With the construction of smart grids, smart meters are gradually being installed in every house. In order to transfer the user data collected by smart meters to the control center, it is necessary to transfer the data to the data aggregation point (DAP) before being transmitted to the control center. The numbers and locations of DAPs affect the communication quality and cost of the smart meter neighborhood network, and because smart meters rely on wireless technology to transmit data, their transmission range is limited. Thus, suburban and rural areas require a large number of DAP installation needs, and it is very important to reduce their numbers. For this problem, this study proposes a grid-based relay DAP placement scheme and presents the corresponding algorithms to reduce the number of DAPs and to avoid the large impact of relay DAP locations on communication quality for the two cases of whether or not the number of relay DAPs is limited. This paper used random smart meter coordinates for testing, and the test results verify that the proposed solution can in fact significantly reduce the number of DAPs and avoid the large impact of relay DAP locations on communication quality.
GhasempourA., Internet of Things in Smart Grid: Architecture, Applications, Services, Key Technologies, and Challenges, Inventions4(1) (2019), 1–12.
2.
FarhangiH., The path of the smart grid, IEEE Power and Energy Magazine8(1) (2010), 18–28.
3.
Ullah KakakhelS.R., KondoroA., WesterlundT., Ben DhaouI. and PlosilaJ., Enhancing Smart Grids via Advanced Metering Infrastructure and Fog Computing Fusion, in Proc. of IEEE 6th World Forum on Internet of Things, New Orleans, LA, USA, (2020), 1–6.
4.
GhosalA. and ContiM., Key Management Systems for Smart Grid Advanced Metering Infrastructure: A Survey, IEEE Communications Surveys & Tutorials21(3) (2019), 2831–2848.
5.
LiH., GongS., LaiL., HanZ., QiuR.C. and YangD., Efficient and Secure Wireless Communications for Advanced Metering Infrastructure in Smart Grids, IEEE Transactions on Smart Grid3(3) (2012), 1540–1551.
6.
SaputroN. and AkkayaK., Investigation of Smart Meter Data Reporting Strategies for Optimized Performance in Smart Grid AMI Networks, IEEE Internet of Things Journal4(4) (2017), 894–904.
7.
RandhawaS. and JainS., Data Aggregation in Wireless Sensor Networks: Previous Research, Current Status and Future Directions, Wireless Personal Communications97 (2017), 3355–3425.
8.
DepuruS.S.S.R., WangL. and DevabhaktuniV., Smart meters for power grid: Challenges, issues, advantages and status, Renewable and Sustainable Energy Reviews15(6) (2011), 2736–2742.
9.
KongP.Y., Effects of Communication Network Performance on Dynamic Pricing in Smart Power Grid, IEEE Systems Journal8(2) (2014), 533–541.
10.
GungorV.C., SahinD., KocakT., ErgutS., BuccellaC., CecatiC. and HanckeG.P., A Survey on Smart Grid Potential Applications and Communication Requirements, IEEE Transactions on Industrial Informatics9(1) (2013), 28–42.
11.
YanY., QianY., SharifH. and TipperD., A Survey on Smart Grid Communication Infrastructures: Motivations, Requirements and Challenges, IEEE Communications Surveys & Tutorials15(1) (2013), 5–20.
12.
AalamifarF., ShiraziG.N., NooriM. and LampeL., Cost-Efficient Data Aggregation Point Placement for Advanced Metering Infrastructure, in Proc. IEEE Conference on Smart Grid Communications, Venice, Italy, (2014), 344–349.
13.
KongP., Cost Efficient Data Aggregation Point Placement with Interdependent Communication and Power Networks in Smart Grid, IEEE Transactions on Smart Grid10(1) (2019), 74–83.
14.
HassanA., PuL., LuoY., WangG. and ZhaoT., Data Aggregation Point Placement in Energy Harvesting Powered Smart Meter Networks, in Proc. of the 11th International Conference on Modelling, Identification and Control, Tianjin, China, (2020), 831–841.
15.
