The industrial Internet of Things (IIoT) is expected to revolutionize the current industry. The capillary introduction of sensors and actuators for real-time monitoring and remote control and their seamless integration into existing information systems will represent a technological breakthrough that will help to reshape the industrial processes. To this aim, the definition of wireless communication standards will play a crucial role in reducing deployment costs and minimizing the time for installation. In this context, the new IPv6 over the TSCH mode of IEEE 802.15.4e communication stack, 6TiSCH, represents the current leading standardization effort that aims at achieving both reliable and timed wireless communication and integration within IPv6 communication networks for industrial systems. In this paper, the network formation dynamics of 6TiSCH networks are assessed, considering the current guidelines for the so-called minimal configuration, a static initial configuration pre-configured to guarantee control communication during network bootstrap. It is shown that the minimal configuration might lead to long network formation and suboptimal performance of the routing algorithm which may result into a disconnected network. In order to overcome this issue, a dynamic resource management algorithm to be executed during network bootstrap is proposed. Simulation and experimental results show that the proposed solution allows to minimize the network formation time and also helps in optimizing routing operations leading to the discovery of better routes.

Time Slotted Channel Hopping (TSCH) is an emerging MAC protocol defined in the IEEE 802.15.4e standard. By combining time slotted access with multi-channel and channel hopping capabilities, it is particularly suitable for critical applications that require high reliability and deterministic latency. In this paper we focus on the formation process of TSCH networks. This relies on periodic advertisement of Enhanced Beacons (EBs), however, the standard does not specify any advertising strategy. By taking a theoretical approach, we first derive a general model of the network formation process and provide an analytical formulation of the average joining time (i.e., the time taken by a node to join the network). Then, we derive an optimal strategy for scheduling EB transmissions that minimizes the average joining time. Finally, we propose a new Model-based Beacon Scheduling (MBS) algorithm that approximates the optimal strategy in real networks. We evaluate the performance of MBS by simulation. Our results show that the proposed algorithm outperforms previous solutions present in the literature.

Recent studies have shown that the IEEE 802.15.4 MAC protocol suffers from severe limitations, in terms of reliability and energy efficiency, when the CSMA/CA parameter setting is not appropriate. However, selecting the optimal setting that guarantees the application reliability requirements, with minimum energy consumption, is not a trivial task in wireless sensor networks, especially when the operating conditions change over time. In this paper we propose a Just-in-Time LEarning-based Adaptive Parameter tuning (JIT-LEAP) algorithm that adapts the CSMA/CA parameter setting to the time-varying operating conditions by also exploiting the past history to find the most appropriate setting for the current conditions. Following the approach of active adaptive algorithms, the adaptation mechanism of JIT-LEAP is triggered by a change detection test only when needed (i.e., in response to a change in the operating conditions). Simulation results show that the proposed algorithm outperforms other similar algorithms, both in stationary and dynamic scenarios.

The overall energy consumption due to ICT equipment has followed an increasing trend over the last years. A considerable fraction of the consumed energy is caused by user devices, such as Personal Computers (PCs) and displays. However, a large part of this energy is wasted due to an inefficient use. Users leave their PCs on for long periods even when unused, especially in workplaces. Hence, significant energy savings could be achieved just turning them off. However, it is not wise to rely on user collaboration, and, thus, automated tools are needed. In this paper, we present E-Net-Manager, a power management system for large environments, which turns unused PCs off and switches them on when the user is about to use them. To this end, E-Net-Manager leverages soft sensors, i.e., software/hardware tools already in use by the users, thus not introducing any additional cost. E-Net-Manager combines information provided by the users and data obtained from a number of these soft sensors. This way, it is possible to accurately determine the user presence/activity near her/his PC and, therefore, eliminate wastes also due to short periods of inactivity.

Recent studies have shown that the IEEE 802.15.4 MAC protocol may suffer from severe limitations in terms of reliability and energy efficiency if a non appropriate parameter setting is used. Hence, a number of solutions have been proposed to select the optimal parameter setting to provide reliability with minimum energy consumption. In this paper we compare, by simulation, three different algorithms that take different approaches to the problem, namely offline computation, model-based adaptation, and measurement-based adaptation. We show that adaptive algorithms perform well, however the model-based adaptive approach has some limitations that make it unsuitable in practical scenarios, where operating conditions may vary over time and transmission errors cannot be neglected. Instead, the measurement-based adaptive approach is flexible and effective.