Abstract—This paper addresses the subject of improving performance of an ad hoc network system suffering from high resource contention by improving its packetdeliveryfraction. The suggested system model assumes that nodes can be divided into two classes: the servers that provide services and the clients that request services provided by servers. It is found that for such a system, it is the server and its adjacent nodes that dominate performance, and tend to cause bottlenecks due to congestion. To reduce the possibility of bottleneck occurrences, it is suggested that resource contention of the area where a server node is located be relieved. This is achieved mainly by decoupling route-requesting from service-providing activities, and reducing the former. On the one hand, for each busy, and thus critical server, a node cluster is created by constructing a server-centered connected dominating set to actively reduce contention level. On the other hand, routes are adaptively selected to avoid high congestion area. It is found that in this way, the overall data packetdeliveryfraction can be improved greatly. Results of simulation experiments demonstrated the effectiveness of the proposed approach.
The primary objective of proposed algorithm (OMR-AODV) is to improve overall network lifetime. It also focuses on improvement of packetdeliveryfraction and normalized routing load. It provides optimized multihop routing by considering residual energy between pair of source and destination nodes. To establish a path to destination, source starts process of route discovery and broadcasts route request packet (RREQ) to all its neighbors same like AODV as illustrated in Fig 1 and Fig 4 respectively. Upon receiving RREQ packet, an intermediate node in AODV sets reverse path entry in its routing table to remember path of source node and re-broadcast same to all its neighbors further as illustrated in Fig 2. When an intermediate node in OMR-AODV receives RREQ, it evaluates its residual energy with predefined threshold value. If it exceeds threshold value, then it re-broadcast RREQ to all its neighbors else node determines that its residual energy is not adequate to participate and node discards received RREQ packet as illustrated in Fig 5. Described process repeats in both AODV and OMR-AODV till Destination receives RREQ. Once destination node receives first RREQ packet, it transmits a route reply packet (RREP) to source node in AODV as illustrated in Fig 3 which always focuses on shortest path while in OMR- AODV it focuses on stronger path as illustrated in Fig 6. Once source node receives RREP, it initiates transmission of actual data packets similarly in AODV and OMR-AODV.
Mobile Ad hoc Network (MANET) can be defined as IP routing protocol functionality suitable for wireless routing application within both static and dynamic topologies with increased dynamics due to node motion and other factors. The best feature of ad hoc networks is wireless communication like wireless internet, where one can move anywhere anytime and still remaining connected with the rest of the world. The MANET is characterized by energy constrained nodes, bandwidth constrained links and dynamic topology where behaviour of communication changes with change in mobility nature of the nodes. In many real-time applications such as audio, video, and real time data processing, the ad hoc networks need for Quality of Service (QoS) in many terms such as delay, bandwidth, genthroughput, energy consumption and packetdeliveryfraction is becoming important. To attain the QoS in ad-hoc networks is a challenging task because of continues changing dynamic nature of network topology and most of the time imprecise state information. Therefore it is very important to have a dynamic routing protocol with fast re-routing capability, which also provides stable route communication during the life- time of the flows.
The reactive (AODV, DSR) and proactive (DSDV) protocol’s internal mechanism leads to considerable performance difference. The performance differentials are analyzed using NS-2 which is the main network simulator, NAM (Network Animator), AWK (post processing script) and were compared in terms of PacketDeliveryFraction (PDF), Average end-to- end Delay and Throughput, in different environments specified by varying network load , mobility rate and number of nodes.
The main observation from the above simulation is that for application oriented metrics such as delay and packetdeliveryfraction, DSR outperforms AODV in less ‘stressful’ situations, i.e., smaller number of nodes and lower load. AODV however outperforms DSR in more stressful situations. The poor delay and PDF performances of DSR are mainly attributed to aggressive use of caching and lack of any mechanism to expire stale routes. Aggressive caching, however seems to help DSR at low loads and also keeps its routing loads down. The mechanism to expire routes and/or determine freshness of routes will benefit DSR’s performance significantly. On the other hand AODV keeps track of actively used routes, destination also can be searched using a single route discovery flood to control routing load, which is totally timer –based.
The simulation carried out 10 times for each mobility model, the sum of times is 40 for the four mobility models, the total number of times is 160 for all mobility models under five parameters. The performance metrics used in this evaluation study are; packetdeliveryfraction (PDF), throughput, no. of lost packets, normalized routing load (NRL) and average end-to-end delay (AED). The main used parameters in this paper are varying no. of nodes, varying speeds, varying pause times , varying simulation area and varying traffic rates. The results are shown in the following Fig.
Abstract: TCP is most widely used transport layer protocol. Most of the applications such as e-mails, file transfers use TCP due to its reliable communication. There are various mechanisms to control the congestion in the network. The variants of TCP implement slow start, congestion avoidance, fast retransmit and fast recovery algorithms in different ways for congestion control. In this paper, we have simulated four TCP variants namely Tahoe, Reno, New-Reno and Vegas in mobile ad hoc network over AODV and DSR routing protocols. Simulation is done in NS2. Comparison of throughput, end-to-end delay and packetdeliveryfraction is made against pause time and node speed variation to determine the performance of these four TCP congestion control algorithms.
