Abstract

     One of the main challenges in the design of wireless ad-hoc and sensor networks is to reduce energy consumption and radio interference and collision, which are strictly and strongly correlated to the transmission range of the nodes. In this article, the transmission ranges of sensors in sparse Wireless Sensor Networks (WSN) is theoretically analyzed and is shown that the transmission ranges of sensors can be significantly reduced in this type of WSNs without losing any connection. Next, a method is presented to calculate reduced transmission ranges. In this method, an iterative transmission range adjustment mechanism is used to select an almost transmission range while no information is needed on the location of nodes or the distance between nodes. This method is very efficient and its communication overhead is low. It can be used as a pre-step of any routing algorithm in order to decrease energy consumption and radio interference in targeted scenarios. The simulation results show the efficiency of the proposed method. We got at least 50% improvement in energy consumption in comparison with full power network, in sparse WSNs.

Introduction

     Wireless Sensor Networks (WSNs) have become increasingly popular in recent years, as they offer a powerful and cost-effective solution for monitoring and collecting data from a variety of environments. A Wireless Sensor Network (WSN) consists of a large number of distributed sensor nodes that cooperatively monitor the physical world [1]. Each node in these networks is typically equipped with a wireless communication device, a small microcontroller, a memory unit and a power source. The nodes in a WSN are typically battery-powered and have limited processing power, memory, and communication bandwidth. Therefore, the design of a WSN requires careful consideration of these constraints, such as the choice of communication protocol, data routing algorithms, and energy management strategies [2].

     The complexity of the WSN domain as well as the presence of many sensor nodes unavoidably introduces a large amount of data in these networks that must be processed, transmitted and received. The sheer amount of data generated by WSNs can pose a significant challenge for the network design and operation. The sensors in a WSN can generate data continuously or periodically, depending on the application requirements. This data must be processed, stored, and transmitted to the base station in a timely and efficient manner. So the complexity of the WSN domain and the presence of many sensor nodes can introduce a large amount of data that must be processed, transmitted, and received [3]. Despite their profound advantages, the utilization of WSNs is often battery-powered and strictly limited due to energy constraints. In fact, most of the energy expenditure of a sensor node occurs during wireless communication, and the remaining energy is consumed during sensing and data processing. The radio transceiver of a node consumes a significant amount of energy during data transmission and reception. Therefore, the energy consumption of a node must be carefully managed to ensure the longevity of the network [4]. Transmission power adjustment for a special transmitter-receiver pair depends on several environmental conditions. The transmission power required to reach the receiver is affected by two main factors including distance and wireless connection quality. Distance affects transmission power. As the distance between the transmitter and receiver increases, the signal strength decreases, resulting in a weaker connection.

Conclusions and future works

     We studied the transmission range of sensors in wireless sensor networks in this study, and proposed a simple yet effective distributed transmission power control algorithm for sparse wireless sensor networks. We showed that this algorithm has advantages in many cases. The main idea of algorithm is simple, but performance of algorithm in targeted scenarios is excellent. We analyzed the performance of this method theoretically and show that using this method is worthwhile in some scenarios, especially in sparse wireless sensor networks with high amount of communication traffic. In these scenarios, our method can be used as a pre-step of any routing algorithm and decrease energy consumption and radio interference. As a future work, we intend to design methods to overcome the collisions problem. Designing methods for resisting the algorithm against the sensor failures is another future work. Furthermore we intend to work on using learning methods like Reinforcement learning for power control. Designing a good energy efficient reward mechanism is one of the main challenges of these methods.

Abstract

The Internet of Things (IoT) connects devices, enabling real-time data acquisition, automation, and collaboration. Wireless sensor networks are one of the important components of the Internet of Things, consisting of many wireless sensor nodes distributed in space. These nodes can perceive environmental information and transmit it to other nodes through wireless communication. In wireless sensor network routing optimization methods, improved ant colony algorithm can be used to find the optimal routing scheme. Ant colony algorithm simulates the behavior of ants in the process of searching for food, and optimizes factors such as transfer probability and pheromone concentration to enable ants to find the shortest path. In wireless sensor networks, node positions can be used as reference nodes and anchor nodes, combined with the objective function of wireless sensor network routing optimization, and improved ant colony algorithm can be used to solve the optimal path, thus obtaining the optimal wireless sensor network routing optimization scheme. Through experimental results, it can be found that the proposed method performs well in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. These optimization results have positive implications for the sustainable development and practical application of the Internet of Things, which can improve the development of the digital economy and enhance the construction of smart cities.

