The exponential growth in global mobile data traffic, especially with regards to the massive deployment of devices envisioned for the fifth generation (5G) mobile networks, has given impetus to exploring new spectrum opportunities to support the new traffic demands. The millimeter wave (mmWave) frequency band is considered as a potential candidate for alleviating the spectrum scarcity. Moreover, the concept of multi-tier networks has gained popularity, especially for dense network environments. In this article, we deviate from the conventional multi-tier networks and employ the concept of control-data separation architecture (CDSA), which comprises of a control base station (CBS) overlaying the data base station (DBS). We assume that the CBS operates on the sub-6 GHz single band, while the DBS possesses a dual-band mmWave capability, i.e., 26 GHz unlicensed band and 60 GHz licensed band. We formulate a multi-objective optimization (MOO) problem, which jointly optimizes conflicting objectives: the spectral efficiency (SE) and the energy efficiency (EE). The unique aspect of this work includes the analysis of a joint radio resource allocation algorithm based on Lagrangian Dual Decomposition (LDD) and we compare the proposed algorithm with the maximal-rate (maxRx), dynamic sub-carrier allocation (DSA) and joint power and rate adaptation (JPRA) algorithms to show the performance gains achieved by the proposed algorithm.

In this paper, we study cross-layer optimization of low-power wireless links for reliability-aware applications while considering both the constraints and the non-ideal characteristics of the hardware in Internet-of-things (IoT) devices. Specifically, we define an energy consumption (EC) model that captures the energy cost—of transceiver circuitry, power amplifier, packet error statistics, packet overhead, etc.—in delivering a useful data bit. We derive the EC models for an ideal and two realistic non-linear power amplifier models. To incorporate packet error statistics, we develop a simple, in the form of elementary functions, and accurate closed-form packet error rate (PER) approximation in Rayleigh block-fading. Using the EC models, we derive energy optimal yet reliability and hardware compliant conditions for limiting unconstrained optimal signal-to-noise ratio (SNR), and payload size. Together with these conditions, we develop a semi-analytic algorithm for resource-constrained IoT devices to jointly optimize parameters on physical (modulation size, SNR) and medium access control (payload size and the number of retransmissions) layers in relation to link distance. Our results show that despite reliability constraints, the common notion—higher-order M-ary modulations (MQAM) are energy optimal for short-range communication—prevails, and can provide up to 180% lifetime extension as compared to often used OQPSK modulation in IoT devices. However, the reliability constraints reduce both their range and the energy efficiency, while non-ideal traditional PA reduces the range further by 50% and diminishes the energy gains unless a better PA is used.

Recent Low Power Wide Area Networks (LPWAN) protocols are receiving increased attention from industry and academia to offer accessibility for Internet of Things (IoT) connected remote sensors and actuators. In this work, we present a formal study of LoRaWAN security, an increasingly popular technology, which defines the structure and operation of LPWAN networks based on the LoRa physical layer. There are previously known security vulnerabilities in LoRaWAN that lead to the proposal of several improvements, some already incorporated into the latest protocol specification. Our analysis of LoRaWAN security uses Scyther, a formal security analysis tool and focuses on the key exchange portion of versions 1.0 (released in 2015) and 1.1 (the latest, released in 2017). For version 1.0, which is still the most widely deployed version of LoRaWAN, we show that our formal model allowed to uncover weaknesses that can be related to previously reported vulnerabilities. Our model did not find weaknesses in the latest version of the protocol (v1.1), and we discuss what this means in practice for the security of LoRaWAN as well as important aspects of our model and tools employed that should be considered. The Scyther model developed provides realistic models for LoRaWAN v1.0 and v1.1 that can be used and extended to formally analyze, inspect, and explore the security features of the protocols. This, in turn, can clarify the methodology for achieving secrecy, integrity, and authentication for designers and developers interested in these LPWAN standards. We believe that our model and discussion of the protocols security properties are beneficial for both researchers and practitioners. To the best of our knowledge, this is the first work that presents a formal security analysis of LoRaWAN.

The deployment of the Internet of Things (IoT) in industry, called the Industrial IoT (IIoT), is supporting the introduction of very desirable improvements such as increasing production flexibility, self-organization and real-time and quick response to events. However, security and privacy challenges are still to be well addressed. The IIoT requires different properties to achieve secure and reliable systems and these requirements create extra challenges considering the limited processing and communication power available to IIoT field devices. In this research article, we present a key distribution protocol for IIoT that is computationally and communicationally lightweight (requires a single message exchange) and handles node addition and revocation, as well as fast re-keying. The scheme can also resist the consequences of node capture attacks (we assume that captured nodes can be detected by the Gateway and previous works have shown this assumption to be acceptable in practice), server impersonation attacks and provides forward/backward secrecy. We show formally the correctness of our protocol and evaluate its energy consumption under realistic scenarios using a real embedded platform compared to previous state-of-the-art key-exchange protocols, to show our protocol reliability for IIoT.

Internet of Things (IoT) is in the booming age of its growth, therefore a vast amount of applications, projects, hardware/software solutions, and customized concepts are being developed. The proliferation of IoT will enable location-based services to be available everywhere for everyone, and this will raise a large number of privacy issues related to the collection, usage, retention, and disclosure of the user’s location information. In order to provide a solution to this unique problem of IoT, this paper proposes Location Privacy Assured Internet of Things (LPA-IoT) scheme, which uses the concepts of Mix-Zone, location-obfuscation along with context-awareness. To the authors’ best knowledge, the proposed LPA-IoT scheme is the first location-based privacy-preserving scheme for IoT that provides flexible privacy levels associated with the present context of the user.

