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Subject: search.filters.subject.400602 Data communications

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    D2D communication based disaster response system under 5G networks : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy (PhD) in Computer and Electronics Engineering, Massey University, Auckland, New Zealand
    (Massey University, 2023-12-14) Ahmed, Shakil
    Many recent natural disasters such as tsunamis, hurricanes, volcanoes, earthquakes, etc. have led to the loss of billions of dollars, resources and human lives. These catastrophic disasters have attracted the researchers’ attention onto the significant damage to communication infrastructure. Further, communication within the first 72 hours after a disaster is critical to get help from rescuers. The advancement of wireless communication technologies, especially mobile devices and technologies, could help improve emergency communication systems. The next generation of mobile networks and technologies such as Device to Device (D2D) communication, the Internet of Things (IoT), Blockchain, and Big Data, can play significant roles in overcoming the drawbacks of the current disaster management system for data analysis and decision making. Next-generation cellular 5G and 6G network will provide several complex services for mobile phones and other communication devices. To integrate those services, the 5G cellular network will have the capabilities to handle the significant volume of data rate and the capacity to handle traffic congestion compared with the 4G or 3G cellular network. D2D communication technology, one of the major technologies in the 5G network, has the capability to exchange a high volume of traffic data directly between User Equipment (UE) without additional control from the Base Station(BS). D2D communication is used with other cell tiers in the 5G heterogeneous network (HetNet). Thus, the devices can form a cluster and cooperate with each other. As a result, the system tremendously increases network capacity as devices inside the cluster reuse the same spectrum or use an unlicensed spectrum. It will help to reduce the network’s traffic load and achieve significant throughput. D2D communication also has the ability to increase area spectral efficiency, reduce device power consumption, outage probabilities and improve network coverage. All of these characteristics are vital parameters for public safety and emergency communication applications. IoT paradigm is another promising technology with exciting features such as heterogeneity, interoperability, and flexibility. IoT has the capability to handle vast amounts of data. This huge amount of data creates Data security and data storage problems. Though, there are many technologies used to overcome the problem of validating data authenticity and data storage. Out of them, the Blockchain system is one of the emerging technologies which provides intrinsic data security. In addition, Big data technology provides data storage, modification, process, visualisation and representation in an efficient and easily understandable format. This feature is essential for disaster applications because it requires quickly collecting and processing vast amounts of data for a prompt response. Therefore, the main focus of this research work is exploring and utilising these emerging technologies (D2D, IoT, Big Data and Blockchain) and validating them with mathematical modelling for developing a disaster response system. This thesis proposes a disaster response framework by integrating the emerging technologies to overcome the problem of data communication, data security, data analysis and visualisation. Mathematical analysis and simulation models for multiple disaster sizes were developed based on D2D communication system. The result shows significant improvement in the disaster framework performance. The Quality of Services (QoS) is calculated for different scales of disaster impact. Approximately 40% disaster-affected people can get 5-10 dB and approximately 20% users get 20-25 dB Signal to Interference and Noise Ratio (SINR) when 70% infrastructure is damaged by a disaster. The network coverage increased by 25% and the network lifetime increased by 8%-14%. The research helps to develop a resilient disaster communication network which minimises the communication gap between the disaster-affected people and the rescue team. It identified the areas according to the needs of the disaster-affected people and offered a viable solution for the government and other stakeholders to visualize the disaster’s effect. This helps to make quick decisions and responses for pre and post-disaster.
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    Improving network lifetime through energy-efficient protocols for IoT applications : thesis submitted to the School of Food and Advanced Technology, Massey University New Zealand, in partial fulfilment of the requirements for the degree of Doctor of Philosophy
    (Massey University, 2022) Mishra, Mukesh
    Sensors are ubiquitous. They can be found in homes, factories, farms, and just about everywhere else. To meet distributed sensing requirements several sensors are deployed and connected on a wireless media to form a Wireless Sensor Network (WSN). Sensor nodes exchange information with one another and with a base station (BS). We begin with a review of recent work on cross-layer WSN design techniques based on the Open System Interconnection (OSI) model. The distributed sensor nodes are often grouped in clusters and a cluster head (CH) is chosen and used to route data from the sensor nodes to the BS. The thesis evaluates constraints-based routing algorithms, which choose a routing path that satisfies administrative or Quality of Service (QoS) constraints. Different algorithms reduce costs, balance network load, and improve security. Clustering sensor nodes in a wireless sensor network is an important technique for lowering sensor energy consumption and thus extending the network's lifetime. The cluster head serves as a router in a network. Furthermore, the cluster head is in charge of gathering and transmitting sensed information from cluster members to a destination node or base station/sink. To safely elect a cluster head, an efficient clustering approach is required. It continues to be an important task for overall network performance. As a result, in this study, we propose a scheme for cluster head selection based on a trust factor that ensures all nodes are trustworthy and authentic during communication. Direct trust is calculated using parameters such as residual energy and node