An Adaptive Middleware for Opportunistic Mobile Sensing

2014

Mobile phones play a pivotal role in supporting ubiquitous and unobtrusive sensing of human activities. However, maintaining a highly accurate record of a user's behavior throughout the day imposes significant energy demands on the phone's battery. In this work, we investigate a new approach that can lead to significant energy savings for mobile applications that require continuous sensing of social activities. This is achieved by opportunistically offloading sensing to sensors embedded in the environment, leveraging sensing that may be available in typical modern buildings (e.g., room occupancy sensors, RFID access control systems).

Mobile smartphones along with embedded sensors have become an efficient enabler for various mobile applications including opportunistic sensing. The hi-tech advances in smartphones are opening up a world of possibilities. This paper proposes a mobile collaborative platform called MOSDEN that enables and supports opportunistic sensing at run time. MOSDEN captures and shares sensor data acrossmultiple apps, smartphones and users. MOSDEN supports the emerging trend of separating sensors from application-specific processing, storing and sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing the efforts in developing novel opportunistic sensing applications. MOSDEN has been implemented on Android-based smartphones and tablets. Experimental evaluations validate the scalability and energy efficiency of MOSDEN and its suitability towards real world applications. The results of evaluation and lessons learned are presented and discussed in this paper.

2012 IEEE Sensors, 2012

Sensors on (or attached to) mobile phones can enable attractive sensing applications in different domains such as environmental monitoring, social networking, healthcare, etc. We introduce a new concept, Sensing-as-a-Service (S 2 aaS), i.e., providing sensing services using mobile phones via a cloud computing system. An S 2 aaS cloud should meet the following requirements: 1) It must be able to support various mobile phone sensing applications on different smartphone platforms. 2) It must be energy-efficient. 3) It must have effective incentive mechanisms that can be used to attract mobile users to participate in sensing activities. In this paper, we identify unique challenges of designing and implementing an S 2 aaS cloud, review existing systems and methods, present viable solutions, and point out future research directions.

2012

Scientific communities extensively exploit simulations to validate their theories. However, the relevance of the obtained results highly depends on the accuracy of the dataset they use. This statement is particularly true when considering human mobility traces, which tend to be highly unpredictable. In this paper, we therefore introduce ANTDROID, a distributed platform for sensing the activities of mobile users. In particular, ANTDROID offers to scientists a participative platform for helping them to easily setup and exploit mobile sensing experiments. Beyond the scientific contribution of this platform, the technical originality of ANTDROID lies in its Cloud orientation, whose implementation is based on an extension of the SCA component model that complies with the REST architectural style. This modular infrastructure provides a scalable yet open environment, which can be configured and customized according to the scientist requirements. We validate this platform by sensing various activities of mobile participants using Android smartphones.

2015 IEEE 14th International Symposium on Network Computing and Applications, 2015

In the participatory sensing model, humans may serve as opportunistic sensors and flexible actuators while also consuming sensing services. Integrating humans into sensing systems has the potential to increase scale and reduce costs. However, contemporary participatory sensing software provides poor consideration of user dynamism, which includes: mobility across networks, mobility across devices and context-awareness. To address these limitations we propose the User Component and User Bindings. The former represents the user as a first class reconfigurable element of evolving and shared participatory sensing platforms. The latter allows the middleware to support multiple communications channels including Online Social Networks (OSN) to connect users with sensing applications. Our approach increases user participation, reduces out-of-context interactions and only consumes a limited amount of energy by sharing context information between applications. We support these claims by evaluating our approach on a two weeks experiment in which three participants take part in three concurrent participatory applications.

Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of sensors installed on the mobile device to deliver enhanced user experience. The sensors on the smartphone provide the opportunity to develop innovative mobile opportunistic sensing applications in many sectors including healthcare, environmental monitoring and transportation. In this paper, we present a collaborative mobile sensing framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on smartphones capturing and sharing sensed data between multiple distributed applications and users. MOSDEN follows a component-based design philosophy promoting reuse for easy and quick opportunistic sensing application deployments. MOSDEN separates the application-specific processing from the sensing, storing and sharing. MOSDEN is scalable and requires minimal development effort from the application developer. We have implemented our framework on Android-based mobile platforms and evaluate its performance to validate the feasibility and efficiency of MOSDEN to operate collaboratively in mobile opportunistic sensing applications. Experimental outcomes and lessons learnt conclude the paper.

