Location Privacy in Spatial Crowdsourcing

Spatial crowdsourcing (SC) is a new platform that engages individuals in collecting and analyzing environmental, social and other spatiotemporal information. With SC, requesters outsource their spatiotemporal tasks to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. This chapter identifies privacy threats toward both workers and requesters during the two main phases of spatial crowdsourcing, tasking and reporting. Tasking is the process of identifying which tasks should be assigned to which workers. This process is handled by a spatial crowdsourcing server (SC-server). The latter phase is reporting, in which workers travel to the tasks' locations, complete the tasks and upload their reports to the SC-server. The challenge is to enable effective and efficient tasking as well as reporting in SC without disclosing the actual locations of workers (at least until they agree to perform a task) and the tasks themselves (at least to workers who are not assigned to those tasks). This chapter aims to provide an overview of the state-of-the-art in protecting users' location privacy in spatial crowdsourcing. We provide a comparative study of a diverse set of solutions in terms of task publishing modes (push vs. pull), problem focuses (tasking and reporting), threats (server, requester and worker), and underlying technical approaches (from pseudonymity, cloaking, and perturbation to exchange-based and encryption-based techniques). The strengths and drawbacks of the techniques are highlighted, leading to a discussion of open problems and future work

Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

Mobile Crowd-Sensing (MCS) has appeared as a prospective solution for large-scale data collection, leveraging built-in sensors and social applications in mobile devices that enables a variety of Internet of Things (IoT) services. However, the human involvement in MCS results in a high possibility for unintentionally contributing corrupted and falsified data or intentionally spreading disinformation for malevolent purposes, consequently undermining IoT services. Therefore, recruiting trustworthy contributors plays a crucial role in collecting high quality data and providing better quality of services while minimizing the vulnerabilities and risks to MCS systems. In this article, a novel trust model called Experience-Reputation (E-R) is proposed for evaluating trust relationships between any two mobile device users in a MCS platform. To enable the E-R model, virtual interactions among the users are manipulated by considering an assessment of the quality of contributed data from such users. Based on these interactions, two indicators of trust called Experience and Reputation are calculated accordingly. By incorporating the Experience and Reputation trust indicators (TIs), trust relationships between the users are established, evaluated and maintained. Based on these trust relationships, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy MCS users to contribute to data sensing tasks. In order to evaluate the performance and effectiveness of the proposed trust-based mechanism as well as the E-R trust model, we deploy several recruitment schemes in a MCS testbed which consists of both normal and malicious users. The results highlight the strength of the trust-based scheme as it delivers better quality for MCS services while being able to detect malicious users. We believe that the trust-based user recruitment offers an effective capability for selecting trustworthy users for various MCS systems and, importantly, the proposed mechanism is practical to deploy in the real world

Delivering IoT Services in Smart Cities and Environmental Monitoring through Collective Awareness, Mobile Crowdsensing and Open Data

The Internet of Things (IoT) is the paradigm that allows us to interact with the real world by means of networking-enabled devices and convert physical phenomena into valuable digital knowledge. Such a rapidly evolving field leveraged the explosion of a number of technologies, standards and platforms. Consequently, different IoT ecosystems behave as closed islands and do not interoperate with each other, thus the potential of the number of connected objects in the world is far from being totally unleashed. Typically, research efforts in tackling such challenge tend to propose a new IoT platforms or standards, however, such solutions find obstacles in keeping up the pace at which the field is evolving. Our work is different, in that it originates from the following observation: in use cases that depend on common phenomena such as Smart Cities or environmental monitoring a lot of useful data for applications is already in place somewhere or devices capable of collecting such data are already deployed. For such scenarios, we propose and study the use of Collective Awareness Paradigms (CAP), which offload data collection to a crowd of participants. We bring three main contributions: we study the feasibility of using Open Data coming from heterogeneous sources, focusing particularly on crowdsourced and user-contributed data that has the drawback of being incomplete and we then propose a State-of-the-Art algorith that automatically classifies raw crowdsourced sensor data; we design a data collection framework that uses Mobile Crowdsensing (MCS) and puts the participants and the stakeholders in a coordinated interaction together with a distributed data collection algorithm that prevents the users from collecting too much or too less data; (3) we design a Service Oriented Architecture that constitutes a unique interface to the raw data collected through CAPs through their aggregation into ad-hoc services, moreover, we provide a prototype implementation

