A Scalable Hybrid MAC Protocol for Massive M2M Networks

In Machine to Machine (M2M) networks, a robust Medium Access Control (MAC) protocol is crucial to enable numerous machine-type devices to concurrently access the channel. Most literatures focus on developing simplex (reservation or contention based)MAC protocols which cannot provide a scalable solution for M2M networks with large number of devices. In this paper, a frame-based Hybrid MAC scheme, which consists of a contention period and a transmission period, is proposed for M2M networks. In the proposed scheme, the devices firstly contend the transmission opportunities during the contention period, only the successful devices will be assigned a time slot for transmission during the transmission period. To balance the tradeoff between the contention and transmission period in each frame, an optimization problem is formulated to maximize the system throughput by finding the optimal contending probability during contention period and optimal number of devices that can transmit during transmission period. A practical hybrid MAC protocol is designed to implement the proposed scheme. The analytical and simulation results demonstrate the effectiveness of the proposed Hybrid MAC protocol

Decentralized control and fair load-shedding compensations to prevent cascading failures in a smart grid

AbstractEvidence shows that a small number of line contingencies in power systems may cause a large-scale blackout due to the effects of cascading failures. With the development of new technologies and the growing number of heterogeneous participants, a modern/smart grid should be able to self-heal its internal disturbances by continually performing self-assessment to deter, detect, respond to and restore from unpredictable contingencies. Along this line, this research focuses on the problem of how to prevent the occurrence of cascading failures through load shedding by considering heterogeneous shedding costs of grid participants. A fair load-shedding algorithm is proposed to solve the problem in a decentralized manner, where a load-shedding participant need only monitor its own operational status and interact with its neighboring participants. Using an embedded feedback mechanism, the fair load-shedding algorithm can determine a marginal compensation price for each load-shedding participant in real time based on the proportional fairness criterion, without knowing the shedding costs of the participants. Such fairly determined compensations can help motivate loaders/generators to actively participate in the load shedding in the face of internal disturbances. Finally, the properties of the load-shedding algorithm are evaluated by carrying out an experimental study on the standard IEEE 30 bus system. The study will offer new insights into emergency planning and design improvement of self-healing smart grids

Decadal Temperature Prediction via Chaotic Behavior Tracking

Decadal temperature prediction provides crucial information for quantifying the expected effects of future climate changes and thus informs strategic planning and decision-making in various domains. However, such long-term predictions are extremely challenging, due to the chaotic nature of temperature variations. Moreover, the usefulness of existing simulation-based and machine learning-based methods for this task is limited because initial simulation or prediction errors increase exponentially over time. To address this challenging task, we devise a novel prediction method involving an information tracking mechanism that aims to track and adapt to changes in temperature dynamics during the prediction phase by providing probabilistic feedback on the prediction error of the next step based on the current prediction. We integrate this information tracking mechanism, which can be considered as a model calibrator, into the objective function of our method to obtain the corrections needed to avoid error accumulation. Our results show the ability of our method to accurately predict global land-surface temperatures over a decadal range. Furthermore, we demonstrate that our results are meaningful in a real-world context: the temperatures predicted using our method are consistent with and can be used to explain the well-known teleconnections within and between different continents