Joint Resource Allocation Optimization of Wireless Sensor Network Based on Edge Computing

Abstract

Resource allocation has always been a key technology in wireless sensor networks (WSN), but most of the traditional resource allocation algorithms are based on single interface networks. The emergence and development of multi-interface and multichannel networks solve many bottleneck problems of single interface and single channel networks, it also brings new opportunities to the development of wireless sensor networks, but the multi-interface and multichannel technology not only improves the performance of wireless sensor networks but also brings great challenges to the resource allocation of wireless sensor networks. Edge computing changes the traditional centralized cloud computing processing method into a method that reduces computing storage capacity to the edge of the network and faces users and terminals. Realize the advantages of lower latency, higher bandwidth, and fast response. Therefore, this paper proposes a joint optimization algorithm of resource allocation based on edge computing. We establish a wireless sensor allocation model and then propose our algorithm model combined with the advantages of edge computing. Compared with the traditional allocation algorithm (PCOA, MCMH, and TDMA), it can further improve the resource utilization, reduce the network energy consumption, increase network capacity, and reduce the complexity of the schemes.

Resource Allocation Approach for Optimal Routing in IoT Wireless Mesh Networks

“…After analyzing the available exact modifications and methods to optimize the mesh network [23][24][25][26][27][28][29][30][31][32][33][34][35], as well as, other types of resource allocation such as bandwidth allocation [44][45][46][47][48][49], throughput optimization [50][51][52][53][54][55] and other different network resources allocations [56][57][58][59][60][61], it can be noted that in fact the mathematical model proposed in the article [32] is optimal for mesh networks using WiMAX technology. A characteristic feature of WiMAX technological processes is considered to be a mechanism of admission to the medium with a limited number of channels and their fixed bands, which in no way allows this system to be adapted to mesh networks with different admission technologies to the transmission medium, methods of allocating channels with a random number of channels, as well as their frequency band.…”

The Optimization Path of Higher Education Resource Allocation in China Based on Fuzzy Set Theory

“…The optimal allocation of higher education resources in China refers to the integration and rational arrangement of educational resources in the area to be determined according to the principles of sustainability and resource sharing to achieve maximum benefits [1][2][3][4]. The priority allocation of educational resources is a condition and prerequisite for the development of education; without sufficient and compliant educational resources, the universalization of education and the improvement of educational standards cannot be discussed [5][6][7][8][9]. The allocation of educational resources reflects the degree of education adapting to social development in a certain period and also reflects the level of efficiency and fairness of education.…”

Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme

“…The centralized hosts within the cloud datacenters can act as an agent between the original content providers and mobile devices, enabling resources sharing in remote datacenters [10]. Hence, the integration of cloud computing and wireless mobile environment allows running computationintensive applications over resource-constrained mobile devices to execute computational intensive tasks [11][12][13]. This is called the mobile cloud computing (MCC).…”

“…According to the researchers, simulation results have shown significant improvement compared with the benchmarked algorithms. Consequently, on virtue of limited performance when allocating better resources across network nodes, a joint optimization algorithm that improved resource utilization, reduced energy consumption, increase network capacity was put forward by [11]. Simulation results show the developed technique can be better compared to the benchmarked schemes.…”