Extend Your Journey: Introducing Signal Strength into Location-based Applications
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Reducing the communication energy is essential to facilitate the growth of emerging mobile applications. In this paper, we introduce signal strength into location-based applications to reduce the energy consumption of mobile devices for data reception. First, we model the problem of data fetch scheduling, with the objective of minimizing the energy required to fetch location-based information without adversely impacting user experience. Then, we propose a dynamic-programming algorithm to solve the fundamental problem and prove its optimality in terms of energy savings. We also provide an optimality condition with respect to signal strength fluctuations. Finally, based on the algorithm, we consider implementation issues. We have also developed a virtual tour system integrated with existing web applications to validate the practicability of the proposed concept. The results of experiments conducted based on real-world case studies are very encouraging.
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Extend Your Journey: Introducing Signal Strength into Location-based Applications
- 1. Extend Your Journey: Introducing Signal Strength into Location-based Applications Chih-Chuan Cheng and Pi-Cheng Hsiu Research Center for IT Innovation, Academia Sinica
- 2. Outline • Motivation • Existing Solutions • Introducing Signal Strength into Location-based Applications A virtual tour system An optimal algorithm An optimality condition • Real-world Case Studies • Conclusion 2
- 3. Location-based Applications • A variety of location-based applications and services have progressively permeated people’s daily life Services for directions or recommendations about nearby attractions Social interaction with friends via location sharing 3
- 4. A Major Challenge & Existing Solutions • Reducing the communication energy is an imminent challenge in stimulating such applications. • Basically, existing approaches leverage the complementary characteristics of WiFi and 3G WiFi to improve energy efficiency 3G to maintain ubiquitous connectivity 4 3G WiFi
- 5. Where Communication Energy Consumption Comes From? • Receiving energy Signal strength has a direct impact on the receiving energy. • Tail energy 8x 3G does not switch from the high to the low power state immediately after each communication. PING Tail Energy (6.67 joules) Signal strength (dBm) -50 -60 -70 -80 -90 -100 Energy cost (Joule/byte) 0.00001 0.00002 0.00004 0.00005 0.00006 0.00008 *measured based on an Android smarphone of HTC EVO 3D in practice 5
- 6. Extend Your Journey: Introducing Signal Strength into Location-based Applications A well-know observation Receiving energy ∝ 1/signal strength The technical problem How to prove the concept of introducing SS into LBA? Contributions A virtual tour system A fundamental algorithm for data fetch scheduling An optimality condition w.r.t signal strength accuracy 6
- 7. A Virtual Tour System 7 Virtual Tour Server Signal Strength DB LBS Providers Src & Dst Estimated SS Fetch schedule LBS Info. Signal Strength DB Example applications The mobile platform
- 8. An Optimal Algorithm -77 -73 -75 -86 -72 -90 -91 1 2 3 4 5 6 7 SS (dBm) 650 4478 500 800 4200 300 0 Objects 9 3 3 3 3 3 0 1 1 1 1 1 1 1 5 4 1 4 4 3 4 0 0 0 1 0 0 1 • Goal: to schedule the fetching locations of the location-based information based on the signal strength such that the communication energy is minimized without adversely impacting original user experience MFC (Kbytes) To Taipei 101 Mitsukoshi is there! a cinema is nearby!? The firework of Taipei 101 is awesome. 8
- 9. An Optimality Condition 1,0 2,3 3,5 4,6 2,2 3,4 1.1 1.38 1.31 0.72 0.98 1.04 1.3 1 ∞ 1,0 2,3 3,5 4,6 2,2 3,4 1.11 1.4 1.35 0.73 1.01 1.07 1.25 0.97 ∞ • Complete directed graph with respect to estimated signal strength constructed based on our algorithm Fluctuations Our proposed algorithm Our proposed algorithm 푟∗ ≡ 1,0 → 2,2 → 3,4 → 4,6 Identical 푟 ∗ ≡ 1,0 → 2,2 → 3,4 → 4,6 9 • Complete directed graph with respect to real signal strength constructed based on our algorithm
