Task scheduling mechanisms for fog computing: A systematic survey

In the Internet of Things (IoT) ecosystem, some processing is done near data production sites at higher speeds without the need for high bandwidth by combining Fog Computing (FC) and cloud computing. Fog computing offers advantages for real-time systems that require high speed internet connectivity. Due to the limited resources of fog nodes, one of the most important challenges of FC is to meet dynamic needs in real-time. Therefore, one of the issues in the fog environment is the optimal assignment of tasks to fog nodes. An efficient scheduling algorithm should reduce various qualitative parameters such as cost and energy consumption, taking into account the heterogeneity of fog nodes and the commitment to perform tasks within their deadlines. This study provides a detailed taxonomy to gain a better understanding of the research issues and distinguishes important challenges in existing work. Therefore, a systematic overview of existing task scheduling techniques for cloud-fog environment, as well as their benefits and drawbacks, is presented in this article. Four main categories are introduced to study these techniques, including machine learning-based, heuristic-based, metaheuristic-based, and deterministic mechanisms. A number of papers are studied in each category. This survey also compares different task scheduling techniques in terms of execution time, resource utilization, delay, network bandwidth, energy consumption, execution deadline, response time, cost, uncertainty, and complexity. The outcomes revealed that 38% of the scheduling algorithms use metaheuristic-based mechanisms, 30% use heuristic-based, 23% use machine learning algorithms, and the other 9% use deterministic methods. The energy consumption is the most significant parameter addressed in most articles with a share of 19%. Finally, a number of important areas for improving the task scheduling methods in the FC in the future are presented

Some Positive Aspects and Limits of System Dynamics in Present Conditions

The paper points out some positive aspects as well as the limits of System dynamics based on author’s own experience with practical applications of System dynamics models without aspiring to assess System dynamics methodology as a whole. The author wants to provide some kind of system detachment and provide information of how System dynamics models help in solving complex problems, mainly with respect to gathering of new knowledge and the integration and communication of the existing one. Questions “What knowledge can be obtained by creating the system dynamics model and its practical application to completion of the changes needed in the system” and “What is the role of the learning process with implementation of these changes in the real world?“ are the starting point for the analysis of advantages and weaknesses of System dynamics. This paper was supported by the Czech Science Foundation grant “System Dynamics Theory and Market Structures,” number GACR 402/05/0502.The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.htmlIFSR 2005 : Proceedings of the First World Congress of the International Federation for Systems Research : The New Roles of Systems Sciences For a Knowledge-based Society : Nov. 14-17, 2144, Kobe, JapanSymposium 7, Session 4 : Foundations of the Systems Sciences Economic and Environmental Issue

System Dynamics Market Model with Aspects of Economic Policy

This paper studies the possibility of the use of the principles, knowledge and tools of System Dynamics. The authors discuss creation of a general system dynamics behavioral market model with interferences to government economic policy. The problem is specified as a creation of a general system dynamics behavioral market model respecting some tools of governmental economic policy application. It is a part of a broader research on general characteristics common for various dynamic market structures determining the customers’ behavior and consecutive processing of general system dynamics behavioral market model from the company’s viewpoint. The paper is one of the results of a project supported by the Czech Science Foundation grant “System Dynamics Theory and Market Structures,” GACR 402/05/0502.The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.htmlIFSR 2005 : Proceedings of the First World Congress of the International Federation for Systems Research : The New Roles of Systems Sciences For a Knowledge-based Society : Nov. 14-17, 2178, Kobe, JapanWorkshop, Session 3 : he New Roles of Systems Sciences for a Knowledge-based Societ

A Probe Survey of Bitcoin Transactions Through Analysis of Advertising in an On-Line Discussion Forum

Cryptocurrencies have become a major phenomenon in recent years. For IT, a breakthrough is both the cryptocurrency itself as a commodity and the technology that cryptocurrency development has brought. The article focuses on the bitcoin cryptocurrency as the most important cryptocurrency. A relatively unexplored topic is what goods or services are purchased for bitcoins. To track what bitcoins are spent on, it is necessary to look for places that are dedicated to trading cryptocurrencies. The bitcointalk.org forum was chosen as a source for our data mining. The aim of the article is to find an answer to the research question: What are bitcoins on the discussion forum bitcointalk.org planned to be spent on? As part of the research, an application was developed using a PHP script to gather information from the discussion forum (bitcointalk.org). There is some evidence which suggests what types of products or services people spend cryptocurrencies on. This research has proven that cryptocurrencies are used to buy and sell goods or services in the electronics and computer world segments. Today, these segments are widespread, which may speed up the integration of cryptocurrencies into everyday life. This applies, of course, only if the risks associated with cryptocurrencies do not increase

