1. The Field of the Invention
The present invention relates to wireless communication networks and related systems and devices. More specifically, embodiments of the present invention relate to systems and methods for using a programmable agent within a wireless device within a distributed communications network to monitor wireless networks, devices, and/or the operation thereof.
2. The Relevant Technology
As a result of advances in technology and enormous increases in the number of wireless device users, the size and complexity of wireless communications networks has greatly increased. One consequence of such increases in size and complexity is that the relative increase in operational and performance problems associated with communications networks has also increased. Reliability issues, such as dropped calls, lack of coverage, poor audio quality, and application failure often lead to user frustration and to increased costs. As new services are introduced that use even more complex technology, exercise different usage modalities, and place additional demands on networks, network performance continues to be a prime concern. In fact, quality of service often has a direct impact on a service provider's profitability. Therefore, improving quality of service is a top priority for service providers.
Network monitoring solutions are well known in the art and widely employed by service providers. Unfortunately, the solutions that are currently available can only monitor and diagnose subsets of the overall telecommunications system in limited ways rather than providing a holistic view of network and device performance that may be needed to efficiently identify and resolve quality issues.
Typical approaches to network monitoring include “self-monitoring.” In this case, a network element reports on its own operational status, performance and any errors that occur during its operation. The metrics reported by a single element can sometimes be indicative of a broader, system-wide problem, but rather than providing answers, problem resolution entails guesswork and extended troubleshooting, which wastes valuable resources.
Another common approach includes placing probes at various points in the network to determine if network elements are functioning according to specification. Sometimes referred to as “sniffers”, “log monitors” or “event monitors,” these monitoring systems are effective at identifying performance issues with a particular network element, but they fail to capture problems that stem from the interfaces among network elements, i.e., these solutions do not address the case where single elements are performing according to specifications, but problems occur when those elements interact with one another. This far more complex and subtle set of problems has costly consequences to network operators when services cannot be delivered to end customers.
Another monitoring approach involves pre-programmed service monitors, where specific elements perform service transactions to emulate “real-world” transaction activity; end to end performance is then monitored and the results reported. While these solutions may catch systematic failures, they cannot detect intermittent or dispersed problems, subtle impairments, or device or end user specific issues. Further, they can only test anticipated usage scenarios and fail to adapt to new usages and interactions between services.
Significantly, the aforementioned solutions lack the ability to monitor network conditions and intelligently and dynamically define and generate data reporting models in response to changing network conditions. Even with the employment of probes in a communications network, it is often the case that the probe provides only an indication of a problem, and actually troubleshooting the problem requires personnel to be dispatched to a physical location on the network, adding significant time and cost to problem identification and resolution. Moreover, these troubleshooting techniques do not provide a multi-faceted view of the different network layers, such as the physical layer, the applications layer, etc., and they do not correlate performance issues across these layers. As a result, numerous quality issues impacting end users go undetected or are misunderstood. Consequently, they may become crises because the performance data provided by currently available network monitoring solutions lacks the detail and timeliness needed to quickly identify, prioritize and resolve network issues.
Furthermore, currently available network monitoring solutions are unable to adequately monitor and report a particular end user's experience, meaning that the service providers must rely upon the end user to report performance problems to a customer service representative. As such, it is frequently the case that by the time the user has reported a problem, the problem cannot be duplicated due to the user's difficulty recounting the specific details of what the user experienced, the timing of the occurrence, along with a lack of underlying data to validate the information provided by the user. Additionally, because the service provider is unable to view network performance holistically, it is difficult for the service provider to understand the true scope of the reported problem and appropriately prioritize the problem for resolution. Therefore, the problem may not be resolved to the reporting end user's satisfaction, creating a disincentive for the end user to diligently report problems in the future. Furthermore, in such situations, the opportunity to prevent the issue from affecting other users of the system and to improve the overall quality of the network is missed.
Thus, situations frequently arise where the end user is alienated from the company providing the communications service, without the service provider even being aware of the cause or source of the end user's dissatisfaction. Moreover, because so many end user problems go unreported, it is almost impossible for service providers to obtain reliable information about the scope of a particular issue. Therefore resources are frequently spent solving issues that are well described, but only affect a small number of end users, versus problems that are poorly described but effect a much larger number of end users. As a result, a service provider with an incorrect understanding of the scope of an issue may allocate an inappropriate amount of resources toward resolution, or it may fail to recognize the value of prioritizing resolution.
Other monitoring solutions known in the art are directed to gathering and analyzing information about the performance of wireless devices. Typically, the monitoring software will be installed on a wireless device at the time of manufacture or by downloading the software onto the device. The software then runs continuously in the background, monitoring device and application performance. When a particular event or error associated with the device occurs, the software collects the data associated with the error or event and may upload it either in real time or at a later time to a data repository for analysis. One problem with such systems and methods, however, is that the systems require a relatively large amount of data to be uploaded to the data repository for analysis, which can drain the resources of the wireless device. Specifically, the frequent transfer of data or the transfer of large amounts of data may decrease the battery life and impact processing performance of the wireless device, which may create further user frustration and dissatisfaction with the communication service.