![Architecture of mobile SOA framework Mobile SOA framework: Similar to our work on MS2A [8], we adopt the open source web container I-Jetty [18] to implement the mobile SOA server, and based on the Android system and the specifications of RESTful Web Services, we develop the mobile SOA framework that integrates with the mobile SOA server, as shown in Fig. 6. But different from MS2A that targets disaster rescue and works independently over wireless ad-hoc networks, in TripleS, the mobile SOA framework functions as a bridge between the mobile device and](https://www.researchgate.net/profile/Qiang-Liu-62/publication/261852657/figure/fig2/AS:392442114265092@1470576899590/Architecture-of-mobile-SOA-framework-Mobile-SOA-framework-Similar-to-our-work-on-MS2A_Q320.jpg)
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A mobile crowdsensing system enhanced by cloud-based social networking services
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This paper presents TripleS, a novel mobile crowdsensing system enhanced by social networking services, which enables mobile users to participate and perform mobile crowdsensing tasks in an efficient manner. TripleS provides a flexible and universal architecture across mobile devices and cloud computing platforms by integrating the service-oriented architecture with multi-agent frameworks for mobile crowdsensing, with extensive supports to application developers and end users. The customized platform of TripleS enables dynamic deployments and collaborations of services and tasks during run-time of mobile devices. Our practical experiments show that TripleS performs its tasks with a considerable computation efficiency, and low computation and communication overhead on mobile devices. Also, the mobile crowdsensing application developed on TripleS demonstrates the functionalities and practical usage of TripleS.
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... Service-oriented MAS architecture for mobile crowdsensing was presented in [24], where application-specific resident agents run crowdsensing services in the participating devices and mobile agents are utilized for augmenting the services of resident agents and transferring their accumulated results. ...
... Campaigns are executed with software agents in the participating devices. This work is based on TripleS [24] that utilizes a cloud platform to coordinate crowdsensing tasks for social networking services. A service-oriented architecture framework in the mobile devices realizes a set of services, implemented as application-specific resident agents: local sensing service, crowdsensing service, context-awareness service, interfaces to the cloud platform and for social networking, and management of services. ...
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.
... Public transport commuters use this game to follow smooth and sustainable routes. TripleS was developed to enhance social networking services [76]. In [77], a framework for traffic monitoring was introduced. ...
Mobile crowdsensing is a useful technique to collect detailed information from mobile devices of the participants. The participants need to participate to sense and transmit valuable information to the servers. Due to the technological growth, various components of mobile devices such as accelerometer, gyroscope, camera and inertial, collect vast volumes of data in a quick, efficient, and cost-effective manner. However, a mobile crowdsensing paradigm may result in serious privacy and security breaches by exposing the mobile devices to various threats and vulnerabilities. This leakage of privacy has an adverse impact on the usage and participation of mobile devices. Motivated by these threats and privacy challenges, we investigate the current approaches used for preserving privacy in mobile crowdsensing applications. After a generic description of mobile crowdsensing systems and their components, we discuss critical issues related to privacy preservation, such as task management, task assignment models, and incentive mechanisms. We also discuss various mobile crowdsensing mechanisms available in the literature. Finally, we highlight numerous research challenges that need to be addressed to improve the performance of future privacy-preserving mechanisms for mobile crowdsensing applications.
... However, by following such a model, it is difficult to react to changes in the opportunistic environment and to the participants' behavior, possibly resulting in a significant reduction of data quality. Conversely, as shown in previous works, e.g., [11,12,13], agents and MAS can be effectively exploited to address the dynamicity in crowdsensing applications. In fact, agent-based development also allows enhancing privacy and energy efficiency in context-aware way, particularly beneficial for resource-constrained devices, such as smartphones [11]. ...
Software agents have been exploited to handle the inherent dynamicity in the Internet of Things (IoT) systems, as agents are capable of autonomous, reactive and proactive operation in response to changes in their local environment. Agents, operating at the network edge, enable leveraging cloud resources into the proximity of the user devices. However, poor interoperability with the existing IoT systems and the lack of a systematic methodology for IoT system development with the agent paradigm have hindered the utilization of software agent technologies in IoT. In this paper, we describe the development process and the system architecture of a mobile crowdsensing service, provided by an agent-based smart object that comprises agents in both edge and user devices. Mobile crowdsensing is an example of such an application that relies on large-scale participatory sensor networks, where participants have active roles in producing information about their environment with their smartphones. This scheme introduces challenges in handling dynamic opportunistic resource availability, due to mobility and unpredicted actions of the participants. We present how ACOSO-Meth (Agent-oriented Cooperative Smart Object-Methodology) guidelines the development process systematically from the analysis to the actual agent-based implementation of a crowdsensing service. The implementation is done with the ROAgent framework that utilizes resource-oriented architecture and REST principles to integrate agent-based smart objects seamlessly with the programmable Web.
