• Energy Research

Forest fires are a significant threat to the ecological system’s stability. Several attempts have been made to detect forest fires using a variety of approaches, including optical fire sensors, and satellite-based technologies, all of which have been unsuccessful. In today’s world, research on flying ad hoc networks (FANETs) is a thriving field and can be used successfully. This paper describes a unique clustering approach that identifies the presence of a fire zone in a forest and transfers all sensed data to a base station as soon as feasible via wireless communication. The fire department takes the required steps to prevent the spread of the fire. It is proposed in this study that an efficient clustering approach be used to deal with routing and energy challenges to extend the lifetime of an unmanned aerial vehicle (UAV) in case of forest fires. Due to the restricted energy and high mobility, this directly impacts the flying duration and routing of FANET nodes. As a result, it is vital to enhance the lifetime of wireless sensor networks (WSNs) to maintain high system availability. Our proposed algorithm EE-SS regulates the energy usage of nodes while taking into account the features of a disaster region and other factors. For firefighting, sensor nodes are placed throughout the forest zone to collect essential data points for identifying forest fires and dividing them into distinct clusters. All of the sensor nodes in the cluster communicate their packets to the base station continually through the cluster head. When FANET nodes communicate with one another, their transmission range is constantly adjusted to meet their operating requirements. This paper examines the existing clustering techniques for forest fire detection approaches restricted to wireless sensor networks and their limitations. Our newly designed algorithm chooses the most optimum cluster heads (CHs) based on their fitness, reducing the routing overhead and increasing the system’s efficiency. Our proposed method results from simulations are compared with the existing approaches such as LEACH, LEACH-C, PSO-HAS, and SEED. The evaluation is carried out concerning overall energy usage, residual energy, the count of live nodes, the network lifetime, and the time it takes to build a cluster compared to other approaches. As a result, our proposed EE-SS algorithm outperforms all the considered state-of-art algorithms.

Falls are critical events among the elderly living alone in their rooms and can have intense consequences, such as the elderly person being left to lie for a long time after the fall. Elderly falling is one of the serious healthcare issues that have been investigated by researchers for over a decade, and several techniques and methods have been proposed to detect fall events. To overcome and mitigate elderly fall issues, such as being left to lie for a long time after a fall, this project presents a low-cost, motion-based technique for detecting all events. In this study, we used IRA-E700ST0 pyroelectric infrared sensors (PIR) that are mounted on walls around or near the patient bed in a horizontal field of view to detect regular motions and patient fall events; we used PIR sensors along with Arduino Uno to detect patient falls and save the collected data in Arduino SD for classification. For data collection, 20 persons contributed as patients performing fall events. When a patient or elderly person falls, a signal of different intensity (high) is produced, which certainly differs from the signals generated due to normal motion. A set of parameters was extracted from the signals generated by the PIR sensors during falling and regular motions to build the dataset. When the system detects a fall event and turns on the green signal, an alarm is generated, and a message is sent to inform the family members or caregivers of the individual. Furthermore, we classified the elderly fall event dataset using five machine learning (ML) classifiers, namely: random forest (RF), decision tree (DT), support vector machine (SVM), naïve Bayes (NB), and AdaBoost (AB). Our result reveals that the RF and AB algorithms achieved almost 99% accuracy in elderly fall-d\detection.

The Internet of Things (IoT) is one of the key components of the ICT infrastructure of smart cities due to its great potential for intelligent management of infrastructures and facilities and the enhanced delivery of services in support of sustainable cities. Smart cities typically rely on IoT, where a wide variety of devices communicate with each other and collaborate across heterogeneous and distributed computing environments to provide information and services to urban entities and urbanites. However, leveraging the IoT within software applications raises tremendous challenges, such as data acquisition, device heterogeneity, service management, security and privacy, interoperability, scalability, flexibility, data processing, and visualization. Middleware for IoT has been recognized as the system that can provide the necessary infrastructure of services and has become increasingly important for IoT over the last few years. This study aims to review and synthesize the relevant literature to identify and discuss the core challenges of existing IoT middleware. Furthermore, it augments the information landscape of IoT middleware with big data applications to achieve the required level of services supporting sustainable cities. In doing so, it proposes a novel IoT middleware for smart city applications, namely Generic Middleware for Smart City Applications (GMSCA), which brings together many studies to further capture and invigorate the application demand for sustainable solutions which IoT and big data can offer. The proposed middleware is implemented, and its feasibility is assessed by developing three applications addressing various scenarios. Finally, the GMSCA is tested by conducting load balance and performance tests. The results prove the excellent functioning and usability of the GMSCA.

