Wireless sensor networks in the Internet of Things   Leave a comment

As the market for the Internet of Things starts to take shape, the role of wireless sensor networks in enabling a range of new applications, has started to take centre stage, with demand growing for extremely low power solutions.

While sensor-based networks for monitoring and control are not new concepts, demand for wireless implementations – previously limited to just a few niche markets – is set to grow as companies look to bene8t from the combination of installation simplicity and low-cost.

Wireless systems need to consume as little power as possible and next generation networks are being developed where batteries require little maintenance over the application’s life time. As an alternative, there is also a focus on energy harvesting to provide the necessary power, removing the need for batteries completely.

Energy harvesting is becoming  more attractive because it doesn’t require continual recharging and does away with the need for communication or power wires. The challenge with its deployment comes when it needs to be located in more challenging environments and especially when power needs to be stored. If you have to incorporate temporary storage capabilities in a design, then the application will, as a necessity, become far more complicated – cost, as a result, then becomes a more important issue, especially if you are distributing hundreds or thousands of sensors.

Applications have different power requirements. A door lock, for example, may only have to report back every hour or when something happens. As a result the power requirements will be low. By comparison, some wearable devices, such as the Apple Watch, consume far too much power to deploy a harvesting solution. The challenge is being able to develop an MCU that is capable of driving an application but which comes with very low power consumption. When powering sensors is that energy harvesting may only be able to supply as little as 10μA ontinuously, especially if the energy source is a small solar panel.

By contrast, a microcontroller executing code will have power requirements hundreds or thousands times greater. As a result, application designs will need to be able to fully leverage various sleep modes and should be designed with a view to putting the application into sleep mode as much as is possible.

Most applications will come with a variety of preset operating modes, so the embedded microcontroller can, for example, be put into a ‘sleep’ or enter an extreme, low-power ‘standby’ mode in between different data samples. If the microcontroller collects sufficient data, it can then switch to a ‘fully on’ mode, where it is awake and running at maximum operating speed. This will require the microcontroller to receive some kind of wake-up event, which could be triggered by an external event or by internal processor activity.

From an ‘always on’ mode to a ‘sleep’ or ‘standby’ mode, where memory stays powered, or a ‘deep sleep’ mode, where the memory is powered down for maximum power savings, the choice of a microcontroller’s power modes can have a significant impact on the overall power requirements of an application.

The problem with energy harvesting solutions is the energy output might be too low to power up the MCU. Starting an MCU  will require a lot of current; more than the energy harvesting component can provide. As a result, you need to deploy a mechanism whereby the MCU will only start-up when sufficient energy has been stored, usually a capacitor to store the energy and a comparator capable of detecting the amount of power available.

Once the application has powered up, it can then return to a more suitable power mode. It means the application has greater stability.

Accurate timing is needed to ensure the wireless sensor transmits information during a predefined assigned time slot, which will enable multiple wireless sensor nodes to work together.

The overall power budget of the wireless system, the transmit power consumption, the receiver power consumption, the standby power consumption, and the start-up time are all important considerations and will determine how much current the unit will consume when transmitting and receiving data.

To minimise the overall power consumption, it is not enough to simply select the lowest-power mode on the microcontroller as the amount of work needed to complete each of the tasks will need to be taken into account and as the Internet of Things develops and the amount of data collated rises, how and when that data is processed will become more important. At the moment, batteries remain the dominant way in which power is stored and delivered. While there has been a lot of discussion around energy harvesting, it really hasn’t taken off.

When you develop a network, there will be a power budget for computation and for communication. Hopping from one node to another to get that data to a point where it can be processed can be described, in effect, as an energy tax that you are taking from that packet or payload. Minimising those hops could be one trade-off leading to more localised processing should those tasks be more power intensive.

In many low-power saving mode systems, the application’s battery life will often be affected by the current consumption of other components in the PCB and this needs to be considered. components in the PCB and this needs to be considered. Designers will also need to  determine which other circuits need to be powered in the low-power state of the application. Power requirements will have an impact on the roll out of the Internet of Things, but everything will depend on the application.

