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A survey on parameters affecting manet performance.

1. Introduction

• The commonly used simulation tools in a MANET are described, covering the advantages and disadvantages of each. Additionally, statistics on the percentage of usage of these simulation tools are collected against 50 recent papers.
• The list of routing protocol parameters that control the routing behavior is provided for three routing protocols. Comprehensive flowcharts for the covered routing protocols are provided. Additionally, the routing parameters’ usage statistics against 50 recent papers are presented.
• The simulation parameters used to define a MANET environment are collected, illustrating the usage of each, and statistics on the literature usage percentage of the simulation parameters are covered. The literature range of values used for each simulation parameter is also provided.
• The main parameters that influence the MANET performance under attack are covered, a list of common attack types on a MANET is collected, and the percentage of usage is shown.
• The evaluation metric terms used for a performance analysis of MANETs are described. Additionally, statistical tables are collected to show the used environment parameters in our survey papers.

2. Routing in MANETs

• Proactive routing protocols: For example, OLSR, each node maintains its routing table by periodically updating its information [ 6 ]; this increases network overhead. On the other hand, routes will always be available with a minimum delay. Proactive protocols provide better performance than reactive protocols as each node continuously updates its awareness of network changes. When a request is received, the packet forwarding procedure is directly handled.
• Reactive routing protocols: For example, AODV and DSR, when a source node tries to perform a packet transmission, it initiates a route discovery mechanism to know how to reach the destination. After the route is determined and updated in the routing table, the packet is forwarded [ 7 ]. Reactive protocols have minimal network overhead, but there is a delay time consumed in the route discovery.
• Hybrid routing protocols: For example, ZRP, the close local neighbors to a node are periodically updated, and the global nodes that are not direct neighbors will be updated on demand such as in reactive routing protocols [ 8 ].

2.1. AODV Routing Protocol

• Destination node address;
• Number of hop counts to reach the destination;
• Intermediate nodes address;
• Route entry expiry time;
• Destination node sequence number.
• Network diameter: The network diameter value sets the maximum number of hop counts between two nodes in MANETs. The network diameter default value is up to thirty-five hops at most as per RFC 3561 standard.
• Node transversal time: The node transversal time is the estimation of packet transversal time between two neighbor nodes; this estimation should consider the network, processing, and transfer delay time. The default configuration time is 40 ms.
• Network transversal time: The network transversal time is the expected time between sending the RREQ packet and the reception of the RREP packet as per the equation [ 14 ]:
• Route request retry: If a route reply is not received by the source node within the maximum network transversal time, the source node can retry to request the route discovery again for a maximum route request retry times. If the route discovery exceeds the route request retry times, the destination node should be considered unreachable. The default value for the route request retry parameter is equal to 2 retries.
• Blacklist timeout: When the RREP transmission from node A to node B fails, node A records node B in its blacklist buffer. During this blocking time, node A discards any RREQ from neighbor node B until the blacklist timeout is reached. After the blacklist timeout expires, node B is removed from the blacklist [ 14 ].
• Route request rate limits: The route request rate limit is the maximum number of RREQ packets for the source node to originate per second. The route request rate limit’s default value is ten packets per second.
• Active route timeout: The neighbor node is recorded in the routing table and considered an active node when the active route timeout is not exceeded. When a neighbor node is active, the recorded route to this neighbor should be used [ 15 ]. The active route timeout default value is 3000 ms.
• Hello interval: All MANET nodes should reveal their existence in the network within a hello interval time [ 16 ]. If a node does not contribute to the routing activities for a hello interval time, it should broadcast a hello message with TTL = 1. Hello interval default value is set to be 1000 ms.
• Allowed hello loss: If a node does not receive any contribution to routing activities from its direct neighbor node for more than (HelloInterval × AllowedHelloLoss), the node should assume a link failure to this neighbor [ 17 ]. The allowed hello loss default value is two link failures.
• DPC: After the delete period constant time is expired, the expired route will be deleted from the routing table [ 18 ]. The default value for DPC is 5 s.

2.2. DSR Routing Protocol

• source node identifier;
• destination node identifier;
• route request identifier;
• record listing the address of all intermediate nodes.
• Discovery hop limit: The discovery hop limit value is defined as the limit to the route request re-broadcast. If the first attempt of RREQ does not reach the destination node, the default value of the discovery hop limit is 255 hops, and the minimum value is one hop.
• Broadcast jitter: The destination node should delay the RREP message by a random value that does not exceed the broadcast jitter’s maximum delay time. The broadcast jitter default value is ten milliseconds.
• Route cache timeout: The route cache timeout is associated with each route entry in the cache [ 23 ]. When the timeout is reached, this means that the related route is not used and needs to be deleted from the node’s cache. Route cache timeout default value is three hundred milliseconds.
• Send buffer timeout: When a packet cannot be transmitted to the next-hop node, this packet is queued inside a buffer to try sending it when possible. Send buffer timeout is the maximum time associated with a packet to be sent before being removed from the send buffer. The default value for send buffer timeout is 30 s.
• Max request period: After a route discovery attempt fails to find a route to the destination node, the time between successive route discovery attempts doubles until the maximum request period is reached. The default value for the maximum request period time is 10 s.
• Re-transmit buffer size: Re-transmit buffer holds the maximum number of packets waiting for the next-hop reachability confirmation. If the buffer is not sufficient to keep the new packet, this packet is discarded without notification. The re-transmit buffer size defines the buffer size with a default value of 50 packets.
• Max maintenance re-transmission: The maximum number of re-transmissions for a packet waiting for a confirmation from the next hop should be limited by the configuration value of the max maintenance re-transmission parameter. The default value is only two transmissions.

2.3. OLSR Routing Protocol

• Willingness: Willingness is a configuration parameter that specifies the node’s willingness to forward traffic packets to other network nodes [ 28 ]. A node may change the willingness during run-time based on conditions such as resource constraints and power limitations. Willingness is an integer value with a range between 0 and 7. ‘WILL_NEVER = 0’ is the lowest willingness value where this node must not be selected as a MPR for any node. ‘WILL_ALWAYS = 7’ is the highest willingness for a node to advertise its willingness to forward traffic on behalf of other network nodes. The default willingness value for a node is ‘WILL_DEFAULT = 3’.
• Hello interval: Hello interval is the set periodic time between two consecutive hello messages in seconds. The default value is 2 s.
• TC interval: This is the interval time in seconds between two consecutive topology control messages that carry the connectivity information. The TC interval default value is 5 s.
• Refresh interval: Each node must cooperate in the network by sending a periodic hello message before the refresh interval period reaches a timeout. A hello interval must be smaller than or equal to the refresh interval. The default value for the refresh interval parameter is 2 s.
• Neighbor hold time: Defines the link expiry time before declaring it as a broken link [ 29 ]. The neighbor hold time default value is 6 s.
• Topology hold time: This is the timeout for the entries in the topology table before being deleted [ 29 ]. The topology hold time default value is 15 s.

