Mobile crowdsensing with mobile agents

We introduce mobile agents for mobile crowdsensing. Crowdsensing campaigns are designed through different roles that are implemented as mobile agents. The role-based tasks of mobile agents include collecting data, analyzing data and sharing data in the campaign. Mobile agents execute and control the campaign autonomously as a multi-agent system and migrate in the opportunistic network of participants’ devices. Mobile agents take into account the available resources in the devices and match participants’ privacy requirements to the campaign requirements. Sharing of task results in real-time facilitates cooperation towards the campaign goal while maintaining a selected global measure, such as energy efficiency. We discuss current challenges in crowdsensing and propose mobile agent based solutions for campaign execution and monitoring, addressing data collection and participant-related issues. We present a software framework for mobile agents-based crowdsensing that is seamlessly integrated into the Web. A set of simulations are conducted to compare mobile agent-based campaigns with existing crowdsensing approaches. We implemented and evaluated a small-scale real-world mobile agent based campaign for pedestrian flock detection. The simulation and evaluation results show that mobile agent based campaigns produce comparable results with less energy consumption when the number of agents is relatively small and enables in-network data processing with sharing of data and task results with insignificant overhead.     

How to trust a few among many

The presence of numerous and disparate information sources available to support decision-making calls for efficient methods of harnessing their potential. Information sources may be unreliable, and misleading reports can affect decisions. Existing trust and reputation mechanisms typically rely on reports from as many sources as possible to mitigate the influence of misleading reports on decisions. In the real world, however, it is often the case that querying information sources can be costly in terms of energy, bandwidth, delay overheads, and other constraints. We present a model of source selection and fusion in resource-constrained environments, where there is uncertainty regarding the trustworthiness of sources. We exploit diversity among sources to stratify them into homogeneous subgroups to both minimise redundant sampling and mitigate the effect of certain biases. Through controlled experiments, we demonstrate that a diversity-based approach is robust to biases introduced due to dependencies among source reports, performs significantly better than existing approaches when sampling budget is limited and equally as good with an unlimited budget.     

Enhancing comparison shopping agents through ordering and gradual information disclosure

The plethora of comparison shopping agents (CSAs) in today’s markets enables buyers to query more than a single CSA when shopping, thus expanding the list of sellers whose prices they obtain. This potentially decreases the chance of a purchase within any single interaction between a buyer and a CSA, and consequently decreases each CSAs’ expected revenue per-query. Obviously, a CSA can improve its competence in such settings by acquiring more sellers’ prices, potentially resulting in a more attractive “best price”. In this paper we suggest a complementary approach that improves the attractiveness of the best result returned based on intelligently controlling the order according to which they are presented to the user, in a way that utilizes several known cognitive-biases of human buyers. The advantage of this approach is in its ability to affect the buyer’s tendency to terminate her search for a better price, hence avoid querying further CSAs, without spending valuable resources on finding additional prices to present. The effectiveness of our method is demonstrated using real data, collected from four CSAs for five products. Our experiments confirm that the suggested method effectively influence people in a way that is highly advantageous to the CSA compared to the common method for presenting the prices. Furthermore, we experimentally show that all of the components of our method are essential to its success.     

The authorship dilemma: alphabetical or contribution?

Scientific communities have adopted different conventions for ordering authors on publications. Are these choices inconsequential, or do they have significant influence on individual authors, the quality of the projects completed, and research communities at large? What are the trade-offs of using one convention over another? In order to investigate these questions, we formulate a basic two-player game theoretic model, which already illustrates interesting phenomena that can occur in more realistic settings. We find that contribution-based ordering leads to a denser collaboration network and a greater number of publications, while alphabetical ordering can improve research quality. Contrary to the assumption that free riding is a weakness of the alphabetical ordering scheme, when there are only two authors, this phenomenon can occur under any contribution scheme, and the worst case occurs under contribution-based ordering. Finally, we show how authors working on multiple projects can cooperate to attain optimal research quality and eliminate free riding given either contribution scheme.     

A little Birdie told me: Social media,organizational legitimacy,and underpricing in initial public offerings

Drawing on the organizational legitimacy literature, we propose and test a set of hypotheses arguing that social media, an artifact of information technology, serves as a mechanism for conferring legitimacy in the market for initial public offerings (IPOs). The extant literature identifies third-party authorities, such as traditional media outlets and industry analysts, as a valuable source of organizational legitimacy. Social media and micro-blogging, however, remain outside current classifications of the phenomenon. This study theoretically develops and empirically tests a new mechanism in the legitimation process: the direct interaction with potential investors and society-at-large via social media. Our findings indicate broad support that having a Twitter account, and the extent to which a firm utilizes Twitter, is associated with systematically higher levels of IPO underpricing. Moreover, we find support that social media variables external to the firm, including number of followers and retweets, also impact the level of underpricing in an IPO. In conclusion, we highlight the emerging role of social media in processes of legitimation and invite additional research at the intersection of IS and management.     

