Context: Ensuring network reliability in IoT communication during adverse weather conditions remains a significant challenge. This case study focuses on an IoT-based Landslide Monitoring System (LMS) deployed in Munnar, southern India. The Packet Delivery Ratio (PDR) of an IoT node is defined as the successful end-to-end delivery of data packets from the IoT node to the data cloud and serves as a key indicator of IoT network reliability. However, the IoT nodes in the LMS experience significant drops in PDR due to harsh weather conditions. Objective: This study aims to enhance the PDR of IoT nodes in the LMS under dynamic weather conditions by leveraging multiple IoT edge networks, thereby improving overall IoT network reliability. Methods: We propose a closed-loop Multi-Criteria Network Digital Twin (MNDT) model that virtually replicates each IoT node in the LMS, along with its associated edge networks. The MNDT monitors real-time PDR of IoT nodes and utilizes live weather data obtained from the Indian Meteorological Department portal. It integrates a supervised bandit model to simulate real-time fluctuations in PDR under dynamic weather conditions and forecast the expected PDR for each edge network. Operating within a closed-loop framework, the MNDT relays this predictive feedback to each IoT node in the LMS, enabling proactive and informed selection of the most suitable edge network. Results: The proffered MNDT model was appraised against benchmark reinforcement learning models, such as Q-learning (value-based) and Advantage Actor-Critic (policy and value-based). Experimental results demonstrate that the MNDT achieves the highest improvement in PDR, with a 19.66% increase, followed by Q-learning at 11.57% and Advantage Actor-Critic at 4.48%. Conclusion: This study presents an MNDT model that enhances the PDR of IoT nodes in an LMS under varying weather, with empirical results validating digital twins for improving network reliability in weather-sensitive deployments.

India’s coal use for electricity jumped 13% in 2021–22. Energy management systems (EnMS) are seen as a solution, but only sustainable EnMS can have a discernable impact on the carbon footprint and the Return On Investment (ROI). Designing a software-intensive sustainable energy management system requires considering technical, environmental, social, and economic factors. This helps evaluate an EnMS’s overall impact and improve its design. We proposed EnSAF for efficient utilization of the energy incurred for the design of sustainability-aware EnMSs. In this work, EnMSs in diverse use cases were selected and analyzed in terms of technical, social, environmental, and economic dimensions of sustainability in collaboration with various stakeholders. The set of application-specific design concerns and Quality Attributes (QAs) were addressed by the Sustainability Assessment Framework (SAF) toolkit. The resultant SAF instances of each EnMS, derived through the analysis and discussion with the stakeholders, were then analyzed to advocate the DMs and SQ model for generic EnMSs. This study demonstrated the following outcomes (i) technical concerns dominate the existing EnMSs (ii) integration of renewable energy resources reduces dependency to the main power grid and nurtures a sustainable environment by diminishing carbon footprint, and minimizing payback time, in the economic dimension; (iii) extant definitions of quality attributes need significant scrutiny and updates apropos of objectives of EnMSs The SAF toolkit was found to be deficient in the representation of relevant design concerns and quality attributes concomitant with sustainable EnMS. Prevailing DMs are inept to factor in stakeholder’s concerns, as the model is ill-equipped to account for spatio-temporal representation of QAs. Pursuant to the insights from the 4 SAF instances, a generic framework, EnSAF, is proposed to tackle the relevant concerns apropos of EnMS sustainability. This work proposed a representation of DMs in the SAF toolkit specifically for sustainability-aware EnMS.

COVID-19 pandemic has challenged the capabilities of hospital healthcare delivery systems worldwide. Among patients admitted in hospitals, sudden severity deterioration leading to out-of-ICU ward crashes are observed in many care areas. During the current pandemic, the major gap in the timely identification of COVID patient deterioration is due to the isolation precautions precluding continuous patient monitoring in wards. To address this challenge, we developed and deployed a wearable IoT integrated system called Remote Early Warning of Out-of-ICU Crashes (REWOC in short), which consists of wearable devices at the patient end and early warning score integrated dashboards for physicians and nurses to monitor patients remotely. We describe the architecture and design of REWOC as well as our deployment experience of REWOC on COVID patients in a large hospital in India. To our knowledge, this is one of the first reports of a real-world deployment using wearable devices for monitoring out-of-ICU ward crashes among COVID patients.

