XRS Corporation, a mobile trucking intelligence company, has announced that Verizon is the official wireless provider for its new collaboration with Samsung Telecommunications America. The new product — NXT — creates what XRS says is the first integrated mobile device and software package designed specifically for the trucking industry.
The new solution brings together powerful XRS compliance and performance tools with Samsung Mobile’s devices, XRS said. Now, NXT is powered by the Verizon 4G LTE network, which provides the platform with superior performance and speed.
“Technology forces in the trucking industry have long been on converging paths, and our new NXT product — powered by XRS in collaboration with Verizon and Samsung Mobile – is a perfect example of how our industry is harnessing the power of technology to benefit drivers and fleet managers,” said Christian Schenk, senior vice president, XRS Corporation. “An integrated platform is the next evolution of convergence, and we are proud to be working with industry leaders like Samsung Mobile and Verizon to deliver the ultimate trucking intelligence solution.”
NXT allows drivers and fleets to purchase select Samsung Mobile devices with an XRS trucking intelligence software subscription and ready for activation on the Verizon 4G LTE network. The first device available is the Samsung Galaxy Tab 7.0, priced at a $100 discount from its suggested retail value. The data plan and subscription cost is $54 per month, including $39 for the monthly XRS fee and $15 for the monthly wireless data fee. This charge covers all required subscriptions and data, including the Relay onboard hardware component, as well as FLX messaging, hours of service, electronic DVIR, IFTA state mileage reporting and more.
“With the explosive growth of mobile electronic devices in the trucking industry, drivers have come to expect reliable and fast wireless service while they are on the road,” said Michael Toto, director of alternate channels for Verizon Enterprise Solutions. “This solution is one example of how Verizon is investing in the success of our channel partners as we collaborate to bring new innovative solutions to the market.”
NXT also offers integration with many enterprise transportation products. Over time, the platform collaboration will expand to introduce additional components, including MDM and wearable products.
The XRS trucking intelligence platform operates in both over-the-road and private carrier configurations, and is suitable for fleets of all sizes. XRS runs on certified smartphones, tablets and rugged handhelds that transmit vehicle and operator data through the cloud to a fleet management dashboard, helping companies to comply with current regulations and soon the pending Electronic Logging Device (ELD) mandate for electronic recording of a driver’s hours-of-service. Nearly 90 percent of drivers already use mobile devices while on the job.
NXT was developed through the Samsung Solutions Exchange, a newly announced strategic engagement model from Samsung Mobile that delivers a range of end-to-end solutions that address real world business challenges. The program is aimed at achieving shared value across Samsung Mobile’s rapidly growing ecosystem of enterprise customers, sales channels and alliances.
“NXT is a holistic solution for trucking that addresses performance, safety, and compliance challenges unique to the industry,” said Tim Wagner, vice president and general manager, enterprise business unit – Samsung Mobile. “Through the Samsung Solutions Exchange, we have worked closely with XRS and Verizon to enable a solution that leverages our respective strengths – which for us includes our robust suite of enterprise-grade devices.”
Customers can buy the NXT product either directly through Verizon or through XRS.
According to a new report by Research and Markets, the winners in making and operating mobile phones will offer the most compelling new functionality, IPS being a major enabler. The winners in making, integrating and operating RTLS will reduce cost and improve usefulness, not least to encompass mobile phones and other mobile computing. The world’s largest companies are locking horns on this.
The term Indoor Positioning Systems (IPS) primarily concerns location-based services on mobile phones where GPS does not work. The term Real Time Locating Systems (RTLS) primarily concerns locating people and things at a distance, securely, using second generation RFID. The subjects are converging with Apple, Samsung, Google, Nokia, Microsoft, Hewlett Packard and IBM clashing for the tens of billions of dollars of business that is emerging.
This subject heavily involves short range communications, notably Wi-Fi and Bluetooth, and inertial navigation and advanced RFID as it progresses to determining 3D position including orientation and line of travel. Emergency services, healthcare, retailing, manufacturing, logistics and many other industries will be transformed by what is becoming possible, Research and Markets said.
The topics of IPS and RTLS embrace a value chain from research and consultancy to software, services, hardware, integration and facilities management. Mobile phone app developers and value added enhancements plus ecosystems of mobile phones, web services and more are also involved.
Most of the development and use is in the USA, but other territories are racing to catch up. For example, the new Indoor Location Alliance came from Europe but has global players and companies, such as Samsung in East Asia, and is taking an exceptionally broad view from new phone design to RTLS in smart cities. Siemens in Europe and several Japanese and U.S. companies seamlessly integrate GPS outdoor navigation and services with IPS and RTLS.
This report consists entirely of evidence-based analysis following seven years of conferences, masterclasses and reports on the subject produced by the PhD level IDTechEx analysts and team.
The main features of the report, which is continuously updated, are the following:
Ten year forecast of the RTLS market 2014-2024, platform hardware vs system integration/services.
Full explanation of what IPS and RTLS are and how these technologies are evolving and converging, with detailed, original graphs and diagrams, largest orders landed and lessons arising. Threats, opportunities and company strategies are revealed.
Comparison of 105 organisations in the IPS/ RTLS value chain by country, basic measuring principle, standards, frequencies, protocol, range, accuracy, applications targeted and background information. Pie charts and graphs give analysis by parameter.
Comparison of 74 case studies of RTLS with many pie charts presenting the lessons arising.
Detailed original interviews carried out from mid 2013 with important organisations in this space.
Glossary of the challenging jargon, which is different between IPS and RTLS yet often refers to the same or similar things.
u‑blox has announced the launch of its newest core positioning platform, the u-blox M8. The new chip forms the basis of u-blox’ upcoming line of positioning modules, which are able to acquire and track different satellite systems concurrently to achieve higher accuracy and reliability.
Supporting all deployed as well as upcoming GNSSs, the platform is based on the UBX-M8030 concurrent multi-GNSS receiver IC which is able to track American GPS, European Galileo, Japanese QZSS, Russian GLONASS, and Chinese BeiDou satellites.
Concurrent tracking of GPS (QZSS) and GLONASS or BeiDou, or concurrent tracking of GLONASS and BeiDou satellites increases performance for applications requiring maximum availability and accuracy. The chip is prepared for the European Galileo system through a future firmware upgrade once the constellation is fully available.
The new platform will ultimately support special functions such as Automotive Dead Reckoning and precision timing to support a wide variety of vehicle, industrial and consumer applications.
To further improve acquisition performance, u-blox’ globally available “AssistNow”assisted-GNSS service for accelerated positioning has been extended for u-blox M8 products; the service supports both GPS and GLONASS, and the validity of downloaded assistance data is now able to support offline operation for up to 35 days.