... TripleS is an S2aaS architecture which utilizes cloud-based social networking services and incorporates open source principles (Hu, X., Liu, Q., Zhu, C., Leung, V. C. M., Chu, T. H. S. & Chan, H. C. B., 2013). TripleS consists of internetworking and opportunistic networking components. ...
Sensing-as-a-Service (S2aaS) is a cloud-inspired service model which enables access to the Internet of Things (IoT) architecture. The IoT denotes virtually interconnected objects that are uniquely identifiable, and are capable of sensing, computing and communicating. Built-in sensors in mobile devices can leverage the performance of IoT applications in terms of energy and communication overhead savings by sending their data to the cloud servers. Sensed data from mobile devices can be accessed by IoT applications on a pay-as-you-go fashion. Efficient sensing service provider search techniques are emerging components of this architecture, and they should be accompanied with effective sensing provider recruitment algorithms. Furthermore, reliability and trustworthiness of participatory sensed data appears as a big challenge. This chapter provides an overview of the state of the art in S2aaS systems, and reports recent proposals to address the most crucial challenges. Furthermore, the chapter points out the open issues and future directions for the researchers in this field.
... Thus, to make the raw sensing data from different sources be context-aware, one possible way is to require service providers to pre-specify the context definition for their sensor devices and register them to the cloud [22]. Further, as introduced in our earlier works, we use a lightweight ontology which contains a modifier using to capture additional information that affects the interpretations of generic concepts [22][23][24]. Specifically, the generic concept in the ontology can have multiple modifiers, each of which indicates an orthogonal dimension of the variations in data interpretation. The data analysis engine can understand the context of data sources and therefore know how to interpret the data based on the values of the modifiers associated with the corresponding context, which is more flexible and adaptable to the dynamic service environment. ...
Advances in sensor cloud computing to support vehicular applications are becoming more important as the need to better utilize computation and communication resources and make them energy efficient. In this paper, we propose a novel approach to minimize energy consumption of processing a vehicular application within mobile wireless sensor networks (MWSN) while satisfying a certain completion time requirement. Specifically, the application can be optimally partitioned, offloaded and executed with helps of peer sensor devices, e.g., a smart phone, thus the proposed solution can be treated as a joint optimization of computing and networking resources. Our theoretical analysis is supplemented by simulation results to show the significance of energy saving by 63% compared to the traditional cloud computing methods. Moreover, a prototype cloud system has been developing to validate the efficiency of sensor cloud strategies in dealing with diverse vehicular applications.
... Contudo, além da coleta de dados, MCS também permite (e algumas vezes exige) que o participante atue no processamento das informações coletadas. Além disso, MCS aceita a participação oportunista [Lane et al. 2010], onde é necessário apenas que o aplicativo capture os dados através dos sensores disponíveis, e a participação mecânica (carros inteligentes, drones, entre outros) na atividade de coleta [Konidala et al. 2013 [Hu et al. 2013], em comparação com sistemas e redes de sensores fixos, com grande complexidade estrutural e logística, MCS possui vantagens como: ...
Mobile Crowd Sensing (MCS) applications allow that participants or users to collect (using different sensors on your mobile devices-microphone, camera, GPS, etc.) and share information in order to assist other users in decision-making (based on third-party opinion) or inform about different events. Examples of MCS applications include coverage of instantly news, traffic congestion, weather disasters, air pollution, parking spaces, among others. Once the operation of MCS applications involves human or mechanical participation (cars, drones, sensors, etc.) to collect data that will be used by end users, the privacy and safety of the participants, not to mention the reliability of generated and received information are important issues that need investigation. In this context, this chapter aims to provide understanding of the environment of MCS applications, focusing mainly on issues related to privacy, security and reliability of the information. Resumo Aplicações de Mobile Crowd Sensing (MCS) são aquelas onde os participantes ou usuá-rios coletam (através dos diferentes sensores em seu dispositivo móvel-microfone, câ-mera, GPS, entre outros) e compartilham informações com o intuito de auxiliar outros usuários na tomada de decisões (baseada na opinião de terceiros) ou informar sobre os mais diversos acontecimentos. Entre exemplos comuns de aplicações de MCS pode-se citar a cobertura de notícias instantaneamente, informações sobre pontos de conges-tionamento, desastres climáticos, poluição do ar, buracos em vias públicas, vagas em estacionamentos, entre outros. Uma vez que o funcionamento de aplicações MCS en-volve a participação humana ou mecânica (automóveis, drones, sensores, entre outros) para coletar os dados que serão usados por usuários finais, a privacidade e a segurança dos participantes, sem falar na confiabilidade das informações geradas e recebidas são questões importantes que precisam investigadas. Neste contexto, este Capítulo objetiva fornecer entendimento sobre o ambiente de aplicações de Mobile Crowd Sensing, focando principalmente nas questões relacionados a privacidade, a segurança e a confiabilidade das informações.