Introduction

At present, wireless sensor networks have been widely applied in various fields such as environmental monitoring, agriculture, and the Internet of Things. In these applications [1,2], wireless communication between nodes in wireless sensor networks plays a crucial role. The Internet of Things has characteristics such as connectivity, intelligence, and real-time. It connects devices through wireless communication technology and endows them with perception, computing, and decision-making capabilities. This enables the Internet of Things to obtain and transmit data in real-time, automate collaboration and data sharing, improve production and operational efficiency, and reduce costs. At the same time, the Internet of Things utilizes big data analysis and predictive maintenance to provide insight into market trends and user needs, helping enterprises make more accurate decisions. In addition, the application of the Internet of Things in fields such as smart homes and health monitoring is improving people’s quality of life and promoting the process of sustainable development. Therefore, the Internet of Things has significant advantages in improving efficiency, reducing costs, enhancing insight, improving quality of life, and promoting sustainable development. The routing optimization of wireless sensor networks has become an important and challenging issue due to the limited energy of sensor nodes, wide and random distribution of nodes, and other characteristics. The goal of wireless sensor network routing optimization is to improve network performance and energy utilization efficiency by selecting appropriate paths, minimizing energy consumption, maintaining low latency, and maximizing network lifespan [3]. It involves techniques and mechanisms such as path selection and transmission scheduling to ensure efficient and accurate data transmission between nodes. The research on routing optimization in wireless sensor networks is of great significance for improving network performance, extending network lifespan, providing reliable data transmission, and supporting real-time applications. By adopting effective routing optimization algorithms and mechanisms, more reliable, intelligent, and efficient wireless sensor network systems can be achieved. Wireless sensor network routing optimization plays an important role in the Internet of Things, as it can improve network performance and energy utilization efficiency, extend network lifespan, provide reliable data transmission and support real-time applications for various application fields. Therefore, routing optimization in wireless sensor networks is of great significance in promoting the development and application of animal networking [4,5].

Conclusion

Wireless sensor network routing optimization is one of the key issues in the Internet of Things, which is of great significance for improving network performance, extending network lifespan, providing reliable data transmission, and supporting real-time applications. The current routing optimization methods have problems such as high energy consumption, high transmission delay, high number of dead nodes, and low network throughput. To address these issues, this article proposes a wireless sensor network routing optimization method based on improved ant colony algorithm in the Internet of Things. Through experimental testing, it has been proven that the proposed method has absolute advantages over the current method in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. This research achievement can not only improve the energy efficiency of wireless sensor networks, but also improve the quality of data transmission and provide better support for real-time applications. At the same time, this method can also optimize network capacity and meet the network performance requirements of different application scenarios. These contributions provide important support for the reliability, intelligence, and efficiency of wireless sensor networks, and promote the further development of wireless sensor network technology in various application fields. However, there are still some challenges in the research of routing optimization in wireless sensor networks. For example, when optimizing routing in a dynamic environment, it is necessary to consider issues such as changes in network topology and the balance between energy consumption and data transmission quality. These challenges require further in-depth research to propose more comprehensive and adaptable routing optimization methods for wireless sensor networks, and to promote the development and application of animal networking technology.

Abstract

In WSN sensor nodes are connected to their neighbor nodes by a wireless channel and these all nodes are operated on a battery to perform any operations. So improving the lifetime of WSN is a major research challenge by effective utilization of each and every node’s energy. The lifetime of WSNs can be improved in three steps: path creation, path selection, and power saving. In existing works, path selection has the major role to utilize and enhance lifetime. In the proposed PStack algorithm, path generation is a major concern, and routing is applied. It takes advantage of and solves the limitations of existing algorithms using the stack concept in WSN. The proposed algorithm shows a better result than the improved LEACH protocol because it uses the data structure with graph theory.

