Constructing a Message-Pruning Tree with Minimum Cost for Tracking Moving Objects in Wireless Sensor Networks Is NP-Complete and an Enhanced Data Aggregation Structure

2008 ◽  
Vol 57 (6) ◽  
pp. 849-863 ◽  
Author(s):  
Bing-Hong Liu ◽  
Wei-Chieh Ke ◽  
Chin-Hsien Tsai ◽  
Ming-Jer Tsai
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Moving Objects ◽  
Minimum Cost ◽  
Wireless Sensor ◽  
Aggregation Structure ◽  
Np Complete

scholarly journals Minimum Cost Data Aggregation for Wireless Sensor Networks Computing Functions of Sensed Data

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Chao Chen ◽  
Kyogu Lee ◽  
Joon-Sang Park ◽  
Seung Jun Baek
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Low Cost ◽  
Cost Savings ◽  
Minimum Cost ◽  
Wireless Sensor ◽  
Weighted Sum ◽  
Extreme Type ◽  
Local Aggregation

We consider a problem of minimum cost (energy) data aggregation in wireless sensor networks computing certain functions of sensed data. We use in-network aggregation such that data can be combined at the intermediate nodes en route to the sink. We consider two types of functions: firstly the summation-type which includessum,mean, andweighted sum, and secondly the extreme-type which includesmaxandmin. However for both types of functions the problem turns out to be NP-hard. We first show that, forsumandmean, there exist algorithms which can approximate the optimal cost by a factor logarithmic in the number of sources. Forweighted sumwe obtain a similar result for Gaussian sources. Next we reveal that the problem for extreme-type functions is intrinsically different from that for summation-type functions. We then propose a novel algorithm based on the crucial tradeoff in reducing costs between local aggregation of flows and finding a low cost path to the sink: the algorithm is shown to empirically find the best tradeoff point. We argue that the algorithm is applicable to many other similar types of problems. Simulation results show that significant cost savings can be achieved by the proposed algorithm.

Energy efficient fixed-cluster architecture for wireless sensor networks

2021 ◽  
Vol 40 (5) ◽  
pp. 8727-8740
Author(s):  
Rajvir Singh ◽  
C. Rama Krishna ◽  
Rajnish Sharma ◽  
Renu Vig
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Energy Efficient ◽  
Search Algorithm ◽  
Cluster Formation ◽  
Wireless Sensor ◽  
Efficient Operation ◽  
Cluster Architecture ◽  
Dynamic Cluster

Dynamic and frequent re-clustering of nodes along with data aggregation is used to achieve energy-efficient operation in wireless sensor networks. But dynamic cluster formation supports data aggregation only when clusters can be formed using any set of nodes that lie in close proximity to each other. Frequent re-clustering makes network management difficult and adversely affects the use of energy efficient TDMA-based scheduling for data collection within the clusters. To circumvent these issues, a centralized Fixed-Cluster Architecture (FCA) has been proposed in this paper. The proposed scheme leads to a simplified network implementation for smart spaces where it makes more sense to aggregate data that belongs to a cluster of sensors located within the confines of a designated area. A comparative study is done with dynamic clusters formed with a distributive Low Energy Adaptive Clustering Hierarchy (LEACH) and a centralized Harmonic Search Algorithm (HSA). Using uniform cluster size for FCA, the results show that it utilizes the available energy efficiently by providing stability period values that are 56% and 41% more as compared to LEACH and HSA respectively.

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