Search

Purpose: Pilonidal sinus incidence is increasing in surgical departments all over the world. Pilonidal sinus is under the influence of several factors. These factors are studied well in various parts of the world; however, these factors are understudied in Pakistan. This study aimed to find out the risk factors of pilonidal sinus among the patients diagnosed with pilonidal sinus. Methodology: This descriptive cross-sectional study was conducted among one hundred and eight patients with pilonidal sinus at Benazir Bhutto Hospital Rawalpindi, for one year from January 2021 to January 2022. Ethical approval and informed consent were obtained before the start of study. Patients’ enrollment was done according to a developed inclusion and exclusion criteria. Convenient sampling technique was also applied for recruitment of participants. Data was collected through a self-designed questionnaire. Descriptive statistics were utilized for the data analysis. Data analysis was performed by SPSS version 25.0 (IBM Corp., Armonk, NY). Findings: Pilonidal sinus frequency was high among the patients who had age group of 11 to 30 years (68.52%), male gender (62.96%), driving profession (37.96%), family history of pilonidal sinus (64.82%), high body mass index(60.18%), daily sitting for more than 6 hours (79.62%), sitting on hard surfaces (63.88%), hairy skin (55.55%), excessive sweating (68.52%), poor hygiene of sacrococcygeal region (77.78%), number of baths less than three times per week (61.11%), and light colored skin (62.04%). Recommendations: This study recommends that health authorities should make people educated regarding the risk factors that lead to pilonidal sinus. The study also recommends that people should reduce their body weight, sit over soft places and should avoid continuous sitting for long periods. People should take care of hygiene, especially of sacrococcygeal regions by shaving hair, drying moisture, and taking frequent baths.

Fifth generation (5G) networks are expected to support a large number of devices, provide spectral efficiency and energy efficiency. Non-orthogonal multiple access (NOMA) has been recently investigated to accommodate a large number of devices as well as spectral efficiency. On the other hand, energy efficiency in 5G networks can be addressed using energy harvesting. In this paper, we investigate NOMA in 5G networks with RF energy harvesting to maximize the number of admitted users as well as system throughput. We model a mathematical framework to optimize user grouping, power allocation, and time allocation for information transfer and energy harvesting while satisfying the minimum data rate and transmit power requirements of users. The proposed framework for optimization is a mixed integer non-linear programming problem. The mesh adaptive direct search (MADS) algorithm is adopted to find solution of the proposed framework. The MADS algorithm provides an epsilon optimal solution. The exhaustive search algorithm is used as a bench mark. Finally, the effectiveness of the proposed framework is supported by simulation results.

Non-Orthogonal Multiple Access (NOMA) has recently been explored to address the challenges in 5G networks such as spectral efficiency, large number of devices, etc. Further, energy harvesting is a promising solution to address the challenges for energy efficiency in 5G networks. In this thesis, joint user grouping, power allocation, and time allocation for NOMA with RF energy harvesting is investigated. We mathematically modeled a framework to optimize user grouping, power allocation, and time allocation for energy harvesting and information transfers. The objective is to maximize data rate while satisfying the constraints on minimum data rate requirement of each user and transmit power. We adopted mesh adaptive direct search (MADS) algorithm to solve the formulated problem. The user grouping in MADS is comparable with the exhaustive search which is computationally very complex. The thesis is supported with simulation results in terms of user grouping, power allocation, user rate, and time sharing.

The Internet of things (IoT) platform can provide product provenance (information that traces a product to its origin) in the food supply chain (FSC). This empowers consumers with the information and opportunity to make the most informed decision. Integrating IoT technology with blockchain can make FSC transparent, making it more productive by providing trustworthy and secure information to both consumers and relevant parties. This paper proposes an architecture of a blockchain- enabled IoT platform to replace the third party present in the FSC, which would authorize the product data. This platform will ensure that the product data collected through sensors from each stage of the supply chain is legitimate and adheres to terms agreed by all parties involved in the system. We present a case study to support the idea of accessible IoT technology being used to create a data network. It details which different technologies that can be used in IoT platform and how it supports the proposed architecture. We show how blockchain can be used in tangent with IoT technologies in order to modernize and optimize the standard FSC.

