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Innovation in the Information and Communication Technology (ICT) sector is critical for organizational growth and societal sustainability. However, the innovation capabilities of developing countries, particularly in the ICT sector, remain underexplored. This study is aimed at assessing the perceptions of senior leadership within Pakistan's ICT sector to identify the factors contributing to the limited scope of innovation. Through qualitative interviews with senior leaders from various ICT organizations, this research explores their views on the definition, types, and driving factors of innovation. Most interview participants reported that the major type of innovation practiced at the ICT sector of Pakistan is "product," the level is "partial replication," and the major driving source is "factors external to the organization." Based on these insights, we developed an innovation readiness framework (IRF) to help organizations enhance their innovation capacity. IRF is expected to provide actionable strategies for organizations to assess their current innovation practices, identify gaps, and adopt a structured approach to enhance both product development and operational processes. By addressing sector‐specific challenges such as limited resources and regulatory barriers, the IRF is aimed at empowering organizations to move from replication to more original, impactful innovations. Hence, implementing this framework is expected to improve the sector's competitiveness, drive sustainable growth, and contribute meaningfully to societal advancement by aligning with broader development goals. [ABSTRACT FROM AUTHOR]

Vehicular sensor networks (VSNs) are expected to revolutionize the transportation systems through automated decision‐making. These networks function on the communication between vehicles, traffic infrastructure, and people. One of the major challenges of VSNs is to deal with heterogeneous traffic due to the diverse nature of information and data generated by different types of sensors and devices within the vehicular environment. To ensure an effective operation of the network, it is crucial to offer a differentiated quality of service to each traffic class. Media access control (MAC) protocols offer an opportunity to provide differentiated channel access to the traffic of different priorities. This work is focused on offering comparison of recent MAC schemes in terms of their performance for heterogeneous traffic generated by VSNs. Two protocols FROG‐MAC and UrgMAC have been selected: FROG‐MAC is developed using a simple concept of packet fragmentation, where the packets of lower priority are fragmented to offer an early transmission opportunity to urgent traffic; on the other hand, urgMAC is designed using advanced cross‐layer techniques of two‐tiered service differentiation mechanism, adaptive data rate adjustment mechanism, urgency‐based contention window size adaptation, traffic type adaptive duty cycle, and multimedia message passing. It has been found that the FROG‐MAC outperforms urgMAC in terms of delay, energy consumption and packet delivery ratio. [ABSTRACT FROM AUTHOR]

The ubiquitous Internet of Things (IoT) and sensing technologies provide an interesting opportunity of remote health monitoring and disease risk categorisation of populations. An end‐to‐end architecture is proposed to facilitate real‐time digital dashboards to visualise general anxiety risks of patients, especially during a pandemic, such as COVID‐19. To collect physiological data related to anxiety (heart rate, blood pressure, and saturation of peripheral oxygen [SPO2]) and communicate them to a centralised dashboard, dubbed 'X‐DASH', a hardware prototype of the proposed architecture was developed using Node‐MCU and diverse sensors. The dashboard presents a smart categorisation of users' data, assessing their anxiety risks, to provide medical professionals and state authorities a clear visualisation of health risks in populations belonging to different regions. We categorised the risk levels as Normal, Mild, Moderate, Elevated, Severe, and Extreme, based on the collected physiological data and pre‐defined threshold values. The developed hardware prototype in this work was used to collect data from about 500 patients present at cardiac clinic of a leading general hospital in Karachi (Pakistan) and the anxiety risk levels were assigned based on pre‐defined threshold values. To validate the reliability of the X‐DASH, the personal physician of each patient was consulted and was requested to identify each of their anxiety risk levels. It was found that the risk levels suggested by X‐DASH, (based on data of heart rate, blood pressure, and SPO2 were more than 90% accurate when compared with diagnoses of physicians. Subsequently, packet loss, delay and network overhead for the platform was compared when using MQTT, CoAP and Modbus. Although MQTT has shown higher delays, but it is still recommended due to having a higher reliability. [ABSTRACT FROM AUTHOR]

Human fertility monitoring, whether for natural conception or artificial insemination and fertilization, presents a significant challenge for both physicians and patients. Detecting the presence of eggs in the fallopian tube and timely communication of this information to the healthcare team and the patient remains an elusive goal; in this paper, we propose a novel, hybrid communication architecture for this purpose. Our architecture combines an intrabody nano‐sensor network for detecting the presence of eggs in the female fallopian tube with a body area network (BAN) to receive information from the intrabody network and transmit it over‐the‐air to the required point (physician, nurse, or patient). To investigate the system's performance, we conducted preliminary simulations using a particle‐based stochastic simulator. The results revealed interesting relationships between information rates achievable, signal‐to‐noise ratio (SNR), distance, number of sensors, detection delay, viscosity, and temperature. Specifically, we found that increasing viscosity led to an increase in detection delay, while increasing temperature had the opposite effect. With data rates of up to 300 Mbps achievable at an SNR of 45, our proposed architecture ensures near‐zero latency, minimum energy consumption, and high throughput for efficient fertility monitoring. By integrating advanced communication technologies and sensor networks, our approach paves the way for improved fertility monitoring, enabling timely interventions and personalized care. These findings contribute to the development of a cooperative intra‐ to extra‐body network that enhances accuracy and enables real‐time monitoring for effective fertility management. [ABSTRACT FROM AUTHOR]

Due to rapid advancements in the field of Internet of Medical Things (IoMT), a continuous influx of health data is being generated at a large scale. The primary objective of IoMT solutions is to transmit critical health data from patients to remote locations in real-time. Apart from remote patient monitoring, the extensive collection of health data offers opportunities for uncovering noteworthy patterns and potential risks associated with future diseases. This study introduces a novel risk prediction approach, namely Association Rule Mining for Risk Prediction (ARMR), which integrates an IoMT framework with the emerging machine learning technique known as Association Rule Mining (ARM). The proposed scheme employs a dataset obtained from various hospitals. The findings demonstrate that ARMR effectively extracts rules to identify a patient's risk of heart disease by considering demographic, physiological, and lifestyle data. Moreover, intriguing, and unexpected patterns and associations in the disease data can be identified, aiding medical professionals in guiding diagnosis and treatment decisions more efficiently. [ABSTRACT FROM AUTHOR]