3. Simulation in MANETs

3.1. manets simulators, 3.2. attacks on manets’ routing protocols.

• Routing table overflow attack: In this attack, the attacking node tends to crowd the network by advertising several non-existing nodes to overflow the routing table [ 33 ]. This prevents legitimate nodes from being aware of network nodes and routing their packets normally.
• Flooding attack: In a flooding attack, malicious nodes tend to waste network resources such as memory, bandwidth, and battery by flooding the network with bogus packets [ 34 ]. For example, flooding RREQ packets prevents the MANET from functioning normally.
• DDoS attack: In a DDoS attack, attackers tend to keep the targeted legitimate node busy by continuously requesting RREQ messages from collaborative attackers at the same time without respecting the TTL time [ 35 ].
• False removal of working route: In this attack, the malicious node advertises a false state of the link with the destination node as if the link is broken. This enforces the source node to re-initiate route discovery protocol to find another path to reach the destination. Additionally, it slows down packet transmission. False removal of working route attack could be used with another collaborative attack to isolate the targeted legitimate node from MANET.
• Node isolation attack: Attackers isolate an innocent node by blocking routing information about this targeted node from the entire network [ 36 ]. This leads to an ignorance of the presence of this innocent node.
• Routing table poisoning: In this attack, the attacker sends false RREQ packets with a higher sequence number to force all nodes to delete the old genuine route to a destination and update this route with a corrupted one.
• Blackhole attack: The attacker tends to change the routing protocol packets to be the best route known for a targeted destination, and when it is requested to forward data packets to the destination node, it starts discarding the received packets to slow down the network performance [ 37 ].
• Grayhole attack: Grayhole attack is an instance of a blackhole attack where an attacker selectively drops some data packets and normally forwards others [ 38 ], or drops all packets but only at a certain time. This makes the attack difficult to detect.
• Wormhole attack: In a wormhole attack, two attacking nodes cooperate where one attacker at a specific location encapsulates some packets and tunnels them to the second attacker, bypassing all intermediate nodes to introduce itself as the fastest route to a destination and then drop the data packets later [ 39 ]. It can also be used to replay the received data packets in the other side of the network to disrupt the routing protocol.
• Rushing attack: In a rushing attack, the malicious node sends RREQ messages with high-power transmission to introduce itself as the shortest path to any destination with only one hop count [ 40 ], this manipulates all network nodes to use this routing path. The rushing attack is most likely used alongside another attack such as dropping the network packets that need forwarding.

3.3. Simulation and Attack Parameters

• Maximum simulation time (s): While running any simulator, a simulation time parameter is set to stop the simulation after this timeout is reached [ 41 ]; for more accurate results it is preferred to increase the simulation time.
• Medium packet rate (packet/s): To avoid interference and packet loss between nodes due to the wireless medium limitation, a packet rate ratio should be pre-set between all MANET nodes. This parameter depends on the road capacity (number of nodes/mile), the available frequency used for packet transfer, and the used wireless protocol (ex. IEEE 802.11) [ 42 ].
• Mobility speed of nodes (m/s): MANET nodes do not have a fixed location, which means that they are moving from one place to another at varying speeds. The speed of nodes affects the result of the simulation.
• Nodes’ mobility movement pattern: The mobility pattern of mobile nodes in a MANET comprises one of the following patterns: (1) random way mobility, (2) linear mobility in a straight line, (3) circle mobility, and (4) stationary mobility for fixed nodes across the network.
• Number of intermediate nodes: Increasing the number of intermediate nodes that forward packets between source nodes and destination decreases the routing protocol performance.
• Number of source nodes: In MANETs, source nodes initiate packet transmission procedures; increasing the number of source nodes in MANET will overload the channel with more packets overhead.
• Number of malicious nodes: Increasing the number of malicious nodes in MANETs decreases overall network performance.
• Position of intermediate nodes: The position of intermediate nodes inside MANETs affects the performance. As the number of intermediate nodes between the source node and the destination node increases, network performance increases.
• Position of malicious nodes: Attackers tend to take a good physical position between source and destination nodes to be able to perform the planned attack and drop the network packets.
• Data packet payload (byte/packet): Data packet payload is the percentage of real data bytes (excluding the control and header data bytes) divided by the overall packet size in bytes. The data packet payload is an indication of the actual gain from packet transmission.
• Simulation area: Simulates the MANETs’ network coverage area in m 2 . The simulation area reflects on the density of nodes inside the network, which impacts the routing protocol mechanisms.
• Antenna type: The following are the antenna types and properties used for wireless communication: (1) the isotropic antenna transmits equal signal power in all directions; (2) the omnidirectional antenna transmits equal power in all horizontal directions, decreasing to zero along the vertical axis; and (3) the directional antenna transmits only in one direction at a specified angle.
• Transportation protocol type: Transport protocol is based on two types: (1) the TCP protocol is a connection-oriented protocol that requires a connection establishment between the sender and the receiver first before sending data packets. This leads to a more secure and guaranteed delivery of data packets. On the other hand, the TCP protocol slows down packet delivery due to the needed overhead of handshaking. (2) UDP is a connectionless protocol that needs no connection establishment, which is faster but less reliable for packet delivery.
• Transmission power: Each node needs to configure the transmission power that defines the range that this node could reach in one hop. Increasing transmission power leads to more coverage, but also means more energy consumption and quick battery drain.
• Mobility speed of malicious nodes: MANETs have a dynamic network structure, which means that at certain times the network consists of some nodes that could leave the network after a while. Malicious node mobility speed is a key factor in affecting network performance. The attacker could use its speed to target an innocent node and isolate it from the network by simply taking a position between this innocent node and the destination node while traveling.
• Transmission power of malicious nodes: The power of transmission for a malicious node could be valuable when the attacker aims to introduce itself as the shortest path between source and destination nodes. This malicious node can then drop the network packets later.

