Software precision — through forecasting, simulation, and optimisation — becomes essential to maintaining energy security and reliability. As India accelerates towards a renewable-first energy system, AI-driven forecasting, digital twins, and cybersecurity are becoming essential for grid reliability and operational efficiency. The article explores how software-defined energy infrastructure is reshaping power system management in the clean energy era.
India’s energy transition is no longer just about adding renewable capacity — it is about reimagining how power systems are planned, operated, and secured in a software-defined world.
Read the full article published in RenewEdge website, where I have discussed India’s aspiration to build the software backbone of a renewable-first grid by embedding intelligence, resilience, and efficiency into its energy systems.
In the last two years, during conversations with Energy & Utilities (E&U) clients, one question consistently comes up: “How can Digital Twin technology help our organization?”Despite its growing popularity, many still perceive a Digital Twin as nothing more than a digital replica or 3D visualization of a physical asset. While visualization is part of the story, it is far from the whole.
The truth is, Digital Twin is not just a model; it is an evolving intelligence layer that spans the entire lifecycle of an asset: from design and engineering, to real-time monitoring, simulation-based optimization, predictive maintenance, and ultimately, decommissioning and recycling.
Read the full article in my LinkedIn post which explores the role of exponential technology in energy and utilities sector.
Generative AI (GenAI) technology is emerging as a powerful catalyst, capable of reshaping how utilities create knowledge, engage customers, and manage complexity. But the story doesn’t end there. The evolution toward Agentic AI (the AI systems that not only generate insights but also reason, plan, and act autonomously): signals a fundamental shift in what is possible. Together, GenAI and Agentic AI chart a pathway from assisted intelligence to autonomous intelligence, unlocking possibilities that mirror Telecom’s leap from chatbots and predictive analytics to self-healing, self-optimizing networks and others.
Read the full article in my LinkedIn post, where I have explained why GenAI is more than just “chatbots plus text generation”. It’s real power lies in combining large language models (LLMs), domain adaptation, multimodal capabilities, and decision support to transform how information is created, consumed, and operationalized.
According to the latest available data, there are approximately 17 billion connected Internet of Things (IoT) devices and this figure is expected to almost double to 29 billion by 2030. As per verified market research, IoT Devices Market size was valued at USD 125 B$ in 2023 and is projected to reach 620 B$ by 2031 (1).
The growing adoption of smart devices has driven demand for IoT devices, making sustainability more critical than ever as IoT’s role in business continues to expand. Most of the IoT devices have dangerous elements inside, ranging from heavy metals such as lead, mercury, cadmium and beryllium to hazardous chemicals like brominated flame retardants.
They are smaller in size!
The good news is; compared to other technology products, IoT devices has lesser environmental impact and they can deliver greener results. IoT devices are small physical devices, so their production requires fewer raw materials and lesser plastics and packaging materials, thus producing less e-waste.
The unique design of IoT devices has inherent restrictions in memory, so they use embedded systems programming which can run in such restricted conditions. IoT industry dictates the development of more complex systems that can be run on limited resources. IoT devices are compact and smaller than regular computers, designed for seamless integration into various environments. Despite their size, they can perform critical tasks, including real-time monitoring and data analysis. Their efficiency and versatility make them essential in smart systems and automation. Manufacturing of IoT devices requires less material as well as less energy than a large computers.
IoT devices are used primarily for data collection. They continuously collect information and send it to data centers for analysis. The whole process takes a lot of energy and is perhaps not always worth it. It would be a good practice to evaluate the real need for IoT use, in terms of what we gain and how much we spend in the race for popular digital instruments.
Where is the challenge?
The challenge is in it’s waste disposal, and associated environmental impact. Some of the critical concerns are:
Increased Volume of E-Waste: The proliferation of IoT devices leads to a significant increase in electronic waste, contributing to the growing global e-waste problem. 17B IoT devices as of today and it is growing exponentially.
Resource Consumption and Toxicity: IoT devices often contain rare earth metals and hazardous materials that are challenging to recycle and can be toxic to the environment if not disposed of properly.
Short Lifespan: Many IoT devices have short lifespans and are not designed for easy disassembly or recycling, leading to more frequent disposal and accumulation of waste in landfills.