Cost and power among the challenges to rolling out of IoT applications, but note that, further down the road, security is another consideration. Consumers will start to worry when they begin to realise the devices they are using are less secure than they thought. When you connect to a network, there are some
expensive operations that need to be carried out and that will have an impact. Any security features will
need to be low power.

The future of your health is on the Internet   Leave a comment

You didn’t know that you could prevent chronic diseases like heart failure, obesity and stroke using the Internet. With tech and healthcare you can.

“Prevention is better than cure.” That’s why we’re encouraged to eat more vegetables than meats, exercise, more than sit, and sleep more than play videogames.

But do these things really help? How would we know for sure that we’re actually becoming healthier with the lifestyle changes we’re making, and not staying the same, or even getting worse?

Juliett Starrett is a CrossFit athlete and the co-founder of San Francisco CrossFit. After giving birth and receiving a blood transfusion, Starrett kept feeling fatigued and getting chronic headaches.

The doctors put her on antibiotics, and thought it was just temporary. But even though Starrett was both extremely health-conscious and fit, she didn’t get better. To help her get through the day, Starrett resorted to drinking eight cups of coffee a day.

Starrett started working with WellnessFX. WellnessFX is a webbased service that combines traditional blood tests with intuitive online data tracking and phone consultations with physicians. Using WellnessFX, Starrett discovered that her iron, vitamin B12 and D levels — indicators of physical energy — were extremely low.

Thanks to WellnessFX’s ability to track biomarkers over time, Starrett changed her lifestyle, diet and supplements to attack her deficiencies. Over the next few months, her energy levels improved significantly, and she could cut her coffee down to a cup a day.

Not only did she feel better, Starrett could actually measure her improvements, using WellnessFX’s regular blood tests and online data tracking, to quantify how her biomarkers changed over time.

Tracking your biomarkers over time isn’t just something for elite athletes — according to the Center for Disease Control and Prevention (CDC) in the United States, chronic diseases and conditions, such as heart disease, obesity and arthritis, are among the most common, and preventable of all health problems.

By monitoring your blood over time, you can track and stop markers like cholesterol, inflammation and blood sugar before they hit unhealthy levels.

Internet-connected health services aren’t just good for preventive care. In 2014, the University of California, San Francisco, began offering patients the use of a miniature wireless device called the CardioMEMS HF System implant.

The CardioMEMS is a battery-free device that’s smaller than a coin. It monitors the patient’s heart rates and artery pressures, and transmits them in real time to the hospital. Not only does it help doctors measure how patients are responding to different treatment therapies, it can tell doctors that a patient’s heart condition is getting worse, even before the patient feels any symptoms.

While having your personal health data online can be convenient, and in some cases, life saving, the one major concern is how secure and private your data can remain once it’s shared. The University of California, Los Angeles (UCLA) Health recently experienced a cyberattack, which may have compromised as many as 4.5 million patient records.

But online healthcare might be a case where the potential rewards will far outweigh the risk — the stakes are as high asthey can ever be when lives are at stake.

Today, there’s a lot of talk about bringing our everyday appliances online, a concept that’s commonly called the Internet of Things; Internet-connected refrigerators and washing machines are real products you can go out and buy right now.

The logical extension of that is surely when our bodies join the Internet of Things. With services like WellnessFX, devices like the CardioMEMS, and consumer wearables becoming more adept at tracking our everyday activity.



Wearables: Revolutionizing Medical Research   Leave a comment

The activity tracker you have on your wrist can do more than just count the number of steps you have taken and the hours you have slept. It has the power to change the way medical research is conducted.

MEDICAL RESEARCH IS A PARAMOUNT COMPONENT of medical studies and is crucial to our understanding to how people react to symptoms, how diseases work, and how effective a particular drug is in the real world.

