2 research outputs found
Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in Space-Air-Ground Integrated Network
In this paper, we investigate a computing task scheduling problem in
space-air-ground integrated network (SAGIN) for delay-oriented Internet of
Things (IoT) services. In the considered scenario, an unmanned aerial vehicle
(UAV) collects computing tasks from IoT devices and then makes online
offloading decisions, in which the tasks can be processed at the UAV or
offloaded to the nearby base station or the remote satellite. Our objective is
to design a task scheduling policy that minimizes offloading and computing
delay of all tasks given the UAV energy capacity constraint. To this end, we
first formulate the online scheduling problem as an energy-constrained Markov
decision process (MDP). Then, considering the task arrival dynamics, we develop
a novel deep risk-sensitive reinforcement learning algorithm. Specifically, the
algorithm evaluates the risk, which measures the energy consumption that
exceeds the constraint, for each state and searches the optimal parameter
weighing the minimization of delay and risk while learning the optimal policy.
Extensive simulation results demonstrate that the proposed algorithm can reduce
the task processing delay by up to 30% compared to probabilistic configuration
methods while satisfying the UAV energy capacity constraint.Comment: 14 pages, 8 figure
Anomaly Detection using Edge Computing in Video Surveillance System: Review
The current concept of Smart Cities influences urban planners and researchers
to provide modern, secured and sustainable infrastructure and give a decent
quality of life to its residents. To fulfill this need video surveillance
cameras have been deployed to enhance the safety and well-being of the
citizens. Despite technical developments in modern science, abnormal event
detection in surveillance video systems is challenging and requires exhaustive
human efforts. In this paper, we surveyed various methodologies developed to
detect anomalies in intelligent video surveillance. Firstly, we revisit the
surveys on anomaly detection in the last decade. We then present a systematic
categorization of methodologies developed for ease of understanding.
Considering the notion of anomaly depends on context, we identify different
objects-of-interest and publicly available datasets in anomaly detection. Since
anomaly detection is considered a time-critical application of computer vision,
our emphasis is on anomaly detection using edge devices and approaches
explicitly designed for them. Further, we discuss the challenges and
opportunities involved in anomaly detection at the edge.Comment: 26 pages, 6 figures, 5 Table