Continuous tracking of a patient's vital signs is now routine, but the air the patient actually breathes is seldom measured by the same inexpensive device, even though air quality is a modifiable factor that affects cardiorespiratory health. Because embedded health monitors and air-quality monitors are usually built as separate products, a caregiver must install, power, and reconcile two devices, and the vital-sign readings arrive without any picture of the environment that produced them. To close this gap, we describe a single low-cost embedded platform that combines a physiological subsystem (a pulse sensor and an LM35 temperature sensor) with an environmental subsystem based on an MQ-135 gas sensor. Both share one Arduino Uno (ATmega328P) edge node, and an ESP32 Wi-Fi gateway relays the readings to the ThingSpeak cloud. A 10-bit ADC digitises every channel; the firmware converts the counts into clinical and air-quality indices, checks them against calibrated thresholds, and reports the outcome through a 16×2 I 2 C LCD, a graduated LED and PWM-buzzer alert stage, and a USART log. Each subsystem was built in hardware and checked against a Proteus simulation. In testing, the physiological subsystem separated normal, tachycardic, and febrile states cleanly (pulse 72-120 bpm; temperature 98.2-101.2°F), and the environmental subsystem classified all four severity levels correctly across five controlled gas trials, with an 8-12 s response time and full simulation-hardware agreement. The assembled prototype costs roughly BDT 1,347 (≈ USD 12.3). Three contributions follow: a single-node architecture serving both domains, a shared threshold-classification and multi-modal alerting scheme that works across them, and a cost model grounded in the actual build. Placing patient vitals and ambient air quality on one affordable node lets the device raise context-aware alerts—such as calling for ventilation when pollutant levels climb near a vulnerable patient—which makes it a practical fit for homes, clinics, and resource-limited settings.
Rwanda’s pursuit of a high-income, knowledge-based economy under Vision 2050 depends on scalable, resilient, and cost-efficient telecommunications infrastructure. Despite achieving near-universal 4G LTE population coverage (97–99%), a persistent usage gap — approximately 62% of the population remains unconnected — reveals that supply-side infrastructure alone is insufficient to bridge the digital divide. This paper investigates how Software-Defined Networking (SDN) and Network Function Virtualization (NFV) can serve as strategic enablers of telecommunications modernization in Rwanda, addressing the twin challenges of cost and operational complexity in a landlocked, resource-constrained environment. Employing a Design Science Research (DSR) methodology, this study evaluates Rwanda’s infrastructure readiness, identifies technical, economic, and regulatory barriers to SDN/NFV adoption, and proposes a context-adapted, phased deployment artifact the Frugal SDN/NFV Framework aligned with Rwanda’s ICT Sector Strategic Plan 2024–2029. The framework is supported by five formally specified mathematical optimization models: the Controller Placement Problem for Rwanda’s 30-district fiber topology, the VNF Resource Allocation mixed integer program, a Network Slice SLA Allocation model, a CAPEX/OPEX Net Present Value cost model, and a joint SDN-MEC task offloading optimization. Numerical projections derived from these models informed by comparable African SDN/NFV deployments including Safaricom Ethiopia’s 2022 greenfield virtualized network and MTN South Africa’s cloud-native 5G core suggest potential CAPEX reductions of 20–68% and OPEX reductions of 20–67%. The proposed three-phase roadmap (Pilot 2025–2026; Scale 2026–2028; Optimize 2028–2029) positions SDN/NFV as a leapfrogging catalyst for equitable digital growth, contributing the first academically grounded SDN/NFV deployment framework for an African national telecommunications network.
The rapid convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is reshaping the hospitality industry by enabling intelligent, automated, and hyper-personalized service ecosystems. This study examines how the integration of AI and IoT—referred to as AIoT—enhances smart service delivery and strengthens customer engagement within hotels and related hospitality environments. The research explores key AI and IoT applications such as smart guest rooms, predictive maintenance, automated check-in systems, and personalized service recommendations, highlighting their impact on operational efficiency and guest satisfaction. Findings indicate that AIoT-driven solutions not only streamline service processes but also create immersive, interactive experiences that increase convenience, engagement, and loyalty. Despite challenges related to security, cost, and technological integration, AIoT offers significant potential for transforming hospitality service models. The study concludes that embracing AI–IoT convergence is essential for hospitality organizations seeking competitive advantage in an increasingly digital and experience-driven marketplace.
