Tom 23 (2023): Zeszyt 1 (March 2023)

The Fog computing concept has been introduced to aid in the data processing of Internet of things applications using Cloud computing. Due to the profitable benefits of this combination, several papers have lately been published proposing the deployment of Blockchain alongside Fog computing in a variety of fields. A comprehensive evaluation and synthesis of the literature on Blockchain-Fog computing integration applications that have emerged in recent years is required. Although there have been several articles on the integration of Blockchain with Fog computing, the applications connected with this combination are still fragmented and require further exploration. Hence, in this paper, the applications of Blockchain-Fog computing integration are identified using a systematic literature review technique and tailored search criteria generated from the study objectives. This article found and evaluated 144 relevant papers. The findings of this article can be used as a resource for future Fog computing research and designs.

Fog computing is one of the emerging forms of cloud computing which aims to satisfy the ever-increasing computation demands of the mobile applications. Effective offloading of tasks leads to increased efficiency of the fog network, but at the same time it suffers from various uncertainty issues with respect to task demands, fog node capabilities, information asymmetry, missing information, low trust, transaction failures, and so on. Several machine learning techniques have been proposed for the task offloading in fog environments, but they lack efficiency. In this paper, a novel uncertainty proof Type-2-Soft-Set (T2SS) enabled apprenticeship learning based task offloading framework is proposed which formulates the optimal task offloading policies. The performance of the proposed T2SS based apprenticeship learning is compared and found to be better than Q-learning and State-Action-Reward-State-Action (SARSA) learning techniques with respect to performance parameters such as total execution time, throughput, learning rate, and response time.

Designing efficient and flexible approaches for placement of Virtual Network Function (VNF) chains is the main success of Network Function Virtualization (NFV). However, most current work considers the constant bandwidth and flow processing requirements while deploying the VNFs in the network. The constant (immutable) flow processing and bandwidth requirements become critical limitations in an NFV-enabled network with highly dynamic traffic flow. Therefore, bandwidth requirements and available resources of the Point-of-Presence (PoP) in the network change constantly. We present an adaptive model for placing VNF chains to overcome this limitation. At the same time, the proposed model minimizes the number of changes (i.e., re-allocation of VNFs) in the network. The experimental evaluation shows that the adaptive model can deliver stable network services. Moreover, it reduces the significant number of changes in the network and ensures flow performance.

Clock synchronization in the Mac layer plays a vital role in wireless sensor network communication that maintains time-based channel sharing and offers a uniform timeframe among different network nodes. Most wireless sensor networks are distributed where no common clock exists among them. Therefore, joint actions are realized by exchanging messages, with time stamps using local sensor clocks. These clocks can easily drift seconds and cause functional problems to the applications that depend on time synchronization. Time synchronization is a major and challenging factor in wireless sensor networks that needs to be studied and explored. In this paper, we propose integrated time synchronization protocols that serve wireless sensor network applications under normal, secured, and unreliable environments. The proposed protocols are discussed and evaluated based on their accuracy, cost, hierarchy, reliability, and security. Simulation results show that the proposed time synchronization protocols outperform the state-of-the-art techniques in achieving a minimum synchronization time.

MANET is an autonomous wireless network without any centralized authority, consisting of a dynamic topology with a multi-hop scenario. Clustering provides centralized authority within MANET and makes the network stable up to some extent. Security is one of the most challenging aspects, directly proportional to load and overhead over the network. The model being proposed uses a cluster-based technique and modified secure routing protocol, which minimizes the network’s load and overhead, improving the network’s security and performance. Two major factors are used as contribution level and stability factor for the choice of cluster head, which minimizes the method of re-selection for cluster head. The node addition using authentication and the Modified Secure Routing Protocol increases the security of the network. The Modified Secure Routing Protocol, based on limited and efficient use of the certificates with encryption and decryption of packets, minimizes the overhead and load over the network. Hence, it provides more efficiency and security.

Embedding the watermark is still a challenge in image watermarking. The watermark should not reduce the visual quality of the image being watermarked and hard to distinguish from its original. Embedding a watermark of a small size might be a good solution. However, the watermark might be easy to lose if there is any tampering with the watermarked image. This research proposes to increase the visual quality of the watermarked image using the Walsh Hadamard transform, which is applied to the singular value decomposition-based image watermarking. Technically, the watermark image is converted into a low bit-rate signal before being embedded in the host image. Using various watermark sizes, experimental results show that the proposed method could produce a good imperceptibility with 47.10 dB on average and also gives robustness close to the original watermark with a normalized correlation close to 1 on average. The proposed method can also recognize the original watermark from the tampered watermarked image at different levels of robustness.

Every country must have an accurate and efficient forecasting model to avoid and manage the epidemic. This paper suggests an upgrade to one of the evolutionary algorithms inspired by nature, the Barnacle Mating Optimizer (BMO). First, the exploration phase of the original BMO is enhanced by enforcing and replacing the sperm cast equation through Levy flight. Then, the Least Square Support Vector Machine (LSSVM) is partnered with the improved BMO (IBMO). This hybrid approach, IBMO-LSSVM, has been deployed effectively for time-series forecasting to enhance the RBF kernel-based LSSVM model since vaccination started against COVID-19 in Malaysia. In comparison to other well-known algorithms, our outcomes are superior. In addition, the IBMO is assessed on 19 conventional benchmarks and the IEEE Congress of Evolutionary Computation Benchmark Test Functions (CECC06, 2019 Competition). In most cases, IBMO outputs are better than comparison algorithms. However, in other circumstances, the outcomes are comparable.

From recent past, shoplifting has become a serious concern for business in both small/big shops and stores. It customarily involves the buyer concealing store items inside clothes/bags and then leaving the store without payment. Unfortunately, no cost-effective solution is available to overcome this problem. We, therefore intend to build an expert monitoring system to automatically recognize shoplifting events in megastores/shops by recognizing object-stealing actions of humans. The method proposed utilizes a deep convolutional-based InceptionV3 architecture to mine the prominent features from video clips. These features are used to custom Long Short Term Memory (LSTM) network to discriminate human stealing actions in video sequences. Optimizing recurrent learning classifier using different modeling parameters such as sequence length and batch size is a genuine contribution of this work. The experiments demonstrate that the system proposed has achieved an accuracy of 89.36% on the synthesized dataset, which comparatively outperforms other existing methods.

In recent days, resource allocation is considered to be a complex task in cloud systems. The heuristics models will allocate the resources efficiently in different machines. Then, the fitness function estimation plays a vital role in cloud load balancing, which is mainly used to minimize power consumption. The optimization technique is one of the most suitable options for solving load-balancing problems. This work mainly focuses on analyzing the impacts of using the Genetic Algorithm and Ant Colony Optimization (GAACO) technique for obtaining the optimal solution to efficiently balance the loads across the cloud systems. In addition to that, the GA and ACO are the kinds of object heuristic algorithms being proposed in the work to increase the number of servers that are operated with better energy efficiency. In this work, the main contribution of the GAACO algorithm is to reduce energy consumption, makespan time, response time, and degree of imbalance.