Abstract
The Internet of Things (IoT) facilitates physical things to detect, interact, and execute activities on-demand, enabling a variety of applications such as smart homes and smart cities. However, it also creates many potential risks related to data security and privacy vulnerabilities on the physical layer of cloud-based Internet of Things (IoT) networks. These can include different types of physical attacks such as interference, eavesdropping, and jamming. As a result, quality-of-service (QoS) provisioning gets difficult for cloud-based IoT. This paper investigates the statistical QoS provisioning of a four-node cloud-based IoT network under security, reliability, and latency constraints by relying on the effective capacity model to offer enhanced QoS for IoT networks. Alice and Bob are legitimate nodes trying to communicate with secrecy in the considered scenario, while an eavesdropper Eve overhears their communication. Meanwhile, a friendly jammer, which emits artificial noise, is used to degrade the wiretap channel. By taking advantage of their multiple antennas, Alice implements transmit antenna selection, while Bob and Eve perform maximum-ratio combining. We further assume that Bob decodes the artificial noise perfectly and thus removes its contribution by implementing perfect successive interference cancellation. A closed-form expression for an alternative formulation of the outage probability, conditioned upon the successful transmission of a message, is obtained by considering adaptive rate allocation in an ON-OFF transmission. The data arriving at Alice’s buffer are modeled by considering four different Markov sources to describe different IoT traffic patterns. Then, the problem of secure throughput maximization is addressed through particle swarm optimization by considering the security, latency, and reliability constraints. Our results evidence the considerable improvements on the delay violation probability by increasing the number of antennas at Bob under strict buffer constraints.
Highlights
Recent advancements in communication technologies and antenna design have drastically increased the amount of data collected from Internet of ings (IoT) environments. ey have catalyzed the growing trend towards big data, where data acquisition and posterior data processing are actionable and trigger intelligent decision-making [1]
Since Markovian processes can incorporate the typical characteristics of the traffic generated by IoT devices, which is comprised of small and burst packets [5, 21, 41], we present the models for four different types of Markov arrival sources. e Markovian described are (i) discretetime Markov source (DTMS), (ii) fluid Markov source (FMS), (iii) discrete-time Markov modulated Poisson source (DMMPS), and (iv) continuous-time Markov modulated Poisson source (CMMPS)
We investigated the statistical QoS provisioning for cloud-based IoT networks under security, reliability, and latency constraints
Summary
Recent advancements in communication technologies and antenna design have drastically increased the amount of data collected from Internet of ings (IoT) environments. ey have catalyzed the growing trend towards big data, where data acquisition and posterior data processing are actionable and trigger intelligent decision-making [1]. Authors in [6] analyzed different types of traffic generated by IoT devices through effective rate transmission and the effective capacity for single-antenna point-to-point communication systems. Erefore, this paper addresses this threat by considering rate control and quality-of-service (QoS) provisioning to minimize the outage secrecy probability and mitigate privacy leakage in IoT networks. In this work, inspired by [20,21,22], we rely on the effective capacity theory in order to examine the joint impact of security, latency, and reliability constraints of cloud-based IoT networks for the four-node multiantenna scenario. Regarding IoT QoS constraints, the authors in [34] proposed the concept of effective secure throughput based on effective capacity metric in order to take into account security and reliability issues, while satisfying certain buffer or delay constraints. A secure effective capacity metric was analyzed in [21] without considering friendly jammer, inspired on [27, 34]. e scenario comprises a single-antenna legitimate pair of IoT devices communicating in the presence of an eavesdropper and using an ON-OFF transmission where secrecy is conditioned on the actual transmission. e metric captures the impact of the source’s arrival traffic, where security, latency, and reliability constraints were considered to evaluate the optimal secure communication rates
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