In order to solve the problems of effective resource allocation in low-power wide-area networks, this thesis investigates the scheduling of end devices in Internet of Things applications using LoRaWAN technology. The main goal of this research is to use RL to improve QoS measures including energy efficiency, throughput, latency, and dependability. This was accomplished by using a simulation-based approach that evaluated the effectiveness of the RL-based scheduling algorithm using NS3 simulations. The main findings show that, in comparison to current scheduling practices, the RL agent greatly improves data transmission reliability and improves network throughput. Furthermore, the suggested approach efficiently lowers average system latency and overall energy usage, improving network resource utilization. These findings imply that using reinforcement learning (RL) for job scheduling in LoRaWAN networks can offer a reliable and expandable solution to present problems, resulting in more intelligent and environmentally friendly IoT systems. In the end, this study finds that using RL-based techniques can help improve resource management in contexts that are dynamic and resource-constrained.
The temperature-sensitive industries including healthcare, agriculture and cold chain logistics the Internet of Things (IoT) has greatly increased monitoring and management of environmental conditions. Minor temperature fluctuations can lead to the deterioration of products, diminished effectiveness of pharmaceuticals, or suboptimal agricultural results. This work presents the design and development of a thermologger system for real-time temperature monitoring and data recording system based on the Internet of Things (IoT). The Internet of Things (IoT) has emerged as a revolutionary solution, facilitating real-time monitoring, sophisticated analysis and automated decision-making across several sectors. The system incorporates low-power digital temperature sensors, a microcontroller unit (MCU) and a wireless communication module based on the ESP-8266. These components work together to collect temperature data and transmit it to a cloud platform for storage and analysis. A key feature of the system is its user-friendly interface, available through a mobile app and a web dashboard. These platforms enable users to view temperature trends, receive alerts when temperatures fall outside of safe ranges and generate reports for further analysis. The alert mechanism is especially useful in high-risk areas such as vaccine storage, greenhouse operations, large industrial cold storage, datacenter and food transportation where timely intervention can prevent significant loss. Real-world testing demonstrates the system’s accuracy, responsiveness, and dependability. These findings validate the system’s efficacy and affordability as a continuous temperature monitoring solution for critical applications. All things considered the proposed thermologger system may enhance operational decision-making, boost safety and optimize resource use across a range of industries.
The rapid rise of Internet of Things (IoT) devices has made cybersecurity much more dangerous and vulnerable, emphasizing the critical necessity for adaptive intrusion detection systems (IDS) to safeguard IoT networks. This study presents a Cyber Threat Intelligence (CTI) model that works in real time and adapts to IoT contexts. The suggested model uses density-based clustering (DBSCAN), deep learning (CNN-LSTM), and reinforcement learning (LDQN) to find, sort, and respond to threats that change over time. A generative model (GAN) is added to make detection better by adding fake data. The model works in three main steps: detection, mitigation and response, and ongoing improvement which is adaptively. During the detecting phase, DBSCAN identifies anomalies by grouping network IoT traffic and separating outliers. A hybrid CNN-LSTM architecture processes anomalies by finding patterns of threats over time, while a Random Forest algorithm classifies typical traffic. During the mitigation and response phase, a Lightweight Deep Q-Network (LDQN) dynamically assigns the actions BLOCK, DROP, INVESTIGATE, or ALLOW based on how serious each threat is. A Generative Adversarial Network (GAN) produces fake data to fix class imbalance and make it easier to find classes that aren't well represented. After being improved, the unified model was able to find IoT intrusions with an accuracy of 92.86%, a precision of 95.16%, and a recall of 95.93%. The system learns about new attack patterns in real time and responds to threats automatically, making it useful for protecting big and changing IoT deployments. This research links classic IDS solutions with cutting-edge AI-driven threat intelligence systems to create an approach for IoT cybersecurity that can grow, is resilient, and improves itself.