Introduction 

Basic foundation of any kind of network communication is path generation from source to destination. Especially in the WSN path generation has a major role. A better path generating between source and destination leads to optimized load balancing and achieves high lifetime. Most of the existing models have single backbone meaning that it creates a single path in network with single backbone, may not be ideal to prolong the lifetime[1]. A simple WSN model is considered with six sensor nodes and a sink as depicted in figure1. It is understood that all sensor nodes are kept active 24X7 irrespective of whether is it in use or not. This leads to unnecessary usage of battery power leading to drain down quickly. Therefore the node automatically disconnected itself from its neighbours[5].

In this work node’s energy means all node’s energy not only the selected nodes energy. Because improving the node’s energy may not able to prolong the lifetime, it can be happen only when all nodes energy will be utilize effectively without wasting any nodes energy. According to the review of literature, there are various techniques introduced and implemented to improve the lifetime of WSN. All these techniques were applied either in routing algorithms or region (area) of WSN as cluster. These techniques may be categories as: 1. WSN with single sink and 2.WSN with more than one sink.

CONCLUSION

PStack algorithm in WSN eliminate the drawbacks of all existing protocol, which is used for the improvement of lifetime of WSN such as AODV, CDS, KMAT, DSR, NBS, VBS and cluster. PStack algorithm results will be in form of the number of stacks with different sizes. Number of stacks represents the number of paths from source to sink and size of stack represents the number of sensor nodes in particular path which having the significant roles for path selection technique. This is the great way to improve the lifetime of any kinds of WSN. This algorithm works more effectively when density of nodes are high. It also supports for static as well as dynamic nodes in WSN.

Limitation of this work is that this assumption may have chances to become wrong if sensor nodes participated in any communication without knowledge of sink, or sink didn’t able to update the energy level of nodes properly

Abstract

Open Access and Permissions

Share and Cite

Article Metrics

Related Articles

Order Article Reprints

Abstract

In recent years, the world’s population living in cities has been rapidly increasing. Cities are transforming their infrastructure in a smarter and more efficient way so that sustainable development forms part of their long-term strategy. However, this transformation does not always result in expected benefits due to a variety of factors such as an absence of social acceptance, a lack of holistic design and the development of unilateral interventions. An analysis of the scientific literature related to the evaluation of the impact of smart city actions revealed a gap in the holistic methods for their assessment. To this end, an accurate evaluation of implemented smart solutions focusing on the energy domain is necessary in order to assess the expected and realized impact of each solution. This paper proposes a seven-step methodology for assessing the impact of smart city interventions and presents a use case for the city of Espoo. A number of major findings were the outcome of our research and development work, such as the need for a thorough analysis of the long-term vision of the city, a combined top-down and bottom-up approach and the ongoing cooperation between all stakeholders involved in urban planning and transformation, in which necessary Key Performance Indicators (KPIs) are defined.

1. Introduction

1.1. The Necessity of Impact Assessment
According to the United Nations [1] 68% of the world’s population is projected to live in urban areas by the year 2050; cities, therefore, are anticipated to face new challenges in sustainably integrating further populace, such as a lack of appropriate infrastructure for sustainable energy and automated systems, applications and commercial arrangements, digital security, network connectivity and data capacity [2]. Cities will be required to transform their infrastructures in a smarter, more efficient and resilient way so that sustainable development forms part of their long-term strategy and a better quality of life is assured to their citizens. The concept of the smart city is multidimensional [3], with the aim of increasing urban sustainability and quality of life while pursuing economic development among its citizens [4]. Additionally, sustainability has gained popularity among scholars, environmental policy makers and government agencies [5]. However, a smart city is not just about creating technically viable urban areas and stimulating economic growth; it requires a multifaceted and multidimensional approach that promotes a structural transformation in the urban ecosystem, directing the development of cities towards their “smartness” in areas such as economy, governance, social, environment, mobility and ICT [6].

Abstract

Keywords

1. Introduction

2. Access control and security system. QToggle

3. Video surveillance. MotionEye

4. Results

4.1. Access control and security using QToggle

4.2. MotionEye screenshots

5. Conclusions

Declaration of Competing Interest

References

Abstract

In the past years smart homes solutions have become more and more popular with the introduction of a high number of both Internet of Things (IoT) applications and smart devices. Home automation and security systems market is in a continuous growth, traditional security systems are evolving fast, and more and more people choose Smart home solutions. In this paper, we propose two IoT based systems in the context of Smart homes: qToggle for multiple home automation, and MotionEyeOS, a video surveillance OS for single-board computers. Most qToggle devices are based on ESP8266/ESP8285 chips or on Raspberry Pi boards and smart sensors, while MotionEye uses Raspberry Pi boards.

