Resource Allocation in Wireless Powered IoT Networks

Abstract

In this paper, efficient resource allocation for the uplink transmission of wireless powered IoT networks is investigated. We adopt LoRa technology as an example in the IoT network, but this work is still suitable for other communication technologies. Allocating limited resources, like spectrum and energy resources, among a massive number of users faces critical challenges. We consider grouping wireless powered IoT users into available channels first and then investigate power allocation for users grouped in the same channel to improve the network throughput. Specifically, the user grouping problem is formulated as a many to one matching game. It is achieved by considering IoT users and channels as selfish players which belong to two disjoint sets. Both selfish players focus on maximizing their own utilities. Then we propose an efficient channel allocation algorithm (ECAA) with low complexity for user grouping. Additionally, a Markov Decision Process (MDP) is used to model unpredictable energy arrival and channel conditions uncertainty at each user, and a power allocation algorithm is proposed to maximize the accumulative network throughput over a finite-horizon of time slots. By doing so, we can distribute the channel access and dynamic power allocation local to IoT users. Numerical results demonstrate that our proposed ECAA algorithm achieves near-optimal performance and is superior to random channel assignment, but has much lower computational complexity. Moreover, simulations show that the distributed power allocation policy for each user is obtained with better performance than a centralized offline scheme.

Self-operating transpiration-driven electrokinetic power generator with an artificial hydrological cycle

“…Nonetheless, these devices (maximum power of less than 1 mW) are highly suitable as an energy source for the vast number of Internet of things (IoT) devices placed in various locations. 7,8 Desirably, the allocation of these devices should not be restricted by place, and a continuous water supply should be guaranteed for their practical use.…”

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

“…Liu et al [70] studied the problem of allocating resources efficiently on IoT that supplies wireless power. In this method, users are first grouped into accessible channels.…”

“…Resource management A distributed cloud network framework [66] Replaces the centralized architecture with a distributed cloud architecture, solves the defects of the centralized cloud architecture, and brings people better experience A MILP model [68] Assigns the requirement of services' resource to heterogeneous network equipment interface A framework for communication used in 5G [69] Transforms the resource allocation problem into a power and channel allocation problem, minimizes the total energy consumption, and improves QoS levels A low complexity channel allocation algorithm [70] Improves throughput of the network and allocates finite resources to a large group of users A resource allocation framework with several bands under cognitive 5G IoT [71] Manages resources more flexibly and reduces energy consumption than common single-band approach A protocol which is distributed and optimal to allocate resources [72] Has excellent adaptability when changing topology of network and dynamically manages resources in the heterogeneous IoT environment A multilevel allocating resources algorithm for IoT communication [73] Has fast data processing rate and very low latency in both saturated and nonsaturated environment…”

The Application of Internet of Things in Metering the Consumption of Utilities in the Czech Republic

“…Except application areas we must mention, that application of IoT is closely connected with other negative factors, for example IoT security challenges (Jain and Singh, 2019;Nouichi et al, 2019;Moin et al, 2019), data security (Sittón-Candanedo, 2019), real-time data analytics (Fernández et al, 2019), energy consumption of IoT devices (Gunturi and Reddy, 2018;Liu et al, 2019), service innovation (Lee, Kao and Yang, 2014).…”