The rapid adoption of Information and Communication Technologies (ICTs) in Kenyan public universities has enhanced administrative efficiency and academic delivery. Still, it has also exposed networks to escalating cyber threats, including intrusions and data breaches. The study reveals challenges faced by institutions of higher learning amid rising threats to their cybersecurity as they advance their information technology infrastructure and expand their reliance on internet-based software to enhance their educational, research, as well as administrative activities. This study conducts an empirical analysis of network vulnerabilities and attack patterns in Kenyan public university networks, leveraging 1,290 Secure Shell (SSH) security event logs from the Kenya Education Network (KENET). Employing a quantitative approach grounded in Design Science Research Methodology (DSRM), we categorize vulnerabilities by severity and Common Vulnerabilities and Exposures (CVEs), revealing that medium-severity attacks dominate (94.4%), with SSH-general (57.3%) and CVE-2023-48795 (37.4%) incidents prevalent, peaking between 01:00–03:00. These findings high- light critical risks, such as protocol downgrade attacks and brute-force attempts, necessitating robust cybersecurity measures. We propose actionable recommendations, including automated vulnerability scanning, real-time monitoring, and multi-factor authentication, to enhance network resilience. This study contributes a context-specific analysis of cybersecurity risks in higher education, addressing a gap in localized threat assessments for developing nations.
This study presents a privacy-preserving learning model designed for cross-border telemedicine in East Africa that keeps raw patient records in country while hospitals collaborate on model quality. The core of this approach is to keep sensitive patient records localized within each country, with hospitals training models locally and only sharing model updates. Using synthetic EHRs split across seven hospitals in Kenya, Tanzania, and Uganda, we compare centralized training, standard federated learning, and federated learning with differential privacy. Federated learning improves utility while maintaining data localization, with accuracy rising by about 0.0665, recall for the positive class improving by about 0.1193, and F1 increasing by about 0.0657 relative to centralized training. Adding differential privacy made the system more resilient to attacks. The success rate of model-inversion attacks dropped from 0.696 in the centralized training scenario to 0.686 with standard FL and further to 0.638 with FL + DP. This represents an absolute reduction of 0.058, or about 8.4 percent, in attack success. Membership-inference leakage has an AUC of around 0.50. The trade-off is tunable utility at a chosen privacy budget, for example accuracy near 0.530 at ε = 0.30. The originality is practical, we pair federated learning with an attack simulator and an ε register that turns privacy into an auditable setting hospitals can manage during cross-border care.
The rapid adoption of cloud computing has transformed organizational operations, offering scalability and flexibility but introducing complex governance, risk, and compliance (GRC) challenges. Increasing regulatory demands, such as GDPR, HIPAA, and PCI-DSS, coupled with rising cybersecurity threats, strain traditional manual GRC processes. These processes are often inefficient, error-prone, and ill-equipped to manage the dynamic nature of cloud environments, leading to compliance violations and heightened risks. As organizations strive for robust GRC frameworks, automation has emerged as a critical solution to streamline compliance monitoring, risk assessment, and policy enforcement, ensuring agility and security in cloud-based operations. This study aims to evaluate the effectiveness of integrating ServiceNow’s GRC platform with the NIST Cybersecurity Framework (CSF) to automate GRC processes in cloud computing environments. The research seeks to demonstrate how this integration enhances audit readiness, reduces compliance violations, and improves real-time risk visibility for organizations. Through a case study of a mid-sized financial institution, we explore the implementation of ServiceNow’s GRC platform aligned with NIST CSF’s core functions (Identify, Protect, Detect, Respond, Recover). The methodology includes deploying automated workflows for continuous compliance monitoring, risk assessment, and policy enforcement. Key features examined include automated evidence collection, real-time dashboards, and incident response automation. The case study reveals a 40% reduction in manual effort for compliance tasks, a 30% improvement in incident response times, and enhanced visibility into risk postures through centralized reporting. These findings highlight the platform’s ability to adapt to dynamic cloud environments while maintaining regulatory compliance. The integration of ServiceNow’s GRC platform with NIST CSF significantly enhances organizational GRC capabilities, offering a scalable solution for cloud environments. By automating critical processes, organizations achieve greater efficiency, reduced errors, and improved audit readiness. The study underscores the potential of automation to transform GRC practices, with implications for industries facing stringent regulations. Future enhancements, such as AI-driven predictive risk analytics, could further strengthen proactive risk management. Limitations, including initial implementation costs and training needs, suggest areas for further research to optimize adoption.
