14,363 research outputs found
Multi -Layer Based Data Aggregation Algorithm for Convergence Platform of IoT and Cloud Computing
Sensor Networks (SN) are deployed in smart domain to sense the environment which is essential to provide the services according to the users need. Hundreds or sometimes thousands of sensors are involved in sensor networks for monitoring the target phenomenon. Large scale of sensory data have to be handle by the sensor network which create several problems such as waste of sensors energy, data redundancy. To overcome these deficiencies one most practice solution is data aggregation which can effectively decrease the massive amount of data generated in SNs by lessening occurrence in the sensing data. The aim of this method is to lessen the massive use of data generated by surrounding nodes, thus saving network energy and providing valuable information for the end user. The effectiveness of any data aggregation technique is largely dependent on topology of the network. Among the various network topologies clustering is preferred as it provides better controllability, scalability and network maintenance phenomenon. In this research, a data aggregation technique is proposed based on Periodic Sensor Network (PSN) which achieved aggregation of data at two layers: the sensor nodes layer and the cluster head layer. In sensor node layer set similarity function is used for checking the redundant data for each sensor node whereas Euclidean distance function is utilized in cluster head layer for discarding the redundancy of data between different sensor nodes. This aggregation technique is implemented in smart home where sensor network is deployed to capture environment related information (temperature, moisture, light, H2 level). Collected information is analyzed using ThinkSpeak cloud platform. For performance evaluation amount of aggregated data, number of pairs of redundant data, energy consumption, data latency, and data accuracy are analyzed and compared with the other state-of-art techniques. The result shows the important improvement of the performance of sensor networks
Resilient networking in wireless sensor networks
This report deals with security in wireless sensor networks (WSNs),
especially in network layer. Multiple secure routing protocols have been
proposed in the literature. However, they often use the cryptography to secure
routing functionalities. The cryptography alone is not enough to defend against
multiple attacks due to the node compromise. Therefore, we need more
algorithmic solutions. In this report, we focus on the behavior of routing
protocols to determine which properties make them more resilient to attacks.
Our aim is to find some answers to the following questions. Are there any
existing protocols, not designed initially for security, but which already
contain some inherently resilient properties against attacks under which some
portion of the network nodes is compromised? If yes, which specific behaviors
are making these protocols more resilient? We propose in this report an
overview of security strategies for WSNs in general, including existing attacks
and defensive measures. In this report we focus at the network layer in
particular, and an analysis of the behavior of four particular routing
protocols is provided to determine their inherent resiliency to insider
attacks. The protocols considered are: Dynamic Source Routing (DSR),
Gradient-Based Routing (GBR), Greedy Forwarding (GF) and Random Walk Routing
(RWR)
Rate-Distortion Classification for Self-Tuning IoT Networks
Many future wireless sensor networks and the Internet of Things are expected
to follow a software defined paradigm, where protocol parameters and behaviors
will be dynamically tuned as a function of the signal statistics. New protocols
will be then injected as a software as certain events occur. For instance, new
data compressors could be (re)programmed on-the-fly as the monitored signal
type or its statistical properties change. We consider a lossy compression
scenario, where the application tolerates some distortion of the gathered
signal in return for improved energy efficiency. To reap the full benefits of
this paradigm, we discuss an automatic sensor profiling approach where the
signal class, and in particular the corresponding rate-distortion curve, is
automatically assessed using machine learning tools (namely, support vector
machines and neural networks). We show that this curve can be reliably
estimated on-the-fly through the computation of a small number (from ten to
twenty) of statistical features on time windows of a few hundreds samples
EZ-AG: Structure-free data aggregation in MANETs using push-assisted self-repelling random walks
This paper describes EZ-AG, a structure-free protocol for duplicate
insensitive data aggregation in MANETs. The key idea in EZ-AG is to introduce a
token that performs a self-repelling random walk in the network and aggregates
information from nodes when they are visited for the first time. A
self-repelling random walk of a token on a graph is one in which at each step,
the token moves to a neighbor that has been visited least often. While
self-repelling random walks visit all nodes in the network much faster than
plain random walks, they tend to slow down when most of the nodes are already
visited. In this paper, we show that a single step push phase at each node can
significantly speed up the aggregation and eliminate this slow down. By doing
so, EZ-AG achieves aggregation in only O(N) time and messages. In terms of
overhead, EZ-AG outperforms existing structure-free data aggregation by a
factor of at least log(N) and achieves the lower bound for aggregation message
overhead. We demonstrate the scalability and robustness of EZ-AG using ns-3
simulations in networks ranging from 100 to 4000 nodes under different mobility
models and node speeds. We also describe a hierarchical extension for EZ-AG
that can produce multi-resolution aggregates at each node using only O(NlogN)
messages, which is a poly-logarithmic factor improvement over existing
techniques
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