distance. Further, K-means clustering algorithm has been employed for cluster head selection. The simulation results show that the proposed solution outperforms the LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol in improving network lifetime, packet delivery ratio, and energy consumption. Furthermore, this strategy can significantly improve performance while discriminating between legitimate and malicious (or compromised) nodes in the network. The use of the IoT in wireless sensor networks (WSNs) presents substantial issues in ensuring network longevity due to the high energy requirements of sensing, processing, and data transmission. Thus, multiple conventional algorithms with optimization methodologies have been developed to increase WSN network performance. These algorithms focus on network layer routing protocols for dependable, energy-efficient communication, extending network life. This thesis proposes multi-objective optimization strategy. It calculates the optimum path for packets from the source to the sink or base station. The proposed model works in two-steps. First, a trust model selects cluster head to control data connection between the BS and cluster nodes. To determine data transmission routes, a novel hybrid algorithm is proposed that combines a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA) .The obtained results validate the proposed approach's efficiency, as it outperforms existing methods in terms of increased energy efficiency, increased network throughput, high packet delivery ratio, and high residual energy across all iterations. Sensor nodes (SNs) have very constrained memory, energy, and computational resources.The limitations are further exacerbated due to the large volume of sensing data generated in a distributed IoT application . Energy can be saved by compressing data at the sensor node or CH level before transmission. The majority of data compression research has been motivated by image and video compression; however, the vast majority of these algorithms are inapplicable on sensor nodes due to memory restrictions, energy consumption, and processing speed. To address this issue, we chose established data compression techniques such as Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE), which require much less resources and can be executed on sensor nodes. Both RLE and AHE can negotiate compression ratio and energy utilisation effectively. This thesis initially evaluates RLE and AHE data compression efficiency. Hybrid-RLEAHE (H-RLEAHE) is then suggested and tested for sensor nodes. Simulations were run to validate the efficacy of the proposed hybrid algorithm, and the results were compared to compression methods using RLE, AHE, and without the use of any compression technique for five different cases. RLE data compression outperforms H-RLEAHE and AHE in energy efficiency, network performance, packet delivery ratio, and energy across all iterations.
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    Increasing the capacity of 5G networks using mobile-cells : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, New Zealand
    (Massey University, 2019) Jaffry, Shan
    Recently, the exponential growth in mobile data demand, fuelled by novel use-cases, such as high- definition video streaming, etc., has caused massive strain on cellular networks. As a solution, the fifth generation (5G) of cellular technology has been introduced to improve network performance through various innovative features, such as millimeter-wave spectrum, device-centric communication, and heterogeneous networks (HetNet). The HetNets will comprise of several small-cells underlaid within macro-cell to serve densely populated regions, like stadiums, malls, etc. On the other hand, due to the constant rise in the use of mobile phones while traveling, the concept of mobile-cells has emerged. Mobile-cells may well be defined as public transport vehicles (e.g., buses or trains etc.) equipped with in- vehicle cellular antenna to serve commuters. The argument for using mobile-cell is based on the observation that commuters often experience poor quality of service (QoS) due to vehicular penetration loss (VPL). Mobile-cell will decouple commuters from the core network, thus eliminating VPL, along with relieving base station off large number of users. Mobile-cells will contain multiple wireless links. Commuters will be served over access link (AL), while the communication with the core network will occur over the backhaul link (BL). On the other hand, neighboring mobile-cells will mutually exchange data over sidehaul links (SLs). Like any other device-centric communication, mobile-cells need to ‘discover’ their neighbors before establishing SLs. Neighborhood discovery is challenging for mobile-cells. Relevant literature on this topic has only focused on static devices, and discovery for mobile devices has not been investigated in detail. Hence, as our first research problem in this thesis, we have focused on the autonomous discovery by a mobile-cell. In general, due to randomness involved in an autonomous process, neighborhood discovery often fails due to collision and half-duplexing effects. This thesis focuses on mitigating these effects. Firstly, we have proposed a modified time-frequency frame structure to subside the collision and half-duplexing effects. Later on, we have presented a more reliable solution that utilizes proximity awareness to adapt transmission probability of individual devices. This scheme has resulted in a drastic increase in the probability of successful discovery as compared to the conventional approaches. On the other hand, actual data exchange via mobile-cell’s links requires interference-free resource allocation for each link. Mobile-cells’ wireless links will cause severe interference to the out-of-vehicle cellular users. Few researchers have assigned separate bands for in-vehicle and out-of-vehicle links. However, given the scarcity of spectral resources, these methods are practically inefficient. Thus, we have addressed the issue of resource allocation as the second research problem in this thesis. Instead of assigning individual resources to each link, we have focused on resource sharing between multiple wireless links. To achieve this goal, we have exploited VPL and utilized successive interference cancellation. Our results have shown high QoS at each individual link. We have also demonstrated the effect of mobility on the proposed resource sharing schemes. The schemes proposed in this thesis will ensure that the mobile-cell increases the capacity of 5G networks through aggressive resource sharing such that more links will use available spectral resources.