Big data in the public sector: Uncertainties and readiness

Big data is being implemented with success in the private sector and science. Yet the public sector seems to be falling behind, despite the potential value of big data for government. Government organizations do recognize the opportunities of big data but seem uncertain about whether they are ready for the introduction of big data, and if they are adequately equipped to use big data. This paper addresses those uncertainties. It presents an assessment framework for evaluating public organizations’ big data readiness. Doing so demystifies the concept of big data, as it is expressed in terms of specific and measureable organizational characteristics. The framework was tested by applying it to organizations in the Dutch public sector. The results suggest that organizations may be technically capable of using big data, but they will not significantly gain from these activities if the applications do not fit their organizations and main statutory tasks. The framework proved helpful in pointing out areas where public sector organizations could improve, providing guidance on how government can become more big data ready in the future.     

Leader’s dilemma game: An experimental design for cyber insider threat research

One of the problems with insider threat research is the lack of a complete 360° view of an insider threat dataset due to inadequate experimental design. This has prevented us from modeling a computational system to protect against insider threat situations. This paper provides a contemporary methodological approach for using online games to simulate insider betrayal for predictive behavioral research. The Leader’s Dilemma Game simulates an insider betrayal scenario for analyzing organizational trust relationships, providing an opportunity to examine the trustworthiness of focal individuals, as measured by humans as sensors engaging in computer-mediated communication. This experimental design provides a window into trustworthiness attribution that can generate a rigorous and relevant behavioral dataset, and contributes to building a cyber laboratory that advances future insider threat study.     

Efficient computation of distance labeling for decremental updates in large dynamic graphs

Since today’s real-world graphs, such as social network graphs, are evolving all the time, it is of great importance to perform graph computations and analysis in these dynamic graphs. Due to the fact that many applications such as social network link analysis with the existence of inactive users need to handle failed links or nodes, decremental computation and maintenance for graphs is considered a challenging problem. Shortest path computation is one of the most fundamental operations for managing and analyzing large graphs. A number of indexing methods have been proposed to answer distance queries in static graphs. Unfortunately, there is little work on answering such queries for dynamic graphs. In this paper, we focus on the problem of computing the shortest path distance in dynamic graphs, particularly on decremental updates (i.e., edge deletions). We propose maintenance algorithms based on distance labeling, which can handle decremental updates efficiently. By exploiting properties of distance labeling in original graphs, we are able to efficiently maintain distance labeling for new graphs. We experimentally evaluate our algorithms using eleven real-world large graphs and confirm the effectiveness and efficiency of our approach. More specifically, our method can speed up index re-computation by up to an order of magnitude compared with the state-of-the-art method, Pruned Landmark Labeling (PLL).     

When do agents outperform centralized algorithms?

Multi-agent systems (MAS) literature often assumes decentralized MAS to be especially suited for dynamic and large scale problems. In operational research, however, the prevailing paradigm is the use of centralized algorithms. Present paper empirically evaluates whether a multi-agent system can outperform a centralized algorithm in dynamic and large scale logistics problems. This evaluation is novel in three aspects: (1) to ensure fairness both implementations are subject to the same constraints with respect to hardware resources and software limitations, (2) the implementations are systematically evaluated with varying problem properties, and (3) all code is open source, facilitating reproduction and extension of the experiments. Existing work lacks a systematic evaluation of centralized versus decentralized paradigms due to the absence of a real-time logistics simulator with support for both paradigms and a dataset of problem instances with varying properties. We extended an existing logistics simulator to be able to perform real-time experiments and we use a recent dataset of dynamic pickup-and-delivery problem with time windows instances with varying levels of dynamism, urgency, and scale. The OptaPlanner constraint satisfaction solver is used in a centralized way to compute a global schedule and used as part of a decentralized MAS based on the dynamic contract-net protocol (DynCNET) algorithm. The experiments show that the DynCNET MAS finds solutions with a relatively lower operating cost when a problem has all following three properties: medium to high dynamism, high urgency, and medium to large scale. In these circumstances, the centralized algorithm finds solutions with an average cost of 112.3% of the solutions found by the MAS. However, averaged over all scenario types, the average cost of the centralized algorithm is 94.2%. The results indicate that the MAS performs best on very urgent problems that are medium to large scale.