P2P Systems Research Articles

Towards a Formal MultipathP2P Protocol

Peer-To-Peer networks are becoming increasingly common as a mean of transferring files over Internet. In this paper, we describe, first, the design and implementation of our P2P system (MultiPathP2P). This latter is based on the social networks concepts where nodes are identified through their virtual addresses, we have designed our protocol based on 1) An architecture exploiting the principle of social networks where nodes are identified by virtual addresses and are able to randomly change their neighbors 2) A process of data request and file sharing differing from those supported in other P2P networks. Second, we present a formal validation of our proposal in order to prove its optimality and completeness.

High-Performance Computing Web Search System Based on Computer Big Data

File sharing, streaming media, collaborative computing, and other P2P systems are all unicast to establish the corresponding overlapping network. The superimposed network is generally carried out based on the existing primary network. In this way, the access of each node is random. At the same time, this will cause the topological structure of the upper and lower layers to be inconsistent. This will increase the communication delay between nodes and cause an excellent bandwidth burden to the underlying network. The existing topology matching methods still face problems, such as poor scalability and long node aggregation time. This paper aims to design a topological distributed node aggregation method based on network coordination and distributed hash table (DHT) algorithm. This paper established a two-dimensional mesh model of nodes based on equal-distance concentric circles and divided into two equal areas. The parts of multiple namespaces correspond one by one according to their location. Because nodes are kept close, neighbours can be aggregated through DHT's primary "publish" and "search" primitives. Experimental results show that the TANRA method can match the network's topology under a slight delay and a large number of nodes. The TANRA method can effectively reduce the path delay in structured networks.

The Promise of Serverless Computing within Peer-to-Peer Architectures for Distributed ML Training

My thesis focuses on the integration of serverless computing with Peer to Peer (P2P) architectures in distributed Machine Learning (ML). This research aims to harness the decentralized, resilient nature of P2P systems, combined with the scalability and automation of serverless platforms. We explore using databases not just for communication but also for in-database model updates and gradient averaging, addressing the challenges of statelessness in serverless environments.

The Potential of Green Engineering Solutions for Energy Conservation in Residential Buildings Towards Sustainability: A Case Study of Saudi Arabia

Developing countries in hot climate regions such as Saudi Arabia have witnessed rapid population growth, which has led to greater resource consumption as a result of the increased demand for new buildings. This research proposes a multi-objective evaluation of the potential green engineering solutions to conserve energy using a building within the ROSHN housing project, which is one of the mega projects in Saudi Arabia, as a case study for this paper with the aid of simulation software, taking into consideration the context of the sustainability concept. The results showed that traditional passive architectural design, whether courtyards or Mashrabiya, had the nearly greatest influence, with percentages ranging from −4% to −5.15% for varied parameters and designs compared to the base case energy usage. Furthermore, energy efficiency solutions for the building envelope’s external insulation and finish system (EIFS) enabled a drop in the U-value that lowered energy usage to −5.40%. However, the wall insulation thickness beyond 300 mm in this system has no substantial influence on energy savings. This research’s most clear finding is that a P2P system for PV panels on a district scale can supply enough energy to meet its needs after implementing the optimal strategy of the other proposed solutions.

Digital Resource Management and Sharing for Teaching and Learning in Higher Education by Integrating Multiple Data Chain Networks

Abstract In this paper, we construct a GCCP multivariate datalink network for teaching and learning resource sharing through the Generalized Control Chaining Primary Protocol (GCCP) combined with mutual cooperative services among the physical, data link and network layers for resource sharing. The solution set is mapped to the distributed dynamic self-organizing network data links through Nash equilibrium. Attribute descriptions for the necessary digital teaching resources are provided in a P2P system for sharing resources in large-scale heterogeneous teaching environments. Finally, simulations are conducted under the Group Service Control Protocol (GCCP) for teaching resource sharing to verify the effectiveness of the scheduling and management model for digital teaching resources. According to the results, the system with a multivariate datalink network maintains an average cluster size of 5 nodes. The response time of the system increases significantly with the increase of scale when RP(k)C-Grid takes k as 10,20,30,40,50 respectively. This paper effectively manages and maintains the entire digital resource management system for teaching and learning in universities, making it more stable and efficient.