D. Deepthi Veronica et. al.,  evaluated various MANET routing protocols such as AODV and DSR. Three network parameters have been chosen in the simulation consists of packetdeliveryfraction, throughput and end to end delay. As usual, NS2 has been used to simulate and evaluate the performance of AODV and DSR. Two experiment scenarios have been setup in the paper with different specific values. The number of nodes in first scenario was 9, whereas the number of nodes in second scenario was 16. The graph or results are varying between AODV and DSR.
In , using NS-2 simulator environment, comparison of AODV, DSDV, DSR, and TORA (Temporally ordered routing algorithm) is described. Several performance parameters has been considered here such as PDF(packetdeliveryfraction), throughput, end-to-end delay. Path optimality, routing traffic generated. This paper concludes that DSr outperforms as compared to DSDV, TORA, and AODV routing protocols.performance evaluation of AODV, DSR, OLSR, and ZRP is done. ZRP and Aodv are declared as best as compared to DSR and OLSR. A brief overview of ZRP (Zone Routing Protocol) is described shortly. QualNet 4.0 simulaor is used for creating a simulation environment. Packetdelivery ratio and throughput are considered as main metrics.
GUO Zhong-hua, SHI Hao-shan puts forward the optimization of DSR (dynamic source routing) protocol with the use of constrained dynamic query localization approach called LDSR protocol in order to solve the routing overhead problem of the Ad Hoc network DSR protocol formed as a result from flooding. Dynamic choice mechanism is used by LDSR protocol in order to constraint query to a small region and full flooding localization which is based on original DSR protocol and constrained by two factors: (1) the small-world theory determines the maximum number of hops of query localization flooding ; (2) mobile critical transmitting range determines the effective routing time for connectivity in Ad Hoc network and the maximum mobile speed of node. simulation results have shown that LDSR protocol can produce better average end-to-end delay ,but packetdeliveryfraction decreased as compared to DSR protocol
This paper has reviewed various works that are related to black hole attack detection mechanism in particularly two main routing protocols in MANET ie DSR and AODV. The various authors have given several proposals for detection and prevention of black hole attacks in MANET but every proposal has its own advantages and disadvantages in their respected solutions .The various schemes are presented in chronological order .In this paper we made a comparison among the existed solutions on various parameters and developed an improved IDS scheme which reduces the normalized routing load considerably and increases the packetdeliveryfraction. The black hole problem is still an active research area for researchers and this paper will help the researchers to understand the various attacks and develop more improved IDS schemes thereby removing the shortcomings of the present IDS.
Abstract—An ad hoc network is a collection of wireless mobile nodes, frequently forming a network topology without the use of any existing network infrastructure or centralized administration. we compare the performance of the three prominent routing protocols for mobile ad hoc networks, Ad hoc On Demand Distance Vector (AODV), Destination Sequenced Distance Vector(DSDV) and Temporally Ordered Routing Protocols (TORA). We have chosen four performance metrics, such as Average Delay, PacketDeliveryFraction, Routing Load and Varying MANET Size, simulation for the popular routing protocols AODV, DSDV and TORA. The simulations are carried out on NS-2. The performance differentials are analyzed using varying network size and simulation times. The simulation results confirm that AODV performs well in terms of Average Delay, PacketDeliveryFraction. As far as Routing Load concerns TORA performs well.
AbhishekDixit(2015) et al: The scenario of directional meta material antenna is simulated for comparing and analyzing of different routing protocols such as AODV, DSR and ZRP using QualNet simulator 6.1. The metrics used for performance evaluation of different routing protocols we used throughput, average unicast end to end delay, and average unicast jitter of routing protocols. Ajay Singh(2014) et al: The performance comparison of MANET mobility models have been analyzed by varying number of nodes, type of traffic (CBR, TCP) and maximum speed of nodes. The comparative conclusions are drawn on the basis of various performance metrics such as: Routing Overhead (packets), PacketDeliveryFraction (%), Normalized Routing Load, Average End-to- End Delay (milliseconds) and Packet Loss (%).
Abstract - In Mobile Ad hoc network (MANETS), no fixed infrastructure is available. Mobile Ad-hoc Networks (MANETs) are future wireless networks consisting entirely of mobile nodes that communicate on-the-move without base stations. Nodes in these networks will both generate user and application traffic and carry out network control and routing protocols. Rapidly changing connectivity, network partitions, higher error rates, collision interference, and bandwidth and power constraints together pose new problems in network control—particularly in the design of higher level protocols such as routing and in implementing applications with Quality of Service requirements. The MANET routing protocols have mainly two classes: Proactive routing (or table-driven routing) protocols and Reactive routing (or on-demand routing) protocols. In this paper, we have analyzed Random based mobility models: Random Waypoint model using AODV and DSDV protocols in Network Simulator (NS 2.35). The performance comparison of MANET mobility models have been analyzed by varying number of nodes type of traffic (TCP) and maximum speed of nodes. The comparative conclusions are drawn on the basis of various performance metrics such as: Routing Overhead (packets), PacketDeliveryFraction (%), Normalized Routing Load, Average End-to-End Delay (milliseconds) and Packet Loss (%).