One of the biggest challenges facing medical researchers is the lack of subjects. The truth is that methods for conducting medical research haven’t really changed in decades. Researchers would try to recruit subjects by putting up flyers, or attract them by offering small rewards for participation. In some cases, university might even make it compulsory for undergraduates to participate. Needless to say, these methods do not provide an accurate a cross-section of the population, thereby limiting our understanding of diseases.

Apple wants to change this. There are already hundreds of millions of iPhones out there and millions of users wearing Apple Watches and other activity trackers. So how can they harness this? The answer is ResearchKit, an opensource software framework that will allow researchers and developers to create apps for medical research.

n a nutshell, ResearchKit will allow researchers and doctors to gather more data by using apps and taking advantage of the millions of iPhones and Apple Watches that are already out there.

For example, one common way to assess Parkinson’s disease is the Parkinson’s Gait Test, where a doctor rates a patient on his walk on a scale of 0 to 4. It’s highly subjective and also troublesome to conduct as it requires patients or subjects to come in and walk in front of a doctor. But by using the accelerometer in the iPhone and Apple Watch, ResearchKit lets researchers and developers create apps that can accurately measure the gait of a patient or subject. It also lets subject do the test wherever they are and whenever they want.

Beyond Parkinson’s disease, ResearchKit will also allow for other apps to be created that can be used to measure and test for other conditions and diseases, allowing research subjects and patients to self-diagnose and take part in research without traveling to a clinic and without the presence and guidance of doctor. It makes things much more convenient and simpler.

Since ResearchKit pulls data out of the Health app, it’s not limited to just the Apple Watch, it will work with any wearable that uses an app that syncs with Apple Health – and that’s a list that includes popular wearables like Jawbone’s Up activity trackers, Withings Activité smartwatches and Polar’s running watches and activity trackers. This allows researchers to gather a larger, more diverse and meaningful amount of data.

Beyond Apple and ResearchKit, Google also wants to use wearables to advance medical research and studies. In June earlier this year, Google’s Google X research division announced a wristband that was
designed specifically for medical research. It will be more accurate than consumer grade activity trackers and it can measure heart rate, heart rhythm, skin temperature and even ambient light exposure and noise levels by the minute.

The intended use of this wristband is for doctors to prescribe them to patients or for use in clinical trials. in future, devices like Google’s wristband will be given to everyone, so that doctors can be alerted to problems and people can catch signs of diseases early.

Like Apple with ResearchKit, Google is hoping that its new wristband will let doctors track their patients more accurately and reliably, especially when they are away from hospital, thereby giving them deeper insights into their conditions and their lives, and also alerting them to any major complications before they can occur. The activity tracker you have on your wrist

Secure path to the Internet of Things   Leave a comment

With a seemingly countless number of connected devices, the Internet of Things (IoT) will be a gigantic growth market in the coming years. With the right solution, developers can concentrate on their core competencies and access the required specialist know-how in the shape of affordable, reliable and pre-validated modules.

The Internet of Things is growing steadily and rapidly. These intelligent objects have their own IP address and are constantly connected to each other over the internet,  making them able to communicate freely with each other. Sensitive data and devices must be protected from unauthorised access.

The first requirement for a network of machines and devices of any kind is secure IoT access. This can be provided either directly or via a gateway. In the first case, a gateway will already be implemented in the individual device. A protocol conversion between the internal and external network is often useful and necessary. Security is a complex issue and involves safety’ (broadly referring to safe operation) and ‘security’ (meaning safe from attacks by outsiders).

Intel quickly realised that this is a major obstacle for widespread access to the IoT. In cooperation with its subsidiaries Wind River and McAfee, Intel set out to develop a  secure end-to-end solution available from one source. This seamless and secure solution combines the individual products and special expertise from each company for selected platforms such as the Intel Atom-38xx family. Wind River supplies the Wind River Intelligent Device Platform XT which includes the operating system (Wind River Linux5.0), prevalidated software stacks, hardware drivers and matching libraries and tools. Functions such as administration, communication, connectivity and security as well as runtime environments such as Java, Lua and OSGi are all supported.

