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This paper investigates the application of graph theory and variants of greedy graph coloring algorithms for the optimization of distributed peer-to-peer networks, with a special focus on private blockchain networks. The graph coloring problem, as an NP-hard problem, presents a challenge in determining the minimum number of colors needed to efficiently allocate resources within the network. The paper deals with the influence of different graph density, i.e., the number of links, on the efficiency of greedy algorithms such as DSATUR, Descending, and Ascending. Experimental results show that increasing the number of links in the network contributes to a more uniform distribution of colors and increases the resistance of the network, whereby the DSATUR algorithm achieves the most uniform color saturation. The optimal configuration for a 100-node network has been identified at around 2000 to 2500 links, which achieves stability without excessive redundancy. These results are applied in the context of a private blockchain network that uses optimal connectivity to achieve high resilience and efficient resource allocation. The research findings suggest that adapting network configuration using greedy algorithms can contribute to the optimization of distributed systems, making them more stable and resilient to loads. [ABSTRACT FROM AUTHOR]

Purpose – The emergence of COVID-19 has exacerbated and spurred the growth of mental health issues in ways that have challenged mental health workers tremendously. The complex nature of COVID-19 has made the need of mental health professionals extremely important. Hospital ward overcrowding, social distancing requirements, sequestrations, limits to face-to-face consultations have created barriers to mental health access, especially those in need of Certified Peer Recovery Specialists. Certified Peer Recovery Specialists play a critical role in the treatment of mental illness through their support and engagement of those recovering from substance abuse. Many in this role are hired because they have a strong skillset for help-oriented clinical work but are often challenged, especially in times like this to development and demonstrate leadership skills. This paper explores the nature and need to develop leadership skills and leadership challenges for people in this field through interpretative phenomenological analysis (IPA) and a content analysis of the current and seminal literature. Design/methodology/approach – This paper explores the nature and need to develop leadership skills and leadership challenges for people in mental health through IPA and a content analysis of the current and seminal literature. Findings – The essential necessity to invest in the cultivation of peer recovery specialists and mental health professionals as organizational leaders. Originality/value – Hospital ward overcrowding, social distancing requirements, sequestrations, limits to face- to- face consultations have created barriers to mental health access, especially those in need of Certified Peer Recovery Specialists. Certified Peer Recovery Specialists play a critical role in the treatment of mental illness through their support and engagement of those recovering from substance abuse.

Substantial evidence has shown that involvement in peer-to-peer farming networks influences whether a farmer decides to try a new practice. Formally organized farmer networks are emerging as a unique entity that blend the benefits of decentralized exchange of farmer knowledge within the structure of an organization providing a variety of sources of information and forms of engagement. We define formal farmer networks as farmer networks with a distinct membership and organizational structure, leadership that includes farmers, and an emphasis on peer-to-peer learning. This study complements existing ethnographic research on the benefits of organized farmer networking by examining farmers in one longstanding formal farmer network, Practical Farmers of Iowa. Using a nested, mixed-method research design, we analyzed survey and interview data to understand how participation and forms of engagement in the network are associated with the adoption of conservation practices. Responses from 677 farmers from a regular member survey disseminated by Practical Farmers of Iowa in 2013, 2017, and 2020 were pooled and analyzed. GLM binomial and ordered logistic regression results indicate that greater participation in the network, particularly through in-person formats, has a strong and significant association with greater adoption of conservation practices. Logistic regression results show that building relationships in the network is the most important variable for predicting whether a farmer reported adopting conservation practices as a result of participation in PFI. In-depth interviews with 26 surveyed member farmers revealed that PFI supports farmers to adopt by providing information, resources, encouragement, confidence building, and reinforcement. In-person learning formats were more important to farmers relative to independent formats because they were able to have side conversations with other farmers, ask questions, and observe results. We conclude that formal networks are a promising way to expand the use of conservation practices, particularly through targeted efforts to increase relationship building in the network through face-to-face learning opportunities.