2005

In the rapidly developing field of sensor networks, bridging the gap between the applications and the hardware presents a major challenge. Although middleware is one solution, it must be specialized to the qualities of sensor networks, especially energy consumption. The work presented here provides two contributions: a new operational setting for sensor networks and a middleware for easing software development in this setting. The operational setting we target removes the usual assumption of a central collection point for sensor data. Instead the sensors are sparsely distributed in an environment, not necessarily able to communicate among themselves, and a set of clients move through space accessing the data of sensors nearby, yielding a system which naturally provides context relevant information to client applications. We further assume the clients are wirelessly networked and share locally accessed data. This scenario is relevant, for example, when relief workers access the information in their zone and share this information with other workers. Our second contribution, the middleware itself, is an extension of LIME, our earlier work on middleware for mobile ad hoc networks. The model makes sensor data available through a tuple space interface, providing the illusion of shared memory between applications and sensors. This paper presents both the model and the implementation of our middleware incorporated with the Crossbow Mote sensor platform.

Pervasive Services, IEEE …, 2007

Data collection has always been a major challenge in sensor networks and various techniques have been proposed to enable efficient data collection. One such methodology is the use of mobile elements within the existing infrastructure to enable data collection. The paper proposes the use of existing mobile elements like mobile phones which have enough spare capacity to act as data carriers within a sensor network to carry sensor data. With advent of technology, mobile devices have become so powerful that they can work in a pervasive environment and make decisions based on context information like presence, location etc. Our proposal is an intelligent heterogeneous network in which the sensor nodes act as the data accumulators and the context-aware mobile phones act as data carriers of the sensed data. A framework that enables the mobile node and sensor node communication over Bluetooth is proposed and a p implementation is presented.

The Internet of Things (IoT) is part of Future Internet and will comprise many billions of Internet Connected Objects (ICO) or `things' where things can sense, communicate, compute and potentially actuate as well as have intelligence, multi-modal interfaces, physical/ virtual identities and attributes. Collecting data from these objects is an important task as it allows software systems to understand the environment better. Many different hardware devices may involve in the process of collecting and uploading sensor data to the cloud where complex processing can occur. Further, we cannot expect all these objects to be connected to the computers due to technical and economical reasons. Therefore, we should be able to utilize resource constrained devices to collect data from these ICOs. On the other hand, it is critical to process the collected sensor data before sending them to the cloud to make sure the sustainability of the infrastructure due to energy constraints. This requires to move the sensor data processing tasks towards the resource constrained computational devices (e.g. mobile phones). In this paper, we propose Mobile Sensor Data Processing Engine (MOSDEN), an plug-in-based IoT middleware for mobile devices, that allows to collect and process sensor data without programming efforts. Our architecture also supports sensing as a service model. We present the results of the evaluations that demonstrate its suitability towards real world deployments. Our proposed middleware is built on Android platform.

The ubiquity of cutting edge very much provisioned cell phones has made the utilization of cloud assets for computational growth less convincing. Rather, there has been an accentuation on building cloud administrations to gather and process sensor and setting information delivered by these cell phones, to upgrade fascinating applications, for example, maps, ongoing ads, and so on. Notwithstanding, these administrations are without further ado custom-made for individualized communications; i.e., they regard every cell phone as a free substance. This paper places that cell phones are infotainment stages, as well Abstract This paper presents a comprehensive survey on the mobile sensor cloud which will provide information on various platforms. The paper discusses about the mobile sensor cloud scope, state of art and challenges faced while designing mobile sensor cloud infrastructure and current mobile sensor cloud applications. We have proposed an architecture framework which aims to address the communication between various sensors and user end applications. As the resource and capability of sensor devices is limited, the cloud computing on the IT infrastructure can be behalf of the sensor management. The proposed framework in this paper also support scaling which is major challenge in cloud computing.