Since then, railway-level crossings have become a significant cause of road and rail accidents, claiming dozens of lives each year, not only in Bangladesh but also at every railway crossing in the world. These accidents are increasing alarmingly owing to manual gate operation, staff negligence, and inadequate infrastructure. This situation creates a considerable challenge that must be overcome in a sophisticated way. To neutralize this issue, our project proposes a cutting-edge automated gate control system for railways that opens and closes the rail crossing gates automatically whenever a train is approaching. The system is equipped with advanced features such as obstacle detection, manual control override, and an emergency stop mechanism. It is built to be future-ready with integrations of solar power, IoT, and AI technologies. Moreover, there is also an arrangement to remotely control all the adjacent gates from an intermediate control room. In addition, the suggested system offers a safe and intelligent solution, particularly designed for rural and semi-urban areas in Bangladesh, where conventional railway crossing mechanisms are often outdated or absent. The system intends to significantly reduce the risk of accidents, ensure smoother train operations, and enhance public safety by maximizing automation and innovative technologies in regions that are typically underserved by modern infrastructure. It also holds potential for adoption in other countries facing frequent railway crossing mishaps. Our motto remains clear: "Automation for a Safer Bangladesh."
The Industrial Internet of Things has enhanced automation, real-time monitoring, and predictive decision-making in modern industries. The study explores the mixed research methods (qualitative and quantitative). However, the growing connectivity of industrial IoT systems has exposed them to severe cyber threats such as Ransomware, MitM, and DDoS attacks, which can disrupt critical operations and compromise safety. Conventional Intrusion Detection Systems (IDS) often face limitations in achieving high accuracy, rapid detection, and low latency while minimizing false alarms. This study proposes a CNN-Fuzzy Logic hybrid model for real-time intrusion detection and prevention in industrial IoT environments. Convolutional Neural Networks (CNN) are employed to extract deep hierarchical features from industrial IoT traffic, while fuzzy logic is integrated to enhance decision-making under uncertainty and reduce false positives. The model was trained and evaluated using Kaggle cybersecurity datasets containing ransomware, MitM, and DDoS attacks. Performance evaluation demonstrates that the CNN-Fuzzy IDS achieves an accuracy of 92.5%, a detection rate of approximately 93%, a false positive rate (FPR) of 2.51%, a reduced latency with an average of 7.14% total latency (which corresponds to 1.207 µsec average latency) is very acceptable for most industrial IoT applications. These results highlight the effectiveness of hybrid intelligent systems in enhancing the resilience and reliability of industrial IoT cybersecurity. The proposed model provides a promising pathway for deploying scalable, adaptive, and real-time IDS solutions in critical industrial infrastructures. On system computational overhead researchers should employ a minimum practical setup with modern multi-core CPU, 8–16 GB RAM, SSD, stable OS (Windows 10 only if hardware is modern) or run a lightweight Linux on edge plus offload heavy tasks elsewhere. Future research should also focus on optimizing hybrid ML architectures for low performance metrics for deployment of resource-constrained industrial IoT devices, integrating the approach for threat detection, and expanding evaluation to real-world industrial environments.
Floods are among the greatest natural disasters, causing immense destruction, particularly in flood-prone regions like Bangladesh. This study introduces SentryLeaf, an innovative IoT-based network for real-time flood monitoring and disaster response. The system integrates water-level sensors, environmental sensors, and communication modules to facilitate continuous monitoring, enabling quick identification of high-risk areas. The major findings of this research include the system's high accuracy in data collection, with water-level sensors providing measurements accurate to ±2 cm under ideal conditions. Additionally, SentryLeaf ensures real-time data transmission and reliable communication even in the absence of traditional networks, thanks to its decentralized architecture. The communication network remained stable over distances of 200 meters, despite obstructions, and the peer-to-peer communication protocol exhibited resilience under harsh conditions. Furthermore, the system’s user interface received positive feedback for its intuitive design and responsiveness, allowing emergency responders to make informed decisions quickly. Overall, SentryLeaf significantly enhances Bangladesh’s disaster preparedness and response capabilities, offering a scalable, cost-effective, and resilient solution for mitigating flood-related damages.
