Wastewater treatment is considered the most important process for reducing pollutants in wastewater to levels that nature can cope with. At many Sewages treatment plants, industrial wastes cause more difficulties in the treatment process than any other single problem where the plant operators have to deal with. These plants may not be designed to handle these types of wastes and the accelerated deterioration of sewage treatment plant structures. In this paper, we propose a new IIoT cloud-based model for real-time wastewater monitoring and controlling. The proposed system monitors the power of hydrogen (pH) and temperature parameters from wastewater inlet which will be treated in Wastewater treatment plant and avoid impermissible industrial wastewater which the plant cannot deal with. The system collects and uploads real-time sensor readings to the cloud via an IIoT Wi-Fi Module. Additionally, it reports observed or identified unexpected industrial wastewater inlets via SMS notifications and alarms and controls the valves of the gates. This is needed to change the path of the water to the industrial wastewater treatment plant that can treat this type of wastes. Experimental work shows the effectiveness of the proposed system compared to related work.

Air pollution is a growing concern due to severe threats to public health and the environment. The need for reliable air quality monitoring solutions has never been more critical. This research paper introduces an innovative approach to addressing this challenge by deploying a low-cost Internet of Things (IoT) air monitoring station and providing a blockchain technology solution to enhance environmental data transparency, reliability, and accessibility. Our paper adopts a concept of merging IoT and blockchain technologies and collecting some parameters that help to assess air quality by using three sensors, DHT11, MQ7, and MQ135, to collect temperature, humidity, carbon monoxide, and carbon dioxide parameters, respectively, to measure the gases and thus indicate the air quality within the surrounding area. Collecting and sharing these types of valuable data will be very important for various stakeholders, such as governmental bodies, researchers, and the public. This approach is consistent with the principles of sustainable development, facilitating informed decision-making and promoting eco-friendly policies. This research explores the technical architecture of the IoT air monitoring stations, offering a promising solution for addressing air pollution concerns while promoting sustainable development goals. The proposed system is a model for leveraging emerging technologies to advance environmental monitoring and create smarter, livable cities. This approach aligns with the principles of sustainable development and eco-friendly initiatives. This research offers a promising model for enhancing environmental monitoring efforts and advancing the creation of smarter, more sustainable urban environments. The proposed IoT, cloud platform and blockchain-based system not only addresses pressing air pollution challenges but also sets a benchmark for leveraging emerging technologies in environmental science.

Open finance is evolving and extending open banking. This creates a large context that implies a financial and identity data exchange paradigm, which faces challenges to balance customer experience, security, and the self-control over personal identity information. We propose Self-Sovereign Banking Identity (SSBI), a Blockchain-based self-sovereign identity (SSI) to secure private data sharing by utilizing trusted customer’s banking cards as a key storage and identity transaction-signing enclave. The design and implementation of the SSI framework is based on the Veramo SDK and Ethereum to overcome the limitation of signing curve availability on the current banking Java Cards needed for Hyperledger Indy. SSBI uses the elliptic curve SECP256K1 for transaction signing, which exists for several payment cards in the market. SSBI enables automated financial services and trust in the service provider communication. This work analyzes the flow and framework components, and evaluates the usability, integration, and performance in terms of throughput, latency, security, and complexity. Furthermore, the proposed approach is compared with related solutions. The presented prototype implementation is based on a test Ethereum network and signing transactions on the banking card. The preliminary results show that SSBI provides an effective solution for integrating the customer’s banking cards to secure open banking identity exchange. Furthermore, it allows the integration of several scenarios to support trusted open banking. The Blockchain layer settings need to be scaled and improved before real-world implementation.

Every year, millions of new devices are added to the Internet of things, which has both great benefits and serious security risks for user data privacy. It is the device owners’ responsibility to ensure that the ownership settings of Internet of things devices are maintained, allowing them to communicate with other user devices autonomously. The ultimate goal of the future Internet of Things is for it to be able to make decisions on its own, without the need for human intervention. Therefore, trust computing and prediction have become more vital in the processing and handling of data as well as in the delivery of services. In this paper, we compute trust in social IoT scenarios using a hybrid approach that combines a distributed computation technique and a global machine learning approach. The approach considers social similarity while assessing other users’ ratings and utilize a cloud-based architecture. Further, we propose a dynamic way to aggregate the different computed trust values. According to the results of the experimental work, it is shown that the proposed approaches outperform related work. Besides, it is shown that the use of machine learning provides slightly better performance than the computing model. Both proposed approaches were found successful in degrading malicious ratings without the need for more complex algorithms.