Peer-to-Peer File Sharing WebApp

Peer-to-peer (P2P) networking has emerged as a promising technology that enables distributed systems to operate in a decentralized manner. P2P networks are based on a model where each node in the network can act as both a client and a server, thereby enabling data and resource sharing without relying on centralized servers. The P2P model has gained considerable attention in recent years due to its potential to provide a scalable, fault-tolerant, and resilient architecture for various applications such as file sharing, content distribution, and social networks.In recent years, researchers have also proposed hybrid architectures that combine the benefits of both structured and unstructured P2P networks. For example, the Distributed Hash Table (DHT) is a popular hybrid architecture that provides efficient lookup and search algorithms while maintaining the flexibility and adaptability of the unstructured network.To demonstrate the feasibility of P2P systems, several prototypes have been developed, such as the BitTorrent file-sharing protocol and the Skype voice-over-IP (VoIP) service. These prototypes have demonstrated the potential of P2P systems for large-scale applications and have paved the way for the development of new P2P-based systems.

Comparative Analysis of Peer-to-Peer PV Trading Strategies under the Influence of Network Constraints with Prosumer Sensitivity towards Network Coefficients

With the increase in rooftop photovoltaic (PV) systems at the residential level, customers owning such renewable resources can act as a source of generation for other consumers in the same network. Peer-to-peer (P2P) energy trading refers to a local trading platform where the residential customers having excess PV power (prosumers) can interact with their neighbors without PV resources (customers) to improve the social welfare of society. However, the performance of a P2P market depends on the power system network constraints and trading strategy adopted for local energy trading. In this paper, we compare different trading strategies, i.e., the rule-based zero intelligent (ZI) strategy and the preference-based game theory (GT) approaches, for a constrained P2P platform. Quadratic trading loss and impedance-based network utilization fee models are suggested to define the network constraints for the P2P system. Additionally, a reluctance-based prosumer-sensitive model is developed to adjust the trading behavior of the participants under the heavy distribution losses/network fee. The presented results show that the suggested trading strategies enhanced the average welfare of the participants by approximately 17%. On average, the customers saved about $33.77 monthly, whereas the average monthly earnings of the prosumers were around $28.3. The ZI strategy enhanced the average monetary advantages of all the market participants by an average of 7% for a system having small distribution losses and a network fee as compared to the GT approach. Contrarily, for a system having high losses/a utilization fee, the GT approach improved the average welfare of the prosumers by around 75% compared to the ZI strategy. However, both trading strategies yielded competitive results compared to the traditional market under the standard values of network coefficients.

Fintech Peer To Peer Lending Sharia as an Alternative Capital for MSME in Sidoarjo

This study aims to explore the role of Islamic peer-to-peer lending fintech as an alternative solution to Sidarjo's MSME capital problem. As we know there are still many MSMEs still unbankable. So when capital issues arise, they are in trouble. Therefore, the existence of Sharia Fintech with P2P system is expected to be the solution to these problems. The study uses a qualitative case study method that collects secondary and primary data through interviews with Sidoarjo economic officials. The conclusion of this study shows that sharia peer-to-peer loan financing to MSME can improve his MSME's performance. Sidoarjo's MSMEs just lack understanding of Sharia Fintech P2P as an alternative to capital. 
 Highlights:
 
 Sharia Fintech P2P as a solution for MSME capital problem
 Qualitative case study using secondary and primary data
 Lack of understanding hindering MSMEs from accessing Sharia P2P finance.
 
 Keywords: Islamic peer-to-peer lending,Fintech,MSMEs,Sidoarjo,Sharia finance

How economic system justification shapes demand for peer‐to‐peer providers

AbstractWith the proliferation of peer‐to‐peer (P2P) exchanges in the marketplace, understanding which consumer factors drive demand for P2P providers is important. We examine the role of consumers' economic system justification (ESJ) beliefs (about the fairness of existing economic arrangements and outcomes), which, despite their growing salience in the marketplace, have been overlooked in extant P2P research. We show that high (vs. low) ESJ increases consumers' interest in purchasing from P2P providers because it heightens perceptions of these providers' entrepreneurial spirit. The effect emerges in the laboratory and in the field with measured and manipulated ESJ, and it is attenuated for traditional commercial providers. The findings offer novel insights and implications for practice and emerging research on P2P exchanges, system justification, and ideological consumption more broadly.

