A Bio-inspired Load Balancing Technique for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of multiple distributed nodes each with limited resources. With their strict resource constraints and application-specific characteristics, WSNs contain many challenging trade-offs. This thesis is concerned with the load balancing of Wireless Sensor Networks (WSNs). We present an approach, inspired by bees’ pheromone propagation mechanism, that allows individual nodes to decide on the execution process locally to solve the trade-off between service availability and energy consumption. We explore the performance consequences of the pheromone-based load balancing approach using a system-level simulator. The effectiveness of the algorithm is evaluated on case studies based on sound sensors with different scenarios of existing approaches on variety of different network topologies. The performance of our approach is dependant on the values chosen for its parameters. As such, we utilise the Simulated Annealing to discover optimal parameter configurations for pheromone-based load balancing technique for any given network schema. Once the parameter values are optimised for the given network topology automatically, we inspect improving the pheromone-based load balancing approach using robotic agents. As cyber-physical systems benefit from the heterogeneity of the hardware components, we introduce the use of pheromone signalling-based robotic guidance that integrates the robotic agents to the existing load balancing approach by guiding the robots into the uncovered area of the sensor field. As such, we maximise the service availability using the robotic agents as well as the sensor nodes

Energy efficient data collection and dissemination protocols in self-organised wireless sensor networks

Wireless sensor networks (WSNs) are used for event detection and data collection in a plethora of environmental monitoring applications. However a critical factor limits the extension of WSNs into new application areas: energy constraints. This thesis develops self-organising energy efficient data collection and dissemination protocols in order to support WSNs in event detection and data collection and thus extend the use of sensor-based networks to many new application areas. Firstly, a Dual Prediction and Probabilistic Scheduler (DPPS) is developed. DPPS uses a Dual Prediction Scheme combining compression and load balancing techniques in order to manage sensor usage more efficiently. DPPS was tested and evaluated through computer simulations and empirical experiments. Results showed that DPPS reduces energy consumption in WSNs by up to 35% while simultaneously maintaining data quality and satisfying a user specified accuracy constraint. Secondly, an Adaptive Detection-driven Ad hoc Medium Access Control (ADAMAC) protocol is developed. ADAMAC limits the Data Forwarding Interruption problem which causes increased end-to-end delay and energy consumption in multi-hop sensor networks. ADAMAC uses early warning alarms to dynamically adapt the sensing intervals and communication periods of a sensor according to the likelihood of any new events occurring. Results demonstrated that compared to previous protocols such as SMAC, ADAMAC dramatically reduces end-to-end delay while still limiting energy consumption during data collection and dissemination. The protocols developed in this thesis, DPPS and ADAMAC, effectively alleviate the energy constraints associated with WSNs and will support the extension of sensorbased networks to many more application areas than had hitherto been readily possible

Use of Inferential Statistics to Design Effective Communication Protocols for Wireless Sensor Networks

This thesis explores the issues and techniques associated with employing the principles of inferential statistics to design effective Medium Access Control (MAC), routing and duty cycle management strategies for multihop Wireless Sensor Networks (WSNs). The main objective of these protocols are to maximise the throughput of the network, to prolong the lifetime of nodes and to reduce the end-to-end delay of packets over a general network scenario without particular considerations for specific topology configurations, traffic patterns or routing policies. WSNs represent one of the leading-edge technologies that have received substantial research efforts due to their prominent roles in many applications. However, to design effective communication protocols for WSNs is particularly challenging due to the scarce resources of these networks and the requirement for large-scale deployment. The MAC, routing and duty cycle management protocols are amongst the important strategies that are required to ensure correct operations of WSNs. This thesis makes use of the inferential statistics field to design these protocols; inferential statistics was selected as it provides a rich design space with powerful approaches and methods. The MAC protocol proposed in this thesis exploits the statistical characteristics of the Gamma distribution to enable each node to adjust its contention parameters dynamically based on its inference for the channel occupancy. This technique reduces the service time of packets and leverages the throughput by improving the channel utilisation. Reducing the service time minimises the energy consumed in contention to access the channel which in turn prolongs the lifetime of nodes. The proposed duty cycle management scheme uses non-parametric Bayesian inference to enable each node to determine the best times and durations for its sleeping durations without posing overheads on the network. Hence the lifetime of node is prolonged by mitigating the amount of energy wasted in overhearing and idle listening. Prolonging the lifetime of nodes increases the throughput of the network and reduces the end-to-end delay as it allows nodes to route their packets over optimal paths for longer periods. The proposed routing protocol uses one of the state-of-the-art inference techniques dubbed spatial reasoning that enables each node to figure out the spatial relationships between nodes without overwhelming the network with control packets. As a result, the end-to-end delay is reduced while the throughput and lifetime are increased. Besides the proposed protocols, this thesis utilises the analytical aspects of statistics to develop rigorous analytical models that can accurately predict the queuing and medium access delay and energy consumption over multihop networks. Moreover, this thesis provides a broader perspective for design of communication protocols for WSNs by casting the operations of these networks in the domains of the artificial chemistry discipline and the harmony search optimisation algorithm

