                                      (2)

To take into account the effects of thermal noise and co-channel interference, node R obtains the SINR from the physical layer at the time of receiving the RREQ packet and infers the PER using the relationship shown in Figure 3. The value of

Neff  is then given by

Neff=Nb×(1-PER)                                          (3)

Equation (3) can be simplified to

Neff=N×( θ180-sinθπ )(1-PER)              (4)

A higher value of

Neffimplies that more nodes have received the RREQ and consequently the value of

Preb  should be lower and vice versa. This suggests an inverse relationship between

Preband

Neff.

Preb=d×1Neff

(5)

Here

dis a constant value representing the dissemination factor. For very low (

≤Nl) and very high

(≥Nu)values of

Neffequation (5) may not hold true so fixed values of

Prebare used in those cases. In general

Prebcan be given as follows:

Preb=Pmax,                                  for  Neff ≤Nl d×1Neff,                   for  Nl<Neff<Nu Pmin,                                      for  Neff≥Nu             (6)

Appropriate values of

Nland

Nucan be derived from an estimated maximum and minimum possible node density and the transmission range of nodes. The implementation of the proposed scheme and its performance evaluation is presented in the next section.

IV.     Performance evaluation of the CAPB algorithm

This section presents the performance evaluation of the proposed CAPB scheme using four metrics namely routing overhead, throughput, end-to-end delay and energy consumption for different node densities, mobility profiles, and traffic load (by varying number of source-destination connections). The proposed CAPB scheme has been compared with three related broadcasting schemes. The first one is pure flooding that is part of the standard AODV routing protocol. The second one is the fixed probabilistic scheme [20] denoted by AODV-P where P shows the rebroadcast probability. The third scheme is DNDP (Dynamic Noise-Dependent Probabilistic) scheme of [16].

A.      Simulation setup

We used ns-2 simulator (2.35v) to implement and evaluate the proposed scheme in MANETs using AODV routing protocol. Standard AODV uses pure flooding. The proposed CAPB scheme and the two other schemes (AODV-P and AODV-DNDP) have been implemented in the route discovery process of AODV. In AODV-P scheme, the value of P is set to 0.6 after running simulation with a range of values for P and choosing the one giving the best performance. The parameters of AODV-DNDP scheme follow recommendations in [16]. For CAPB, we set

Nl=7,

Nu=16,  Pmax=0.7,

P min=0.3and

d=5. These values are partly heuristic and partly simulation guided.

Fig. 6.  PDF of Preb

The MANET related simulation parameters generally follow [16][27]. The radio propagation is based on 2-ray Ground Reflected Model. The network bandwidth is set to 6 Mbps and the medium access control (MAC) protocol is simulated using the ns2 library dei80211mr [26]. This library calculates the PER using pre-determined curves (PER Vs. SINR) for the given packet size (shown in Figure 3). The SINR value is computed from the received signal strength, thermal noise and co-channel interference. Thermal noise is set to -95dBm following recommendations in [28].

The node mobility is modelled using Random Waypoint [29] mobility model with variable node speed and pause time set to zero. Nodes are placed randomly in an area of 1000×1000 square meters. Transmission power, path loss and receive power threshold are set such that the effective transmission range is 250m. Each node has a FTP (File Transfer Protocol) agent attached to it such that node i is downloading a file of infinite size from node i+M/2 for i=1,2,…,M/2 where M is the total number of nodes for density and mobility scenarios. For energy consumption analysis, each node has initial energy of 1000 joules.

B.      Simulation results and analysis

We used three different simulation scenarios namely the density-scenario, the mobility-scenario, and the traffic-scenario to see effects of varying node density, mobility and traffic load respectively on the performance metrics (routing overhead, throughput, end-to-end delay and energy consumption). There are three variables, mobility, number of nodes, and traffic. In each scenario, one is varied while the other two parameters are fixed. The density and traffic load scenarios use a fixed node speed of 6km/hour for each node. The mobility and the traffic load scenarios use fixed number of nodes (set to 100).

Simulation results are obtained by averaging the results of 30 runs for each scenario within the same confidence interval of 95%, each using a different seed value and lasting for 800 seconds. The seed value is used in the mobility model to yield different mobility profiles and to set the initial location for each node. Since the direct outcome of the proposed CAPB algorithm is the probability

Prebof rebroadcasting RREQ, we collected

Prebvalues over a number of runs from three scenarios. The mean and variance of

Prebis found to be 0.5 and 0.01 respectively. Figure 6 shows that the distribution of

Prebfollows closely the normal distribution truncated at below 0.3 and above 0.7 with the same mean and deviation.

Fig. 7. Routing Overhead vs Number of Nodes (density-scenario)

Fig. 9. Routing Overhead vs traffic load (traffic-scenario)

Fig. 8. Routing Overhead vs Node Speed (mobility-scenario)

Fig. 10. Total number of RREQ packets transmitted for different number of nodes (density-scenario)

1)     Routing overhead

Routing overhead is defined as the ratio of the number of routing packets (control packets) transmitted per data packet received. Figure 7, Figure 8 and Figure 9 show the average routing overhead as a function of node density, node speed and traffic load respectively.