Objective: The primary objective of this study is to develop a QoS-aware task scheduling algorithm for LoRaWAN IoT applications using a Reinforcement Learning (RL) approach. Introduction: LoRaWAN is a widely adopted Low Power Wide Area Network (LPWAN) protocol designed for Internet of Things (IoT) applications due to its long-range communication and low power consumption. However, ensuring QoS in LoRaWAN networks remains challenging due to limited bandwidth, high device density, and dynamic traffic patterns. Existing scheduling algorithms often fail to balance competing QoS requirements effectively. Reinforcement Learning (RL) offers a promising solution by enabling intelligent decision-making through interaction with the network environment. Case representation: The proposed model employs a Deep Q-Network (DQN) to optimize task scheduling in LoRaWAN networks. The RL agent interacts with a simulated LoRaWAN environment built using NS-3, where it learns to make scheduling decisions based on real-time network states. Key parameters, such as delay, PDR, PER, and throughput, are used as inputs to the reward function to guide the learning process. Performance is evaluated against existing models like RT-LoRa, and LoRa+ under varying node densities and traffic scenarios. Result: The simulation results demonstrate that the proposed RL-based task scheduling algorithm outperforms existing models across multiple Quality of Service (QoS) metrics. It achieves the lowest delay at approximately 40 ms, significantly outperforming RT-LoRa, which has a delay of around 120 ms, and LoRa+, which experiences a delay of about 80ms. In terms of Packet Delivery Ratio (PDR), the model maintains a competitive value of approximately 85%, comparable to LoRa+ at 87%. Additionally, it records the lowest Packet Error Rate (PER) at around 5%, outperforming RT-LoRa and LoRa+, which exhibit PER values of approximately 15% and 10%, respectively. Furthermore, the model achieves the highest throughput of approximately 250 kbps, surpassing RT-LoRa at 150 kbps and LoRa+ at 200 kbps, demonstrating its superior performance in optimizing network efficiency. Discussion: The proposed model demonstrates significant strengths in reducing delay and PER while maximizing throughput, making it suitable for time-sensitive IoT applications. However, its marginal improvement in PDR compared to existing models highlights an area for further optimization. Additionally, energy efficiency was not explicitly addressed in this study, which is critical for LPWAN applications like LoRaWAN. These limitations suggest potential directions for future research. Conclusion: This research successfully develops a QoS-aware task scheduling algorithm using reinforcement learning for LoRaWAN IoT applications. By dynamically adapting to network conditions, the proposed model achieves superior performance across multiple QoS metrics compared to state-of-the-art algorithms. Future work will focus on incorporating energy efficiency into the model and extending its applicability to multi-gateway scenarios.
There is increasing popularity of Big data and cloud computing in recent years, and it is offering both individuals and businesses a number of advantages. But as data volume and complexity rise, data security and privacy have become a serious problem. In order to safeguard sensitive data stored in the cloud from sophisticated cyberattacks, it is crucial to have strong security measures in place. Although multi-factor authentication (MFA) has gained popularity as a security mechanism, Because of the lack of in depth analysis of its efficacy in large data systems based in the cloud is not fully known. In order to determine if MFA is effective in large data environments based on the cloud, this study will examine how well it can defend against different types of cyberattacks. The study will analyze the benefits and drawbacks of MFA in this situation as well as the trade-offs that must be made between security and usability when putting this security measure into place. This study aims to evaluate the efficacy of MFA in cloud-based big data environments in order to offer insightful recommendations for the most effective ways to secure sensitive data in the cloud.
Cloud is used in various fields for storage of big data with the major challenge regarding this storage being security.Existing conventional encryption systems can be vulnerable to brute-force attacks.The goal of this project was to develop a hybrid encryption scheme that will only allow authorized users to access and download files stored online, thus enhancing file storage security in the cloud.Rapid Application Development (RAD) methodology was used to create the proposed system, allowing for modifications to be made to the system as it was being developed.The hybrid encryption scheme employs both symmetric and asymmetric encryption.The AES (Advanced Encryption Standard) algorithm and RSA (Rivest-Shamir-Adleman) algorithm were combined to develop the proposed hybrid encryption system.PHP, JavaScript and Laravel were the programming languages and web framework used to implement the system.The proposed system was tested and evaluated by users.The experimental results show that the proposed hybrid encryption scheme was fast and provided a high level of security but had some drawbacks which include increase in file size after it was encrypted and inability to sort files in the web app.Overall, the proposed system enhances confidentiality and data protection in cloud environments, guarding against potential breaches and unauthorized access.