As we discussed at the beginning, IoT devices often contain heavy metals such as lead, mercury, and cadmium. These metals can leach into soil and water, causing environmental contamination and health issues like neurological damage, kidney disease, and cancer. Lead exposure can cause high blood pressure and brain, kidney and reproductive health issues in adults. Exposure to mercury may cause irritation to the eyes, skin, and stomach, cough, chest pain, or difficulty breathing, insomnia, irritability, among others. IoT devices often contain lithium-ion batteries in them, which can pose fire hazards if damaged. Improper disposal can lead to toxic leaks, causing soil and water contamination.
What are sustainable approaches?
To reduce the environmental impact of IoT devices in terms of e-waste, several approaches can be considered:
Design for Longevity and Repairability: Develop IoT devices with longer lifespans, modular components, and easy repairability to reduce the need for frequent replacements.
Use of Sustainable Materials: Utilise recyclable and non-toxic materials in the manufacturing of IoT devices to minimise environmental harm.
Improved Recycling Programs: Establish and promote efficient e-waste recycling programs to ensure proper disposal and recovery of valuable materials from discarded IoT devices.
Standardisation and Interoperability: Encourage standardisation of components and interoperability between devices to reduce the need for multiple, redundant gadgets.
Manufacturer Take-Back Schemes: Implement take-back programs where manufacturers are responsible for collecting and recycling their products at the end of their lifecycle.
Consumer Awareness and Education: Educate consumers about the environmental impact of e-waste and encourage responsible purchasing, usage, and disposal of IoT devices.
Legislation and Regulation: Advocate for stricter regulations and policies that mandate environmentally friendly practices in the production, usage, and disposal of IoT devices.
Can GenAI come to the rescue?
Identifying alternative materials used in IoT devices is crucial as we use more and more connected devices and in-order-to address environmental concerns. The search for eco-friendly materials can reduce the environmental impact and promote the development of greener technologies. Generative AI (GenAI) can play a pivotal role in this process by analysing vast amounts of data to predict and discover new materials with desirable properties. GenAI can simulate the performance of these materials in various conditions, accelerating the research and development process. Additionally, GenAI can optimise manufacturing processes to incorporate these new materials efficiently, ensuring that IoT devices are not only sustainable but also cost-effective and high-performing.
Conclusion
The market demand for IoT devices is rapidly increasing as industries and consumers want to harness the power of connected technologies for enhanced efficiency, convenience, and innovation. However, this surge in demand brings about significant environmental challenges due to the materials and manufacturing processes currently used. Adopting sustainable approaches in the production and deployment of IoT devices is critical to mitigating these environmental impacts. Sustainable practices, such as using eco-friendly materials, improving energy efficiency, and implementing robust recycling programs, ensure that the growth of IoT does not come at the expense of our planet. By prioritising sustainability, manufacturers not only will meet regulatory requirements but also will contribute to a greener future while maintaining the technological advancements that IoT devices offer.
Climate change is a global phenomenon, meaning it affects all parts of the world, regardless of geographic location. This interconnected impact is due to the Earth’s atmosphere and oceans being part of a global system, where changes in one region can have ripple effects across the globe. Here are several examples to justify why climate change is a global phenomenon:
Rising Sea Levels is due to various factors: increase in temperature driven by CO2 emissions into the atmosphere since late 19thcentury resulting into melting of polar ice that is adding more water to the oceanic system and expansion of water volume as the oceanic temperature increases. And we can see the global impact of rising sea levels that threaten coastal communities worldwide, including cities like New York, Shanghai, and Mumbai. This also impacts island nations such as the Maldives, which face the threat of becoming uninhabitable.
“While climate change is a global issue, effective mitigation and adaptation actions need to be localised“
“Formulating climate solutions requires a thorough understanding of the specific influencing factors, constraints, and opportunities within each region. Technology will play a more crucial role than ever before in enabling these localised solutions”
Over the past few weeks, the author has focused his research on climate change, particularly examining the region of Odisha, a coastal state in India. His aim has been to understand the various dynamics driving extreme weather conditions and to explore potential approaches for developing solutions addressing the climate change in this region.
Key Extreme Weather Events witnessed by people of Odisha are:
Heatwaves. A significant increase in the frequency and intensity of heatwaves have been observed where the state regularly experiences temperatures soaring above 40°C during summer, particularly affecting the western and interior regions. This has severe health impacts, leading to increased cases of heatstroke and dehydration.
