Priority-Aware Actuation Update Scheme in Heterogeneous Industrial Networks.

In heterogeneous wireless networked control systems (WNCSs), the age of information (AoI) of the actuation update and actuation update cost are important performance metrics. To reduce the monetary cost, the control system can wait for the availability of a WiFi network for the actuator and then conduct the update using a WiFi network in an opportunistic manner, but this leads to an increased AoI of the actuation update. In addition, since there are different AoI requirements according to the control priorities (i.e., robustness of AoI of the actuation update), these need to be considered when delivering the actuation update. To jointly consider the monetary cost and AoI with priority, this paper proposes a priority-aware actuation update scheme (PAUS) where the control system decides whether to deliver or delay the actuation update to the actuator. For the optimal decision, we formulate a Markov decision process model and derive the optimal policy based on Q-learning, which aims to maximize the average reward that implies the balance between the monetary cost and AoI with priority. Simulation results demonstrate that the PAUS outperforms the comparison schemes in terms of the average reward under various settings.

Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks.

The observation satellite can exploit its own storage and computational resources to reduce the transmission delay. However, too excessive usage of these resources can have negative effects on the queuing delay at the relay satellite and/or on conducting other tasks at each observation satellite. In this paper, we proposed a new resource- and neighbor-aware observation transmission scheme (RNA-OTS). In RNA-OTS, each observation satellite decides whether to use its resources and the resources of the relay satellite at each time epoch by considering its resource utilization and transmission policies of neighbor observation satellites. For the optimal decision of each observation satellite in a distributed manner, the operation of observation satellites is modeled by means of a constrained stochastic game, and a best-response-dynamics-based algorithm is devised to find the Nash equilibrium. The evaluation results demonstrate that RNA-OTS can decrease the delay to deliver the observation to the destination by up to 87% compared to a relay-satellite-based scheme while guaranteeing a sufficiently low average utilization of the resources of the observation satellite.

Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things.

In a non-orthogonal multiple access (NOMA) environment, an Internet of Things (IoT) device achieves a high data rate by increasing its transmission power. However, excessively high transmission power can cause an energy outage of an IoT device and have a detrimental effect on the signal-to-interference-plus-noise ratio of neighbor IoT devices. In this paper, we propose a neighbor-aware NOMA scheme (NA-NOMA) where each IoT device determines whether to transmit data to the base station and the transmission power at each time epoch in a distributed manner with the consideration of its energy level and other devices' transmission powers. To maximize the aggregated data rate of IoT devices while keeping an acceptable average energy outage probability, a constrained stochastic game model is formulated, and the solution of the model is obtained using a best response dynamics-based algorithm. Evaluation results show that NA-NOMA can increase the average data rate up to 22% compared with a probability-based scheme while providing a sufficiently low energy outage probability (e.g., 0.05).

Distributed Group Location Update Algorithm for Massive Machine Type Communication.

Frequent location updates of individual Internet of Things (IoT) devices can cause several problems (e.g., signaling overhead in networks and energy depletion of IoT devices) in massive machine type communication (mMTC) systems. To alleviate these problems, we design a distributed group location update algorithm (DGLU) in which geographically proximate IoT devices determine whether to conduct the location update in a distributed manner. To maximize the accuracy of the locations of IoT devices while maintaining a sufficiently small energy outage probability, we formulate a constrained stochastic game model. We then introduce a best response dynamics-based algorithm to obtain a multi-policy constrained Nash equilibrium. From the evaluation results, it is demonstrated that DGLU can achieve an accuracy of location information that is comparable with that of the individual location update scheme, with a sufficiently small energy outage probability.

EERS: Energy-Efficient Reference Node Selection Algorithm for Synchronization in Industrial Wireless Sensor Networks.

Time synchronization is an essential issue in industrial wireless sensor networks (IWSNs). It assists perfect coordinated communications among the sensor nodes to preserve battery power. Generally, time synchronization in IWSNs has two major aspects of energy consumption and accuracy. In the literature, the energy consumption has not received much attention in contrast to the accuracy. In this paper, focusing on the energy consumption aspect, we introduce an energy-efficient reference node selection (EERS) algorithm for time synchronization in IWSNs. It selects and schedules a minimal sequence of connected reference nodes that are responsible for spreading timing messages. EERS achieves energy consumption synchronization by reducing the number of transmitted messages among the sensor nodes. To evaluate the performance of EERS, we conducted extensive experiments with Arduino Nano RF sensors and revealed that EERS achieves considerably fewer messages than previous techniques, robust time synchronization (R-Sync), fast scheduling and accurate drift compensation for time synchronization (FADS), and low power scheduling for time synchronization protocols (LPSS). In addition, simulation results for a large sensor network of 450 nodes demonstrate that EERS reduces the whole number of transmitted messages by 52%, 30%, and 13% compared to R-Sync, FADS, and LPSS, respectively.