Ethereum, currently the most widely utilized smart contracts platform, anchors the security of myriad smart contracts upon its own robustness. Its foundational peer-to-peer network facilitates a dependable node connection mechanism, whereas an efficient data-sharing protocol constitutes as the bedrock of Blockchain network security. In this paper, we propose NodeHunter, an Ethereum network detector implemented through the application of simulation technology, which is capable of aggregating all node records within the network and the interconnectedness between them. Utilizing this connection information, NodeHunter can procure more comprehensive insights for network status analysis compared to preceding detection methodologies. Throughout a three-month period of unbroken surveillance of the Ethereum network, we obtained an excess of two million node records along with over one hundred million node acquaintances. Analysis of the gathered data revealed that an alarming 49% or more of these node records were maliciously forged. [ABSTRACT FROM AUTHOR]

This paper investigates the application of graph theory and variants of greedy graph coloring algorithms for the optimization of distributed peer-to-peer networks, with a special focus on private blockchain networks. The graph coloring problem, as an NP-hard problem, presents a challenge in determining the minimum number of colors needed to efficiently allocate resources within the network. The paper deals with the influence of different graph density, i.e., the number of links, on the efficiency of greedy algorithms such as DSATUR, Descending, and Ascending. Experimental results show that increasing the number of links in the network contributes to a more uniform distribution of colors and increases the resistance of the network, whereby the DSATUR algorithm achieves the most uniform color saturation. The optimal configuration for a 100-node network has been identified at around 2000 to 2500 links, which achieves stability without excessive redundancy. These results are applied in the context of a private blockchain network that uses optimal connectivity to achieve high resilience and efficient resource allocation. The research findings suggest that adapting network configuration using greedy algorithms can contribute to the optimization of distributed systems, making them more stable and resilient to loads.

This paper studies a few randomized algorithms (e.g., random walks, gossip) in peer-to-peer networks. We leverage the Docker virtual container technology to develop implementations of the peer-to-peer networks and of these distributed randomized algorithms executing on top of them. We can thus analyze their behavior and performance in realistic settings. We further consider the problem of identifying high-risk bottleneck links in the network with the objective of improving the resilience and reliability of peer-to-peer networks. We propose a randomized algorithm to solve this problem and evaluate its performance by simulations. [ABSTRACT FROM AUTHOR]

Ethereum, currently the most widely utilized smart contracts platform, anchors the security of myriad smart contracts upon its own robustness. Its foundational peer-to-peer network facilitates a dependable node connection mechanism, whereas an efficient data-sharing protocol constitutes as the bedrock of Blockchain network security. In this paper, we propose NodeHunter, an Ethereum network detector implemented through the application of simulation technology, which is capable of aggregating all node records within the network and the interconnectedness between them. Utilizing this connection information, NodeHunter can procure more comprehensive insights for network status analysis compared to preceding detection methodologies. Throughout a three-month period of unbroken surveillance of the Ethereum network, we obtained an excess of two million node records along with over one hundred million node acquaintances. Analysis of the gathered data revealed that an alarming 49% or more of these node records were maliciously forged. [ABSTRACT FROM AUTHOR]

The object of this study is the protocol for detecting nodes in the Rootstock blockchain network and crawling tools. Node discovery protocols are the foundation of any decentralized peer-to-peer network. In blockchain systems, full nodes store and maintain a complete copy of all transactions performed in the network. However, they do not store information about all other nodes, such as their IDs or IP addresses. Each node usually maintains an incomplete list of nodes to which it connects to exchange blockchain data. In decentralized networks, nodes join and leave the network and their IP addresses can change, making it impractical to maintain a complete, up-to-date list of all nodes. Therefore, the only way to get a list of all nodes in the network is to poll each node sequentially. The developed method involves sending specially formed messages to nodes to obtain their neighbors. The graph search algorithm is used to traverse all received neighboring nodes. This allows one to consistently detect all network nodes. Identifying the desired sequence of messages requires a preliminary analysis of the node software RSKj in the part of node discovery protocol. Effectiveness of the proposed method was verified using the developed software and an experiment in the main network. 6 verification nodes were deployed in different physical locations and at different times. All test nodes were detected in less than 10 minutes. The developed method found 222 nodes that have 209 unique IP addresses. Results of this study show how to perform analysis of node discovery protocol. They provide the means to obtain information about the available nodes of the Rootstock blockchain system, enabling the analysis of both the blockchain network in general and individual node. [ABSTRACT FROM AUTHOR]