Evaluating Peer-to-Peer Electricity Markets across the U.S. Using an Agent-Based Modeling Approach

The diffusion of distributed energy resources can overcome some challenges associated with the historical centralized model of electric power distribution. Decentralized generation by residential solar photovoltaic cells creates the potential for peer-to-peer (P2P) electricity trading, where households can act as consumers and prosumers to buy and sell renewable electricity. P2P energy markets are emerging at locations across the globe, and market performance is affected by various social, economic, and environmental factors. This research applies an agent-based modeling (ABM) framework to simulate electricity trades between heterogeneous households in a decentralized market. The P2P system is tested for 15 locations in the United States that vary in climate parameters and local economic factors. The results from these simulations are compared to assess how differences in climate, demand pattern, retail rate, and irradiance affect market performance. Simulations demonstrate that market outcomes rely on the ratio of prosumers to consumers, environmental factors, and geographic conditions. Battery energy storage overcomes limitations associated with faulty forecasting and improves the flexibility of household-generated solar resources to increase the proportion of production that is sold in the P2P market. The application of the agent-based modeling framework demonstrates how P2P markets can be expected to perform for various locations and can be applied to assess alternative locations for market performance.

Energy storage with Power-to-Power systems relying on photovoltaic and hydrogen: modelling the operation with secondary reserve provision

Energy storage is increasingly required in order to cope with the fluctuations of renewable energy sources, especially in power generation. In many countries, the electric market is undergoing regulatory transformations that aim at increasing the type and number of technologies that can provide grid services, either alone or as virtual aggregates. In this context, this work investigates the economic profitability of Secondary Frequency Reserve (SFR) provision to the electric grid through integrated generation-and-storage systems. In particular, the case of Italy is assessed, where recent projects opened the Ancillary Service Market (ASM) to relevant generation units or aggregates of units, including programmable and non-programmable renewable sources, consumption units, and energy storage systems. The operation of a hydrogen-based Power-to-Power (P2P) system, designed to supply an electric load with locally generated electricity from a solar PV field, is studied when acting on both the Day-Ahead Market (DAM) and the ASM. A mixed-integer linear optimization problem is set up that minimizes the annual average cost of electricity to the load. The analysis considers a broad range of options in terms of electricity prices on the DAM and price-quantity combinations of accepted offers on the ASM. Results show that the economic advantages deriving from the utilization of P2P systems increase with more fluctuating prices. The provision of SFR services with an optimal bidding strategy and asymmetric bids allows one to reduce the average cost of electricity up to about 15 % with respect to the operation on the DAM only.

Data Dissemination Performance in P2P-Based Vehicular Communications for Smart City Environments

Peer-to-Peer (P2P) networks, as well as vehicular networks, have been extensively studied in the literature. However, the statistical characteristics of connection times among vehicles on the performance of a P2P network to allow Vehicle-to-Vehicle (V2V) communications have been largely overlooked. Specifically, the periods that vehicles remain connected to neighboring vehicles to directly communicate among them in different road characteristics (urban or highway) and road traffic conditions (low, medium, or high) are of main importance for the adequate performance of a P2P system. Indeed, these connection times establish the average number of peers and hence the bandwidth available in the communication system. These times have not been studied nor characterized before for the purpose of calculating the download probability and average download times, which are calculated in this work using a teletraffic analysis. We also present simulation results considering the CSMA/CA random access scheme established in the IEEE 802.11p communication protocol for V2V communications to investigate the effect of the random access protocol on the system performance.

System Design Choice in the Sharing Economy: How Different Institutional Logics Drive Consumer Perception and Consumers’ Intention to Use Sharing Systems

The sharing economy provides consumers with temporary access to various products. As a growing business trend that continuously attracts new consumers, it motivates businesses to rapidly develop new system designs. In this study, we investigate how the system design choices of sharing systems for products affect consumers’ perceptions of the system and consequently their intention to use a system. Building on institutional logics, we examine how the logics inherent in two system designs—the community logic in peer-to-peer (P2P) systems and the corporate logic in business-to-consumer (B2C) systems—affect consumer perceptions. We argue that consumers perceive P2P and B2C logics differently regarding logics’ economic benefits, product scarcity, sustainability benefits, and social benefits. To test our theory, we conducted a scenario experiment with 1259 participants from the UK. Our findings suggest that consumers perceive P2P systems as yielding higher economic, sustainability, and social benefits than B2C systems, and that these benefits increase consumers’ intention to use the system. However, we also find that P2P systems suffer from the risk of product scarcity, reducing consumers’ intention to use such systems. In summary, our findings show that system design affects consumers’ perceptions and that different designs attract consumer groups with different preferences.