In general, the average routing overhead increases with increasing node density and traffic load because higher number of neighbouring nodes and traffic load lead to higher contention and PER which result in redundant retransmission of control packets.  Similarly, increasing node speed makes the network topology more dynamic. Routes get expired quickly and new route discovery mechanism is triggered more often to replace the expired routes. This can be verified by observing the total number of RREQ packets transmitted as shown in Figure 10, Figure 11 and Figure 12.

The proposed CAPB scheme uses the least number of RREQ packets. Increasing the number of RREQ broadcasts increases the reachability of nodes on one hand but on other hand, it may increases the co-channel interference leading to higher PER which may limit the reachability and require restart the route discovery process.

This is the reason of higher overhead of pure AODV scheme.  Fixed probabilistic scheme (AODV-0.6) limits the number of RREQ blindly which often limits the reachability of RREQ packets to the destination node and route discovery mechanism has to be triggered more often leading to higher overhead. It is interesting to note that the routing overhead of pure AODV is better than AODV-0.6 scheme. In fact, thermal noise plus co-channel interference act as natural limiters for the traffic; the former is static while the latter is adaptive because it increases with traffic intensity. This reduces the chances of getting duplicate RREQs from the neighbouring nodes and adapts to the traffic intensity very well.

In presence of natural and adaptive limiters (thermal noise and co-channel interference), the artificial limiter (reducing the rebroadcast probability without considering the effect of interference and thermal noise), does not work well because it limits the reachability of RREQs independent of the traffic intensity. Nodes have to try several times before they get a valid route which increases the routing overhead.  In AODV-DNDP, the probability is not fixed and is drawn from a distribution without considering the current level of noise and interference.  The proposed CAPB scheme is able to achieve significantly lower routing overhead as compared to other schemes as shown in Figure 7, 8 and 9. The comparative savings in routing overhead increases with the increase in node density, node speed and traffic load.

Fig. 11. Total number of RREQ packets transmitted (mobility-scenario)

Fig. 14. Average throughput vs. Node Speed (mobility-scenario)

Fig. 12. Total number of RREQ packets transmitted vs. traffic load (traffic-scenario)

Fig. 15. Average throughput vs. traffic load (traffic-scenario)

2)     Average throughput

Throughput is defined as the amount of data received by a node per unit time. Figure 13 shows the average throughput, measured at the application layer, for all nodes as a function of number of nodes. Figure 14 shows the average throughput as a function of node speed and Figure 15 shows the average throughput as a function of traffic load.

Fig. 13 Average throughput vs. Number of Nodes (density-scenario)

In general, the average throughput decreases by increasing the number of nodes and traffic load due to increased contention ratio and higher collision rate. The average throughput also decreases with increasing node speed because routes are broken more often due to changing neighbourhood and network topology causing a temporary pause in data transmission till the new route is established. The time required to establish new routes to replace the broken ones and the routing overhead affect the throughput significantly.

Inefficient or blind decision of rebroadcasting the RREQ packets may not result in a successful route establishment at first attempt and the process may have to be initiated repeatedly. This would increase the time to establish a route from the source node to the destination node. The FTP application has to wait longer before it could start sending data. Moreover, node mobility invalidates old routes more frequently and interrupts the data supply until an alternative route is established. The proposed scheme is able to achieve significant throughput gain over the other schemes. This is because the rebroadcasting decision in CAPB takes into account SINR and nodal density in the neighbourhood which increases the reachability of RREQ to the destination node while keeping the routing overhead at minimum.

Fig. 19. Energy Consumption vs. Number of Nodes (density-scenario)

Fig. 17. Average end to end delay vs. node speed (mobility-scenario)

3)     Average end-to-end delay

The average end-to-end delay shows the time a packet takes to arrive at the destination node from the source node. It includes all possible delays which include buffering during route discovery, queuing at the interface queue, retransmission delays at the MAC, propagation delay and transmission delay. Figure 16 shows the average end-to-end delay for data packets for all nodes as a function of number of nodes, Figure 17 presents the average end to end delay for all nodes as a function of node speed and Figure 18 presents the average end to end delay for all nodes as a function of traffic load.

Fig. 16. Average end to end delay vs. number of node (density-scenario)

It can be seen that for all schemes, the average end-to-end delay increases with increasing number of nodes, node speed and traffic load.  By increasing the number of node and traffic load, contention increases leading to higher queuing delay at the transmitter’s buffer and higher packet loss rate due to increased collision. A data packet may need to be retransmitted multiple times. With increased mobility, route breaking and repairing takes places more often leading to higher average delay.