We consider the following network model motivated, in particular, by blockchains and peer-to-peer live streaming. Data packet flows arrive at the network nodes and need to be disseminated to all other nodes. Packets are relayed through the network via links of finite capacity. A packet leaves the network when it is disseminated to all nodes. Our focus is on two communication disciplines, which determine the order in which packets are transmitted over each link, namely Random-Useful (RU) and Oldest-Useful (OU). We show that RU has the maximum stability region in a general network. For the OU we demonstrate that, somewhat surprisingly, it does not in general have the maximum stability region. We prove that OU does achieve maximum stability in the important special case of a symmetric network, given by the full graph with equal capacities on all links and equal arrival rates at all nodes. We also give other stability results, and compare different disciplines' performances in a symmetric system via simulation. Finally, we study the cumulative delays experienced by a packet as it propagates through the symmetric system, specifically the delay asymptotic behavior as N → ∞ . We put forward some conjectures about this behavior, supported by heuristic arguments and simulation experiments. [ABSTRACT FROM AUTHOR]

Substantial evidence has shown that involvement in peer-to-peer farming networks influences whether a farmer decides to try a new practice. Formally organized farmer networks are emerging as a unique entity that blend the benefits of decentralized exchange of farmer knowledge within the structure of an organization providing a variety of sources of information and forms of engagement. We define formal farmer networks as farmer networks with a distinct membership and organizational structure, leadership that includes farmers, and an emphasis on peer-to-peer learning. This study complements existing ethnographic research on the benefits of organized farmer networking by examining farmers in one longstanding formal farmer network, Practical Farmers of Iowa. Using a nested, mixed-method research design, we analyzed survey and interview data to understand how participation and forms of engagement in the network are associated with the adoption of conservation practices. Responses from 677 farmers from a regular member survey disseminated by Practical Farmers of Iowa in 2013, 2017, and 2020 were pooled and analyzed. GLM binomial and ordered logistic regression results indicate that greater participation in the network, particularly through in-person formats, has a strong and significant association with greater adoption of conservation practices. Logistic regression results show that building relationships in the network is the most important variable for predicting whether a farmer reported adopting conservation practices as a result of participation in PFI. In-depth interviews with 26 surveyed member farmers revealed that PFI supports farmers to adopt by providing information, resources, encouragement, confidence building, and reinforcement. In-person learning formats were more important to farmers relative to independent formats because they were able to have side conversations with other farmers, ask questions, and observe results. We conclude that formal networks are a promising way to expand the use of conservation practices, particularly through targeted efforts to increase relationship building in the network through face-to-face learning opportunities. [ABSTRACT FROM AUTHOR]

Communication via digital means, such as mobile messaging applications (apps), plays an increasingly important role in everyday life. However, most messaging apps employ centralized computing principles that relinquish control of their users’ personal data to social network platform providers. Decentralization has been proposed as an alternative that provides trustworthiness and data confidentiality, but this comes at expense of fewer provided features and non-intuitive user experience. To address this issue, we hereby present two interconnected decentralized messaging tools, developed in the scope of the HELIOS platform, which can support new decentralized social networks. The first tool is a framework that supports the development of context-aware decentralized messaging apps in mobile Android devices by organically tying together many of the platform’s standalone decentralized operations. The second is a decentralized messaging app, called helios.TALK, that builds on the framework but accommodates additional design considerations from the perspective of end-users.

Graph centrality measures have found widespread use ranking agents in networks by characterizing their "importance" for the purpose of predicting and managing network outcomes. One major application of such a graph centrality problem is found in decentralized, peer-to-peer networks; in particular, the EigenTrust algorithm for trust management aims to reduce the amount of malware distributed in peer-to-peer networks by using graph centrality as a metric for reputation. This popular scheme has been successfully applied to many different types of peer-to-peer networks. However, the effect malicious agents can have on the reliability of schemes like EigenTrust through overt or covert behavior has not yet been fully investigated. In this work, we analyze vulnerabilities in EigenTrust by extending classical eigenvalue/eigenvector sensitivity theory to the nonsmooth setting, where the presence of nonsmoothness in this problem arises from defense mechanisms built into EigenTrust. This enables us to compute the sensitivity of trust scores to ratings provided by individual agents, revealing vulnerabilities in EigenTrust. Our findings indicate that malicious agents can have a large impact on a network despite having relatively low centrality themselves (i.e., appearing untrustworthy). [ABSTRACT FROM AUTHOR]

