Towards Energy-Fairness in LoRa Networks

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordLoRa has become one of the most promising networking technologies for Internet-of-Things applications. Distant end devices have to use a low data rate to reach a LoRa gateway, causing long in-the-air transmission time and high energy consumption. Compared with the end devices using high data rates, they will drain the batteries much earlier and the network may be broken early. Such an energy unfairness can be mitigated by deploying more gateways. However, with more gateways, more end devices may choose small spreading factors to reach closer gateways, increasing the collision probability. In this paper, we propose a networking solution for LoRa networks, EF-LoRa, that can achieve energy fairness among end devices by carefully allocating network resources, including frequency channels, spreading factors and transmission power. We develop a LoRa network model to study the energy consumption of the end devices, considering the unique features of LoRa networks such as LoRaWAN MAC protocol and the capacity limitation of a gateway. We formulate the energy fairness allocation as an optimization problem, and propose a greedy allocation algorithm to achieve max-min fairness of energy efficiency. Simulation results show that EF-LoRa can improve the energy fairness of the state-of-the-art works by 177.8%

A Survey on LoRaWAN Technology: Recent Trends, Opportunities, Simulation Tools and Future Directions

Low-power wide-area network (LPWAN) technologies play a pivotal role in IoT applications, owing to their capability to meet the key IoT requirements (e.g., long range, low cost, small data volumes, massive device number, and low energy consumption). Between all obtainable LPWAN technologies, long-range wide-area network (LoRaWAN) technology has attracted much interest from both industry and academia due to networking autonomous architecture and an open standard specification. This paper presents a comparative review of five selected driving LPWAN technologies, including NB-IoT, SigFox, Telensa, Ingenu (RPMA), and LoRa/LoRaWAN. The comparison shows that LoRa/LoRaWAN and SigFox surpass other technologies in terms of device lifetime, network capacity, adaptive data rate, and cost. In contrast, NB-IoT technology excels in latency and quality of service. Furthermore, we present a technical overview of LoRa/LoRaWAN technology by considering its main features, opportunities, and open issues. We also compare the most important simulation tools for investigating and analyzing LoRa/LoRaWAN network performance that has been developed recently. Then, we introduce a comparative evaluation of LoRa simulators to highlight their features. Furthermore, we classify the recent efforts to improve LoRa/LoRaWAN performance in terms of energy consumption, pure data extraction rate, network scalability, network coverage, quality of service, and security. Finally, although we focus more on LoRa/LoRaWAN issues and solutions, we introduce guidance and directions for future research on LPWAN technologies

On the Efficacy of Particle Swarm Optimization for Gateway Placement in LoRaWAN Networks

The efficacy of the Particle Swarm Optimization (PSO) in determining the optimal locations for gateways in LoRaWAN networks is investigated. A modified PSO approach, which introduces gateway distancing measures during the initialization phase and flight time, is proposed. For the ease of comparisons and the understanding of the behavior of the algorithms under study, a square LoRaWAN area is used for simulations. Optimization results on a LoRaWAN script, implemented in NS-3, show that the modified PSO converges faster and achieves better results than the traditional PSO, as the number of gateways increases. Results further show that the modified PSO approach achieves similar performance to a deterministic approach, in which gateways are uniformly distributed in the network. This shows that for swarm intelligence techniques such as PSO to be used for gateway placement in LoRaWAN networks, gateway distancing mechanisms must be incorporated in the optimization process. These results further show that these techniques can be easily deployed in geometrically more complex LoRaWAN figures such as rectangular, triangular, circular and trapezoidal shapes. It is generally difficult to figure out a deterministic gateway placement mechanism for such shapes. As part of future work, more realistic LoRaWAN networks will be developed by using real geographical information of an area

Designing Intelligent Energy Efficient Scheduling Algorithm To Support Massive IoT Communication In LoRa Networks