Cyclones. Odisha’s coastline is prone to cyclones originating from the Bay of Bengal. The frequency and intensity of these cyclones have increased, with notable recent examples including Cyclone Fani (2019) and Cyclone Amphan (2020). Cyclones cause widespread damage to infrastructure, homes, and agriculture. They also result in significant economic losses and displacement of communities.
Flooding. The monsoon season brings heavy rainfall, often leading to severe flooding in low-lying and coastal areas. The erratic nature of monsoon patterns due to climate change exacerbates the frequency and severity of floods. Flooding results in loss of life, damage to property, and disruption of livelihoods. It also poses health risks due to waterborne diseases and hampers access to clean water and sanitation.
Drought. While certain areas of Odisha experience excessive rainfall, others suffer from prolonged dry spells. This irregularity in rainfall patterns contributes to drought conditions, especially in the western and interior districts. Droughts lead to water scarcity, reduced agricultural productivity, and increased food insecurity. They also affect the availability of drinking water and can lead to conflicts over water resources.
Causes of extreme weather
Global Climate Change. The increase in global temperatures due to greenhouse gas emissions is a primary driver of extreme weather events. Higher temperatures exacerbate heatwaves and influence ocean temperatures, contributing to more intense cyclones.
Deforestation. Deforestation and changes in land use, such as mining excavations, urbanization and industrialization, reduce the land’s natural ability to regulate temperature and absorb rainfall, leading to increased vulnerability to extreme weather.
Oceanic and Atmospheric Circulation. The Bay of Bengal’s warm waters and atmospheric conditions play a crucial role in the formation and intensification of cyclones. Changes in oceanic circulation patterns due to climate change further enhance these conditions.
These above are the obvious reasons for climate change. But, the author wants to introduce a new dynamics to this discussion that is topology of a region and how it plays a bigger role in trapping or releasing heat Odisha, plays a significant role in how heat is trapped on the land. Several geographical and climatic factors contribute to this phenomenon:
Geographical Features
The Eastern Ghats: The Eastern Ghats run parallel to the coast but are relatively low in altitude compared to the other parts of Eastern Ghat and Western Ghats. They do not act as significant barriers to the movement of moist air from the Bay of Bengal, allowing more humid air to move inland but not sufficiently high to block or reflect solar radiation. So Mountainous relief also called orographic lifting phenomenon to provide rlief from heat doesn’t occur.
Coastal Plains: Odisha’s coastal plains are flat and expansive, making them more susceptible to the accumulation of heat. The lack of significant elevation means there is little to disrupt the direct heating of the land surface by solar radiation.
Inland Water bodies: The presence of large rivers like the Mahanadi and various lakes and reservoirs can also affect local microclimates. Water bodies absorb heat during the day and release it slowly at night, which can moderate temperature fluctuations but also contribute to higher overall temperatures.
Solution Approach
In this case, we not only have man made challenges, but we have the topological adversity playing a bigger role. High humidity levels, especially after the monsoon season, can trap heat in the lower atmosphere, leading to increased night-time temperatures. This reduces the cooling effect that typically occurs during the night.
While everyone is working towards reduced CO2 emission to tackle the global temperature rise, this may not yield greater result for region like Odisha unless we look at the holistic view and have adaptation actions for topological adversity discussed above.
How to reduce direct heating of the land?
Planation and agriculture practices that can use humidity from the atmosphere and doesn’t rely on monsoon rain.
Some of the strategy considered to reduce direct heating are:
Reforestation and Afforestation: Planting trees and restoring forests in rural and peri-urban areas can significantly increase the amount of shaded land and improve local microclimates.
Deploying solar panels: Covering the open space and surface of the water bodies with solar panels, will help to reduce the amount of heat absorbed by the land and water. Solar panels on water bodies in turn, can help to reduce evaporation, save water and maintain a healthy aquatic ecosystem.
Mangroves and Coastal Forests: Coastal forests, including mangrove ecosystems, play a significant role in cooling the coastal areas. These green cover areas provide shade, reduce the heat island effect, and help maintain cooler temperatures through transpiration.
Reflective: Using light-colored or reflective materials for buildings, roads, and other infrastructure that will reflect more sunlight and absorb less heat.
