The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management

Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

A survey of network lifetime maximization techniques in wireless sensor networks

Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteri

Energy Harvesting Wireless Communications: A Review of Recent Advances

This article summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed as well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications (Special Issue: Wireless Communications Powered by Energy Harvesting and Wireless Energy Transfer

Smart cities: potential and challenges

This paper aims to discuss a few fundamental questions related to the smart city paradigm, such as “what is actually a smart city?”, “what can we expect from a smart city?”, and “which problems have to be addressed and solved in order to turn a standard (dumb) city into a smart one?” Starting from a discussion of the Smart City concept, we will illustrate some of the most popular smart services using the results of proof-of-concept experiments carried out in different cities around the world. Successively, we will describe the fundamental functions required to build a smart service and the corresponding enabling technologies. We will then describe the main research challenges that need to be addressed in order to fulfill the Smart City vision, and we will conclude with some final remarks and considerations about the possible evolution of the Smart City concept

Energy-aware Gossip Protocol for Wireless Sensor Networks

Dissertação de mestrado em Engenharia InformáticaIn Wireless Sensor Networks (WSNs), typically composed of nodes with resource constraints, leveraging efficient processes is crucial to enhance the network longevity and consequently the sustainability in ultra-dense and heterogeneous environments, such as smart cities. Epidemic algorithms are usually efficient in delivering packets to a sink or to all it’s peers but have poor energy efficiency due to the amount of packet redundancy. Directional algorithms, such as Minimum Cost Forward Algorithm (MCFA) or Directed Diffusion, yield high energy efficiency but fail to handle mobile environments, and have poor network coverage. This work proposes a new epidemic algorithm that uses the current energy state of the network to create a topology that is cyclically updated, fault tolerant, whilst being able to handle the challenges of a static or mobile heterogeneous network. Depending on the application, tuning in the protocol settings can be made to prioritise desired characteristics. The proposed protocol has a small computational footprint and the required memory is proportional not to the size of the network, but to the number of neighbours of a node, enabling high scalability. The proposed protocol was tested, using a ESP8266 as an energy model reference, in a simulated environment with ad-hoc wireless nodes. It was implemented at the application level with UDP sockets, and resulted in a highly energy efficient protocol, capable of leveraging extended network longevity with different static or mobile topologies, with results comparable to a static directional algorithm in delivery efficiency.Em Redes de Sensores sem Fios (RSF), tipicamente compostas por nós com recursos lim-itados, alavancar processos eficientes é crucial para aumentar o tempo de vida da rede e consequentemente a sustentabilidade em ambientes heterogéneos e ultra densos, como cidades inteligentes por exemplo. Algoritmos epidêmicos são geralmente eficientes em en-tregar pacotes para um sink ou para todos os nós da rede, no entanto têm baixa eficiência energética devido a alta taxa de duplicação de pacotes. Algoritmos direcionais, como o MCFA ou de Difusão Direta, rendem alta eficiência energética mas não conseguem lidar com ambientes móveis, e alcançam baixa cobertura da rede. Este trabalho propõe um novo protocolo epidêmico que faz uso do estado energético atual da rede para criar uma topologia que por sua vez atualizada ciclicamente, tolerante a falhas, ao mesmo tempo que é capaz de lidar com os desafios de uma rede heterogênea estática ou móvel. A depender da aplicação, ajustes podem ser feitos às configurações do protocolo para que o mesmo priorize determinadas características. O protocolo proposto tem um pequeno impacto computacional e a memória requerida é proporcional somente à quantidade de vizinhos do nó, não ao tamanho da rede inteira, permitindo assim alta escalabilidade. O algoritmo proposto foi testado fazendo uso do modelo energético de uma ESP8266, em um ambiente simulado com uma rede sem fios ad-hoc. Foi implementado à nível aplicacional com sockets UDP, e resultou em um protocol energeticamente eficiente, capaz de disponibilizar alta longevidade da rede mesmo com diferentes topologias estáticas ou móveis com resultados comparáveis à um protocolo direcional em termos de eficiência na entrega de pacotes

Utilization of Internet of Things and wireless sensor networks for sustainable smallholder agriculture

Agriculture is the economy’s backbone for most developing countries. Most of these countries suffer from insufficient agricultural production. The availability of real-time, reliable and farm-specific information may significantly contribute to more sufficient and sustained production. Typically, such information is usually fragmented and often does fit one-on-one with the farm or farm plot. Automated, precise and affordable data collection and dissemination tools are vital to bring such information to these levels. The tools must address details of spatial and temporal variability. The Internet of Things (IoT) and wireless sensor networks (WSNs) are useful technology in this respect. This paper investigates the usability of IoT and WSN for smallholder agriculture applications. An in-depth qualitative and quantitative analysis of relevant work over the past decade was conducted. We explore the type and purpose of agricultural parameters, study and describe available resources, needed skills and technological requirements that allow sustained deployment of IoT and WSN technology. Our findings reveal significant gaps in utilization of the technology in the context of smallholder farm practices caused by social, economic, infrastructural and technological barriers. We also identify a significant future opportunity to design and implement affordable and reliable data acquisition tools and frameworks, with a possible integration of citizen science