Secure Transmission of Delay-Sensitive Data Over Wireless Fading Channels

Abstract

In this paper, throughput and energy efficiency of secure wireless transmission of delay sensitive data generated by random sources is studied. A fading broadcast model in which the transmitter sends confidential and common messages to two receivers is considered. It is assumed that the common and confidential data, generated from Markovian sources, is stored in buffers prior to transmission, and the transmitter operates under constraints on buffer/delay violation probability. Under such statistical quality of service (QoS) constraints, effective capacity of time-varying wireless transmissions and effective bandwidth of Markovian sources are employed to determine the throughput. In particular, secrecy capacity is used to describe the service rate of buffers containing confidential messages. Moreover, energy per bit is used as the energy efficiency metric and energy efficiency is studied in the low signal-to-noise (SNR) regime. Specifically, minimum energy per bit required for the reliable communication of common and confidential messages is determined and wideband slope expressions are identified. The impact of buffer/delay constraints, correlation between channels, source characteristics/burstiness, channel knowledge at the transmitter, power allocation, and secrecy requirements on the throughput and energy efficiency of common and confidential message transmissions is identified.Index Terms-Common and confidential messages, effective bandwidth, effective capacity, energy efficiency, fading broadcast channel, Markovian arrivals, secrecy capacity, statistical buffer/delay QoS constraints, throughput.

Effective Capacity in Wireless Networks: A Comprehensive Survey

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Physical Layer Authentication in Mission-Critical MTC Networks: A Security and Delay Performance Analysis

“…In the disassociation attack, the frame-level service process s k in (7) follows the same model as in the baseline scenario (10) (i.e., frames are dropped with the false alarm rate and N k is given by its baseline distribution). We consider independent Bernoulli attack attempts from Eve with probability p Attack and to model the impact on the queueing performance, we divide the data flow from device i to the access point into blocks consisting of K RC frames each and define the aggregated arrival process as a ′ l = KRC(l+1)−1 k=KCNl a k and service process as…”

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