1. Introduction

The Internet of Things (IoT) [1], [2] represents a new paradigm in computing, in which billions of smart devices that are connected to the Internet, directly communicate with each other and with the users. IoT uses the Internet Protocol (IP) to connect devices, which include smartphones, tablets and digital assistants to various types of sensors, appliances, and systems such as lighting, temperature, or security. The characteristics of the IoT are very wide and include a variety of physical elements, as shown in Fig. 1 [3].

In the last years, the IoT concept has had a strong evolution, being currently used in various domains such as smart homes, telemedicine, industrial environments, etc. The associated network can bring several challenges in the applications development process. The IoT is not a single technology, but it is a concept in which new things are connected and enabled, and this brings many business opportunities [3]. According to Intel [4] the IoT world is continuously growing, from two billion objects in 2006, to a projected 200 billion by 2020. This means around twenty-six smart devices for every human being on Earth.

Intelligent Building Management Systems (BMS) represent an important part of the IoT paradigm that aims to control and monitor the mechanical and electrical equipment of a building. Allowing objects and devices in a building to be connected to the Internet enables users to remotely monitor and control them. From light switches that can be controlled to turn on and off by using a smartphone, tablet, laptop or by voice command, to thermostats that will adjust the indoor temperatures, access control, or air conditioning on/off switching, intelligent BMS solutions have become popular in the last years and more nowadays. Smart home aims to integrate home automations. Home automation represents a smart solution that offers several advantages, including comfort, security, energy saving, money saving in the long run, convenience, and peace of mind. Fig. 2 shows an example of an intelligent building employed with different IoT connected utilities. Although, it presents only a few examples, there are many more applications that can be implemented, upon request.

Abstract

     The sudden emergence of the COVID-19 pandemic has introduced new norms largely revolving around the use of social media, disrupting the mentality of Internet users, especially the youth, resulting in an increase in cyberbullying. The rise in the popularity of many apps that facilitate online interactions has increased the risk of cyberbullying incidents. Not only did the COVID-19 pandemic transform social life, work, and education towards online modes of interaction, but it has also contributed to the ongoing digitization of bullying. As work moved to the home, so did bullying. This study aims to understand how the COVID-19 pandemic, which affects social media usage, increases the incidence of cyberbullying. We tested our hypotheses using a sample of 200 Malaysian participants. The results showed that the relationship between the COVID-19 pandemic’s influence and cyberbullying was significant. However, we failed to find any statistical evidence that gender moderates this relationship. This study found an increase in cyberbullying incidents resulting from the increased use of social media due to the COVID-19 pandemic’s influence. Our findings contribute to the body of knowledge on the prevalence of cyberbullying in Malaysia, which may benefit future research.

Introduction

     It has been considerable amount of time since the COVID-19 pandemic began impacting the world in numerous ways and imparting upon people new perspectives. Technology has taken over many aspects of the world, and the COVID-19 virus has accelerated the transformation of many manual jobs and tasks to that of electronic-assisted and enabled ones. The COVID-19 pandemic has forced millions of students to stay indoors, adapt to the “new normal,” and engage in distance learning at home, thus placing online learning in the spotlight (Munir et al., 2021). Many businesses have ceased operations due to various obstacles presented by the pandemic (Hu & Kee, 2021), while some businesses have converted the COVID-19 experience to that of entrepreneurial opportunity identification (Tunde et al., 2021). Arguably, the COVID-19 pandemic has further accelerated the adoption of technology via its use across many remote activities, such as in online classrooms, shopping, delivery, and work applications, as well as in the form of video conferencing applications (Lim, 2021). The phenomenon of working and studying online by default has become widespread, which has led to the spread of many virtual applications and programs aimed at electronically facilitating many operations. In the case of social distancing, it is the Internet connection that makes us aware of local and global developments, and through which we are able to run our businesses and maintain a measure of mental and physical well-being.