A theoretical analysis of industrial 4.0 in the South African SMMEs

Abstract: A number of challenges has been identified in the manufacturing sector in the past three decades. The industrialized uprising is currently known as the industry 4.0. Consequently, the concept of industry 4.0 has become the buzz word amongst scholars and industry practitioners. Despite the fact that the concept of industry 4.0 is attending high level of significance in the western and eastern countries i.e. America, Asia, etc., due to its ability of smoothing of business environment, in Africa, particularly in South Africa the integration and adoption of industry 4.0 concept is facing several challenges. To date, over the past decade many studies has been conducted to investigate the benefit of implementing industry 4.0 in manufacturing companies at global level. However very few studies have been conducted in the South African SMMEs manufacturing industry 4.0 perspective, thus, the overall goal of this paper is to fill these gaps by means of critically analyzing studies that were conducted or develop in the field of industry 4.0 over the past decade. This segment is too experiencing encounters that face unceasingly altering requirements of customer at an international level, pleasing into justification that they essential familiarize to the fluctuations to safeguard tenable. The manufacturing space is progressing at an effective means of production and the adoption of novel expertise

Evaluation of Trust in the Internet Of Things: Models, Mechanisms And Applications

In the blooming era of the Internet of Things (IoT), trust has become a vital factor for provisioning reliable smart services without human intervention by reducing risk in autonomous decision making. However, the merging of physical objects, cyber components and humans in the IoT infrastructure has introduced new concerns for the evaluation of trust. Consequently, a large number of trust-related challenges have been unsolved yet due to the ambiguity of the concept of trust and the variety of divergent trust models and management mechanisms in different IoT scenarios. In this PhD thesis, my ultimate goal is to propose an efficient and practical trust evaluation mechanisms for any two entities in the IoT. To achieve this goal, the first important objective is to augment the generic trust concept and provide a conceptual model of trust in order to come up with a comprehensive understanding of trust, influencing factors and possible Trust Indicators (TI) in the context of IoT. Following the catalyst, as the second objective, a trust model called REK comprised of the triad Reputation, Experience and Knowledge TIs is proposed which covers multi-dimensional aspects of trust by incorporating heterogeneous information from direct observation, personal experiences to global opinions. The mathematical models and evaluation mechanisms for the three TIs in the REK trust model are proposed. Knowledge TI is as “direct trust” rendering a trustor’s understanding of a trustee in respective scenarios that can be obtained based on limited available information about characteristics of the trustee, environment and the trustor’s perspective using a variety of techniques. Experience and Reputation TIs are originated from social features and extracted based on previous interactions among entities in IoT. The mathematical models and calculation mechanisms for the Experience and Reputation TIs also proposed leveraging sociological behaviours of humans in the real-world; and being inspired by the Google PageRank in the web-ranking area, respectively. The REK Trust Model is also applied in variety of IoT scenarios such as Mobile Crowd-Sensing (MCS), Car Sharing service, Data Sharing and Exchange platform in Smart Cities and in Vehicular Networks; and for empowering Blockchain-based systems. The feasibility and effectiveness of the REK model and associated evaluation mechanisms are proved not only by the theoretical analysis but also by real-world applications deployed in our ongoing TII and Wise-IoT projects

Quality-aware Tasking in Mobile Opportunistic Networks - Distributed Information Retrieval and Processing utilizing Opportunistic Heterogeneous Resources.

