Industrial Wireless Sensor Networks (IWSNs) designedfor mission- and time-critical applications require timelyand deterministic data delivery within stringent deadline bounds.Exceeding delay limits for such applications can lead to system malfunction or ultimately dangerous situations that can threaten human safety. In this paper, we propose SS-MAC, an efficient slot stealing MAC protocol to guarantee predictable and timely channel access for time-critical data in IWSNs. In the proposed SS-MAC, aperiodic time-critical traffic opportunistically steals time slots assigned to periodic non-critical traffic. Additionally, a dynamic deadline-based scheduling is introduced to provide guaranteed channel access in emergency and event-based situations where multiple sensor nodes are triggered simultaneously to transmit time-critical data to the controller. The proposed protocol is evaluated mathematically to provide the worst-case delay bound for the time-critical traffic. Performance comparisons are carried out between the proposed SS-MAC and WirelessHARTstandard and they show that, for the time-critical traffic, theproposed SS-MAC can achieve, at least, a reduction of almost 30% in the worst-case delay with a significant channel utilization efficiency.

LoRa and the LoRaWAN specification is a technology for Low Power Wide Area Networks (LPWAN) designed to allow connectivity for connected objects, such as remote sensors. Several previous works revealed various weaknesses regarding the security of LoRaWAN v1.0 (the official 1st draft) and these led to improvements included in LoRaWAN v1.1, released on Oct 11, 2017. In this work, we provide the first look into the security of LoRaWAN v1.1. We present an overview of the protocol and, importantly, present several threats to this new version of the protocol. Besides, we propose our own ramification strategies for the mentioned threats, to be used in developing next version of LoRaWAN. The threats presented were not previously discussed, they are possible even within the security assumptions of the specification and are relevant for practitioners implementing LoRa-based applications as well researchers and the future evolution of the LoRaWAN specification.

We see a shift from todays Internet-of-Things (IoT)to include more industrial equipment and metrology systems,forming the Industrial Internet of Things (IIoT). However, thisleads to many concerns related to confidentiality, integrity,availability, privacy and non-repudiation. Hence, there is a needto secure the IIoT in order to cater for a future with smart grids,smart metering, smart factories, smart cities, and smart manufacturing.It is therefore important to research IIoT technologiesand to create order in this chaos, especially when it comes tosecuring communication, resilient wireless networks, protectingindustrial data, and safely storing industrial intellectual propertyin cloud systems. This research therefore presents the challenges,needs, and requirements of industrial applications when it comesto securing IIoT systems.

The vision of connecting billions of battery operated devices to be used for diverse emerging applications calls for a wireless communication system that can support stringent reliability and latency requirements. Both reliability and energy efficiency are critical for many of these applications that involve communication with short packets which undermine the coding gain achievable from large packets. In this paper, we study a cross-layer approach to optimize the performance of low-power wireless links. At first, we derive a simple and accurate packet error rate (PER) expression for uncoded schemes in block fading channels based on a new proposition that shows that the waterfall threshold in the PER upper bound in Nakagami-m fading channels is tightly approximated by the m-th moment of an asymptotic distribution of PER in AWGN channel. The proposed PER approximation establishes an explicit connection between the physical and link layers parameters, and the packet error rate. We exploit this connection for cross-layer design and optimization of communication links. To this end, we propose a semi-analytic framework to jointly optimize signal-to-noise ratio (SNR) and modulation order at physical layer, and the packet length and number of retransmissions at link layer with respect to distance under the prescribed delay and reliability constraints.

