Engineering “Smart” Villages Within Pakistan - Rejuvenating Rural Accessibility
Engineering “Smart” Villages Within Pakistan - Rejuvenating Rural Accessibility
Laiba Anwar
Email: laibaanwar07@gmail.com
As per the Smart Villages Initiative, 2015 Pakistan currently faces a global energy crisis, with 1 billion people lacking access to electricity and 3 billion relying on biomass-based inefficient cookstoves. Smoke inhalation from cookstoves has led to the death of 4 million prematurely per year. The ‘’Smart Village’ initiative,which aligns with Sustainable Development Goal (SDG) 7 offers a transformative concept of leveraging technology innovatively to promote rural development and sustainable indigenous living with improved quality of life in underserved areas. (Kasinathan et al., 2022) notes that smart villages built on disruptive technologies like Big Data, AI, IoT will also contribute to SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure). Key components of a smart village include digital education, modern healthcare and digital economic and educational facilities. Smart villages help enhance climate resistance within farmers through integration of technological practices in rural farming, which helps empower the marginalized communities. (Rahoveanu et al., 2022) highlights the core requirements of a Smart Village as having reliable communication networks, education as a key driver of success and gender equity within knowledge of IT. Smart practices through digital integration can support the ecosystem within the villages and promote biodiversity. Similarly as noted by Kumar and RamaKrishna, factors like the rapid urbanization in overcrowded cities leaving villages underdeveloped positions Smart Villages as a tool to maintain a balance between urban and rural development while enhancing the quality of village life.
Indraprahasta, Dinaseviani & Jayanthi, 2022 built upon the concept of a Smart Village (SV) with emphasis on the importance of digital technology like internet access, mobile connectivity and ICT services. Smart villages can help reduce rural poverty and improve agricultural innovation. With better technology, social inclusion of the community also occurs. Zaman mentions how initiatives like the Digital Dera which is a network of climate resilient SVs helps elevate livelihoods through responsible and regenerative agriculture approaches while also helping educate people with the necessary skills and knowledge to become technologically equipped. Tanveer, Naurin, Mumtaz, & Muhammad, talk about the Gokina Smart Village project as an example that aims to digitally transform rural living, and is supported by Huawei, ITU, the Federal Ministry of Information Technology and Telecommunication (MoITT), Universal Service Fund (USF Pakistan), social enterprise TeleTaleem, and one-stop telemedicine provider Sehat Kahani. Okuda further adds that the Gokina Village is part of the Smart Villages and Smart Islands (SVSI) aimed at connecting unconnected communities. However emphasis is still placed upon addressing challenges faced by rural communities and reducing urban-rural. Aziiza, & Susanto highlight the global trends in smart villages that can be analyzed and then applied to transformation in Pakistan, such as in Indonesia (as discussed in the case of Banyuwangi). The Banyuwangi Regency with its "Smart Kampung" program integrates ICT driven governance to tackle local needs. Indonesia has seen the emergence of successful digital startups like TaniHub and eFishery aimed at helping farmers and Pakistan could learn from such initiatives to adapt its initiatives to its rural contexts.
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Tanveer, H., Naurin, S., Mumtaz, S. & Muhammad, S., 2024. Addressing Urban-Rural Disparities in Pakistan: A Targeted Smart Village Model. Journal of Social & Organizational Matters, 3(4), pp.260-289. DOI: 10.56976/jsom.v3i4.136.
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Deconstructing Neuromorphic Computing & Its Relevance in Pakistan’s Public Policy Stratosphere
Neuromorphic computing inspired by the human brain emulates biological neurons to process information efficiently. It replicates the behavior of neurons and synapses of the human brainThis technology has far reaching implications for Pakistan’s public policy sector especially within healthcare and resource allocation.
As noted by Furber, 2016, some ongoing neuromorphic computing systems include IBM TrueNorth chip, SpiNNaker machine. And given the energy efficient nature of neuromorphic systems, they can help tackle Pakistan’s ongoing energy crisis through development of smart grids and AI systems that allow optimization of energy distribution. It could also advance the healthcare sector by bringing improvements within the diagnostic systems and developing autonomous robots for healthcare within the rural regions. Precision agriculture can make use of neuromorphic computing to monitor environmental conditions, crop management. (Ahmed and Shereif, 2022 highlight how neuromorphic computing can be leveraged within fields such as AI, electronics and computational biology to foster innovation. As an emerging tech industry, Pakistan can utilize neuromorphic computing to reduce dependence of foreign technological imports. Neuromorphic computing can be further utilized for traffic management, public safety, pollution monitoring, and telemedicine. As neuromorphic computing evolves, Pakistan’s policymakers can create frameworks that foster innovation by focusing on incentivizing research and addressing material challenges especially given the rise of post CMOS technologies like photonics and spintronics (Mehonic et al.)