LiY., WangT. and WangS., Cost-efficient approximation algorithm for aggregation points planning in smart grid communications, Wireless Networks26(1) (2020), 521–530.
16.
KongP., Optimal Configuration of Interdependence Between Communication Network and Power Grid, IEEE Transactions on Industrial Informatics15(7) (2019), 4054–4065.
17.
SultanaA., BardalaiA. and SarmaK.K., Wireless Sensor Network Based Smart Grid Supported by a Cognitively Driven Load Management Decision Making, Neural Processing Letters52 (2020), 663–678.
18.
RolimG., PassosD., MoraesI. and AlbuquerqueC., Modelling the Data Aggregator Positioning Problem in Smart Grids. in Proc. IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Liverpool, UK, (2015), 632–639.
19.
RolimG., PassosD., AlbuquerqueC., MoraesI., CarranoR., SousaC., BettiolA., PassosL., HommaR., AndradeR. and MolinaF., Scalability evaluation of the data aggregator positioning problem in smart grids, in Proc. IEEE PES Transmission & Distribution Conference and Exposition-Latin America, Morelia, Mexico, (2016), 1–6.
20.
RolimG., PassosD., AlbuquerqueC. and MoraesI., MOSKOU: A Heuristic for Data Aggregator Positioning in Smart Grids, IEEE Transactions on Smart Grid9(6) (2018), 6206–6213.
21.
WangG., ZhaoY., HuangJ. and WinterR.M., On the Data Aggregation Point Placement in Smart Meter Networks, in Proc. International Conference on Computer Communication and Networks, Vancouver, BC, Canada, (2017), 1–6.
22.
WangG., ZhaoY., YingY., HuangJ. and WinterR.M., Data Aggregation Point Placement Problem in Neighborhood Area Networks of Smart Grid, Mobile Networks and Applications23 (2018), 696–708.
23.
SungT.W., HuX. and OuH., Review of Several Address Assignment Mechanisms for Distributed Smart Meter Deployment in Smart Grid, Advances in Intelligent Systems and Computing1261 (2020), 171–178.
24.
AiniA. and SalehipourA., Speeding up the Floyd–Warshall algorithm for the cycled shortest path problem, Applied Mathematics Letters25(1) (2012), 1–5.
25.
ZhangJ.K. and TaoD., Partition Connectivity Recovery Based on Relay Node Deployment for Wireless Sensor Networks, Journal of Computers29(6) (2018), 193–200.
26.
TisseliK., BenzaidC., LaslaN. and BadacheN., Connectivity-aware Relay Node Deployment in Grid-based Wireless Sensor Networks, in Proc. Wireless Days, Manchester, UK, (2019), 1–4.
27.
KumarR. and AmgothT., Adaptive cluster-based relay-node placement for disjoint wireless sensor networks, Wireless Networks26 (2020), 651–666.
28.
PanJ.S., KongL., SungT.W., TsaiP.W. and VaclavS., Alpha-Fraction First Strategy for Hierarchical Model in Wireless Sensor Networks, Journal of Internet Technology19(6) (2018), 1717–1726.
29.
XuY., ZhengX. and LiR., A Grid-based Unequal Clustering Algorithm for the Multi-hop Routing in WSN, in Proc. International Conference on Computer Science and Service System, Bangkok, Thailand, (2014), 728–731.
30.
SungT.W. and YangC.S., A Cell-based Sensor Deployment Strategy with Improved Coverage for Mobility-Assisted Hybrid Wireless Sensor Networks, International Journal of Ad Hoc and Ubiquitous Computing5(3) (2010), 189–198.
31.
XuY., SungT.W. and ChangK.C., A Grid-Based DAP Placement Approach for Smart Grids, Advances in Intelligent Systems and Computing1339 (2021), 538–547.
32.
PanJ.S., KongL., SungT.W., TsaiP.W. and VaclavS., A Clustering Scheme for Wireless Sensor Networks based on Genetic Algorithm and Dominating Set, Journal of Internet Technology19(4) (2018), 1111–1118.