Scalable wireless sensor networks for dynamic communication environments: simulation and modelling

This thesis explores the deployment of Wireless Sensor Networks (WSNs) on localised maritime events. In particular, it will focus on the deployment of a WSN at sea and estimating what challenges derive from the environment and how they affect communication. This research addresses these challenges through simulation and modelling of communication and environment, evaluating the implications of hardware selection and custom algorithm development. The first part of this thesis consists of the analysis of aspects related to the Medium Access Control layer of the network stack in large-scale networks. These details are commonly hidden from upper layers, thus resulting in misconceptions of real deployment characteristics. Results show that simple solutions have greater advantages when the number of nodes within a cluster increases. The second part considers routing techniques, with focus on energy management and packet delivery. It is shown that, under certain conditions, relaying data can increase energy savings, while at the same time allows a more even distribution of its usage between nodes. The third part describes the development of a custom-made network simulator. It starts by considering realistic radio, channel and interference models to allow a trustworthy simulation of the deployment environment. The MAC and Routing techniques developed thus far are adapted to the simulator in a cross-layer manner. The fourth part consists of adapting the WSN behaviour to the variable weather and topology found in the chosen application scenario. By analysing the algorithms presented in this work, it is possible to find and use the best alternative under any set of environmental conditions. This mechanism, the environment-aware engine, uses both network and sensing data to optimise performance through a set of rules that involve message delivery and distance between origin and cluster hea

