In this paper, efficient resource allocation for the uplink transmission of wireless powered IoT networks is investigated. We adopt LoRa technology as an example in the IoT network, but this work is still suitable for other communication technologies. Allocating limited resources, like spectrum and energy resources, among a massive number of users faces critical challenges. We consider grouping wireless powered IoT users into available channels first and then investigate power allocation for users grouped in the same channel to improve the network throughput. Specifically, the user grouping problem is formulated as a many to one matching game. It is achieved by considering IoT users and channels as selfish players which belong to two disjoint sets. Both selfish players focus on maximizing their own utilities. Then we propose an efficient channel allocation algorithm (ECAA) with low complexity for user grouping. Additionally, a Markov Decision Process (MDP) is used to model unpredictable energy arrival and channel conditions uncertainty at each user, and a power allocation algorithm is proposed to maximize the accumulative network throughput over a finite-horizon of time slots. By doing so, we can distribute the channel access and dynamic power allocation local to IoT users. Numerical results demonstrate that our proposed ECAA algorithm achieves near-optimal performance and is superior to random channel assignment, but has much lower computational complexity. Moreover, simulations show that the distributed power allocation policy for each user is obtained with better performance than a centralized offline scheme.

Physical-layer security (PLS) schemes use the randomness of the channel parameters, namely, channel state information (CSI) and received signal strength indicator (RSSI) to generate the secret keys. There has been limited work in PLS schemes in long-range (LoRa) wide-area networks (LoRaWANs) which hinder their widespread application. Limitations observed in existing studies include the requirement of high correlation between channel parameter measurements for secret key generation in the proposed schemes and the evaluation of the schemes has only been done in either fully indoor or outdoor environments. The real-world wireless sensor networks (WSNs) and LoRa use cases might not meet both requirements thus making the current PLS schemes inappropriate for these systems. By considering the limitations found in existing PLS schemes, this article proposes LoRA-LiSK, a practical and efficient shared secret key generation scheme for LoRa networks. Our proposed LoRa-LiSK scheme consists of several preprocessing techniques (timestamp matching, two sample Kolmogorov–Smirnov tests, and a Savitzky–Golay filter), multilevel quantization, information reconciliation using Bose–Chaudhuri–Hocquenghem (BCH) codes, and finally, privacy amplification using secure hash algorithm SHA-2. The LoRa-LiSK scheme is extensively evaluated on real WSN/IoT devices in practical application scenarios: 1) indoor to outdoor and 2) LoRa static and mobile outdoor links. It outperforms existing schemes by generating keys with channel parameter measurements of low correlation values (0.2–0.6), while still essentially achieving high key generation rates, and low key disagreement rates (10%–20%). The scheme updates a key in approximately 1 h using an application profile with high transmission rate compared to 3 h reported by existing works while still respecting the duty cycle regulation. It also incurs less communication overhead compared to the existing works.

This brief adds dynamic variables to decentralized periodic event-triggered control (DPETC), denoting dynamic DPETC (DDPETC) for wireless cyber-physical systems whose sensors are distributed and bandwidths are limited, and studies the stability and L₂-gain. In DDPETC, dynamic variables that require only local information are designed and added to the event-triggered mechanisms (ETMs). This dynamic variable can guarantee that the ETMs still be localized which does not require information from other nodes. For implementations with DDPETC, at each periodic sampling time the ETMs decide events based on new measurements from local sensors. Those measurements that trigger the events are transmitted to controllers, and after network transmission delays, they are received and the controller computes the control actions. The transmissions of these control actions from controllers to actuators are also decided by ETMs. The feasibility of the approach is shown via two examples. Numerical results show that, with the same stability and performance, DDPETC can save more than 40% events, making it more suitable for wireless cyber-physical systems.

Pipeline networks have been widely utilised in the transportation of water, natural gases, oil and waste materials efficiently and safely over varying distances with minimal human intervention. In order to optimise the spatial use of the pipeline infrastructure, pipelines are either buried underground, or located in submarine environments. Due to the continuous expansion of pipeline networks in locations that are inaccessible to maintenance personnel, research efforts have been ongoing to introduce and develop reliable detection methods for pipeline failures, such as blockages, leakages, cracks, corrosion and weld defects. In this paper, a taxonomy of existing pipeline failure detection techniques and technologies was created to comparatively analyse their respective advantages, drawbacks and limitations. This effort has effectively illuminated various unaddressed research challenges that are still present among a wide array of the state-of-the-art detection methods that have been employed in various pipeline domains. These challenges include the extension of the lifetime of a pipeline network for the reduction of maintenance costs, and the prevention of disruptive pipeline failures for the minimisation of downtime. Our taxonomy of various pipeline failure detection methods is also presented in the form of a look-up table to illustrate the suitability, key aspects and data or signal processing techniques of each individual method. We have also quantitatively evaluated the industrial relevance and practicality of each of the methods in the taxonomy in terms of their respective deployability, generality and computational cost. The outcome of the evaluation made in the taxonomy will contribute to our future works involving the utilisation of sensor fusion and data-centric frameworks to develop efficient, accurate and reliable failure detection solutions.

In this article, we provide, for the first time, a comprehensive understanding of long-range (LoRa) waveform theory in order to quantify its orthogonality. We present LoRa waveform expressions in continuous- and discrete-time domains, and analyze measures of orthogonality between different LoRa spreading factors (SFs) through cross-correlation functions. The cross-correlation functions are analytically expressed in a general form and they account for diverse configuration parameters (bandwidth, SF, etc.) and different cases of signal displacements (time delay shift, frequency shift, etc.). We quantify their mean and maximum in all time domains. We highlight the impact of the temporal displacement and different bandwidths. The general result is that LoRa modulation is nonorthogonal. First, we observe that for same bandwidths, the largest maximum cross-correlation happens for same SF and is equal to 100% due to same symbols; whereas for different bandwidths, the largest maximum cross-correlation is no longer observed at the same SF. Second, the maximum cross-correlation is less than 26% between different SFs, is higher for closer SFs, and decreases as the difference between SFs increases. After downchirping, the maximum cross-correlation increases and the mean decreases compared to those before downchirping. Moreover, the maximum cross-correlation is insignificantly impacted by the temporal delay, which makes it valid to adopt for the performance analysis of both synchronous and asynchronous systems. Finally, we analyze by simulating the bit error probability statistics for different bandwidth ratios and highlighting their correlated behavior with the insights obtained from the maximum cross-correlation expressions.