Natural disasters have always threatened the lives of humans and other creatures. One of the significant challenges for quickly responding to an earthquake is the need for precise and comprehensive information. Given that part of the environmental infrastructure is destroyed, quickly acquiring the required information is a serious challenge. Due to the ubiquity of smartphones, which have sensing, processing, and communication capabilities, this paper proposes CrowdBIG, a crowdsourcing-based architecture for information acquisition from the disaster environment. CrowdBIG architecture consists of four layers: sensing, fog, cloud, and application. Given that the reliability of crowdsourcing systems is dependent on the quality of user data, detecting malicious users, as well as scoring, and selecting useful users are of great importance. The CrowdBIG system is equipped with a reputation management component, which contains two sub-components: malicious user detection and user scoring. To evaluate the CrowdBIG system, first, we validate the information acquisition and dissemination workflow of the system using a scenario-based method. We then simulate the disaster environment through several well-known scenarios. The results show that CrowdBIG can detect malicious users appropriately. The CrowdBIG system can also score non-malicious users reasonably based on their usefulness and information completeness rates. The simulation results reveal that the reliability of the CrowdBIG system is 92%. Finally, the usability evaluation survey shows that more than 80% of the participants rated the usability of the proposed information-gathering tool as good or excellent.
Crowdsensing can be an enabler of the Social Internet of Things (SIoT), among a plethora of other systems, elements, infrastructure, and applications. Although in the short term crowdsensing can be supported within the traditional wireless cellular infrastructure, in the longer term, it will be an important component of the evolving Smart City paradigm. Given the expected increase of urban populations in the next 35 years, this application not only will assist in the process of “greening the environment” but also make city living more livable. Clearly, mobility is at the core of crowdsensing in particular, and SIoT in general. While several mobility management techniques have emerged, an extensive body of applicable research has been developed in the past twenty years, in the form of the Mobile IPv6 (MIPv6) and related protocols. As of press time over seventy RFCs had been published by the IETF on MIPv6 and related MIPv6 mobility protocols; yet, MIPv6 has received relatively little attention up to now in the IoT. Broad deployment of SIoT will benefit from MIPv6 technologies. This chapter describes key MIPv6 features and its propitious applicability to crowdsensing and SIoT, particularly given 3rd Generation Partnership Project (3GPP) recent adoption of it for some 4G/5G scenarios.
A large portion of the human population lives in cities, and by 2050 even a larger fraction of people will be urban dwellers. Typically, cities have aging and limiting infrastructure. Some areas have seen tremendous real estate development, yet the roads, water mains, sewers, and power grids have seen no or extremely limited upgrades. Whatever infrastructure is in place is aging and may, unfortunately, be subject to temporary use rationing as upgrades are made. It is clear that technological solutions are needed to manage the increasingly scarce infrastructure resources under the limitations imposed by population growth, limited financial resources, and political inertia. This predicament creates an urgent challenge to Quality of Life. The IoT offers the promise of improving the resource management of many assets related to city life, the flow of goods, people, and vehicles, and the greening of the environment by optimizing energy consumption and maximizing life‐activity efficiency. This chapter assesses some of the application issues, the technical requirements, and the technical solutions associated with a broad‐based deployment of IoT in urban settings to enjoy the benefits of a smart city.
Sensing-as-a-Service (S2aaS) is a cloud-inspired service model which enables access to the Internet of Things (IoT) architecture. The IoT denotes virtually interconnected objects that are uniquely identifiable, and are capable of sensing, computing and communicating. Built-in sensors in mobile devices can leverage the performance of IoT applications in terms of energy and communication overhead savings by sending their data to the cloud servers. Sensed data from mobile devices can be accessed by IoT applications on a pay-as-you-go fashion. Efficient sensing service provider search techniques are emerging components of this architecture, and they should be accompanied with effective sensing provider recruitment algorithms. Furthermore, reliability and trustworthiness of participatory sensed data appears as a big challenge. This chapter provides an overview of the state of the art in S2aaS systems, and reports recent proposals to address the most crucial challenges. Furthermore, the chapter points out the open issues and future directions for the researchers in this field.
