An Efficient Algorithm in Computing Optimal Data Concentrator Unit Location in IEEE 802.15.4g AMI Networks
Keywords:optimal placement algorithm, data concentrator unit, smart grids, IEEE 802.15.4g, AMI
With a view to achieve several goals in the smart grid (SG) such as making the production and delivery of electricity more cost-effective as well as providing consumers with available information which assists them in controlling their cost, the advanced metering infrastructure (AMI) system has been playing a major role to realize such goals. The AMI network, as an essential infrastructure, typically creates a two-way communication network between electricity consumers and the electric service provider for collecting of the big data generated from consumer’s smart meters (SM). Specifically, there is a crucial element called a data concentrator unit (DCU) employed to collect the boundless data from smart meters before disseminating to meter data management system (MDMS) in the AMI systems. Hence, the location of DCU has significantly impacted the quality of service (QoS) of AMI network, in particular the average throughput and delay. This work aims at developing an efficient algorithm in determining the minimum number of DCUs and computing their optimum locations in which smart meters can communicate through good quality wireless links in the AMI network by employing the IEEE 802.15.4g with unslotted CSMA/CA channel access mechanism. Firstly, the optimization algorithm computes the DCU location based on a minimum hop count metric. Nevertheless, it is possible that multiple positions achieving the minimum hop count may be found; therefore, the additional performance metric, i.e. the average throughput and delay, will be utilized to select the ultimately optimal location. In this paper, the maximum throughput with the acceptable averaged delay constraint is proposed by considering the behavior of the AMI meters, which is almost stationary in the AMI network. In our experiment, the algorithm is demonstrated in different scenarios with different densities of SM, including urban, suburban, and rural areas. The simulation results illustrate that the smart meter density and the environment have substantially impacted on a decision for DCU location, and the proposed methodology is significantly effective. Furthermore, the QoS in urban area, i.e. a highly populated area for SM, of the AMI network is better than those in the suburban and rural areas, where the SM density is quite sparse, because multiple available hops and routes created by neighboring meters in the dense area can help improve the average throughput and delay with the minimum hop count.
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