Filtering Dishonest Trust Recommendations in Trust Management Systems in Mobile Ad Hoc Networks

Trust recommendations, having a pivotal role in computation of trust and hence confidence in peer to peer (P2P) environment, if hampered, may entail in colossal attacks from dishonest recommenders such as bad mouthing, ballot stuffing, random opinion etc. Therefore, mitigation of dishonest trust recommendations is stipulated as a challenging research issue in P2P systems (esp in Mobile Ad Hoc Networks). In order to cater these challenges associated with dishonest trust recommendations, a technique named “intelligently Selection of Trust Recommendations based on Dissimilarity factor (iSTRD)” has been devised for Mobile Ad Hoc Networks. iSTRD exploits personal experience of an “evaluating node” in conjunction with majority vote of the recommenders. It successfully removes the recommendations of “low trustworthy recommenders” as well as dishonest recommendations of “highly trustworthy recommenders”. Efficacy of proposed approach is evident from enhanced accuracy of “recognition rate”, “false rejection” and “false acceptance”. Moreover, experiential results depict that iSTRD has unprecedented performance compared to contemporary techniques in presence of attacks asserted.

SMART: A Subspace based Malicious Peers Detection algorithm for P2P Systems

In recent years, reputation management schemes have been proposed as promising solutions to alleviate the blindness during peer selection in distributed P2P environment where malicious peers coexist with honest ones. They indeed provide incentives for peers to contribute more resources to the system and thus promote the whole system performance. But few of them have been implemented practically since they still suffer from various security threats, such as collusion, Sybil attack and so on. Therefore, how to detect malicious peers plays a critical role in the successful work of these mechanisms, and it will also be our focus in this paper. Firstly, we define malicious peers and show their influence on the system performance. Secondly, based on Multiscale Principal Component Analysis (MSPCA) and control chart, a Subspace based MAlicious peeRs deTecting algorithm (SMART) is brought forward. SMART first reconstructs the original reputation matrix based on subspace method, and then finds malicious peers out based on Shewhart control chart. Finally, simulation results indicate that SMART can detect malicious peers efficiently and accurately.

Coordination of commercial prosumers with distributed demand-side flexibility in energy sharing and management system

Driven by advances in smart grid technologies, building prosumers have closer interaction in sharing energy and information towards net-zero energy communities with energy flexibility and smart readiness. Many studies have focused on transactive energy systems, whereas limited attention was paid to coordinated prosumer management involving electric and electro-thermal energy storages. In this paper, a hierarchical energy management system was developed for a commercial neighbourhood, where distributed prosumers can share their generation and batteries, and manage cooling loads and thermal storages, in subtropical Hong Kong. Several operational scenarios were formulated considering building-level and neighbourhood-level controls, to explore the synergies through peer-to-peer (P2P) sharing and demand flexibility. Techno-economic-environmental impacts were analysed comprehensively from the perspectives of the network, the utility, and each participant. Results show that by integrating the demand-side flexibility, the P2P system could use electricity more effectively and economically. The formulated P2P operation strategy utilising neighbourhood-level control minimised the operational cost by 24.6% and net equivalent CO2 emission by 7.1%, compared to the traditional peer-to-grid operation. The profit for each participant can be allocated by regulating the compensating price in the dynamic internal trading. The study provides prosumers energy-flexible and grid-friendly operation to facilitate the sustainability of the smart grid.

An Approach to Analyze Reliability Indices in Peer-To-Peer Communication Systems

In this work, the performance of a peer-to-peer (P2P) network has been discussed. As being one of the most promising technology in today’s world, P2P systems exploit computation, bandwidth and other resources at the edge of the Internet, with the lower cost of building and maintenance. In the past, a lot of research has been done to improve the reliability of P2P systems focusing on paradigms like- routing strategies, reliable file exchange, resource location and placement, security. However, not much effort has been focused on studying their overall reliability incorporating essential failure causes altogether. In this research work, the authors have designed a mathematical model for a P2P network using Markov Process to holistically determine the availability, reliability and MTTF (Mean time to failure) and examined the most vulnerable components in the system whose failure rates can be controlled to achieve a more reliable network. In such networks, performance is affected to some degree due to failure or disturbance in any component.