Despite the availability of the sensor and smart-phone devices to fulfill the ubiquitous computing vision, the-state-of-the-art falls short of this vision. We argue that the reason for this gap is the lack of an infrastructure to task/utilize these devices for collaboration. We propose that microblogging services like Twitter can provide an "open" publish-subscribe infrastructure for sensors and smartphones, and pave the way for ubiquitous crowd-sourced sensing and collaboration applications. We design and implement a crowd-sourced sensing and collaboration system over Twitter, and showcase our system in the context of two applications: a crowd-sourced weather radar, and a participatory noise-mapping application. Our results from real-world Twitter experiments give insights into the feasibility of this approach and outline the research challenges in sensor/smartphone integration to Twitter.
A new field in distributed computing, called Ambient Intelligence, has emerged as a consequence of the increasing availability
of wireless devices and the mobile networks they induce. Developing software for mobile networks is extremely hard in conventional
programming languages because the network is dynamically demarcated. This leads us to postulate a suite of characteristics
of future Ambient-Oriented Programming languages. A simple reflective programming language, called AmbientTalk, that meets the characteristics is presented. It
is validated by implementing a collection of high level language features that are used in the implementation of an ambient
messenger application.
As a prominent subcategory of cyber-physical systems, mobile cyber-physical systems could take advantage of widely used mobile devices, such as smartphones, as a convenient and economical platform that facilitates sophisticated and ubiquitous mobile sensing applications between humans and the surrounding physical world. This paper presents Vita, a novel mobile cyber-physical system for crowdsensing applications, which enables mobile users to perform mobile crowdsensing tasks in an efficient manner through mobile devices. Vita provides a flexible and universal architecture across mobile devices and cloud computing platforms by integrating the service-oriented architecture with resource optimization mechanism for crowdsensing, with extensive supports to application developers and end users. The customized platform of Vita enables intelligent deployments of tasks between humans in the physical world, and dynamic collaborations of services between mobile devices and cloud computing platform during run-time of mobile devices with service failure handling support. Our practical experiments show that Vita performs its tasks efficiently with a low computation and communication overhead on mobile devices, and eases the development of multiple mobile crowdsensing applications and services. In addition, we present a mobile crowdsensing application, Smart City, developed on Vita to demonstrate the functionalities and practical usage of Vita.
This paper proposes a novel service-oriented vehicular social networking platform called VSSA for transportation efficiency. VSSA consists of two layers: service layer and application layer. The service layer is implemented based on the service-oriented architecture (SOA) to ease application developments. By extending and composing the web services in this layer, application developers can efficiently and flexibly develop different new functions or applications for transportation scenarios. The application layer is oriented to vehicular users and it integrates with functions of mobile social networks. With dynamic and automatic service collaboration support, it enables people to easily collaborate and help each other through their mobile devices in transportation situations. Moreover, VSSA provides a context-awareness mechanism to support automatically and intelligently predicting the potential incoming traffic congestions.
This paper proposes a novel service-oriented mobile social networking platform called MS2A for disaster situations. MS2A consists of two layers: service layer and application layer. The service layer is implemented based on the service-oriented architecture (SOA) to ease application developments. By extending and composing the web services at this layer, application developers can efficiently and flexibly develop different new functions or applications for mobile disaster rescue and recovery applications. The application layer is oriented to mobile users and it integrates with functions of mobile social networks. With dynamic and automatic service collaboration support, it enables people to easily collaborate and help each other through their mobile devices in disaster situations. The results of our experiments and demonstration examples given in the paper show the feasibility and desired functionality of MS2A for relieving disaster situations with mobile devices.
Mobile gaming is a big driver of app marketplaces. However, few mobile games deliver truly distinctive gameplay experiences for ad hoc collocated users. As an example of such an experience, consider a sword fight dual between two users facing each other ...
The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowd-sourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowd-sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks.
The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resolve to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.
A growing class of smartphone applications are tasking applications that run continuously, process data from sensors to determine the user's context (such as location) and activity, and optionally trigger certain actions when the right conditions occur. Many such tasking applications also involve coordination between multiple users or devices. Example tasking applications include location-based reminders, changing the ring-mode of a phone automatically depending on location, notifying when friends are nearby, disabling WiFi in favor of cellular data when moving at more than a certain speed outdoors, automatically tracking and storing movement tracks when driving, and inferring the number of steps walked each day. Today, these applications are non-trivial to develop, although they are often trivial for end users to state. Additionally, simple implementations can consume excessive amounts of energy. This paper proposes Code in the Air (CITA), a system which simplifies the rapid development of tasking applications. It enables non-expert end users to easily express simple tasks on their phone, and more sophisticated developers to write code for complex tasks by writing purely server-side scripts. CITA provides a task execution framework to automatically distribute and coordinate tasks, energy-efficient modules to infer user activities and compose them, and a push communication service for mobile devices that overcomes some shortcomings in existing push services.