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Mobile crowd sensing (MCS) is a new paradigm enabled by Internet of Things (IoT) in which sensor-rich ubiquitous devices collect and share the data over a large geography. Human behaviour attributes (perception, comprehension and projection) play a key role in the decision-making process for sharing and processing the data. This study aims to understand how situation awareness plays an important role in MCS in an IoT ecosystem.

A systematic literature review was conducted by following a rigorous search protocol that identified a total of 470 peer-reviewed research papers. These papers were further filtered and finally 31 relevant papers were selected.

The major issues and concerns arising due to human participation in the MCS system were identified. Further, probable strategies were explored to deal with the challenges resulting due to certain human behaviour attributes.

This study provides the recommendations to address the major challenges related to the MCS system, which in turn may enhance the adoption of emerging smart technology-driven services.

The study is original and is based on the existing literature and its interpretation.

The purpose of this paper is to investigate the role of emerging technologies in the promotion of health and well-being at the urban, domestic and bodily scale, through the systematic examination of technologies such as physical sensing systems and physiological data monitoring, that are currently explored as drivers for achieving sustainable healthcare within a multi-scalar approach.

A comprehensive study of the various technologies associated with smart healthcare is provided, first investigating smart cities, physical sensing systems and geospatial data as potential enablers of public health. Then the discourse shifts towards exploring Smart Home technologies for healthcare, first reviewing strategies of enhancing the home environment with multisensory components, and then discussing the emergence of physiological monitoring devices and their interconnection with the domestic and urban environment.

While the implementation of Internet of Things, physical sensing systems and geospatial analytics in extracting and analyzing the multiple information layers of the urban, the domestic and the bodily environment, has been widely explored, there is little consideration on the transition from the domestic to the urban level, and while within each of the different scales, the need for a multi-componential approach is addressed, there is minimal effort towards its materialization.

The major contribution of this study therefore lies in laying the ground for further research towards a multi-scalar relational approach that views smart healthcare as a trajectory, binding the bodily, to the domestic and the urban fabric.

The purpose of this paper is to understand the impact of procedural justice on team members’ commitment and the role of task routineness and participatory safety in this relationship.

Survey method was used to collect data from 177 respondents from 33 software development teams. Participatory Safety Scale from Anderson and West’s Team Climate Inventory, Colquitt’s Procedural Justice Scale, a modified version of Mowday et al.’s Organizational Commitment Scale and Daft and Macintosh’s Task Routineness Scale were used to measure the variables studied. Regression analysis was used to test the main, mediating and moderating effects.

Results showed a significant positive impact of procedural justice perception on participatory safety dimensions and team commitment. Task routineness did not show any significant moderation effect. Perception of participatory safety had a partial mediation effect.

A relatively smaller sample size, purposive sampling technique and absence of relevant control variables are the key limitations of this study.

The findings will provide managers insights on designing the team tasks and procedures to nurture participatory safety and commitment in teams.

The study is unique in terms of selection of variables, design (moderation and mediating effects) and the context (software development teams). The study provides a holistic picture of team dynamics by studying variables related to procedures, task and psychological states of the individual.

This paper aims to present a project in Australia, where participants use smartphones to measure the level of traffic noise in their homes. Through the data collected, participants learn if they are subjected to sleep disturbances and, if so, understand how they can manage the issue to protect their health. The project also has a secondary purpose: the local council would like to engage its community through the exercise and be seen as acting on the community’s problems.

The approach taken was the development of a mobile app call 2Loud? that turns the smartphones of participants into noise sensors with accuracies comparable to professional sound-meters. The data collected are analyzed by environment and acoustic experts, and personalized feedback, in the form of mitigation strategies, is then provided. The strategies are delivered through the app to allow participants to share within the community and hence, propagate the solution to non-participants.

Participants who are technologically literate find a sense of empowerment as a result. They confirmed the importance of “closing the loop” with the feedback they received after their voluntary data collection effort. They also reported some sense of satisfaction with the technology as an interim solution and noted the council’s creative approach.

This project first showcases how a participatory setup could be extended to create a “closed-loop” feedback system that further empowers its users. It is also a case example of how an organization could engage and manage its stakeholders’ expectations through innovative use of participatory sensing systems.

The ability to gather, store, and make meaning from large amounts of sensor data is becoming a technological and financial reality for cities. Many of these initiatives are happening through deals brokered between vendors, developers, and cities. They are made manifest in the environment as infrastructure – invisible to citizens and communities. We assert that in order to have community-centered smart cities, we need to transform sensor data collection and usage from invisible infrastructure into visible and legible interface. In this chapter, we compare two different urban sensing initiatives and examine the methods used for feedback between sensors and people. We question how value gets produced and communicated to citizens in urban sensing projects and what kind of oversight and ethical considerations are necessary. Finally, we make a case for “seamful” interfaces between communities, sensors, and cities that reveal their inner workings for the purposes of civic pedagogy and dialogue.

Citizen Science – public participation in scientific projects – is becoming a global practice engaging volunteer participants, often non-scientists, with scientific research. Citizen Science is facing major challenges, such as quality and consistency, to reap open the full potential of its outputs and outcomes, including data, software and results. In this context, the principles put forth by Data Science and Open Science domains are essential for alleviating these challenges, which have been addressed at length in these domains. The purpose of this study is to explore the extent to which Citizen Science initiatives capitalise on Data Science and Open Science principles.