Engineering Self-Adaptive Collective Processes for Cyber-Physical Ecosystems

The pervasiveness of computing and networking is creating significant opportunities for building valuable socio-technical systems. However, the scale, density, heterogeneity, interdependence, and QoS constraints of many target systems pose severe operational and engineering challenges. Beyond individual smart devices, cyber-physical collectives can provide services or solve complex problems by leveraging a “system effect” while coordinating and adapting to context or environment change. Understanding and building systems exhibiting collective intelligence and autonomic capabilities represent a prominent research goal, partly covered, e.g., by the field of collective adaptive systems. Therefore, drawing inspiration from and building on the long-time research activity on coordination, multi-agent systems, autonomic/self-* systems, spatial computing, and especially on the recent aggregate computing paradigm, this thesis investigates concepts, methods, and tools for the engineering of possibly large-scale, heterogeneous ensembles of situated components that should be able to operate, adapt and self-organise in a decentralised fashion. The primary contribution of this thesis consists of four main parts. First, we define and implement an aggregate programming language (ScaFi), internal to the mainstream Scala programming language, for describing collective adaptive behaviour, based on field calculi. Second, we conceive of a “dynamic collective computation” abstraction, also called aggregate process, formalised by an extension to the field calculus, and implemented in ScaFi. Third, we characterise and provide a proof-of-concept implementation of a middleware for aggregate computing that enables the development of aggregate systems according to multiple architectural styles. Fourth, we apply and evaluate aggregate computing techniques to edge computing scenarios, and characterise a design pattern, called Self-organising Coordination Regions (SCR), that supports adjustable, decentralised decision-making and activity in dynamic environments.Con lo sviluppo di informatica e intelligenza artificiale, la diffusione pervasiva di device computazionali e la crescente interconnessione tra elementi fisici e digitali, emergono innumerevoli opportunità per la costruzione di sistemi socio-tecnici di nuova generazione. Tuttavia, l'ingegneria di tali sistemi presenta notevoli sfide, data la loro complessità—si pensi ai livelli, scale, eterogeneità, e interdipendenze coinvolti. Oltre a dispositivi smart individuali, collettivi cyber-fisici possono fornire servizi o risolvere problemi complessi con un “effetto sistema” che emerge dalla coordinazione e l'adattamento di componenti fra loro, l'ambiente e il contesto. Comprendere e costruire sistemi in grado di esibire intelligenza collettiva e capacità autonomiche è un importante problema di ricerca studiato, ad esempio, nel campo dei sistemi collettivi adattativi. Perciò, traendo ispirazione e partendo dall'attività di ricerca su coordinazione, sistemi multiagente e self-*, modelli di computazione spazio-temporali e, specialmente, sul recente paradigma di programmazione aggregata, questa tesi tratta concetti, metodi, e strumenti per l'ingegneria di ensemble di elementi situati eterogenei che devono essere in grado di lavorare, adattarsi, e auto-organizzarsi in modo decentralizzato. Il contributo di questa tesi consiste in quattro parti principali. In primo luogo, viene definito e implementato un linguaggio di programmazione aggregata (ScaFi), interno al linguaggio Scala, per descrivere comportamenti collettivi e adattativi secondo l'approccio dei campi computazionali. In secondo luogo, si propone e caratterizza l'astrazione di processo aggregato per rappresentare computazioni collettive dinamiche concorrenti, formalizzata come estensione al field calculus e implementata in ScaFi. Inoltre, si analizza e implementa un prototipo di middleware per sistemi aggregati, in grado di supportare più stili architetturali. Infine, si applicano e valutano tecniche di programmazione aggregata in scenari di edge computing, e si propone un pattern, Self-Organising Coordination Regions, per supportare, in modo decentralizzato, attività decisionali e di regolazione in ambienti dinamici

Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects

Sensor Networks and Their Applications: Investigating the Role of Sensor Web Enablement

The Engineering Doctorate (EngD) was conducted in conjunction with BT Research on state-of-the-art Wireless Sensor Network (WSN) projects. The first area of work is a literature review of WSN project applications, some of which the author worked on as a BT Researcher based at the world renowned Adastral Park Research Labs in Suffolk (2004-09). WSN applications are examined within the context of Machine-to-Machine (M2M); Information Networking (IN); Internet/Web of Things (IoT/WoT); smart home and smart devices; BT’s 21st Century Network (21CN); Cloud Computing; and future trends. In addition, this thesis provides an insight into the capabilities of similar external WSN project applications. Under BT’s Sensor Virtualization project, the second area of work focuses on building a Generic Architecture for WSNs with reusable infrastructure and ‘infostructure’ by identifying and trialling suitable components, in order to realise actual business benefits for BT. The third area of work focuses on the Open Geospatial Consortium (OGC) standards and their Sensor Web Enablement (SWE) initiative. The SWE framework was investigated to ascertain its potential as a component of the Generic Architecture. BT’s SAPHE project served as a use case. BT Research’s experiences of taking this traditional (vertical) stove-piped application and creating SWE compliant services are described. The author’s findings were originally presented in a series of publications and have been incorporated into this thesis along with supplementary WSN material from BT Research projects. SWE 2.0 specifications are outlined to highlight key improvements, since work began at BT with SWE 1.0. The fourth area of work focuses on Complex Event Processing (CEP) which was evaluated to ascertain its potential for aggregating and correlating the shared project sensor data (‘infostructure’) harvested and for enabling data fusion for WSNs in diverse domains. Finally, the conclusions and suggestions for further work are provided