Noise power estimation is central to efficient radio resource allocation in modern wireless communication systems. In the literature, there exist many noise power estimation methods that can be classified based on underlying theoretical principle; the most common are spectral averaging, eigenvalues of sample covariance matrix, information theory, and statistical signal analysis. However, how these estimation methods compare against each other in terms of accuracy, stability, and complexity is not well studied, and the focus instead remains on the enhancement of individual methods. In this paper, we adopt a common simulation methodology to perform a detailed performance evaluation of the prominent estimation techniques. The basis of our comparison is the signal-to-noise ratio estimation in the simulated industrial, scientific and medical band transmission, while the reference noise signal is acquired from an industrial production plant using a software-defined radio platform, USRP-2932. In addition, we analyze the impact of different techniques for noise-samples' separation on the estimation process. As a response to defects in the existing techniques, we propose a novel noise-samples' separation algorithm based on the adaptation of rank-order filtering. Our analysis shows that the proposed solution, apart from its low complexity, has a very good root-mean-squared error of 0.5 dB and smaller than 0.1-dB resolution, thus  achieving a performance comparable with the methods exploiting information theory concepts.

Small cell networks (SCNs) offer a cost-effective coverage solution to wireless applications demanding high data rates. However in SCNs, a challenging problem is the proper management of backhaul links to small cell base stations(SCBSs). To make a good backhaul link, perfect line-of-sight (LoS) communication between the SCBSs and the core network plays a vital role. In this study, we use the idea of employing unmanned aerial vehicles (UAVs) to provide connectivity betweenSCBSs and the core network. We focus on the association of SCBSs with UAVs by considering multiple communication-related factors including data rate limit and available bandwidth resources of the backhaul. In particular, we address the optimum placement of UAVs to serve a maximum number of SCBSswhile considering available resources using the unsupervised k-means algorithm. Numerical results show that the proposed approach outperforms the conventional approach in terms of associatedSCBSs, bandwidth consumption, available link utilization, and sum-rate maximization.

Industrial wireless sensor networks are becoming popular for critical monitoring and control applications in industrial automation systems. For such type of applications, providing reliable real-time performance regarding data delivery is considered as a fundamental challenge. The problem becomes more prominent with mixed-criticality systems, where different data flow with different levels of criticality (importance) coexist and characterized by different requirements regarding delay and reliability. In this paper, we propose a wireless fieldbus protocol to enable real-time communication and service differentiation for cluster-based mixed-criticality networks. A process monitoring scenario of plastic extrusion is used to define the protocol requirements and elaborate the working principle of the proposed work. In our proposed protocol, each data flow is scheduled for channel access based on its criticality level using a distributed prioritized medium access mechanism that ensures a guaranteed channel access for the most critical traffic over other traffic types. The performance of the proposed protocol is analyzed analytically using a discrete-time Markov chain model to evaluate the performance in terms of delay and throughput. Moreover, the extensive simulations are conducted to prove the analytical claims and different performance assessments are provided, which also demonstrate the effectiveness of the proposed approach compared with the related existing work.

Energy sampling-based interference detection and identification (IDI) methods collide with the limitations of commercial off-the-shelf (COTS) IoT hardware. Moreover, long sensing times, complexity and inability to track concurrent interference strongly inhibit their applicability in most IoT deployments. Motivated by the increasing need for on-device IDI for wireless coexistence, we develop a lightweight and efficient method targeting interference identification already at the level of single interference bursts. Our method exploits real-time extraction of envelope and model-aided spectral features, specifically designed considering the physical properties of signals captured with COTS hardware. We adopt manifold supervised-learning (SL) classifiers ensuring suitable performance and complexity trade-off for IoT platforms with different computational capabilities. The proposed IDI method is capable of real-time identification of IEEE 802.11b/g/n, 802.15.4, 802.15.1 and Bluetooth Low Energy wireless standards, enabling isolation and extraction of standard-specific traffic statistics even in the case of heavy concurrent interference. We perform an experimental study in real environments with heterogeneous interference scenarios, showing 90%–97% burst identification accuracy. Meanwhile, the lightweight SL methods, running online on wireless sensor networks-COTS hardware, ensure sub-ms identification time and limited performance gap from machine-learning approaches.

Low-power wide-area network (LPWAN) technologies are gaining momentum for internet-of-things (IoT) applications since they promise wide coverage to a massive number of battery-operated devices using grant-free medium access. LoRaWAN, with its physical (PHY) layer design and regulatory efforts, has emerged as the widely adopted LPWAN solution. By using chirp spread spectrum modulation with qausi-orthogonal spreading factors (SFs), LoRa PHY offers coverage to wide-area applications while supporting high-density of devices. However, thus far its scalability performance has been inadequately modeled and the effect of interference resulting from the imperfect orthogonality of the SFs has not been considered. In this paper, we present an analytical model of a single-cell LoRa system that accounts for the impact of interference among transmissions over the same SF (co-SF) as well as different SFs (inter-SF). By modeling the interference field as Poisson point process under duty-cycled ALOHA, we derive the signal-to-interference ratio (SIR) distributions for several interference conditions. Results show that, for a duty cycle as low as 0.33%, the network performance under co-SF interference alone is considerably optimistic as the inclusion of inter-SF interference unveils a further drop in the success probability and the coverage probability of approximately 10% and 15%, respectively for 1500 devices in a LoRa channel. Finally, we illustrate how our analysis can characterize the critical device density with respect to cell size for a given reliability target.