Jones and Anjomshoa, 2024 notes the advantages of real time processing with neuromorphic computing with increased parallelism, flexible timing and dynamic resource utilization. However, challenges like scaling of the neuromorphic computing systems, and lack of standardized frameworks exists. Regardless of which it has potential for application within Pakistan with considerable social impact such as leveraging AI technologies to build smart cities, and facilitate healthcare solutions. Uddin et al., 2024 talks about novel anomaly detection methods like spatially aware auto encoder techniques which can be particularly useful for anomaly detection in geospatial data, and be used in sectors like energy and transportation. (Yu et al) also notes how neuromorphic systems because of their distributed nature are fault tolerant which makes them suitable for applications where reliability is essential. (Al-Rodhan, 2016) highlights how if Pakistan is to compete globally in the AI world, it must invest in educating and training its workforce in the AI industry as well as prioritize government funded research initiatives to help develop the sector of neuromorphic computing to become better suited to local needs of the region. This can be furthered by a collaboration between the public and private tech firms allowing for a boost in technological innovation and establishing regulatory frameworks to ensure ethical use of neuromorphic computing, especially in areas like public safety and healthcare.
https://www.researchgate.net/publication/306248734_Large-scale_neuromorphic_computing_systems
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Accentuating the Role of Artificial Intelligence to Pervade Cyber Threats
With the rise of digitalization, the world faces a surge in cyberthreats which poses a threat to national security and privacy. AI offers innovative solutions to help tackle cyber risks.
As noted by Dambe, Gochhait & Ray, 2024, AI equips organizations to protect sensitive data and information, and automate processes. With its ability for real time threat detection and processing, any unusual patterns in traffic or user behavior are immediately identified and addressed (Dambe, Gochhait & Ray, 2024). AI uses technologies like Autoencoders (AE) to detect anomalies and identify deviations, Artificial Neural Networks (ANN) for nonlinear classification of threat detection and Machine Learning (ML) to predict cyber threats, allowing for its prevention (Dambe, Gochhait & Ray, 2024). Similarly, Gopalsamy, Mani. (2023) highlights that AI helps mitigate cyber threats via real time processing and detection of vast data sets of malicious patterns like DDoS attack, phishing which helps take countermeasures. Some notable research insights demonstrate the mitigation of cyber threats through the use of supervised and unsupervised ML techniques, such as SVM (support vector machine) achieving a 93.36% success in classification of hashtag relevance. Moreover, Kaur, Gabrijelčič, and Klobučar, 2023 note that AI, based on the NIST Cybersecurity Framework can help identity, protect, detect, respond and recover data to help minimize risks within an organization. Some of the specific uses of AI in cybersecurity can be defined as analysis of network traffic to track abnormal behaviour, behavioral monitoring, user authentication and intrusion detection (Kaur, Gabrijelčič, and Klobučar, 2023). Technologies like expert systems within AI support problem solving and decision making within cyber security and IDS (Intrusion Detection Systems) help detect unauthorized activity in a network (Kharbanda, 2023).
Omar et al. (2013) quoted by Rajbangshi et al (2023) helps further expand on IDS by focusing on the technical challenge involved in intrusion detection systems by placing emphasis on challenges like feature extraction, classifier construction, and sequential pattern prediction. This helps explain how ML can enhance IDS to predict potential threats. Fakhar & Haile, 2022 further add that NLP (Natural Language Processing) can help analyze unstructured data from diverse sources to help identify any potential cyber threats. AI also enhances the effectiveness of security operation centers through automation of data analysis and incident responses, which reduces the burden on human analysts by allowing them to focus on other strategic tasks (Fakhar & Haile, 2022). Traditional methods often tend to be reactive as they mostly rely on static controls which makes them insufficient against evolving threats, and high profile hijacks like the Equifax hack (2017) highlight these vulnerabilities( Morovat, 2020). Furthermore, with traditional approaches against cybersecurity being insufficient, AI offers a proactive defense strategy such as through its ability to integrate with other security systems like SIEM (Rehman & Liu, 2021). However, Zhang, Al Hamadi, Damiani, Yeun, and Taher, 2022 point out some challenges within AI based cybersecurity models like operating within a ‘black box’ meaning they lack transparency and interpretability which makes it difficult for security experts to understand how decisions are made and also undermines user confidence. Some other challenges of AI integration are complex implementation, adversarial attacks, and risk of bias (Aldhamer, 2024)
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