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Defending against false data attacks in 3D grid-based MANET using soft computing approaches

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Abstract

Quality of service (QoS) is one of the major factors for current applications in mobile ad hoc networks (MANET). Due to the limited wireless link capabilities, a path with heavy load will lost battery within short period of time and maximizes the possibility of path disconnection and packet drops. Hence, optimal path is necessary to increase residual energy of the node. Dual CHs are selected by mobile node energy level, distance and expected relative mobility. The dual CH selection considers the residual energy of nodes, average distance to neighbors and number of neighbor nodes. Previously proposed multipath routing techniques reduce network traffic among multiple routes paths instead of sending packets via only one path. There are two research issues are still arisen in multipath routing that are packet redundancy and quality. We address these issues in the proposed optimal routing paradigm where the best path is selected instead of routing among all available paths. We propose hybrid routing scheme that combines type 2 fuzzy logic system to find available paths from source to the destination node and particle swarm optimization to identify a best path among the selected paths. Due to the lack of central monitoring system in MANET, security breach called false data injection attack may occur. To reveal this task, false data predicted on the source digital signature by upcoming cluster member. We implement our proposed approach in 3D grid-based MANET environment using NS3.26. We also compare our proposed approach performance with the conventional 2D routing schemes. Finally, the proposed approach shows higher QoS than others.

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Abbreviations

\( T_{\text{MN}} \) :

Sum of mobile nodes in network

R :

Population of PSO algorithm

\( k_{\text{opt}} \) :

The number of optimal clusters in each round

\( T_{\text{L - I}}^{n} ,T_{\text{L - II}}^{n} \) :

Energy threshold for selecting CHs

\( p \) :

Random prime value

\( a,b \) :

Integers

\( {\mathcal{F}} \) :

Finite field

\( H_{\text{f}} \) :

Hash function

\( D_{m} \) :

Euclidean distance

\( {\text{rm}}_{i,j}^{t} \) :

The relative mobility of the node \( n_{j} \) at instant t

\( \varepsilon \left( t \right) \) :

Remaining energy

\( \beta_{1} \), \( \beta_{2} \) :

Weighting coefficients

\( \alpha_{1} ,\alpha_{2} ,\alpha_{3} \) :

Random integers

\( F_{1} ,F_{2} \) :

Learning factors

\( \mu ,\tau \) :

Random numbers between 0 and 1

V, X :

Particle velocity and position, respectively

\( P_{\text{ID}} \) :

Best particle position

\( P_{\text{GD}} \) :

Global best particle position

\( \omega \) :

Inertia weight

\( f\left( i \right) \) :

Fitness function

TA:

Trusted authority

\( {\text{Pu}}_{k} \) :

Public key

\( {\text{Pi}}_{k} \) :

Private key

DS:

Digital signature

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Authors and Affiliations

  1. Department of ECE, SACS MAVMM Engineering College, Madurai, TN, India

    B. Muneeswari

  2. Department of ECE, Thiagarajar College of Engineering, Madurai, TN, India

    M. S. K. Manikandan

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  1. B. Muneeswari
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  2. M. S. K. Manikandan
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Correspondence to B. Muneeswari.

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Communicated by Sahul Smys.

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Muneeswari, B., Manikandan, M.S.K. Defending against false data attacks in 3D grid-based MANET using soft computing approaches. Soft Comput 23, 8579–8595 (2019). https://doi.org/10.1007/s00500-019-03899-3

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