Wireless Sensor Networks for Fire Detection and Control

Due to current technological progress, the manufacturing of tiny and low price sensors became technically and economically feasible. Sensors can measure physical surroundings related to the environment and convert them into an electric signal. A huge quantity of these disposable sensors is networked to detect and monitor fire. This paper provides an analysis of utilisation of wireless sensor networks for fire detection and control

Agilla: A Mobile Agent Middleware for Sensor Networks

Agilla is a mobile agent middleware for sensor networks. Mobile agents are special processes that can migrate across sensors. They increase network flexibility by enabling active in-network reprogramming. Neighbor lists and tuple spaces are used for agent coordination. Agilla was originally implemented on Mica2 motes, but has been ported to other platforms. Its Mica2 implementation consumes 41.6KB of code and 3.59KB of data memory. Agents can move five hops in less than 1.1s with over 92% success. Agilla was used to develop multiple applications related to fire detection and tracking, cargo container monitoring, and robot navigation

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

The design and implementation of fuzzy query processing on sensor networks

Sensor nodes and Wireless Sensor Networks (WSN) enable observation of the physical world in unprecedented levels of granularity. A growing number of environmental monitoring applications are being designed to leverage data collection features of WSN, increasing the need for efficient data management techniques and for comparative analysis of various data management techniques. My research leverages aspects of fuzzy database, specifically fuzzy data representation and fuzzy or flexible queries to improve upon the efficiency of existing data management techniques by exploiting the inherent uncertainty of the data collected by WSN. Herein I present my research contributions. I provide classification of WSN middleware to illustrate varying approaches to data management for WSN and identify a need to better handle the uncertainty inherent in data collected from physical environments and to take advantage of the imprecision of the data to increase the efficiency of WSN by requiring less information be transmitted to adequately answer queries posed by WSN monitoring applications. In this dissertation, I present a novel approach to querying WSN, in which semantic knowledge about sensor attributes is represented as fuzzy terms. I present an enhanced simulation environment that supports more flexible and realistic analysis by using cellular automata models to separately model the deployed WSN and the underlying physical environment. Simulation experiments are used to evaluate my fuzzy query approach for environmental monitoring applications. My analysis shows that using fuzzy queries improves upon other data management techniques by reducing the amount of data that needs to be collected to accurately satisfy application requests. This reduction in data transmission results in increased battery life within sensors, an important measure of cost and performance for WSN applications

Adaptive Middleware for Resource-Constrained Mobile Ad Hoc and Wireless Sensor Networks

Mobile ad hoc networks: MANETs) and wireless sensor networks: WSNs) are two recently-developed technologies that uniquely function without fixed infrastructure support, and sense at scales, resolutions, and durations previously not possible. While both offer great potential in many applications, developing software for these types of networks is extremely difficult, preventing their wide-spread use. Three primary challenges are: 1) the high level of dynamics within the network in terms of changing wireless links and node hardware configurations,: 2) the wide variety of hardware present in these networks, and: 3) the extremely limited computational and energy resources available. Until now, the burden of handling these issues was put on the software application developer. This dissertation presents three novel programming models and middleware systems that address these challenges: Limone, Agilla, and Servilla. Limone reliably handles high levels of dynamics within MANETs. It does this through lightweight coordination primitives that make minimal assumptions about network connectivity. Agilla enables self-adaptive WSN applications via the integration of mobile agent and tuple space programming models, which is critical given the continuously changing network. It is the first system to successfully demonstrate the feasibility of using mobile agents and tuple spaces within WSNs. Servilla addresses the challenges that arise from WSN hardware heterogeneity using principles of Service-Oriented Computing: SOC). It is the first system to successfully implement the entire SOC model within WSNs and uniquely tailors it to the WSN domain by making it energy-aware and adaptive. The efficacies of the above three systems are demonstrated through implementation, micro-benchmarks, and the evaluation of several real-world applications including Universal Remote, Fire Detection and Tracking, Structural Health Monitoring, and Medical Patient Monitoring

