View their issues first-hand instead of talking them through the problem over chat or a phone call. To find out how to use remote troubleshooting Click Here. Remotely troubleshoot issues on mobile devices As an IT administrator, you've probably come across issues such as malfunctioning devices, failed client installations, apps that just keep crashing, and so on.
Effortless, real-time support over-the-air. Remote control Samsung Android devices only Remote screen sharing via Wi-Fi or cellular data No extra installed agents Compatible with devices running Android 5. The server reconstructs the probability density functions of the original distributions using the sensed values, but without knowing the participants' actual data. These theoretical ideas must be put in practice to observe results respecting real world scenarios. In [ 90 — 92 ] the authors focused on the safe processing of the phone sensed data in that mitigating attacks by malware and other attacking software is an important challenge.
The problem analyzed in [ 90 , 91 ] is trustworthiness: Confidentiality and integrity properties are analyzed between the outer sensors and the phone [ 92 ]. All the reviewed works need to verify that the running Android application is safe and differ in the way they test privacy: This is a very cheap solution which does not include additional hardware; perhaps they should consider including the modern Trust Computing Platforms incorporated in recent commercial phones.
On the contrary, [ 92 ] requires additional, expensive and sophisticated mechanisms that can limit its applicability. That is, each sensor had a cryptographic key which is known to the Secure Data SD card which has a key also known to the outer sensors. The key distribution is done directly between the SD, without the intervention of the phone and the outer sensors.
In this way, the malware will not be aware of the keys. Among the seven limitations they exposed, one curious limitation was that data cannot be displayed to the patient because it would be vulnerable to malware. They suppose the outer sensors communicate with the phone using Zigbee , Bluetooth or its secure protocol, so in practice they can only directly use Bluetooth technology, and support its secure protocol over it, because Zigbee is not directly supported in present phones.
Trusted computing platform is used in recently released phones. Energy saving is a very important issue in mobile App design and implementation. A kernel module of the modern smart phone's operating system manages it using energy profilers. For example, Eprof [ 94 ] considered that optimizing energy consumption is of critical importance and it was the first fine-grained energy profiler for phone Apps. It was implemented on Android and Windows Mobile.
The aim of Eprof was guessing where the energy was spent inside any App, for example in storage [ 95 ]. Energy saving is a key issue for continuous sensing because the phone battery drains rapidly [ 96 ].
For this reason, the main objective of energy saving in continuous sensing is to control the actions of sensors and suspend them when necessary. To do this, three different kind of sensors were identified in [ 97 ]: Energy saving has mainly been studied in theoretically the past, but in [ 98 ] a report in the healthcare domain was presented which explained the lessons learnt after their system was tested with several people for a long time.
There are several approaches to control energy saving. Among them we review Green Technology and specific middlewares for energy saving. In this context energy saving in mobile devices follows three directions [ 99 ]: The optimization of the sensor duty cycles was studied in [ 97 , ] using different mathematical models. A Markov chains model was formulated [ ] for minimizing the expected user state estimation error, while maintaining an energy consumption budget.
The results were numerically compared against uniform periodic sampling and they found that the performance gains depend upon the user state transition probabilities. Machine learning algorithms performing offline training of the inference models were used to observe the value stability provided by the sensors in order to disconnect them for some time.
During that time interval, last read values were used. The calculation of the intervals of time in which to use the last read values of the sensors in order to optimize the energy saving is challenging.
These theoretical models must be verified with real scenario experiments because there are several issues that influence energy consumption. Those issues can be taken into account theoretically, for example, the sporadic variation of the wireless channel in the presence of obstacles. AS sampling frequency versus sensed data accuracy impacts energy savings. This affects human activity recognition. For example, [ ] showed a sequence of moderately-long lasting activities, and many of these commonplace activities can be classified quite accurately, without requiring sophisticated features or high sampling rates.
The main objective of energy saving middleware is to accommodate the energy consumption taking into account real world problems and minimizing its energy consumption overhead. In [ ], Acquisitional Context Engine ACE middleware observes the behavior of the participant in different physical contexts home, driving a car, in office… and correlates sensor values that could define the location context attributes in which the user could be.
It dynamically learned relationships among various context attributes and basically used inference caching for opportunistically inferring one context attribute and try to do speculative sensing. In [ ], Lee et al. Group formation, the distribution of sensing planning, and the mobility of participants were identified as challenges.