4. Evaluation Metrics and Performance Analysis in MANETs

• THPT: Throughput is the rate of successfully delivered packets that reached the receiver node per time slot [ 43 ]. Throughput is affected by topology changes, noise on communication links, the power of transmission from the source node, and the existence of malicious nodes affecting the throughput ratio.
• AETED: Average end-to-end delay is the average time taken to send a packet to the destination node [ 44 ]. This delay is due to many reasons such as route discovery queuing and process latency, delays caused by wireless links, and processing delays at both the sender and the receiver sides.
• PDR: Packet delivery ratio is the ratio of packets that are received by the destination across the overall transmitted packets from the source node [ 45 ]. The packet delivery ratio represents the maximum throughput that can be achieved by the MANET network.
• PLR: Packet loss ratio is the opposite of PDR; PLR measures the total lost packets that did not reach the destination node across the overall transmitted packets [ 46 ].
• ROR: Routing overhead ratio is the size of control and header packets needed by the protocol for route discovery and maintenance over the total data packets received by the destination node [ 47 ].
• NRL: Normalized routing load is the ratio between the total number of control packets sent by a source node over the total number of data packets received by a destination node [ 48 ]. An increase in normalized routing load metric indicates the efficiency of the used routing protocol.
• NL: The network load is the average amount of data packets that are being carried by the entire network over time [ 49 ]. Increasing the network load ratio increases the possibility of data collision in the wireless medium.

5. Related Work

6. conclusions and future work, author contributions, data availability statement, conflicts of interest.

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Share and Cite

Eltahlawy, A.M.; Aslan, H.K.; Abdallah, E.G.; Elsayed, M.S.; Jurcut, A.D.; Azer, M.A. A Survey on Parameters Affecting MANET Performance. Electronics 2023 , 12 , 1956. https://doi.org/10.3390/electronics12091956

Eltahlawy AM, Aslan HK, Abdallah EG, Elsayed MS, Jurcut AD, Azer MA. A Survey on Parameters Affecting MANET Performance. Electronics . 2023; 12(9):1956. https://doi.org/10.3390/electronics12091956

Eltahlawy, Ahmed M., Heba K. Aslan, Eslam G. Abdallah, Mahmoud Said Elsayed, Anca D. Jurcut, and Marianne A. Azer. 2023. "A Survey on Parameters Affecting MANET Performance" Electronics 12, no. 9: 1956. https://doi.org/10.3390/electronics12091956

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Open Access

Peer-reviewed

Research Article

Introducing a new routing method in the MANET using the symbionts search algorithm

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Computer Engineering, Higher Education Complex of Saravan, Saravan, Iran

• Shayesteh Tabatabaei

• Published: August 25, 2023
• https://doi.org/10.1371/journal.pone.0290091
• Reader Comments

A wireless MANET network is a grouping of wireless nodes that communicate without the use of a centralized network infrastructure. The lack of reliable elements, such as routers, as well as severe resource constraints, have an important impact on the performance of these networks. To improve the efficiency of MANET, intelligent routing algorithms are required; in fact, the design of a smart and efficient routing algorithm can significantly affect the efficiency of these networks. In this regard, this paper proposes an intelligent routing method for MANET networks based on (SOS) symbiotic organism search. The proposed method is implemented in dynamic environments by considering four important criteria: available bandwidth, remaining battery energy, mobility speed, and hop count. The simulation results show that the learning process of the symbionts search algorithm has a significant impact on network performance and outperforms the FBRP algorithms in terms of throughput rate, data loss rate, packet delivery rate, and the number of hops.

Citation: Tabatabaei S (2023) Introducing a new routing method in the MANET using the symbionts search algorithm. PLoS ONE 18(8): e0290091. https://doi.org/10.1371/journal.pone.0290091

Editor: Chakchai So-In, Khon Kaen University, THAILAND

Received: June 3, 2022; Accepted: August 1, 2023; Published: August 25, 2023

Copyright: © 2023 Shayesteh Tabatabaei. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Because of the growing popularity of mobile devices and wireless networks in recent years, the MANET network has become one of the most animated and dynamic fields of communication. MANET is essentially an organization-less network of transportable devices with wireless communication capabilities that can dynamically connect at any time and location. These networks are made up of portable devices such as laptops, smartphones, sensors, and so on that communicate with one another via wireless links [ 1 ]. These devices work together to provide essential network functions without an immovable organization in a distributed manner.

MANET networks allow devices to move freely and autonomously in any direction. The nodes can detach and reconnect to the network at will. As a result, variations in the node’s link state with other nodes are experienced regularly. The movement in the MANET network changes link states and other wireless transmission characteristics such as attenuation, multipath propagation, interference, and so on. This issue poses challenges for routing protocols operating in MANET.

Furthermore, the power of MANET nodes for communication is provided by a limited-capacity battery. This battery may run out of power and turn off during data routing. This issue invalidates the route between source and destination, so optimal energy consumption in this type of network is essential [ 2 ].

As a result, in order to prevent reducing the quality of services in such a network (whose topology is constantly changing and whose nodes have energy constraints), it is necessary to design intelligent routing protocols that minimize energy consumption and provide stable routes between sources and destinations.

Achieving an optimal routing algorithm in MANETs is an NP-hard problem that can be solved by soft computing and optimization techniques [ 3 ]. Based on this, in recent years, many smart algorithms have been presented for routing mobile ad hoc networks, with different aims, among which FBRP (to reduce energy consumption) can be mentioned.

In MANETs, other routing criteria, such as available bandwidth and mobility speed should also be checked to improve the quality of the link and the performance of the routing protocols. Each criterion can be viewed as a collection of measurements that helps the routing algorithms to estimate the new weight for each step/link in the intended routes. Although so far, various routing criteria have been proposed to evaluate link quality, only a few of them have been implemented and tested in a real network simultaneously [ 4 ]. Not paying attention to any of these criteria can reduce the efficiency of the network; thus, considering a limited number of them in routing can be a significant weakness for such protocols.

As said above, changes in the network topology are one of the prominent features of the MANETs, so always establishing a stable path between source and destination in this type of network is a major challenge; therefore, routing protocols should be created to discover routes with high reliability until the end of the data transmission phase.

In this study, a new method for MANET routing using the symbiotic organisms search algorithm is proposed.