Fig 1 congatec’s current offering on the hardware and software sides of the IOT topology, with the Intel
processor selection on the left, and the matching form factors on the right.

iot topology

McAfee’s security software, McAfee Embedded Control, provides features such as dynamic application whitelisting (only registered and verified applications can run) and change control (all modifications of the code and the environment must be explicitly approved before execution). Intel provides the hardware platform itself plus hardware feature enhancements such as TPM (tamper proof module) and matching hardware-related software and stacks. The essential point here is that Intel validates the end solution as a whole; the complete processor board including all firmware.

Standard Modules

For those who neither want to rely on finished, commercially available devices nor go through the complicated and time-consuming process of certifying their own developments with Intel, the use of pre-certified function blocks makes good sense. Many industry sectors already use modular computer systems that are highly scalable for the specific application and based on proven standards such as Qseven or COM Express. The use of modules that are precertified for the Intel solution not only saves time and cost when implementing secure Internet connectivity, they also open up all the advantages of modular computer systems. Important criteria when selecting a module supplier includes support of the relevant standards, quality of the modules and the ability of the module manufacturer to effectively support the system manufacturer in the development of its own systems.

The the conga-QA3 Qseven module from congatec with processors from the Intel Atom E3800 family is particularly  well suited for connecting to the Intel Gateway Solutions for the Internet of Things. It enables the use of Intel Atom processors with up to four cores and clock speeds from 1.33 to 1.91GHz. Depending on the system and its application, the total power consumption ranges between as little as 4.5W to 12W. This enables the development of very economical and extremely powerful embedded PCs, that can be hermetically sealed and operate fan lessly in an extended temperature range. The maximum RAM size is 8GB DDR3L memory, and the integrated Intel HD graphics can support two independent Full HD displays via DisplayPort, HDMI or LVDS. Numerous interfaces and functions (including Gigabit Ethernet and USB3.0), enable fast and cost effective realisation of high-performance embedded systems with low power consumption such as Box PCs or other customised solutions.

Figure 2 - congatec's certified Intel Gateway Solution for the IoT

Fig 2 congatec’s certified Intel Gateway Solution for the Internet of Things

The combination of reliable hardware and a consistent software package, including everything from firmware to operating system and applications, provides a totally secure root of trust for IoT gateway applications. Thanks to outstanding performance, it is possible to carry out additional demanding tasks such as evaluation, consolidation, storage and visualisation of data, as well as sophisticated protocol conversions between the individual connection levels.

QSys is a modular embedded PC from TQSystems based on the Intel Atom E38xx. The combination of the MB-Q7-2 mainboard and thecongatec conga-QA3 module provides a highly compact embedded computer system and an ideal platform for use with the Intel Gateway Solutions for the Internet of Things.

The compact box design, with external dimensions of only 100x100x23mm³ and many interfaces and functions, is an example of how to quickly and cost-effectively implement a high-performance, passively cooled embedded system for gateway applications. Hardware security features such as TPM 1.2/2.0, the Sentinel HL Security Controller and integrated secure EEPROM enable the realisation of embedded systems with an exceptional level of security. The example has shown how quick and easy it is with congatec’s modular system to build concrete solutions for secure IoT gateways. The right know-how and technology can, however, bring further benefits. Thanks to the 70x70mm compact form factor of the Qseven module it is easy to transfer the system layout to a customised system, making the development of complete single board computer systems a simple and inexpensive task. The re-validation effort is relatively low because key components, such as processor, I/O system, network peripherals and firmware, require no or little modification. congatec has, for example, already implemented a complete mini- ITX single board solution.

As an ODM (Original Device Manufacturer) congatec can also develop complete customised systems and validate them for the customer, or use its know-how to help customers validate their own developments. The cost optimisation of this approach is particularly interesting where large production runs are concerned.