The proposed CAPB scheme achieves much lower end-to-end delay as compared to other schemes. This is because the proposed scheme produces fewer routing traffic, which helps to decrease the contention and collision, and it increases the reachability of RREQ packets to the destination which helps to establish or repair routes faster.

Fig. 18. Average end to end delay vs. traffic load (traffic-scenario)

4)     Average energy consumption

Energy consumption accounts for the energy consumed in transmitting, forwarding and receiving packets (both data and routing packets). We used energy model in NS2 to measure energy consumption of AODV-0.6, AODV, AODV-DNDP and AODV-CAPB. As implemented in NS2, energy Model is a node attribute and it represents energy level in a mobile node.  It has an initial value which is the level of energy the node has at the beginning of the simulation, and also has a given energy usage for every packet it transmits and receives. These are txPower_ and rxPower, and we used the default value for them which is 281.8mW [26] .

Figure 19, Figure 20 and Figure 21 depict the average energy consumption for all nodes for different number of nodes, node speed, and traffic load respectively. The proposed scheme CAPB achieves better energy efficiency as compared to the other schemes.  The energy saving of CAPB is achieved by adapting the rebroadcasting of RREQ packets to current channel conditions and number of neighbouring nodes which helps to reduce unnecessary transmissions of RREQ packet.

Fig. 21. Energy Consumption vs. traffic load (traffic-scenario)

However, the savings in energy is not in proportion to the saving in RREQ packets (see Figure 7, Figure 8 and Figure 9). This is because the CAPB achieves much higher throughput as well which consumes extra energy.

Fig. 20. Energy Consumption vs. Node Speed (mobility-scenario)

V.  Conclusion and future work

Broadcasting is a vital part of route discovery phase of on-demand routing protocols in MANETs. Many on-demand routing protocols (e.g., AODV) use pure flooding to broadcast the RREQ packet. However, pure flooding generates excessive control traffic which may lead to broadcast storm problem. A number of probabilistic broadcasting schemes have been proposed to limit the broadcast traffic but these schemes do not consider the thermal noise and the co-channel interference and hence do not perform well in realistic noisy MANETs. Node density in the neighbourhood is another important factor to determine the rebroadcast probability. This paper has presented a novel Channel Adaptive Probabilistic Broadcast (CAPB) scheme that adapts the rebroadcast probability to the thermal noise, co-channel interference and node density in the neighbourhood dynamically. Extensive ns-2 simulations have shown that the proposed CAPB scheme outperforms the standard AODV and the two related schemes significantly in terms of routing overhead, throughput, end-to-end delay and energy consumption. Simulation results also revealed that the distribution of the rebroadcast probability follows normal distribution closely. The proposed scheme is simple and does not require any extra information to be exchanged among the neighbouring nodes.

The proposed scheme shows the potential gains of considering thermal noise, co-channel interference, and node density in the neighbourhood. However, the proposed scheme depends on carefully chosen values of certain parameters

(Nl,

Nu,  Pmax,

Pminand

d). These parameters were chosen partly heuristically and partly simulation guided in this work. However, research on a systematic approach to find out the optimal values of the aforementioned parameters would be a potential extension of this work.

 

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Haitham Y. Adarbah obtained his PhD degree in Wireless Networks from De Montfort University, Leicester, United Kingdom in 2015. He earned a Master degree in Computer Science from Amman Arab University, Amman, Jordan in 2009 and a Bachelor degree in Computer Science from Al-Zaytoonah University of Jordan, Amman, in 2004.

He is an assistant professor and programme leader at Gulf College, Muscat, Oman. He also served as Moodle Coordinator for almost a year. His research interests include Route Discovery Schemes, Routing Techniques, Bandwidth Utilization, Power Consumption, Delay, Security Wireless Network, and Network Simulation & Modelling. He is the author of more than 9 articles which were all published in various international conferences / journals.

C:UsersahmadsGoogle Drivemyprofileshakeel pr 1.pngShakeel Ahmad received the PhD (Dr.-Ing) degree from the University of Konstanz, Konstanz, Germany, in 2008 for his work on optimized network-adaptive multimedia transmission over packet erasure channels. He received the MSc. degree in Information and Communication Systems from Hamburg University of Technology, Hamburg, Germany, in 2005 and the BSc. (Hons) degree in Electronics and Communication Engineering from the University of Engineering and Technology, Lahore, Pakistan, in 2000. He is an associate professor in computer networks at Southampton Solent University, UK. Shakeel Ahmad has extensive software experience in networking, communication, and information technology. His research interests include computer networks, multimedia communications, Mobile Ad-hoc Networks, Peer-to-Peer Networks, and simulation and modelling.


Haitham Y. Adarbah is with Faculty of Foundation, Gulf College, Muscat, Oman (haitham.adarbah@gulfcollege.edu.om)

Shakeel Ahmad  is with School of Media Arts & Technology

Southampton Solent University, Southampton SO14 0YN, UK shakeel.ahmad@solent.ac.uk)



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