Optimal Secrecy Capacity-Delay Tradeoff in Large-Scale Mobile Ad Hoc Networks


During this paper, we have a tendency to investigate the impact of data-theoretic secrecy constraint on the capability and delay of mobile ad hoc networks (MANETs) with mobile legitimate nodes and static eavesdroppers whose location and channel state information (CSI) are both unknown. We assume $n$ legitimate nodes move consistent with the fast i.i.d. mobility pattern and each needs to speak with one randomly selected destination node. There also are $n^nu$ static eavesdroppers located uniformly within the network and we tend to assume the quantity of eavesdroppers is abundant larger than that of legitimate nodes, i.e., $nu>1$. We tend to propose a completely unique easy secure communication model, i.e., the secure protocol model, and prove its equivalence to the widely accepted secure physical model underneath some technical assumptions. Primarily based on the proposed model, a framework of analyzing the secrecy capacity and delay in MANETs is established. Given a delay constraint $D$, we have a tendency to notice that the optimal secrecy throughput capability is $widetildeTheta(W((D/n))^(2/3))$ , where $W$ is the info rate of every link. We have a tendency to observe that: one) the capacity-delay tradeoff is freelance of the number of eavesdroppers, that indicates that adding a lot of eavesdroppers will not degenerate the performance of the legitimate network so long as $nu>1$; a pair of) the capacity-delay tradeoff of our paper outperforms the previous result $Theta((1/npsi_e))$ in , where $psi_e=n^nu-1=omega(one)$ is the density of the eavesdroppers. Throughout this paper, for functions $f(n)$ and $g(n)$ , we denote $f(n)=o(g(n))$ if $lim_nrightarrowinfty(f(n)/g(n))=zero$ ; $f(n)=omega(g(n))$ if $g(n)=o(f(n))$ ; $f(n)=O(g(n))$ if there's a positive constant $c$ such that $f(n)leq cg(n)$ for sufficiently giant $n$; $f(n)=Omega(g(n))$ if $g(n)=O(f(n))$; $f(n)=Theta(g(n))$ if each $f(n)=O(g(n))$ and $f(n)=Omega(g(n))$ hold. Besides, the order notation $widetildeTheta$ omits the polylogarithmic factors for higher readability.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Reinforcement Learning-based Collision Avoidance and Optimal Trajectory Planning in UAV Communication Networks ABSTRACT: In this paper, we investigate optimal trajectory planning for unmanned aerial vehicle (UAV)
PROJECT TITLE : PRIME: An Optimal Pricing Scheme for Mobile Sensors-as-a-Service ABSTRACT: In this article, we propose a pricing scheme for provisioning mobile Sensors-as-a-Service (mSe-aaS) in the mobile sensor-cloud (MSC) architecture.
PROJECT TITLE : Fast Globally Optimal Transmit Antenna Selection and Resource Allocation Scheme in mmWave D2D Networks ABSTRACT: The process of transmit antenna selection, abbreviated as TAS at base stations, has been the subject
PROJECT TITLE : Optimal Sensor Placement for Source Localization: A Unified ADMM Approach ABSTRACT: Source localization is an important part of many different applications, including radar, wireless communications, and communications
PROJECT TITLE : Optimal Scale Combination Selection Integrating Three-Way Decision With Hasse Diagram ABSTRACT: In the field of machine learning, the multi-scale decision system, also known as MDS, is a useful tool for describing

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry