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Side-channel attacks are an unpredictable risk factor in cryptography. Therefore, observations of leakages through physical parameters, i.e., power and electromagnetic (EM) radiation, etc., of digital devices are essential to minimise vulnerabilities associated with cryptographic functions. Compared to costs in the past, performing side-channel attacks using inexpensive test equipment is becoming a reality. Internet-of-Things (IoT) devices are resource-constrained, and lightweight cryptography is a novel approach in progress towards IoT security. Thus, it would provide sufficient data and privacy protection in such a constrained ecosystem. Therefore, cryptanalysis of physical leakages regarding these emerging ciphers is crucial. EM side-channel attacks seem to cause a significant impact on digital forensics nowadays. Within existing literature, power analysis seems to have considerable attention in research whereas other phenomena, such as EM, should continue to be appropriately evaluated in playing a role in forensic analysis. The emphasis of this thesis is on lightweight cryptanalysis. The preliminary investigations showed no Correlation EManalysis (CEMA) of PRESENT lightweight algorithm. The PRESENT is a block cipher that promises to be adequate for IoT devices, and is expected to be used commercially in the future. In an effort to fill in this research gap, this work examines the capabilities of a correlation EM side-channel attack against the PRESENT. For that, Substitution box (S-box) of the PRESENT was targeted for its 1st round with the use of a minimum number of EM waveforms compared to other work in literature, which was 256. The attack indicates the possibility of retrieving 8 bytes of the secret key out of 10 bytes. The experimental process started from a Simple EMA (SEMA) and gradually enhanced up to a CEMA. The thesis presents the methodology of the attack modelling and the observations followed by a critical analysis. Also, a technical review of the IoT technology and a comprehensive literature review on lightweight cryptology are included.

In Wireless Sensor Networks, with small, limited capacity devices now more prevalent, the issue of Neighbour Discovery has shifted. These devices utilise duty cycling methods in order to conserve battery power. Hence, the main issue is now that these devices may be awake at the same time in order to discover each other. When mobility increases complexity further. Rather than attempt to negate the issue of mobility, instead this thesis seeks to utilise a predictable sink mobility pattern in order to influence the duty cycling of static nodes. Literature demonstrates a move towards Mobility Awareness in Neighbour Discovery in mobile Wireless Sensor Networks. However, there is a gap identified with sink mobility in use. Therefore, this thesis aims to establish to what extent the mobility pattern of a Mobile Sink Node in a Wireless Sensor Network may be exploited at the MAC layer, to influence the performance of static nodes. Such that network efficiency may be improved with energy consumption reduced and balanced across nodes. This study proposes three novel lightweight algorithms, with processing which does not add to the energy consumption within sensor nodes, these being Mobility Aware Duty Cycling Algorithm (MADCAL), Mobility Aware Duty Cycling and Dynamic Preambling Algorithm (MADCaDPAL) and Dynamic Mobility and Energy Aware Algorithm (DMEAAL). These located in the MAC layer of static nodes and utilising knowledge of predictable sink node mobility. This is in order to create a dynamic communication threshold between static nodes on the sink path and the sink itself. Subsequently lessening competition for sink communication between nodes. In MADCAL this threshold is used to influence the sleep function in order that static nodes only awake and move to Clear Channel Assessment once the sink is within their threshold, improving energy consumption by up to 15%. The MADCaDPAL algorithm takes this approach further, using the threshold to directly influence Clear Channel Assessment and the sending of preambles, as such, closing off the threshold when the sink leaves it. This shows energy consumption lessening by close to 80% with a significant improvement in frame delivery to the sink. Finally, the DMEAAL algorithm utilises previous results to influence energy consumption in real-time by utilising a cross-layer approach, comparing current consumption to optimal target energy consumption and adjusting the threshold for each static node accordingly. This shows benefit in evening out results across nodes, thus improving network lifetime. All algorithms are achieved without the energy-consuming beacon messaging associated with Neighbour Discovery. Analysis and simulation results, tested on a lightweight implementation of a carrier-sense multiple-access-based MAC protocol, show a significant improvement in energy consumption and frame delivery in both controlled and random environments. In utilising a cross-layer approach to access energy consumption in static nodes, is it also shown to be possible to even out energy consumption across nodes by altering the communication threshold in real-time. As such, improving network lifetime by removing spikes in energy consumption in individual nodes.

