2,871 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
Seamless connectivity:investigating implementation challenges of multibroker MQTT platform for smart environmental monitoring
Abstract. This thesis explores the performance and efficiency of MQTT-based infrastructure Internet of Things (IoT) sensor networks for smart environment. The study focuses on the impact of network latency and broker switching in distributed multi-broker MQTT platforms. The research involves three case studies: a cloud-based multi-broker deployment, a Local Area Network (LAN)-based multi-broker deployment, and a multi-layer LAN network-based multi-broker deployment. The research is guided by three objectives: quantifying and analyzing the latency of multi-broker MQTT platforms; investigating the benefits of distributed brokers for edge users; and assessing the impact of switching latency at applications. This thesis ultimately seeks to answer three key questions related to network and switching latency, the merits of distributed brokers, and the influence of switching latency on the reliability of end-user applications
Location-aware computing: a neural network model for determining location in wireless LANs
The strengths of the RF signals arriving from more access points in a wireless LANs are related to the position of the mobile terminal and can be used to derive the location of the user. In a heterogeneous environment, e.g. inside a building or in a variegated urban geometry, the received power is a very complex function of the distance, the geometry, the materials. The complexity of the inverse problem (to derive the position from the signals) and the lack of complete information, motivate to consider flexible models based on a network of functions (neural networks). Specifying the value of the free parameters of the model requires a supervised learning strategy that starts from a set of labeled examples to construct a model that will then generalize in an appropriate manner when confronted with new data, not present in the training set. The advantage of the method is that it does not require ad-hoc infrastructure in addition to the wireless LAN, while the flexible modeling and learning capabilities of neural networks achieve lower errors in determining the position, are amenable to incremental improvements, and do not require the detailed knowledge of the access point locations and of the building characteristics. A user needs only a map of the working space and a small number of identified locations to train a system, as evidenced by the experimental results presented
Framework for Content Distribution over Wireless LANs
Wireless LAN (also called as Wi-Fi) is dominantly considered as the most pervasive
technology for Intent access. Due to the low-cost of chipsets and support for high data
rates, Wi-Fi has become a universal solution for ever-increasing application space
which includes, video streaming, content delivery, emergency communication,
vehicular communication and Internet-of-Things (IoT).
Wireless LAN technology is defined by the IEEE 802.11 standard. The 802.11
standard has been amended several times over the last two decades, to incorporate the
requirement of future applications. The 802.11 based Wi-Fi networks are
infrastructure networks in which devices communicate through an access point.
However, in 2010, Wi-Fi Alliance has released a specification to standardize direct
communication in Wi-Fi networks. The technology is called Wi-Fi Direct. Wi-Fi
Direct after 9 years of its release is still used for very basic services (connectivity, file
transfer etc.), despite the potential to support a wide range of applications. The reason
behind the limited inception of Wi-Fi Direct is some inherent shortcomings that limit
its performance in dense networks. These include the issues related to topology
design, such as non-optimal group formation, Group Owner selection problem,
clustering in dense networks and coping with device mobility in dynamic networks. Furthermore, Wi-Fi networks also face challenges to meet the growing number of Wi
Fi users. The next generation of Wi-Fi networks is characterized as ultra-dense
networks where the topology changes frequently which directly affects the network
performance. The dynamic nature of such networks challenges the operators to design
and make optimum planifications.
In this dissertation, we propose solutions to the aforementioned problems. We
contributed to the existing Wi-Fi Direct technology by enhancing the group formation
process. The proposed group formation scheme is backwards-compatible and
incorporates role selection based on the device's capabilities to improve network
performance. Optimum clustering scheme using mixed integer programming is
proposed to design efficient topologies in fixed dense networks, which improves
network throughput and reduces packet loss ratio. A novel architecture using
Unmanned Aeriel Vehicles (UAVs) in Wi-Fi Direct networks is proposed for
dynamic networks. In ultra-dense, highly dynamic topologies, we propose cognitive
networks using machine-learning algorithms to predict the network changes ahead of
time and self-configuring the network
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