Automation can now be found in nearly every industry. However, home automation has yet to reach Pakistan. This paper presents an Internet of Things smart kitchen project that includes automation and monitoring. In this project, a system was developed that automatically detects the kitchen temperature. It also monitors the humidity level in the kitchen. This system includes built-in gas detection sensors that detect any gas leaks in the kitchen and notify the user if the gas pressure in the kitchen exceeds a certain level. This system also allows the user to remotely control appliances such as freezers, ovens, and air conditioners using a mobile phone. The user can control gas levels using their phone with this system. In this paper, the ESP32, DHT11 Sensor, 5 V Relay X 8, and MQ-135 gas sensors create a smart kitchen by controlling the temperature, managing humidity, and detecting gas leakage. The system was built on an Arduino board that is connected to the Internet. The hardware was integrated and programmed using an Arduino, and a user Android application was developed. The project’s goal is to allow any Android smartphone to remotely control devices. This method is commonly used in homes, businesses, and grocery stores. Users will be able to control all of their instruments from anywhere, including switches, fans, and lights. Furthermore, simulation was performed using Matlab2016b on multiple houses. In the simulation, not only was the kitchen considered, but also two, four, and six houses. Each house has two bedrooms, one living room, one guest room, two bathrooms, and one kitchen. The results revealed that using this system will have a scientifically significant impact on electricity consumption and cost. In the case of the houses, the cost was USD 33.32, 32.64, 22.32, and 19.54 for unscheduled, two, four, and six houses, respectively. Thus, it was observed that the cost and power are directly proportional to each other. The results reveal that the proposed solution efficiently reduces the cost as compared to that of unscheduled houses.

In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.

Les chaînes de blocs sont une nouvelle approche de la création de réseaux distribués qui a été introduite pour la première fois en 2008. Il permet la formation de réseaux peer-to-peer basés sur le consensus, formant des chaînes à partir de blocs acceptés sans nécessiter d'autorité centrale ou de contrôleur centralisé. Une application importante de cette technologie est son utilisation dans les systèmes de stockage décentralisés. Les individus dans les réseaux de stockage décentralisés louent de l'espace de stockage matériel inutilisé à d'autres individus. Un réseau décentralisé utilisant un cryptage de bout en bout élimine le risque de perte de données associé au contrôle centralisé des données en permettant aux clients de transmettre leurs fichiers en toute sécurité. Les fournisseurs de stockage doivent prouver qu'ils ont conservé des fichiers inchangés dans ce réseau pendant cette période. De nombreuses études ont été menées dans ce domaine spécifique, la plupart ciblant la capacité et l'efficacité du stockage, mais une lacune en matière de sécurité, d'intégrité et de confidentialité doit être comblée. Cet article présente un aperçu des systèmes de stockage basés sur la blockchain et de leur fonctionnement, suivi d'une comparaison avec les réseaux de stockage basés sur le cloud et d'une enquête sur divers réseaux de stockage décentralisés tels que SIA, File coin et Storj disponibles sur le marché. Ensuite, nous discutons des avantages et des inconvénients du stockage basé sur la blockchain. Dans notre discussion finale, nous examinons les problèmes de sécurité des réseaux de stockage décentralisés et explorons les solutions potentielles et les orientations de recherche pour l'avenir. Las cadenas de bloques son un nuevo enfoque para crear redes distribuidas que se introdujo por primera vez en 2008. Permite la formación de redes peer-to-peer basadas en el consenso, formando cadenas a partir de bloques aceptados sin requerir una autoridad central o un controlador centralizado. Una aplicación destacada de esta tecnología es su uso en sistemas de almacenamiento descentralizados. Las personas en redes de almacenamiento descentralizadas alquilan espacio de almacenamiento de hardware no utilizado a otras personas. Una red descentralizada que utiliza cifrado de extremo a extremo elimina el riesgo de pérdida de datos asociado con el control centralizado de datos al permitir a los clientes transmitir sus archivos de forma segura. Los proveedores de almacenamiento deben demostrar que han mantenido inalterados los archivos en esta red durante este tiempo. Se han realizado muchos estudios en este ámbito específico, la mayoría dirigidos a la capacidad y eficiencia de almacenamiento, pero es necesario abordar una laguna de seguridad, integridad y privacidad. Este documento presenta una descripción general de los sistemas de almacenamiento basados en blockchain y cómo funcionan, seguido de una comparación con las redes de almacenamiento basadas en la nube y una encuesta de varias redes de almacenamiento descentralizadas como SIA, File coin y Storj disponibles en el mercado. A continuación, discutimos las ventajas y desventajas del almacenamiento basado en blockchain. En nuestra discusión final, examinamos los problemas de seguridad de las redes de almacenamiento descentralizadas y exploramos posibles soluciones y direcciones de investigación para el futuro. Blockchains are a new approach to creating distributed networks that were first introduced in 2008. It allows the formation of peer-to-peer networks based on consensus, forming chains from accepted blocks without requiring a central authority or centralized controller. A prominent application of this technology is its use in decentralized storage systems. Individuals in decentralized storage networks rent unused hardware storage space to other individuals. A decentralized network utilizing end-to-end encryption eliminates the risk of data loss associated with centralized data control by enabling clients to transmit their files securely. The storage providers must prove that they have kept unaltered files in this network for this time. Many studies have been conducted in this specific domain, most targeting storage capacity and efficiency, but a security, integrity and privacy loophole need to be addressed. This paper presents an overview of blockchain-based storage systems and how they work, followed by a comparison with cloud-based storage networks and a survey of various decentralized storage networks like SIA, File coin, and Storj available on the market. Next, we discuss the advantages and disadvantages of blockchain-based storage. In our final discussion, we examine the security problems of decentralized storage networks and explore potential solutions and research directions for the future. تعد البلوك تشين نهجًا جديدًا لإنشاء شبكات موزعة تم تقديمها لأول مرة في عام 2008. يسمح بتكوين شبكات نظير إلى نظير بناءً على الإجماع، وتشكيل سلاسل من الكتل المقبولة دون الحاجة إلى سلطة مركزية أو وحدة تحكم مركزية. ومن التطبيقات البارزة لهذه التكنولوجيا استخدامها في أنظمة التخزين اللامركزية. يقوم الأفراد في شبكات التخزين اللامركزية بتأجير مساحة تخزين الأجهزة غير المستخدمة لأفراد آخرين. تعمل الشبكة اللامركزية التي تستخدم التشفير من طرف إلى طرف على القضاء على مخاطر فقدان البيانات المرتبطة بالتحكم المركزي في البيانات من خلال تمكين العملاء من نقل ملفاتهم بشكل آمن. يجب على موفري التخزين إثبات أنهم احتفظوا بملفات غير معدلة في هذه الشبكة لهذا الوقت. وقد أجريت العديد من الدراسات في هذا المجال المحدد، ومعظمها يستهدف سعة التخزين والكفاءة، ولكن هناك حاجة إلى معالجة ثغرة الأمن والنزاهة والخصوصية. تقدم هذه الورقة نظرة عامة على أنظمة التخزين القائمة على البلوك تشين وكيفية عملها، تليها مقارنة مع شبكات التخزين القائمة على السحابة ومسح لمختلف شبكات التخزين اللامركزية مثل SIA و File coin و Storj المتاحة في السوق. بعد ذلك، نناقش مزايا وعيوب التخزين القائم على البلوك تشين. في مناقشتنا الأخيرة، ندرس المشاكل الأمنية لشبكات التخزين اللامركزية ونستكشف الحلول والتوجهات البحثية المحتملة للمستقبل.