The authors analysed 48 Citizen Science projects related to pollution and its effects. They compared each project against a set of Data Science and Open Science indicators, exploring how each project defines, collects, analyses and exploits data to present results and contribute to knowledge.

The results indicate several shortcomings with respect to commonly accepted Data Science principles, including lack of a clear definition of research problems and limited description of data management and analysis processes, and Open Science principles, including lack of the necessary contextual information for reusing project outcomes.

In the light of this analysis, the authors provide a set of guidelines and recommendations for better adoption of Data Science and Open Science principles in Citizen Science projects, and introduce a software tool to support this adoption, with a focus on preparation of data management plans in Citizen Science projects.

Participatory wireless sensor networks (PWSN) is an emerging paradigm that leverages existing sensing and communication infrastructures for the sensing task. Various environmental phenomenon – P monitoring applications dealing with noise pollution, road traffic, requiring spatio-temporal data samples of P (to capture its variations and its profile construction) in the region of interest – can be enabled using PWSN. Because of irregular distribution and uncontrollable mobility of people (with mobile phones), and their willingness to participate, complete spatio-temporal (CST) coverage of P may not be ensured. Therefore, unobserved data values must be estimated for CST profile construction of P and presented in this paper.

In this paper, the estimation of these missing data samples both in spatial and temporal dimension is being discussed, and the paper shows that non-parametric technique – Kernel Regression – provides better estimation compared to parametric regression techniques in PWSN context for spatial estimation. Furthermore, the preliminary results for estimation in temporal dimension have been provided. The deterministic and stochastic approaches toward estimation in the context of PWSN have also been discussed.

For the task of spatial profile reconstruction, it is shown that non-parametric estimation technique (kernel regression) gives a better estimation of the unobserved data points. In case of temporal estimation, few preliminary techniques have been studied and have shown that further investigations are required to find out best estimation technique(s) which may approximate the missing observations (temporally) with considerably less error.

This study addresses the environmental informatics issues related to deterministic and stochastic approaches using PWSN.

The key purpose of this paper is to overview the many issues related to the integration of social sensing and pervasive sensing in the support of adaptive context-aware services.

From the analysis of existing proposals and prototypes, the authors found out that the process of integrating social and pervasive sensing can follow a limited number of approaches, which enables the authors to properly frame the proposals existing in the literature (and/or available as prototype infrastructures) according to a simple taxonomy, which is very useful to make the survey much more effective than a simple list of systems and proposals.

The taxonomy shows that, when integrating social sensing with pervasive sensing, it is possible, at one extreme, to exploit social network as a mere source of information and have such information flow towards the infrastructure supporting the execution of pervasive computing services. At the other extreme, it is possible exploiting a social network as an infrastructure for the integration, by having data from pervasive devices flow towards social networks. In between the extremes, different means can consider to have social networks and pervasive infrastructures converge towards each other to enable the integration of social and pervasive sensing.

Besides introducing the main concepts related to social sensing and framing the key approaches that can be undertaken to pursue the integration with traditional pervasive sensing, the authors go further discussing open issues and key research challenges behind their seamless integration.

The proliferation of mobile phones with integrated sensors makes large scale sensing possible at low cost. During mobile sensing, data mostly contain sensitive information of users such as their real-time location. When such information are not effectively secured, users’ privacy can be violated due to eavesdropping and information disclosure. In this paper, we demonstrated the possibility of unauthorized access to location information of a user during sensing due to the ineffective security mechanisms in most sensing applications. We analyzed 40 apps downloaded from Google Play Store and results showed a 100% success rate in traffic interception and disclosure of sensitive information of users. As a countermeasure, a security scheme which ensures encryption and authentication of sensed data using Advanced Encryption Standard 256-Galois Counter Mode was proposed. End-to-end security of location and motion data from smartphone sensors are ensured using the proposed security scheme. Security analysis of the proposed scheme showed it to be effective in protecting Android based sensor data against eavesdropping, information disclosure and data modification.

Participatory geographic information systems (PGIS) have been increasingly employed for decision-making in planning, environmental conservation, zoning, and development. This research explores the use of PGIS and its significant role for environmental zoning plans (EZP). PGIS methodology intends to incorporate local knowledge, increase data access, multiple realities, and bottom-up decision-making in EZP in a very sensitive water body in Southern Sri Lanka.

This research presents the lessons learned from a case study of Madu Ganga estuary in Galle District, Sri Lanka. Madu Ganga is an extraordinarily stretch of water body (an estuary) with abundant natural resources and beauty. In recent years, Madu Ganga has faced serious environmental threats due to increase in human activities leading to overexploitation of natural resources. To protect and manage this valuable environment, a team of geographers from the Center for Environmental Studies, University of Peradeniya, Sri Lanka was invited by the Coastal Resource Management Project to undertake the zoning study. Various participatory methods including PGIS applications were adopted and affected communities were also involved in developing a zoning plan. The study illustrates that the use of PGIS approach is effective in incorporating local people into environmental planning, and it also supports affected people to be actively involved in development activities in their own communities.