- 10. Case Studies Route@campus Route@downtown 10 Route Ch. Route@ campus Route@ downtown Signal strength (dBm) Relatively weak (i.e., -77,-75,- 78,-86,-79,-91,- 91) Relatively strong (i.e., -65,-72,- 78,-76,-58,-60) Location-based Info. Sparse (i.e., 54 objects including 24 map tiles, 7 street views, 22 photos, and 1 video) Dense (i.e., 239 objects including 21 map tiles, 1 street view, 214 photos, and 3 videos) Taipei City Hall MRT Station VIESHOW & Taipei 101 Main Entrance of Academia Sinica The Institute of History and Philology
- 11. Experimental Results • Impacts of the amount of information and the velocities • LBS1 (Google maps): 59-70% reduction along Route@campus and 61% reduction along Route@downtown • LBS2 (Google maps and Panoramio): 49- 53% reduction along Route@campus and 18-35% reduction along Route@downtown • LBS3 (Google maps, Panoramio and YouTube): 35-46% reduction along Route@campus and 27-43% reduction along Route@downtown 1. Signal strength distortion 2. The round trip time of requests 1. Large number of objects 2. Significantly varied signal strength Amortized by the videos 11
- 12. Demo – HTC EVO 3D 12 Taipei City Hall VIEWSHOW http://www.youtube.com/watch?v=NGVi1JPzxeE
- 13. Conclusions • This work introduces signal strength into location-based applications to reduce the energy consumption of mobile devices for data reception. • We have deployed a virtual tour system to prove this concept. An HTC EVO 3D smartphone can achieve 30-70% of energy savings for data reception. We will import Taiwan’s signal database acquired from OpenSignalMaps and release the mobile application program. 13
- 14. Thank You 14
- 15. How To Estimate Energy Cost 15 OpenSignalMaps Power monitor (Downlink DR, SS) Polynomial regression method Power consumption at each power state
- 16. How To Determine Maximum Fetch Size Maximum fetch size = (Distance/Speed)*Downlink DR 16 RF signal tracker Effective regions 83 m/min 216 m/min 667 m/min
Notes de l'éditeur
- Good afternoon everyone. Thanks for coming for my presentation. Today, I will present how we introduce signal strength into location-based applications to reduce the energy consumption of mobile device for data reception. This paper was co-authored with Pi-Cheng Hsiu, from the same institute.
- This is the outline for today’s talk. Regarding motivation, recently location-based applications are becoming more and more popular. To stimulate the growth of such applications, reducing the communication energy of mobile devices is an imminent challenge. To address this challenge, existing solutions attempted to leverage the complementary characteristics of WiFi and 3G. However, we were thinking that if there exists other ways to further improve energy efficiency. The key point is that it has been observed that signal strength has a direct impact on the communication energy, which inspires us to introduce signal strength into location-based applications to reduce the communication energy. But the problem is how to applied the concept. To study the fundamental problem, we implemented a virtual tour system. And then, another problem raised, that is when signal strength information is available in the system, how to decide which location-based information should be fetched at which locations such that the communication energy is minimized. To address this problem, we proposed a dynamic-programming algorithm, and proved its optimality in terms of energy savings. However, a data fetch schedule is derived based on estimated signal strength. In fact, signal strength cannot be estimated accurately. So, we determined an optimality condition to analyze the robustness of our proposed algorithm with respect to signal strength accuracy. Then, we applied our proposed algorithm in two real-world case studies to see if the concept is practicable, how much energy can be saved, and what insights can we gain from the experiments. We have to emphasize that once the concept is proved practicable, it can be extended and applied to other location-based applications. Finally, I will summarize our work and finish the talk.