In wake-up radio (WuR)-enabled wireless sensor networks, data communication among nodes is triggered in an on-demand manner, by either a sender or a receiver. For receiver-initiated WuR (RI-WuR), a~receiving node wakes up sending nodes through a wake-up call. Correspondingly sending nodes transmit packets in a traditional way by competing with one another multiple times in a single operational cycle. In~this paper, we propose a receiver-initiated consecutive packet transmission WuR (RI-CPT-WuR) medium access control (MAC) protocol, which eliminates multiple competitions to achieve higher energy efficiency. Furthermore, we develop two associated discrete time Markov chains (DTMCs) for evaluating the performance of RI-CPT-WuR and an existing RI-WuR MAC protocol. Using the solutions from the DTMC models, closed-form expressions for network throughput, average delay, packet reliability ratio, energy consumption and lifetime, and energy efficiency for both protocols are obtained. Numerical results demonstrate the superiority of the RI-CPT-WuR protocol.

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.

With the prevalence of low-power wireless devices in industrial applications, concerns about timeliness and reliability are bound to continue despite the best efforts of researchers to design Industrial Wireless Sensor Networks (IWSNs) to improve the performance of monitoring and control systems. As mobile devices have a major role to play in industrial production, IWSNs should support mobility. However, research on mobile IWSNs and practical tests have been limited due to the complicated resource scheduling and rescheduling compared with traditional wireless sensor networks. This paper proposes an effective mechanism to guarantee the performance of handoff, including a mobility-aware scheme, temporary connection and quick registration. The main contribution of this paper is that the proposed mechanism is implemented not only in our testbed but in a real industrial environment. The results indicate that our mechanism not only improves the accuracy of handoff triggering, but also solves the problem of ping-pong effect during handoff. Compared with the WirelessHART standard and the RSSI-based approach, our mechanism facilitates real-time communication while being more reliable, which can help end-to-end packet delivery remain an average of 98.5% in the scenario of mobile IWSNs.

In recent years, the adoption of industrial wireless sensor and actuator networks (IWSANs) has greatly increased. However, the time-critical performance of IWSANs is considerably affected by external sources of interference. In particular, when an IEEE 802.11 network is coexisting in the same environment, a significant drop in communication reliability is observed. This, in turn, represents one of the main challenges for a wide-scale adoption of IWSAN. Interference classification through spectrum sensing is a possible step towards interference mitigation, but the long sampling window required by many of the approaches in the literature undermines their run-time applicability in time-slotted channel hopping (TSCH)-based IWSAN. Aiming at minimizing both the sensing time and the memory footprint of the collected samples, a centralized interference classifier based on support vector machines (SVMs) is introduced in this article. The proposed mechanism, tested with sample traces collected in industrial scenarios, enables the classification of interference from IEEE 802.11 networks and microwave ovens, while ensuring high classification accuracy with a sensing duration below 300 ms. In addition, the obtained results show that the fast classification together with a contained sampling frequency ensure the suitability of the method for TSCH-based IWSAN

The Internet of Things (IoT) is captivating the society because of its potential to rapidly transform businesses and people’s lives. It is widely believed that IoT will also transform the industrial automation business in terms of improved productivity, less cost, flexibility, and increased revenues. Hence, there are some challenges that needs to be addressed when IoT is introduced to the industrial automation domain. This paperaims to present realistic requirements and highlights identified challenges such as security, interoperability, deterministic and low latency communication, and how the required availability (uptime) can be kept. Moreover, the paper also point out the need of standardization and sustainable business models. The conclusion is that introducing IoT devices and connecting them directly to cloud services is not straightforward for process automation.

Energy harvesting (EH) promises an extended lifetime for wireless sensor networks (WSNs), supplying sensor nodes with accumulated energy from natural sources. Different from battery powered sensors, nodes in EH-enabled WSNs are equipped with an energy harvesting accessory in order to extract energy from surrounding sources. Based upon the harvested energy, sensor nodes perform data exchange activities. In this paper, we develop two discrete time Markov chain (DTMC)models to analyze the performance of packet transmissionsin such a WSN while employing a generic medium access control (MAC) protocol. Using the proposed DTMC models, we investigate the effect of EH over a synchronous MAC protocol with respect to average packet delay and network throughput. Furthermore, the analytical results are validated via discrete event simulations. Numerical results indicate the accuracy of the models and reveal the behavior of packet transmissions relying on harvested energy.