IMPLICATIONS AND CONCLUSIONS

     This study has several important implications regarding cyberbullying among Malaysian youths. First, the literature on cyberbullying and mental health has been expanded through a study conducted among Malaysians. This study is the first to empirically investigate cyberbullying on social media under the influence of the COVID-19 pandemic among Malaysian youths. This study found that the proportion of cyberbullies who used online social networking applications is much higher than that of their victims. Based on the survey conducted, it was found that Malaysians perceived Facebook as the most used application of cyberbullying, and that physical appearance was the most attacked aspect that cyberbullies target in their victims. This study also found a significant impact of the COVID-19 pandemic on cyberbullying, which acts as a determinant of Malaysians’ behavior in using social media more often. In other words, this study has found that the COVID-19 pandemic has caused a sharp increase in cyberbullying among the youth surveyed in Malaysia.

     The findings of this study highlight the urgency of combating cyberbullying, as it is a growing problem among Malaysians—and especially young people—with cyberbullying seriously affecting their mental health. The rising popularity of social media—which facilitates online interactions—has increased the risk of cyberbullying. Policymakers and educators in schools, colleges, and universities should consider the following: education and psychology professionals should provide interventions concerning technology, its uses, and abuse as part of the curriculum, along with ethics, so as to possibly reduce the potential threat of cyberbullying. These interventions could potentially positively impact an individual’s critical thinking skills, self-awareness, and independence online, possibly enabling Malaysian youths to respond to cyberbullying incidents in a self-organized manner. The findings of this study indicate that the general approach to cyberbullying should be preventive and proactive, rather than reactive, and should be based on apprehending and engaging the perpetrators, as well as by creating safe and respectful environments for young people.

Abstract

     Every aspect of human activities has been influenced by social media, but how this influence is affecting individual decision making in different context had not been studied yet. This article tries to investigate social media influences at individual level with respect to different contexts such as organization, marketplace, and social environment. 132 articles had been selected for the review process. The findings have been presented using the lens of Theory, Context, Characteristics and Methodology. This article presents social media influence phenomenon within an individual. The article concludes by highlighting literature gaps and future research directions. This review makes the relevant contribution to the field of computer mediated communications.

Introduction

     Twitter, Facebook, and LinkedIn are popular social media platforms each of it being used by different stakeholders across the world to interact with each other (Gong et al., 2017; Kane et al., 2014; Kitsios et al., 2022; Obembe et al., 2021). Social media is interactive and networked (Bechmann & Lomborg, 2013; Park et al., 2018). Every individual is motivated towards participating in virtual communities in exchange for rewards in form of friendship, appreciation, knowledge, participation (Sipahi, 2017) leading to crowd wisdom, financial support, collective creation, and many more. Individuals on social media are both producers and consumers of information. This had led to new term “pro-consumer”. Organizations used social media for reaching potential customers (Buechel & Berger, 2018) and shaping consumer’s opinion regarding products (Gkikas et al., 2022; Ruggeri & Samoggia, 2018). Social media empower individuals in decision-making (Bulut & Karabulut, 2018; Sadovykh et al., 2015; Zhang et al., 2017). Similarly social media strengthens organizations relationship with stakeholders (Blevins & Ragozzino, 2019; Schniederjans et al., 2013; Youn & Jin, 2017). Careful adoption of social media is beneficial for the organizations for increasing sales, customer acquisition, corporate reputation (Sipahi E. ve Artantaş E, 2017) and many more (Ancillai et al., 2019; Cui et al., 2018; Dolan et al., 2019; Huang et al., 2015; Kumar et al., 2017; Meire et al., 2017; Vieira et al., 2019). Mismanagement of social media may lead to negative perception among stakeholders (Benthaus et al., 2016).

Conclusion and implication for research

     Social media platforms unite the people, facilitates sharing of information for various purposes such as self enhancement, product usability, learning, branding, innovation, customer engagement and many more. Also, social media platforms satisfy the human need and desire for social connection and relationship. The article presents the social media influence phenomenon which tries to explain how social media posts is influencing an individual decision making. The article also summarizes the theories and methodologies being used in prior literature for studying different characteristics in different contexts. The study also identifies literature gaps illustrated in implication for research section. Social media can be used for understanding social, economic, and behavioral aspects of the mankind with the help of different characteristics categories such as: individual profile attributes, user generated content analyzed algorithmically, network analytics and platform characteristics.