Mobile sensor networks for environmental monitoring

Vulnerability to natural disasters and the human pressure on natural resources have increased the need for environmental monitoring. Proper decisions, based on real-time information gathered from the environment, are critical to protecting human lives and natural resources. To this end, mobile sensor networks, such as wireless sensor networks, are promising sensing systems for flexible and autonomous gathering of such information. Mobile sensor networks consist of geographically deployed sensors very close to a phenomenon of interest. The sensors are autonomous, self-configured, small, lightweight and low powered, and they become mobile when they are attached to mobile objects such as robots, people or bikes. Research on mobile sensor networks has focused primarily on using sensor mobility to reduce the main sensor network limitations in terms of network topology, connectivity and energy conservation. However, how sensor mobility could improve environmental monitoring still remains largely unexplored. Addressing this requires the consideration of two main mobility aspects: sampling and mobility constraints. Sampling is about where mobile sensors should be moved, while mobility constraints are about how such movements should be handled, considering the context in which the monitoring is carried out. This thesis explores approaches for sensor mobility within a wireless sensor network for use in environmental monitoring. To achieve this goal, four sub-objectives were defined: Explore the use of metadata to describe the dynamic status of sensor networks. Develop a mobility constraint model to infer mobile sensor behaviour. Develop a method to adapt spatial sampling using mobile, constrained sensors. Extend the developed adaptive sampling method to monitoring highly dynamic environmental phenomena. Chapter 2 explores the use of metadata to describe the dynamic status of sensor networks. A context model was proposed to describe the general situation in which a sensor network is monitoring. The model consists of four types of contexts: sensor, network, sensing and organisation, where each of the contexts describes the sensor network from a different perspective. Metadata, which are descriptors of observed data, sensor configurations and functionalities, are used as parameters to describe what is happening in the different contexts. The results reveal that metadata are suitable for describing sensor network status within different contexts and reporting the status back to other components, systems or users. Chapter 3 develops a model which describes mobility constraints for inferring mobile sensor behaviour. The proposed mobility constraint model consists of three components: first, the context typology proposed in Chapter 2 to describe mobility constraints within the different contexts; second, a context graph, modelled as a Bayesian network, to encode dependencies of mobility constraints within the same or different contexts, as well as among mobility constraints and sensor behaviour; and third, contextual rules to encode how dependent mobility constraints are expected to constrain sensor behaviour. Metadata values for the monitored phenomenon and sensor properties are used to feed the context graph. They are propagated through the graph structure, and the contextual rules are used to infer the most suitable behaviour. The model was used to simulate the behaviour of a mobile sensor network to obtain a suitable spatial coverage in low and high fire risk scenarios. It was shown that the mobility constraint model successfully inferred behaviour, such as sleeping sensors, moving sensors and deploying more sensors to enhance spatial coverage. Chapter 4 develops a spatial sampling strategy for use with mobile, constrained sensors according to the expected value of information (EVoI) and mobility constraints. EVoI allows decisions to be made about the location to observe. It minimises the expected costs of wrong predictions about a phenomenon using a spatially aggregated EVoI criterion. Mobility constraints allow decisions to be made about which sensor to move. A cost-distance criterion is used to minimise unwanted effects of sensor mobility on the sensor network itself, such as energy depletion. The method was assessed by comparing it with a random selection of sample locations combined with sensor selection based on a minimum Euclidian distance criterion. The results demonstrate that EVoI enables selection of the most informative locations, while mobility constraints provide the needed context for sensor selection. Chapter 5 extends the method developed in Chapter 4 for the case of highly dynamic phenomena. It develops a method for deciding when and where to sample a dynamic phenomenon using mobile sensors. The optimisation criterion is to maximise the EVoI from a new sensor deployment at each time step. The method was demonstrated in a scenario in which a simulated fire in a chemical factory released polluted smoke into the open air. The plume varied in space and time because of variations in atmospheric conditions and could be only partially predicted by a deterministic dispersion model. In-situ observations acquired by mobile sensors were considered to improve predictions. A comparison with random sensor movements and the previous sensor deployment without performing sensor movements shows that the optimised sensor mobility successfully reduced risk caused by poor model predictions. Chapter 6 synthesises the main findings and presents my reflections on the implications of such findings. Mobile sensors for environmental monitoring are relevant to improving monitoring by selecting sampling locations that deliver the information that most improves the quality of decisions for protecting human lives and natural resources. Mobility constraints are relevant to managing sensor mobility within sampling strategies. The traditional consideration of mobility constraints within the field of computer sciences mainly leads to sensor self-protection rather than to the protection of human beings and natural resources. By contrast, when used for environmental monitoring, mobile sensors should above all improve monitoring performance, even thought this might produce negative effects on coverage, connectivity or energy consumption. Thus, mobility constraints are useful for reducing such negative effects without constraining the sampling strategy. Although sensor networks are now a mature technology, they are not yet widely used by surveyors and environmental scientists. The operational use of sensor networks in geo-information and environmental sciences therefore needs to be further stimulated. Although this thesis focuses on wireless sensor network, other types of informal sensor networks could be also relevant for environmental monitoring, such as smart phones, volunteer citizens and sensor web. Finally, the following recommendations are given for further research: extend the sampling strategy for dynamic phenomena to take account of mobility constraints; develop sampling strategies that take a decentralised approach; focus on mobility constraints related to connectivity and data transmission; elicit expert knowledge to reveal preferences for sensor mobility under mobility constraints within different types of environmental applications; and validate the proposed strategies in operational implementations. </p

Forests

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms |, |, |, |, |, |, and |. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.U01 OH010841/OH/NIOSH CDC HHSUnited States/U54 OH007544/OH/NIOSH CDC HHSUnited States

Wireless Sensor Networks

The aim of this book is to present few important issues of WSNs, from the application, design and technology points of view. The book highlights power efficient design issues related to wireless sensor networks, the existing WSN applications, and discusses the research efforts being undertaken in this field which put the reader in good pace to be able to understand more advanced research and make a contribution in this field for themselves. It is believed that this book serves as a comprehensive reference for graduate and undergraduate senior students who seek to learn latest development in wireless sensor networks

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society