Modular systems consisting of pre-integrated hardware and software modules enable manufacturers of IoT-enabled systems to  develop secure solutions quickly and costeffectively, without having to deal in any detail with the complex security issues. On the one hand, security is safeguarded by a global player such as Intel bundling its expertise with that of its subsidiaries Wind River and McAfee in an end-toend, validated solution. On the other hand, they can rely on the manufacturer of the appropriate certified standard module, who is responsible for high manufacturing quality and practical support during the implementation of the complete solution. It is important to select the manufacturer carefully to avoid unwelcome surprises later on.

While current modules are primarily designed to provide gateway functionality for applications in the areas of industrial electronics, mechanical engineering, energy supply and transportation, subsequent modules and validation packages will cover additional functionalities and industry segments. The possibilities offered by the IoT are virtually unlimited and hold a rich potential for further development

Posted November 5, 2015 by Anoop George Joseph in Internet

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Smart Homes, Sentrollers and ZigBee   Leave a comment

The estimates of how many connected devices will constitute the Smart Home market are all over the map. This sector is going to be big and will be extremely important to the future of the electronics industry

Smart Home: let us define the Smart Home as being a network of Sentrollers  – sensors, controllers and actuators, connected to a central home control box and from there to an intelligent dashboard in the cloud that can be monitored and controlled by web connected devices like smart phones.

Currently, there are 600m connected homes in the world- connected homes have some kind of internet connection. Of these, on the average, each has about ten connected wifi devices. This includes computers, laptops, phones, games, entertainment systems etc.

As the number of connected homes grows, there will be an increasing movement to incorporate sentrollers into the home. Whereas there are about 10 WiFi devices in the home today, within
ten years, we expect to see about 100 or more sentroller devices within each home.

This will include a network of different kinds of sensors spread throughout the home- temperature, motion, position, security, humidity, etc. that will track the life activity of those in the home. This includes position sensors for measuring whether a door or window is open or closed, motion sensors that track where in the house a certain occupant may be, temperature sensors to ensure that the house is at the proper temperature, while at the same time ensuring optimal use of energy (i.e. not heating or cooling the house when nobody is home. This can also include humidity sensors adjacent to plumbing fixtures to provide alerts in case of leaks.

Intelligence can enable many of these sensors to provide dual uses, i.e. when no one is in the home and the security system is on, a motion sensors can send an alarm, if it detects someone moving around. However, when the However, when the residents are home, the motion sensors can turn on lights to illuminate a path during the night or customise temperature for the room in which the person is in. The motion sensor could even activate the appropriate music selections to follow the person as they move from room to room.

Depending on their function, these sensors can communicate among themselves and to a central home control unit, which connects all the home’s systems to the internet and allows control and monitoring of the home via cloud intelligence, smart phones, tablets or other web connected devices.

Within the homes however, the devices need to talk to each other and to the central home router/network. There are a variety of wireless technology standards that can be used to tie together these sentrollers and make sure that they can reach the home control box (i.e. the router).

We believe that there will be three open standard based networks within the home. For example, WiFi – with its wide bandwidth (and power hungry) requirements, will be used for big data applications, like video streaming, music, phones, gaming, etc. In contrast, Bluetooth will be used for short range communications between low data rate devices like wearables and medical health (heart rate monitoring, fitness band). However, for most sensor applications in the home, IEEE 802.15.4 based protocols like
ZigBee will be the most suitable.

Operating in the 2.4GHz frequency range, able to transmit through walls, floors and furniture, and cover an entire house, IEEE 802.15.4 based wireless offers an ideal convergence of robustness, bandwidth, power requirement and cost.

Designers can essentially consider ZigBee as low data rate, low power WiFi. Most consumers have grown accustomed to charging their portable WiFi devices every day or so. In contrast, no one will want to
regularly change batteries on the  hundred or so sensor devices in the Smart Home of the future.
ZigBee offers the ability for a battery powered device to run for up to ten years without having to change or recharge the battery. Because ZigBee only needs to send small data packets on an occasional basis, it
does not require the power used by WiFi which continually transmits millions of data packets.