Cool Roofs: Installing cool roofs with reflective coatings or materials can reduce the amount of heat absorbed by buildings, thereby lowering surrounding temperatures.
Urban Greening: Planting trees, shrubs, and other vegetation in urban areas can provide shade and reduce surface temperatures through transpiration.
Parks and Green Spaces: Designing urban areas with ample parks and green spaces can reduce overall temperatures by providing shaded areas and promoting natural cooling.
Permeable Surfaces: Using permeable materials for pavements and roads allows rainwater to infiltrate the ground, which can reduce surface temperatures and promote cooling through evaporation.
Conclusion
Odisha’s vulnerability to extreme weather events due to climate change necessitates a comprehensive approach involving mitigation, adaptation, and community resilience-building. The combination of Odisha’s flat coastal plains, low mountain ranges, humid climate, extensive deforestation, and urbanization leads to the trapping of heat on the land. These factors, combined with seasonal weather patterns, result in the region experiencing high temperatures, especially during the post-monsoon and dry seasons. Understanding these geographical and climatic influences is crucial for developing strategies to mitigate heat-related impacts in the region.
This is initial findings and thoughts from the author which needs more detail study in-order to come-up with effective mitigation and adaptation actions. These solution approaches need to be combined with policy actions because of the inhernet complexity and broader scope.
Author is open for comments and potential collaborative works in this area.
Generative AI is dominating conversations due to its transformative impact across industries. From generating creative content to aiding in problem-solving, these systems are revolutionizing how we approach tasks. With their ability to innovate, automate, and personalize, Generative AI technologies are at the forefront of shaping the future of technology and human interaction. Ensuring technology advancements benefit all, not just the privileged few, is paramount. While major tech players and governments strive for inclusive and equitable technology use, individuals must also acquire basic skills to prevent widening skill gaps. Accessible technology empowers everyone to participate fully in the digital age, fostering a more equitable and prosperous society.
Crafting well-designed prompts empowers users to steer the AI towards desired outcomes, maximizing creativity and relevance
Introduction
Rule based systems vs Trained systems
Trained systems learn from the data, they adapt and improve over time. Whereas, programmed systems follow predefined instructions without any learning capabilities. Today’s intelligent systems (using AI technology) are trained, not programmed. Instead of programming or writing specific rules to solve a problem, AI systems are fed with examples of ‘what it will encounter’ in the real world. AI systems then detect the patterns and produce their own ‘rules’ based on the examples. While trained systems offer flexibility and adaptation, programmed ones excel in precise execution.
So, Artificial Intelligence (AI) is the science and engineering of making intelligent machines, that can perform tasks which typically requires human intelligence (John McCarthy, 2007). At its simplest form, Artificial Intelligence is a field which combines Computer Science with robust data set.
AI systems typically perform functions like ‘classifying data’ (e.g. assigning labels to images), ‘grouping data’ (e.g. identifying customer segments with similar purchasing behavior), or ‘choosing actions’ (e.g. steering an autonomous vehicle).
What is Generative AI?
Generative AI (GenAI) is the latest development in AI field, where the primary function of the AI system is to generate content, content that is similar to/ indistinguishable from human-created content.
Generative AI is crucial because it empowers us to create new content, ranging from art to music to text, with minimal human intervention. Mastering generative AI allows for innovative problem-solving, creative expression, and automation across various fields, making it a valuable skill in today’s rapidly evolving technological landscape.
Who are the consumers?
Consumers of generative AI systems can be diverse, ranging from individual creators and artists to businesses and organizations across industries such as entertainment, marketing, design, education, healthcare, and more. These systems cater to anyone seeking automated content generation, personalized recommendations, enhanced creativity, or improved efficiency in their work processes. Additionally, consumers could include developers and researchers interested in advancing the capabilities of generative AI itself.
Why to embrace generative AI?
Understanding generative AI skills can be incredibly valuable for several reasons:
Creative Expression: Generative AI empowers individuals to express their creativity in new and exciting ways, whether it’s through generating art, music, literature, or even entirely new concepts. This skill allows for innovative exploration and pushes the boundaries of traditional creative processes.
Problem Solving: Generative AI techniques can be applied to various problem-solving scenarios, such as generating synthetic data for training machine learning models, designing optimized structures in engineering, or simulating complex systems for research purposes. By mastering generative AI, individuals gain a powerful toolset for addressing real-world challenges.