A cluster-based trusted routing method using fire hawk optimizer (FHO) in wireless sensor networks (WSNs)

Abstract Today, wireless sensor networks (WSNs) are growing rapidly and provide a lot of comfort to human life. Due to the use of WSNs in various areas, like health care and battlefield, security is an important concern in the data transfer procedure to prevent data manipulation. Trust management is an affective scheme to solve these problems by building trust relationships between sensor nodes. In this paper, a cluster-based trusted routing technique using fire hawk optimizer called CTRF is presented to improve network security by considering the limited energy of nodes in WSNs. It includes a weighted trust mechanism (WTM) designed based on interactive behavior between sensor nodes. The main feature of this trust mechanism is to consider the exponential coefficients for the trust parameters, namely weighted reception rate, weighted redundancy rate, and energy state so that the trust level of sensor nodes is exponentially reduced or increased based on their hostile or friendly behaviors. Moreover, the proposed approach creates a fire hawk optimizer-based clustering mechanism to select cluster heads from a candidate set, which includes sensor nodes whose remaining energy and trust levels are greater than the average remaining energy and the average trust level of all network nodes, respectively. In this clustering method, a new cost function is proposed based on four objectives, including cluster head location, cluster head energy, distance from the cluster head to the base station, and cluster size. Finally, CTRF decides on inter-cluster routing paths through a trusted routing algorithm and uses these routes to transmit data from cluster heads to the base station. In the route construction process, CTRF regards various parameters such as energy of the route, quality of the route, reliability of the route, and number of hops. CTRF runs on the network simulator version 2 (NS2), and its performance is compared with other secure routing approaches with regard to energy, throughput, packet loss rate, latency, detection ratio, and accuracy. This evaluation proves the superior and successful performance of CTRF compared to other methods

A fuzzy logic-based secure hierarchical routing scheme using firefly algorithm in Internet of Things for healthcare

Abstract The Internet of Things (IoT) is a universal network to supervise the physical world through sensors installed on different devices. The network can improve many areas, including healthcare because IoT technology has the potential to reduce pressure caused by aging and chronic diseases on healthcare systems. For this reason, researchers attempt to solve the challenges of this technology in healthcare. In this paper, a fuzzy logic-based secure hierarchical routing scheme using the firefly algorithm (FSRF) is presented for IoT-based healthcare systems. FSRF comprises three main frameworks: fuzzy trust framework, firefly algorithm-based clustering framework, and inter-cluster routing framework. A fuzzy logic-based trust framework is responsible for evaluating the trust of IoT devices on the network. This framework identifies and prevents routing attacks like black hole, flooding, wormhole, sinkhole, and selective forwarding. Moreover, FSRF supports a clustering framework based on the firefly algorithm. It presents a fitness function that evaluates the chance of IoT devices to be cluster head nodes. The design of this function is based on trust level, residual energy, hop count, communication radius, and centrality. Also, FSRF involves an on-demand routing framework to decide on reliable and energy-efficient paths that can send the data to the destination faster. Finally, FSRF is compared to the energy-efficient multi-level secure routing protocol (EEMSR) and the enhanced balanced energy-efficient network-integrated super heterogeneous (E-BEENISH) routing method based on network lifetime, energy stored in IoT devices, and packet delivery rate (PDR). These results prove that FSRF improves network longevity by 10.34% and 56.35% and the energy stored in the nodes by 10.79% and 28.51% compared to EEMSR and E-BEENISH, respectively. However, FSRF is weaker than EEMSR in terms of security. Furthermore, PDR in this method has dropped slightly (almost 1.4%) compared to that in EEMSR

A cluster-tree-based trusted routing algorithm using Grasshopper Optimization Algorithm (GOA) in Wireless Sensor Networks (WSNs).

In wireless sensor networks (WSNs), existing routing protocols mainly consider energy efficiency or security separately. However, these protocols must be more comprehensive because many applications should guarantee security and energy efficiency, simultaneously. Due to the limited energy of sensor nodes, these protocols should make a trade-off between network lifetime and security. This paper proposes a cluster-tree-based trusted routing method using the grasshopper optimization algorithm (GOA) called CTTRG in WSNs. This routing scheme includes a distributed time-variant trust (TVT) model to analyze the behavior of sensor nodes according to three trust criteria, including the black hole, sink hole, and gray hole probability, the wormhole probability, and the flooding probability. Furthermore, CTTRG suggests a GOA-based trusted routing tree (GTRT) to construct secure and stable communication paths between sensor nodes and base station. To evaluate each GTRT, a multi-objective fitness function is designed based on three parameters, namely the distance between cluster heads and their parent node, the trust level, and the energy of cluster heads. The evaluation results prove that CTTRG has a suitable and successful performance in terms of the detection speed of malicious nodes, packet loss rate, and end-to-end delay