ZigBee offers various other advantages to the device developer. As an international standard, it enables design engineers the knowledge that their device can be used anywhere in the world, unlike some other non-standard wireless solutions like Zwave and EnOcean. Also, as it is an open international networking standard, there are many different companies offering ZigBee radio chips – this enables manufacturers to multisource the radio chips, not locking them into a single provider.

The new Smart Home – the Intelligent smart home – will be composed of a network of sensors, actuators and controllers – all connected by a reliable, robust and power efficient wireless connectivity standard, provides a path to the next generation, multi trillion dollar  electronic component and smart home devices market.


Location Based Social Networks   Leave a comment

Location-based Social Networks (LBSNs) can be considered as a special Online Social Network category. Actually, an LBSN has the same OSN’s properties, but qualifies location as the core object of its structure.

Recently, advances in broadband wireless networks and location sensing technologies led to the emergence of smart mobile phones, tablets etc. that allowed ubiquitous access to the Web. In this new era, users can benefit by getting ubiquitous access to location-based services from anywhere via mobile devices. Moreover, users can share location-related information with each other to leverage the collaborative social knowledge by using LBSNs.

LBSNs allow users to see where their friends are, to search location-tagged content within their social graph, and to meet others nearby. LBSNs consist of the new social structure made up of individuals connected by the interdependency derived from their locations in the physical world as well as their location-tagged media content, such as photos, video, and texts. LBSNs are a type of social networking in which geographic services and capabilities such as geo-coding and geo-tagging are used to enable additional social dynamics. It presents three layers, namely, the user, the location, and the content layers. It is obvious that someone can exploit information from each layer independently to leverage recommendations. For instance, we can compute the geographical distance (i.e. Euclidean distance) between each pair of places in the location layer. Moreover, we can calculate the similarity among users based on the social network that exists in the user layer. Regarding the content layer, we can compute similarity among the information objects (i.e. video, tags etc.) based on their metadata. Please also notice the ternary relation among entities (i.e. user, location, content), which goes through all layers.

Acquiring this abundant contextual information, LBSNs can improve the quality of services on: (1)generic (non-personalized) recommendations of social events, locations, activities and friends, (2) personalized recommendations of social events, locations, activities and friends, and (3) user and group mobility behavior modeling and community discovery.

Generic Recommendations

Generic Recommendations compute the same recommendation list (location, activity,event etc.) for all users, regardless the personalized preferences of each individual user. The most simple recommender systems are those based on counting frequencies of occurrences or co-occurrences of some given dimension. For example, a simple recommender system could just count the number of check-ins per
place, rank them and recommend those places with the larger number of check-ins.

A location recommender, for any user who travels in a specific city (e.g. New York), can first count each location’s frequency of check-ins. Then, it can recommend the top-n locations by sorting these locations in decreasing order of their scores and selecting the n most popular. Notice that an interesting location can be defined as a cultural place, such as the Acropolis of Athens (i.e., popular tourist destinations), and commonly frequented public areas, such as shopping streets, restaurants, etc. As far as the activity recommendations is concerned, an activity recommender can provide a user with the most popular activities that may take place at a given location, e.g. dinning or shopping.  A target user can provide to the system the activity she wants to do and the place she is (e.g. coffee in New York). Then, the system provides a map with coffee places, which are nearby the user’s location (i.e. EuroPan Cafe in location A) andwere visited many times (i.e. 17 times) from 16 people. All the aforementioned recommendations can guide a user in an unknown place of visit.

Personalized Recommendations

The personalized recommender systems rely on past “check-in” history of users. Then, they correlate them with other users that have similar preferences and suggest to them new locations, activities and events. In particular, a personalized recommender exploits the time that someone has visited a location and her explicit ratings or comments on that location and predicts her interest in unvisited places. As there are three approaches that have emerged in the context of recommender systems: collaborative filtering (CF), content-based Filtering (CB) and hybrid methods. In the following, we briefly discuss, the special characteristics of each approach in the LBSN field.

