Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

Deep Learning in Mobile and Wireless Networking: A Survey

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research

Exploring Audio Sensing in Detecting Social Interactions Using Smartphone Devices

In recent years, the fast proliferation of smartphones devices has provided powerful and portable methodologies for integrating sensing systems which can run continuously and provide feedback in real-time. The mobile crowd-sensing of human behaviour is an emerging computing paradigm that offers a challenge of sensing everyday social interactions performed by people who carry smartphone devices upon themselves. Typical smartphone sensors and the mobile crowd-sensing paradigm compose a process where the sensors present, such as the microphone, are used to infer social relationships between people in diverse social settings, where environmental factors can be dynamic and the infrastructure of buildings can vary. The typical approaches in detecting social interactions between people consider the use of co-location as a proxy for real-world interactions. Such approaches can under-perform in challenging situations where multiple social interactions can occur within close proximity to each other, for example when people are in a queue at the supermarket but not a part of the same social interaction. Other approaches involve a limitation where all participants of a social interaction must carry a smartphone device with themselves at all times and each smartphone must have the sensing app installed. The problem here is the feasibility of the sensing system, which relies heavily on each participant's smartphone acting as nodes within a social graph, connected together with weighted edges of proximity between the devices; when users uninstall the app or disable background sensing, the system is unable to accurately determine the correct number of participants. In this thesis, we present two novel approaches to detecting co-located social interac- tions using smartphones. The first relies on the use of WiFi signals and audio signals to distinguish social groups interacting within a few meters from each other with 88% precision. We orchestrated preliminary experiments using WiFi as a proxy for co-location between people who are socially interacting. Initial results showed that in more challenging scenarios, WiFi is not accurate enough to determine if people are socially interacting within the same social group. We then made use of audio as a second modality to capture the sound patterns of conversations to identify and segment social groups within close proximity to each other. Through a range of real-world experiments (social interactions in meeting scenarios, coffee shop scenarios, conference scenarios), we demonstrate a technique that utilises WiFi fingerprinting, along with sound fingerprinting to identify these social groups. We built a system which performs well, and then optimized the power consumption and improved the performance to 88% precision in the most challenging scenarios using duty cycling and data averaging techniques. The second approach explores the feasibility of detecting social interactions without the need of all social contacts to carry a social sensing device. This work explores the use of supervised and unsupervised Deep Learning techniques before concluding on the use of an Autoencoder model to perform a Speaker Identification task. We demonstrate how machine learning can be used with the audio data collected from a singular device as a speaker identification framework. Speech from people is used as the input to our Autoencoder model and then classified against a list of "social contacts" to determine if the user has spoken a person before or not. By doing this, the system can count the number of social contacts belonging to the user, and develop a database of common social contacts. Through the use 100 randomly-generated social conversations and the use of state-of-the-art Deep Learning techniques, we demonstrate how this system can accurately distinguish new and existing speakers from a data set of voices, to count the number of daily social interactions a user encounters with a precision of 75%. We then optimize the model using Hyperparameter Optimization to ensure that the model is most optimal for the task. Unlike most systems in the literature, this approach would work without the need to modify the existing infrastructure of a building, and without all participants needing to install the same ap

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Evolutionary Approach to Efficient Provisioning and Self-organization in Wireless Sensor Networks (WSN)

