Pervasive Service Discovery (SD) in Low-power and Lossy Networks (LLNs) is expected to play a major role in realising the Internet of Things (IoT) vision. Such a vision aims to expand the current Internet to interconnect billions of miniature smart objects that sense and act on our surroundings in a way that will revolutionise the future. The pervasiveness and heterogeneity of such low-power devices requires robust, automatic, interoperable and scalable deployment and operability solutions. At the same time, the limitations of such constrained devices impose strict challenges regarding complexity, energy consumption, time-efficiency and mobility. This research contributes new lightweight solutions to facilitate automatic deployment and operability of LLNs. It mainly tackles the aforementioned challenges through the proposition of novel component-based, automatic and efficient SD solutions that ensure extensibility and adaptability to various LLN environments. Building upon such architecture, a first fully-distributed, hybrid pushpull SD solution dubbed EADP (Extensible Adaptable Discovery Protocol) is proposed based on the well-known Trickle algorithm. Motivated by EADPs’ achievements, new methods to optimise Trickle are introduced. Such methods allow Trickle to encompass a wide range of algorithms and extend its usage to new application domains. One of the new applications is concretized in the TrickleSD protocol aiming to build automatic, reliable, scalable, and time-efficient SD. To optimise the energy efficiency of TrickleSD, two mechanisms improving broadcast communication in LLNs are proposed. Finally, interoperable standards-based SD in the IoT is demonstrated, and methods combining zero-configuration operations with infrastructure-based solutions are proposed. Experimental evaluations of the above contributions reveal that it is possible to achieve automatic, cost-effective, time-efficient, lightweight, and interoperable SD in LLNs. These achievements open novel perspectives for zero-configuration capabilities in the IoT and promise to bring the ‘things’ to all people everywhere.

Since the sequential inference framework determines the number of total samples in real-time based on the history data, it yields quicker decision compared to its fixed-sample-size counterpart, provided the appropriate early termination rule. This advantage is particularly appealing in the system where data is acquired in sequence, and both the decision accuracy and latency are of primary interests. Meanwhile, the Internet of Things (IoT) technology has created all types of connected devices, which can potentially enhance the inference performance by providing information diversity. For instance, smart home network deploys multiple sensors to perform the climate control, security surveillance, and personal assistance. Therefore, it has become highly desirable to pursue the solutions that can efficiently integrate the classic sequential inference methodologies into the networked multi-agent systems. In brief, this thesis investigates the sequential hypothesis testing problem in multi-agent networks, aiming to overcome the constraints of communication bandwidth, energy capacity, and network topology so that the networked system can perform sequential test cooperatively to its full potential. The multi-agent networks are generally categorized into two main types. The first one features a hierarchical structure, where the agents transmit messages based on their observations to a fusion center that performs the data fusion and sequential inference on behalf of the network. One such example is the network formed by wearable devices connected with a smartphone. The central challenges in the hierarchical network arise from the instantaneous transmission of the distributed data to the fusion center, which is constrained by the battery capacity and the communication bandwidth in practice. Therefore, the first part of this thesis is dedicated to address these two constraints for the hierarchical network. In specific, aiming to preserve the agent energy, Chapter 2 devises the optimal sequential test that selects the "most informative" agent online at each sampling step while leaving others in idle status. To overcome the communication bottleneck, Chapter 3 proposes a scheme that allows distributed agents to send only one-bit messages asynchronously to the fusion center without compromising the performance. In contrast, the second type of networks does not assume the presence of a fusion center, and each agent performs the sequential test based on its own samples together with the messages shared by its neighbours. The communication links can be represented by an undirected graph. A variety of applications conform to such a distributed structure, for instance, the social networks that connect individuals through online friendship and the vehicular network formed by connected cars. However, the distributed network is prone to sub-optimal performance since each agent can only access the information from its local neighborhood. Hence the second part of this thesis mainly focuses on optimizing the distributed performance through local message exchanges. In Chapter 4, we put forward a distributed sequential test based on consensus algorithm, where agents exchange and aggregate real-valued local statistics with neighbours at every sampling step. In order to further lower the communication overhead, Chapter 5 develops a distributed sequential test that only requires the exchange of quantized messages (i.e., integers) between agents. The cluster-based network, which is a hybrid of the hierarchical and distributed networks, is also investigated in Chapter 5.