“With the proliferation of multiple new GNSS systems beyond GPS, our u-blox M8 platform is designed to take full advantage of the increasing number of visible satellites to further increase accuracy and availability, particularly in urban and vehicle-based applications,” said Daniel Ammann, executive vice president, head of the Positioning Product Centre, and co-founder of u-blox, “At the same time we realize the ongoing requirement for extremely low-power and cost-sensitive portable applications where operation with a single GNSS system is more than sufficient. That is why we will continue to offer both u-blox M8 and u-blox 7 based products to the market.
The new u-blox M8 chip is at the heart of u-blox’ next generation of positioning modules based on the company’s popular MAX, NEO and LEA module form factors.
u-blox M8 chips feature low power consumption in concurrent reception mode, thanks to an innovative single-die architecture combined with sophisticated software algorithms. The extended supply voltage supply range and 1.8 V/3.0 V I/O compliance supports a wide variety of system architectures. Sophisticated radio architecture and interference suppression using active jamming detection ensure maximum performance even in GNSS hostile environments. UBX-M8030 chips are available in miniature WL-CSP (2.99 x 3.21 x 0.36 mm) and QFN (5.00 x 5.00 x 0.59 mm) packages. The chip is also available in automotive quality grade according to AEC-Q100.
The new platform maintains backwards compatibility with u-blox 7 modules and QFP chip products which remain in the company’s portfolio as the industry’s lowest power standalone satellite positioning receivers. u‑blox’ capability of delivering GNSS technology in both integrated circuit and form-factor consistent modules provides maximum design flexibility and protects customers’ development investments over successive product generations.
First samples of the multi-GNSS receiver chip UBX-M8030 are available for customer evaluation. Soon, module customers can easily migrate to the MAX, NEO, and LEA form factors, u-blox’ popular, industry-standard module form factors.
Sales of smart glasses, smart watches and wearable fitness trackers reached 8.3 million units worldwide in 2012, up from 3.1 million devices in the previous year, according to researchers at Berg Insight. Growing at a compound annual growth rate of 50.6 percent, total shipments of wearable technology devices are expected to reach 64.0 million units in 2017.
According to the announcement, today wearable fitness and activity trackers constitute the vast majority of the shipments. By the end of the forecast period, smart watches are predicted to incorporate much of the functionality of these and will then be the largest wearable device segment. “A perfect storm of innovation within low power wireless connectivity, sensor technology, big data, cloud services, voice user interfaces and mobile computing power is coming together and paves the way for connected wearable technology,” said Johan Svanberg, senior analyst, Berg Insight.
The first generation of products appeal to specific markets and certain use cases, but refinement in design, technology and connectivity will broaden application areas and speed up market adoption. Initially, the wrist is the most attractive location for wearable devices, which is shown by the success of the Pebble smart watch and the popularity of wristband activity trackers such as the Nike Fuelband and the Fitbit Flex.
“However, today’s devices need to evolve into something more than single purpose fitness trackers or external smartphone notification centers in order to be truly successful,” continues Svanberg.
Berg Insight predicts that wearable technology will shift from being smartphone accessories into becoming proper stand-alone computing devices. Furthermore, closeness to the body and always aware capabilities will enable them to be more than merely miniaturized smartphones. Google, Sony and Samsung have already launched products and other major players such as Apple and LG are expected to soon enter the market. Wide market availability of wearable devices also raises privacy concerns. “It is still uncertain where lines should be drawn, but as in the case with most new technology, individual users and solution providers have the responsibility not to misuse the capabilities enabled by wearable tech,” concludes Svanberg.
If we wish to obtain consistently usable positions indoors using a mobile phone, we can augment its GPS or GNSS receiver with other unfettered sensing technologies such as gyroscopes and accelerometers supplemented by radio signals of opportunity. But is all of this actually feasible? The authors have conducted tests of a multi-system approach to positioning indoors with favorable results.
INNOVATION INSIGHTS by Richard Langley
IS GPS REALY A GLOBAL POSITIONING SYSTEM? Well, that depends on your definition of “global.” If it means that GPS operates well all over the world in environments where it was designed to work, then, yes, it is a global system. But, if you define global as meaning that GPS operates well everywhere not only outdoors with a clear view of the sky but also indoors and in other restricted environments, then (as some have argued), GPS is not truly global.
So why doesn’t GPS work (for the most part) indoors? Our mobile phones do and they use similar bits of the electromagnetic spectrum. The basic problem is that the signals from GPS (and other GNSS) satellites are just too weak to easily penetrate buildings. They are more than strong enough to yield excellent positioning, navigation, and timing (or PNT) results if the antenna connected to the receiver can “see” the satellites unobstructed. But even outdoors, trees, buildings, and mountains can block the signals from one or more satellites at a time. And indoors, the signals are usually attenuated by walls, floors, and ceilings so much that a conventional receiver cannot lock onto them.
Receiver manufacturers have developed more sensitive receivers that can operate, at least to some degree, indoors but with a good antenna. And receiver chips or modules with this more sensitive technology are often found in modern mobile phones. But they don’t typically provide reliable indoor positioning because they are being used with inexpensive, suboptimal antennas. Some potential improvement in indoor positioning capability is possible by supplying the receiver with satellite orbit and clock information through the mobile network rather than having the receiver acquire this information directly from the satellite signals. This assisted-GNSS technique allows a receiver to work with weaker signals. But it is not a panacea. Gaps or holes still exist for positioning indoors or in other obstructed environments, prompting one industry wag to liken GNSS coverage to Swiss cheese.
So, what are we to do if we wish to obtain consistently usable positions indoors using a mobile phone? As we will see in this month’s column, we can augment or bypass its GPS or GNSS receiver with other unfettered sensing technologies such as gyroscopes and accelerometers. These devices can be made very small using microelectromechanical technology and are already included in some mobile phones.
However, there are some issues with these devices for positioning, not the least of which is rapid position drift. We can restrain the drift by using magnetometers, for example – also present in some mobile phones. We can also use radio signals of opportunity to help in the positioning – signals available in the phone such as multi-generation mobile signals, Bluetooth, and Wi-Fi through their signal strength “fingerprints.” But is all of this actually feasible?
The authors of the article in this month’s column have conducted tests of such a multi-system approach to positioning indoors with quite favorable results. Are we at the stage of accurate positioning (and tracking) everywhere? Not quite, but we are getting closer.
“Innovation” is a regular feature that discusses advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. He welcomes comments and topic ideas.
