As the new paradigm of data collection enabled by the advancements in wireless technology and digital electronics, small sensing devices have started to be used in everyday life. These devices are capable of sensing, computing, communicating, and forming a wireless sensor network (WSN) which is necessary to provide sensing services and to monitor various conditions. In addition to WSNs, the idea of Internet of Things (IoT) has started to draw more attention. IoT is defined as an interconnection between identifiable devices within the internet for sensing and monitoring processes. This dissertation addresses the development of wireless network and process control for two challenging IoT applications, namely smart agriculture and industrial lyophilization.<div><br><div>Smart agriculture refers to the concept of using modern technology to increase the quantity and quality of agricultural products. This dissertation presents a novel hybrid large-area IoT network by combining the low-power wide-area network (LPWAN) as well as ultra-low-power wireless personal area networks (WPAN) that delivers wide-area coverage while maintaining low-power operation. </div><div><br></div><div>Lyophilization is the process of removing water content from a material with the objective of increasing its stability and, hence, its shelf life. Continuous inline process tracking is imperative to a successful lyophilization process in industrial pharmaceuticals. To address this need, this dissertation presents two wireless sensing systems that are capable of monitoring lyophilization process with detailed design and demonstration<br></div></div>

[pt] Um dos principais desafios em redes de sensores sem fio (RSSF) é reduzir o consumo de energia dos nós sensores. Um método usado para economizar a bateria que alimenta os nós sensores é o duty cycling (DC) do rádio, onde o rádio fica desligado na maior parte do tempo e fica ligado por pouco tempo para verificar se existe alguma mensagem. O DC é usado com frequência em aplicações de monitoramento onde apenas uma mensagem é transmitida depois da leitura do sensor. Geralmente a leitura do sensor só volta a acontecer depois de minutos, logo poucas mensagens são transmitidas por unidade de tempo. Neste trabalho, analisamos o uso da técnica de DC em um contexto diferente, o da disseminação de código, onde várias mensagens são enviadas em um curto espaço de tempo, e que usa mensagens broadcast, ao contrário do monitoramento, que utiliza mensagens unicast. Analisamos dois algoritmos de disseminação específicos, um para um ambiente de máquinas virtuais executando nos motes, onde o código disseminado é um script com tamanho da ordem de bytes, e outro para disseminação de códigos da aplicação inteira, onde o tamanho é bem maior que no caso do script, da ordem de kbytes. O objetivo deste trabalho foi avaliar qual o impacto do DC na latência e quanto de energia foi economizado quando comparado a deixar o rádio ligado o tempo todo, que é como ambos algoritmos funcionam em sua forma original. / [en] One of the key challenges in wireless sensor networks (WSN) is to save energy at motes. One method to save battery is radio duty cycling (DC), which keeps the radio turned off in most of the time and turns the radio on for a short time to verify if any there are any messages. DC is frequently used in monitoring applications where only one message is transmitted after the mote reads its sensor. Usually the mote reads its sensor only once every few minutes, so few unicast messages are transmitted in the network per time unit. This work analyzes the use of the DC method in code dissemination. In this context, multiple broadcast messages are transmitted in a short time. We examined two specific dissemination algorithms, one of them proposed for a virtual machine environment, in which the disseminated code is a small script, and a second one originally proposed for disseminating the code of an entire application, typically much larger than a script. The objective of this study is to evaluate the impact of DC on latency and how much energy was saved when compared to leaving the radio on all the time, which is how both algorithms work in their original form.

Machine-to-Machine technology has been intensively researched recently which is believed to take the role of leading ICT industry development. Wireless Sensor Networks provide solution to integrate numerous numbers of machines who require features include low power, low cost, and flexible, which can be fulfilled by applying Zigbee technique. This thesis devotes an effort into Wireless Sensor Network development that a Multi- Tag System operating on 2.4-GHz wireless network is developed. A theoretical study about ZigBee protocol and its bottom layers IEEE 802.15.4 standard is provided to lay a foundation of the design work. The thesis also includes a practical usage of low cost TI CC2530 Systom-on-Chip, together with the illustration of software development inside the chip. The design work provides GUI Platform for users to register themselves into the network and central monitoring platform to track all users within the area. Both GUI platforms are developed based on VB IDE. The Multi-Tag Access System is suitable for attendee control functionality in big-scale conference, events, and lecture, which is also a prototype expecting more functionality to be added in the future.

Energy consumption in Wireless Sensor Networks (WSN) involving multiple sensor nodes is a crucial parameter in many applications like smart healthcare systems, home automation, environmental monitoring, and industrial use. Hence, an energy-efficient cluster-head (CH) selection strategy is imperative in a WSN to improve network performance. So to balance the harsh conditions in the network with fast changes in the energy dynamics, a novel energy-efficient adaptive fuzzy-based CH selection approach is projected. Extensive simulations exploited various real-time scenarios, such as varying the optimal position of the location of the base station and network energy. Additionally, the results showed an improved performance in the throughput (46%) and energy consumption (66%), which demonstrated the robustness and efficacy of the proposed model for the future designs of WSN applications.

