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Underwater Acoustic Sensor Networks (UASNs) play a critical role in the remote monitoring of a wide range of time-sensitive underwater applications, such as in the oil/gas pipeline to avoid oil spills. In this type of application, the transmission of collected information to the onshore infrastructure within a period of time is critical. Despite the advantages of UASNs over the limitations of Terrestrial Wireless Sensor Networks (TWSNs), the applicability of UASNs in different use-cases requires further investigation. In this paper, we investigate different MAC protocols and study the impact of non-environmental factors that may degrade performance. We simulate different MAC protocol approaches based on available underwater commercial modems to find the most efficient MAC protocol approach for the oil/gas industry based on core performance metrics. Our extensive simulation results show that the contention-based random access approach is the most suitable for time-sensitive application where the Network Size (NS) followed by Network Load (NL), Data Rate (DR), and Packet Size (PS), respectively have the strongest impact on delay.

The introduction of WebAssembly in 2017 opened a new door for performing computation in the browser at 0.9 the speed of C/C++ code (Haas et al. ACM SIGPLAN Notices 52(6), 185–200, 2017). As browsers are the most ubiquitous software, it is now possible to build universal applications that run on every machine that has a web browser installed on it. In this paper we propose a design to build web applications that take advantage of the new performance capabilities in the browser. We also implemented this design and showed that it increases the overall performance of the web applications in our experiment.

Scheduler timer architecture has significant impact on operating system performance and power consumption. The current generation of Linux kernel supports multiple timer implementations, including periodic ticks, Dyntick-idle and Adaptive-ticks. Adaptive-ticks kernel offers the benefits of previous generations with additional improvement in power consumption and performance. In this paper, we evaluate the impact of Adaptive-ticks on power consumption with Linux kernel version 5.4.0 on an Intel Core i9-9900K. The current generation of Adaptive-ticks feature does not support multiple tasks in a ready queue; however, with the increase in application parallelism, not having support for multiple tasks in the ready queue poses a significant disadvantage to this feature. To support multi-threaded applications, we propose an application optimization technique which splits threads into two main categories, lightweight and heavyweight, with proper affinity settings for better power consumption. In addition, this study proposes a possible implementation strategy to extend the Adaptive-ticks feature to support multiple tasks in the ready queue. Our tests use in-band “RAPL” for profiling power consumption, and synthetic benchmarks such as Livermore, RAMSpeed, and SysBench, as the workloads. For real-world application benchmarking, we use Linux kernel compilation. The study shows that Adaptive-ticks kernel can reduce power consumption by 1–2.7% and the application optimization technique provides a 2.4% enhancement in power consumption.

Electronic healthcare records (EHRs) have become popular because they facilitate sharing of information, and they take small space compared to paper chart systems. Significant improvements continue to be seen in the EHR field because of technological advancement. The current study introduces Health Record Chain (HRC), a mobile application that facilitates communication between patients and caregivers. The technology eliminates the need for physical examination, which encourages social distancing, especially during the COVID-19 pandemic. However, it raises concerns about user’s privacy and adherence to relevant regulations, For instance, Health Insurance Portability and Accountability Act and General Data Protection Regulation GDPR. The suggested system integrates blockchains to control access to user data, ensuring privacy and confidentiality.

We propose general-purpose methods for data representation and data concealment via multivector decompositions and a small subset of functions in the three dimensional Clifford geometric algebra. We demonstrate mechanisms that can be explored for purposes from plain data manipulation to homomorphic data processing with multivectors. The wide variety of algebraic representations in Clifford geometric algebra allow us to explore concepts from integer, complex, vector and matrix arithmetic within a single, compact, flexible and yet powerful algebraic structure in order to propose novel homomorphisms. Our constructions can be incorporated into existing applications as add-ons as well as used to provide standalone data-centric algorithms. We implement our representation and concealment mechanisms in the Ruby programming language to demonstrate the ideas discussed in this work.

Fingerprinting has been widely adopted by first- and third-party websites for the purpose of online tracking. It collects properties of operating systems, browsers, and even the hardware, for generating unique identifiers for visitors on websites. However, fingerprinting has raised both privacy and security concerns. In this paper, we present a traffic-based fingerprinting detection framework, FPExcavator. By analyzing the difference on values carried in outgoing requests from different browsers and machines, FPExcavator detects possible identifiers, as the generated fingerprints, in request header and payload. We implemented FPExcavator with OpenStack, Java, and some command scripts, and evaluated it on 100 websites in a lab setting and 100 websites selected from real-world. FPExcavator achieved 100% detection accuracy rate on 100 testing websites and 99% detection accuracy rate on 100 real-world websites. Meanwhile, it identified 12 new online tracking domains that have not been reported by previous research work. The evaluation results demonstrate that FPExcavator is useful and effective.

The skeletal system and central nervous system are two common sites of metastatic spread in solid tumors. Radiotherapy is a highly effective treatment modality for these sites of metastatic disease. Given that the intent of treatment is not curative, the goals in palliative radiotherapy are centered on symptom management and quality of life. Therefore, the decisions around dose fractionation and radiotherapy technique in palliative therapy can be complex, taking into account patient performance status, prognosis, and goals of therapy. There is increasing interest in the use of hypo-fractionated and stereotactic radiotherapy in the treatment of skeletal and brain metastases due to the convenience of shorter treatments, high rates of local control, and low toxicity rates. This chapter discusses the role for alternate fractionation in palliative radiotherapy to bone metastases, spinal cord compression, and brain metastases.

The virtualization of cloud network requires flexible and effective techniques to accommodate the rapid changes in the network configurations and updates. OpenFlow protocol has attracted attentions for cloud networks since it facilitates managing and sharing the network resources, and it can be utilized to create an overlay abstraction on top of the network infrastructure for the flow setup in the cloud. However, the traditional reactive flow setup of OpenFlow introduces higher flow latency and overhead on the network controller. This paper discusses the issues of the reactive flow setup and presents two optimized overlay network virtualization methods that leverage OpenFlow to control and forward the tenants’ traffic. The proposed methods enable tenants to use their own MAC/IP addresses in the cloud. We have implemented and evaluated the proposed overlay methods, and the experimental results show that our methods have less flow latency than the traditional reactive approach, and higher performance than the popular overlay tunneling protocols such as VXLAN, STT, and NVGRE.

The search for a uniform risk analysis approach for critical infrastructures has prompted a reexamination of the Risk Analysis and Management for Critical Asset Protection (RAMCAP) methodology to see if it can accommodate emerging threats from climate change, aging infrastructure and cyber attacks. This chapter examines the challenges involved in taking a site-specific formulation and turning it into a general model capable of analyzing performance under a full range of simulated conditions. The AWWA J100-10 standard provides the blueprint for a basic RAMCAP model that calculates risk as an attenuation of consequences via probability estimates of vulnerability, threat, resilience and countermeasures. The RAMCAP model was subjected to varying scenario loads in deterministic simulations that examined all hypothetical conditions and probabilistic simulations that examined likely conditions. RAMCAP performance was measured by the average net benefit and represented by the distribution of component values. Contrary to expectations, RAMCAP performance did not improve as the number of scenarios increased in the simulations. The methods and results of this study may hold implications for other critical infrastructure risk methodologies that are based on consequence, threat and vulnerability.