In recent years, there has been increasing interest in ubiquitous positioning — accurate location fixes in any environment, outdoors and indoors. We have all become used to the availability and performance of global navigation satellite systems (GNSS) for accurate outdoor radio positioning with a reasonable degree of reliability and availability. However, indoor radio positioning is more challenging because GNSS signals do not penetrate buildings well, and we must instead rely on local infrastructure and other available inputs to aid the user.
Indoor radio positioning is, however, available to the general public today through the use of signal strength fingerprint databases managed and provided by third-party providers such as Skyhook. These typically use Wi-Fi and cellular signals because of their ubiquity and the prevalence of appropriate receiver circuits in consumer devices. The user can also access the fingerprint database through these media. These systems, therefore, have two clear constraints: the database must have been previously built via some form of survey process, and the user must have a data connection available to obtain it. A more scalable system would not rely on such constraints, and would instead develop its own database during operation.
The benefits of such a system are significant: it can provide location-based services, situational awareness, and asset tracking in new and unknown environments for consumers, emergency services, the military, lone workers, security personnel, and autonomous vehicles. There is no requirement for a data link to function, nor any prior surveying of the radio environment, nor any other prior knowledge such as a floor plan or map. However, the system can also be used to quickly and easily generate maps of the radio environment or floor plans, which can be beneficial for organizations wishing to provide positioning services to the public using a simpler positioning method; that is, this method can be used to rapidly survey an area and generate a signal fingerprint database for other users to exploit. Best of all, all of this can be achieved today in real time using an app for a consumer smartphone.
The Digital Swiss Army Knife
The last couple of decades have seen steady improvements in a variety of sectors that have led to new and flexible navigation capabilities — and all of these improvements can now be found in the little chunks of silicon, plastic, and glass in our pockets and handbags. Moore’s Law and the miniaturization of electronics have enabled us all to carry handheld programmable supercomputers around with us every day. Microelectromechanical systems technologies and the demand for better gaming and augmented reality experiences on our smartphones mean that any new phone contains the same types of sensors for enhancing user experiences that cruise missiles and smartbombs use to ensure they hit their targets precisely.
Finally, your smartphone contains more radios than you probably realize. GPS (or GNSS); 2G, 3G, and 4G network radios; near field communications, like RFID; Bluetooth; Wi-Fi; and even a VHF FM chip might be tucked away in there somewhere. The near future is likely to bring a “whitespace” radio (using re-assigned vacated spectrum) along with a 60-GHz wireless USB transceiver. We are bathed in a phenomenal number of radio signals as we go about our daily lives, completely oblivious to the rich tapestry we are walking through — an invisible, permanent, detailed map just waiting to be sensed by our smartphones and annotated for our navigation purposes.
So, just what is possible with a commodity smartphone and its arsenal of features?
Pedestrian Motion Modeling
We can begin with the accelerometers, magnetometers, gyroscopes and barometers found in recent smartphones. These sensors collectively form an inertial measurement unit (IMU) that can be used to track the motion of a user through any environment, regardless of the availability of GNSS (at least in theory).
Unfortunately, there are many stumbling blocks in the way for any new navigator starting down this road. The standard approach for inertial navigation involves using the gyroscopes to maintain an estimate of the orientation of the device relative to the Earth, and to integrate the accelerometer measurements to calculate the system velocity and subsequently the change in position with each measurement update. A key aspect of this process is the removal of the effect of gravity, which requires us to estimate the value of the local gravity field strength (which varies with location across the globe) and its direction (which we do based on the estimated orientation of the device according to the gyroscopes). There are inevitably some errors associated with the estimates of both of these quantities.
In addition, the sensors themselves suffer noise, biases, instabilities, non-linearities, and other effects that only decrease the system performance further. These errors accumulate over time because the position and orientation estimates at any moment depend on the cumulative sum of all measurements since the start of the journey. The result is rapid and unbounded growth in position and orientation error. The cost of the sensors is, of course, tightly correlated with their quality, and so the rate at which the navigation performance degrades. The quality of the sensors in smartphones is so low that this approach is rendered useless within the first few seconds of use. To make progress we must apply regular position corrections to the system by applying external constraints or incorporating external sensor measurements.
Alternative. GNSS measurements provide constraints and corrections for inertial navigation systems, but here we are considering operating indoors where these are unavailable or severely degraded. An alternative solution for most smartphone users is to use the inertial sensors in a different manner, within a so-called pedestrian dead- reckoning (PDR) approach. Here, it is assumed that the device being tracked is held by (or attached to) someone walking in a manner that can be modeled. The inertial sensors are not now used to reproduce the full 3D motion of the device at the update rate of the sensors, but instead used simply to detect stepping motions and to infer that the user has moved some number of steps. Looking for patterns in the accelerometer data where minimum and maximum thresholds are exceeded within a certain time window is a surprisingly robust step counter when the user walks “normally” (more complicated actions such as side steps and stumbles require more complex algorithms). The smartphone can estimate its orientation by fusing together its gyroscope (which offers good short-term orientation-tracking) and its magnetic compass (good long-term orientation-tracking with periodic fluctuations from local magnetic anomalies). The step length of the user (a surprisingly consistent quantity) and any bias in the gyro-smoothed compass heading can both be measured and modeled during periods of GNSS availability such that the best possible estimates are available when GNSS is lost.
FIGURE 1 shows the functional flow diagrams for a strapdown inertial navigation system (top) and a PDR system (bottom). Note that the PDR scheme accumulates error more slowly than the INS scheme (involves fewer integrations over lower-rate data) but is heavily dependent on the performance of the gait recognition, floor-change detection, and step-length-estimation algorithms.
FIGURE 1. Functional flow diagrams for a strapdown inertial navigation system (top) and a pedestrian dead-reckoning system (bottom).
However, PDR techniques still accumulate error, resulting in gradual position drift, but with much higher performance than would be achieved by integrating the raw data in the traditional INS manner. Typical PDR schemes can track the user with an accuracy of a few percent of the distance walked, although this performance degrades with any un-modeled motions that confuse the step detector, such as infrequent backward or sidesteps. So how do we deal with this issue?
Machine Learning
The accuracy of PDR schemes is dependent on the validity of the pedestrian motion model. Any un-modeled action has the potential to generate false positive events in the step detector and hence contribute to position error. Users may stoop, crawl, jump, hop, or shake their device while static — motions that are all very difficult to unambiguously discriminate in raw sensor data.
There are many approaches to solving this problem of gait recognition, and most exploit machine learning techniques. The basic principle of supervised machine learning is that a large set of labeled training data (that is, lots of manually categorized data of each type) is analyzed by a computer in order to extract patterns, statistics, or certain measurement sequences from the inertial sensor measurements that reveal the type of step that was taken. In unsupervised learning, the clusters and categories within the data must be found by the algorithms themselves.