The ever-growing increase in modern and ubiquitous applications of wireless sensor networks (WSNs) is causing energy scarcity, which is a serious threat to the lifetime of the network. Wireless power transfer emerges as a promising solution to replenish the sensor nodes. In wireless power transfer, energy is transferred to sensor nodes through dedicated energy transmitters. In addition, software-defined WSNs (SDWSNs) have been recently realized to fully explore and efficiently utilize the resources of WSNs. In this paper, we present an energy efficient SDWSN with wireless power transfer. We propose a mechanism to place energy transmitters and determine minimum number of energy transmitters. For placement of energy transmitters, a tradeoff between maximum energy charged in the network and fair distribution of energy is studied. We present this mechanism by defining a utility function to maximize both total energy charged and fairness. For minimum number of energy transmitters, an optimization problem is formulated and solved while satisfying the constraint on minimum energy charged by each sensor node. We also propose an energy-efficient scheduling scheme for energy transmitters for the given tasks of energy charging. The focus is to minimize the energy consumption of energy transmitters while keeping sensor nodes sufficiently charged. Finally, this paper is supported by extensive simulation results, which illustrate the performance of energy-efficient SDWSNs with wireless power transfer in terms of energy charged, fairness, number of energy transmitters, number of tasks, and energy consumption.

The Internet of Things (IoT) offers connectivity of massive low-power devices and sensors through the Internet which requires spectrum and energy efficient solutions. Recently, non-orthogonal multiple access (NOMA) has been investigated to address the challenges associated with spectral efficiency and dense deployment of a large number of devices in 5G cellular networks. Further, energy harvesting can enhance the energy efficiency of IoT devices. In this paper, we propose an energy-efficient resource allocation scheme for NOMA (EERA-NOMA) in cellular IoT with RF energy harvesting to address the above mentioned challenges. We model a framework to optimize user grouping of IoT devices in most appropriate resource blocks, power allocation, and time allocation for information transfer and energy harvesting. The objective is to maximize energy efficiency while satisfying constraints on the minimum data rate requirement of each user and transmit power. We adopted mesh adaptive direct search (MADS) algorithm to solve the formulated problem. Simulation results are presented to show the performance of proposed framework in comparison with existing work.

The rapid growth of modern wireless applications results in spectrum and energy scarcity. Cognitive radio (CR) technology is pivotal to resolve spectrum shortage. However, energy consumption is a critical issue in CR networks (CRNs) due to the unique functionality of spectrum sensing. Besides RF energy harvesting has come up as a potential solution to provide energy to CR devices. In this paper, we first provide an overview of spectrum sensing in CRNs with RF energy harvesting. Then, we propose a framework for multi-band spectrum sensing in CRNs with RF energy harvesting. We formulate a problem to optimize sensing time for throughput maximization while protecting primary users and keeping a minimum level of residual energy. Simulation results show the performance of multi-band cooperative spectrum sensing in terms of average throughput and energy harvested.

Recently cognitive radio technology gets attention to enhance the performance of smart grid communication networks. In this paper, we present a cognitive radio enabled smart grid architecture. We then discuss major cyber security challenges in smart grid deployment and additional challenges introduced by cognitive radio technology. Spectrum sensing is one of the important aspect for opportunistic spectrum access in cognitive radio enabled smart grid networks. Cooperative spectrum sensing can improve the sensing performance in which multiple cognitive radio users cooperate to sense primary user bands. However, cooperative spectrum sensing is vulnerable to incumbent emulation and spectrum sensing data falsification (SSDF) attacks. Thus, we propose a two-stage scheme for defense against SSDF attacks. Simulation results show that the proposed two-stage scheme can identify and exclude the attackers accurately.

The intensity in the requirements of Internet of Things and mobile internet makes the efficiency of fifth-generation (5G) wireless communications very challenging to achieve. Accomplishing the drastically increasing demand of massive connectivity and high spectral efficiency is a strenuous task. Because of the very large number of devices, 5G wireless communication systems are inevitable to satisfy the traffic requirements. Recently, nonorthogonal multiple-access (NOMA) schemes are immensely being explored to address the challenges in 5G, which include effective bandwidth utilization, support for a massive number of devices, and low latency. This paper provides the reader with a holistic view of multiple-access schemes, methods, and strategies for optimization in NOMA. First, we discuss the taxonomy of multiple-access schemes in the literature; then, we provide a detailed discussion of objectives, constraints, problem types, and solution approaches for NOMA. This paper also discusses the decoding methods and key performance indicators used in NOMA. Finally, we outline future research directions.