A mutual information based sensor selection and information controlled transmission power adjustment

Chithra, B.; Muniraj, N. J. R.

doi:10.1109/icoiss.2013.6678432

Cited by 5 publications

(7 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main idea of the method is to optimize an information utility function using the defined metrics. In [6], the authors propose a mutual-information based sensor selection (MISS) algorithm, which allows the sensor nodes with the highest mutual information about the target state to transfer data first, and the other nodes no longer send their sensed data when the sink received enough data to estimate the target state with the required accuracy. In [7], the authors propose a sensor selection approach based on maximum entropy fuzzy clustering to address the target tracking problem in large-scale sensor networks.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, in order to fully compare and verify our proposed algorithms, we conduct experiments under some different network environments, and for each simulation, we run at least 20 times with different random node distributions and the average results are shown. We also compare our simulation results with the distance-based method in [4], the entropy-based method in [6] and the optimal theory-based method in [19] in terms of mean square error, execution time and energy cost and so on.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…To solve the node selection problem, the distance-based methods (such as [2,3,4]) are proposed, and they need less computation but cannot reach competitive tracking accuracy. To improve the tracking accuracy, the entropy-based methods (such as [5,6,7]) and the optimal theory-based methods (such as [8,9,10]) are proposed. Although they achieve good tracking accuracy, these methods are computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Node and Sensed Module Management for Multisensory Wireless Sensor Networks

Jiang

Shi

2018

Sensors

View full text Add to dashboard Cite

In target tracking wireless sensor networks, choosing a part of sensor nodes to execute tracking tasks and letting the other nodes sleep to save energy are efficient node management strategies. However, at present more and more sensor nodes carry many different types of sensed modules, and the existing researches on node selection are mainly focused on sensor nodes with a single sensed module. Few works involved the management and selection of the sensed modules for sensor nodes which have several multi-mode sensed modules. This work proposes an efficient node and sensed module management strategy, called ENSMM, for multisensory WSNs (wireless sensor networks). ENSMM considers not only node selection, but also the selection of the sensed modules for each node, and then the power management of sensor nodes is performed according to the selection results. Moreover, a joint weighted information utility measurement is proposed to estimate the information utility of the multiple sensed modules in the different nodes. Through extensive and realistic experiments, the results show that, ENSMM outperforms the state-of-the-art approaches by decreasing the energy consumption and prolonging the network lifetime. Meanwhile, it reduces the computational complexity with guaranteeing the tracking accuracy.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Node and Sensed Module Management for Multisensory Wireless Sensor Networks

Jiang

Shi

2018

Sensors

View full text Add to dashboard Cite

show abstract

“…The main idea of entropy-based approaches is to optimize an information utility function using the defined metrics. In [12], a mutual-information based sensor selection (MISS) algorithm was implemented for involvement in the mixture procedure. MISS allowed the sensor nodes with the highest mutual information about the target state to transfer data so that the energy ingestion was reduced while the preferred target position estimation accuracy was met.…”

Section: Related Workmentioning

confidence: 99%

“…(1) input set of candidate nodes CN = { 1 ⋅ ⋅ ⋅ }, ∈ [1, ] (2) set of selected nodes SN = { 1 ⋅ ⋅ ⋅ }, ∈ [1, ] (3) = 1 (4) Max residual energy = 0 (5) while ( < ) (6) for each ( ∈ CN) (7) if (residual energy( ) > Max residual energy) (8) Max residual energy = residual energy( ) (9) Max residual energy N = (10) end if (11) end for (12) = + 1 (13) end while (14) =…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Efficient and Adaptive Node Selection for Target Tracking in Wireless Sensor Network

2016

View full text Add to dashboard Cite

In target tracking wireless sensor network, choosing the proper working nodes can not only minimize the number of active nodes, but also satisfy the tracking reliability requirement. However, most existing works focus on selecting sensor nodes which are the nearest to the target for tracking missions and they did not consider the correlation of the location of the sensor nodes so that these approaches can not meet all the goals of the network. This work proposes an efficient and adaptive node selection approach for tracking a target in a distributed wireless sensor network. The proposed approach combines the distance-based node selection strategy and particle filter prediction considering the spatial correlation of the different sensing nodes. Moreover, a joint distance weighted measurement is proposed to estimate the information utility of sensing nodes. Experimental results show that EANS outperformed the state-of-the-art approaches by reducing the energy cost and computational complexity as well as guaranteeing the tracking accuracy.

show abstract

Convex optimization approach to design sensor networks using information theoretic measures

Magbool Jan

2023

AIChE Journal

View full text Add to dashboard Cite

Accurate and precise estimation of process variables is key to effective process monitoring. The estimation accuracy depends on the choice of the sensor network. Therefore, the article aims at developing convex optimization formulations for designing the optimal sensor network using information‐theoretic measures in linear steady‐state data reconciliation. To this end, the estimation errors are characterized by a multivariate Gaussian distribution, and thus the analytical form for entropy and Kullback–Leibler divergences (forward, reverse, and symmetric) of estimation errors can be obtained to formulate the optimal sensor network design. The proposed information theoretic‐based optimal sensor selection problems are shown to be integer semi‐definite programming problems where the relaxation of binary decision variables results in solving a convex optimization problem. Thus, we use a branch and bound method to obtain a globally optimal sensor network design. Demonstrative case studies are presented to illustrate the efficacy of the proposed optimal sensor selection formulations.

show abstract

A mutual information based sensor selection and information controlled transmission power adjustment

Cited by 5 publications

References 39 publications

Efficient Node and Sensed Module Management for Multisensory Wireless Sensor Networks

Efficient Node and Sensed Module Management for Multisensory Wireless Sensor Networks

Efficient and Adaptive Node Selection for Target Tracking in Wireless Sensor Network

Convex optimization approach to design sensor networks using information theoretic measures

Contact Info

Product

Resources

About