The outputs from such algorithms are typically thresholds, signatures, and other learned metrics that can be installed in a smartphone and used to dynamically classify movements. It is also possible to deploy the learning algorithms on the device itself so that it can learn what the particular user’s signatures are to permit better step and gait detection (like training a speech-recognition program to understand your accent). A simple example of this is running an error-state Kalman filter while GNSS signals are available to determine the user step length and to detect any background compass bias that is corrupting the system.
A problem yet to be resolved for PDR schemes is a basic physical one: the laws of physics are the same for an object at rest as for one moving at constant speed. This means that it is theoretically possible for a suitably skilled person to simulate the “already moving at constant velocity” version of any of these motions while static by moving the device in just the right manner, effectively spoofing as many steps or motions as they like. The opening and closing phases of a journey (that is, the very first and last steps) are critical in distinguishing real and spoofed motion if only inertial sensing is used to disambiguate real and spoofed motion through an environment. We will, however, return to this problem in a moment.
Simultaneous Localization and Mapping
The application of machine learning can be extended to the entire indoor navigation problem using a technique called Simultaneous Localization and Mapping (SLAM). A key aspect here is the hypothesis that there are some measurements that can be taken within an indoor environment that vary rapidly on the spatial scale but only slowly on a temporal scale. These opportunistic measurements are typically of radio signal strength (Wi-Fi, cellular, television, VHF FM, and so on) and magnetic field strength, although in principle many other metrics could be used such as light level and temperature. They are deemed to be opportunistic because they already exist in the environment and have not been generated specifically for this positioning system. Moving along a corridor is expected to result in a particular sequence of measurements that is repeatable on the next visit to that corridor with a confidence based on the time since the last visit. Tight agreement is expected within the next few minutes, close agreement within the next few days, and so on. It is not expected that these fingerprints will necessarily be valid for months or years, as objects may move around the environment; for example, large items may be relocated and Wi-Fi access points may be moved. The ability to exploit the expectation of high repeatability over short time periods of a few hours is the key to developing a system that can learn about its environment and improve its performance during use.
As the device moves through the indoor environment (with position estimate driven by the PDR estimation), the opportunistic fingerprints are captured and stored. If the device returns to a region it has been in before, then it will record a sequence of measurements that will agree closely with the previous sequence that was recorded in the past. This provides a constraint to the system: whatever path was taken in between, it has converged with a section of its historical path and “closed a loop.” Any offset in these two path sections at this point reveals the inertial error that has accumulated during this loop. The system can therefore correct its own inertial error growth, allowing extended operations in GNSS-denied areas.
Fingerprint Maps. The gathered opportunistic measurements can also be used to generate fingerprint maps of the areas that can be shared with other users to allow them to accurately position themselves within those areas in the future, reducing everyone’s reliance on PDR schemes and removing the need for environments to be manually surveyed for their environmental maps. The maps are automatically calibrated and corrected by the SLAM process. As more users operate in the environment and more data accumulate it is easier to identify and remove erroneous data that does not fit into the consensus being formed by the “intelligence of crowds.” This opportunistic navigation scheme can also feed back into the PDR scheme to aid with motion detection — as fingerprints are expected to vary on a fine spatial scale as users move through an environment. They can be used to detect when a PDR device is in reality static, but being moved in a manner that is erroneously triggering the step-detection routine.
FIGURE 2 shows a plot of the magnetic-field-strength variations recorded during four walks down the same corridor of a building at four different times of day on four different days. The traces have been manually aligned by the clear drop in field strength at step number 40. A fixed step length was assumed, and the relative stretching evident across the traces is due to small differences in walking speeds across the tests. Step-length changes can be estimated using changes in the stepping frequency, and the typical step length can be observed and calibrated during periods of GNSS availability.
FIGURE 2. Repeatability tests of the magnetic field strength from four walks along an indoor corridor at four different times during the day on four different days.
There are two distinct classes of SLAM algorithm for PDR. The most common class involves an iterative batch process applied after the data have been collected (that is, offline). This process (which might be least-squares fitting or maximum likelihood estimation, for example) identify loop closure points and provide an optimal joint estimation of the path taken by the user that satisfies these constraints and the raw odometry data as much as possible. The
Wi-Fi SLAM approaches, Gaussian Processes Latent Variables and GraphSLAM, both use such schemes. The results are typically robust, but the offline processing stage can be lengthy.
SLAM can, however, be performed in real time, even on a smartphone, by exploiting an efficient multi-hypothesis scheme. As the user moves, we retain multiple hypotheses for their position and, crucially, record the history of each hypothesis. This is typically done using a particle filter, where each particle represents a unique hypothesis. In this context, we must store the tree of ancestors for each particle at each epoch. When we detect a loop closure, we prune the history to remove all hypotheses that did not result in a loop closure at that point and therefore dynamically correct our errors. Note that each particle can even be assigned different parameter values, such as step length or heading bias, and if a gait detection scheme cannot confidently identify the type of step taken, new particles representing every possible user motion at that epoch can be generated.
Occupancy Grid. Rather than running a specific loop closure algorithm, an occupancy grid is used, whereby the environment is defined by a grid of small cells, for example, one meter by one meter squares. As each particle propagates, representing a hypothesis of the user path, it posts its identity and the current step number into the occupancy grid. As the user continues to move, the particles check the grid cells they move through for any previous visits. If a particle has visited a cell before, the current sensor measurements are compared to those recorded at the time of the last visit. If there is close agreement (typically scored using metrics such as the Euclidean or Mahalanobis distances) then that particular particle is given a high weight. Conversely, poor agreement results in a low weighting.
The entire particle cloud can be reweighted accordingly with low-scoring particles being killed and high-scoring particles being duplicated. The result is the particle cloud collapsing towards the region of close agreement between old and new sensor measurements. Because the occupancy grid contains the historical path of each particle stored via their IDs and step-number sequence, when a reweighting of particles occurs, the historical path of the user is updated and improved accordingly along with the current estimate of the user’s location.
The SLAM estimate can be improved by many types of observations, not just loop closures. If the user moves outside and confident GNSS locations become available, these can also be used to reweight the particle cloud. If the user moves into a region where the floor plan of the building is available to the positioning engine, particles can be pruned whenever they try to cross walls. If desired, even direct user interaction such as manually tapping the map on the smartphone display could be used to provide a position estimate and so constrain the particle cloud.
FIGURE 3 shows six stages from a walk around the corridors of a building using an indoor positioning smartphone app to track the user. The red dashed line shows the trace using just the PDR scheme, which exhibits gradual degradation in positioning accuracy. The green solid line shows the trace using SLAM to constrain the PDR error growth using magnetic anomalies and Wi-Fi signal strengths.