     The authors of the study had come up with table 5 which can be used by the research scholars for future research projects. Table 5 presents the characteristics categories as rows and contexts as the columns. The values in each cell contains the characteristics which can be studied by the scholar, for example, (a) by applying algorithms on content the sentiments and emotions of the consumers can be computed in marketplace along with consumers preferences; (b) by computing network parameters such as co-membership, centrality, tie and tie strength within the social environment the social influence and information diffusion can be measured. Scholars have indicated real time conversation on social media can be used for analyzing various matters, concerns and issues (Lai & Turban, 2008; Sundararajan et al., 2013). Some scholars indicated that big data analytics can be used for studying networks on social media (Cao et al., 2015; Grover & Kar, 2017). Cao et al. (2015) had presented the evolution of social networks within the information system research.

Abstract

Introduction

Related works

Integrated cybersecurity risk management (i-CSRM)

Integrated cyber security risk management tool (i-CSRMT)

Evaluation of i-CSRM

Discussion

Conclusion

Declarations

References

Abstract

     Cyber security risk management plays an important role for today’s businesses due to the rapidly changing threat landscape and the existence of evolving sophisticated cyber attacks. It is necessary for organisations, of any size, but in particular those that are associated with a critical infrastructure, to understand the risks, so that suitable controls can be taken for the overall business continuity and critical service delivery. There are a number of works that aim to develop systematic processes for risk assessment and management. However, the existing works have limited input from threat intelligence properties and evolving attack trends, resulting in limited contextual information related to cyber security risks. This creates a challenge, especially in the context of critical infrastructures, since attacks have evolved from technical to socio-technical and protecting against them requires such contextual information. This research proposes a novel integrated cyber security risk management (i-CSRM) framework that responds to that challenge by supporting systematic identification of critical assets through the use of a decision support mechanism built on fuzzy set theory, by predicting risk types through machine learning techniques, and by assessing the effectiveness of existing controls.

Introduction

     Critical infrastructures (CIs), such as energy and healthcare, heavily rely on Information and Communication Technology (ICT) to support reliable service delivery. Such integration of ICT to CIs introduces a number of advantages, such as higher degree of flexibility, scalability and efficiency in the communication and coordination of advanced services and processes. On the other hand, the increase usage of ICT in CIs creates new opportunities for cyber attacks and increases the vulnerability of those systems. Due to the importance of critical infrastructures, there are recently an increased number of attacks that are evolving in terms of sophistication, persistence and the resources that attackers have available. Such attacks consider not just the technical limitations of the relevant technologies but also the contextual information related to the critical infrastructure.

     Despite of several existing works on cybersecurity risk management, the literature fails to present works that consider such contextual information when performing risk management for critical infrastructures. Moreover, existing works focus more on the prediction of risks and do not consider—as part of the same process—necessary controls that mitigate those risks. Our work advances the state of the art through the integration of cyber threat intelligence (CTI) to the risk management process, to understand contextual information related to the threat actor’s behaviour, tactics, techniques and procedures (TTP) and indicators. Moreover, it provides a unified process that integrates both risk prediction and risk mitigation with the aid of machine learning.

Conclusion

     Risk management is a continuous process for maintaining the effective functioning of critical assets for any organisational context. In particular, critical infrastructures need resilience for the service delivery and risk management is an essential component to achieve this. The threat landscape is constantly evolving with new techniques and more sophisticated organised attacks. Therefore, it is necessary for the risk management activities to consider the threat context to assess and manage the risks. This research proposes the integrated cyber security risk management framework (i-CSRM) that adopts various existing standards and cyber threat intelligence data for risk management. i-CSRM also includes machine learning (ML) models to predicate the risk types so that organisations can undertake the necessary proactive measures to tackle the risks. The framework also includes a tool support to automate some of the risk management activities. Finally, i-CSRM is applied in a CI-based industrial context and the results of applying the framework are very promising. Specifically the studied context was able to identify and assess risks using i-CSRM and determine the right level of control for the overall business continuity. The participants’ observation is that i-CSRM is a practical approach for the risk management, and integration of CTI makes the risk management activities more effective. We believe that the proposed i-CSRM framework, its process and supporting tool will significantly impact the cybersecurity domain and state of the art in general. The i-CSRM framework focuses only on the supervised learning method, which requires labelled dataset. As a part of our future research, we would like to deploy the i-CSRM in different CI context and implement different data sets for the risk type predication. Additionally, it is necessary to develop a checklist to make the process easy to use for risk assessment and management.