Advances in low-power digital integration and microelectro-mechanical systems (MEMS) have paved the way for micro-sensors. These sensors are equipped with data processing capabilities along with sensory circuits. Sensor data are processed on these individual sensors and transmitted to the target (sink). Lowcost integration and small sizes of these sensors have generated special interest in the area of disposable-sensors and large scale platform management. Queries to these sensors are addressed to nodes which have data satisfying the same condition. However, these sensors may be constrained in energy, bandwidth, storage, and processing capabilities. Large number of such sensors along with these constraints creates a sensor-management problem. At the network layer it amounts to setting up the efficient route that transmits the non-redundant data from source to the sink in order to maximize one or more sensor objectives (e.g. battery (and sensor's) life, Sensor-Data yield). This is done while adapting to changing connectivity due to failure of some nodes and new nodes powering up. First part of the thesis propose a reduced-complexity genetic algorithm (GA) for optimization of multi-hop battery-constrained sensor networks. The goal of the system is to generate optimal number of sensor-clusters with cluster-heads. It results in minimization of the power consumption of the sensor system while maximizing the sensor objectives (coverage and exposure). The genetic algorithm is used to adaptively create various components such as cluster-members, cluster-heads, and next-cluster. These components are then used to evaluate the average fitness of the system based on the sequence of communication links towards the sink. We then enhance the genetic algorithm (GA) approach for secure deployment of resource constrained multi-hop sensor networks. The goal in this case is to achieve secure coverage and improve battery life by dynamically optimizing security attributes (Like authentication and encryption). Further, we augment the GA approach for intrusion detection of resource constrained multi-hop sensor networks. Traditional intrusion detection mechanisms have limited applicability to the sensor networks due to scarce battery and processing resources. Therefore, we propose an effective scheme that would offer a power efficient and lightweight approach to identify malicious attacks. We evaluate sensor node attributes by measuring the perceived threat and its suitability to host local monitoring node (LMN) that acts as trusted proxy agent for the sink and capable of securely monitoring its neighbors. Security attributes in conjunction with genetic algorithm jointly optimizes the selection of monitoring nodes (i.e., LMN) by dynamically evaluating node fitness by profiling workloads patterns, packet statistics, utilization data, battery status, and quality-of-service compliance. Second part of the thesis delves into application of Information Technology (and Industrial) Systems and devices where the use of sensor networks can deliver non-intrusive and effective telemetry for group-based server management. These systems (Like Data Centers or Shipment tracking) face major challenges in seamless integration of telemetry and control data that is essential to various autonomic management functions related to power, thermal, reliability, predictability, survivability, locality and adaptability. Such systems that are supported by a dense network of sense-points operating in noisy environment (Metals, Cables) are required to deliver reliable trends, measurements and analysis in a timely fashion. The traditional approaches to provide distributed observability and control using wired solutions are static, expensive, and nonscalable. We apply the proposed GA approach for this unique environment that replaces static wired sensors with dynamically reconfigurable battery-powered wireless sensors. The proposed technique employs machine learning approach to optimize sensor node function assignment, clustering decisions, route establishment and data collection trees for improved throughput that results in effective controls

Context awareness and related challenges: A comprehensive evaluation study for a context-based RAT selection scheme towards 5G networks

Ο αποτελεσματικός σχεδιασμός των δικτύων είναι απαραίτητος για να αντιμετωπιστεί ο αυξανόμενος αριθμός των συνδρομητών κινητού διαδικτύου και των απαιτητικών υπηρεσιών δεδομένων, που ανταγωνίζονται για περιορισμένους ασύρματους πόρους. Επιπλέον, οι βασικές προκλήσεις για τα συνεχώς αναπτυσσόμενα δίκτυα LTE είναι η αύξηση των δυνατοτήτων των υφιστάμενων μηχανισμών, η μείωση της υπερβολικής σηματοδότησης (signaling) και η αξιοποίηση ενός αποτελεσματικού μηχανισμού επιλογής τεχνολογίας ασύρματης πρόσβασης (RAT). Υπάρχουν ποικίλες προτάσεις στην βιβλιογραφία σχετικά με αυτές τις προκλήσεις, μερικές από τις οποίες παρουσιάζονται εδώ. Ο σκοπός της εργασίας αυτής είναι να ερευνήσει τις τρέχουσες εξελίξεις στα δίκτυα LTE σχετικά με την ενσωμάτωση EPC και WiFi και την επίγνωση πλαισίου (context awareness) στην διαχείριση κινητικότητας, και να προτείνει τον αλγόριθμο COmpAsS, έναν μηχανισμό που χρησιμοποιεί ασαφή λογική (fuzzy logic) για να επιλέξει την πιο κατάλληλη τεχνολογία ασύρματης πρόσβασης για τα κινητά. Επιπλέον, έχουμε ποσοτικοποιήσει το κόστος σηματοδότησης του προτεινόμενου μηχανισμού σε σύνδεση με τις σημερινές προδιαγραφές του 3GPP και εκτελέσαμε μια ολοκληρωμένη ανάλυση. Τέλος, αξιολογήσαμε τον αλγόριθμο μέσω εκτεταμένων προσομοιώσεων σε ένα πολύπλοκο και ρεαλιστικό σενάριο χρήσης 5G, που απεικονίζονται τα σαφή πλεονεκτήματα της προσέγγισής μας όσον αφορά τη συχνότητα μεταπομπών (handover) και τις μετρήσεις βασικών QoS τιμών, όπως ρυθμός μετάδοσης και καθυστέρηση.Effective network planning is essential to cope with the increasing number of mobile internet subscribers and bandwidth-intensive services competing for limited wireless resources. Additionally, key challenges for the constantly growing LTE networks is increasing capabilities of current mechanisms, reduction of signaling overhead and the utilization of an effective Radio Access Technology (RAT) selection scheme. There have been various proposals in literature regarding these challenges, some of which are discussed here. The purpose of this work is to research the current advances in LTE networks regarding EPC - WiFi integration and context awareness in mobility management, and propose the COmpAsS algorithm, a mechanism using fuzzy logic to select the most suitable Radio Access Technology. Furthermore, we quantify the signaling overhead of the proposed mechanism by linking it to the current 3GPP specifications and performing a comprehensive analysis. Finally, we evaluate the novel scheme via extensive simulations in a complex and realistic 5G use case, illustrating the clear advantages of our approach in terms of handover frequency and key QoS metrics, i.e. the user-experienced throughput and delay

