Extend Your Journey: Considering Signal Strength and Fluctuation in Location-Based Applications android project | android project free download
Extend Your Journey: Considering Signal Strength and Fluctuation in Location-Based Applications
ABSTRACT:
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 impacting the application's semantics adversely. To solve the fundamental problem, we propose a dynamic-programming algorithm and prove its optimality in terms of energy savings. Then, we perform post-optimal analysis to explore the tolerance of the algorithm 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 and demonstrate the applicability of the proposed algorithm toward signal strength fluctuations.
EXISTING SYSTEM:
v Location-based applications will become more diverse and pervasive due to the potential for a range of highly personalized and context-aware services. However, the trend will lead to a significant boost in mobile data traffic and, consequently, result in further pressure on the limited battery capacity of mobile devices. Thus, reducing the communication energy is an imminent challenge in stimulating the development of emerging location-based applications.
v Many existing approaches leverage the complementary characteristics of Wi-Fi and 3G—i.e., WiFi to improve energy efficiency, and 3G to maintain ubiquitous connectivity. Recently, it has been observed that signal strength has a direct impact on the communication energy consumption.
v The communication energy per bit when the signal is weak could be as much as six times more than that when the signal is strong. This phenomenon has proved evident in both Wi-Fi and 3G. The reason for such a phenomenon results mainly from the adaptive modulation and power control employed in wireless network systems.
v Based on the observation, it could be promising to exploit signal strength information to reduce the communication energy of mobile devices. However, the challenge is how to exploit this observation to gain energy efficiency. In particular, signal strength may fluctuate with time due to multipath fading, so attention has to be paid to the impact of signal fluctuations on the practicability of the proposed approaches in real-world environments.
DISADVANTAGES OF EXISTING SYSTEM:
v The significant boost in mobile data traffic and, consequently, result in further pressure on the limited battery capacity of mobile devices.
v The communication energy per bit when the signal is weak could be as much as six times more than that when the signal is strong. This phenomenon has proved evident in both Wi-Fi and 3G. The reason for such a phenomenon results mainly from the adaptive modulation and power control employed in wireless network systems. Based on the observation, it could be promising to exploit signal strength information to reduce the communication energy of mobile devices.
v However, the challenge is how to exploit this observation to gain energy efficiency. In particular, signal strength may fluctuate with time due to multipath fading, so attention has to be paid to the impact of signal fluctuations on the practicability of the proposed approaches in real-world environments.
PROPOSED SYSTEM:
v In this paper, our major contribution is to introduce signal strength into location-based applications to reduce the energy consumption of mobile devices for data reception.
v To validate the practicability of the concept, we developed a virtual tour system comprised of an online server and a mobile application program based on Android.
v First, we model the fundamental problem in the virtual tour system as a data fetch scheduling problem.
v Second, we propose a dynamic-programming algorithm to solve the fundamental problem. The solution involves scheduling the fetching of location-based information at appropriate locations so as to minimize the total energy consumption. We prove that the algorithm is optimal in terms of energy savings.
v Third, we perform post optimal analysis to explore how the algorithm responds to signal strength fluctuations, especially the fluctuation range within which the derived solution remains optimal or feasible. The analysis helps to understand the impact of signal fluctuations on the practicability of this new concept in real-world environments.
v Fourth, we discuss technical implementation issues that arise when introducing signal strength into location-based applications for energy savings.
v Fifth, we conducted a series of experiments in Taipei City, Taiwan, for real-world case studies. The results show that an Android smartphone of HTC EVO 3D can achieve a significant energy reduction when accessing location-based applications.
v Finally, we discuss the limitations of our work and highlight issues that require further investigation. The concept, once proved practicable and embraced gradually, could be extended and applied to other variants of location-based applications based on the knowledge learned from this work.
ADVANTAGES OF PROPOSED SYSTEM:
ü Exploitation signal strength information has been done to reduce the communication energy of mobile devices.
ü A feasible fetch schedule that minimizes the total energy consumption for data reception
ü Through real-world case studies, we have demonstrated the practicability of introducing signal strength into location-based applications.
MODULES:
1. Application security
2. Location feeds
3. Querier
4. Location estimation
5. Energy cost estimation
MODULE DESCRIPTION:
Application security:
Application has the user registration and password verification for the security purpose. User's registered information's are uploaded to the server. Where the user's activities are witnessed and maintained by the server admin
Location feeds:
The location information is uploaded by the server admin and the signal strength in the particular area can be added with the location and each routes also.
Querying user:
The user is the querier who searches for the place as a query from his/her mobile application. The user will get the routes and signal strength in each routes. These routes are generated using the shortest path algorithm.
Location estimation:
Given a trajectory T (P0 P1), if the location of departure point P0 is known, we can roughly estimate the location of P1 by accumulating the trajectory segments. However, due to the inaccuracy of step size and orientation measurement, errors may be introduced during the estimation of each segment. With the number of segments increase, the errors are accumulated, thus the estimated location will be far away from the actual location. To overcome this drawback, we use the encounter opportunity of nodes to improve the estimation accuracy, which is introduced in the following subsection.
Energy Cost Estimation:
The energy cost (joules per byte) at a checking location is defined as the mobile device's power consumption (watts) divided by the downlink data rate (bytes per second). The downlink data rate has a strong relationship with the signal strength. To plot their relationship, we installed the application program developed by Open Signal Maps on an HTC EVO 3D smartphone to measure the signal strengths and data rates at various locations in Taipei City. We gathered over 3000 pairs of such data within the coverage of 3G/3.5G signals provided by Chunghwa Telecom. Then, we applied the polynomial regression method to the gathered data and modeled the relationship with a monotonic function. It is no doubt that the more (and diverse) the data gathered, the more accurate the monotonic function, and the less the effect due to signal fluctuations. Furthermore, we observed that the signal strength at a location is generally stable over time, which also agrees with the phenomenon observed in. Based on our measurement, the signal strength at a checking location is close to the expected signal strength with a standard deviation up to 4 dBm, and the standard deviations are smaller than 2 dBm at most locations. The power consumption depends mainly on the communication chip adopted by the mobile device. Fortunately, the accuracy of the power model will only affect the amount of energy saved if the scheduled objects can be fetched successfully at every checking location; therefore, other device models could also benefit even if their accurate power models have not be acquired. The receive mode of 3G/3.5G has four/five states, and the state transition adheres to the radio resource control protocol specified in UMTS/HSPA of the 3GPP standard. In practice, we used the power monitor produced by Monsoon Solutions to measure the power consumption of the HTC EVO 3D smartphone. Fig. 6 shows the power consumption of each state during an ICMP ping. The radio interface is initiated in CELL_IDEL, which consumes almost no power. Then, it transits to CELL_DCH with HS-DSCH, a state supporting high speed data downlink, and consumes 1050 mW when remaining in the state for data reception. Thus, the energy cost at a location can be computed by dividing 1.05 W by the downlink data rate there. After that, the interface starts to release the radio resources, resulting in a state demotion, and lasts in CELL_DCH with power consumption of 590 mW until an inactive timer of 5 s expires.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
Ø System : Pentium IV 2.4 GHz.
Ø Hard Disk : 40 GB.
Ø Floppy Drive : 1.44 Mb.
Ø Monitor : 15 VGA Colour.
Ø Mouse : Logitech.
Ø Ram : 512 Mb.
Ø MOBILE : ANDROID
SOFTWARE REQUIREMENTS:
Ø Operating system : Windows 7.
Ø Coding Language : Java 1.7
Ø Tool Kit : Android 2.3 ABOVE
Ø IDE : Eclipse
VIDEO OUTPUT
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