Techno-economic feasibility analysis of Renewable-fed Power-to-Power (P2P) systems for small French islands

In this paper, the feasibility analysis of H2 and Battery based Power-to-Power (P2P) systems are presented from a techno-economic perspective. For this study, 21 small islands of France based in Europe are selected, being naturally endowed with renewable energy sources (RES) of solar and wind. For each island, three distinct energy storage options, i.e., hydrogen storage, battery storage and hydrogen + battery combined storage are discussed and explored. Optimum sizing of RES and P2P systems are achieved through employing particle swarm optimization (PSO) algorithm, keeping Levelized Cost of Energy (LCOE) as an objective function. The hydrogen and battery storage combination with an average LCOE value of 420Euro/MWh was found to be the most suitable option for all 21 reference islands. The associated Net Present Cost (NPC) was EUR564,388,050 which was lower than that for the other two storage options. The hybrid storage selection necessitated installation of 41 MW of photovoltaic (PV) modules and 122MW of wind turbines at these islands. The required storage capacity comprised of 7700MWh of hydrogen and 7MWh of battery storage combined with the auxiliaries. A considerable emphasis is also laid on highlighting the annual reduction of greenhouse gases (GHGs) and other air pollutants through the adoption of the proposed RES-P2P integrated systems. The annual carbon dioxide equivalent measure (CO2,e) of all islands was computed to be around 99507.52tons. It was revealed that more than 99 thousand tons of CO2 emissions can be prevented by avoiding consumption of more than 34 million liters of diesel per year.

Semantic data management in P2P systems driven by self-esteem

Abstract This paper is a contribution to semantic data management in P2P systems. It is based on the previous works of the same authors in which a declarative semantics for P2P systems is defined: under this semantics, only facts not making the local databases inconsistent are imported (Weak Models) and the Maximal Weak Models are those in which peers import maximal sets of facts not violating integrity constraints. The proposal, presented in this paper, stems from the following two observations: (i) the Maximal Weak Model Semantics ensures that locally consistent P2P systems always admit a maximal weak model, but fails for locally inconsistent P2P systems; (ii) the self-esteem degree of a peer should impact on the interaction with other peers in the system so that driving the integration process. From the above-mentioned observations a more general framework is presented. Our proposal is able to manage local inconsistencies and allows to model a peer integration process driven by the self-esteem degree of each peer. Different self-esteem degrees could be considered; in this paper we focus our attention on three different scenarios in which a generic peer can declare an high, low or medium self-esteem degree, stating respectively that it trusts its own knowledge more, less or equally with respect to the knowledge that can be provided from the rest of the system. Three different basic semantics are proposed, High, Low, Medium Self-Esteem Semantics and results about the computational complexity of P2P logic queries are investigated by considering brave and cautions reasoning. The paper also presents an extension of the basic framework of the Self-Esteem Semantics that models a finer-grained self-esteem concept and allows each peer to exhibit different levels of self-esteem each of them defined with respect to a subset of its mapping rules.

Pricing and Energy Trading in Peer-to-Peer Zero Marginal-Cost Microgrids

Efforts to utilize 100% renewable energy in community microgrids require new approaches to energy markets and transactions to efficiently address periods of scarce energy supply. In this paper we contribute to the promising approach of peer-to-peer (P2P) energy trading in two main ways: analysis of a centralized, welfare-maximizing economic dispatch that characterizes optimal price and allocations, and a novel P2P system for negotiating energy trades that yields physically feasible and at least weakly Pareto-optimal outcomes. Our main results are 1) that optimal pricing is insufficient to induce agents with batteries to take optimal actions, 2) a novel P2P algorithm to address this while keeping private information, 3) a formal proof that this algorithm converges to the centralized solution in the case of two agents negotiating for a single period, and 4)numerical simulations of the P2P algorithm performance with up to 10 agents and 24 periods that show it converges on average to total welfare within 0.1% of the social optimum in on the order of 10s to 100s of iterations, increasing with the number of agents, time periods, and total storage capacity.