Modelling and optimisation of resource usage in an IoT enabled smart campus

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilized efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organizations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory. The building blocks of this thesis consist of three pillars of execution, namely, IoT deployment, predictive modelling, and optimization. Together, these components create an end-to-end framework that provides informed decisions to estate manager in regards to the optimal allocation of campus resources. The main contributions of this thesis are three application domains, which lies on top of the execution pillars, defining campus resources as classrooms, car parks, and transit buses. Specifically, our contributions are: i) We evaluate several IoT occupancy sensing technologies and instrument 9 lecture halls of varying capacities with the most appropriate sensing solution. The collected data provides us with insights into attendance patterns, such as cancelled lectures and class tests, of over 250 courses. We then develop predictive models using machine learning algorithms and quantile regression technique to predict future attendance patterns. Finally, we propose an intelligent optimisation model that allows allocations of classes to rooms based on the dynamics of predicted attendance as opposed to static enrolment number. We show that the data-driven assignment of classroom resources can achieve a potential saving in room cost of over 10\% over the course of a semester, while incurring a very low risk of disrupting student experience due to classroom overflow; ii) We instrument a car park with IoT sensors for real-time monitoring of parking demand and comprehensively analyse the usage data spanning over 15 months. We then develop machine learning models to forecast future parking demand at multiple forecast horizons ranging from 1 day to 10 weeks, our models achieve a mean absolute error (MAE) of 4.58 cars per hour. Finally, we propose a novel optimal allocation framework that allows campus manager to re-dimension the car park to accommodate new paradigms of car use while minimizing the risk of rejecting users and maintaining a certain level of revenue from the parking infrastructure; iii) We develop sensing technology for measuring an outdoor orderly queue using ultrasonic sensor and LoRaWAN, and deploy the solution at an on campus bus stop. Our solution yields a reasonable accuracy with MAE of 10.7 people for detecting a queue length of up to 100 people. We then develop an optimisation model to reschedule bus dispatch times based on the actual dynamics of passenger demand. The result suggests that a potential wait time reduction of 42.93% can be achieved with demand-driven bus scheduling. Taken together, our contributions demonstrates that there are significant resource efficiency gains to be realised in a smart-campus that employs IoT sensing coupled with predictive modelling and dynamic optimisation algorithms

Modelling and optimisation of resource usage in an IoT enabled smart campus

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilized efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organizations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory. The building blocks of this thesis consist of three pillars of execution, namely, IoT deployment, predictive modelling, and optimization. Together, these components create an end-to-end framework that provides informed decisions to estate manager in regards to the optimal allocation of campus resources. The main contributions of this thesis are three application domains, which lies on top of the execution pillars, defining campus resources as classrooms, car parks, and transit buses. Specifically, our contributions are: i) We evaluate several IoT occupancy sensing technologies and instrument 9 lecture halls of varying capacities with the most appropriate sensing solution. The collected data provides us with insights into attendance patterns, such as cancelled lectures and class tests, of over 250 courses. We then develop predictive models using machine learning algorithms and quantile regression technique to predict future attendance patterns. Finally, we propose an intelligent optimisation model that allows allocations of classes to rooms based on the dynamics of predicted attendance as opposed to static enrolment number. We show that the data-driven assignment of classroom resources can achieve a potential saving in room cost of over 10\% over the course of a semester, while incurring a very low risk of disrupting student experience due to classroom overflow; ii) We instrument a car park with IoT sensors for real-time monitoring of parking demand and comprehensively analyse the usage data spanning over 15 months. We then develop machine learning models to forecast future parking demand at multiple forecast horizons ranging from 1 day to 10 weeks, our models achieve a mean absolute error (MAE) of 4.58 cars per hour. Finally, we propose a novel optimal allocation framework that allows campus manager to re-dimension the car park to accommodate new paradigms of car use while minimizing the risk of rejecting users and maintaining a certain level of revenue from the parking infrastructure; iii) We develop sensing technology for measuring an outdoor orderly queue using ultrasonic sensor and LoRaWAN, and deploy the solution at an on campus bus stop. Our solution yields a reasonable accuracy with MAE of 10.7 people for detecting a queue length of up to 100 people. We then develop an optimisation model to reschedule bus dispatch times based on the actual dynamics of passenger demand. The result suggests that a potential wait time reduction of 42.93% can be achieved with demand-driven bus scheduling. Taken together, our contributions demonstrates that there are significant resource efficiency gains to be realised in a smart-campus that employs IoT sensing coupled with predictive modelling and dynamic optimisation algorithms