Due to the ever-increasing number and diversity of data sources, and the continuous flow of data that are inevitably redundant and unused to the cloud, the Internet of Things (IoT) brings several problems including network bandwidth, the consumption of network energy, cloud storage, especially for paid volume, and I/O throughput as well as handling huge amount of stored data in the cloud. These call for data pre-processing at the network edge before data transmission over the network takes place. Data reduction is a method for mitigating such problems. Most state-of-the-art data reduction approaches employ a single tier, such as gateways, or two tiers, such gateways and the cloud data center or sensor nodes and base station. In this paper, an approach for IoT data reduction is proposed using in-networking data filtering and fusion. The proposed approach consists of two layers that can be adapted at either a single tier or two tiers. The first layer of the proposed approach is the data filtering layer that is based on two techniques, namely data change detection and the deviation of real observations from their estimated values. The second layer is the data fusion layer. It is based on a minimum square error criterion and fuses the data of the same time domain for specific sensors deployed in a specific area. The proposed approach was implemented using Python and the evaluation of the approach was conducted based on a real-world dataset. The obtained results demonstrate that the proposed approach is efficient in terms of data reduction in comparison with Least Mean Squares filter and Papageorgiou’s (CLONE) method.

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The Internet of Things (IoT) is emerging from its infancy and establishing itself as a component of the future Internet. However, the ability to manage a huge number of IoT devices is one of the IoT technical challenges. To implement access control, traditional schemes typically rely on a trusted central organization. Traditional centralized access control systems of the IoT lead to the shortcomings of a single point of failure, low overall system efficiency, and ethical and privacy issues. To overcome such challenges, an attribute-based access control model using Hyperledger Fabric blockchain (ABAC-HLFBC) is proposed in this paper. By adopting ABAC, it is no longer to create access control lists (ACLs) or assign roles to all system users. Instead, ABAC grants access based on the attributes presented by the target. No one is permitted access unless he/she possesses sufficient attributes that correspond to the access policy. The Hyperledger Fabric Raft consensus mechanism has been used to verify the transaction on the proposed model because it has a demanding feature for faster and less complicated consensus in comparison to the Kafka ordering service. To evaluate the proposed model, it has been tested against the most recent previous work, called fabric-iot model. The proposed model has been tested and evaluated in two parts. The first part tests the cost time using a client test program written in Golang. The second part tests the latency and throughput using the Hyperledger Caliper benchmark tool. Results show that the proposed model efficiently outperforms the previous work in terms of the performance metrics mentioned above.