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Resource Optimization Algorithm for Sparse Time-Driven Sensor Networks [chapter]

María Luisa Santamaría, Sebastià Galmés, Ramon Puigjaner
2010 Lecture Notes in Computer Science  
Time-driven sensor networks are devoted to the continuous reporting of data to the user.  ...  However, despite the enhanced lifetime, the overall link distance is not optimized, fact that increases the need for additional resources (relay nodes).  ...  Finally, our research results are progressively being integrated in etLife, a software tool for planning time-driven sensor networks.  ... 
doi:10.1007/978-3-642-12963-6_22 fatcat:evuk6iguprhqblzzkhoypeg5im

Sparse Distributed Memory Approach for Reinforcement Learning Driven Efficient Routing in Mobile Wireless Network System

Varshini Vidyadhar, Nagaraj R, G Sudha
2021 International Journal of Advanced Computer Science and Applications  
The Kanerva-coding is a sparse memory with distributed reading and writing mechanism which enables optimal compression and state abstractions for learning with fewer parameterized components making it  ...  In addition, the time required for agent learning in the training phase is too long, which makes it difficult for the agent to generalize the observation state efficiently.  ...  Fig. 11 11 Fig.11demonstrates the analysis of the average routing time of each algorithm. Routing time is defined as the time required by the algorithm to search for a suitable path in any network.  ... 
doi:10.14569/ijacsa.2021.0121117 fatcat:hh6siqah6jemlmoidxtia756ce

Model-Driven Public Sensing in Sparse Networks [chapter]

Damian Philipp, Jarosław Stachowiak, Frank Dürr, Kurt Rothermel
2014 Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering  
In this work, we present an adaptive model-driven PS system that deals with both dense and sparse networks.  ...  Existing solutions for limiting the energy drain through model-driven optimizations are limited to dense networks where there is a high probability for every point of interest to be covered by a smartphone  ...  We address this challenge by presenting an approach for optimized modeldriven PS that works in both dense and sparse networks. To this end, we extend our previous model-driven approach.  ... 
doi:10.1007/978-3-319-11569-6_2 fatcat:e6oirdy6are7lmzrthjd7uk5q4

EDICS for the IEEE Trans. on Signal Processing

2018 IEEE Transactions on Signal Processing  
and protocols in energy systems NEG-ASAL NEG-CLRD NEG-ENGY Adaptive sensing algorithms Cross-layer design Energy effi cient sensor network algorithms SPE-F SPE-M SPE-PS Demand, renewable generation, and  ...  for SP SSP-SPEC Spectral analysis and spectral estimation OPT-NCVX Non-convex methods for SP SSP-SPRS SP methods for structured low dimensional models OPT-SOPT Sparse optimization techniques for SP SSP-SSAN  ... 
doi:10.1109/tsp.2018.2839143 fatcat:kebdk7me6vegrmi5p3lh363wdm

EDICS for the IEEE Trans. on Signal Processing

2018 IEEE Transactions on Signal Processing  
and protocols in energy systems NEG-ASAL NEG-CLRD NEG-ENGY Adaptive sensing algorithms Cross-layer design Energy effi cient sensor network algorithms SPE-F SPE-M SPE-PS Demand, renewable generation, and  ...  for SP SSP-SPEC Spectral analysis and spectral estimation OPT-NCVX Non-convex methods for SP SSP-SPRS SP methods for structured low dimensional models OPT-SOPT Sparse optimization techniques for SP SSP-SSAN  ... 
doi:10.1109/tsp.2018.2858372 fatcat:ve7y3fvdpvhrbgnoryvks5lb24

EDICS for the IEEE Trans. on Signal Processing

2017 IEEE Transactions on Signal Processing  
and protocols in energy systems NEG-ASAL NEG-CLRD NEG-ENGY Adaptive sensing algorithms Cross-layer design Energy effi cient sensor network algorithms SPE-F SPE-M SPE-PS Demand, renewable generation, and  ...  for SP SSP-SPEC Spectral analysis and spectral estimation OPT-NCVX Non-convex methods for SP SSP-SPRS SP methods for structured low dimensional models OPT-SOPT Sparse optimization techniques for SP SSP-SSAN  ... 
doi:10.1109/tsp.2017.2747233 fatcat:4hgqzevshrfd5mcafw4q7daaem

EDICS for the IEEE Trans. on Signal Processing

2017 IEEE Transactions on Signal Processing  
and protocols in energy systems NEG-ASAL NEG-CLRD NEG-ENGY Adaptive sensing algorithms Cross-layer design Energy effi cient sensor network algorithms SPE-F SPE-M SPE-PS Demand, renewable generation, and  ...  for SP SSP-SPEC Spectral analysis and spectral estimation OPT-NCVX Non-convex methods for SP SSP-SPRS SP methods for structured low dimensional models OPT-SOPT Sparse optimization techniques for SP SSP-SSAN  ... 
doi:10.1109/tsp.2017.2747207 fatcat:pv3l27nggbflrfrjyzmu63iqri

EDICS for the IEEE Trans. on Signal Processing

2017 IEEE Transactions on Signal Processing  
and protocols in energy systems NEG-ASAL NEG-CLRD NEG-ENGY Adaptive sensing algorithms Cross-layer design Energy effi cient sensor network algorithms SPE-F SPE-M SPE-PS Demand, renewable generation, and  ...  for SP SSP-SPEC Spectral analysis and spectral estimation OPT-NCVX Non-convex methods for SP SSP-SPRS SP methods for structured low dimensional models OPT-SOPT Sparse optimization techniques for SP SSP-SSAN  ... 
doi:10.1109/tsp.2017.2773959 fatcat:s75s6mbx6vfjleoyzkggwmrofm

EDICS for the IEEE Trans. on Signal Processing

2018 IEEE Transactions on Signal Processing  
and protocols in energy systems NEG-ASAL NEG-CLRD NEG-ENGY Adaptive sensing algorithms Cross-layer design Energy effi cient sensor network algorithms SPE-F SPE-M SPE-PS Demand, renewable generation, and  ...  for SP SSP-SPEC Spectral analysis and spectral estimation OPT-NCVX Non-convex methods for SP SSP-SPRS SP methods for structured low dimensional models OPT-SOPT Sparse optimization techniques for SP SSP-SSAN  ... 
doi:10.1109/tsp.2018.2839155 fatcat:5fxpomytp5efjlvs4djs4g6hfq

EDICS for the IEEE Trans. on Signal Processing

2018 IEEE Transactions on Signal Processing  
and protocols in energy systems NEG-ASAL NEG-CLRD NEG-ENGY Adaptive sensing algorithms Cross-layer design Energy effi cient sensor network algorithms SPE-F SPE-M SPE-PS Demand, renewable generation, and  ...  for SP SSP-SPEC Spectral analysis and spectral estimation OPT-NCVX Non-convex methods for SP SSP-SPRS SP methods for structured low dimensional models OPT-SOPT Sparse optimization techniques for SP SSP-SSAN  ... 
doi:10.1109/tsp.2018.2858307 fatcat:avmcww6dlzadvhbmudcmwzynzm

Energy Aware Compressive Sensing Scheme in Wireless Sensor Network

K.Vaishnu Sankareshwari, R. v, Dr.K. Ramasamy
2017 International Journal Of Engineering And Computer Science  
In wireless sensor network energy consumption plays major role .The main reason for designing a enhanced optimized energy efficient routing protocol is to reduce the scarcity of energy resource .The major  ...  node formation and improves the overall network lifetime when compared with the existing protocols .It can be achieved by applying the concepts of energy aware activity scheduling algorithm for buffer  ...  Gravitational Search Algorithm In proposed method uses GSA algorithm for constructing optimal path for transmitting data to the base station.  ... 
doi:10.18535/ijecs/v6i3.23 fatcat:sfxnlcculvf5zptmfk6qalwc4u

STACK: Sparse Timing of Algorithms Using Computational Knowledge [chapter]

Vasanth Iyer, S. Sitharama Iyengar, Garmiela Rama Murthy, Kannan Srinathan, Mandalika B. Srinivas, Regeti Govindarajulu
2011 Lecture Notes in Electrical Engineering  
The pre-processing approach of the modified algorithm for sparse sensing gives better consistency and the application based calibration allowing coherent view of the data and at the same time reduces the  ...  The STACK implementation runs 4 times faster than the normal program based optimizations for static and dynamic scheduling.  ...  Acknowledgments One of the authors would like to thank the members of the Intel Parallel Processing lab at IIIT-Hyderbad and specially Nayan Mujadiya for extending his time and effort during CS4200 with  ... 
doi:10.1007/978-3-642-17943-3_16 fatcat:vuld6apdznewbisu3rzydj5hda

Table of contents

2020 IEEE Communications Letters  
Wang 2574 SENSOR NETWORKS AND INTERNET OF THINGS Time Synchronized Node Localization Using Optimal H-Node Allocation in a Small World WSN ..................... .........................................  ...  Loreti 2555 Optimal Resource Allocation for Wireless Powered Sensors: A Perspective From Age of Information ................. ..................................................................... Q.  ... 
doi:10.1109/lcomm.2020.3031678 fatcat:vceu3lry2zbk5ldqav4545lweu

Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks

Ruisong Wang, Gongliang Liu, Wenjing Kang, Bo Li, Ruofei Ma, Chunsheng Zhu
2018 Sensors  
Information acquisition in underwater sensor networks is usually limited by energy and bandwidth. Fortunately, the received signal can be represented sparsely on some basis.  ...  Therefore, it is significant to adjust the sensor node selection scheme according to these factors for the superior performance.  ...  For example, when the number of selected sensor nodes is 30, the optimized scheme makes full use of 2.3 times energy to prolong network lifetime more.  ... 
doi:10.3390/s18082568 pmid:30082602 fatcat:5lv6dz63nnegvbgiaf4ieolw64

Using of compressed sensing in energy sensitive WSN applications

Ondrej Karpiš, Juraj Miček, Veronika Olešnaníková
2015 Proceedings of the 2015 Federated Conference on Computer Science and Information Systems  
As a case study, we use compressed sensing for monitoring of mains voltage deformation.  ...  In this case we can assume that the measured signal is sparse in frequency domain and using of methods of compressed sensing is meaningful.  ...  ACKNOWLEDGMENT This contribution is the result of the project implementation Centre of excellence for systems and services of intelligent transport I.  ... 
doi:10.15439/2015f167 dblp:conf/fedcsis/KarpisMO15 fatcat:kg7m2icfpfcu5pai4mhtb3w5ay
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