A primary challenge to form a geometric pattern for a fleet of randomly distributed agents is the determination of their locations on the boundary of a formation. The challenge is more acute if the agents need to be uniformly distributed on the formation. This study addresses this challenge for polygon formations via a two-phase procedure. In the first phase, the agents form an enclosing circle to the location of the formation. This prevents the agents from location-conflicts on the polygon and hence each agent can uniquely ascertain its projection point on the formation. In the second phase, the agents establish their projected points and move toward their locations on the polygon, while mitigating collisions. The circle formation is also used as a regrouping feature before the agents reconfigure themselves into a different polygon formation. The formation control laws developed are verified through simulation for circle formation, convex polygons, and some categories of concave polygons. The control laws include the cases for rotation, translation (relocation), and scaling of polygons as well. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) networks are distributed systems with a communication model in which no central authority governs the behavior of individual peers. These networks currently account for a considerable percentage of all bandwidth worldwide. However, this communication model also has a clear disadvantage: it has a multitude of vulnerabilities and security threats. The nature of the P2P philosophy itself means that there is no centralized server responsible for uploading, storing, and verifying the authenticity of the shared files and packets. A direct consequence of this is that P2P networks are a good choice for hackers for the spread of malicious software or malware in general since there is no mechanism to control what content is shared. In this paper, we present a mathematical model for P2P networks to study the effect of two different attacks on these systems, namely, malware and denial of service. To analyze the behavior of the cyber attacks and identify important weaknesses, we develop different Markov chains that reflect the main dynamics of the system and the attacks. Specifically, our model considers the case in which a certain number of nodes are infected with a cyber worm that is spread throughout the network as the file is shared among peers. This allows observation of the final number of infected peers when an initial number (we evaluate the system for from 1 to 14 initial nodes) of malicious nodes infect the system. For the DoS attack, our model considers the portion of peers that are unable to communicate and the average attack duration to study the performance degradation of such an attack. A two-pronged approach was used to study the impact of the attacks on P2P networks; the first focused only on the P2P network, and the second focused on the attacks and the network. [ABSTRACT FROM AUTHOR]

Several protocols for private set intersection are presented in the literature. When the sets are large these tend to be too slow for many purposes. We consider the problem of two parties finding out how many common friends they have in a privacy preserving way. This problem has arisen in designing a peer-to-peer platform called HELIOS. We present our solution for the problem that is more efficient than older protocols but still sufficiently privacy-friendly for our purposes. The solution is based on iteratively revealing information about hash values of friends identities in small increments.

A Peer-to-peer (P2P) network system has proved to be a popular and efficient approach for large-scale live video streaming and file sharing over the Internet. Such a system is always challenged by various issues and requires to be monitored and controlled for significant performance. Among them, one of the major performance issues in P2P networks is the flash crowd. When many users switch to a popular channel in a short span of time, the P2P system suffers from a serious join congestion problem, called the flash crowd problem. In such situations, systems can scale only up to a limit, both by available surplus bandwidth and intense competition among peers for their start-up, and hence, it not only decreases the efficiency but also creates a blockage of services for connected peers. Therefore, management of the flash crowd has been important and becoming paramount with the increased use of online services and the proliferation of various technologies like IoT, Edge & Mobile Computing, Crypto Currencies, and Torrenz-like services. A hierarchy-based admission control model with multiple threshold layers is proposed to mitigate the flash crowd in the P2P network paradigm. A fuzzy logic-based prediction mechanism is proposed to predict the control scheme applicable to the requested peers based on the parameters such as buffer size, bandwidth, etc. In case of the occurrence of the flash crowd, the model applies different control schemes depending upon which threshold layer it is predicted. These include saving the system resources, granting preferences to best-performing peers, dropping requests of worst-performing peers, a slight decrease in service quality, etc. We also characterize the credit score of peers, which denotes their performances. The model is simulated in OMNET++ along with the OverSim framework. Results from the simulation reveal a reduction in request drops, processing delays, and startup delays compared to other management schemes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Gestão & Tecnologia is the property of Revista Gestao & Tecnologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Peer (P2P) networks have emerged as an efficient and affordable means of transmitting videos to numerous end-users via the Internet. The dynamic and heterogeneous nature of P2P streaming systems (P2PSS) makes testing, analyzing and verification a cumbersome task. However, formal methods offer efficient approaches to rigorously analyze and verify P2PSS. This paper demonstrates the use of formal verification techniques for analyzing the behavioral properties of P2PSS. We use temporal logics to analyze whether all the possible behaviors within the P2P streaming systems conform to the defined specifications. Specifically, we apply model checking to check the consistency, completeness and certainty of the model if the temporal properties of the proposed system satisfies the required specifications. Furthermore, the P2PSS framework was modeled and verified using Simulink Design Verifier (SDV) in MATLAB simulation tool. The simulations results showed 100% validation for all frames and 50% validation for I-frames prioritisation. Further, the probability of a peer capable of forwarding frames while receiving is at most 0.5.