An identified barrier was that although the participants were closely located less than 10 m apart sometimes they did not easily share their resources with other unknown people. They also have to treat the problem of service disruptions due to Bluetooth channel issues. A cloud can be used to balance opportunistic sensing by observing the proximity of participants and their trajectory [ ]. The authors simulated fair scheduling algorithms of sensing operations among the participants. Their objective was to eliminate redundant sensed data and improve energy savings.
More work must be done to take into account more complex phone user movements and energy wasted in the communications with the cloud and close phones. Machine learning algorithms can be used to dynamically predict device energy-efficient consumption [ ]. In this case, authors showed that the best results were obtained using neural networks and k-nearest neighbor algorithms. Fusion, learning, security, privacy and energy saving are normally implemented in the phone and they are sometimes coordinated by a sensing server. In this section we will present a holistic view of the design of a MSS that can include a WSN, a cloud and a social network.
There are theoretical research works that consider the optimization of the above issues when a WSN is considered for extending the sensing capacity of phones. Other works consider cloud and social network technology. We review these works that have not been included in other surveys and present some new ideas of the design of practical MSS. In Figure 1 we show our MSS schema.
The sensor-rich phone executes a sensing mobile App.
Our Strategy
This App is downloaded from an App store, uses the phone's sensors and can access a WSN using a middleware. Moreover, several geographically near phones can communicate locally their sensing tasks to improve sensing. The phone can receive data from the coordinator of the WSN, extending the sensing capability. Moreover, the phone can upload sensing instructions to the WSN coordinator.
The phone reports data to the server application using Web services. The server always makes the presentation of the sensing results to the consumer. In case the sensing process is organized by an agency, this server will be in charge of distributing sensing tasks among the participants the same applies for crowd MSS. This server can be executed in a machine or it can be executed in several geographically distributed machines in order to distribute the computing load configured as a cloud service. In case the consumers were part of a social network they can use it to share sensed data, processing and visualization.
- 1. Introduction.
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We propose the following ideas for efficiently implementing sensing, fusion, learning, security, privacy and energy saving:. Next we present some research works that justify our proposals and our identified research challenges. A server can implement fusion and learning based on the sensed data coming from a phone. For example, Cui et al. The energy consumption awareness App makes corrective actions to control the rate, and the sampling duration of phones'sensors. The design of accurate activity and context detection algorithms can be achieved with several Android phones [ ] collecting sensing data sets, in several parts of the World, tagged with appropriate ground truth information about the user activity.
Context-aware applications can be built using cloud services for visualization and reasoning [ ]. They provide a set of tips for optimizing the communication among the application and the Internet cloud. A context oriented programming model proposed that each component, referred to as a Widget, maintain updated information about a specific context [ ].
A directory-based service to update information about the overall collection of Widgets was used and applications could read the last updated context accessing that directory. The main barrier to implementation of that proposal is users' mistrust, since the privacy is not guaranteed if the mobile devices would not be under the owner's control. Web services must coordinate the data gathering reporting process from sensors integrated by a middleware and communication among consumers using social network software.
Mobile Sensing Systems
Normally a MSS uses particular mechanisms for data gathering. Next we present some recent initiatives to guide the implementation of middleware and Web services appropriate for efficiently reporting sensed data to a server. A client App has to register the mobile device with the Web service a Java application using the Client's Google account cloud service. That cloud service must determine which client devices were close to the client that requested the Orienteer service. SensOrchestra [ ] relied on a server allocated in Internet to infer location by fusing built in sensor data such as audio recordings and images, and a list of nearby phones, coming from mobile phones forming a Bluetooth ad hoc network.
The server combined the correlated sensor data using a classifier fusion model and sent the recognition results back to the phones. Web services are often used to report and share sensed data using very simple mechanisms. Some ideas about the implementation of MSS using Web services were presented in [ ].
They reviewed requisites, challenges and applications of Web services.
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We state that a MSS must adapt its communication to the new service architecture of the future Internet [ ]. In particular the reporting service description , discovery , access and composition is the key aspect when designing a MSS. Several Web services have been designed with this purpose in mind. It is very simple to program and it could be used by MSS. Heterogeneity is a challenging topic in sensed data discovery and access. The coordination of the transparent access to heterogeneous data from different sensors using a middleware is presented in [ ].
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That middleware runs on the WSN and the phone Android 2. The sensor manager finds the minimal set of sensors to provide the answer to application requests. The resource coordinator mediates among sensors when concurrent requests are issued to them.
The application broker receives the requests from the applications. Web services must also facilitate the composition of the sensed data. This composition ranges from the simple data integration to the complex computation of the semantic associated to users and sensors [ ].