scholarly journals Power-Efficient Routing Based on Ant-Colony-Optimization and LMST for In-Network Data Aggregation in Event-Based Wireless Sensor Networks

2018 ◽  
Vol 7 (6) ◽  
pp. 321-336
Author(s):  
Nadjib BENAOUDA,et al.
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Data Aggregation ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Network Data ◽  
Power Efficient ◽  
Event Based

IN-NETWORK DATA AGGREGATION VIA ANT-COLONY OPTIMIZATION IN WIRELESS SENSOR NETWORKS

2012 ◽  
Vol 13 (03n04) ◽  
pp. 1250013
Author(s):  
MENG XIE ◽  
HONGCHI SHI
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Search Space ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Network Data ◽  
Hop Count ◽  
Conventional Algorithm

Energy efficiency is an important issue of wireless sensor networks. In-network data aggregation is a data collection technique that improves energy efficiency and alleviates congestive routing traffic by reducing data forwarding in wireless sensor networks. Ant-colony aggregation is a distributed algorithm that provides an intrinsic way of exploring the search space to optimize settings for optimal data aggregation. This paper aims to refine the heuristic function and the aggregation node selection method to maximize energy efficiency and to extend network lifetime. Two proposed algorithms are shown to yield longer maximum lifetime than the conventional algorithm with the same hop-count delay. One of the proposed algorithms is shown to have improved scalability than the conventional algorithm.

Ant Colony Algorithms for Steiner Trees

2011 ◽  
pp. 1551-1575 ◽  
Author(s):  
Gurdip Singh ◽  
Sanjoy Das ◽  
Shekhar V. Gosavi ◽  
Sandeep Pujar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Steiner Trees ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Graph Theoretic ◽  
Ant Colony Algorithms ◽  
Complex Optimization

This chapter introduces ant colony optimization as a method for computing minimum Steiner trees in graphs. Tree computation is achieved when multiple ants, starting out from different nodes in the graph, move towards one another and ultimately merge into a single entity. A distributed version of the proposed algorithm is also described, which is applied to the specific problem of data-centric routing in wireless sensor networks. This research illustrates how tree based graph theoretic computations can be accomplished by means of purely local ant interaction. The authors hope that this work will demonstrate how innovative ways to carry out ant interactions can be used to design effective ant colony algorithms for complex optimization problems.

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