A further modern advance is in computer vision: the use of cameras and algorithms to monitor and interpret features in the environment. The movement of features within the field of view from frame to frame can be used to determine the motion of the camera if it is assumed that the majority of the objects tracked through the view are actually static in the environment. Consistency checks between features allow those corresponding to other moving objects to be filtered out.
The result of this visual odometry scheme is the ability to determine the speed and heading changes of the camera by observing the optical flow of the environment. As with PDR approaches, integrating over visual odometry measurements results in motion tracking with much slower reduction in accuracy over time and distance than for systems built upon traditional IMU integration (accelerometers and gyroscopes) alone. If specific objects or features can be uniquely identified and recognized when seen again in the future, then SLAM techniques can also be applied. At the moment, smartphones are powerful enough to apply computer vision techniques and calculations at moderate update rates of a few frames per second. As smartphones become more powerful, or if mobile operating systems will, in future, permit these computer vision algorithms to be deployed on the dedicated graphical processing units, or even perhaps if devices such as Google Glass result in the deployment of dedicated computer vision chips within devices, we will see computer vision coupled with augmented reality move to the forefront of smartphone navigation.
The Future
Our desire for accurate positioning and tracking anywhere will never go away. The availability of cheap, accurate GPS over the last decade has resulted in accurate positioning, navigation, and timing not only being something we take for granted, but something society has come to depend upon. The positioning capabilities of our smartphones will continue to improve, not only because of the new developments and capabilities described above, but because of new infrastructure developments.
The In-Location Alliance is a large consortium of companies including big names like Nokia and CSR who are defining standards for Bluetooth and other beacon-based positioning technologies for dedicated deployments in indoor environments such as shopping centers, airports, and museums. The new 4G LTE signal structure also contains a dedicated ranging signal to permit traditional timing-based positioning schemes to be easily deployed using these new cellular standards. All infrastructure-based schemes incur costs associated with deployment and maintenance that ultimately limit their scope of deployment; opportunistic schemes are the key to truly ubiquitous positioning.
While billions of dollars are being spent worldwide on deploying and maintaining new GNSS, there will always be scenarios and environments where these weak signals are blocked or severely corrupted. In these cases, opportunistic sensing powered by smart algorithms running on consumer devices costing a few hundred dollars will be there to fill those gaps.
Ramsey Faragher is a senior research associate at the University of Cambridge and an associate editor for the journal of the Royal Institute of Navigation. Previously he was a principal scientist at the BAE Systems Advanced Technology Centre, near Chelmsford in the United Kingdom, where he developed the NAVSOP GNSS-denied positioning system. His research interests include opportunistic positioning, sensor fusion, and machine learning.
Robert Harle is a senior lecturer at the University of Cambridge with research interests in positioning, sensor fusion, and wireless sensor networks. He has worked on indoor positioning since 2000, developing a series of infrastructure-based and infrastructure-free solutions.
FURTHER READING
• Simultaneous Localization and Mapping
“SmartSLAM – An Efficient Smartphone Indoor Positioning System Exploiting Machine Learning and Opportunistic Sensing” by R.M. Faragher and R.K. Harle in Proceedings of ION GNSS+ 2013, the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, September 16–20, 2013 (in press).
“Opportunistic Radio SLAM for Indoor Navigation Using Smartphone Sensors,” by R. Faragher, C. Sarno, and M. Newman in Proceedings of PLANS 2012, Institute of Electrical and Electronics Engineers / Institute of Navigation Position, Location and Navigation Symposium, Myrtle Beach, South Carolina, April 23–26, 2012, pp. 120-128.
“Efficient, Generalized Indoor WiFi GraphSLAM” by J. Huang, D. Millman, M. Quigley, D. Stavens, S. Thrun, and A. Aggarwal in Proceedings of 2011 IEEE International Conference on Robotics and Automation, Shanghai, May 9–13, 2011, pp. 1038–1043, doi: 10.1109/ICRA.2011.5979643.
“WiFi-SLAM Using Gaussian Process Latent Variable Models” by B. Ferris, D. Fox, and N. Lawrence in Proceedings of IJCAI-07, the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6–12, 2007, R. Sangal, H. Mehta, and R. K. Bagga (Eds.), published by Morgan Kaufmann Publishers Inc., San Francisco, California, pp. 2480–2485.
“Simultaneous Map Building and Localization for an Autonomous Mobile Robot” by J.J. Leonard and H.F. Durrant-Whyte in Proceedings of IROS’91, Institute of Electrical and Electronics Engineers / Robotics Society of Japan International Workshop on Intelligence for Mechanical Systems, Osaka, Japan, November 3–5, 1991, pp. 1442–1447, doi: 10.1109/IROS.1991.174711.
• Integrated Indoor Navigation
“A Survey of Indoor Inertial Positioning Systems for Pedestrians” by R. Harle in IEEE Communications Surveys & Tutorials, Vol. 15, No. 3, 2013, pp. 1281–1293, doi: 10.1109/SURV.2012.121912.00075.
Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Second Edition, by P.D. Groves, published by Artech House, Boston, Massachusetts, 2013.
• Wi-Fi Positioning
“Wi-Fi Azimuth and Position Tracking Using Directional Received Signal Strength Measurements” by J. Seitz, T. Vaupel, S. Haimerl, J.G. Boronat, and J. Thielecke in Proceedings of 2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications, Bonn, September 4–6, 2012, pp. 72–77, doi: 10.1109/SDF.2012.6327911.
“Comparison of WiFi Positioning on Two Mobile Devices” by P.A. Zandbergen in Journal of Location Based Services, Vol. 6, No. 1, 2012, pp. 35–50, doi: 10.1080/17489725.2011.630038.
• Step Length and Pedestrian Navigation
“Step Length Estimation Using Handheld Inertial Sensors” by V. Renaudin, M. Susi, and G. Lachapelle in Sensors, Vol. 12, No. 7, 2012, pp. 8507–8525, doi: 10.3390/s120708507.
• Computer Vision and Navigation
“Improving the Accuracy of EKF-Based Visual-Inertial Odometry” by L. Mingyang and A.I. Mourikis in Proceedings of 2012 IEEE International Conference on Robotics and Automation, Saint Paul, Minnesota, May 14–18, 2012, pp. 828–835, doi: 10.1109/ICRA.2012.6225229.
• Machine Learning
Information Theory, Inference and Learning Algorithms by D.J.C. MacKay, published by Cambridge University Press, Cambridge, U.K., 2003.
The Bellagio Hotel & Casino in Las Vegas, Nevada. Photo credit: Photographersnature.
