40,364 research outputs found
Adaptive Synchronization of Robotic Sensor Networks
The main focus of recent time synchronization research is developing
power-efficient synchronization methods that meet pre-defined accuracy
requirements. However, an aspect that has been often overlooked is the high
dynamics of the network topology due to the mobility of the nodes. Employing
existing flooding-based and peer-to-peer synchronization methods, are networked
robots still be able to adapt themselves and self-adjust their logical clocks
under mobile network dynamics? In this paper, we present the application and
the evaluation of the existing synchronization methods on robotic sensor
networks. We show through simulations that Adaptive Value Tracking
synchronization is robust and efficient under mobility. Hence, deducing the
time synchronization problem in robotic sensor networks into a dynamic value
searching problem is preferable to existing synchronization methods in the
literature.Comment: First International Workshop on Robotic Sensor Networks part of
Cyber-Physical Systems Week, Berlin, Germany, 14 April 201
Adaptive service discovery on service-oriented and spontaneous sensor systems
Service-oriented architecture, Spontaneous networks, Self-organisation, Self-configuration, Sensor systems, Social patternsNatural and man-made disasters can significantly impact both people and environments. Enhanced effect can be achieved through dynamic networking of people, systems and procedures and seamless integration of them to fulfil mission objectives with service-oriented sensor systems. However, the benefits of integration of services will not be realised unless we have a dependable method to discover all required services in dynamic environments. In this paper, we propose an Adaptive and Efficient Peer-to-peer Search (AEPS) approach for dependable service integration on service-oriented architecture based on a number of social behaviour patterns. In the AEPS network, the networked nodes can autonomously support and co-operate with each other in a peer-to-peer (P2P) manner to quickly discover and self-configure any services available on the disaster area and deliver a real-time capability by self-organising themselves in spontaneous groups to provide higher flexibility and adaptability for disaster monitoring and relief
Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment
We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population
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Self-organizing peer-to-peer social networks
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 The Authors.Peer-to-peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organizing peer-to-peer social networks (SoPPSoNs) to self-organize distributed peers in a decentralized way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules.National Natural Science of Foundation of Chin
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Local Search in Unstructured Networks
We review a number of message-passing algorithms that can be used to search
through power-law networks. Most of these algorithms are meant to be
improvements for peer-to-peer file sharing systems, and some may also shed some
light on how unstructured social networks with certain topologies might
function relatively efficiently with local information. Like the networks that
they are designed for, these algorithms are completely decentralized, and they
exploit the power-law link distribution in the node degree. We demonstrate that
some of these search algorithms can work well on real Gnutella networks, scale
sub-linearly with the number of nodes, and may help reduce the network search
traffic that tends to cripple such networks.Comment: v2 includes minor revisions: corrections to Fig. 8's caption and
references. 23 pages, 10 figures, a review of local search strategies in
unstructured networks, a contribution to `Handbook of Graphs and Networks:
From the Genome to the Internet', eds. S. Bornholdt and H.G. Schuster
(Wiley-VCH, Berlin, 2002), to be publishe
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