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Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SoA) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, prevalent dynamic offloading (PoA) solutions often come with hidden costs that impact sensitive applications, including decision time, networks congested and distance offloading. This paper introduces the Hybrid Offloading (HybOff) algorithm, which substantially enhances load balancing and resource utilization in fog networks, addressing issues in both static and dynamic approaches while leveraging clustering theory. Its goal is to create an uncomplicated low-cost offloading approach that enhances IoT application performance by eliminating the consequences of hidden costs regardless of network size. Experimental results using the iFogSim simulation tool show that HybOff significantly reduces offloading messages, distance, and decision-offloading consequences. It improves load balancing by 97%, surpassing SoA (64%) and PoA (88%). Additionally, it increases system utilization by an average of 50% and enhances system performance 1.6 times and 1.4 times more than SoA and PoA, respectively. In summary, this paper tries to introduce a new offloading approach in load balancing research in fog environments. [ABSTRACT FROM AUTHOR]

Fog computing has evolved as a promising computing paradigm to support the execution of latency-sensitive Internet of Things (IoT) applications. The mobile devices connected to the fog environment are resource constrained and non-stationary. In such environments, offloading mobile user's computational task to nearby fog servers is necessary to satisfy the QoS requirements of time-critical IoT applications. Moreover, the fog servers are also susceptible to numerous attacks which induce security and privacy issues.Offloading computation task to a malicious fog node affects the integrity of users' data. Despite the fact that there are many integrity-preserving strategies for fog environments, the majority of them rely on a reliable central entity that might have a single point of failure. Blockchain is a promising strategy that maintains data integrity in a decentralized manner. The state-of-art blockchain offloading mechnanisms have not considered the mobility during secure offloading process. Besides, it is necessary to ensure QoS constraints of the IoT applications while considering mobility of user devices. Hence, in this paper, Blockchain assisted Mobility-aware Secure Computation Offloading (MSCO) mechanism is proposed to choose the best authorized fog servers for offloading task with minimal computational and energy cost. To address the optimization issue, a hybrid Genetic Algorithm based Particle Swarm Optimization technique is employed. Experimental results demonstrated the significant improvement of MSCO when compared to the existing approaches in terms of on average 11 % improvement of total cost which includes the parameters of latency and energy consumption. [ABSTRACT FROM AUTHOR]

The emergence of a new generation of applications such as the Internet of Things, Artificial Intelligence, and 5G has brought fog computing as cloud development toward the edge network. It is done by adding an extra layer of resources to meet low latency, high performance, mobility, and interoperability challenges. The development of fog markets and new beneficial business paradigms such as fog services considerably affect the future of fogonomics (which is defined as the economics of fog computing). Thus, it is a challenging issue to design a market-oriented model to deal with the economics-based resource management in fog computing, which adjusts the resource supply of Fog Resource Providers (FRPs) and the demands of User Services (USs) based on users' requirements while satisfying all parties' utilities. This paper proposes the Fog-Marketing scheme consisting of a Resource Propagation Component (RPC) and a Contract-aware Market Selection Component (CMSC) for resource provisioning in multi-tier fog computing. The RPC is deployed in each fog node to perform hierarchical resource propagation of FRPs to auction-based Fog Decentralized Markets (FDMs) close to USs to increase revenue opportunities, and embedded CMSC in the user agent applies a matching strategy to assign USs and FDMs aims to promote USs' utility. The simulation results under extensive experiments confirmed that the proposed scheme succeeded in reducing the Round-Trip-Time, increasing the revenue of FRPs, and improving the utility of USs compared to existing models. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) devices has given rise to applications that demand real-time responses and minimal delay. Fog computing has emerged as a suitable platform for processing IoT applications, extending cloud computing services to the edge of the network. This enables more cost-effective and time-efficient processing at the network's edge. However, determining how to allocate tasks to fog nodes presents a fundamental challenge, involving factors like energy consumption and limited fog server capacity, impacting quality of service parameters such as delay. This paper introduces a mathematical formula for resource allocation to minimize delay and energy consumption while considering quality of service criteria. The subsequent step involves presenting a hybrid genetic algorithm (GA) and the gray wolf optimization (GWO), constituting an improved hybrid approach where the GA exhaustively explores the solution space to reduce the risk of converging to a locally optimal point. The combination of these algorithms produces multiple solutions. Despite incurring processing costs and computation delays, the implementation of these algorithms is crucial for enhancing the Quality of Service (QoS). In conclusion, the results indicate that the simultaneous use of positive aspects from both algorithms significantly improves execution time, final task completion time compared to the other methods. [ABSTRACT FROM AUTHOR]

Due to dynamic changes, applications designed for the Internet of Things (IoT) require new storage resources. Storing IoT data in a fog infrastructure is a common practice to guarantee the IoT application's high performance. Multiple fog nodes spread the data replica to accommodate multiple users and reduce access latency. However, such data replicas are subjected to intentional and accidental corruption. This paper proposes a data integrity verification approach in a distributed fog environment. The proposed work uses fuzzy clustering and consensus mechanism to verify the integrity of multiple replicas stored on distributed fog nodes. Security analysis shows that the proposed approach is highly resistant to replace, replay, and forgery attacks. Performance analysis demonstrates the practicability of the proposed approach in a fog environment. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices generate and transfer a vast volume of data in interaction with various objects. These data rely on cloud computing (CC) or fog computing (FC) for processing and calculations. Unlike CC, FC delivers task processing closer to IoT devices. Consequently, FC leads to reduced latency, increased efficiency, ease of deployment, and flexibility. The primary concern between the service requester and provider when agreeing to task offloading is trust. While several types of research are based on intelligent models for trust management, trust management in FC requires direct and indirect trust computation due to the presence of malicious nodes. To ensure security and maintain the quality of service when interacting with fog servers, trust is essential. In this paper, a new trust model for FC is proposed using a combination of the Genetic Algorithm (GA) and Learning Automaton (LA). The crossover operator in GA by LA is improved. The changes in the search space and finding optimal solutions are dependent on the crossover rate. Thus, determining an optimal value for the crossover operator impacts the problem’s solutions. The proposed methods evaluation was done in a MATLAB 2020 simulation environment. The results show that the performance of the proposed method with 200 iterations and 1000 tasks is superior in terms of reliability rate, energy consumption, and delay compared to GA and particle swarm optimization (PSO) algorithm. [ABSTRACT FROM AUTHOR]