Peer-to-peer (P2P) networks are distributed systems with a communication model in which no central authority governs the behavior of individual peers. These networks currently account for a considerable percentage of all bandwidth worldwide. However, this communication model also has a clear disadvantage: it has a multitude of vulnerabilities and security threats. The nature of the P2P philosophy itself means that there is no centralized server responsible for uploading, storing, and verifying the authenticity of the shared files and packets. A direct consequence of this is that P2P networks are a good choice for hackers for the spread of malicious software or malware in general since there is no mechanism to control what content is shared. In this paper, we present a mathematical model for P2P networks to study the effect of two different attacks on these systems, namely, malware and denial of service. To analyze the behavior of the cyber attacks and identify important weaknesses, we develop different Markov chains that reflect the main dynamics of the system and the attacks. Specifically, our model considers the case in which a certain number of nodes are infected with a cyber worm that is spread throughout the network as the file is shared among peers. This allows observation of the final number of infected peers when an initial number (we evaluate the system for from 1 to 14 initial nodes) of malicious nodes infect the system. For the DoS attack, our model considers the portion of peers that are unable to communicate and the average attack duration to study the performance degradation of such an attack. A two-pronged approach was used to study the impact of the attacks on P2P networks; the first focused only on the P2P network, and the second focused on the attacks and the network.

The ability of a P2P network to scale its throughput up in proportion to the arrival rate of peers has recently been shown to be crucially dependent on the chunk sharing policy employed. Some policies can result in low frequencies of a particular chunk, known as the missing chunk syndrome, which can dramatically reduce throughput and lead to instability of the system. For instance, commonly used policies that nominally “boost” the sharing of infrequent chunks such as the well-known rarest-first algorithm have been shown to be unstable. We take a complementary viewpoint, and instead consider a policy that simply prevents the sharing of the most frequent chunk(s), that we call mode-suppression. We also consider a more general version that suppresses the mode only if the mode frequency is larger than the lowest frequency by a fixed threshold. We prove the stability of mode-suppression using Lyapunov techniques, and use a Kingman bound argument to show that the total download time does not increase with peer arrival rate. We then design versions of mode-suppression that sample a small number of peers at each time, and construct noisy mode estimates by aggregating these samples over time. We show numerically that mode suppression stabilizes and outperforms all other recently proposed chunk sharing algorithms, and via integration into BitTorrent implementation operating over the ns-3 that it ensures stable, low sojourn time operation in a real-world setting. [ABSTRACT FROM AUTHOR]

With the rapid growth in clean distributed energy resources involving micro-generation and flexible loads, users can actively manage their own energy and have the capability to enter in a market of energy services as prosumers while reducing their carbon footprint. The coordination between these distributed energy resources is essential in order to ensure fair trading and equality in resource sharing among a community of prosumers. Peer-to-Peer (P2P) networks can provide the underlying mechanisms for supporting such coordination and offer incentives to prosumers to participate in the energy market. In particular, the federation of energy clusters with P2P networks has the potential to unlock access to energy resources and lead to the development of new energy services in a fast-growing sharing energy economy. In this paper, we present the formation and federation of smart energy clusters using P2P networks with a view to decentralise energy markets and enable access and use of clean energy resources. We implement a P2P framework to support the federation of energy clusters and study the interaction of consumers and producers in a market of energy resources and services. We demonstrate how energy exchanges and energy costs in a federation are influenced by the energy demand, the size of energy clusters and energy types. We conduct our modelling and analysis based on a real fish industry case study in Milford Haven, South Wales, as part of the EU H2020 INTERREG piSCES project.

Copyright of Boğaziçi Journal: Review of Social, Economic & Administrative Studies is the property of Bogazici University, Faculty of Economics & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)