Cognitive networking for next generation of cellular communication systems

This thesis presents a comprehensive study of cognitive networking for cellular networks with contributions that enable them to be more dynamic, agile, and efficient. To achieve this, machine learning (ML) algorithms, a subset of artificial intelligence, are employed to bring such cognition to cellular networks. More specifically, three major branches of ML, namely supervised, unsupervised, and reinforcement learning (RL), are utilised for various purposes: unsupervised learning is used for data clustering, while supervised learning is employed for predictions on future behaviours of networks/users. RL, on the other hand, is utilised for optimisation purposes due to its inherent characteristics of adaptability and requiring minimal knowledge of the environment. Energy optimisation, capacity enhancement, and spectrum access are identified as primary design challenges for cellular networks given that they are envisioned to play crucial roles for 5G and beyond due to the increased demand in the number of connected devices as well as data rates. Each design challenge and its corresponding proposed solution are discussed thoroughly in separate chapters. Regarding energy optimisation, a user-side energy consumption is investigated by considering Internet of things (IoT) networks. An RL based intelligent model, which jointly optimises the wireless connection type and data processing entity, is proposed. In particular, a Q-learning algorithm is developed, through which the energy consumption of an IoT device is minimised while keeping the requirement of the applications--in terms of response time and security--satisfied. The proposed methodology manages to result in 0% normalised joint cost--where all the considered metrics are combined--while the benchmarks performed 54.84% on average. Next, the energy consumption of radio access networks (RANs) is targeted, and a traffic-aware cell switching algorithm is designed to reduce the energy consumption of a RAN without compromising on the user quality-of-service (QoS). The proposed technique employs a SARSA algorithm with value function approximation, since the conventional RL methods struggle with solving problems with huge state spaces. The results reveal that up to 52% gain on the total energy consumption is achieved with the proposed technique, and the gain is observed to reduce when the scenario becomes more realistic. On the other hand, capacity enhancement is studied from two different perspectives, namely mobility management and unmanned aerial vehicle (UAV) assistance. Towards that end, a predictive handover (HO) mechanism is designed for mobility management in cellular networks by identifying two major issues of Markov chains based HO predictions. First, revisits--which are defined as a situation whereby a user visits the same cell more than once within the same day--are diagnosed as causing similar transition probabilities, which in turn increases the likelihood of making incorrect predictions. This problem is addressed with a structural change; i.e., rather than storing 2-D transition matrix, it is proposed to store 3-D one that also includes HO orders. The obtained results show that 3-D transition matrix is capable of reducing the HO signalling cost by up to 25.37%, which is observed to drop with increasing randomness level in the data set. Second, making a HO prediction with insufficient criteria is identified as another issue with the conventional Markov chains based predictors. Thus, a prediction confidence level is derived, such that there should be a lower bound to perform HO predictions, which are not always advantageous owing to the HO signalling cost incurred from incorrect predictions. The outcomes of the simulations confirm that the derived confidence level mechanism helps in improving the prediction accuracy by up to 8.23%. Furthermore, still considering capacity enhancement, a UAV assisted cellular networking is considered, and an unsupervised learning-based UAV positioning algorithm is presented. A comprehensive analysis is conducted on the impacts of the overlapping footprints of multiple UAVs, which are controlled by their altitudes. The developed k-means clustering based UAV positioning approach is shown to reduce the number of users in outage by up to 80.47% when compared to the benchmark symmetric deployment. Lastly, a QoS-aware dynamic spectrum access approach is developed in order to tackle challenges related to spectrum access, wherein all the aforementioned types of ML methods are employed. More specifically, by leveraging future traffic load predictions of radio access technologies (RATs) and Q-learning algorithm, a novel proactive spectrum sensing technique is introduced. As such, two different sensing strategies are developed; the first one focuses solely on sensing latency reduction, while the second one jointly optimises sensing latency and user requirements. In particular, the proposed Q-learning algorithm takes the future load predictions of the RATs and the requirements of secondary users--in terms of mobility and bandwidth--as inputs and directs the users to the spectrum of the optimum RAT to perform sensing. The strategy to be employed can be selected based on the needs of the applications, such that if the latency is the only concern, the first strategy should be selected due to the fact that the second strategy is computationally more demanding. However, by employing the second strategy, sensing latency is reduced while satisfying other user requirements. The simulation results demonstrate that, compared to random sensing, the first strategy decays the sensing latency by 85.25%, while the second strategy enhances the full-satisfaction rate, where both mobility and bandwidth requirements of the user are simultaneously satisfied, by 95.7%. Therefore, as it can be observed, three key design challenges of the next generation of cellular networks are identified and addressed via the concept of cognitive networking, providing a utilitarian tool for mobile network operators to plug into their systems. The proposed solutions can be generalised to various network scenarios owing to the sophisticated ML implementations, which renders the solutions both practical and sustainable