Career Opportunities: With the increasing integration of AI technologies across industries, proficiency in generative AI can open up numerous career opportunities. From roles in software development and data science to creative fields like design and entertainment, individuals with generative AI skills are in high demand and can command competitive salaries in the job market.
Can Gen AI systems make mistakes?
A big yes. While it strives for accuracy and coherence in it’s response; there might be occasions where AI systems can misinterpret the context and provide information that’s outdated or inaccurate.
Large Language Models (LLM)
Large language models are advanced AI systems trained on vast amounts of text data, capable of understanding and generating human-like language. They exhibit impressive language comprehension, enabling them to perform tasks such as text generation, summarization, translation, and question answering.
Generative AI systems are based on large language models which forces users to understand how they are trained. Training a LLM involves feeding it vast amounts of text data to learn patterns and language nuances. The process begins with pre-training on a diverse dataset to impart general knowledge and language understanding to the model. Fine-tuning is the next step which involves training the LLM on a more specific dataset for a particular task or domain. During training, the model adjusts its internal parameters through iterations to minimize prediction errors and improve performance. Finally, validation datasets are used to monitor the model’s performance and prevent overfitting during training.
Despite their capabilities, challenges such as ethical concerns, biases, and energy consumption highlight the need for responsible development and deployment of these models.
Key advantages of Gen AI systems
Creative Output: Gen AI can generate novel and creative content, including images, music, and text, which can be valuable for artistic endeavors, content creation, and design projects.
Automation of Tasks: Gen AI can automate repetitive tasks, such as data entry, image generation, and text summarization, freeing up human resources for more complex and creative endeavors.
Personalization: Gen AI can analyze large datasets and user preferences to generate personalized recommendations and experiences, enhancing customer engagement and satisfaction in various industries such as e-commerce, entertainment, and marketing.
Problem-solving: Gen AI can assist in problem-solving by generating solutions, optimizing processes, and identifying patterns in data, enabling organizations to make data-driven decisions and innovate more effectively.
Efficiency and Scalability: Gen AI can improve efficiency and scalability by automating workflows, optimizing resource allocation, and accelerating innovation cycles, leading to cost savings and competitive advantages for businesses and organizations.
Known dis-advantages of Gen AI systems.
While Generative AI (Gen AI) offers numerous benefits, it also presents some disadvantages:
Ethical Concerns: Gen AI raises ethical concerns regarding the authenticity and ownership of generated content. There are debates surrounding the potential misuse of AI-generated content for spreading misinformation, creating fake news, and manipulating public opinion.
Bias and Discrimination: Gen AI models trained on biased datasets may inadvertently perpetuate biases and discrimination in generated content. This can lead to unfair outcomes, reinforce stereotypes, and exacerbate societal inequalities.
Security Risks: Gen AI poses security risks, including the potential for malicious actors to exploit vulnerabilities in AI systems to generate fake identities, bypass authentication mechanisms, or launch cyberattacks such as phishing scams and social engineering attacks.
Loss of Jobs: The automation capabilities of Gen AI may lead to job displacement and unemployment in certain industries, as AI systems can perform tasks more efficiently and cost-effectively than humans. This can have socio-economic implications and require workforce retraining and reskilling initiatives.
Hallucination/ Dependence on Data: Gen AI models require large volumes of high-quality data for training, which can be expensive, time-consuming, and resource-intensive. Additionally, reliance on data may raise privacy concerns and require compliance with data protection regulations. Moreover, if the data is biased or incomplete, it can lead to inaccurate or unreliable AI-generated outputs.
As per Mckinsey report, economic potential of Generative AI is $4.1 Trillion each year, that is 4.4% of the total economic output. Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities. Generative AI will have a significant impact across all industry sectors. Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI.
How to unlock the potential of Gen AI?
To unlock the potential of Generative AI as an individual, one should learn and experiment by diving into learning Generative AI techniques/ tools/ frameworks. Experiment with small projects to understand its capabilities and limitations.
Continuous Learning is needed to keep up with the latest research, advancements, and best practices in Generative AI. Individual may want to share findings, insights, and projects output with the community in order to foster collaboration and exchange ideas, expertise for growth and learning.
The key is to also stay informed about ethical considerations surrounding generative AI, such as bias mitigation and privacy concerns; and ensure responsible usage and deployment of AI-generated content.
