8,110 research outputs found
Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks
In this paper, a family of ant colony algorithms called DAACA for data
aggregation has been presented which contains three phases: the initialization,
packet transmission and operations on pheromones. After initialization, each
node estimates the remaining energy and the amount of pheromones to compute the
probabilities used for dynamically selecting the next hop. After certain rounds
of transmissions, the pheromones adjustment is performed periodically, which
combines the advantages of both global and local pheromones adjustment for
evaporating or depositing pheromones. Four different pheromones adjustment
strategies are designed to achieve the global optimal network lifetime, namely
Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data
aggregation algorithms, DAACA shows higher superiority on average degree of
nodes, energy efficiency, prolonging the network lifetime, computation
complexity and success ratio of one hop transmission. At last we analyze the
characteristic of DAACA in the aspects of robustness, fault tolerance and
scalability.Comment: To appear in Journal of Computer and System Science
From carbon nanotubes and silicate layers to graphene platelets for polymer nanocomposites
In spite of extensive studies conducted on carbon nanotubes and silicate layers for their polymer-based nanocomposites, the rise of graphene now provides a more promising candidate due to its exceptionally high mechanical performance and electrical and thermal conductivities. The present study developed a facile approach to fabricate epoxy–graphene nanocomposites by thermally expanding a commercial product followed by ultrasonication and solution-compounding with epoxy, and investigated their morphologies, mechanical properties, electrical conductivity and thermal mechanical behaviour. Graphene platelets (GnPs) of 3.5
CODE: description language for wireless collaborating objects
This paper introduces CODE, a Description Language for Wireless Collaborating Objects (WCO), with the specific aim of enabling service management in smart environments. WCO extend the traditional model of wireless sensor networks by transferring additional intelligence and responsibility from the gateway level to the network. WCO are able to offer complex services based on cooperation among sensor nodes. CODE provides the vocabulary for describing the complex services offered by WCO. It enables description of services offered by groups, on-demand services, service interface and sub-services. The proposed methodology is based on XML, widely used for structured information exchange and collaboration. CODE can be directly implemented on the network gateway, while a lightweight binary version is stored and exchanged among sensor nodes. Experimental results show the feasibility and flexibility of using CODE as a basis for service management in WCO
Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS) for Non-smooth Data Field
Compressive Sensing (CS) has been applied successfully in a wide variety of
applications in recent years, including photography, shortwave infrared
cameras, optical system research, facial recognition, MRI, etc. In wireless
sensor networks (WSNs), significant research work has been pursued to
investigate the use of CS to reduce the amount of data communicated,
particularly in data aggregation applications and thereby improving energy
efficiency. However, most of the previous work in WSN has used CS under the
assumption that data field is smooth with negligible white Gaussian noise. In
these schemes signal sparsity is estimated globally based on the entire data
field, which is then used to determine the CS parameters. In more realistic
scenarios, where data field may have regional fluctuations or it is piecewise
smooth, existing CS based data aggregation schemes yield poor compression
efficiency. In order to take full advantage of CS in WSNs, we propose an
Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS)
scheme. The proposed schemes dynamically chooses sparsity values based on
signal variations in local regions. We prove that A-HDACS enables more sensor
nodes to employ CS compared to the schemes that do not adapt to the changing
field. The simulation results also demonstrate the improvement in energy
efficiency as well as accurate signal recovery
- …