34,856 research outputs found
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks
NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based
technology that offers a range of flexible configurations for massive IoT radio
access from groups of devices with heterogeneous requirements. A configuration
specifies the amount of radio resource allocated to each group of devices for
random access and for data transmission. Assuming no knowledge of the traffic
statistics, there exists an important challenge in "how to determine the
configuration that maximizes the long-term average number of served IoT devices
at each Transmission Time Interval (TTI) in an online fashion". Given the
complexity of searching for optimal configuration, we first develop real-time
configuration selection based on the tabular Q-learning (tabular-Q), the Linear
Approximation based Q-learning (LA-Q), and the Deep Neural Network based
Q-learning (DQN) in the single-parameter single-group scenario. Our results
show that the proposed reinforcement learning based approaches considerably
outperform the conventional heuristic approaches based on load estimation
(LE-URC) in terms of the number of served IoT devices. This result also
indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve
almost the same performance with much less training time. We further advance
LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative
Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario,
thereby solve the problem that Q-learning agents do not converge in
high-dimensional configurations. In this scenario, the superiority of the
proposed Q-learning approaches over the conventional LE-URC approach
significantly improves with the increase of configuration dimensions, and the
CMA-DQN approach outperforms the other approaches in both throughput and
training efficiency
Cost Adaptation for Robust Decentralized Swarm Behaviour
Decentralized receding horizon control (D-RHC) provides a mechanism for
coordination in multi-agent settings without a centralized command center.
However, combining a set of different goals, costs, and constraints to form an
efficient optimization objective for D-RHC can be difficult. To allay this
problem, we use a meta-learning process -- cost adaptation -- which generates
the optimization objective for D-RHC to solve based on a set of human-generated
priors (cost and constraint functions) and an auxiliary heuristic. We use this
adaptive D-RHC method for control of mesh-networked swarm agents. This
formulation allows a wide range of tasks to be encoded and can account for
network delays, heterogeneous capabilities, and increasingly large swarms
through the adaptation mechanism. We leverage the Unity3D game engine to build
a simulator capable of introducing artificial networking failures and delays in
the swarm. Using the simulator we validate our method on an example coordinated
exploration task. We demonstrate that cost adaptation allows for more efficient
and safer task completion under varying environment conditions and increasingly
large swarm sizes. We release our simulator and code to the community for
future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 201
Survey of Inter-satellite Communication for Small Satellite Systems: Physical Layer to Network Layer View
Small satellite systems enable whole new class of missions for navigation,
communications, remote sensing and scientific research for both civilian and
military purposes. As individual spacecraft are limited by the size, mass and
power constraints, mass-produced small satellites in large constellations or
clusters could be useful in many science missions such as gravity mapping,
tracking of forest fires, finding water resources, etc. Constellation of
satellites provide improved spatial and temporal resolution of the target.
Small satellite constellations contribute innovative applications by replacing
a single asset with several very capable spacecraft which opens the door to new
applications. With increasing levels of autonomy, there will be a need for
remote communication networks to enable communication between spacecraft. These
space based networks will need to configure and maintain dynamic routes, manage
intermediate nodes, and reconfigure themselves to achieve mission objectives.
Hence, inter-satellite communication is a key aspect when satellites fly in
formation. In this paper, we present the various researches being conducted in
the small satellite community for implementing inter-satellite communications
based on the Open System Interconnection (OSI) model. This paper also reviews
the various design parameters applicable to the first three layers of the OSI
model, i.e., physical, data link and network layer. Based on the survey, we
also present a comprehensive list of design parameters useful for achieving
inter-satellite communications for multiple small satellite missions. Specific
topics include proposed solutions for some of the challenges faced by small
satellite systems, enabling operations using a network of small satellites, and
some examples of small satellite missions involving formation flying aspects.Comment: 51 pages, 21 Figures, 11 Tables, accepted in IEEE Communications
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