Not All Wireless Sensor Networks Are Created Equal: A Comparative Study On Tunnels

Wireless sensor networks (WSNs) are envisioned for a number of application scenarios. Nevertheless, the few in-the-field experiences typically focus on the features of a specific system, and rarely report about the characteristics of the target environment, especially w.r.t. the behavior and performance of low-power wireless communication. The TRITon project, funded by our local administration, aims to improve safety and reduce maintenance costs of road tunnels, using a WSN-based control infrastructure. The access to real tunnels within TRITon gives us the opportunity to experimentally assess the peculiarities of this environment, hitherto not investigated in the WSN field. We report about three deployments: i) an operational road tunnel, enabling us to assess the impact of vehicular traffic; ii) a non-operational tunnel, providing insights into analogous scenarios (e.g., underground mines) without vehicles; iii) a vineyard, serving as a baseline representative of the existing literature. Our setup, replicated in each deployment, uses mainstream WSN hardware, and popular MAC and routing protocols. We analyze and compare the deployments w.r.t. reliability, stability, and asymmetry of links, the accuracy of link quality estimators, and the impact of these aspects on MAC and routing layers. Our analysis shows that a number of criteria commonly used in the design of WSN protocols do not hold in tunnels. Therefore, our results are useful for designing networking solutions operating efficiently in similar environments

Traffic-differentiation-based modular QoS localized routing for wireless sensor networks

A new localized quality of service (QoS) routing protocol for wireless sensor networks (WSN) is proposed in this paper. The proposed protocol targets WSN's applications having different types of data traffic. It is based on differentiating QoS requirements according to the data type, which enables to provide several and customized QoS metrics for each traffic category. With each packet, the protocol attempts to fulfill the required data-related QoS metric(s) while considering power efficiency. It is modular and uses geographical information, which eliminates the need of propagating routing information. For link quality estimation, the protocol employs distributed, memory and computation efficient mechanisms. It uses a multisink single-path approach to increase reliability. To our knowledge, this protocol is the first that makes use of the diversity in data traffic while considering latency, reliability, residual energy in sensor nodes, and transmission power between nodes to cast QoS metrics as a multiobjective problem. The proposed protocol can operate with any medium access control (MAC) protocol, provided that it employs an acknowledgment (ACK) mechanism. Extensive simulation study with scenarios of 900 nodes shows the proposed protocol outperforms all comparable state-of-the-art QoS and localized routing protocols. Moreover, the protocol has been implemented on sensor motes and tested in a sensor network testbed

Predicting Types of Failures in Wireless Sensor Networks Using an Adaptive Neuro-fuzzy Inference System

In this paper, Adaptive Neuro-Fuzzy Interference System (ANFIS) technique is used to develop models to predict two conditions commonly found in a Wireless Sensor Network's deployment; these conditions are failure due to (i) poorly deployed environment and (ii) human movements. ANFIS models are trained using parameters obtained from actual ZigBee PRO nodes' Neighbour Table experimented under the influence of associated network challenges. These parameters are Mean RSSI, Standard Deviation RSSI, Average Coefficient of Variation RSSI and Neighbour Table Connectivity. The individual and combined effects of parameters are investigated in-depth. Results showed the mean RSSI is a critical parameter and the combination of mean RSSI, ACV RSSI and NTC produced the best prediction results (~92%) for all ANFIS models

On Designing a Machine Learning Based Wireless Link Quality Classifier

Ensuring a reliable communication in wireless networks strictly depends on the effective estimation of the link quality, which is particularly challenging when propagation environment for radio signals significantly varies. In such environments, intelligent algorithms that can provide robust, resilient and adaptive links are being investigated to complement traditional algorithms in maintaining a reliable communication. In this respect, the data-driven link quality estimation (LQE) using machine learning (ML) algorithms is one of the most promising approaches. In this paper, we provide a quantitative evaluation of design decisions taken at each step involved in developing a ML based wireless LQE on a selected, publicly available dataset. Our study shows that, re-sampling to achieve training class balance and feature engineering have a larger impact on the final performance of the LQE than the selection of the ML method on the selected data.Comment: accepted in PIMRC 2020. arXiv admin note: text overlap with arXiv:1812.0885