Twenty years ago, if someone said that every object could have its own identity, no one would agree and some might even think that was crazy. However, it turns out that the wild imagination is possible today. With the help of the Internet of Things (IoT), it is convenient to identify any objects with RFID (Radio Frequency Identification) and control the objects via the Internet. In the near future, people will even make the IoT network visible, thus all the information on the Internet can become dynamic and much easier to understand than numbers to be.   At the moment, Guiyang Municipal Science & Technology Bureau is planning to design and apply visualization technique to logistics, the focus of Guizhou Provincial logistic network in the IoT era. This is a good opportunity for different kinds of enterprises in theGuizhoudistrict or even in the whole country.   This thesis focuses on three problems, namely, discussion on the use of visualization techniques in IoT, the necessary preparation of manufacturing industry to join in the visible IoT and measures available that the government can adopt. The exploratory case study in this thesis is about the visualization technique in IoT in manufacturing industry in theGuizhouProvince. A company was selected for the case study to explore the situation inGuizhouProvince. The related information was collected through interviews with relevant personnel and observation in the company. To bring a clear view of the situation and provide enterprises with information for reference, SWOT analysis is adopted to evaluate the strength and weakness in both the internal and external environment. Those measures that government can take to promote its development include unification in standards, support in research and development of technology and emphasis on personal privacy.   The conclusion shows that the use of visualization techniques in IoT can promote information transmission both in effectiveness and efficiency, and control the supply chain as well as special processes in an efficient way. Discussions have been conducted on four techniques which are able to realize visualization, including GPS, RFID, bar code and machine vision. The preparation that needs to be done in a progressive way, of manufacturing enterprises mainly involves three aspects: equipment, system, and management; which have been discussed in detail in this study. Only totally combining the three aspects, not a single one can be omitted, can enterprises achieve the goal of growth in benefit and costs reduction through the use of IoT. Due to immaturity of the emerging network and technology, in the future, the IoT still has a long way to go. Certainly, we should not ignore the followed huge benefit and improvement that IoT can bring.

In the "Internet of Things" (IoT), the things will be able to sense, communicate, and interact. They will also exchange data, information and knowledge, and locate themselves and other things that surround them. In order to be able to interact, the things need to recognize that they are in proximity of other things. It is anticipated that the most widespread components of the IoT will be passive radio frequency identification (RFID) tags because they are inexpensive and provide automatic identification. However, passive RFID tags are not capable of performing complex operations, such as proximity detection and localization, which will be required in future networks. In this thesis, we describe existing problems with current RFID systems and survey potential solutions for localization and proximity detection. Then we present a new RFID device called "Sense-a-Tag" (ST) that can passively detect and decode backscattered signals from tags in its proximity. There have already been an attempt to use this device for tracking. However, detailed analysis of the performance of the ST especially for proximity detection has not been performed yet. We show that when STs are added to a standard RFID system, the problems of proximity detection and localization with RFID tags can readily be solved. Then we applied ST-based system for identifying people and object interactions. The potential uses of ST as an augmented device for IoT applications are discussed in this thesis. Advantages and limitations of an ST based RFID system have been investigated in details for each application. Results obtained from real experiments illustrate that an ST-based RFID system is feasible for proximity detection applications. In addition, a special software is developed in C\# to process the data and run a localization algorithm based on proximity detection information. The same software has been used for tracking people's activity. Different scenarios have been considered in the experiments. We tried to consider majority of factors that might affect the accuracy in the experiments including: angle and distance between the reader/ST and tags, timing in sending queries, presence of human body, etc. The simulations based on real experiments and results illustrates that an ST-based RFID system can be a realistic solution for proximity detection and localization for Location Positioning systems (LPS) and activity monitoring in future IoT.