- Nowadays, we have witnessed a variety of location-based applications and services have progressively permeated peoples daily life. One well-known application is google maps which provide mobile users with directions or recommendations about nearby attractions. Other applications are panoramio and facebook. Mobile users can use the applications to share their experiences at certain locations with friends. With the emergence of smart mobile devices, it is expecting that location-based applications will become increasingly diverse and the mobile data traffic will skyrocket, thereby imposing a significant pressure on the limited energy of mobile devices.
- As such, to stimulate such applications, reducing the communication energy is an imminent challenge. Basically, existing approaches were trying to leverage the complementary characteristics of WiFi and 3G while using WiFi to improve energy efficiency and using 3G to maintain ubiquitous connectivity. The problem is that if we can connect only to 3G networks, what can we do to improve energy efficiency?
- To answer the question, we looked into where communication energy consumption comes from, we found that it comprises of receiving energy and tail energy. Regarding the receiving energy, it has been observed that signal strength has a direct impact on it. The table shows that the energy cost at locations with weak signal is as much as 8 times higher than that at locations with strong signal. Now, what we learnt from this phenomenon. It means that we should fetch data at locations with strong signal rather than at locations with weak signal. The tail energy incurs when 3G does not switch from the high to the low power state immediately after each communication. Its value is approximately 6.67 joules, implying that we should reduce the number of data transmissions to avoid tail energy costs.
- Based on the previous slide, a well known observation is the receiving energy is proportional to the reciprocal of signal strength. Then, the technical problem is how to exploit this observation to location-based applications to improve energy efficiency. To study the fundamental problem, we implemented a virtual tour system. In the system, suppose that a mobile user carrying a smartphone at location A wants to go to location B. The system provides him with the directions and the corresponding information to guide him. The information includes map tiles, photos, and video clips. Now, when the signal information is available, the problem is how to fetch the information along the route such that the communication energy is minimized. To address this problem, we proposed a dynamic programming algorithm and proved its optimality in terms of energy savings. In this work, a data fetch schedule is derived based on estimate signal strength. We cannot expect the estimated signal strength is 100 percent accurate. So, we determined an optimality condition to indicate how large the distortion of the estimated signal strength can be tolerated by our proposed algorithm.
- This is the system architecture of the virtual tour system. We have implemented a virtual tour server on a cloud server and two mobile programs for mobile devices. In this system, we considered three services, including google maps, panoramio, and youtube. Signal strength information is stored in the signal strength database. The signal strength information can be collected by three ways depending on locations. For example, it can be uploaded by mobile programs, like OpenSignalMaps, or by street cars, like google does. Or, it can be estimated by radio propagation model. In this work, we adopted the second approach. These two figures show the interfaces of the mobile programs. We installed the programs on an Android smartphone of HTC EVO 3D, and used the power monitor to measure the energy consumption of the smartphone. Finally, we installed all the devices on the mobile platform to conduct experiments. Our proposed algorithm is executed at the server.
- The goal of the proposed algorithm is to schedule the fetching locations of the location-based information based on the signal strength such that the communication energy is minimized without adversely impacting original user experience. What does it mean – without adversely impacting original user experience?. The mobile user uses location-based applications to get information, so when he reaches a certain location, the corresponding information should be available on his device; otherwise he may get lost or miss some valuable information. It means that the information should be pre-fetched. However, the problem is that when he travels along the route, the amount of data he can fetch at a location depends on the downlink data rate at the location. As such, our proposed algorithm must schedule the fetching of the information under these constraints. These are some difficulties we should overcome when designing the algorithm. For more details, please refer to our paper. This animation illustrates how the user fetches the information along the route.
- In this work, we can’t expect signal strength can be estimated accurately. Signal strength accuracy may impact the optimality and feasibility of our proposed algorithm. So, we wanted to analyze the robustness of our proposed algorithm against signal strength fluctuation. To this end, we applied our proposed algorithm to determine data fetch schedules based on estimated and the real signal strength respectively. The complete directed graph is constructed based on our proposed dynamic programming algorithm, and each vertex represents a subproblem in the formula. If the derived data fetch schedules are the same. We can conclude that the derived schedule based on the estimated signal strength remains optimal in the real environment.