Research Context and Aims: The swift expansion of internet technologies has rendered the digital network essential to contemporary life, highlighting the significance of cybersecurity. Network data not only encompasses personal information management and communication but also pertains to the protection of confidential corporate, governmental, and vital national infrastructures. Consequently, cybersecurity has become a critical issue in current society. Frequent occurrences like privacy violations, data thefts, and national security threats underline the critical need for enhanced network defenses and protective measures. Research Approach: This study examines the state of cybersecurity by assessing pertinent studies across public databases such as PubMed, CNKI, and CrossRef. It compiles and evaluates significant cybersecurity events including data breaches, malware attacks, and phishing, outlining key security challenges faced by networks. The paper also evaluates the current cybersecurity technologies and methods, pinpointing their effectiveness and limitations in addressing network threats. Research Findings: The findings reveal that cyber attackers have refined their methods, employing sophisticated, covert techniques for prolonged periods, which often outpace current defenses. In cases of data breaches, perpetrators frequently utilize precise social engineering or deploy advanced persistent threats (APTs). Additionally, the proliferation of IoT technology has not only obscured the boundaries of cybersecurity but also broadened potential attack vectors. Despite the implementation of security measures like encryption and multi-factor authentication, these can be compromised by managerial or operational oversights. Research Conclusions: With the cybersecurity landscape becoming increasingly challenging, future defenses will likely prioritize the adoption of integrated, proactive strategies. It is crucial to foster the development of smart security solutions, such as leveraging artificial intelligence to detect and respond to anomalies. Furthermore, boosting security awareness among users and ensuring standardized practices are imperative. Ultimately, formulating future cybersecurity policies will require a holistic approach, integrating technological, managerial, legal, and educational initiatives to forge a robust network defense architecture.
The Internet of Things (IoT) has lately attracted a lot of interest owing to the fact that it has several applications in a variety of fields and makes communication easier across a variety of levels. The IoT is made up of three unique levels, which are the physical layer, the network layer, and the application layer at the most fundamental level. The purpose of this study is to examine security threats and the responses that correspond to them for each layer of the IoT architecture. Additionally, the article investigates the implications that arise from security breaches on IoT devices. In addition to providing a detailed taxonomy of attacks, this research reveals security weaknesses that are present inside each tier of the IoT network. In addition to this, the article investigates a variety of modern security frameworks, investigates probable security flaws, and investigates remedies that correspond to those vulnerabilities. In conclusion, the article proposed the "Unified Federated Security Framework," which is an all-encompassing security architecture made specifically for IoT networks. In order to facilitate the ability of users inside the security layer to acquire access to resources situated within a separate security layer, the proposed framework is based on the building of trust across the three levels. This allows users to gain access to resources without having to utilise the account of another user.
AI is a potential game changer for Africa to address the specific challenges she faces in sectors like healthcare, climate change and water-related issues. However, the regulation of AI is still largely underdeveloped in Africa with some existing policies and frameworks still being young. Therefore, as the adoption of AI systems spreads across Africa, so does the need for a structured methodology to guide organizations in either developing new AI systems or onboarding existing ones while maintaining the quality and ethicality of these systems. This paper aims to develop a holistic methodology that provides comprehensive guidance to companies considering to develop new AI systems or onboard existing systems. The goal is to support the development and deployment of AI systems tailored to the specific needs of Africa. The proposed methodology employs a lifecycle approach that integrates both Agile and Waterfall frameworks. By combining the adaptive flexibility of Agile with the structured progression of Waterfall, this methodology ensures adaptability and thoroughness throughout the AI system's development and implementation phases. The integration of these methodologies offers a robust, adaptable framework that can be tailored to the unique demands of AI projects in Africa, from design to implementation, deployment as well as maintenance phases, thereby maximizing the potential impact of AI technologies in the region.
Quality of Service (QoS) is the degree to which a service provided by an operator promotes customer satisfaction. In telecommunications sector service quality is a set of specific parameters provided by service providers to their customers, which are necessary for achieving the required functionality of the requested service. In current global competitive telecommunication business market, the quality of a service is being considered as a differentiator for users if service features or price of services of service providers are similar. The potential of growth and scope for telecommunication services in general and mobile broadband services in developing countries like Pakistan is encouraging. The objective of this research work is to analyze the impact of service quality gaps with customer loyalty in the mobile broadband sector of Pakistan. The cellular mobile operators in Pakistan are facing issue of declining customer loyalty and increasing churn rate of customers in search of their expected quality of service. In this research the service quality gaps will be considered as independent variables while customer loyalty as dependent variable. The SERVQUAL model proposed by Parasuraman, Zeithaml and Berry (1988) will be used to probe the effects of five dimensions of service quality viz. tangibility, assurance, responsiveness, reliability, and empathy on consumer loyalty. Data of 200 current LTE broadband consumers using mobile service of four cellular mobile operators of Pakistan (Jazz-PMCL, Ufone-PTML, Zong-CMPak and Telenor) will be collected through structured questionnaires. The response received from end users of broadband services through these questionnaires will be analyzed through SPSS to determine the causal relationship of service quality dimensions and customer loyalty. The results of analysis will depict that the service quality dimensions will have significant and positive impact on customer loyalty.