At the heart of the Internet of Things (IoTs) are the Low-power and Lossy networks (LLNs), a collection of interconnected battery-operated and resource-constrained tiny devices that enable the realization of a wide range of applications in multiple domains. For an efficient operation, such networks require the design of efficient protocols especially at the network layer of their communication stack. In this regards, the Routing Protocol for LLNs (RPL) has been developed and standardised by the IETF to fulfil the routing requirements in such networks. Proven efficient in tackling some major issues, RPL is still far from being optimal in addressing several other routing gaps in the context of LLNs. For instance, the RPL standard lacks in a scalable routing mechanism in the applications that require bidirectional communication. In addition, its routing maintenance mechanism suffers from relatively slow convergence time, limiting the applicability of the protocol in time-critical applications, and a high risk of incorrect configurations of its parameters, risking the creation of sub-optimal routes. Furthermore, RPL lacks in a fair load-distribution mechanism which may harm both energy and reliability of its networks. Motivated by the above-mentioned issues, this thesis aimed at overcoming the RPL's weaknesses by developing more efficient routing solutions, paving the way towards successful deployments and operations of the LLNs at different scales. Hence, to tackle the inefficiency of RPL's routing maintenance operations, a new routing maintenance algorithm, namely, Drizzle, has been developed characterized by an adaptive, robust and configurable nature that boosts the applicability of RPL in several applications. To address the scalability problem, a new downward routing solution has been developed rendering RPL more efficient in large-scale networks. Finally, a load-balancing objective function for RPL has been proposed that enhances both the energy efficiency and reliability of LLNs. The efficiency of the proposed solutions has been validated through extensive simulation experiments under different scenarios and operation conditions demonstrating significant performance enhancements in terms of convergence time, scalability, reliability, and power consumption.

The post-installation verification of wind turbine performance is an essential part of a wind energy project. Data collected from meteorological instruments and from the turbine is analysed to produce an estimate of the annual energy production (AEP) which is compared against expectations. However, turbine warranties can impose very strict data filtering criteria which can lead to high rates of data loss. As a consequence, measurement campaigns may last longer than expected and incur additional costs for the development. This project aims to investigate the extent of the problem and the potential of alternative data filtering strategies with respect to data loss, AEP estimates and the dispersion of points in the power curve scatter plot. In doing so, it targets a wide range of meteorological parameters with theoretical relationships to wind turbine power production with particularinterest in those not accounted for in the current standard. The identification of viable filtering strategies with lower data loss would provide significant benefits to wind energy development projects in terms of greater control over timescales and reduced costs. Data from a sample of power performance tests is analysed to explore the range and severity of the problem of data loss. It confirms the wide variation in warranty conditions, demonstrates the extent and likelihood of data losses and quantifies the financial implications within the limits of commercial sensitivity. When indirect costs are taken into consideration, the impact of extended measurement campaigns can theoretically reach tens of millions of pounds. A new, high-fidelity dataset is then compiled so that the effects of alternative filtering strategies can be examined. The dataset covers the whole of 2017 and consists of over 700 parameters of which 74 are selected for investigation here. The eFAST method of global sensitivity analysis is used in combination with correlation analysis to reduce this number to11 parameters which are then used to define alternative filtering criteria. Similar AEP estimates are obtained by application of conventional and experimental criteria to the research dataset. In the case of the experimental filters, however, the data loss was 11% compared to 63% data loss with conventional filters. Conventional filters were also shown to increase the dispersion in the power curve scatter plot by over 10%, while dispersion did not increase significantly with the experimental filters.

Sensor nodes and their underlying communication technologies are characterised by restricted power resources, restricted processing, limited storage capacities, low data rates and lossy links, and they may also comprise up to a thousand nodes. Wireless Sensor Networks (WSNs) require effective methods for data aggregation, forwarding and processing in order to preserve the limited nodes resource. Energy efficiency in WSNs has been widely investigated; it is still a challenging dilemma, and new mechanisms are required to fulfil the identified gaps in the literature. In most of WSNs applications, the energy cost has a significant effect on the network lifetime. Along with the energy efficiency, the Network Scalability and Data Reliability are other challenges affecting the performance of robust sensor network. Consequently, network reliability increases at the expense of energy consumption due to the traffic generated to maintain forwarding paths. On the other hand, scalability is a key component of WSNs because, for example, the network may need to grow to cover more space. Therefore, adding more nodes to the network will increase the number of data traffic within the network. Thus, these challenges impose the need to develop new mechanisms that cope with this sensor network requirements and be able to scale while providing efficient data routing and less energy consumption. Clustering mechanisms are among the most commonly recommended approaches by the research community to sustaining a sensor network throughout its lifetime and provide a scalable architecture and reliable data delivery. Despite a number of research activities associated with clustering in WSNs, some aspects of clustering have not yet been adequately investigated. The conflict between high energy consumption and reliability results in excessive energy waste; this is mainly caused by a high number of control messages exchanged to select cluster-head nodes and frequent re-clustering process in traditional cluster-based mechanisms. The re-clustering of the entire network in each round, or when one of the clusters depletes their energy, is not effective because of the extensive overhead and re-clustering process used. The former problems have been solved using tree novel mechanisms that enhance the network's Energy-Efficiency, Reliability and Scalability while considering the node's limited resources. The performance of the proposed mechanisms is validated under realistic network settings through extensive simulation experiments under different scenarios. The realistic energy consumption model is considered based on Chipcon CC2420 advanced radio modules implemented in Castalia simulator. The results obtained revealed that proposed mechanisms outperform the existing mechanisms in term of energy consumption while achieving reliability and scalability.

Wireless Body Area Network (WBAN) facilitates efficient and cost-effective e-health care and well-being applications. The WBAN has unique challenges and features compared to other Wireless Sensor Networks (WSN). In addition to battery power consumption, the vulnerability and the unpredicted channel behavior of the Medium Access Control (MAC) layer make channel access a serious problem. MAC protocols based on Time Division Multiple Access (TDMA) can improve the reliability and efficiency of WBAN. However, conventional static TDMA techniques adopted by IEEE 802.15.4 and IEEE 802.15.6 do not sufficiently consider the channel status or the buffer requirements of the nodes within heterogeneous contexts. Although there are some solutions that have been proposed to alleviate the effect of the deep fade in WBAN channel by adopting dynamic slot allocation, these solutions still suffer from some reliability and energy efficiency issues and they do not avoid channel deep fading. This thesis presents novel and generic TDMA based techniques to improve WBAN reliability and energy efficiency. The proposed techniques synchronise nodes adaptively whilst tackling their channel and buffer status in normal and emergency contexts. Extensive simulation experiments using various traffic rates and time slot lengths demonstrate that the proposed techniques improve the reliability and the energy efficiency compared to the de-facto standards of WBAN, i.e. the IEEE 802.15.4 and the IEEE 802.15.6. In normal situations, the proposed techniques reduce packet loss up to 61% and 68% compared to the IEEE 802.15.4 and IEEE 802.15.6 respectively. They also reduce energy consumption up to 7.3%. In emergencies, however, the proposed techniques reduce packets loss up to 63.4% and 90% with respect to their counterparts in IEEE 802.15.4 and 802.15.6. The achieved results confirm the significant enhancements made by the developed scheduling techniques to promote the reliability and energy efficiency of WBAN, opening up promising doors towards new horizons and applications.

Internet of Things (IoT) has been paving the way for a plethora of potential applications, which becomes more spatial and demanding. The goal of this work is to optimise the performance within the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) in the network layer. RPL still suffers from unbalanced load traffic among the candidate parents. Consequently, the overloaded parent node drains its energy much faster than other candidate parent nodes. This may lead to an early disconnection of a part of the network topology and affect the overall network reliability. To solve this problem, a new objective function (OF) has been proposed to usher better load balancing among the bottleneck candidate parents, and keep the overloaded nodes lifetime thriving to longer survival. Moreover, several IoT applications have antagonistic requirements but pertinent, which results in a greater risk of affecting the network reliability, especially within the emergency scenarios. With the presence of this challenging issue, the current standardised RPL OFs cannot sufficiently fulfil the antagonistic needs of Low-power and Lossy Networks (LLNs) applications. In response to the above issues, a context adaptive OF has been proposed to facilitate exchanging the synergy information between the application and network layers. Thus, the impact of the antagonistic requirements based on context parameters will be mitigated via rationalizing the selection decision of the routing path towards the root node. We implemented the proposed protocol and verified all our findings through excessive measurements via simulations and a realistic deployment using a real testbed of a multi-hop LLNs motes. The results proved the superiority of our solution over the existing ones with respect to end-to-end delay, packet delivery ratio and network lifetime. Our contribution has been accepted initially to be adopted within the standard body Internet Engineering Task Force (IETF).

With the resource constrained nature of mobile devices and the resource abundant offerings of the cloud, several promising optimisation techniques have been proposed by the green computing research community. Prominent techniques and unique methods have been developed to offload resource/computation intensive tasks from mobile devices to the cloud. Most of the existing offloading techniques can only be applied to legacy mobile applications as they are motivated by existing systems. Consequently, they are realised with custom runtimes which incur overhead on the application. Moreover, existing approaches which can be applied to the software development phase, are difficult to implement (based on manual process) and also fall short of overall (mobile to cloud) efficiency in software qualityattributes or awareness of full-tier (mobile to cloud) implications. To address the above issues, the thesis proposes a model-driven architecturefor integration of software quality with green optimisation in Mobile Cloud Applications (MCAs), abbreviated as Mango architecture. The core aim of the architecture is to present an approach which easily integrates software quality attributes (SQAs) with the green optimisation objective of Mobile Cloud Computing (MCC). Also, as MCA is an application domain which spans through the mobile and cloud tiers; the Mango architecture, therefore, takesinto account the specification of SQAs across the mobile and cloud tiers, for overall efficiency. Furthermore, as a model-driven architecture, models can be built for computation intensive tasks and their SQAs, which in turn drives the development – for development efficiency. Thus, a modelling framework (called Mosaic) and a full-tier test framework (called Beftigre) were proposed to automate the architecture derivation and demonstrate the efficiency of Mango approach. By use of real world scenarios/applications, Mango has been demonstrated to enhance the MCA development process while achieving overall efficiency in terms of SQAs (including mobile performance and energy usage compared to existing counterparts).

Wireless Sensor Network (WSN) technology has been one of the major avenues of Internet of Things (IoT) due to their potential role in digitising smart physical environments. WSNs are typically composed of a vast number of low-power, low–cost and multifunctional sensor nodes within an area that automatically cooperate to complete the application task. This emerging technology has already contributed to the advancement of a broad range of applications. Nevertheless, the development of WSNs is a challenging issue due to significant concerns, which need to be resolved to take full benefit of this remarkable technology. One of the main challenges of WSNs is how to reduce the energy consumption of a single node, in order to extend the network lifetime and improves the quality of service. For that reason, a newly design energy efficient communication protocol is required to tackle the issue. The clustering protocols designed for communication are alleged to be one of the most efficient solutions that can contribute to network scalability and energy consumption in WSNs. While different clustering protocols have been proposed to tackle the aforementioned issue, those solutions are either not scalable or do not provide the mechanisms to avoid a heavy loaded area. This thesis presents new adaptive energy management mechanisms, through which the limited critical energy source can be wisely managed so that the WSN application can achieve its intended design goals. Three protocols are introduced to manage the energy use. The first protocol presents an intra-cluster CH rotation approach that reduces the need for the execution of a periodical clustering process. The second protocol relates to load balancing in terms of the intra and inter-cluster communication patterns of clusters of unequal sizes. This proposed approach involves computing a threshold value that, when reached, triggers overall network re-clustering, with the condition that the network will be reconfigured into unequal cluster size. The third protocol proposes new performance factors in relation to CH selection. Based on these factors, the aggregated weight of each node is calculated, and the most suitable CH is selected. A comparison with existing communication protocols reveals that the proposed approaches balance effectively the energy consumption among all sensor nodes and significantly increase the network lifetime.