Smart grid technology has given users the ability to regulate their home energy use more efficiently and effectively. Home Energy Management (HEM) is a difficult undertaking in this regard, as it necessitates the optimal scheduling of smart appliances to reduce energy usage. In this research, we introduce a metaheuristic-based HEM system which incorporates Earth Worm Algorithm (EWA) and Harmony Search Algorithms (HSA). In addition, a hybridization based on the EWA and HSA operators is used to optimize energy consumption in terms of electricity cost and Peak-to-Average Ratio (PAR) reduction. Hybridization has been demonstrated to be beneficial in achieving many objectives at the same time. Extensive simulations in MATLAB were used to test the performance of the proposed hybrid technique. The simulations were run for multiple homes with multiple appliances, which were categorized according to the usage and nature of the appliance, taking advantage of appliance scheduling in terms of the time-varying retail pricing enabled by the smart grid two-way communication infrastructure algorithms EWA and HSA, along with a Real-Time Price scheme. These techniques helped us to find the best usage pattern for energy consumption to reduce electricity costs. These metaheuristic techniques efficiently reduced and shifted the load from peak hours to off-peak hours and reduced electricity costs. In comparison to HSA, the simulation results suggest that EWA performed better in terms of cost reduction. In comparison to EWA and HSA, HSA was more efficient in terms of PAR. However, the proposed hybrid approach EHSA gave the maximum reduction in cost which was 2.668%, 2.247%, and 2.535% in the case of 10, 30, and 50 homes, respectively, as compared to EWA and HSA.

The demand for sustainable alternative-fuels in the transportation and agriculture domains is essential due to the quick depletion of petroleum supplies and the growing environmental challenges. The ternary-blends (diesel, biodiesel, and Methyl oleate) have the ability to report the existing challenges in this area because they offer significant promise for reducing exhaust emissions and improving engine performance. In the current work, soy methyl ester is blended with methyl oleate and diesel. The emissions and performance of blended biodiesel was conducted in common rail direct injection engine (CRDI). The characterization and physical properties were also evaluated by utilizing various methods like Fourier-Transform Infrared Spectroscopy (FTIR), UV-vis Spectroscopy (UV-vis), and Nuclear Magnetic Resonance. FTIR spectra showed the existence of the strong C=O, indicating the presence of FAME at 1745 cm−1. Again, UV-vis has reported the appearance of conjugated dienes in the oxidized biodiesel. The results indicated all blended samples retained the properties of diesel. The addition of methyl oleate improved brake specific fuel consumption of blended biodiesel almost near to diesel. D50::S80:M20 produced a mean reduction in hydrocarbon 42.64% compared to diesel. The average carbon monoxide emission reduction for D50::S80:M20 was 49.36% as against diesel.