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As the Internet of Things (IoT) continues to revolutionize value-added services, its conventional architecture exhibits persistent scalability and security vulnerabilities, jeopardizing the trustworthiness of IoT-based services. These architectural limitations hinder the IoT’s Sensor-as-a-Service (SEaaS) model, which enables the commercial transmission of sensed data through cloud platforms. This study proposes an innovative computational framework that integrates decentralized blockchain technology into the IoT architectural design, specifically enhancing SEaaS efficiency. This research contributes to an optimized IoT architecture with decentralized blockchain operations and simplified public key encryption. Furthermore, this study introduces an advanced SEaaS model featuring innovative trading operations for sensed data among diverse stakeholders. At its core, this model presents a unique blockchain-based data-sharing mechanism that manages multiple aspects, from enrollment to validation. Evaluations conducted in a standard Python environment indicate that the proposed SEaaS model outperforms existing blockchain-based data-sharing models, demonstrating approximately 40% less energy consumption, 18% increased throughput, 16% reduced latency, and a 25% reduction in algorithm processing time. Ultimately, integrating a lightweight authentication mechanism using simplified public key cryptography within the blockchain establishes the model’s potential for efficient and secure data-sharing in IoT.

This paper introduces a computational strategic game model capable of mitigating the adversarial impact of node misbehaviour in large-scale Internet of Things (IoT) deployments. This security model’s central concept is to preclude the participation of misbehaving nodes during the routing process within the ad hoc environment of mobile IoT nodes. The core of the design is a simplified mathematical algorithm that can strategically compute payoff embrace moves to maximise gain. At the same time, a unique role is given to a node for restoring resources during communication or security operations. Adopting an analytical research methodology, the proposed model uses public and private cloud systems for integrating quality service delivery with secure agreements using a Global Trust Controller and core node selection controller to select an intermediate node for data propagation. The initiation of the game model is carried out by identifying mobile node role followed by choosing an optimal payoff for a normal IoT node. Finally, the model leads to an increment of gain for selecting the regular IoT node for routing. The findings of the evaluation indicate that the proposed scheme offers 36% greater accuracy, 25% less energy, 11% faster response time, and 27% lower cost than the prevalent game-based models currently used to solve security issues. The value added by the proposed study is the simplified game model which balances both security demands and communication demands.

Sales enablement sensing-as-a-service (SESaaS) is an organisation’s future process management for any sales management operation. With an expanding base of dynamic customer demands and the adoption of multiple technological advancements, there is a high possibility that human-centric sales management will be transformed into a fully automated form aimed at increasing productivity and being able to cater to effectively a broader customer base. A review of the relevant literature demonstrates that machine learning is one of the most prevalent techniques in analytics for predicting sales behaviour. However, SESaaS includes many features beyond the sales component. Internet-of-Things (IoT) can additionally be used for networking and data analytics to enrich sales data. Therefore, the proposed scheme introduces a novel SESaaS model capable of balancing the sales team’s needs with those of the customers to maximise profits. The proposed model also presents a novel learning scheme in the IoT environment that aids in projecting the service quality score to the final customer, thereby positively influencing the customer to pay a service fee for a superior and desired quality of experience. Unlike any existing sales management scheme, the proposed scheme offers a novel research methodology for improving sales enablement practices, emphasising service scalability, and forecasting company profit. In contrast to any existing system for sales management, the proposed scheme provides greater accuracy, higher service quality, and faster response time in its predictive strategy for projecting the cost of the adoption of SESaaS, which is not reported in any existing studies. In an extensive testing environment, it is determined that the proposed scheme achieves accuracy and service quality of approximately 98.75% and 92.91%, respectively. In addition, the proposed SESaaS model has a significantly faster response time of 1.256 s. These quantifiable outcomes were validated after being compared with commonly adopted learning programs.