51,909 research outputs found
Distributed data fusion for the internet of things
© Springer International Publishing AG 2017. The ubiquitous Internet of Things is underpinned by the recent advancements in the wireless networking technology, which enabled connecting previously scattered devices into the global network. IoT engineers, however, are required to handle current limitations and find the right balance between data transferring range, throughput, and power consumption of wireless IoT devices. As a result, existing IoT systems, based on collecting data from a distributed network of edge devices, are limited by the amount of data they are able to transfer over the network. This means that some sort of data fusion mechanism has to be introduced, which would be responsible for filtering raw data before sending them further to a next node through the network. As a potential way of implementing such a mechanism, this paper proposes utilising Complex Event Processing and introduces a hierarchical distributed architecture for enabling data fusion at various levels
A Hybrid Approach for Data Analytics for Internet of Things
The vision of the Internet of Things is to allow currently unconnected
physical objects to be connected to the internet. There will be an extremely
large number of internet connected devices that will be much more than the
number of human being in the world all producing data. These data will be
collected and delivered to the cloud for processing, especially with a view of
finding meaningful information to then take action. However, ideally the data
needs to be analysed locally to increase privacy, give quick responses to
people and to reduce use of network and storage resources. To tackle these
problems, distributed data analytics can be proposed to collect and analyse the
data either in the edge or fog devices. In this paper, we explore a hybrid
approach which means that both innetwork level and cloud level processing
should work together to build effective IoT data analytics in order to overcome
their respective weaknesses and use their specific strengths. Specifically, we
collected raw data locally and extracted features by applying data fusion
techniques on the data on resource constrained devices to reduce the data and
then send the extracted features to the cloud for processing. We evaluated the
accuracy and data consumption over network and thus show that it is feasible to
increase privacy and maintain accuracy while reducing data communication
demands.Comment: Accepted to be published in the Proceedings of the 7th ACM
International Conference on the Internet of Things (IoT 2017
Channel Estimation Techniques for Quantized Distributed Reception in MIMO Systems
The Internet of Things (IoT) could enable the development of cloud
multiple-input multiple-output (MIMO) systems where internet-enabled devices
can work as distributed transmission/reception entities. We expect that spatial
multiplexing with distributed reception using cloud MIMO would be a key factor
of future wireless communication systems. In this paper, we first review
practical receivers for distributed reception of spatially multiplexed transmit
data where the fusion center relies on quantized received signals conveyed from
geographically separated receive nodes. Using the structures of these
receivers, we propose practical channel estimation techniques for the
block-fading scenario. The proposed channel estimation techniques rely on very
simple operations at the received nodes while achieving near-optimal channel
estimation performance as the training length becomes large.Comment: Proceedings of the 2014 Asilomar Conference on Signals, Systems &
Computer
Cross-domain fusion in smart seafloor sensor networks
Many of the socio-economic and environmental challenges of the 21st century like the growing energy and food demand, rising sea levels and temperatures put stress on marine ecosystems and coastal populations. This requires a significant strengthening of our monitoring capacities for processes in the water column, at the seafloor and in the subsurface. However, present-day seafloor instruments and the required infrastructure to operate these are expensive and inaccessible. We envision a future Internet of Underwater Things, composed of small and cheap but intelligent underwater nodes. Each node will be equipped with sensing, communication, and computing capabilities. Building on distributed event detection and cross-domain data fusion, such an Internet of Underwater Things will enable new applications. In this paper, we argue that to make this vision a reality, we need new methodologies for resource-efficient and distributed cross-domain data fusion. Resource-efficient, distributed neural networks will serve as data-analytics pipelines to derive highly aggregated patterns of interest from raw data. These will serve as (1) a common base in time and space for fusion of heterogeneous data, and (2) be sufficiently small to be transmitted efficiently in resource-constrained settings
ODIN: Obfuscation-based privacy-preserving consensus algorithm for Decentralized Information fusion in smart device Networks
The large spread of sensors and smart devices in urban infrastructures are motivating research in the area of the Internet of Things (IoT) to develop new services and improve citizens’ quality of life. Sensors and smart devices generate large amounts of measurement data from sensing the environment, which is used to enable services such as control of power consumption or traffic density. To deal with such a large amount of information and provide accurate measurements, service providers can adopt information fusion, which given the decentralized nature of urban deployments can be performed by means of consensus algorithms. These algorithms allow distributed agents to (iteratively) compute linear functions on the exchanged data, and take decisions based on the outcome, without the need for the support of a central entity. However, the use of consensus algorithms raises several security concerns, especially when private or security critical information is involved in the computation.
In this article we propose ODIN, a novel algorithm allowing information fusion over encrypted data. ODIN is a privacy-preserving extension of the popular consensus gossip algorithm, which prevents distributed agents from having direct access to the data while they iteratively reach consensus; agents cannot access even the final consensus value but can only retrieve partial information (e.g., a binary decision). ODIN uses efficient additive obfuscation and proxy re-encryption during the update steps and garbled circuits to make final decisions on the obfuscated consensus. We discuss the security of our proposal and show its practicability and efficiency on real-world resource-constrained devices, developing a prototype implementation for Raspberry Pi devices
ODIN: Obfuscation-based privacy-preserving consensus algorithm for Decentralized Information fusion in smart device Networks
The large spread of sensors and smart devices in urban infrastructures are motivating research in the area of the Internet of Things (IoT) to develop new services and improve citizens’ quality of life. Sensors and smart devices generate large amounts of measurement data from sensing the environment, which is used to enable services such as control of power consumption or traffic density. To deal with such a large amount of information and provide accurate measurements, service providers can adopt information fusion, which given the decentralized nature of urban deployments can be performed by means of consensus algorithms. These algorithms allow distributed agents to (iteratively) compute linear functions on the exchanged data, and take decisions based on the outcome, without the need for the support of a central entity. However, the use of consensus algorithms raises several security concerns, especially when private or security critical information is involved in the computation.
In this article we propose ODIN, a novel algorithm allowing information fusion over encrypted data. ODIN is a privacy-preserving extension of the popular consensus gossip algorithm, which prevents distributed agents from having direct access to the data while they iteratively reach consensus; agents cannot access even the final consensus value but can only retrieve partial information (e.g., a binary decision). ODIN uses efficient additive obfuscation and proxy re-encryption during the update steps and garbled circuits to make final decisions on the obfuscated consensus. We discuss the security of our proposal and show its practicability and efficiency on real-world resource-constrained devices, developing a prototype implementation for Raspberry Pi devices
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