Qualcomm and Cisco Collaborate to Improve Indoor Navigation
Las Vegas — home of gambling, shows, and massive hotel/entertainment/resort complexes. It’s not always easy to find what you’re looking for amid miles and miles of indoor floorspace.
Previous Bellagio visitors had to rely on a static map to find their way around the massive Bellagio resort.
In May, Qualcomm Atheros and Cisco showcased its collaboration to enhance indoor location services at a customer deployment at the Bellagio Resort and Casino in Las Vegas. The event took place in cooperation with MGM Resorts International during the Interop information technology conference. Participants had the opportunity to try out Qualcomm and Cisco’s approach to indoor location services, which uses the Qualcomm IZat indoor location platform with Cisco’s Connected Mobile Experience. According to the companies, the combination improves location accuracy and allows users to discover services with context awareness in sprawling retail, travel, and hospitality venues, such as Las Vegas resorts.
The companies began their collaboration in November 2012. The Bellagio mobile app, available for iOS and Android, is now offered as a free download for guests using their smartphones, tablets, and other mobile devices.
At the Interop event, participants were given Samsung devices with Qualcomm IZat software, which tracked their position within the Bellagio on a map as they moved through the hotel — a definite advantage over less-advanced apps which only provide a static map.
Based on the person’s location, the mobile app provides recommendations of nearby services such as restaurants, shows, spa services, and bars and lounges on the property. Guests can become a loyalty member and be alerted to discounts at local restaurants, shops, and wine bars. “This creates a truly unique mobile experience for guests and visitors, putting all the amenities of indoor-location-enabled spaces at their fingertips,” according to Cisco.
Event participants pick up Samsung phones equipped with the Bellagio app.
Qualcomm Atheros, which is Qualcomm Technologies’ networking and connectivity subsidiary, recently enhanced its IZat location platform to enable more precise positioning (within 3–5 meters) inside buildings to make indoor positioning more useful to consumers.
The Cisco Connected Mobile Experience offers a Wi-Fi Passpoint (HotSpot 2.0) solution to integrate indoor location and real-time analytic technologies to deliver personalized mobile services and content. The solution is built upon the Cisco Mobility Services Engine, which uses the Bellagio’s existing wireless access-point infrastructure to determine indoor location for mobile devices. Cisco worked with MGM Resorts’ service provider Mobilitie and its partner Meridian to link the mobile app, context-aware services, and wireless connectivity experience together.
The solution is designed to help app developers deploy mobile applications and services that engage the customer more effectively, the companies said.
In recent years, the sporting world has seen an explosion in the use of GPS. You will rarely spot a runner or cyclist on the road without either a smartphone strapped to their arm or a dedicated GPS device clamped to their handlebars, tracking their every move.
The amount of information that the modern sportsperson — from casual amateur to full-time professional — logs, analyzes, and shares is phenomenal. There are now dozens of ways of uploading data for the whole world to share and study.
As more manufacturers come to this market with the hope of capturing a share of it, they face the challenge of effectively developing and then testing their devices. Among many factors to consider, new products must have capability for local constellations such as BeiDou, GLONASS, and QZSS, not just GPS alone. New market entrants won’t have the same budget as the established big players, and constantly traveling to China or Japan to try out a new gadget will escalate costs to an unsustainable degree.
Then there’s the issue of getting out into the kind of environment in which you imagine your new sporting GPS device will be put to use. In many cases this will be remote: forests, hills, and mountains. Stepping outside to the office car park does not constitute a sufficient test for satellite acquisition and retention. Neither does simply driving the commute route home with it.
A GPS simulator or replay device allows for bench testing, but such devices are expensive. They might not actually fulfill your testing requirements, either: a traditional GPS simulator outputs its scenarios based on constellation modeling, either as a perfect signal or one that has simulated multipath. But you need to genuinely know how your new product will operate through, say, a forest on a downhill mountain bike run, or during a city marathon through urban canyons, or on a trail under wet trees. Adventure sport participants want to record their achievements wherever they go.
How do you obtain this kind of realistic scenario? It will require the use of a GNSS recorder, and in an ideal world you would lend it to someone who actually does some of this stuff. Perhaps one of your colleagues is an (insane) downhill skier — who better to capture exactly that type of data, which you can replay back in a nice warm lab?
The trouble is that a person of this sporting ilk will be unwilling or unable to carry bulky equipment that weighs several kilos. It will slow them down, so a GNSS recorder that can be easily carried without affecting the sporting activity is essential. It has to be easy to use: self-contained, with a battery that will last a couple of hours, and with one big button to start and stop recording. The user shouldn’t need any training in its operation. And ideally, it won’t need a large ground-plane antenna to capture usable data; a well-designed unit will employ a sensitive GPS engine allowing for as complete a signal as possible to be logged through a standard passive antenna.
Looking further afield, other industries will soon be seeking a device with this level of convenience. For instance, agricultural and automotive manufacturers want the ability to send test engineers out to record drive-cycle tests easily and in a variety of vehicles. Additional features, such as controlled area network (CAN) and inertial sensor logging, synchronized with the GNSS data, will also find favor.
The nature of the simulation market is changing: increasing numbers of developers need not just a traditional constellation simulator, but rather a replay device that is feature-rich and that doesn’t cost the earth.
Economies of scale will likely dictate the way that this develops, and GNSS simulation will no longer be the specialist and exclusive field it once was.
Mark Sampson is the LabSat product manager for RaceLogic, based in Buckingham, UK.
Avenza’ PDF Maps app is now available at the Google Play Store.
Avenza Systems Inc., producers of MAPublisher cartographic software for Adobe Illustrator and Geographic Imager geospatial tools for Adobe Photoshop, announce that PDF Maps app is now available on the Google Play Store. The first and only geospatial PDF and GeoTIFF reader for Android devices, Avenza said, the PDF Maps app is unique to the space due to its extensive collection of more than 100,000 detailed maps sourced from well-established publishers, cartographers, government agencies and aficionados of outdoor recreational activities, all of which are downloadable directly from within the app.
PDF Maps take advantage of geospatial technology that allows consumers to view maps and measure real world distances and areas. Paired together with mobile devices that use GPS such as Androids, the PDF Maps app provides constant access to geographic locations and even points of interest without the risk of losing reception due to cell tower proximity.
Designed with its audience of travelers and outdoor enthusiasts in mind, Avenza’s PDF Maps app has already garnered accolades from the International Map Industry Association (IMIA) and Geospatial World for its innovative use of technology on the iOS platform in 2011 and 2012. Since then, its versatility for recreational or business purposes out in the field has been recognized across several industries and it’s gaining momentum.