With the rapid development of information technology, applications referring to the Internet of things are booming. Applications that gather information from sensors and affect the context environment with actuators can provide customized and intelligent behaviour to users. These applications have become widely used nowadays in daily life and have initiated the multi-dimensional range query demand referring to the Internet of things. As the data information is fully distributed and the devices like sensors, mobile phones, etc., has limited resources and finite energy, supporting efficient range query is a tough challenge. In this paper, we have proposed a distributed range query framework for Internet of things. In order to save energy costs and reduce the network traffic, we suggest a reporting data range mechanism in the sensing peers, which choose to report a data range and report again only when the peer senses an abnormal data instead of the common moving data method. In addition, we selected some strong peers to be used as the super peers to create a data index by collecting the reporting data range, which will be used for performing range queries. The study has shown that our proposal framework could reduce resource costs in the less strong peers like sensors and mobile phones, and reduce network traffic among all the peers within the network, as well as support a range query function. According the evaluation results, the reporting data range method could greatly reduce the data migration times and save energy costs, and the data index could significantly reduce accessing unnecessary peers and diminish the network traffic.

We present platooning as a special case of crowd-sensing framework. After offering a policy that governs platooning, we review common scenarios and components surrounding platooning. We present a prototype that illustrates efficiency of road usage and vehicle travel time derived from platooning. We have argued that beyond the commonly reported benefits of platooning, there are substantial savings in acquisition and processing of sensory data sharing the road. Our results show that data transmission can be reduced to low of 3% compared to normal data transmission using a platoon formation with sensor sharing.

Internet of Things (IoT) is becoming commonplace in people's daily life. Even, many governments' authorities have already deployed a very large number of IoT sensors toward their smart city initiative and development road-map. However, lack of semantics in the presentation of IoT-based sensory data represents the perception complexity by general people. Adding semantics to the IoT sensory data remains a challenge for smart cities and environments. In this thesis proposal, we present an implementation that provides a meaningful IoT sensory data notifications approach about indoor and outdoor environment status for people and authorities. The approach is based on analyzing spatio-temporal thresholds that compose of multiple IoT sensors readings. Our developed IoT sensory data analytics adds real-time semantics to the received sensory raw data stream by converting the IoT sensory data into meaningful and descriptive notifications about the environment status such as green locations, emergency zone, crowded places, green paths, polluted locations, etc. Our adopted IoT messaging protocol can handle a very large number of dynamically added static and dynamic IoT sensors publication and subscription processes. People can customize the notifications based on their preference or can subscribe to existing semantic notifications in order to be acknowledged of any concerned environmental condition. The thesis is supposed to come up with three contributions. The first, an IoT approach of a three-layer architecture that extracts raw sensory data measurements and converts it to a contextual-aware format that can be perceived by people. The second, an ontology that infers a semantic notification of multiple sensory data according to the appropriate spatio-temporal reasoning and description mechanism. We used a tool called Protégé to model our ontology as a common IDE to build semantic knowledge. We built our ontology through extending a well-known web ontology called Semantic Sensor Network (SSN). We built the extension from which six classes were adopted to derive our SENS-IT ontology and fulfill our objectives. The third, a fuzzy system approach is proposed to make our system much generic of providing broader semantic notifications, so it can be agile enough to accept more measurements of multiple sensory sources.

