This article evaluates the quality-of-service performance and scalability of the recently released Bluetooth Mesh protocol and provides general guidelines on its use and configuration. Through extensive simulations, we analyzed the impact of the configuration of all the different protocol's parameters on the end-to-end reliability, delay, and scalability. In particular, we focused on the structure of the packet broadcast process, which takes place in time intervals known as Advertising Events and Scanning Events. Results indicate a high degree of interdependence among all the different timing parameters involved in both the scanning and the advertising processes and show that the correct operation of the protocol greatly depends on the compatibility between their configurations. We also demonstrated that introducing randomization in these timing parameters, as well as varying the duration of the Advertising Events, reduces the drawbacks of the flooding propagation mechanism implemented by the protocol. Using data collected from a real office environment, we also studied the behavior of the protocol in the presence of WLAN interference. It was shown that Bluetooth Mesh is vulnerable to external interference, even when implementing the standardized limitation of using only 3 out of the 40 Bluetooth Low Energy frequency channels. We observed that the achievable average delay is relatively low, of around 250 ms for over 10 hops under the worst simulated network conditions. However, results proved that scalability is especially challenging for Bluetooth Mesh since it is prone to broadcast storm, hindering the communication reliability for denser deployments.
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 tradeoff 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.
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 lack of coordinated spectrum access for IoT wireless technologies in unlicensed bands creates inefficient spectrum usage and poses growing concerns in several IoT applications. Spectrum awareness becomes then crucial, especially in the presence of strict quality-of-service (QoS) requirements and mission-critical communication. In this work, we propose a lightweight spectral analysis framework designed for strongly resource-constrained devices, which are the norm in IoT deployments. The proposed solution enables model-based reconstruction of the spectrum of single radio-bursts entirely onboard without DFT processing. The spectrum sampling exploits pattern-based frequency sweeping, which enables the spectral analysis of short radio-bursts while minimizing the sampling error induced by non-ideal sensing hardware. We carry out an analysis of the properties of such sweeping patterns, derive useful theoretical error bounds, and explain how to design optimal patterns for radio front-ends with different characteristics. The experimental campaign shows that the proposed solution enables the estimation of central frequency, bandwidth, and spectral shape of signals at runtime by using a strongly hardware-limited radio platform. Finally, we test the potential of the proposed solution in combination with a proactive blacklisting scheme, allowing a substantial improvement in real-time QoS of a radio link under interference.
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