Wireless sensor networks (WSNs) is a virtual layer in the paradigm of the Internet of Things (IoT). It inter-relates information associated with the physical domain to the IoT drove computational systems. WSN provides an ubiquitous access to location, the status of different entities of the environment, and data acquisition for long-term IoT monitoring. Since energy is a major constraint in the design process of a WSN, recent advances have led to project various energy-efficient protocols. Routing of data involves energy expenditure in considerable amount. In recent times, various heuristic clustering protocols have been discussed to solve the purpose. This article is an improvement of the existing stable election protocol (SEP) that implements a threshold-based cluster head (CH) selection for a heterogeneous network. The threshold maintains uniform energy distribution between member and CH nodes. The sensor nodes are also categorized into three different types called normal, intermediate, and advanced depending on the initial energy supply to distribute the network load evenly. The simulation result shows that the proposed scheme outperforms SEP and DEEC protocols with an improvement of 300% in network lifetime and 56% in throughput.

Indiana University-Purdue University Indianapolis (IUPUI) / Today, Wireless Sensor Networks (WSNs) are forming an exciting new area to have dramatic impacts on science and engineering innovations. New WSN-based technologies, such as body sensor networks in medical and health care and environmental monitoring sensor networks, are emerging. Sensor networks are quickly becoming a flexible, inexpensive, and reliable platform to provide solutions for a wide variety of applications in real-world settings. The increase in the proliferation of sensor networks has paralleled the use of more heterogeneous systems in deployment. In this thesis, our work attempts to develop a new network management and data collection framework for heterogeneous wireless sensor networks called as Heterogeneous Wireless Sensor Networks Management System (H-WSNMS), which enables to manage and operate various sensor network systems with unified control and management services and interface. The H-WSNMS framework aims to provide a scheme to manage, query, and interact with sensor network systems. By introducing the concept of Virtual Command Set (VCS), a series of unified application interfaces and Metadata (XML files) across multiple WSNs are designed and implement the scalability and flexibility of the management functions for heterogeneous wireless sensor networks, which is demonstrated though through a series of web-based WSN management Applications such as Monitoring, Configuration, Reprogram, Data Collection and so on. The tests and application trials confirm the feasibility of our approach but also still reveal a number of challenges to be taken into account when deploying wireless sensor and actuator networks at industrial sites, which will be considered by our future research work.

Wireless Sensor Networks (WSNs) experiences several technical challenges such as limited energy, short transmission range, limited storage capacities, and limited computational capabilities. Moreover, the sensor nodes are deployed randomly and massively over an inaccessible or hostile region. Hence, WSNs are vulnerable to adversaries and are usually operated in a dynamic and unreliable environment. Animal tracking using wireless sensors is one such application of WSN where power management plays a vital role. In this paper, an energy-efficient hybrid routing method is proposed that divides the whole network into smaller regions based on sensor location and chooses the routing scheme accordingly. The sensor network consists of a base station (BS) located at a distant place outside the network, and a relay node is placed inside the network for direct communications from nodes nearer to it. The nodes are further divided into two categories based on the supplied energy; such that the ones located far away from BS and relay have higher energy than the nodes nearer to them. The network performance of the proposed method is compared with protocols like LEACH, SEP, and SNRP, considering parameters like stability period, throughput and energy consumption. Simulation result shows that the proposed method outperforms other methods with better network performance.

Flood is one of the most devastating natural disasters. It is estimated that flooding from sea level rise will cause one trillion USD to major coastal cities of the world by the year 2050. Flood not only destroys the economy, but it also creates physical and psychological sufferings for the human and destroys infrastructures. Disseminating flood warnings and evacuating people from the flood-affected areas help to save human life. Therefore, predicting flood will help government authorities to take necessary actions to evacuate humans and arrange relief for the people. This licentiate thesis focuses on four different aspects of flood prediction using wireless sensor networks (WSNs). Firstly, different WSNs, protocols related to WSN, and backhaul connectivity in the context of predicting flood were investigated. A heterogeneous WSN network for flood prediction was proposed. Secondly, data coming from sensors contain anomaly due to different types of uncertainty, which hampers the accuracy of flood prediction. Therefore, anomalous data needs to be filtered out. A novel algorithm based on belief rule base for detecting the anomaly from sensor data has been proposed in this thesis. Thirdly, predicting flood is a challenging task as it involves multi-level factors, which cannot be measured with 100% certainty. Belief rule based expert systems (BRBESs) can be considered to handle the complex problem of this nature as they address different types of uncertainty. A web based BRBES was developed for predicting flood. This system provides better usability, more computational power to handle larger numbers of rule bases and scalability by porting it into a web-based solution. To improve the accuracy of flood prediction, a learning mechanism for multi-level BRBES was proposed. Furthermore, a comparison between the proposed multi-level belief rule based learning algorithm and other machine learning techniques including Artificial Neural Networks (ANN), Support Vector Machine (SVM) based regression, and Linear Regression has been performed. In the light of the research findings of this thesis, it can be argued that flood prediction can be accomplished more accurately by integrating WSN and BRBES.

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