The symbiotic organisms search algorithm has three separate and strong processes: mutualism, communication, and parasitic life. The proposed method improves the solutions in the population with the help of these three processes. One of the advantages of this algorithm is not having any parameters to adjust the two operations between exploration and productivity. Therefore, the processing cost is expected to be reduced due to the simplicity of implementing the algorithm. This issue decreases the energy consumption in MANET.

One of the important features of the SOSBRA algorithm is that it encourages nodes to thoroughly investigate their surroundings before converging on the best global route. As a result, it prevents falling into the local optimum. Therefore, paths with high stability are discovered.

To evaluate the proposed method, in addition to this method, the FBRP algorithm (as a well-known intelligent protocol) is also simulated in OPNET software.

An important point that needs to be mentioned is that: the proposed method is implemented in dynamic environments by considering four important criteria of available bandwidth, remaining battery energy, mobility speed, and the number of hops. This issue is considered an important strength of this algorithm, and it will guarantee the achievement of reliable routes in different time and place conditions. However, other research works have yet to consider these four parameters simultaneously.

This paper’s main contribution is the use of the Modified routing algorithm to enhance the performance of mobile ad hoc networks as follows:

The hello message is used to identify the new neighbor nodes, and the neighbor table is updated as new responses are received from the neighbor node.

The proposed method is implemented in dynamic environments.

We consider available bandwidth, remaining battery energy, mobility speed, and hop count to choose the best path.

We employ a sophisticated routing technique based on (SOS) symbiotic organism search, which has been shown to perform well and achieve high convergence accuracy when optimizing multidimensional functions with higher dimensions.

The proposed routing algorithms implement over mobile ad hoc nodes using a network simulator (OPNET).

This article is divided into the following sections: The second section describes related work on routing protocols in MANETs, the third section states the proposed method, and the fourth section provides simulation and simulation results in the OPNET simulator. The conclusion is stated in the fifth section.

2. Related works

Omar et al. [ 5 ] presented a protocol based on source routing, in which the source node inserts routing information into data packets and allows the intermediate nodes to find the route to the destination. In the proposed method, instead of complete information about the address of the intermediate nodes, the data packets carry only a small numerical value that is a summary of the route to the destination and helps the data packets find their way to the destination.

The proposed method is divided into route discovery and data packet routing phases. If a source node’s cache does not contain any routes to its destination, the route discovery phase is started by using route request packets (RREQ) before sending data. After discovering the route, the data is sent from the source to the destination. In this article, only the mobility criterion was used for routing, and the critical criterion of battery energy level was not considered.

Khalid et al. [ 6 ] introduced a response rate-aware routing protocol for MANET. In the proposed method, nodes check the ongoing transmissions to estimate the transmission rate. In addition, instead of updating the entities in the routing table, a uniform rate of Hello messages is sent to the sending node. If the sending node receives that message, it will know that it has the correct information about the status of the sending rate. This node then stores this information and sends it to all of the nodes from which the data is received. Compared to other algorithms, this algorithm finds reliable routes while reducing the overhead caused by sending Hello messages periodically.

In the article [ 6 ], only two criteria, Medium Time Metric, and the hop count were used. This paper did not take into account the amount of energy consumption, the mobility of nodes, and the available bandwidth for routing, which are among the basic and determining parameters in MANET routing protocols.

Sumathi and Priyadharshini [ 7 ] introduced a new routing algorithm that uses multiple channels to improve the efficiency of MANETs. The proposed interlayer method employs a channel allocation strategy and collision control techniques. Channels are divided into two categories: data channels and control channels.

The data channel is used for data transmission, while the control channel is used for scheduling. The authors of this article assume that each node has a wireless network card and can send or receive data on any channel at any time. In the article, only the packet capacity criterion was checked, and the other important criteria were not mentioned.

Wang and Garcia [ 8 ] presented a new interlayer routing protocol that uses the multi-factor Q-learning algorithm to control transmission rate and node energy. This method assigns a reward to the next steps by continuously observing the reward of all status-actions, then uses the links with the highest reward for each route leading to the destination. The proposed algorithm reduces the amount of energy consumed by mobile nodes.

In this article, the criteria of node mobility speed and available bandwidth of the node were not checked.

Tabatabaei and Behravesh [ 9 ] used the reinforcement learning method to find a highly reliable route in MANETs. To calculate the reward, this method uses fuzzy logic with three inputs: the available bandwidth, the number of steps, and the remaining energy of the battery. In this article, mobile MANET routing was done once with fuzzy logic, once with reinforcement learning, and finally with a combination of these two algorithms. Simulations showed that the combined method produced a better result regarding energy consumption, efficiency rate, and latency. In this article, the mobility criterion (which changes the topology with the nodes’ mobility) was not considered.

Chettibi and Chikhi [ 10 ] proposed an Ad hoc On-Demand Distance Vector (AODV) routing protocol based on the fuzzy method to reduce energy consumption in MANETs. This algorithm makes the decision about sending the route request using a Mamdani fuzzy logic system with the inputs, the remaining battery energy, and the amount of energy loss of mobile nodes.

In this article, only the energy level of the battery was considered, and other important criteria, such as mobility and available bandwidth, were not considered.

Chatterjee and Das [ 11 ] used the ant colony optimization algorithm to improve the dynamic source routing protocol (DSR). Routing in the proposed method is done using a technique similar to the biological behavior of ants to find food, where ants first scatter in all directions. This method discovers multiple routes and has a high packet delivery rate, low end-to-end delay, and low energy consumption.

In this article, only three criteria, length, congestion, and reliability of the path, were used for choosing the paths; and the important criteria, such as the level of battery energy and mobility, were not considered.

In [ 12 ], the best routes were chosen using an interlayer routing method and a fuzzy method considering decision criteria such as node mobility and energy parameters. This algorithm allows mobile nodes to find better routes with more bandwidth and higher quality of service. This method also extends the network lifetime and improves multimedia flow quality.

Unfortunately, this article did not consider important criteria such as available bandwidth, distance to the destination, or the number of hops.

In [ 13 ], a Fuzzy logic-based on-demand routing protocol (in brief FBRP) for MANETs was proposed that selects routes based on battery energy level and mobile node speed. The simulation results showed that the proposed method has good performance and high error tolerance, especially during node failure.

It should be noted that in this article, only two criteria of battery energy level and mobility were considered for routing.

