Exponential Reliability Coefficient based Reputation Mechanism for isolating selfish nodes in MANETs

AbstractIn mobile ad hoc networks, cooperation among active mobile nodes is considered to play a vital role in reliable transmission of data. But, the selfish mobile nodes present in an ad hoc environment refuse to forward neighbouring nodes’ packet for conserving its own energy. This intentional selfish behaviour drastically reduces the degree of cooperation maintained between the mobile nodes. Hence, a need arises for devising an effective mechanism which incorporates both energy efficiency and reputation into account for mitigating selfish behaviour in MANETs. In this paper, we propose an Exponential Reliability Coefficient based reputation Mechanism (ERCRM) which isolates the selfish nodes from the routing path based on Exponential Reliability Coefficient (ExRC). This reliability coefficient manipulated through exponential failure rate based on moving average method highlights the most recent past behaviour of the mobile nodes for quantifying its genuineness. From the simulation results, it is evident that, the proposed ERCRM approach outperforms the existing Packet Conservation Monitoring Algorithm (PCMA) and Spilt Half Reliability Coefficient based Mathematical Model (SHRCM) in terms of performance evaluation metrics such as packet delivery ratio, throughput, total overhead and control overhead. Further, this ERCRM mechanism has a successful rate of 28% in isolating the selfish nodes from the routing path. Furthermore, it also aids in framing the exponential threshold point of detection as 0.4, where a maximum number of selfish nodes are identified when compared to the existing models available in the literature

Bayesian signaling game based efficient security model for MANETs

Game Theory acts as a suitable tool offering promising solutions to security-related concerns in Mobile Ad Hoc Networks (i.e., MANETs). In MANETs, security forms a prominent concern as it includes nodes which are usually portable and require significant coordination between them. Further, the absence of physical organisation makes such networks susceptible to security breaches, hindering secure routing and execution among nodes. Game Theory approach has been manipulated in the current study to achieve an analytical view while addressing the security concerns in MANETs. This paper offers a Bayesian-Signaling game model capable of analysing the behaviour associated with regular as well as malicious nodes. In the proposed model, the utility of normal nodes has been increased while reducing the utility linked to malicious nodes. Moreover, the system employs a reputation system capable of stimulating best cooperation between the nodes. The regular nodes record incessantly to examine their corresponding nodes’ behaviours by using the belief system of Bayes-rules. On its comparison with existing schemes, it was revealed that the presented algorithm provides better identification of malicious nodes and attacks while delivering improved throughput and reduced false positive rate

Laplace Stleltjes Transform based Conditional Survivability Coefficient Model for mitigating Selfish Nodes in MANETs

In MANETs, the cooperation is considered as an important entity for enabling reliable data dissemination among the mobile nodes. But, the existence of selfish nodes weakens the degree of cooperation and in turn reduces the network performance. Hence, the computation of reputation level for each and every node in the network becomes essential in order to make optimal routing decisions. In this paper, we propose a Laplace Stleltjes Transform based Conditional Survivability Coefficient Model (LCSCM), which manipulates the survivability of the network through a parameter called Conditional Survivability Coefficient (CSC). This Conditional Survivability Coefficient aids in determining the reputation level of mobile nodes as well as quantifies the survivability of the entire network. The performance of this conditional probabilistic approach is analyzed using ns-2 based on the network related parameters such as packet delivery ratio, throughput, total overhead, and control overhead by varying the number of mobile nodes in the network. The results obtained through these extensive simulations make it obvious that, this approach outperforms PCMA model with a successful detection rate of 24%. This LCSCM also facilitates in framing 0.25 as the saddle point for selfish node detection

An Enhanced Hybrid Pareto Metaheuritic Algorithm-based Multicast Tree Estimation for Reliable Multicast Routing in VANETs

Prompt and reliable data dissemination among the vehicular nodes of the network is indispensable as itsmobility rate and limited coverage characteristics introduce the possibility of frequent topology changes. The effectiveand efficient sharing of critical information in the event of emergency necessitates either direct interaction or RoadSide Units (RSUs)-based vehicular communication in the primitive place. Multicast routing is confirmed to be thesignificant scheme of data transfer since they establish reliable data dissemination between the source and destinationvehicular nodes by estimating an optimal multicast tree. Moreover, QoS-constraint enforced meta-heuristic approachesare considered to be excellent for determining optimal multicast tree under multicasting. An Enhanced Hybrid ParetoMetaheuritic Algorithm-based Multicast Tree Estimation Scheme (EHPMA-MTES) is contributed for reliable multicastrouting. The proposed EHPMA-MTES is confirmed to reduce the cost of transmission by 28% through the minimizationof the multicast tree count formed during the process of multicast routing

Erlang coefficient based conditional probabilistic model for reliable data dissemination in MANETs

In MANETs, reputation plays a significant role in reliable dissemination of data for establishing maximum degree of cooperation among the mobile nodes in the network. But, the presence of selfish nodes drastically reduces the level of cooperation between the nodes and further reduces the life time of the network. Moreover, when the number of selfish nodes increases in the network, the packet delivery ratio and throughput decreases which in turn increases the number of retransmissions. Hence, an effective mechanism for isolating selfish nodes in order to increase the packet delivery rate and the throughput for reliable dissemination of data becomes vital. This paper proposes an Erlang coefficient based conditional probabilistic model (ECCPM) which makes the decision of isolating selfish nodes through the manipulation of Conditional Probabilistic Coefficient (CPC) factor. This Conditional Probabilistic Coefficient acts as the reputation factor for estimating the level of negative impact produced by selfish nodes toward the resilience of the network. The proposed work is simulated in ns-2 and from the results, it is obvious that ECCPM showed better performance in terms of packet delivery ratio, throughput, control overhead and total overhead than existing mitigation mechanisms like RCSBMM, RFBMM, SHRCM and PCMA proposed for selfish nodes

A novel cluster head selection using Hybrid Artificial Bee Colony and Firefly Algorithm for network lifetime and stability in WSNs

Wireless Sensor Networks (WSNs) are capable of achieving data dissemination between them such that exploration of their potential could be performed based on their frequency range. It is considered to be highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issue of network lifetime, coverage area, scheduling and data aggregation. In particular, prolonging network lifetime confirms the success together with the energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation. Clustering schemes are considered to be highly suitable for effectively utilising the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. In this paper, a Hybrid Modified Artificial Bee Colony and Firefly Algorithm (HMABCFA) -Based Cluster Head Selection is proposed for ensuring energy stabilisation, delay minimisation and inter-node distance reduction for improving the network lifetime. This proposed HMABCFA integrates the benefit of the Firefly optimisation algorithm for generating a new position that which has the capability of replacing the position, which is not updated in the scout bee phase of ABC. This incorporation of Firefly optimisation algorithm into the ABC algorithm prevents the limitations of premature convergence, slow convergence and the possibility of being trapped into the local point of optimality in the clustering process. The modified ABC-based clustering process is phenomenal in improving the feasible dimensions for enhancing the process of exploitation and exploration. The results of the HMABCFA, on an average are confirmed to enhance the network lifetime by 23.21%, energy stability by 19.84% and reduce network latency by 22.88%, compared to the benchmarked approaches