Be a prompt engineer
Prompt engineering is a strategic and creative endeavor that shapes the interactions between humans and artificial intelligence systems. This is the process of crafting effective and specific prompts to guide artificial intelligence systems. It involves designing questions or statements that elicit desired responses from AI models. It is crucial in influencing the output and behavior of AI, ensuring accuracy and relevance in the generated content. By carefully constructing prompts, engineers can control the direction and focus of AI models, leading to more tailored and useful responses.
The effectiveness of prompt engineering can greatly impact the overall user experience and usefulness of AI-powered tools and services.
Conclusion
In summary, prompt engineering serves as a strategic tool for harnessing the power of Generative AI, by guiding the model’s output towards desired outcomes. Crafting well-designed prompts can steer the generative AI model to produce outputs that align with specific goals or criteria, ensuring precision and relevance in the generated content. Prompt engineering can help mitigate biases in generative AI outputs by providing prompts that encourage fair and unbiased content generation, promoting diversity and inclusivity. And finally, tailoring prompts to specific domains or tasks enables generative AI models to produce content that is relevant and useful within those contexts, enhancing their practical applicability.
Every year 19-23 million tonnes of plastic waste leaks into aquatic ecosystems; polluting lakes, rivers and seas (UNEP). This pollution chokes marine wildlife, damages soil and poisons groundwater, and can cause serious health impacts. This is not the only contributor for environmental degradation, but there are many: burning of fossil fuel for Energy generation and transportation, hazardous chemicals used for agriculture and fashion industry and many more.
Increased consumptions is a distinguishing feature of 21st century where more products and services are made available than even before. Total global consumption of construction materials, ore and minerals, fossil fuels, and biomass is ten times of the volume that was in 1900 (Mills, 2012). Large scale farming practices supported the population growth, but widespread usage of fertilisers not only degraded the soil quality over the time but also contributed to environmental pollution in a much larger scale.
Over past 70 years, the world has experienced unprecedented growth, and as a result, extraction of the natural resources was a must to fuel the growth. Currently, the population uses the equivalent of 1.7 of earth planet to provide resources needed to produce the goods and absorb waste. This means earth takes 1 year and 7 months to regenerate what has been used in 1 year (Global Footprint Network, 2020). With no much surprises, this plastic has reached the world’s deepest ocean trench – Mariana Trench which is roughly 11 kilometre below the sea surface.
We are able to raise awareness about environmental impacts and have made some good progress on sustainability reporting and monitoring in production, supply chains; but we have paid much less attention to the design of sustainable products, services and systems.
This is a global problem, YES. But this needs to be actioned at individual level to move the needle.
As an individual, planting trees to create green spaces is not practically possible, but taking actions which is within individual’s control will provide numerous environmental benefits. Adopting the “3 Rs” principle (Reduce, Reuse, Recycle) can significantly decrease waste generation.
Actions for individual
Say no to Single-Use Plastics: Avoid single-use plastics such as plastic bags, straws, and water bottles. Use reusable alternatives like cloth bags, stainless steel straws, and refillable water bottles to reduce plastic waste.
Choose Sustainable Transportation: Walking, biking, carpooling, or using public transportation will reduce greenhouse gas emissions.
Responsible Consumption: This includes conserve energy, save water, change food habit by reducing meat consumption (particularly from high-emission sources like beef and lamb). What about choosing locally sourced, organic, and seasonal foods to minimize the environmental impact of food production and transportation? Choosing products with minimal packaging, made from recycled materials, and produced using sustainable practices will add to the bigger goal to protect environment.
Advocate for Change: Raise awareness, support policies and initiatives that promote environmental protection and sustainability. Prioritise and speak environmental issues and advocate for sustainable practices in your community and workplace.
By incorporating these practices into their daily lives, individuals can contribute to a healthier planet and mitigate the impacts of climate change. Every small action adds up to make a meaningful difference in preserving the environment for future generations.
Railway systems/networks are divided into sections (also known as blocks) to avoid collision between trains; because more than one train is NOT permitted to run on the same section of the track at the same time. The recent train accident in India along with the casualty numbers has warranted to revisit the train signalling system and how technology can come for rescue. The probe report flags multiple protocol breaches including signalling system as reason for the accident (1).
