In current power systems, electrical energy is generated whenever there is a demand for it. Therefore, load forecasting, which estimates the active load in advance, is imperative for power system planning and operations. Based on the time horizon, load forecasting is classified as very short-term (below one day), short-term (a day to two weeks), medium-term (two weeks to three years) and long-term (over three years). This paper focuses on the short-term forecasting. The complex multi-level seasonality of load series (e.g., the load in a given hour is not only dependent on load in the previous hour, but also on the previous day.s load in the same hour, and on the previous week's load in the same day-of-the-week and hour) makes this task challenging, especially when the load data is represented in 1d numerical series. However, in multi-channel images, the patterns in spatial neighbourhood of one channel and the patterns in the neighbourhood along the channel dimension are able to be captured by 3d image processing operations. Hence, this study proposes to transform electrical load data from 1d series to 3d images and transform the problem from future series forecasting to missing patch inpainting. Furthermore, it proposes a recurrent neural network to model the temporal trends in the series by convolutional operations on the spatial neighbourhood in the images. Experimental results demonstrate the effectiveness of the proposed method on two benchmarks and show the capability of inferring the future load from related series if there is a lack of history.

Delay Tolerant Network (DTN) is developed to cope with intermittent connectivity and long delay in wireless networks. Due to limited connectivity, DTN is vulnerable to flooding attack in which malicious nodes flood the network with superfluous data to deplete the network resources. Existing works mitigate internal flooding attacks by rate-limit to constrain the number of messages that nodes can generate per time slot. However, rate-limit cannot flexibly accommodate burst traffic in which nodes may have sending demands higher than the rate-limit for a short period. In this paper, we propose FDER to detect flooding attack and yet allow legitimate burst traffic simultaneously. Nodes exchange their histories of encounter records (ER) which record the sent messages during their previous encounters. The ER history is used to infer a node's new message transmission rate over time and the number of forwarded replicas per message. The adversary nodes that send too many messages or replicas can thus be detected. Since ERs serve as useful tools for monitoring the sending behavior of nodes over a long time period, FDER could detect the burst traffic violation efficiently. We also design FP - a fairness forwarding policy to ensure fairness in the delivery performance fairness between nodes with normal traffic and those with burst traffic. FP uses ER information to observe nodes' rate of new message generation and adjust their forwarding priorities accordingly. Simulation results show that FDER can detect flooding attack at a higher accuracy and a lower delay compared to state-of-the-art scheme with affordable overhead. Moreover, FP could mitigate the smart flooding attack and still provide the performance fairness to support bursty traffic scenario.

Mobile edge computing (MEC) is viewed as a promising technology to address the challenges of intensive computing demands in hotspots (HSs). In this paper, we consider a unmanned aerial vehicle (UAV)-assisted backscattering MEC system. The UAVs can fly from parking aprons to HSs, providing energy to HSs via RF beamforming and collecting data from wireless users in HSs through backscattering. We aim to maximize the long-term utility of all HSs, subject to the stability of the HSs' energy queues. This problem is a joint optimization of the data offloading decision and contract design that should be adaptive to the users' random task demands and the time-varying wireless channel conditions. A deep reinforcement learning based contract incentive (DRLCI) strategy is proposed to solve this problem in two steps. Firstly, we use deep Q-network (DQN) algorithm to update the HSs' offloading decisions according to the changing network environment. Secondly, to motivate the UAVs to participate in resource sharing, a contract specific to each type of UAVs has been designed, utilizing Lagrangian multiplier method to approach the optimal contract. Simulation results show the feasibility and efficiency of the proposed strategy, demonstrating a better performance than the natural DQN and Double-DQN algorithms.