Algorithmic Trading Using Continuous Action Space Deep Reinforcement
Learning
Price movement prediction has always been one of the traders' concerns in
financial market trading. In order to increase their profit, they can analyze
the historical data and predict the price movement. The large size of the data
and complex relations between them lead us to use algorithmic trading and
artificial intelligence.
This paper aims to offer an approach using
Twin-Delayed DDPG (TD3) and the daily close price in order to achieve a trading
strategy in the stock and cryptocurrency markets. Unlike previous studies using
a discrete action space reinforcement learning algorithm, the TD3 is
continuous, offering both position and the number of trading shares. Both the
stock (Amazon) and cryptocurrency (Bitcoin) markets are addressed in this
research to evaluate the performance of the proposed algorithm.
The achieved
strategy using the TD3 is compared with some algorithms using technical
analysis, reinforcement learning, stochastic, and deterministic strategies
through two standard metrics, Return and Sharpe ratio. The results indicate
that employing both position and the number of trading shares can improve the
performance of a trading system based on the mentioned metrics.
Swarm of UAVs for Network Management in 6G: A Technical Review
Fifth-generation (5G) cellular networks have led to the implementation of
beyond 5G (B5G) networks, which are capable of incorporating autonomous
services to swarm of unmanned aerial vehicles (UAVs). They provide capacity
expansion strategies to address massive connectivity issues and guarantee
ultra-high throughput and low latency, especially in extreme or emergency
situations where network density, bandwidth, and traffic patterns fluctuate. On
the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish
ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks,
on the other hand, rely on new enabling technologies such as air interface and
transmission technologies, as well as a unique network design, posing new
challenges for the swarm of UAVs. Keeping these challenges in mind, this
article focuses on the security and privacy, intelligence, and
energy-efficiency issues faced by swarms of UAVs operating in 6G mobile
networks. In this state-of-the-art review, we integrated blockchain and AI/ML
with UAV networks utilizing the 6G ecosystem. The key findings are then
presented, and potential research challenges are identified. We conclude the
review by shedding light on future research in this emerging field of research.
Perspectives on a 6G Architecture
Mobile communications have been undergoing a generational change every ten
years. Whilst we are just beginning to roll out 5G networks, significant
efforts are planned to standardize 6G that is expected to be commercially
introduced by 2030. This paper looks at the use cases for 6G and their impact
on the network architecture to meet the anticipated performance requirements.
The new architecture is based on integrating various network functions in
virtual cloud environments, leveraging the advancement of artificial
intelligence in all domains, integrating different sub-networks constituting
the 6G system, and on enhanced means of exposing data and services to third
parties.
On Routing Optimization in Networks with Embedded Computational Services
Modern communication networks are increasingly equipped with in-network
computational capabilities and services. Routing in such networks is
significantly more complicated than the traditional routing. A legitimate route
for a flow not only needs to have enough communication and computation
resources, but also has to conform to various application-specific routing
constraints. This paper presents a comprehensive study on routing optimization
problems in networks with embedded computational services. We develop a set of
routing optimization models and derive low-complexity heuristic routing
algorithms for diverse computation scenarios. For dynamic demands, we also
develop an online routing algorithm with performance guarantees. Through
evaluations over emerging applications on real topologies, we demonstrate that
our models can be flexibly customized to meet the diverse routing requirements
of different computation applications. Our proposed heuristic algorithms
significantly outperform baseline algorithms and can achieve close-to-optimal
performance in various scenarios.
Network Intrusion Detection System in a Light Bulb
Internet of Things (IoT) devices are progressively being utilised in a
variety of edge applications to monitor and control home and industry
infrastructure. Due to the limited compute and energy resources, active
security protections are usually minimal in many IoT devices. This has created
a critical security challenge that has attracted researchers' attention in the
field of network security. Despite a large number of proposed Network Intrusion
Detection Systems (NIDSs), there is limited research into practical IoT
implementations, and to the best of our knowledge, no edge-based NIDS has been
demonstrated to operate on common low-power chipsets found in the majority of
IoT devices, such as the ESP8266. This research aims to address this gap by
pushing the boundaries on low-power Machine Learning (ML) based NIDSs. We
propose and develop an efficient and low-power ML-based NIDS, and demonstrate
its applicability for IoT edge applications by running it on a typical smart
light bulb. We also evaluate our system against other proposed edge-based NIDSs
and show that our model has a higher detection performance, and is
significantly faster and smaller, and therefore more applicable to a wider
range of IoT edge devices.