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Fog computing complemented cloud computing integration services in the Internet of Things (IoT) and the web of real-time interactivity. Fog offers faster computing and other services facilities sitting close to user applications. However, secure data transfer in the fog is still a challenging issue requiring attention and efficient deployment of a secure data security scheme. We present an Identity Based Encryption (IBE) scheme to secure data security and transmission in fog clouds and IoT ecosystems. We devise and develop a four-level Hierarchical Identity Based Architecture for Fog Computing (HIBAF) data security scheme to enhance data security. We also analyze the system's performance regarding response time, CPU utilization, run-time encryption-decryption, and key generation time in the fog computing paradigm to an increasing number of users data-loads. Moreover, we evaluate our scheme and compare the outcomes with different cryptography structures to discern our scheme's effectiveness. Furthermore, we also evaluate secret key updating time, re-encrypted key updating time, and file revoking time by launching DDoS attacks both in the cloud and fog computing environment to compare improvements of HIBAF in the fog computing paradigm. Finally, through this overall evaluation, we have found that the developed HIBAF scheme provides a 33% performance enhancement in a fog environment in terms of data processing, provision, and management compared to the cloud environment. [ABSTRACT FROM AUTHOR]

The article attempts to bridge global and local models of service-oriented systems. It is stated that a service-oriented system is collection of independent services that interact with one another through message exchanges. In this article, the author investigates the issue of local enforceability of global service interaction models. They introduce two algorithms: an algorithm for determining if a global model is locally enforceable and algorithm for generating local models from global ones. They also show how local models are mapped into templates of Business Process Execution Language (BPEL) process definitions.

The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively. [ABSTRACT FROM AUTHOR]