CF methods recommend those locations, activities and events in a city to the target user, that have been rated highly by other users with similar preferences and tastes. In most CF approaches, only the locations and users’ ratings are accessible and no additional information, i.e. locations or users, is provided. User-based CF, employs users’ similarities for the formation of the neighborhood of nearest users. User-based CF is an effective approach in terms of accurate recommendations. However, it cannot scale-up easily due to the high computation of user similarity matrix. In contrast, location-based CF algorithm employs locations’ similarities for the formation of the neighborhood of nearest users, reducing the problem of scalability. In any case, a pitfall of both user-based and location-based CF is the cold start problem: new locations have received only few ratings, so they cannot be recommended; new users have performed only few visits, so there can be hardly found other users similar to them.

CB methods assume that each user operates independently. As a result, it exploits only information derived from location features. For example, a restaurant may have features such as cuisine and cost. If a user, in her profile, has set her preferable cuisine to be Chinese, then the Chinese restaurants will be presented to her. Apparently, the limitation of these systems lies upon the fact that other people’s preferences are not considered. In particular, it exploits a set of attributes that describes the location and recommend other locations similar to those that existin the user’s profile. This way, the cold start problems, faced by CF methods, for new locations and new users are alleviated. However, the pitfall of CB is that there is no diversity in the location and activity recommendations.

The combination of social with geographical data, is becoming a way of handling shortcomings when only one type of data is taken into consideration. For example, the social graph (i.e. trust/friend connections) is not dealing with location analysis, whereas collaborative filtering maintains a user profile mainly based on rating data. The idea of a hybrid approach suggests that by using both data (i.e. social and rating data) it is possible to overcome each other’s shortcomings and make the recommendation result to be more accurate. A hybrid system is where geographical data are combined with social data to provide location
and activity recommendations. GPS location data, user ratings and user activities to propose recommendations to interested users along with appropriate explanations.

Integrating Sensors in Social Networks   Leave a comment

A number of sensor applications in recent years collect data which can be directly associated with human interactions. Some examples of such applications include GPS applications on mobile devices, accelerometers, or location sensors designed to track human and vehicular traffic. Such data lends itself to a variety of rich applications in which one can use the sensor data in order to model the underlying relationships and interactions. It also leads to a number of challenges, since such data may often be private, and it is important to be able to perform the mining process without violating the privacy of the users.

Social networks have become extremely popular in recent years, because of numerous online social networks such as Facebook, LinkedIn and MySpace. In addition, many chat applications can also be modeled as social networks.  Social networks provide a rich and flexible platform for performing the mining process with different kinds of data such as text, images, audio and video. Therefore, a tremendous amount of research has been performed in recent years on mining such data in the context of social networks. In particular, it has been observed that the use of a combination of linkage structure and different kinds of data can be a very powerful tool for mining purposes. How one can combine the text in social networks with the linkage structure in order to implement more effective classification models. Other recent work uses the linkage structure in image data in order to perform more effective mining and search in information networks. Therefore, it is natural to explore whether sensor data processing can be tightly integrated with social network construction and analysis. Most of the afore-mentioned data types on a social network are static and change slowly over time. On the other hand, sensors collect vast amounts of data which need to be stored and processed in real time. There are a couple of important drivers for integrating sensor and social networks:

-One driver for integrating sensors and social networks is to allow the actors in the social network to both publish their data and subscribe to each other’s data either directly, or indirectly after discovery of useful information from such data. The idea is that such collaborative sharing on a social network can increase real-time awareness of different users about each other, and provide unprecedented information and understanding about global behavior of different actors in the social network. The vision of integrating sensor processing with the real world.