Due to the competitive nature of the global economy, organisations are continuously seeking ways of cutting costs and increasing efficiency to gain a competitive advantage. Field service organisations that offer after sales support seek to gain a competitive advantage through downtime minimisation. Downtime is the time between service requests made by a customer or triggered by equipment failure and the completion of the service to rectify the problem by the field service team. Researchers have identified downtime as one of the key performance indicators for field service organisations. The lack of real-time access to information and inaccuracy of information are factors which contribute to the poor management of downtime. Various technology advancements have been adopted to address some of the challenges faced by field service organisations through automation. The emergence of an Internet of Things (IoT), has brought new enhancement possibilities to various industries, for instance, the manufacturing industry. The main research question that this study aims to address is “How can an Internet of Things be used to optimise field service automation?” The main research objective was to develop and evaluate a model for the optimisation of field services using an IoT’s features and technologies. The model aims at addressing challenges associated with the inaccuracy or/and lack of real-time access to information during downtime. The model developed is the theoretical artefact of the research methodology used in this study which is the Design Science Research Methodology (DSRM). The DSRM activities were adopted to fulfil the research objectives of this research. A literature review in the field services domain was conducted to establish the problems faced by field service organisations. Several interviews were held to verify the problems of FSM identified in literature and some potential solutions. During the design and development activity of the DSRM methodology, an IoT model for FSA was designed. The model consists of:The Four Layered Architecture; The Three Phase Data Flow Process; and Definition and descriptions of IoT-based elements and functions. The model was then used to drive the design, development, and evaluation of “proof of concept” prototype, the KapCha prototype. KapCha enables the optimisation of FSA using IoT techniques and features. The implementation of a sub-component of the KapCha system, in fulfilment of the research. The implementation of KapCha was applied to the context of a smart lighting environment in the case study. A two-phase evaluation was conducted to review both the theoretical model and the KapCha prototype. The model and KapCha prototype were evaluated using the Technical and Risk efficacy evaluation strategy from the Framework for Evaluation of Design Science (FEDS). The Technical Risk and Efficacy strategy made use of formative, artificial-summative and summative-naturalistic methods of evaluation. An artificial-summative evaluation was used to evaluate the design of the model. Iterative formative evaluations were conducted during the development of the KapCha. KapCha was then placed in a real-environment conditions and a summative-naturalistic evaluation was conducted. The summative-naturalistic evaluation was used to determine the performance of KapCha under real-world conditions to evaluate the extent it addresses FSA problems identified such as real-time communication and automated fault detection.

The objective of this paper is to perform a quantitative comparison of Dweet.io and SensibleThings from different aspects. With the fast development of internet of things, the platforms for internet-of-things face bigger challenges. This paper will evaluate both systems in four parts. The first part shows the general comparison of input ways and output functions provided by the platforms. The second part shows the security comparison, which focuses on the protocol types of the packets and the stability during the communication. The third part shows the scalability comparison when the value becomes bigger. The fourth part shows the scalability comparison when speeding up the processes. After the comparisons, I concluded that Dweet.io is more easy to use on devices and supports more programming languages. Dweet.io realizes visualization and it can be shared. Dweet.io is safer and more stable than SensibleThings. SensibleThings provides more openness. SensibleThings has better scalability in handling big values and quick speed.

Pervasive Service Discovery (SD) in Low-power and Lossy Networks (LLNs) is expected to play a major role in realising the Internet of Things (IoT) vision. Such a vision aims to expand the current Internet to interconnect billions of miniature smart objects that sense and act on our surroundings in a way that will revolutionise the future. The pervasiveness and heterogeneity of such low-power devices requires robust, automatic, interoperable and scalable deployment and operability solutions. At the same time, the limitations of such constrained devices impose strict challenges regarding complexity, energy consumption, time-efficiency and mobility. This research contributes new lightweight solutions to facilitate automatic deployment and operability of LLNs. It mainly tackles the aforementioned challenges through the proposition of novel component-based, automatic and efficient SD solutions that ensure extensibility and adaptability to various LLN environments. Building upon such architecture, a first fully-distributed, hybrid pushpull SD solution dubbed EADP (Extensible Adaptable Discovery Protocol) is proposed based on the well-known Trickle algorithm. Motivated by EADPs’ achievements, new methods to optimise Trickle are introduced. Such methods allow Trickle to encompass a wide range of algorithms and extend its usage to new application domains. One of the new applications is concretized in the TrickleSD protocol aiming to build automatic, reliable, scalable, and time-efficient SD. To optimise the energy efficiency of TrickleSD, two mechanisms improving broadcast communication in LLNs are proposed. Finally, interoperable standards-based SD in the IoT is demonstrated, and methods combining zero-configuration operations with infrastructure-based solutions are proposed. Experimental evaluations of the above contributions reveal that it is possible to achieve automatic, cost-effective, time-efficient, lightweight, and interoperable SD in LLNs. These achievements open novel perspectives for zero-configuration capabilities in the IoT and promise to bring the ‘things’ to all people everywhere.