Mecanismos de rede para swarms de drones em ambientes de monitorização aquática

With the development of intelligent platforms for environment sensing, drones present themselves as a fundamental resource capable of responding to the widest range of applications. Monitoring aquatic sensing environments is one such application and the communication between them becomes a key aspect for both navigation and sensing tasks. Testing an aquatic environment with a high number of Unmanned Surface Vehicles (USVs) is very costly, requiring a lot of time and resources. Therefore, simulation platforms become elements of great importance . In this dissertation a simulator is developed containing a modular architecture, based on a delay tolerant network, being capable of simulating aquatic environments as similar as possible to real aquatic environments. In addition to the developed simulator, this dissertation presents methods and strategies of cluster formation, allowing the aquatic drones to select, in a distributed way, the gateways of each cluster that will be responsible for forwarding collected data towards the gateway on land. Two gateway selection methods were implemented, one focused on the energy of aquatic drones, and one considering different metrics such as link quality, centrality and energy. The proposed methods were evaluated across several cases and scenarios, with clusters built and changed in a dynamic way, and it was observed that the election of gateways with a method based on several metrics, together with appropriated control strategy, provides a better outcome of the network behaviour throughout the aquatic monitoring tasks.Com o desenvolvimento de plataformas inteligentes que permitem monitorizar vários ambientes, os drones apresentam-se como um recurso fundamental capaz de responder às mais vastas aplicações. A monitorização de meios aquáticos com recurso a drones é uma destas aplicações e a comunicação entre os mesmos torna-se um aspeto fundamental, tanto em tarefas de navegação como em tarefas de sensorização. Testar um ambiente aquático com um elevado número de drones aquáticos é muito caro, requer muito tempo e vários recursos, por isso, plataformas de simulação tornam-se elementos de grande importância. Nesta dissertação é desenvolvido um simulador, com uma arquitetura modular, tendo por base uma rede tolerante a atrasos, sendo capaz de simular ambientes aquáticos o mais semelhante possível a ambientes aquáticos reais. Para além do simulador desenvolvido, esta dissertação propõe métodos e estratégias de formação de clusters de drones, permitindo que os drones aquáticos elejam, de uma forma distribuída, os gateways de cada cluster que serão responsáveis por encaminhar os dados recolhidos pelos drones em direção à estação em terra. Foram implementados dois métodos de eleição de gateway, um focado na energia dos drones aquáticos, e outro capaz de considerar diferentes métricas, tais como a qualidade de ligação, a centralidade e a energia. Os métodos propostos foram avaliados através de vários cenários em que os clusters são construídos e alterados de forma dinâmica, e foi observado que a escolha de gateways com um método baseado em várias métricas, e juntamente com uma estratégia de controlo apropriada, proporciona um melhor comportamento da rede ao longo das tarefas de monitorização aquática.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Reliable load-balancing routing for resource-constrained wireless sensor networks

Wireless sensor networks (WSNs) are energy and resource constrained. Energy limitations make it advantageous to balance radio transmissions across multiple sensor nodes. Thus, load balanced routing is highly desirable and has motivated a significant volume of research. Multihop sensor network architecture can also provide greater coverage, but requires a highly reliable and adaptive routing scheme to accommodate frequent topology changes. Current reliability-oriented protocols degrade energy efficiency and increase network latency. This thesis develops and evaluates a novel solution to provide energy-efficient routing while enhancing packet delivery reliability. This solution, a reliable load-balancing routing (RLBR), makes four contributions in the area of reliability, resiliency and load balancing in support of the primary objective of network lifetime maximisation. The results are captured using real world testbeds as well as simulations. The first contribution uses sensor node emulation, at the instruction cycle level, to characterise the additional processing and computation overhead required by the routing scheme. The second contribution is based on real world testbeds which comprises two different TinyOS-enabled senor platforms under different scenarios. The third contribution extends and evaluates RLBR using large-scale simulations. It is shown that RLBR consumes less energy while reducing topology repair latency and supports various aggregation weights by redistributing packet relaying loads. It also shows a balanced energy usage and a significant lifetime gain. Finally, the forth contribution is a novel variable transmission power control scheme which is created based on the experience gained from prior practical and simulated studies. This power control scheme operates at the data link layer to dynamically reduce unnecessarily high transmission power while maintaining acceptable link reliability

