Low-Power Wide Area Networks (LPWANs), such as LoRaWAN, are pivotal for large-scale IoT deployments. However, traditional stationary gateways (GWs) impose scalability and cost constraints. We propose an AI-driven mobile GW architecture that leverages reinforcement learning (RL) to dynamically adapt GW mobility based on real-time network conditions. Our custom mobility module, integrated into OMNeT++ with FLoRa and TensorFlow Lite Micro, enables on-the-fly decision-making to optimize Packet Delivery Ratio (PDR) and fairness. The results of the simulations show that the introduced RL-GW achieves up to 99.94 % PDR in high-fidelity simulations, outperforming static and heuristic mobile strategies. The RL policy generalizes robustly across network sizes and scenarios, offering a scalable, low-overhead solution for adaptive LPWAN infrastructure.

After a rapid deployment worldwide over the past few years, 5G is expected to have reached a mature deployment stage to provide measurable improvement of network performance and user experience over its predecessors. In this study, we aim to assess 5G deployment maturity via three conditions: (1) Does 5G performance remain stable over a long time span (1 year)? (2) Does 5G provide better performance than its predecessor Long-Term Evolution (LTE)? (3) Does the technology offer similar performance across diverse geographic areas and cellular operators? We answer this important question by conducting two year-long measurement campaigns of 5G uplink performance leveraging a custom Android app: one crowd-sourced, cross-sectional campaign spanning 8 major cities in 7 countries and two different continents (Europe and North America), and one controlled campaign focusing on mmWave deployment at a fixed location in the downtown area of Boston, MA. Our datasets show that 5G deployment in major cities appears to have matured, with no major performance improvements observed over a one-year period, but 5G does not provide consistent, superior measurable performance over LTE, especially in terms of latency, and further there exists clear uneven 5G performance across the 8 cities. Our study suggests that, while 5G deployment appears to have stagnated, it is short of delivering its promised performance and user experience gain over its predecessor.

Low-Power Wide Area Networks (LPWAN) offer long-range communication with low energy consumption, making them ideal for IoT applications powered by energy harvesting. However, unpredictable energy harvesting rates can lead to sub-optimal device operation. To tackle this, the intermittent computing paradigm has been proposed. In this paper, we explore the combination of intermittent computing and LPWANs by proposing four different communication solutions based on LoRa and designed to match common application scenarios and requirements. For the solutions, we also present experimental energy estimation models, which we evaluate against measurements.

Time Slotted Channel Hopping (TSCH) is a medium access protocol defined in the IEEE 802.15.4 standard which have been proven to be one of the most reliable options when it comes to industrial applications. TSCH has been designed to be utilized in static network topologies. Thus, if an application scenario requires a mobile network topology, TSCH does not perform reliably. In this paper we introduce active connectivity for mobile application scenarios, such as mobile robots. This is a feature that enables the option to regulate physical characteristics such as the speed of a node as it moves, in order to keep being connected to the TSCH network. We model the active connectivity approach through a basic example where two nodes are moving towards the same direction to infer the main principles of the introduced approach. We evaluate the active connectivity feature through simulations and quantify trade-off between connectivity and application-layer performance.

TSCH is a MAC protocol able to provide reliable wireless communication to IoT networks. For applications where mobility is necessary research has shown that TSCH presents a suboptimal performance. One of the main issues is that the time required for a device to join a TSCH network is unpredictable and theoretically has no upper limit. The joining process to a TSCH network can take several minutes in some cases which can ensue significant delay and power overheads. In this paper we examine how TSCH can be adapted to support mobile applications, by improving the joining and re-joining procedure. Relevant information about the network topology is aggregated throughout the network to be utilized during the joining process. The results show that the joining time can be reduced by 50% and the re-joining time by 90%.

Due to their popularity, large coverage areas, and diversity of radio technologies, Low Power Wide Area Networks (LPWAN) will experience high levels of cross-technology interference. In this work, we investigate the impact of LoRa interference on IEEE 802.15.4 (Wi-SUN) networks. Using hardware experiments, we characterize the interaction between these two very different radio technologies. In particular, we observe that the IEEE 802.15.4 Clear Channel Assessment (CCA) mechanism does not reliably detect interfering LoRa transmissions. We therefore propose an enhanced CCA mechanism based on a Multi-Layer Perceptron classifier and show that it significantly reduces the number of unsuccessful transmissions, while remaining compatible with the IEEE 802.15.4 standard.

We present some preliminary results on LoRaWAN and IEEE 802.15.4-SUN interference in urban environments. The results are based on a simple simulation that is parameterized using PHY layer measurements of controlled interference scenarios.

The rapid growth of new radio technologies for Smart City/Building/Home applications means that models of cross-technology interference are needed to inform the development of higher layer protocols and applications. We systematically investigate interference interactions between LoRa and IEEE 802.15.4g networks. Our results show that LoRa can obtain high packet reception rates, even in presence of strong IEEE 802.15.4g interference. IEEE 802.15.4g is also shown to have some resilience to LoRa interference. Both effects are highly dependent on the LoRa radio's spreading factor and bandwidth configuration, as well as on the channelization. The results are shown to arise from the interaction between the two radios' modulation schemes. The data have implications for the design and analysis of protocols for both radio technologies.

With the advent of Internet of Things (IoT) Wireless Sensor Networks (WSN) seem to play key a role in the connectivity of smart objects. The limited resources of WSN devices and the increased demand for new and more sophisticated services call for new and more efficient architectures. The new architectures should ensure energy-efficiency, flexibility, reliability and robustness. We believe that using SDN principles in WSN will improve many important features, such as routing, robustness and reconfiguration of the networking as well as the applications. In this research, We will find ways to increase the robustness of WSN using an SDN inspired architecture and a statistical model. The initial plan is to use statistical machine learning to look for periodic interference and other periodic behaviour in typical WSN networks.

Fault-tolerant event detection is fundamental to wireless sensor network applications. Existing approaches usually adopt neighborhood collaboration for better detection accuracy, while need more energy consumption due to communication. Focusing on energy efficiency, this paper makes an improvement to a hybrid algorithm for dynamic event region detection, such as real-time tracking of chemical leakage regions. Considering the characteristics of the moving away dynamic events, we propose a return back condition for the hybrid algorithm from distributed neighborhood collaboration, in which a node makes its detection decision based on decisions received from its spatial and temporal neighbors, to local non-communicative decision making. The simulation results demonstrate that the improved algorithm does not degrade the detection accuracy of the original algorithm, while it has better energy efficiency with the number of messages exchanged in the network decreased.