“The market is currently saturated with map apps that are limited in map data, or too simplified to be functional for offline navigating. We wanted to address those issues by providing a free navigational app that catered to a segment of users who needed something more substantial than the average turn-by-turn digital maps offered today, while providing map-publishers with an iTunes-like environment for distributing their maps direct to devices” said Ted Florence, President of Avenza Systems Inc.
“With Avenza’s PDF Maps app Android users can do more than just view their location. PDF Maps provides a meaningful interface to measure distances, drop placemarks and share personal recorded data in various formats. It’s more than just a viewing tool, but will provide the Android market the best of both worlds — access to maps from well-known paper map publishers that work in tandem with the functionality of GPS devices. We’re thrilled to finally make it available to a new market.”
Unlike other map apps that provide one view of a location using GPS coordinates as most maps do, Avenza’s PDF Maps app expands a traveler’s choices, allowing them to access detailed geography or points of interest created by specific map publishers for use on land, sea or air. PDF Maps app for Android allows consumers to access information while at a destination, providing users an opportunity to make the most of their time experiencing their environment rather than searching for cell reception to access directions.
Currently, Avenza’s vast PDF Maps app library covers maps for domestic and international travel organized by state and area. Android users will appreciate the breadth of tool management features available. All maps — free and purchased — are accessible through the in-app map store and offer the following capabilities:
Add maps from the file system, Dropbox, a URL, email, or Map Store
Browse, purchase, and download maps from the Avenza Map Store (existing iOS PDF Maps accounts are compatible)”
Show GPS position on maps
Add Placemarks
Import and export KML
Find Coordinates
Measure Distance or Area
Open current view in Google Maps
Avenza’s PDF Maps in-app Map Store features a variety of publishers that focus on recreational activities as well as all segments of the map-use market. Below is a small sampling of maps available:
Camping and hiking including National Park Service maps and other regions of the world
Nautical and marine navigation including NOAA and FAA charts for North America and other regions of the world
Topographic use including USGS and Canadian Topographic maps and other regions of the world
Maps for tourists, transit, travel, special events, historic and much more
PDF Maps is available now in the Google Play Store free of charge. For more information about PDF Maps, visit the Avenza website at www.avenza.com/pdf-maps. Pricing of each map is set by the publisher and free maps remain free to users through the PDF Maps app in-app store.
eTrak has announced the release of its latest product, the PetTrak GPS+ tracking system. Now available for purchase online, PetTrak is a small, lightweight device used to track the location of most pets.
PetTrak uses GPS+, eTrak’s patent-pending technology utilizing Wi-Fi, Cell ID, and GPS to deliver accurate location, indoors or out. Users will be able to utilize PetTrak to always know the location of their animal companions. PetTrak also allows users to set up specific safety circle zones, around designated areas where pets spend most of their time. If the pet leaves or enters this zone, the pet’s owner will be notified via text or email.
“One of the best features of PetTrak is the safety circle, which enables pet owners to draw a boundary circle around the house, yard, or any place that provides a protective perimeter for a pet,” said John Harris, eTrak founder and CEO. “If a pet wanders off beyond the circle, PetTrak will send an email and text message to the owner’s cellphone and computer. Not only that, but it will even include a Google map with the pet’s location.”
The PetTrak device can be attached to Simply attached to a pet’s collar. The animal can then be tracked in virtually any environment around the clock, from any smartphone, computer or tablet. With more than six million pets lost every year, PetTrak is designed to help owners rest assured knowing their furry friends are safe.
A watershed moment may be approaching for the connected vehicle market. The National Highway Traffic and Safety Administration (NHTSA) is about to start on the path towards mandating connected vehicle technology. Interest in the market is not limited to a few countries; last week I moderated a GPS World webinar on the connected vehicle that drew registrants from 40 countries. Other news in the industry includes Sprint removing Sprint Navigation and TeleNav GPS Navigator from bundled data and data add-on plans. A new report shows it has become more expensive to acquire app users. And despite no longer being preinstalled on iOS devices, Google Maps is doing pretty well with Apple iOS users.
During our GPS World connected vehicle webinar, held September 19, I noticed differences in how the audience characterized the connected vehicle. The connected vehicle enables information to be exchanged with other vehicles, devices and/or road infrastructure to provide safety, mobility and consumer functionality. The devices that are used with the connected vehicle can be nomadic (phone, tablet, personal navigation devices), vehicle embedded and aftermarket devices. Communication options are currently cellular, Wi-Fi or DSRC/WAVE.
Regulation Pushing Connected Vehicle Forward. In a recent statement, the National Highway Traffic and Safety Administration (NHTSA) asserts that connected vehicle technology “can transform the nation’s surface transportation safety, mobility and environmental performance.” NHTSA is expected to start rulemaking on the connected vehicle later this year, which could result in a connected car industry mandate in the U.S. While it could take five or more years for final rules and several more years for rules to take effect, it would be a transformative event. “In six years, I expect to see vehicles widely using the technology,” said Scott McCormick of the Connected Vehicle Trade Association. “Vehicle manufacturers are eager for connectivity in vehicles, but need to understand the regulations that will be in play. This hasn’t been idle time, as vehicle makers are ahead of the game and have already embedded some connected vehicle technology into vehicles that can later be activated.”
The commercial fleet market has been the first adopter of connected vehicle technology as efficiencies provide cost savings, but the automotive market is poised to catch up. “Fleets now have access to actionable intelligence from the field,” said Andrew Maliszewski of Micronet, as well as an industry consultant. “Business decisions are now being made from data, including fuel levels, driver behaviors, vehicle performance, weather and traffic conditions, and even real-time trailer connect/disconnect events.”
Ownership of Data is Tricky. Some of the data that is produced inside a vehicle will be of great value to marketers. It will reveal personal information, including your driving habits, where you go, and how you react to in-vehicle marketing. David Jumpa of Airbiquity asserts, “There is uncertainty on who will own the data, but the sensory data, such as how you brake and accelerate, would be owned by the vehicle OEM.” When polled, many listeners of the webinar opined that content and app providers, and not vehicle OEMs or data infrastructure companies, will own personal data generated.
Making Money, or Not. The technology of the connected vehicle market hasn’t been easy, but it has been much simpler than finding the revenue models that will support companies in this market. “In the past, the vehicle market would use a tier-one manufacturer to deliver the entertainment solution, including maps and routing,” said Scott Sedlik of Inrix. “That isn’t the case now, and multiple suppliers work together and are also having to carry the risk that the vehicle OEMs had solely carried.” Some of the content and app providers are making money; others are figuring out the right business model. One of the questions that remain is whether the OEMs will pay for in-vehicle services and content. This is a pivot point of business, Sedlik adds.