Improved Accurate Localization using PSO and the Weighted Dijkstra Algorithm in Software Defined Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are crucial in various fields, including monitoring the environment, surveillance, and healthcare. They rely on localization services for accurate data transfer and optimal network performance. Traditional WSN techniques struggle to adapt to dynamic environmental changes beyond the intended task scope. A synergy between Software-Defined Networking (SDN) and WSN has been suggested to address this issue. This research paper presents proposed approach for machine learning-based localization in Software Defined Wireless Sensor Networks (SDWSNs) using Particle Swarm Optimization (PSO) technique and the Weighted Dijkstra algorithm. PSO technique is used for clustering, the weighted Dijkstra algorithm (WDA) for finding the shortest path and sending data packets, and machine learning algorithms like AdaBoost and Naïve Bayes for data classification. The effectiveness of the proposed approach is measured using energy consumption, throughput, network lifespan, and packet delivery ratio, outperforming existing models like OEERP, LEACH, DRINA, and BCDCA. The machine learning algorithms' performance is also evaluated, with Naïve Bayes achieving the highest accuracy of 78.24% and AdaBoost 98.90%

Sustainable modular IoT solution for smart cities applications supported by machine learning algorithms

The Internet of Things (IoT) and Smart Cities are nowadays a big trend, but with the proliferation of these systems several challenges start to appear and put in jeopardy the acceptance by the population, mainly in terms of sustainability and environmental issues. This Thesis introduces a new system composed by a modular IoT smart node that is self-configurable and sustainable with the support of machine learning techniques, as well as the research and development to achieve a innovative solution considering data analysis, wireless communications and hardware and software development. For all these, concepts are introduced, research methodologies, tests and results are presented and discussed as well as the development and implementation. The developed research and methodology shows that Random Forest was the best choice for the data analysis in the self-configuration of the hardware and communication systems and that Edge Computing has an advantage in terms of energy efficiency and latency. The autonomous communication system was able to create a 65% more sustainable node, in terms of energy consumption, with only a 13% decrease in quality of service. The modular approach for the smart node presented advantages in the integration, scalability and implementation of smart cities projects when facing traditional implementations, reducing up to 45% the energy consumption of the overall system and 60% of messages exchanged, without compromising the system performance. The deployment of this new system will help Smart Cities, in a worldwide fashion, to decrease their environmental issues and comply with rules and regulations to reduce CO2 emission.A Internet das Coisas (IoT) e as Cidades Inteligentes são hoje uma grande tendência, mas com a rápida evolução destes sistemas são vários os desafios que põem em causa a sua aceitação por parte das populações, maioritariamente devido a problemas ambientais e de sustentabilidade. Esta Tese introduz um novo sistema composto por nós de IoT inteligentes que são auto-configuáveis e sustentáveis suportados por de aprendizagem automática, e o trabalho de investigação e desenvolvimento para se obter uma solução inovadora que considera a análise de dados, comunicações sem fios e o desenvolvimento do hardware e software. Para todos estes, os conceitos chave são introduzidos, as metodologias de investigação, testes e resultados são apresentados e discutidos, bem como todo o desenvolvimento e implementação. Através do trabalho desenvolvido mostra-se que as Árvores Aleatórias são a melhor escolha para análise de dados em termos da autoconfiguração do hardware e sistema de comunicações e que a computação nos nós tem uma vantagem em termos de eficiência energética e latência. O sistema de configuração autónoma de comunicações foi capaz de criar um nós 65% mais sustentável, em termos en- ergéticos, comprometendo apenas em 13% a qualidade do servi ̧co. A solução modular do nó inteligente apresentou vantagens na integração, escalabilidade e implementação de projectos para Cidades Inteligentes quando comparado com soluções tradicionais, reduzindo em 45% o consumo energético e 60% a troca de mensagens, sem comprometer a qualidade do sistema. A implementação deste novo sistema irá ajudar as cidades inteligentes, em todo o mundo, a diminuir os seus problemas ambientais e a cumprir com as normas e regulamentos para reduzir as emissões de CO2