Trust Management Model for Cloud Computing Environment

Software as a service or (SaaS) is a new software development and deployment paradigm over the cloud and offers Information Technology services dynamically as "on-demand" basis over the internet. Trust is one of the fundamental security concepts on storing and delivering such services. In general, trust factors are integrated into such existent security frameworks in order to add a security level to entities collaborations through the trust relationship. However, deploying trust factor in the secured cloud environment are more complex engineering task due to the existence of heterogeneous types of service providers and consumers. In this paper, a formal trust management model has been introduced to manage the trust and its properties for SaaS in cloud computing environment. The model is capable to represent the direct trust, recommended trust, reputation etc. formally. For the analysis of the trust properties in the cloud environment, the proposed approach estimates the trust value and uncertainty of each peer by computing decay function, number of positive interactions, reputation factor and satisfaction level for the collected information.Comment: 5 Pages, 2 Figures, Conferenc

Predicting topology propagation messages in mobile ad hoc networks: The value of history

This research was funded by the Spanish Government under contracts TIN2016-77836-C2-1-R,TIN2016-77836-C2-2-R, and DPI2016-77415-R, and by the Generalitat de Catalunya as Consolidated ResearchGroups 2017-SGR-688 and 2017-SGR-990.The mobile ad hoc communication in highly dynamic scenarios, like urban evacuations or search-and-rescue processes, plays a key role in coordinating the activities performed by the participants. Particularly, counting on message routing enhances the communication capability among these actors. Given the high dynamism of these networks and their low bandwidth, having mechanisms to predict the network topology offers several potential advantages; e.g., to reduce the number of topology propagation messages delivered through the network, the consumption of resources in the nodes and the amount of redundant retransmissions. Most strategies reported in the literature to perform these predictions are limited to support high mobility, consume a large amount of resources or require training. In order to contribute towards addressing that challenge, this paper presents a history-based predictor (HBP), which is a prediction strategy based on the assumption that some topological changes in these networks have happened before in the past, therefore, the predictor can take advantage of these patterns following a simple and low-cost approach. The article extends a previous proposal of the authors and evaluates its impact in highly mobile scenarios through the implementation of a real predictor for the optimized link state routing (OLSR) protocol. The use of this predictor, named OLSR-HBP, shows a reduction of 40–55% of topology propagation messages compared to the regular OLSR protocol. Moreover, the use of this predictor has a low cost in terms of CPU and memory consumption, and it can also be used with other routing protocols.Peer ReviewedPostprint (published version

A new design principle of robust onion-like networks self-organized in growth

Today's economy, production activity, and our life are sustained by social and technological network infrastructures, while new threats of network attacks by destructing loops have been found recently in network science. We inversely take into account the weakness, and propose a new design principle for incrementally growing robust networks. The networks are self-organized by enhancing interwoven long loops. In particular, we consider the range-limited approximation of linking by intermediations in a few hops, and show the strong robustness in the growth without degrading efficiency of paths. Moreover, we demonstrate that the tolerance of connectivity is reformable even from extremely vulnerable real networks according to our proposed growing process with some investment. These results may indicate a prospective direction to the future growth of our network infrastructures.Comment: 21 pages, 10 figures, 1 tabl

Checkpoint placement algorithms for mobile agent system

2006-2007 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Part 3: Systemic risk in ecology and engineering

The Federal Reserve Bank of New York released a report -- New Directions for Understanding Systemic Risk -- that presents key findings from a cross-disciplinary conference that it cosponsored in May 2006 with the National Academy of Sciences' Board on Mathematical Sciences and Their Applications. ; The pace of financial innovation over the past decade has increased the complexity and interconnectedness of the financial system. This development is important to central banks, such as the Federal Reserve, because of their traditional role in addressing systemic risks to the financial system. ; To encourage innovative thinking about systemic issues, the New York Fed partnered with the National Academy of Sciences to bring together more than 100 experts on systemic risk from 22 countries to compare cross-disciplinary perspectives on monitoring, addressing and preventing this type of risk. ; This report, released as part of the Bank's Economic Policy Review series, outlines some of the key points concerning systemic risk made by the various disciplines represented - including economic research, ecology, physics and engineering - as well as presentations on market-oriented models of financial crises, and systemic risk in the payments system and the interbank funds market. The report concludes with observations gathered from the sessions and a discussion of potential applications to policy. ; The three papers presented in this conference session highlighted the positive feedback effects that produce herdlike behavior in markets, and the subsequent discussion focused in part on means of encouraging heterogeneous investment strategies to counter such behavior. Participants in the session also discussed the types of models used to study systemic risk and commented on the challenges and trade-offs researchers face in developing their models.Financial risk management ; Financial markets ; Financial stability ; Financial crises

Managing Complex Networks inEmerging Markets: The Story of AMUL

Firms that desire to do business in large emerging markets need to develop a new paradigm for looking at opportunities in these markets. The success of many such firms has depended on how well have they formed or managed to become a part of an existing network of suppliers and consumers. These economies are complex and have unique characteristics that range from underdeveloped markets to small and fragmented supplier base. Clearly, traditional business models are not adequate for this environment. Successful firms participate in the development of both these elements of the supply chain. The prize, needless to mention, is significant sales in a large market. In this paper we describe a successful business model using the example of AMUL. AMUL is a dairy cooperative in the western India that has been primarily responsible, through its innovative practices, for India to become the worlds largest milk producer. The distinctive features of this paradigm involves managing a large decentralized network of suppliers and producers, simultaneous development of markets and suppliers, lean and efficient supply chain, and breakthrough leadership. This paper draws various lessons from the experiences of AMUL that would be useful to firms contemplating entry into emerging market.