Mobile Ad-hoc Networks (MANETs) have lately come to be widely used in everyday applications. Their usability and capability have attracted the interest of both commercial organizations and research communities. Recently, the Vehicular Ad-hoc Network (VANET) is a promising application of MANETs. It has been designed to offer a high level of safety for the drivers in order to minimize a number of roads accidents. Broadcast communication in MANETs and VANETs, is essential for a wide range of important services such as propagating safety messages and Route REQuest (RREQ) packets. Routing is one of the most challenging issues in MANETs and VANETs, which requires high efficient broadcast schemes. The primitive and widely deployed method of implementing the broadcast is simple ‘flooding'. In this approach, each node ‘floods' the network, with the message that it has received, in order to guarantee that other nodes in the network have been successfully reached. Although flooding is simple and reliable, it consumes a great deal of network resources, since it swamps the network with many redundant packets, leading to collisions contention and huge competition, while accessing the same shared wireless medium. This phenomenon is well-known in MANETs, and is called the Broadcast Storm Problem. The first contribution of this thesis is to design and develop an efficient distributed route discovery scheme that is implemented based on the probabilistic concept, in order to suppress the broadcast storm problem. The proposed scheme is called a Probabilistic Disturbed Route Discovery scheme (PDRD), and it prioritizes the routing operation at each node with respect to different network parameters such as the number of duplicated packets, and local and global network density. The performance of the proposed scheme PDRD has been examined in MANETs, in terms of a number of important metrics such as RREQ rebroadcast number and RREQ collision number. Experimental results confirm the superiority of the proposed scheme over its counterparts, including the Hybrid Probabilistic-Based Counter (HPC) scheme and the Simple Flooding (SF) scheme. The second contribution of this thesis is to tackle the frequent link breakages problem in MANETs. High mobility nodes often have frequent link breakages; this potentially leads to re-discovery of the same routes. Although different probabilistic solutions have been suggested to optimize the routing in MANETs, to the best of our knowledge they have not focused on the problem of frequent link breakages and link stability. II Unlike other existing probabilistic solutions, this thesis proposes a new Velocity Aware-Probabilistic (VAP) route discovery scheme, which can exclude unstable nodes from constructing routes between source and destination. The main idea behind the proposed schemes is to use velocity vector information to determine the stable nodes and unstable nodes. A proper rebroadcast probability and timer are set dynamically according to the node stability. Simulation results confirm that the new proposed scheme has much better performance in terms of end-to-end delay, RREQ rebroadcast number and link stability. The routing in VANETs is very critical and challenging in terms of the number of broken links and packet overheads. This is mainly due to the fast vehicles' speed and different vehicles' movement directions. A large number of routing protocols such as Ad-hoc On-demand Distance Vector (AODV) and Dynamic Source Routing (DSR) have been proposed to deal with the routing in MANETs. However, these protocols are not efficient and cannot be applied directly to VANETs context due to its different characteristics. Finally toward this end, this thesis proposes new probabilistic and timer probabilistic routing schemes in order to improve the routing in VANETs. The main aim of the proposed schemes is to set up the most stable routes to avoid any possible link breakage. These schemes also enhance the overall network performance by suppressing the broadcast storm problem, which occurs during the route discovery process. The proposed schemes also make AODV protocol suitable and applicable for VANETs. Simulation results show the benefit of the new routing schemes in terms of a number of metrics such as RREQ rebroadcast number, link stability and end-to-end delay.