Abstract

Introduction

Background

The Application of Viable System Model (VSM) in IT governance

Methodology

Findings

Conclusion

Declarations

References

Abstract

     In recent years, some standards and frameworks proposed the risk management structures for managing and controlling IT risk that is the main component of enterprise governance of IT. Unfortunately, these frameworks have a retrospective view of threat analysis and less pay attention to future threats in the business environment, and do not propose adaptable solutions. At the same time, the current risk framework is not based on a strong scientific system modeling. In this research, the researcher proposed the Viable System Model (VSM) as an adaptable and comprehensive framework that is based on scientific modeling with the systematic approach for managing and controlling IT risk in today’s complex business environment. This research did in a systematic action research methodology in the banking context in Iran. The results showed that by applying the VSM as a framework for managing IT risk, adaptability of IT risk criteria according to the future threats can be achieved by this framework. A comprehensive risk management framework (retrospective and prospective view) with a systematic approach could be achieved by this system modeling.

Introduction

     The development of new technologies in IT caused the increased complexity of this feld in recent years. Due to this complexity, the risk related to IT becomes more critical and if these risks ignored, it could be dangerous for achieving business objectives. Nowadays, IT risk management system became the main concern of senior managers of organizations. Meanwhile, many organizations that have not a good understanding of IT risk management systems have spent a lot of money to reduce IT risks, but they just waste their money and not successful in managing the IT risks. “Many organizations struggle with risk assessment and some believe that it shouldn’t be practiced at all! Many do some form of risk assessment, but often badly, or incompletely. Some just don’t bother, preferring an approach which relies on standards and baselines to manage the common risks, some just ignore the problem and trust to hope. (Coles and Moulton 2003)”.

     Risk is a combination of the probability of an event and its impacts (negative and positive) in business, which is usually referred to as the negative impacts that could be afected the business goals. Information Security Risk (ISR) is defne by ISO 31000 as “a combination of two factors: probability and consequences. It asks two basic questions: what is the probability that a particular information security event will occur in the future? And what consequences would this event produce or what impact would it have if it actually occurred? Information security risks often emerge because potential security threats are identifed that could exploit vulnerabilities in an information asset or group of assets and therefore cause harm to an organization” (Fazlida and Said 2015).

Conclusion

     In this research, by applying the soft system models such as the VSM as an adaptable framework for managing IT risk, we can guarantee the adaptability and viability of the IT risk management system. The VSM has a strong scientifc foundation, which based on system science, cybernetics approach, and variety engineering and is a good candidate as a framework for IT risk management system because the dynamic changes of business /IT environment require an adaptable framework for managing IT risks for aligning and integrating IT risk strategy with the business risk strategy.

     By using the variety engineering (Ashby law) in VSM, the front line employee is encouraged to manage the IT risk (creating autonomous system 1) and this can be helpful to increase the quality of IT risk management systems by managing risk at the front line (detecting and mitigating risk at the source). The results show us the best approach to manage the IT risk is building an autonomous system (system1) to deal with risk at the front line and VSM brings these benefts for us in system 1. By the communication channels between subsystems in VSM, a big picture of the current IT risk profle shared between the operational units (S1) and the management system and this can be helpful to establish a holistic approach in the IT risk management system. Therefore, the results show us that VSM can propose a holistic approach for IT risk management system, which has not been paying attention to this important point in any other risk framework. Not paying attention to a holistic approach in a risk management system can be a threat of duplicating risk analysis (wasteful work) in diferent units, which are working in the isolated environment.

Introduction

One of the most noteworthy arcs of the 21st century has been the explosive growth seen in the usage and deployment of technology. More specifically, the advancement of information and communications technology (ICT) has been the foundation for many of the innovations seen over this century thus far. The wide-spanning adoption of the Internet for social media, e-commerce, news consumption, and more has redefined much of our everyday lives and industries across the world. Due to the advancements of ICT, there are many exciting opportunities for what is yet to come. One longstanding opportunity for the future is the vision of the smart city.