In 2019, to evaluate the fit of each node in WSNs, a clustering method-based optimized invasive weed algorithm using fuzzy modeling was proposed [ 14 ]. This method can choose the best node to be the cluster head and extend the network’s lifespan. The simulation results showed that it could reduce the number of dead nodes in each run while lowering sensor energy consumption.

Although the proposed method can quickly find the global optimal using the optimized aggressive weed algorithm, it suffers from a high processing cost (due to using fuzzy logic).

Gorgich and Tabatabaei [ 15 ] presented a new method for improving energy consumption in wireless sensor networks using a fish swarm optimization algorithm. To evaluate the proposed protocol, the protocol was compared with the ERA (Energy Aware Routing Algorithm) protocol. The disadvantage of this method is that fish optimization is used in static environments, and the convergence speed may be reduced in dynamic environments such as sensor networks.

In [ 16 ], a fuzzy inference system used the shuffled frog leaping algorithm to automatically configure and optimize the base rule table. This method has two steps: cluster head selection (CH) and parent selection. The CHs are chosen from the candidate nodes based on the fuzzy output, energy threshold, and the overlap rate of adjacent CHs. The parent selection phase starts with determining the network’s CHs level. The parents of each CH are determined at the end of this step. Finally, the information gathered by the CHs is forwarded to the BS by their parents. Although this method saves energy, it has a high processing cost.

In [ 17 ], an improved protocol based on AODV was proposed that works based on clustering in VANET. This protocol uses fuzzy logic to select reliable routes between cluster members. Fuzzy logic input parameters are the link expiration time and link reliability. Also, in this article, Tabu search was used for routing between the cluster heads and the sinks based on criteria distance, direction, and velocity. The simulation results indicated the suitable performance of the proposed protocol in terms of the average packet delivery rate, the average end-to-end delays, and the number of packet losses. In this method, the energy level of the battery was not considered.

In [ 18 ], an inter-vehicle routing protocol using fuzzy logic was proposed to select the most reliable link and to avoid packet replays in the geographic range. This method uses effective distance, direction, and velocity parameters to find the next-hop node. The simulation results show that the proposed protocol increases packet delivery rate and decreases packet loss rate and end-to-end delay.

In this article, only two parameters, the link expiration time and the probability adapt density, were considered. The proposed method had a high processing cost.

In [ 19 ], the division of the dedicated short-range communication channel with three types of priority was used to transfer the data message quickly. Furthermore, the multi-hop directional routing method was used to increase reliability. Moreover, the storage and distribution method was used during communication gaps to increase efficiency. The processing cost was high in the proposed method since several methods were combined.

In [ 19 ], a fuzzy logic-based routing method with authentication capability in VANET was presented. This method includes the clustering, routing, and authentication phases. In the clustering phase, vehicles are clustered. Data routing is done in the routing phase. In the authentication phase for secure data, the packets use an authentication mechanism based on message authentication code and symmetric key cryptography. The simulations showed that the proposed method improves end-to-end delay, packet collision, packet delivery rate, packet loss rate, and throughput. However, it increases the routing overhead slightly.

According to what has been said above, the common routing protocols in MANETs consider only one or two of four important parameters: available bandwidth, remaining battery energy, mobility speed, and the number of hops to discover the best routes.

But not paying attention to any of these parameters in different times or place situations can prevent finding the local optimum and discovering reliable routes.

As a result, Implementing a comprehensive protocol that considers all the above four parameters in its evaluation simultaneously is essential.

3. Proposed method

3-1 introduction.

A MANET is a network of battery-powered mobile nodes with limited bandwidth. One of the most fundamental challenges in this type of network is routing. An intelligent routing protocol based on symbiont search algorithms is presented in this study. Cheng and Prayogo [ 20 ] are the pioneers of the symbiont search algorithm.

This algorithm is a population search method that simulates how living things interact with each other to survive in nature. The symbiont search algorithm begins with a population known as an ecosystem. This algorithm searches the answer space of the problem for the best answers using the ecosystem and its mechanism. The ecosystem comprises a number of organisms or living things, each representing a candidate to answer the problem. In fact, every organism is a point with the number of components corresponding to the number of problem decision variables. As a result, the value of the objective function for each organism can be calculated. The value of the obtained objective function indicates the organism’s compatibility with nature. The task of generating a new population in the symbiotic organism search algorithm is the responsibility of three actors, each representing a type of symbiosis including mutualism, commensalism, and parasitism.

The most important symbiotic relationships between creatures in nature are:

• Mutualism symbiosis (a relationship between two different species that benefits both),
• Commensalism symbiosis (a relationship between two different species that benefits one but has no effect on the other), and
• Parasitic symbiosis (a symbiosis between two different species that is beneficial for one and harmful for the other.).

Thus, this search method, which is inspired by the pattern of symbiosis between living things in nature, employs three stages: mutualism symbiosis, commensalism symbiosis, and parasitic symbiosis. Each phase of this algorithm is described below:

• Phase one: mutualism symbiosis (win, win)

At this phase, one answer X j (i ≠ j) is chosen randomly from the population for each answer X i in the population. Then, to improve the fit, both X i and X j answers enter into a mutualism symbiosis relationship. X i and X j are produced according to Eqs ( 1 ) to ( 3 ).

In the above relationships, rand (0,1) is a random number in the range [0,1], and X best represents the population’s best response. BF 1 and BF 2 are two coefficients that can be either 0 or 1. In fact, these coefficients demonstrate that the degree of the benefit provided by two beings to each other in a symbiotic relationship is not always equal; and it can even be many times more for one of the two creatures than the other. This algorithm considers this amount to be a maximum of two times. If the fit of the answers X inew and X jnew be better than the fit of the answers X i and X j , these two new answers replace the previous values; otherwise, the previous answers will be preserved.

• Phase two: commensalism or self-help symbiosis (win—indifferent)

At this stage, one answer X j (i ≠ j) is randomly selected from the population for each answer X i in the population. Then, X i and X j form a self-help symbiotic relationship to improve the fit of answers. The new candidate’s answer X inew is generated by Eq (4) .

The reason for selecting the interval [-1,1] to generate a random number in this equation is to increase the scanning power of the search for symbionts. After using this step, If the fit of the answer X inew is better than the fit of the answer X i , X inew replaces X i ; Otherwise, X i will be preserved.

• Phase three: parasitism symbiosis (win-lose)

At this phase, one answer X j (i ≠ j), is chosen randomly for each answer X i in the population. The answer X k is then generated by applying a random change to X i ; if the fit of X k is better than X j , it replaces X j , and otherwise, X k will be disappeared.

