In urban logistics, the last-mile delivery from the warehouse to the consumer’s home has become more and more challenging with the continuous growth of E-commerce. It requires elaborate planning and scheduling to minimize the global traveling cost, but often results in unattended delivery as most consumers are away from home. In this paper, we propose an effective large-scale mobile crowd-tasking model in which a large pool of citizen workers are used to perform the last-mile delivery. To efficiently solve the model, we formulate it as a network min-cost flow problem and propose various pruning techniques that can dramatically reduce the network size. Comprehensive experiments were conducted with Singapore and Beijing datasets. The results show that our solution can support real-time delivery optimization in the large-scale mobile crowd-sourcing problem.

Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions

The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools

Comprehensive and systematic review of the service composition mechanisms in the cloud environments

Cloud based Smart City hubs are an attractive approach to addressing some of the complex issues faced when deploying PaaS infrastructure for Smart Cities. In this paper we introduce the general notion of IoT hubs and then discuss our work to generalize our IoT hub as a Smart City PaaS. We briefly describe our approach and discuss our experiences deploying two cloud-based Smart City hubs, one in the UK and the other in Canada.

/pdf/city-hub-a-cloud-based-iot-platform-for-smart-cities-27icn2tchv.pdf

City Hub: A Cloud-Based IoT Platform for Smart Cities

Service composition is emerging as an effective vehicle for integrating existing web services to create value-added and personalized composite services. As web services with similar functionality are expected to be provided by competing providers, a key challenge is to find the “best” web services to participate in the composition. When multiple quality aspects (e.g., response time, fee, etc.) are considered, a weighting mechanism is usually adopted by most existing approaches, which requires users to specify their preferences as numeric values. We propose to exploit the dominance relationship among service providers to find a set of “best” possible composite services, referred to as a composite service skyline. We develop efficient algorithms that allow us to find the composite service skyline from a significantly reduced searching space instead of considering all possible service compositions. We propose a novel bottom-up computation framework that enables the skyline algorithm to scale well with the number of services in a composition. We conduct a comprehensive analytical and experimental study to evaluate the effectiveness, efficiency, and scalability of the composite skyline computation approaches.

Efficient Service Skyline Computation for Composite Service Selection

A platform that connects citizens effectively to local government, letting them contribute to their community's general well-being, would be an elegant way to make cities smarter. CrowdSC is a crowdsourcing framework designed for smarter cities. The framework lets users combine data collection, selection, and assessment activities in a crowdsourcing process to achieve sophisticated goals within a predefined context. Depending upon this process's execution strategy, different outcomes are possible. The authors describe CrowdSC's process model and evaluate three execution strategies.

https://hal.archives-ouvertes.fr/hal-00803702/document

CrowdSC: Building Smart Cities with Large-Scale Citizen Participation

Social networks (SNs) have become a powerful tool for the jihadism as they serve as recruitment assets, live forums, psychological warfare, as well as sharing platforms. SNs enable vulnerable individuals to reach radicalized people, hence triggering their own radicalization process. There are many vulnerability factors linked to socio-economic and demographic conditions that make jihadist militants suitable targets for their radicalization. We focus on these vulnerability factors, studying, understanding, and identifying them on the Internet. Here, we present a set of radicalization indicators and a model to assess them using a data set of tweets published by several Islamic State of Iraq and Sham sympathizers. Results show that there is a strong correlation between the values assigned by the model to the indicators.

Measuring the Radicalisation Risk in Social Networks

Nowadays, the exploding number of functionally similar Web services has led to a new challenge of selecting the most relevant services using quality of service (QoS) aspects. Traditionally, the relevance of a service is determined by computing an overall score that aggregates individual QoS values. Users are required to assign weights to QoS attributes. This is a rather demanding task and an imprecise specification of the weights could result in missing some user desired services. Recent approaches focus on computing service skyline over a set of QoS aspects. This can completely free users from assigning weights to QoS attributes. However, two main drawbacks characterize such approaches. First, the service skyline often privileges services with a bad compromise between different QoS attributes. Second, as the size of the service skyline may be quite large, users will be overwhelmed during the service selection process. In this paper, we introduce a new concept, called alpha-dominant service skyline, to address the above issues and we develop a suitable algorithm for computing it efficiently. Experimental evaluation conducted on synthetically generated datasets, demonstrates both the effectiveness of the introduced concept and the efficiency of the proposed algorithm.

On the Use of Fuzzy Dominance for Computing Service Skyline Based on QoS

Quality of service (QoS) has been considered as a significant criterion for selecting among functionally similar Web services. Recent approaches focus on computing the skyline over a set of QoS attributes. This can completely free users from assigning weights to QoS attributes. However, these approaches are not sufficient in a dynamic Web service environment where the delivered QoS by a Web service is inherently uncertain. In this paper, we tackle the problem of skyline on uncertain QoS. We represent each QoS attribute of a Web service using a possibility distribution and introduce two skyline extensions on uncertain QoS called pos-dominant skyline and nec-dominant skyline. We then develop appropriate algorithms to efficiently compute both the pos-dominant skyline and nec-dominant skyline. Finally, we present our experimental results that show both the effectiveness of the introduced skyline extensions and the efficiency of the proposed algorithms.

Selecting Skyline Web Services from Uncertain QoS

With the increasing number of Web services published on the Web, many of services provide the same functionality with different quality of service. Ranking similar web services based on QoS is then an important issue. This paper proposes a hybrid approach to rank-order Skyline Web services, which mixes several methods borrowed from Multi-Criteria Decision Making field. The Skyline method is used to reduce the decision space and focusing only on interesting Web services that are not dominated by any other service. For weighting QoS criteria, we aggregate objective and subjective weights. The objective Entropy weights are extracted directly from invocation history data, however, the subjective weights are calculated using Fuzzy AHP from user opinions. Promethee method is leveraged to rank Skyline Web services, by taking advantage of the outranking relationships between Skyline Web services and generating positive, negative and Net flows. An efficient algorithm to rank-order Skyline Web services on the basis of Net flow is developed. A case study is presented to illustrate the different steps of our approach. The experimental evaluation conducted on real-world datasets demonstrates that our approach can better capture the user preferences and retrieve the best ranked Skyline Web services.

Karim Benouaret

Papers

CrowdSC: Building Smart Cities with Large-Scale Citizen Participation

Measuring the Radicalisation Risk in Social Networks

On the Use of Fuzzy Dominance for Computing Service Skyline Based on QoS

Selecting Skyline Web Services from Uncertain QoS

SEFAP: an efficient approach for ranking skyline web services