Nakema Deonauth

A greedy model with small world for improving the robustness of heterogeneous Internet of Things

Robustness is an important and challenging issue in the Internet of Things (IoT), which contains multiple types of heterogeneous networks. Improving the robustness of topological structure, i.e., withstanding a certain amount of node failures, is of great significance especially for the energy-limited lightweight networks. Meanwhile, a high-performance topology is also necessary. The small world model has been proven to be a feasible way to optimize the network topology. In this paper, we propose a Greedy Model with Small World properties (GMSW) for heterogeneous sensor networks in IoT. We first present the two greedy criteria used in GMSW to distinguish the importance of different network nodes, based on which we define the concept of local importance of nodes. Then, we present our algorithm that transforms a network to possess small world properties by adding shortcuts between certain nodes according to their local importance. Our performance evaluations demonstrate that, by only adding a small number of shortcuts, GMSW can quickly enable a network to exhibit the small world properties. We also compare GMSW with a latest related work, the Directed Angulation toward the Sink Node Model (DASM), showing that GMSW outperforms DASM in terms of small world characteristics and network latency.

Folksonomy based socially-aware recommendation of scholarly papers for conference participants

Due to the significant proliferation of scholarly papers in both conferences and journals, recommending relevant papers to researchers for academic learning has become a substantial problem. Conferences, in comparison to journals have an aspect of social learning, which allows personal familiarization through various interactions among researchers. In this paper, we improve the social awareness of participants of smart conferences by proposing an innovative folksonomy-based paper recommendation algorithm, namely, Socially-Aware Recommendation of Scholarly Papers (SARSP). Our proposed algorithm recommends scholarly papers, issued by Active Participants (APs), to other Group Profile participants at the same smart conference based on similarity of their research interests. Furthermore, through computation of social ties, SARSP generates effective recommendations of scholarly papers to participants who have strong social ties with an AP. Through a relevant real-world dataset, we evaluate our proposed algorithm. Our experimental results verify that SARSP has encouraging improvements over other existing methods.

Socially-Aware Venue Recommendation for Conference Participants

Current research environments are witnessing high enormities of presentations occurring in different sessions at academic conferences. This situation makes it difficult for researchers (especially juniors) to attend the right presentation session(s) for effective collaboration. In this paper, we propose an innovative venue recommendation algorithm to enhance smart conference participation. Our proposed algorithm, Social Aware Recommendation of Venues and Environments (SARVE), computes the Pearson Correlation and social characteristic information of conference participants. SARVE further incorporates the current context of both the smart conference community and participants in order to model a recommendation process using distributed community detection. Through the integration of the above computations and techniques, we are able to recommend presentation sessions of active participant presenters that may be of high interest to a particular participant. We evaluate SARVE using a real world dataset. Our experimental results demonstrate that SARVE outperforms other state-of-the-art methods.

A small world model for improving robustness of heterogeneous networks

Robustness is an important and challenging issue in internet of things (IoT), where contains multiple types of heterogeneous networks. To solve this problem, we design and realize a greedy model with small world properties (GMSW) for heterogeneous sensor network of IoT. In GMSW, two greedy criteria are presented firstly, which are used to distinguish importance of different network nodes. On the basis, we define the concept of local importance of node and design a shortcut-added strategy, by which way prompts the network present small world phenomenon. The endpoints of these shortcuts are super sensor nodes with more powerful hardware. Simulation results show that GMSW can quickly present and maintain small word characteristics in the case of adding a few of shortcuts. Besides, it has good performance of network latency no matter suffering a random or specific failure.

An Extended Framework for Recovering From Trust Breakdowns in Online Community Settings

The violation of trust as a result of interactions that do not proceed as expected gives rise to the question as to whether broken trust can possibly be recovered. Clearly, trust recovery is more complex than trust initialization and maintenance. Trust recovery requires a more complex mechanism to explore different factors that cause the decline of trust and identify the affected individuals of trust violation both directly and indirectly. In this study, an extended framework for recovering trust is presented. Aside from evaluating whether there is potential for recovery based on the outcome of a forgiveness mechanism after a trust violation, encouraging cooperation between interacting parties after a trust violation through incentive mechanisms is also important. Furthermore, a number of experiments are conducted to validate the applicability of the framework and the findings show that the e-marketplace incorporating our proposed framework results in improved efficiency of trading, especially in long-term interactions.