One of the primary difficulties of fishermen engaged in deep-sea fishing is the lack of effective communication systems to the shore. The Offshore Communication Network(OCN) resolves this problem by providing Internet over the ocean through a fishing vessel network. OCN is a multi-layered architecture with heterogeneous connectivity ranges, directionality, resources, and mobility patterns. Connectivity maintenance is challenging due to the lack of infrastructure, expanded mobility, network sparsity, and sea-wave-induced movements. This paper discusses a framework to improve OCN connectivity with a multi-level optimization strategy. We propose a predictive model to generate real-time forecasts of link status. At the physical level, node position re-orientations to higher connectivity locations are suggested. The transmission queue management and prioritized scheduling in the link-layer minimize the queuing delay. A reinforcement routing strategy in the network layer determines the best next-hop for message dissemination. The proposed three-level optimization approach facilitates communication capability enhancement in OCN.

Remote health monitoring and delivery through mobile devices and wireless networks offers unique challenges related to performance, reliability, data size, power management, and analytical complexity. We present a multi-layered architecture that matches communication performance to medical importance of data being monitored. The priority of vital data and the context of sensing are used to select the communication medium and the power management policies. Further smartness is introduced into data summarization by employing a severity level quantizer, followed by a consensus abnormality motif discovery and an alert mechanism that prioritizes doctors' consultative time. We also present our successful implementation of the above multi-layered architecture in a system developed to remotely monitor cardiac patients.

Most of the critical challenges seen in the past decades have impacted citizens in a global way. Given shrinking resources, educationists find preparing students for the global market place a formidable challenge. Hence exposing students to multi-lateral educational initiatives are critical to their growth, understanding and future contributions. This paper focuses on European Union's Erasmus Mundus programs, involving academic cooperation amongst international universities in engineering programs. A phased undergraduate engineering program with multiple specializations is analyzed within this context. Based on their performance at the end of first phase, selected students were provided opportunities using scholarship to pursue completion of their degree requirements at various European universities. This paper will elaborate the impact of differing pedagogical interventions, language and cultural differences amongst these countries on students in diverse engineering disciplines. The data presented is based on on the feedback analysis from Eramus Mundus students (N=121) that underwent the mobility programs. The findings have given important insights into the structure of the initiative and implications for academia and education policy makers for internationalizing engineering education. These included considering digital interventions such as MOOCs (Massive Open Online Courses) and Virtual Laboratory (VL) initiatives for systemic reorganization of engineering education.

In this paper, we present a real time remote triggered laboratory which has multi-set, multi-group of wireless sensor network experimental setup which is envisioned to provide a practical experience of designing and implementing wireless sensor networks' algorithms in both indoor and outdoor conditions. The architecture provides a remote code editing mechanism using deluge protocol that offers the user a flexible environment for the experimentation. Central and local authentication agents serve a two level security mechanism which makes the system robust to security threats. The lab is accessible for all the students in the world through internet and it will provide an intuitive web-based interface, where registered users can access the code and do code editing.

Sensor nodes in wireless sensor network are powered by batteries and thus the utilization of effective energy management techniques becomes one of the most important challenges in realistic design of WSN. This paper deals with an optimal energy management scheme in Landslide detection system deployed in Kerala. Based on the meteorological, hydrological and soil parameters, sensors will be dynamically prioritized, scheduled and selects appropriate sensors for event handling. The results of this research work shows that the life time of the network has been improved due to the implementation of this adaptive energy management scheme.

The post disaster mitigation is the immediate task to be carried out in disaster affected areas in order to reduce the extent of damage and for early rehabilitation and reconstruction. This paper proposes a design framework for an optimal control strategy to efficiently perform surveillance over a wide disaster hit area using a network of flying robots to determine the extent of damage promptly so that the rescue operation can be carried out efficiently. The main focus of the paper is to develop a low cost and an adaptive energy efficient strategy with less power dissipation and delay compared to traditional methods. The routing protocol proposed in the paper efficiently determines the best route by taking account of the residual energy, signal strength and various environmental factors. Simulation results show that the proposed routing scheme achieves much higher performance than the classical routing protocols.