-A second driver for integrating sensors and social networks is to better understand or measure the aggregate behavior of self-selected communities or the external environment in which these communities function. Examples may include understanding traffic conditions in a city, understanding
environmental pollution levels, or measuring obesity trends. Sensors in the possession of large numbers  of individuals enable exploiting the crowd for massively distributed data collection and processing.
Recent literature reports on several efforts that exploit individuals for data collection and processing purposes such as collection of vehicular GPS trajectories as a way for developing street maps, collectively locating items of interest using cell-phone reports, such as mapping speed traps using the Trapster application, use of massive human input to translate documents, and the development of protein folding games that use competition among players to implement the equivalent of global optimization algorithms.

The above trends are enabled by the emergence of large-scale data collection opportunities, brought about by the proliferation of sensing devices of every-day use such as cell-phones, piedometers, smart energy meters, fuel consumption sensors (standardized in modern vehicles), and GPS navigators.
The proliferation of many sensors in the possession of the common individual creates an unprecedented potential for building services that leverage massive amounts data collected from willing participants, or involving such participants as elements of distributed computing applications. Social networks, in a sensor-rich world, have become inherently multi-modal data sources, because if the richness of the data collection process in the context of the network structure.  In recent years, sensor data collection techniques and services have been integrated into many kinds of social networks. These services have caused a computational paradigm shift, known as crowd-sourcing, referring to the involvement of the general population in data collection and processing. Crowd-sourcing, arguably pioneered by programs such as SETI, has become remarkably successful recently due to increased networking, mobile connectivity and geo-tagging. Some examples of integration of social and sensor networks are as follows:

-The Google Latitude application collects mobile position data of uses, and shares this data among different users. The sharing of such data among users can lead to signi􀂿cant events of interest. For example, proximity alerts may be triggered when two linked users are within geographical proximity of one another. This may itself trigger changes in the user-behavior patterns, and therefore the underlying sensor values. This is generally true of many applications, the data on one sensor can influence data in the other sensors.

-The City Sense application  collects sensor data extracted from fixed sensors, GPS-enabled cell phones and cabs in order to determine where the people are, and then carries this information to clients who subscribe to this information. The information can also be delivered to clients with mobile devices. This kind of social networking application provides a “sense” as to where the people in the city are, and can be used in order to effectively plan activities. A similar project, referred to as WikiCity,  developed at MIT, uses the mobile data collected from cell phones in order to determine the spatial trends in a city, and which the social streets might be.

-This general approach of collecting individual location data from mobile phones can also be used in order to generate interesting business decisions. For example, the project MacroSense analyzes customers location behaviors, in order to determine individuals which behave in a similar way to a given target. The application is able to perform real time recommendations, personalization and discovery from real time location data.

Automotive Tracking Application: A number of real-time automotive tracking applications determine the important points of congestion in the city by pooling GPS data from the vehicles in the city. This can be used by other drivers in order to avoid points of congestion in the city. In many applications, such objects may have implicit links among them. For example, in a military application, the different vehicles may have links depending upon their unit membership or other related data. Another related application is that of sharing of bike track paths by different users. The problem of fnding bike routes is naturally a trialand- error process in terms of finding paths which are safe and enjoyable.
The designs Biketastic, which uses GPS-based sensing on a mobile phone application in order to create a platform which enables rich sharing of biker experiences with one another. The microphone and the accelerometer embedded on the phone are sampled to infer route noise level and roughness. The speed can also be inferred directly from the GPS sensing abilities of the mobile phone. The platform combines
this rich sensor data with mapping and visualization in order to provide an intuitive and visual interface for sharing information about the bike routes.

Animal Tracking: In its most general interpretation, an actor in a social network need not necessary be a person, but can be any living entity such as an animal. Recently, animal tracking data is collected with the use of radio-frequency identifiers. A number of social links may exist between the different animals such as group membership, or family membership. It is extremely useful to utilize the sensor information in order to predict linkage information and vice-versa. A recent project called MoveBank has made tremendous advances in collecting such data sets. A similar approach may be used for commercial product-tracking applications, though social networking applications are generally relevant to living entities, which are most typically people.


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