 Ajay Singh(2014) et al: Mobile Ad Hoc Networking (MANET) is a group of independent network mobile devices that are connected over various wireless links. It is relatively working on a constrained bandwidth. The network topologies are dynamic and may vary from time to time. Each device must act as a router for transferring any traffic among each other. This network can operate by itself or incorporate into large area network (LAN). In this paper, we have analyzed various Random based mobility models: Random Waypoint model, Random Walk model, Random Direction model and Probabilistic Random Walk model using AODV,DSDV and ZRP protocols in Network Simulator (NS 2.35). The performance comparison of MANET mobility models have been analyzed by varying number of nodes, type of traffic (CBR, TCP) and maximum speed of nodes. The comparative conclusions are drawn on the basis of various performance metrics such as: Routing Overhead (packets), PacketDeliveryFraction (%), Normalized Routing Load, Average End-to- End Delay (milliseconds) and Packet Loss (%).
In order to measure the performance of proposed Ant Based on Demand Routing Algorithm ABDRA effectively, we compare the performance of ABDRA with AODV for varying pause time. Nodes we have considered are 50 and they move within an area of 1500m x 300 m using Random Waypoint Model. The maximum speed of nodes we have considered in simulation is 10m/s. The channel capacity is 2 Mbps and transmission range of each node is taken as 250m. Traffic Type is 20 CBR (continuous bit rate) traffic sources each send 4 packets per second with a packet size of 64 bytes. PacketDeliveryFraction and End to End Delay is considered as evaluation parameter for this simulation. The simulation time was taken to be of 1000 seconds. Also, we have considered nodes with Omni-Antenna and Two Ray Ground Radio Propagation method. Simulation parameters are appended in Table-1.
where 𝑝 and 𝑞 are the probabilities of forward and backward packet reception over a link, respectively. In the initialization phase, each node broadcasts the control packets and stores the number of successfully received packets from its neighbors in the routing neighbourhood table. Then, the destination node sets its transmission cost to zero and broadcasts this value to its neighbors, when a node receives a transmission cost included in a packet.
Where Td is the busy-tone detection delay which depends on the communication hardware and might not be negligible, TDATA is the transmission time of a data packet that can be calculated based on the date transmission rate and packet length. The timer TD1 is used to account the time in which node B should receive the data packet from node A if no helper exists. If no data packet is received in TD1, node B turns off its BTr busy tone. Node A monitors the BTr signal and waits for the CTS packet from node B. Once the CTS packet is received, node A sets up a new timer TS2 = Td + τ , and waits for response from potential helpers. If there is no BTh signal detected before the timer TS2 expires, which means that no helper has the ability to improve the instantaneous throughput of transmission from node A to node B, node A turns off its BTt signal and starts to send the data packet to node B with the transmission parameters (e.g., modulation mode and coding rate) chosen according to the SNR information included in the CTS packet. Otherwise, node A keeps transmitting its BTt signal until it receives the ready-to-help (RTH) packet from an optimal helper. Note that different from DBTMA, where an existing receiver (e.g., node F in Fig. 1) turns on its BTr busy tone during the data packet reception, in our scheme it is the potential receiver (e.g., node B) which just received an RTS packet that turns on its BTr busy tone to protect the reception of the RTH.
PacketDelivery Ratio(PDR)in Mobile Scenario with different Geocast Region dimensions In Figure 10, the packetdelivery ratio graph is represented at the different geocast region dimensions. The geocast region dimensions specified in the graph as “Geocast Region 1100” implies a geocast region of 1100 × 1100 square meters. The same is to be considered for all the cases. When the geocast region is small, in such a scenario less number of nodes exists in the geocast region. For example from the figure we can infer that in case of 1100 × 1100 square meters dimension of the geocast region, PDR is 100% in almost all the cases. The red colored bar in the graph is depicting the geo- cast region of dimension 1100 × 1100 square meters. As the geocast region size increases the packetdelivery ratio achieved reaches from 71% to 100%. In all other sce- narios with geocast region dimensions being varied at 1100 × 1100, 1300 × 1300 and 1500 × 1500, the average packetdelivery ratio is above 85%.
Brahmankar et al. in this paper, a detailed review is taken to reduce the routing overhead in ad-hoc network. The techniques given here may be utilized to enhance the performance of routing. They have their own advantages or disadvantages. Because of less redundant rebroadcast, the NCPR protocol moderates the network collision and contention, which increases the packetdelivery ratio and decreases the average end-to-end delay.