DaGCM: A Concurrent Data Uploading Framework for Mobile Data Gathering in Wireless Sensor Networks

Abstract

Data uploading time constitute a large portion of mobile data gathering time in wireless sensor networks. By equipping multiple antennas on the mobile collector, data uploading time can be greatly shortened. However, previous works only treated wireless link capacity as a constant and ignored power control on sensors, which would significantly deviate from the real wireless environments. To overcome this problem, in this paper we propose a new data gathering cost minimization framework for mobile data gathering in wireless sensor networks by considering dynamic wireless link capacity and power control jointly. Our new framework not only allows concurrent data uploading from sensors to the mobile collector, but also determines transmission power under elastic link capacities. We study the problem under constraints of flow conservation, energy consumption, elastic link capacity, transmission compatibility and sojourn time. We employ the subgradient iteration algorithm to solve the minimization problem. We first relax the problem with Lagrangian dualization, then decompose the original problem into several sub problems, and present distributed algorithms to derive data rate, link flow and routing, power control and

transmission compatibility. For the mobile collector, we also propose a sub algorithm to determine sojourn time at different stopping locations. Finally, we provide extensive simulation results to demonstrate the convergence and robustness of proposed algorithms. The results reveal 20% shorter data collection latency on average with lower energy consumptions compared to previous works as well as lower data gathering cost and robustness in case of node failures.

Aim

The main aim is to generate a new data gathering cost minimization framework for mobile data gathering in wireless sensor networks by considering dynamic wireless link capacity and power control jointly.

Scope

The scope is to employ the subgradient iteration algorithm to solve the minimization problem. And then the problem with Lagrangian dualization, then decompose the original problem into several sub problems, and present distributed algorithms to derive data rate, link flow and routing, power control and transmission compatibility. For the mobile collector, it also provide a sub algorithm to determine sojourn time at different stopping locations.

Existing System

Wireless sensor networks (WSNs) play an increasingly important role in a wide range of applications, e.g., wildlife tracking, habitat monitoring and battlefield intelligence. These applications usually involve hundreds or even thousands of sensor nodes powered by batteries with limited energy over a large field. During the operation, sensors organize themselves into a network and report sensing data to the sink(s) periodically. How to aggregate data from sensors largely determines the energy consumptions of the network. In recent years, extensive research efforts have been devoted to data gathering in WSNs. Most of them focused on static data gathering where sensing data is gathered by a static sink. In an optimal routing and data aggregation scheme for WSNs was proposed to maximize network lifetime by jointly optimizing data aggregation and routing.

Disadvantages

Data uploading time constitute a large portion of mobile data gathering time in wireless sensor networks. By equipping multiple antennas on the mobile collector, data uploading time can be greatly shortened. However, previous works only treated wireless link capacity as a constant and ignored power control on sensors, which would significantly deviate from the real wireless environments.

The problem is of tree construction for maximizing network lifetime with a single base station in the network. Energy efficient and collision-free polling schedules in multi-hop clusters to reduce energy consumption. However, these schemes suffer from the notorious energy hole problem in which the neighboring nodes closer to the sink consume more energy due to relaying more data. The congested area around the sink could easily cause service interruptions or packet loss that can severely degrade network performance.

Proposed System

We propose a comprehensive data gathering cost minimization (DaGCM) framework. We first define data gathering cost with respect to the amount of data a sensor uploads to anchor points. Then we minimize the total data gathering cost by integrating the constraints of flow conservation, energy consumption, elastic link capacity and compatibility required by MIMO transmission and bound of the total sojourn time into one optimization problem. Upon discovering the original problem is non-convex, we convert it into a convex one by introducing auxiliary variables and logarithmic transformation. By applying Lagrangian dualization, we decompose the problem into several subproblems. We further provide distributed cross-layer subalgorithms to calculate data rates, link flow and transmission power for each sensor as well as sojourn time at different anchor points for the SenCar. Our numerical results reveal that the proposed algorithms can converge to the optimum in about 50 iterations. We also conduct extensive simulations to show that our framework can significantly reduce data gathering time and total energy consumption compared to the algorithms without concurrent data uploading and power control. 

Advantages

·      This is the first work to explore optimal solutions with concurrent data uploading given the selection of anchor points. To the best of our knowledge, this is also the first work that integrates elastic link capacity and transmission power control into an optimization problem using MIMO communications in WSNs.

·      This project not only allows concurrent data uploading from sensors to the mobile collector, but also determines transmission power under elastic link capacities.

System Architecture

System Configuration

Hardware Requirements

• Speed                  -    1.1 Ghz
• Processor              -    Pentium IV
• RAM                    -    512 MB (min)
• Hard Disk            -    40 GB
• Key Board                    -    Standard Windows Keyboard
• Mouse                  -    Two or Three Button Mouse
• Monitor                -     LCD/LED

 Software requirements

• Operating System              : Windows 7             
•  Front End                           : ASP.Net and C#
• Database                             : MSSQL
• Tool                                    : Microsoft Visual studio

References

Songtao Guo , Yuanyuan Yang “DAGCM: A CONCURRENT DATA UPLOADING FRAMEWORK FOR MOBILE DATA GATHERING IN  WIRELESS SENSOR NETWORKS” INFOCOM, 2012 Proceedings IEEE.