This paper presents the results of a field study undertaken all over the Punjab, Pakistan, to evaluate the socio-economic and climatic impact of photovoltaic-operated high-efficiency irrigation systems (HEIS), i.e., drip and sprinkler irrigation systems. Nearly half of the rural population relies on agriculture for a living, and the recent energy crisis has had a negative impact on rural communities. Farmers’ reliance on fossil fuels for the operation of irrigation systems has increased exponentially, resulting in the high costs of agricultural production. Primary data regarding on-farm agriculture and irrigation practices used in this study were collected through an intensive on-farm survey, while secondary data were taken from published reports and statistics. The results of the current investigation show that the installation of PV systems has resulted in the increased adoption of high-efficiency irrigation systems, a reduction in the high operational costs incurred on account of old diesel-powered pumping systems (with an annual saving of 6.6 million liters of diesel), a 100% increase in farmer’s income, a reduction of 17,622 tons of CO₂ emissions per annum, and 41% savings in water. The unit cost of PV-powered HEIS was found to be 0.1219 USD/kWh, which was 4% and 66% less than subsidized electricity cost and diesel cost, respectively.

This paper presents the optimal design of a photovoltaic (PV) drip irrigation system. Designing a PV system is based on calculated motor power, solar irradiance level and other meteorological parameters at a certain geographical location. Therefore, a simulation study of the designed PV system were performed by a PVGIS simulation tool. The PVGIS simulation tool analyzes the potential of power generation with optimal PV modules tilt angle and orientation on a monthly and annual basis, and an analysis of the overall shading situation (horizon) as well as the internal shading between the PV module rows. The selection of water pump and motor depends upon the depth of water table and desired discharge and head to operate the irrigation system. Furthermore, a locally developed Solar-Drip Simulation Tool (SoSiT) was used for load and supply optimization. Based on ambient temperature, solar irradiation and water requirements, SoSiT calculates net generation by a PV system and resultant water output of the irrigation system. The particular drip irrigation site has two zones; the maximum water requirement for zone 1 (row crop) is 50,918.40 Liters/day and for zone 2 (orchards) is 56,908.80 L/day. From PVGIS simulation results, the maximum daily energy production of the designed PV system was 6.48 kWh and monthly energy production was 201 kWh in the month of May. SoSiT results showed that the PV system fulfilled the required crop requirement by only using 28% of the potential water supply, and 72% of the potential water supply from a solar-powered pump was not used. This value is high, and it is recommended to grow more or different crops to utilize the fuel-free electricity from the PV system. The unit cost of PV-powered drip irrigation is USD 0.1013/kWh, which is 4.74% and 66.26% lower than the cost of subsidized electricity and diesel, respectively.

In recent few years, flying ad hoc networks are utilized more for interconnectivity. In the topological scenario of FANETs, IoT nodes are available on ground where UAVs collect information. Due to high mobility patterns of UAVs cause disruption where intruders easily deploy cyberattacks like DoS/DDoS. Flying ad hoc networks use to have UAVs, satellite, and base station in the physical structure. IoT-based UAV networks are having many applications which include agriculture, rescue operations, tracking, and surveillance. However, DoS/DDoS attacks disturb the behaviour of entire FANET which lead to unbalance energy, end-to-end delay, and packet loss. This research study is focused about the detail study of machine learning-based IDS. Also, cognitive lightweight-LR approach is modeled using UNSW-NB 15 dataset. IoT-based UAV network is introduced using machine learning to detect possible security attacks. The queuing and data traffic model is utilized to implement DT, RF, XGBoost, AdaBoost, Bagging and logistic regression in the environment of IoT-based UAV network. Logistic regression is the proposed approach which is used to estimate statistical possibility. Overall, experimentation is based on binomial distribution. There exists linear association approach in logistic regression. In comparison with other techniques, logistic regression behaviour is lightweight and low cost. The simulation results presents logistic regression better results in contrast with other techniques. Also, high accuracy is balanced well in optimal way.

With emerging population and transportation in today’s world, traffic has become a challenging issue to be addressed. Most of the metropolitan cities are facing various traffic‐related issues. This poses the need for a smart traffic system, which could tackle the external environment and provide energy efficient transportation system. Intelligent transportation system (ITS) is required to support traffic management system in smart cities. The existing systems concentrate on the traffic prediction to yield better results. The work in this paper proposes a Smart Traffic Prediction and Congestion Avoidance System (s‐TPCA) which helps in better identification of the traffic scenario that in turn helps in predicting and avoiding the congestion. The proposed work uses Poisson distribution for prediction of vehicle arrivals from recurring size. The framework comprises traffic identification, prediction, and congestion avoidance phases. The system checks for the fitness function to determine the traffic intensity and further use predictive analytics to determine the traffic level in future. This also integrates fuel consumption model to save time and energy. The proposed s‐TPCA system outperforms the conventional systems in terms of delay and proves to conserve energy. The fuel conservation is observed to be 20% higher than the other existing systems.

For the past decade, the main problem that has attracted researchers’ attention in aerial robotics is the position estimation or Simultaneous Localization and Mapping (SLAM) of Unmanned Aerial Vehicles (UAVs) where the GPS signal is poor or denied. This article reviews the strengths and weaknesses of existing methods in the field of aerial robotics. There are many different techniques and algorithms that are used to overcome the localization and mapping problem of these UAVs. These techniques and algorithms use different sensors, such as Red Green Blue-Depth (RGB_D), Light Detecting and Ranging (LIDAR), and Ultra-wideband (UWB). The most common technique is used, i.e., probability-based SLAM, which uses two algorithms: Linear Kalman Filter (LKF) and Extended Kalman Filter (EKF). LKF consists of five phases and this algorithm is just used for linear system problems. However, the EKF algorithm is used for non-linear systems. Aerial robots are used to perform many tasks, such as rescue, transportation, search, control, monitoring, and different military operations because of their vast top view. These properties are increasing their demand as compared to human service. In this paper, different techniques for the localization of aerial vehicles are discussed in terms of advantages and disadvantages, practicality and efficiency. This paper enables future researchers to find the suitable SLAM solution based on their problems; either the researcher is dealing with a linear problem or a non-linear problem.

The demand for cloud computing has drastically increased recently, but this paradigm has several issues due to its inherent complications, such as non-reliability, latency, lesser mobility support, and location-aware services. Fog computing can resolve these issues to some extent, yet it is still in its infancy. Despite several existing works, these works lack fault-tolerant fog computing, which necessitates further research. Fault tolerance enables the performing and provisioning of services despite failures and maintains anti-fragility and resiliency. Fog computing is highly diverse in terms of failures as compared to cloud computing and requires wide research and investigation. From this perspective, this study primarily focuses on the provision of uninterrupted services through fog computing. A framework has been designed to provide uninterrupted services while maintaining resiliency. The geographical information system (GIS) services have been deployed as a test bed which requires high computation, requires intensive resources in terms of CPU and memory, and requires low latency. Keeping different types of failures at different levels and their impacts on service failure and greater response time in mind, the framework was made anti-fragile and resilient at different levels. Experimental results indicate that during service interruption, the user state remains unaffected.