The most significant advantage of symbiont organisms is that they lack a specific parameter that can reduce the number of calculations (except the population parameter, which is also present in other metaheuristic methods, no other parameter is seen in this algorithm).

The following section describes how to use this algorithm to route a MANET.

3-2 Determining the best route by the symbionts search algorithm

In this stage, the source node creates a special packet called the route request packet to know the next hop to the destination. The source node then sends the request packet to neighbors as a multicast to find the location of the destination. Before sending the packet, the number of hops is reduced to zero. When a route request packet arrives at an intermediate node, the attribute pairs of the source address and the request ID are first checked in a local table that stores records of such packets to see if it has already been received and processed. If it is a duplicate, the packet will be deleted, and its processing will be terminated. If it is not a duplicate, these attribute pairs are entered in the record table to prevent a similar package from being processed in the future, and the processing process continues. The receiver then searches its routing table to find the destination address. If a new route to this destination is discovered, a route reply packet is returned to the source so that the source knows how to reach the destination. If the receiver does not have a new route to the destination, it increments the value of the hop counter by one unit and republishes the route reply package around it.

This process is repeated until the route request message arrives at the destination. The destination node generates a route reply packet in response to the incoming request. The above node copies the source and destination address fields (extracted directly from the route request packet) to the route reply packet. This packet is sent only to the node through which the route request packet was received. A unit is added to the value of the hop counter field in each node so that each node that sees it knows how far it is from the destination node.

Each intermediate node for each destination in the routing table checks the existence of the candidate node for the next hop. If there are at least two candidate answers for a destination, that destination is considered an element of the candidate answer in the last node. On the other hand, only nodes can implement the ultra-innovative symbionts search algorithm if the candidate answer contains at least one element. As a result, the candidate answer of a node may have three elements, while the candidate answer of the neighboring node may have only one element. Assume k is the set of candidate answers and, is the answer i th of the candidate.

Also, suppose that the vectors X j , Y j and Z j are respectively the locations of the solution of element K i , on the X, Y and Z axes, so that X ij , Y ij and Z ij are the locations of the selected next hop to the destination for the j th of the destination node.

Now, each node that has the candidate answers chooses m random answers, where m is the size of the ecosystem’s population.

In short, and to summarize the stated content, it can be said that:

In the route discovery phase, the route request packet that is generated and sent by the source will be received by intermediate nodes. For each intermediate node, there is a table called the routing table, which contains information on the available routes from that intermediate node to the specified destination. For the specified destination in the route request packet, the intermediate node checks the existence of the candidate node for the next hop from its routing table. Nodes having the candidate answer add a unit to the variable routing_num_iteration and set the variable I to 1. Then, these nodes send the route request message containing the IP of the candidate node to its neighbors (as the next step for the destinations in the routing table) in a multicast manner.

Then the intermediate node sends a useful message containing four parameters of available bandwidth, mobility speed, number of hops, and the energy level of the neighboring node to the next hop node to the destination so that the next hop node calculates the fitness of the best node.

The usefulness of each intermediate node’s candidate answer is then calculated using Eq (9) , and the best candidate answer is identified and placed in k best . Then, for each candidate answer k i , the vectors X i ، Y i and Z i are calculated, and finally, the following steps are performed:

Two-way utility stage.

In this step, for each intermediate node (current candidate node) k i , a candidate node named k j (i ≠ j) (new candidate node) is randomly selected.

Then, to improve the fit of both answers (current and new), the vectors X i ، Y i and Z i form a symbiotic relationship. The new candidate answers for X i ، Y i and Z are generated as a result of the mutual symbiosis between the two responses using Eqs ( 10 ) to ( 18 ).

The nearest neighboring node to X inew , Y inew and Z inew is considered the candidate answer for K i . Also, the nearest neighboring node to X jnew , Y jnew and Z jnew is considered the candidate answer for K j . The usefulness of K inew and K jnew is then calculated using an Eq (9) .

If the usefulness of candidate answers K inew and K jnew is greater than that of K i and K j candidate answers, respectively, K inew and K jnew will replace K i and K j .

One-way utility stage.

At this step, one response K j (i ≠ j) is randomly chosen from the population for each response k i . The vectors X i ، Y i and Z i are then Calculated; these vectors enter a commensalism symbiotic relationship. Eqs ( 4 ), ( 19 ) and ( 20 ) are used to generate new candidate answers for X i ، Y i and Z i .

The nearest neighbor node to X inew , Y inew and Z inew is considered as the candidate answer K inew . Eq (9) is used to calculate the usefulness of the candidate answer K inew . If the usefulness of K inew is greater than that of K i , K inew will replace K i .

Parasitism stage.

In this step, for the IP of each candidate node k i in the route request packet, k j (i ≠ j) is randomly selected from among the new candidate nodes. The corresponding vectors X i ، Y i and Z i are then obtained.

The worst candidate answer K Worst is Identified from the set of candidate answers. Using the location of nodes K j , K Worst and K best on the X, Y and Z axes, the approximation position of the parasitic vector K parasitic is calculated (using Eqs ( 21 )–( 23 ).

The nearest neighbor node to X parasite , Y parasite , and Z parasite is considered the candidate answer K parasitic .

If the usefulness of K parasite is greater than the usefulness of K j , K parasite replaces K j .

The above steps are repeated until the termination condition is reached (the current answer becomes better than the previous answer) so that the best route to the destination can be found.

The general procedure of the proposed algorithm is shown in Fig 1 .

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0290091.g001

By using such method, it is expected that the SOSBRA algorithm shows that a suitable efficiency (as depicted in Figs 4–7) due to advantages such as the following:

First, by moving towards the best global path, the SOSBRA algorithm prevents premature convergence. Second, it encourages nodes to thoroughly investigate their surroundings before converging on the best global route. Furthermore, as stated, the proposed method is implemented by considering four important criteria: available bandwidth, remaining battery energy, mobility speed, and the number of hops simultaneously.

4. Simulation of the proposed method

4-1 simulation environment.