Advances in wireless technology have facilitated direct communication among mobile devices in recent years, enabling opportunistic networks. Opportunistic networking among mobile devices is often utilized to offload and save cellular network traffic and to maintain communication in case of impaired communication infrastructure, such as in emergency situations. With a plethora of built-in capabilities, such as built-in sensors and the ability to perform even intensive operations, mobile devices in such networks can be used to provide distributed applications for other devices upon opportunistic contact. However, ensuring quality requirements for such type of distributed applications is still challenging due to uncontrolled mobility and resource constraints of devices. Addressing this problem, in this thesis, we propose a tasking methodology, which allows for assigning tasks to capable mobile devices, considering quality requirements. To this end, we tackle two fundamental types of tasks required in a distributed application, i.e., information retrieval and distributed processing. Our first contribution is a decentralized tasking concept to obtain crowd collected data through built-in sensors of participating mobile devices. Based on the Named Data Networking paradigm, we propose a naming scheme to specify the quality requirements for crowd sensing tasks. With the proposed naming scheme, we design an adaptive self-organizing approach, in which the sensing tasks will be forwarded to the right devices, satisfying specified quality requirements for requested information. In our second contribution, we develop a tasking model for distributed processing in opportunistic networks. We design a task-oriented message template, which enhances the definition of a complex processing task, which requires multiple processing stages to accomplish a predefined goal. Our tasking concept enables distributed coordination and an autonomous decision of participating device to counter uncertainty caused by the mobility of devices in the network. Based on this proposed model, we develop computation handover strategies among mobile devices for achieving quality requirements of the distributed processing. Finally, as the third contribution and to enhance information retrieval, we integrate our proposed tasking concept for distributed processing into information retrieval. Thereby, the crowd-collected data can be processed by the devices during the forwarding process in the network. As a result, relevant information can be extracted from the crowd-collected data directly within the network without being offloaded to any remote computation entity. We show that the obtained information can be disseminated to the right information consumers, without over-utilizing the resource of participating devices in the network. Overall, we demonstrate that our contributions comprise a tasking methodology for leveraging resources of participating devices to ensure quality requirement of applications built upon an opportunistic network

A Neural Network-Based Situational Awareness Approach for Emergency Response

publishedVersio

Place, recreation and local development

Proceedings of the 9th International Conference on Monitoring and Management of Visitors in Recreational and Protected Areas (MMV9), Bordeaux, FRA, 29-/08/2018 - 31/08/2018It is our pleasure to welcome you to the 9th international Conference on Monitoring and Management of Visitors in Recreational and Protected Areas (MMV9) with a program including keynote speeches, organized and poster sessions, a half-day field trip, social events and post conference trips. This is the first time that France has hosted an MMV Conference. Our country is ranked as the world's top tourist destination, thanks largely to its culture, art, and gastronomy, as well as popular cities such as Paris and Bordeaux. On the other hand, France's potential as a destination for outdoor recreation and nature-based tourism is not hugely publicized, despite its many unique features in this respect: varied climate and natural assets (shoreline, mountains, lakes, and forests), large expanses of countryside, and a network of protected natural areas, to name but a few. France's protected areas are often free to access for the general public. However, in contrast with other countries, nature conservation in specific areas is much less widespread. Where it does take place, it is often centered on territories that are perceived to be "attractive", and where many conflicting activities are practiced. This may be one of the reasons why contractual tools and regional park systems are quite popular in France. The MMV Conference offers an excellent opportunity to discuss the situation in France in greater depth. The theme proposed for the conference was "recreation, place and local development". This reflects our assumption that recreational areas are not just physical assets designed to receive visitors for the purpose of leisure - which in itself would already be something of great importance - but that they reflect deeper social phenomena, as demonstrated through the range of organized sessions dedicated to discussing questions such as environmental education and economic development, but also emerging themes such as social integration, community resilience, environmental justice, and health. The traditional topics covered by MMV Conference reflect an evolving society: with innovations in monitoring techniques (both on people and nature), focus on new populations (Y generation, ethnic minority) and a larger concern for individual engagement and participative management. The 9th Edition of MMV is co-hosted by Irstea and BSA. This would not have been possible without significant contributions from a large number of additional partners and sponsors as well as our national scientific and organizing committee. We would like to take this opportunity to thank everyone for their help. After two years of planning, we are proud to announce that we have more than 160 presentations from 30 countries, meaning that the conference will host over 200 participants from across the globe. We are honored that the International Steering Committee has given us the opportunity to be part of this great MMV community, which organized its first meeting in 2002. We hope you will enjoy the conference as much as we enjoyed organizing it. If you can't be with us in person, we hope that you will enjoy reading our publications