Internet of Things (IoT) devices along with associated software have proliferated at an unprecedented pace, presenting the challenge of high energy use combined with latency during complex, time-sensitive transactions. Fog computing, i.e., a distributed computing paradigm, may be a potential remedy. However, despite these efforts, it is highly strenuous to regulate service latency and energy efficiency within the fog computing layer for IoT centered consumer electronics. In our research, we propose an algorithm called Dynamic Deep Reinforcement learning-based Task Offloading (DDTO). Therefore, in multi-modal IoT-Fog systems, the intelligent distribution of fog computing resources by DDTO is considered on the basis of resources constraints and the timeliness of completion of tasks. We use a log-normal distribution to describe delay and energy consumption so as to make up for varying modalities. Besides, a task prioritization problem, which is described as an integer programming problem, that minimizes service latency and energy consumption in fog servers is further described. DDTO yields better performance than a conventional Q–learning with respect to long-term expected rewards since it uses task priority weights derived from statistical latency and energy data. Experimental results demonstrate the benefits of DDTO on reducing service latency and consumption of energy, when compared to benchmark strategies, discussing issues with multiple modalities within IoT-Fog systems.

Smartwatches with cutting-edge sensors are becoming commonplace in our daily lives. Despite their widespread use, it can be challenging to interpret accelerometer and gyroscope data efficiently for Human Activity Recognition (HAR). An effective remedy is the incorporation of active learning strategies. This study explores this junction, intending to maximize the use of smartwatch technology across a range of applications. The previous research on the dataset used in our article did not provide results with a higher accuracy, which could make it difficult to make predictions. This paper proposes a novel approach to predict human activity from the Heterogeneity human activity recognition (HHAR) dataset that joins active learning with machine learning models: Random Forest (RF),Extreme Gradient Boosting (XGBoost), K-nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting (GB) and Light Gradient Boosting Machine (LGBM) classifier to predict heterogeneous activities accurately. We evaluated our approach to these models on the HHAR dataset that was generated using an accelerometer and gyroscope that were present in smartwatches. The dataset was evaluated on 3 iterations; the evaluation measures demonstrated that we can predict human activity with the highest accuracy and F1-Score of 99.99%. The results indicate that this approach is the most accurate and effective compared to the conventional machine learning approaches.

The new data processing systems required reliable. Fast and low latency data services for smart cities operations and unmanned vehicle systems for quick and fast decisions. At present the low latency and speed data for video decoding for real time are required for intelligent decisions. In this research work we present video decoding model based on for decoding data in real time. This proposed model is based on hybrid Edge-Fog-Cloud orchestration layer that perform decoding task in real time according to the network congestion and this technique ensure data integrity, traceable task distribution and protect the data from tempering by using IoT backbone secured by blockchain technology. To reduce the risk of end-to-end latency and packet loss in worst conditions a novel Temporal-Spatial Predictive Decoding (TSPD) method is used. The AI model deep reinforcement learning is used for fast decisions. After analyzing it can be concluded that a 47.8% improvement in decoding throughput, a 62% reduction in jitter and 38% improvement QoE. This shows satisfactory performance from proposed model. By optimizing energy-latency and combining decentralized system with IoT-driven communication for autonomous aerial system can be used in future 6G network.

In this paper, we present our research, where residents purchase and sell locally produced renewable energy from each other. In this research work, we integrate block chain technology to create peer-to-peer marketplaces for distributed energy. First, we use a smart energy meter to measure various important parameters of electrical energy. In this proposed work, the initial measurements of the current are being performed with the help of hall effect sensor due to its accuracy. Second, supply voltage is calculated with the help of a transformer and DC biasing method. The apparent power is calculated by multiplying the measured current and voltage, and then the energy parameter is obtained. Using this smart energy meter, we obtain our desired parameters with sufficient accuracy of nearly 90 percent. These obtained parameters and results are directly uploaded to Amazon Web Server(AWS). We also develop an android app that connects with the AWS server. The designed system facilitates the users to perceive these electrical parameters on meter having built-in LCD display which shows all measured results including a number of units consumed, and billing information anywhere around the world. Finally, we develop a smart contract on Ethereum block chain which can transact the number of tokens according to the units sold or purchased by the users. The Consumer can set their selling rate through the app. Any user with this app is able to take their readings live to their smart phone.

Pakistan’s energy sector has been in crises since 2005. Major reasons behind crises include huge dependence on imported fossil assets and lack of integrated energy planning. These crises has challenged energy sector and required special attention to solve through proper planning using energy modeling tools. In this research, Long-range Energy Alternatives Planning System (LEAP) is used for modeling Pakistan’s energy sector for period 2020 to 2070. Energy demand, production and CO2 emissions are forecasted under the Progressive (PROG) and Renewable Energy Sources, Technologies and Efficiency Measures (RESTEM) scenarios by considering techno-economic parameters. Results of this study suggest that transition towards defossilized energy system is possible through RESTEM scenario. Model results shows that energy demand for 2020 is 112 TWh and for 2070 is 2,684 TWh. On supply side, energy production for 2020 is 114 TWh and for 2070 is 2,696 TWh. Share of renewable sources in total energy mix is increased from 38.5% in 2021 to 59.1% in 2070 and share of non-renewable sources is reduced from 53.1% in 2021 to 40.9% in 2070. CO2 emissions are also reduced from 153,083.7 million tons to 56,152 million tons under RESTEM scenario. Recommendations devised from this study are presented in conclusion section.