There are countless different ideas and definitions for what exactly qualifies as a smart city. A more general definition, provided by Bakıcı et al. in [1], defines smart cities as being high-tech cities that serve to connect people, information, and city-wide infrastructure and resources using advances in technology to improve sustainability, innovation, and overall quality-of-life. The extent to which how pervasive (or intrusive) technologies are incorporated to accomplish such goals is often a point of contention in the literature [2]. However, the many different visions for what define smart cities all generally involve heavy utilization of technology to improve the maintenance of cities to further improve and/or optimize city-specific operations for different stakeholders (e.g., citizens, the environment).

Realizing the ambitious vision of smart cities requires seamlessly making decisions for a host of different applications and domains with minimal sacrifice with respect to performance and resource costs. Some applications of interest for smart cities include, but are not limited to, smart traffic light control, smart information spread (or advertisement), smart energy distribution, smart waste management, etc. Ideally, data generated in urban environments can be collected in smart cities using CT-powered infrastructure to train and inform models designed for these applications to make realtime decisions. For instance, insistent collection of traffic congestion data from roadside units(RSU) and other sources can be sent to ICT infrastructure to train machine learning models how to make optimal decisions related to traffic lights to reduce congestion. This approach is becoming more within reach given the advances in vehicular ad hoc networks (VANET) [3, 4, 5] and other Internetof-Vehicles (IoV) systems [6, 7, 8].

Making smart decisions in a timely fashion will require a powerful pipeline ready to support intense amounts of data collection, analytics, and adaptive decision-making. For instance, video feeds at road intersections — to empower models for smart traffic light control — are large in size (in terms of bytes) and will require automated processes to make meaningful decisions in real-time. However, video cameras that provide such video feeds will be unable, on their own, to perform such analytics and decision-making. Such tasks would require more computational resources than would be available at video cameras and other similar sensors that can be found in urban environments. Thus, for such applications, smart cities will need to be able to provide heavy computational power on-demand, even for devices that do not have such resources locally. Applications (e.g., big data analytics, video classification) all require large amounts of compute resources in order to adequately process. Because Internet-of-Things (IoT) devices like smartphones are often ill-equipped to meet the needs of such processes [9, 10], a standard approach for providing these services on these devices is to rely on remote resources.

Abstract

Document Sections

I. Introduction (heading 1)

II. Basic Technology of Blockchain

III. Blockchain Technology Architecture

IV. The Applications of Blockchain

V. Challenges and Future Trends

VI. Discussion and Future Directions

Abstract:

Starting from the basic concept of blockchain technology, the paper introduces the architecture of blockchain technology and the typical application of blockchain technology, and finally summarizes the challenges faced by blockchain technology, so as to provide some reference for the technological path innovation and data value reconstruction of deep integration of blockchain technology and various fields.

I. INTRODUCTION (HEADING 1)

Blockchain Technology (BT) is a new decentralized infrastructure and distributed computing paradigm, which uses encrypted chained data blocks to verify and store data, uses distributed node consensus algorithm to generate and update data, and uses smart contract to program and operate data. Its outstanding feature is that it doesn’t rely on the third-party trusted organization to realize the value communication between nodes without trust relationship.

Blockchain is a novel type of distributed infrastructure and computing paradigm that integrates technologies including distributed data storage, peer-to-peer (P2P) transmission, consensus algorithm, and cryptography [1]. It uses block-chain data structures to store and verify data, uses distributed consensus algorithms to generate and update data, uses cryptography technologies to ensure the security of data transmission and access, and uses smart contracts composed of automated script codes to program and operate data [2]. Through the integration of multiple technologies, the cluster jointly maintains the security and operation of the blockchain network and builds trust with machines.

Blockchain has four major supporting technologies: distributed database, consensus algorithm, secure cryptographic protocol, and smart contract. Based on the underlying key technologies of the blockchain, it has the following characteristics [3]: (1) Decentralization. There is no centralized hardware or management organization in the blockchain network. The rights and obligations of all nodes are equal, and the data in the system is maintained together. The suspension of any node does not affect the overall operation of the system.