Mobile App Marketing Cost at High. For brands that proactively market their apps, the cost of acquiring a loyal user increased in July to $1.80 according to Fiksu’s Cost per Loyal User Index. This is a jump of 30 cents from June, falling just a penny short of the December 2011 price of $1.81. Fiksu attributes the cost rise to brands leveraging Facebook’s mobile app ads, which target consumers based on app and games access on smartphones.
Mobile Map Usage. More than 60 percent of iOS users accessed Apple Maps at least once during the previous 30 days, reports Mobidia. That isn’t too surprising given that it comes installed on the phone. However, 20 percent of iOS users accessed Google Maps during the same period — impressive, since the user has to go to the effort of installing the software. Google Maps usage is heavy, although not as heavy as Apple Maps use. 55 percent of iOS users that use Google Maps, use it weekly; 80 percent of Apple Maps users use it weekly. Not bad, Google.
In the wake of Microsoft’s recent purchase of Nokia’s mobile phone business, the Nokia unit formerly known as Navteq, and now know as HERE, has opportunities, but also a hard-to-guess future. At least one industry analyst believes that Navteq/HERE was not included in the Microsoft deal because it was too expensive.
“While much ado has been made of the Nokia/Microsoft deal in the press, I was interested in why Mr. Softy did not acquire Navteq/HERE with the other assets of interest. There are several possibilities to explain this omission,” said Mike Dobson, TeleMapics president. “First, it could be the case that Nokia did not want to sell Navteq/HERE. Second, it is possible that Microsoft had no interest in acquiring its current map database supplier. Third, maybe the price for Navteq/HERE was too high. My vote is for number three.”
Dobson said that Nokia clearly would like to sell HERE, as it does not fit with the company’s profile, growth strategies, or competencies, on a going-forward basis. “Just as Navteq was not a good fit for Nokia in 2007, it is now a less comfortable fit for the reconstituted company, which is being focused on network infrastructure services,” he said. “Conversely, I suspect Microsoft was ambivalent about a deal that included [Navteq/HERE].” Under the proposed Nokia/Microsoft deal, Nokia’s mapping assets are to be licensed for a four-year term by Microsoft, which gives them time to firm up their future strategy for spatial data. Note that the price of the license for the mapping products was not part of the $7.1 billion transaction, Dobson said.
“Why was Mr. Softy gun shy? First, I suspect that Microsoft concluded that owning a mapping company was not core to any of Microsoft’s current initiatives, including its bumbling approach to location and connected car services,” Dobson said. “Next, Microsoft has enough problems competing with companies in its distribution chain, without adding another business that would serve to complicate its relationship with manufacturers and resellers. Of course, all of these objections could have been overcome if the price was right, it wasn’t, but that does not mean it won’t be in the future.”
Where Does Navteq Go from HERE?
Dobson says Navteq, Nokia and HERE are in a world of pain. “While the ‘new’ Nokia will have the ability to fund all of the development to enhance the Navteq database that it has deferred over the past five years, I think it is unlikely to do so. Nokia does not appear to understand the fundamentals of the location market, the automotive navigation market, or the connected car market,” Dobson said. “Perhaps most importantly, they have lagged Google in evolving their map compilation process into a modern, synergistic, information sourcing engine. The Navteq approach to crowdsourcing hinders their potential speed to market with updated map information and has allowed Google to reach parity with Navteq in some areas, while exceeding it in quality in other markets.”
The future battleground in the location markets will devolve into a scarp for ownership of the last mile, Dobson said. “The type of thinking that believes that the ‘last mile’ is all about road geometry, simply does not understand the problem. People want to know that the map will support their journey to a destination, but they are focused on the destination and the various opportunities that it presents,” he said. “For example, the mobile phone has promoted an egocentric view of the world focused on ‘what’s around me?’ Providing the spatial detail of the total environment that surrounds the user is key to winning the last mile battle and I do not see Nokia having the assets to participate in this market.”
Nokia announced that HERE, at the recent Frankfurt Motor Show, partnered with Mercedes Benz, Continental Corporation and Magneti Marelli to offer connected products and services beyond navigation. Nokia believes that connecting the car to the cloud is one of the biggest opportunities for the automotive industry.
“Whether the concept of the connected car offers Nokia a lifeline is unclear. Connectivity may suck the spatial data out of the car and into phone based systems,” Dobson said. “Others would argue that smart cars will require a detailed, highly accurate database of spatial information to manage the safety systems in the automobile of the future. I’m not wise enough to predict the future, but I think the Nokia is going to have a rocky road with Navteq/HERE.”
Dobson said that it is interesting that Microsoft has loaned Nokia 1.5 billion Euros in three tranches of convertible bonds. “The bonds will be redeemed and netted against the deal proceeds, although the loan is not conditional on the deal closing, nor is Nokia obligated to exercise its option,” he said. “However, it would appear that Mr. Softy and Nokia are not quite through with each other: if Nokia exercises these options, Microsoft will become a shareholder in Nokia.”
Millennial Media has announced the launch of Omni Measurement Solutions, a suite of measurement products designed to evaluate and demonstrate the effectiveness of mobile campaigns. The new solutions will combine Millennial Media’s extensive first-party data with best-in-class third-party data sources to show the impact on key advertiser metrics driven by a campaign.
“Measurement is one of the most important issues in mobile advertising today,” said Mollie Spilman of Millennial Media. “Brands need to feel confident that the dollars they are spending in mobile advertising are truly moving the needle, and our Omni Measurement Solutions represent the most comprehensive, data rich solution at this scale in mobile advertising.”
Omni Measurement Solutions currently consists of the following products:
Door Open Rate – Measures the impact on foot traffic to a given retail location generated by a mobile campaign.
Register Ring Rate –Measures the impact in total credit card spend at a retail location due to a mobile campaign, including number of transactions per purchaser and total basket size.
Brand Lift Rate – Measures the impact on high funnel activities such as awareness, intent, consideration, and recall.
For every measurement product in the Omni Measurement Solutions suite, Millennial Media partners with a third party, and matches mobile IDs against exposed and control groups to judge the effectiveness of marketing campaigns with target audiences. Data analytics company Neustar and location analytics firm Placed are among the launch partners. Millennial Media will offer end-of-campaign reports that will accurately show the impact on advertiser KPIs, and give advertisers credible and qualified insights to use for future marketing efforts.
To power Door Open Rate, Millennial Media selected Placed. “The product we’re working on with Millennial Media allows advertisers to track conversions beyond the mobile device itself, and extend measurement into the physical world,” said David Shim of Placed. “By combining Placed Attribution with Millennial Media’s industry leading scale, we’re able to measure the impact on in-store visits in a way that was simply not possible before.”
Additional products will be added to the suite in the coming weeks, including a product in collaboration with comScore that measures online consumer behavior after mobile ad exposure.