We are about to enter a new world with sixth sense ability – “Network as a sensor -6G”. The driving force behind digital sensing abilities is IoT. Due to their capacity to work in high frequency, 6G devices have voracious energy demand. Hence there is a growing need to work on green solutions to support the underlying 6G network by making it more energy efficient. Low cost, low energy, and long-range communication capability make LoRa the most adopted and promising network for IoT devices. Since LoRaWAN uses ALOHA for multi-access of channels, collision management is an important task. Moreover, in massive IoT, due to the increased number of devices and their Adhoc transmissions, collision becomes and concern. Furthermore, in long-range communication, such as in forests, agriculture, and remote locations, the IoT devices need to be powered using a battery and cannot be attached to an energy grid. LoRaWAN originally has a star network wherein IoT devices communicated to a single gateway. Massive IoT causes increased traffic at a single gateway. To address Massive IoT issues of collision and gateway load handling, we have designed a reinforcement learning-based scheduling algorithm, a Deep Deterministic policy gradient algorithm with channel activity detection (CAD) to optimize the energy efficiency of LoRaWAN in cross-layer architecture in massive IoT with star topology. We also design a CAD-based simulator for evaluating any algorithms with channel sensing. We compare energy efficiency, packet delivery ratio, latency, and signal strength with existing state of art algorithms and prove that our proposed solution is efficient for massive IoT LoRaWAN with star topology

An Orthogonal Air Pollution Monitoring Method (OAPM) Based on LoRaWAN

International audienceHigh accuracy air pollution monitoring in a smart city requires the deployment of a huge number of sensors in this city. One of the most appropriate wireless technologies expected to support high density deployment is LoRaWAN which belongs to the Low Power Wide Area Network (LPWAN) family and offers long communication range, multi-year battery lifetime and low cost end devices. It has been designed for End Devices (EDs) and applications that need to send small amounts of data a few times per hour. However, a high number of end devices breaks the orthogonality of LoRaWAN transmissions, which was one of the main advantages of LoRaWAN. Hence, network performances are strongly impacted. To solve this problem, we propose a solution called OAPM (Orthogonal Air Pollution Monitoring) which ensures the orthogonality of LoRaWAN transmissions and provides accurate air pollution monitoring. In this paper, we show how to organize EDs into clusters and sub-clusters, assign transmission times to EDs, configurate and synchronize them, taking into account the specificities of LoRaWAN and the features of the air pollution monitoring application. Simulation results corroborate the very good behavior of OAPM

An Optimized IoT Architecture based on Fog Computing with a new Method of Data Transfer Control

Over the years, distributed and grid computing paradigms have evolved to cloud computing, which has become a common approach applied in the Internet of Things (IoT). The growing popularity of the cloud computing paradigm lies mainly in the simple management of end devices, uniform access to many services, elasticity of available resources and cost savings. In addition to these advantages, the expansion of IoT devices and the demand for speed and data volume have provided an opportunity for the emergence of new computing paradigms. The fog computing paradigm brings data processing nearer to the end devices while preserving the cloud connection, leading to lower latency, higher efficiency and location awareness. The overall aim of the dissertation is the design and implementation of an optimised IoT network architecture which adopts the fog computing paradigm. To eliminate the need to build completely new infrastructure, the optimised network architecture is based on LoRaWAN, which has already been deployed at many locations and offers long-distance communication with low-power consumption. This raises several challenges which need to be overcome. For correct functioning of the fog computing paradigm, it was necessary to explore a new method of controlling the data transfer between IoT gateways and the cloud service. The methods explored in this dissertation are both static (based on predefined values) and dynamic (based on machine learning).V průběhu let se výpočetní modely vyvíjely od distribuovaných a gridových ke cloud computingu, který se stal nejčastěji používaným přístupem v oblasti Internetu věcí. Rostoucí popularita cloud computingu spočívá především v jednoduché správě koncových uzlů, jednotném přístupu k velkému počtu služeb, elasticitě dostupných zdrojů a šetření jednotlivých nákladů. Přes všechny své přínosy však narůstající počet připojených zařízení a nároků na rychlost dávají příležitost vzniku nových výpočetních modelů. Fog computing model přenáší výpočetní výkon blíže ke koncovým zařízením při zachování spojení s cloudem, což vede ke snížení latence, zvýšení efektivity a umožnění reagovat na základě aktuálních podmínek. Výsledným cílem této disertační práce je návrh a implementace optimalizované síťové IoT architektury s podporou pro fog computing. Pro eliminaci nutnosti budovat kompletně novou infrastrukturu počítá výsledné optimalizované řešení s integrací do LoRaWAN, která je již nasazena na mnoha místech a nabízí komunikaci na velké vzdálenosti při nízké spotřebě energie. Tato integrace však přináší několik úskalí, jež je potřeba překonat. K dosažení správné funkčnosti fog computingu bylo potřeba provést výzkum metody pro řízení přenosu dat mezi síťovou bránou a cloud službou. Zkoumané metody jsou jak statické (založené na předdefinovaných hodnotách), tak dynamické (využívající strojového učení).440 - Katedra telekomunikační technikyvyhově