Introduction
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The fault-tolerant algorithm should have an effective route failure-handling mechanism to ensure the integrity of the network.
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Integrity should be maintained even in the events of congestion, bottlenecks or broken links which are prone to happen under a highly dynamic condition in MANET.
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Whenever a path breaks, the algorithm should try to use an alternate path, instead of initiating a new route discovery.
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The secure routing algorithm addresses the security issues by incorporating the concept of trust-based reputation mechanism to overcome the misbehaving entities.
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Trust evaluation using the EigenTrust fuzzy system helps to make routing decision for secure data transmission.
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Developing a fault-tolerant routing in case of route failures or node failures
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QoS metrics such as packet delivery ratio, throughput and delay should be considered to achieve a QoS-constrained routing
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Developing a secure routing by trust-based reputation mechanism which evaluates the trust worthiness of a node in order to continue the data forwarding along that node
Related work
Intelligent fault-tolerant routing
Watchdog mechanism
Misbehaviour detection and trust management
Reputation management
Fuzzy-based trust management
Title | Algorithm | Concept | Issues |
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Security through collaboration and trust in MANETs [38] | Gossip-based outlier detection algorithm | • Outlier detection uses local view formation, local view exchange, local view update, and global view formation | Longer time to converge to a global view if more nodes in MANET |
Trust evaluation in wireless ad hoc networks using the fuzzy system [48] | Fuzzy trust algorithm | • Calculates the trust level of a route from source to destination | Suitable only for low mobile ad hoc networks |
Friend-based ad hoc routing using challenges to establish security in MANET system [37] | FACES algorithm | • Sends challenges and shares friend lists to provide a list of trusted nodes to the source node through which data transmission finally takes place | More control overhead due to challenge request and challenge reply |
Outlier detection using naïve Bayes in wireless ad hoc networks [49] | Naïve Bayes classifier | • Predicts the reliability of trust information provided by other adjacent nodes | High overhead |
A reputation-based trust mechanism for ad hoc networks [50] | Reputation-based trust management algorithm | • Monitors the behavior of neighboring nodes and computing reputation based on monitoring | High computation overhead |
Malicious node detection using fuzzy-based trust level in MANETs [47] | Fuzzy-based trust management | • Certificate authority employs fuzzy-based analyzer to distinguish between trusted and malicious behaviour of nodes by distributing the certificates only to the trusted nodes | More control overhead |
A novel approach for misbehavior detection in ad hoc networks [51] | Fuzzy logic | • System learns the behaviour and applies the fuzzy logic concept for misbehaviour detection | More delay for control packet transmission due to more control packet overhead which effects the data packet transmission |
• Trust parameter is computed for each node which depends on the input parameters | |||
A secure trusted auction-oriented clustering-based routing protocol for MANET [49] | Markov chain analysis of trust model, credit, and reputation scheme | • Effectively detects selfish nodes by credit and reputation scheme to enforce cooperation between nodes | High communication overhead |
Malicious node detection in MANETs: a behavior analysis approach [52] | Trust management | • The approach proposes observing the behavior of mobile nodes depending on different factors | More control overhead |
• Each node in the network can recognize the malicious nodes and prevent them to participate in the communication | |||
AOMDV-based TRIUMF implementation and performance evaluation [53] | Trust management | • The protocol uses an incentive mechanism for selfish node to declare its selfishness behaviour | Unable to detect more than one malicious node in the route |
• It also uses two node-disjoint routes to reduce the malicious searching time |
Methodology
Fault-tolerant and secure routing
Route discovery phase
Route selection and route maintenance phase
Trust-based secure routing
Local trust evaluation using EigenTrust
Certificate authority election
Fuzzy-based trust system
Secure data transmission
Simulation analysis and results
Parameter | Values |
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Radio range of single node | 250 m |
MAC layer protocol | IEEE 802.11 |
Traffic pattern | CBR |
Data packet size | 512 bytes |
Simulation area | 3,000 m × 3,000 m |
Number of nodes | 100 |
Node mobility speed | 0 to 30 m/s |
Simulation time | 100, 200, 300, 400 and 500 ms |
Mobility model | Random way point model |
Misbehaving nodes | 10, 20, 30, 40 and 50 |
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Packet delivery ratio
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The ratio of the data packets delivered to the destinations to those generated by the constant bit rate (CBR) source.
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Throughput
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Throughput of the routing protocol means that in certain time, the total size of useful packets received at all the destination nodes.
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Routing overhead
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It is the ratio of routing packets to the total number of packets generated by the source.
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Malicious detection effectiveness
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This measures the performance of the algorithm. This is measured as total number of detected nodes divided by the total number of malicious nodes in the network.