Combination of Blockchain, IoT and AI technology will play a big role in addressing this real-life challenge.
Signalling system plays a vital role in a Railways network to monitors the trains movement and operates the trains in a safe manner. Finding issues with signal failure before time will not save lives and national infrastructure. Key objectives of a Rail signalling system are:
Maintain a safe distance between trains
Control the trains movement at junctions
Prioritise track allotments based on business rules (Fast train vs slower trains vs goods trains)
Internet of Things (IoT) based event logger are being used in many countries to monitor and log the data related to train movements, but many a times this logger is not integrated or overlooked by personnels during the decision making which are primarily manual in nature. Also the usage of IoT systems are limited to predict failure of equipments to alert signal engineer for maintenance rather than using in real-time decision makings.
Problem statements:
In the above diagram, a passenger train has got priority over a goods train. When both the trains approaches a station which is equipped with a “passing loop” track, Peer to Peer (P2P) negotiation takes place. In this case, passenger trains negotiate with Station master to get access to the main line, and good train negotiate with station master to divert into passing loop track. Just imagine when trains are also passing in opposite track at the same time, the station master will get engaged in similar P2P negotiations. Such a P2P coordination can become with more number of trains participating in the network and also if train lines are crossing each other (diamond crossing). The real treat to safety is, the outcome of such a bilateral coordination (P2P) is provisional until all the coordinations are completed (between station master and participating trains).
Concurrent one-to-one coordinations are potentially complex and inefficient or even dangerous if done manually or by unsophisticated approach. All the coordinations between peers must be able to execute the same safeguarding logic that take into consideration of all relevant actors within the given railway network.
Secondly, in some cases it is seen that, once a section of track is allowed to one train post P2P negation, the track fails to switch as intended and hence creating accident situation.
Solution Components:
Blockchain: Blockchain technology provides both centralized and decentralized aspects of a solution.
It will hold the network-wide data at central level which is unique truth and can be shared across all participating nodes (information like train routes, timetables, passenger reservations, infrastructure reservations etc.). It will hold network-wide rules to safeguard trains in terms of smart contracts (stored on the ledger). These are software defined rules and will get executed when certain conditions are met.
The governance will be decentralized, business network administration is based on consensus and on transparency. Transactions are verified (endorsed) and ordered (serialized) by peers/orderers distributed over the participating network. When participating trains in a section or within a defined proximity met certain criteria consensus being taken in defined channel and corresponding smart contracts are verified and ordered for execution. This way, any bottleneck from central broker that is responsible for information distribution is avoided.
Internet of Thing: IoT devices are used to provide real-time situational data from the ground.
Once consensus is arrived in Signalling system for a train to get access to a particular section, the Switch operators makes sure the required switches happens before a green signal provided to the train driver.
So the IoT system will be a participant of the Blockchain system to provide a finality to the transactions before it get executed. IoT devices can be used to monitor the track changes and pass/fail flag will come back to Blockchain system as input before it commits the transaction. Computer vision/ camera solutions can be also used alternatively.
The status of the signal (green or red) on a particular track can be easily broadcasted by a wireless transmitter to any near-by approaching train. This come handy especially early morning hrs with less visibility where train driver need not have to look for the signal outside, but will get the status inside the driver cabin itself.
IoT devices using RFID and other technology can be used to build Anti Collision Systems for railway which can sense static or slow moving objects on the track. Indian railway has been using Train Collision Avoidance System in certain sections of the network successfully (2).
Conclusion
The Blockchain-based signalling System will be a software defined safety mechanisms that can be considered to automate train traffic management. Exponential technologies such as Edge Computing, 5G, IoT and AI can be used to bring right capability for implementation of such a system.
Blockchain and IoT solutions together will provide the real time data to all the stakeholders including Station Masters, Section Controllers, Signal Engineers and others. By sharing real time data on train movement with data integrity and transparency in place, this solution will enhance decision making capability. All the participating trains can communicate with each other, and decisions will be executed through smart contract that can avoid train delays which typically happens due to information gap and lack of trust with the existing systems.
Data will be stored in a distributed ledger using cryptography which will ensure data integrity and tamper proof. Applications and user interfaces can be build on Blockchain system to authenticate participants (driver, dispatcher, station master) based on face recognition or similar technology to avoid security risks.