- We were curious about if our proposed algorithm can be applied in real environment, and when the algorithm is applied how much energy can be saved and what insights can we gain from the experiments. So, we applied our proposed algorithm to two real-world case studies with diverse characteristics. Route@campus is a path in the campus of Academia Sinica situated in the suburb of Taipei City, and Route@downtown represents a crowded street in the urban area of Taipei City. So, the signal strength is relatively weak along Route@campus, and strong along Route@downtown. And, the location-based information is sparse along Route@campus and dense along Route@downtown.
- In the experiments, we wanted to investigate the impacts of the amount of information and the velocities of mobile users on our proposed algorithm. So, we considered three scenarios. In LBS1, only google maps service is considered. The performance metric is the energy consumption required for data reception. We compare our proposed algorithm with two approaches. The first one is the native approach adopted by Google maps, and the other is the theoretical results of our proposed algorithm. We compared our proposed algorithm with the theoretical one to gain further insights into their gap. We call the approaches NATIVE, OPT, and OPT_THEORY for short. The results show that the energy consumption of OPT decreases as the velocity decrease. This is because that the lower the velocity, the higher is the amount of data can be fetched at a location. However, the native approach is not impacted by the velocity. This is because the native approach fetches information when it is required. In addition, one interesting thing is that the gap between OPT and OPT_THEORY is large. This is because of the distortion of the estimated signal strength and the round trip time of data requests for the files about location-based information. The energy reduction achieved by OPT is around 59-70%. In LBS2, google maps and panoramio services are considered. Since LBS2 contains much more information than that of LBS1, the decreasing of the energy consumption with respect to the velocities is manifest, and the discrepancy between OPT and OPT_THEORY is significantly increased. The energy reduction achieved by OPT is around 18-53%. In LBS3, in addition to google maps and panoramio services, youtube service is considered. The results show that the gap between OPT and OPT_THEORY is considerably reduced. The reason is that LBS3 contains some videos of large sizes and the additional energy consumed by the round trip time is amortized by that consumed by the videos. In a nutshell, the impact of the round trip time is significant, and thus unignorable. So, what we can do is to batch the files of location-based information for data reception, or consider the round trip time in the system model, which, however, will make the problem more complex.
- We also made a video demonstration of the virtual tour system along Route@downtown. The color shows the distribution of the signal strength. In the video, our proposed algorithm was applied on the left device, and the right device adopted the native approach. The arrow on the map indicates the current location of the mobile. The green texts indicate the instant signal strength and the corresponding energy cost, and the white texts show the energy consumed by our proposed algorithm and the native approach. In the video, we can see that our proposed algorithm scheduled the fetching of the information at the locations with strong signal. Therefore, eventually it achieved 40.53% of energy savings. I put the youtube link of the video here, so you are interested in it, you can watch the full version on youtube.
- In this work, we introduce signal strength into location-based applications to reduce the energy consumption of mobile devices for data reception. Then, we have implemented a virtual tour system to prove this concept. The experiment results show that an HTC EVO 3D smartphone that installs our mobile application program can achieve 30-70% of energy saving for data reception when accessing location-based services. In the future, we will import Taiwan’s signal strength database acquired form OpenSignalMaps – thanks to them by the way – and release the mobile application program in an Android marketplace to identify more issues in this research direction.
- First of all, we installed an OpenSignalMaps app on an Android smartphone to measure downlink data rate with respect to signal strength, and store the information in the signal strength database. Next, we measure the power consumption of 3G component at each power state. Then, we applied the polynomial regression method to the gathered data and modeled the relationship with a monotonic function.
- First of all, we installed a RF signal tracker app on an Android smartphone to capture the changes of signal strength along a route. Then, we identified the effective regions of the locations covered by the same signal strength. Based on the effective regions, along with user speed and downlink data rates, we can calculate the maximum fetch sizes.