In this paper, the proposed method is simulated and compared with the FBRP protocol using OPNET modeler simulation software. Table 1 shows the parameters of the implemented model for each layer, and Table 2 shows the simulation parameters. Fig 2 depicts the proposed network connection method, which considers 50 nodes. In the first scenario, mobile nodes are randomly distributed in the environment and are routed using the fuzzy method (FBRP) described in the article [ 13 ]. In the second scenario, nodes are randomly distributed and routed using the Symbiotic Organism Search Based Routing Algorithm (SOSBRA).

https://doi.org/10.1371/journal.pone.0290091.g002

https://doi.org/10.1371/journal.pone.0290091.t001

https://doi.org/10.1371/journal.pone.0290091.t002

The model node editor is shown in Fig 3 .

https://doi.org/10.1371/journal.pone.0290091.g003

Each node in a network is free to move independently in any direction, changing its connections to other devices frequently as a result. Each node also acts as a router by forwarding traffic unrelated to its own use.

4-2 Simulation results

The following criteria are used to evaluate the effectiveness of the proposed method.

• DR (delivery ratio): The ratio of the total number of messages delivered (m del ) to the total number of messages generated (m cre ) defines this parameter.
• Packet loss rate: The ratio of data packets lost at the destination to the packets generated at the source defines this parameter as a result of route failure or buffer overload.
• Throughput ratio: It is the number of bits transmitted in a unit of time between the source and destination.
• Route length: The number of steps / links that exist between the two points.

Fig 4 depicts the throughput rate. The horizontal axis represents simulation time, and the vertical axis represents the number of packets delivered at the time or throughput rate.

https://doi.org/10.1371/journal.pone.0290091.g004

According to Fig 4 , the ratio of the number of packets successfully delivered to the destination node to the total packets transmitted by the network nodes is lower in the FBRP protocol than the proposed SOSBRA method.

The most important feature of the SOSBRA algorithm is that it encourages nodes to thoroughly investigate their surroundings before converging on the best global route. As a result, it prevents falling into the local optimum. Therefore, paths with high stability are discovered. Furthermore, in the FBRP protocol, only two criteria are taken into account: mobility speed and node energy, while other important criteria, such as available bandwidth or distance to the destination, are not. On the other hand, the proposed method considers all four above parameters simultaneously; thus, the throughput rate is higher in this scenario.

The data packet loss rate is depicted in Fig 5 . The horizontal axis represents simulation time, while the vertical axis represents the data packet loss rate. Fig 5 shows that the FBRP protocol has a higher data packet loss rate than the SOSBRA method.

https://doi.org/10.1371/journal.pone.0290091.g005

Since the lack of access to a route to the destination can be one of the reasons for the loss of data packets in the network, the SOSBRA method attempts to select routes that are stable, at least until the end of the data transfer phase. This issue reduces the number of lost data packets and the average delay.

It should be noted that the data loss rates in the FBRP protocol may also increase due to a lack of available bandwidth.

Fig 6 compares the data packet delivery rate of the proposed SOSBRA method with the FBRP protocol. The SOSBRA method will have a data packet delivery rate higher than the FBRP protocol because it finds a new route before failing caused by mobility or a lack of necessary bandwidth.

https://doi.org/10.1371/journal.pone.0290091.g006

It should be noted that if the proposed method is not very successful in finding the route before failure, Since it prefers longer routes but more stable to the shortest unstable routes, It could take longer than FBRP.

According to Fig 6 , the proposed method’s data packet delivery rate increases faster than the FBRP protocol as simulation time increases. Because the proposed method is capable of determining the most appropriate and stable links for data transmission, in other words, routes are created that will most likely lead the package to its destination.

Fig 7 depicts the number of hops required to reach the destination using the proposed SOSBRA method and the FBRP protocol. The horizontal axis represents simulation time, while the vertical axis represents the number of hops. Fig 7 shows that the proposed protocol chooses a shorter route than the FBRP protocol. This problem originates from the fact that in the proposed protocol, the parameter of hops number is considered one of the four important criteria in routing. At the same time, route stability and distance issues are also taken into account. Furthermore, in the FBRP protocol, the route may be invalidated due to a lack of bandwidth.

https://doi.org/10.1371/journal.pone.0290091.g007

5. Conclusion

In this article, considering that meta-heuristic algorithms inspired by nature are widely used to find the right answer in optimization problems, a new method using the search algorithm symbiotic organisms called SOSBRA has been proposed. The symbionts search algorithm is one of the most recent meta-heuristic algorithms that has demonstrated good performance and high convergence in optimizing multidimensional functions with higher dimensions. The proposed method can increase the data delivery rate by selecting routes with high stability between source and destination. In the OPNET simulator, SOSBRA and the FBRP protocols are simulated. Comparing the results shows that SOSBRA performs better for network features such as power consumption, throughput rate, number of hops, and latency. By moving toward the best global direction, the SOSBRA algorithm prevents premature convergence. Furthermore, it encourages nodes to thoroughly investigate their surroundings before converging on the best global route.

According to the high speed of MANETs or other factors such as; the rapid disconnection of routing links, the sudden shutdown of nodes due to battery depletion, and the high probability of error in this type of network, the SOSBRA proposed algorithm considers four criteria, battery level energy, mobility speed, number of hops and bandwidth for routing at the same time that can be very efficient and productive in routing.

Supporting information

S1 dataset..

https://doi.org/10.1371/journal.pone.0290091.s001

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Routing Algorithms for MANET-IoT Networks: A Comprehensive Survey

• Published: 12 May 2022
• Volume 125 , pages 3501–3525, ( 2022 )

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• Vu Khanh Quy   ORCID: orcid.org/0000-0002-0242-5606 1 ,
• Vi Hoai Nam 1 ,
• Dao Manh Linh 1 &
• Le Anh Ngoc 2  

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With the powerful evolution of wireless communication systems in recent years, mobile ad hoc networks (MANET) are more and more applied in many fields such as environment, energy efficiency, intelligent transport systems, smart agriculture, and IoT ecosystems, as well as expected to contribute role more and more important in the future Internet. However, due to the characteristic of the mobile ad hoc environment, the performance is dependent mainly on the deployed routing protocol and relative low. Therefore, routing protocols should be more flexible and intelligent to enhance network performance. This paper surveyed and analysed a series of recently proposed routing protocols for MANET-IoT networks. Results have shown that these protocols are classified into four main categories: performance improvement, quality of service (QoS-aware), energy-saving, and security-aware. Most protocols are evolved from these existing traditional protocols. Then, we compare the performance of the four traditional routing protocols under the different movement speeds of the network node aim determines the most stable routing protocol in smart cities environments. The experimental results showed that the proactive protocol work is good when the movement network nodes are low. However, the reactive protocols have more stable and high performance for high movement network scenarios. Thus, we confirm that the proposal of the routing protocols for MANET becomes more suitable based on improving the ad hoc on-demand distance vector routing protocol. This study is the premise for our further in-depth research on IoT ecosystems.