Identifying and Mitigating Security Risks in Multi-Level Systems-of-Systems Environments

In recent years, organisations, governments, and cities have taken advantage of the many benefits and automated processes Information and Communication Technology (ICT) offers, evolving their existing systems and infrastructures into highly connected and complex Systems-of-Systems (SoS). These infrastructures endeavour to increase robustness and offer some resilience against single points of failure. The Internet, Wireless Sensor Networks, the Internet of Things, critical infrastructures, the human body, etc., can all be broadly categorised as SoS, as they encompass a wide range of differing systems that collaborate to fulfil objectives that the distinct systems could not fulfil on their own. ICT constructed SoS face the same dangers, limitations, and challenges as those of traditional cyber based networks, and while monitoring the security of small networks can be difficult, the dynamic nature, size, and complexity of SoS makes securing these infrastructures more taxing. Solutions that attempt to identify risks, vulnerabilities, and model the topologies of SoS have failed to evolve at the same pace as SoS adoption. This has resulted in attacks against these infrastructures gaining prevalence, as unidentified vulnerabilities and exploits provide unguarded opportunities for attackers to exploit. In addition, the new collaborative relations introduce new cyber interdependencies, unforeseen cascading failures, and increase complexity. This thesis presents an innovative approach to identifying, mitigating risks, and securing SoS environments. Our security framework incorporates a number of novel techniques, which allows us to calculate the security level of the entire SoS infrastructure using vulnerability analysis, node property aspects, topology data, and other factors, and to improve and mitigate risks without adding additional resources into the SoS infrastructure. Other risk factors we examine include risks associated with different properties, and the likelihood of violating access control requirements. Extending the principals of the framework, we also apply the approach to multi-level SoS, in order to improve both SoS security and the overall robustness of the network. In addition, the identified risks, vulnerabilities, and interdependent links are modelled by extending network modelling and attack graph generation methods. The proposed SeCurity Risk Analysis and Mitigation Framework and principal techniques have been researched, developed, implemented, and then evaluated via numerous experiments and case studies. The subsequent results accomplished ascertain that the framework can successfully observe SoS and produce an accurate security level for the entire SoS in all instances, visualising identified vulnerabilities, interdependencies, high risk nodes, data access violations, and security grades in a series of reports and undirected graphs. The framework’s evolutionary approach to mitigating risks and the robustness function which can determine the appropriateness of the SoS, revealed promising results, with the framework and principal techniques identifying SoS topologies, and quantifying their associated security levels. Distinguishing SoS that are either optimally structured (in terms of communication security), or cannot be evolved as the applied processes would negatively impede the security and robustness of the SoS. Likewise, the framework is capable via evolvement methods of identifying SoS communication configurations that improve communication security and assure data as it traverses across an unsecure and unencrypted SoS. Reporting enhanced SoS configurations that mitigate risks in a series of undirected graphs and reports that visualise and detail the SoS topology and its vulnerabilities. These reported candidates and optimal solutions improve the security and SoS robustness, and will support the maintenance of acceptable and tolerable low centrality factors, should these recommended configurations be applied to the evaluated SoS infrastructure

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications