Mohammad Abdul Azim, Z. Aung, S. Moad, N. Bouabdallah, M. E. Rivero-Angeles, Israel Leyva-Mayorga
{"title":"无线传感器网络通信中高度相关时空环境的节能方法","authors":"Mohammad Abdul Azim, Z. Aung, S. Moad, N. Bouabdallah, M. E. Rivero-Angeles, Israel Leyva-Mayorga","doi":"10.4236/WSN.2014.65009","DOIUrl":null,"url":null,"abstract":"Continuous-monitoring (CM) \nof natural phenomenon is one of the major streams of applications in wireless \nsensor networks (WSNs), where aggregation and clustering techniques are \nbeneficial as correlation dominates in both spatial and temporal aspects of \nsensed phenomenon. Conversely, in Event Driven Reporting (EDR), the efficient \ntransmission of sensitive data related to some predefined alarm cases is of \nmajor importance. As such, reporting latency is a more important performance \nparameter. However, in some applications, the transmission of both CM and EDR \ndata is encouraged or even required. For either CM or EDR applications, system \nperformance can be greatly improved when both the number of packets to be \ntransmitted as well as the packet size is reduced. This is especially true for \nhighly dense sensor networks where many nodes detect the same values for the \nsensed phenomenon. Building on this, this paper focuses on studying and \nproposing compression techniques to improve the system performance in terms of \nenergy consumption and reporting latency in both CM and EDR applications. \nFurthermore, we extend our analysis to hybrid networks where CM and EDR are \nrequired simultaneously. Specifically, this paper presents a simple aggregation \ntechnique named smart aggregation (SAG) for the CM applications and an event \ndriven scheme named compression cluster scheme in spatial correlated region \n(CC_SCR). The proposed SAG exploits both spatial and temporal correlations \nwhere CC_SCR exploits the spatial correlation of such networks by data \ncompression. Rationalizing the developments is attained by simulations that \ncompare energy efficiency of the proposed SAG with k-hop aggregation and CM based event driven reporting (CMEDR) \nschemes. Results of CC_SCR show that the technique may reduce the energy \nconsumption drastically. In some specific cases the reduction becomes more \nthan 10 times compared to a classical clustering scheme. Two different strategies \nfor the transmission of event reports through the CM infrastructure are \nincorporated: PER and NPER protocols. Both strategies take advantage of the \ncluster-based architecture which assigns a TDMA schedule for the CM data \ntransmission while using NP/CSMA for the transmission of the event information. \nConsequently, no extra energy is consumed for separate event clusters. As such, \nthe number of packets to be transmitted is greatly reduced.","PeriodicalId":58712,"journal":{"name":"无线传感网络(英文)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Energy-Efficient Methods for Highly Correlated Spatio-Temporal Environments in Wireless Sensor Network Communications\",\"authors\":\"Mohammad Abdul Azim, Z. Aung, S. Moad, N. Bouabdallah, M. E. Rivero-Angeles, Israel Leyva-Mayorga\",\"doi\":\"10.4236/WSN.2014.65009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Continuous-monitoring (CM) \\nof natural phenomenon is one of the major streams of applications in wireless \\nsensor networks (WSNs), where aggregation and clustering techniques are \\nbeneficial as correlation dominates in both spatial and temporal aspects of \\nsensed phenomenon. Conversely, in Event Driven Reporting (EDR), the efficient \\ntransmission of sensitive data related to some predefined alarm cases is of \\nmajor importance. As such, reporting latency is a more important performance \\nparameter. However, in some applications, the transmission of both CM and EDR \\ndata is encouraged or even required. For either CM or EDR applications, system \\nperformance can be greatly improved when both the number of packets to be \\ntransmitted as well as the packet size is reduced. This is especially true for \\nhighly dense sensor networks where many nodes detect the same values for the \\nsensed phenomenon. Building on this, this paper focuses on studying and \\nproposing compression techniques to improve the system performance in terms of \\nenergy consumption and reporting latency in both CM and EDR applications. \\nFurthermore, we extend our analysis to hybrid networks where CM and EDR are \\nrequired simultaneously. Specifically, this paper presents a simple aggregation \\ntechnique named smart aggregation (SAG) for the CM applications and an event \\ndriven scheme named compression cluster scheme in spatial correlated region \\n(CC_SCR). The proposed SAG exploits both spatial and temporal correlations \\nwhere CC_SCR exploits the spatial correlation of such networks by data \\ncompression. Rationalizing the developments is attained by simulations that \\ncompare energy efficiency of the proposed SAG with k-hop aggregation and CM based event driven reporting (CMEDR) \\nschemes. Results of CC_SCR show that the technique may reduce the energy \\nconsumption drastically. In some specific cases the reduction becomes more \\nthan 10 times compared to a classical clustering scheme. Two different strategies \\nfor the transmission of event reports through the CM infrastructure are \\nincorporated: PER and NPER protocols. Both strategies take advantage of the \\ncluster-based architecture which assigns a TDMA schedule for the CM data \\ntransmission while using NP/CSMA for the transmission of the event information. \\nConsequently, no extra energy is consumed for separate event clusters. 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Energy-Efficient Methods for Highly Correlated Spatio-Temporal Environments in Wireless Sensor Network Communications
Continuous-monitoring (CM)
of natural phenomenon is one of the major streams of applications in wireless
sensor networks (WSNs), where aggregation and clustering techniques are
beneficial as correlation dominates in both spatial and temporal aspects of
sensed phenomenon. Conversely, in Event Driven Reporting (EDR), the efficient
transmission of sensitive data related to some predefined alarm cases is of
major importance. As such, reporting latency is a more important performance
parameter. However, in some applications, the transmission of both CM and EDR
data is encouraged or even required. For either CM or EDR applications, system
performance can be greatly improved when both the number of packets to be
transmitted as well as the packet size is reduced. This is especially true for
highly dense sensor networks where many nodes detect the same values for the
sensed phenomenon. Building on this, this paper focuses on studying and
proposing compression techniques to improve the system performance in terms of
energy consumption and reporting latency in both CM and EDR applications.
Furthermore, we extend our analysis to hybrid networks where CM and EDR are
required simultaneously. Specifically, this paper presents a simple aggregation
technique named smart aggregation (SAG) for the CM applications and an event
driven scheme named compression cluster scheme in spatial correlated region
(CC_SCR). The proposed SAG exploits both spatial and temporal correlations
where CC_SCR exploits the spatial correlation of such networks by data
compression. Rationalizing the developments is attained by simulations that
compare energy efficiency of the proposed SAG with k-hop aggregation and CM based event driven reporting (CMEDR)
schemes. Results of CC_SCR show that the technique may reduce the energy
consumption drastically. In some specific cases the reduction becomes more
than 10 times compared to a classical clustering scheme. Two different strategies
for the transmission of event reports through the CM infrastructure are
incorporated: PER and NPER protocols. Both strategies take advantage of the
cluster-based architecture which assigns a TDMA schedule for the CM data
transmission while using NP/CSMA for the transmission of the event information.
Consequently, no extra energy is consumed for separate event clusters. As such,
the number of packets to be transmitted is greatly reduced.