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Acknowledgements

This research is funded by Hung Yen University of Technology and Education under grand number UTEHY.L.2022.06.

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Vu Khanh Quy, Vi Hoai Nam & Dao Manh Linh

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Dr. V.K.Q has performed the study conception and deployment. Data collection and analysis were performed by V.K.Q, V.H.N, L.A.N and D.M.L. The first manuscript was written by V.K.Q. L.A.N and V.K.Q proofreading the final manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. The author corresponding is V.K.Q.

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Quy, V.K., Nam, V.H., Linh, D.M. et al. Routing Algorithms for MANET-IoT Networks: A Comprehensive Survey. Wireless Pers Commun 125 , 3501–3525 (2022). https://doi.org/10.1007/s11277-022-09722-x

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• Sugumaran V Rajaram A (2023) Lightweight blockchain-assisted intrusion detection system in energy efficient MANETs Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology 10.3233/JIFS-231340 45 :3 (4261-4276) Online publication date: 1-Jan-2023 https://dl.acm.org/doi/10.3233/JIFS-231340
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IJERT-A Study on Manet: Applications, Challenges and Issues

2020, International Journal of Engineering Research and Technology (IJERT)

https://www.ijert.org/a-study-on-manet-applications-challenges-and-issues https://www.ijert.org/research/a-study-on-manet-applications-challenges-and-issues-IJERTCONV8IS03003.pdf Wireless ad hoc networks have become an important area of research in wireless communications systems. Mobile Ad hoc Network is an ad hoc network that can be formed to allow nodes to communicate without any infrastructure. The set up of MANET makes it very popular as compared to the traditional wireless network. In traditional wireless network, mid-point is required for overall process of the network, whereas MANET is self-organized and infrastructure-free network, which is considered as a good approach for some specific applications such as battlefield survivability, communication in the natural or manmade disaster areas, emergency or rescue operations. This research work attempts to provide a wide overview of this active field and it first explains detailed survey of MANET in different fields and then takes over several challenges and issues in the Ad hoc networking area. Finally specify the active application areas of MANET and describes the future work.

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Due to growth in number of wireless devices, MANET have become focus of research. This paper discusses characteristics, routing algorithms, security issues, applications and design challenges in Mobile Ad-hoc Networks. We expect that this paper will give an introduction of Mobile Ad-hoc networks to students who want to research in this field.

Gajendra Kumar Ahirwar , Mahendra Kumar Ahirwar

— A mobile ad hoc network is the self-configure and self-organized infrastructure less network that is used for wireless. Mobile ad hoc network (MANET) are not used any centralized device to manage the nodes [1]. The range or the paths between the nodes are limited. When a node are move far from the range that node are out of network. This paper presents the study of the Manet Types, Features and its Challenges.

International Journal of Engineering and Computer Science

Khalid Bilal

Mobile Ad Hoc Network (MANET) is the one of the type of ad hoc network, the MANET is a collection of two or more devices or nodes or terminals with wireless communications and networking capability that communicate with each other without the aid of any centralized administrator also the wireless nodes that can dynamically form a network to exchange information without using any existing fixed network infrastructure. And it's an autonomous system in which mobile hosts connected by wireless links are free to be dynamically and sometime act as routers at the same time, In MANET, the mobile nodes require to forward packets for each other to enable communication among nodes outside of transmission range. The nodes in the network are free to move independently in any direction, leave and join the network arbitrarily. Thus a node experiences changes in its link states regularly with other devices. Eventually, the mobility in the ad hoc network, change of link states and other proper...

International Journal of Emerging Trends in Engineering and Development

SUNITI PURBEY

Gnana Jayanthi

Mobile Adhoc NETwork (MANET) is a current and a hot topic in the Internet world. Because the Internet usage has increased and moved from the wired communication to wireless communication. In a MANET, the architecture and routing process follow arbitrary manner. MANET has unique characteristics based on its usage in several application areas such as military, commercial sectors, disaster areas, etc. This paper deals detailed concepts of MANET applications, architecture in OSI layers and various routing protocols.

International Journal of Scientific Research in Science, Engineering and Technology IJSRSET

As the popularity of mobile device and wireless networks significantly increase over the past few years, wireless ad-hoc networks has now become one of the most vibrant and active field of communications of networks. Due to serve challenges the special features of MANET bring the technology great opportunities together. Our project describes fundamental problems of ad-hoc network by giving its related research background including the concept, features, status and vulnerabilities of MANET.

International Journal of Scientific Research in Science and Technology

International Journal of Scientific Research in Science and Technology IJSRST

MANET is a collection of various mobile nodes that are helpful to communicate with each other without any centralized control which are wireless and work as dynamic network. The current generation is much advanced in using such type of networks, which is very flexible and portable to the end users, which is most economical. The applications of this MANETs are used world wide due to its advantages, but still there are some issues which need to be resolved, known as challenges. This paper is on the various challenges and characteristics of MANET.

Ad-hoc networks are the collection of autonomous nodes where all the nodes are configured dynamically without any centralized management system. Mobile Ad-hoc Networks (MANETs) are self-configuring network of mobile routers connected via a wireless link. A MANET is a most promising and rapidly growing technology and it have become a very popular research topic in recent years. This paper gives an overview of MANETs with respect to services, parameters, applications, attacks and challenges.

Indonesian Journal of Electrical Engineering and Computer Science

Mashkuri Yaacob

Mobile ad hoc networks or MANETs, also referred to as mobile mesh networks at times, are self-configuring networks of mobile devices that are joined using wireless channels. These represent convoluted distributed systems comprising of wireless mobile nodes which are free to move and self-organise dynamically into temporary and arbitrary, ad hoc topologies. This makes it possible for devices as well as people to internetwork seamlessly in such regions that have no communication infrastructure in place. Conventionally, the single communication networking application following the ad hoc concept had been tactical networks. Lately, new technologies have been introduced such as IEEE 802.11, Hyperlan and Bluetooth that are assisting in the deployment of commercial MANETs external to the military realm. Such topical evolutions infuse a new and rising interest in MANET research and development. This paper provides an overview of the dynamic domain of MANETs. It begins with the discussion on...

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