A Historical-Beacon-Aided Localization Algorithm for Mobile Sensor Networks

A Historical-Beacon-Aided Localization Algorithm for Mobile Sensor Networks

ABSTRACT:

Range-free localization approaches are cost-effective for mobile sensor networks (because no additional hardware support is required). However, existing range-free localization approaches for mobile sensor networks suffer from either sparse anchor node problem or high communication cost. Due to economic considerations, mobile sensor networks typically have sparse anchor nodes which makes most range-free localization algorithms inaccurate. On the other hand, due to the power limitation of mobile sensor nodes (i.e., they are battery-operated) and high power consumption by communication, high communication cost will significantly reduce the network life time. For solving these two problems, in this paper, we use historical beacons (i.e., anchor nodes’ announcements delivered in previous time slots) and received signal strength (RSS) to derive three constraints. By the aid of the three constraints, we introduce a low-communication-cost range-free localization algorithm (only one-hop beacon broadcasting is required). According to the theoretical analysis and simulation results, our three constraints can indeed improve the accuracy. Simulation results also show that our algorithm outperforms even in irregular-radio-signal environments. In addition, a hardware implementation running on sensor nodes, Octopus Xs, confirms theoretical analysis and simulation results.

PROJECT OUTPUT VIDEO: (Click the below link to see the project output video):

EXISTING SYSTEM:

A range-based localization algorithm calculates locations with absolute point-to-point distances, while a range-free localization algorithm calculates locations without these distances.

DISADVANTAGES OF EXISTING SYSTEM:

  • It is impractical to equip each sensor node with a GPS device in large-scale WSNs.
  • Distance estimation techniques usually require additional expensive hardware support (e.g., angle of arrival (AoA) and time difference of arrival (TDoA)), or have low accuracy (e.g., received signal strength (RSS)-based approaches). Due to the hardware limitations of WSNs, range-free solutions are being pursued as an alternative to range-based solutions.
  • Most of prior range-free localization algorithms were designed for static sensor networks and not applicable to mobile ones.
  • Existing range-free localization approaches for mobile sensor networks usually suffer from sparse anchor node problem and high communication cost.

 PROPOSED SYSTEM:

  • In this paper, we introduce a range-free localization algorithm for mobile sensor node networks. In order to address the sparse anchor node problem and high communication cost problem, our algorithm fully utilizes the advantages of the communication ranges (of nodes), historical beacons, and RSS (of beacons), which are free of communication cost. To the best of our knowledge, our algorithm is the first one to use the RSS of historical beacons in mobile sensor node localization. Our algorithm includes three new constrained regions.
  • A constrained region is a region that can cover the location of the target normal node, e.g., the communication range of a one-hop neighboring anchor node (which is widely adopted in existing range-free algorithms.
  • The three types of RSS-constrained regions:
    • Current-current-RSS-constrained region (CC-region, for short),
    • Current-historical-RSSconstrained region (CH-region, for short), and
    • Historical-historical-RSS-constrained region (HH-region, for short).

 ADVANTAGES OF PROPOSED SYSTEM:

  • Our algorithm has low communication cost (only one-hop beacon broadcasting is required). Simulation results also show that our algorithm outperforms even in irregular-radio-signal environments.
  • According to the theoretical analysis and simulation results, the three constrained regions can indeed improve the localization accuracy.

ALGORITHM USED:

  • The HitBall Algorithm

 

SYSTEM ARCHITECTURE:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

  • System :         Pentium IV 2.4 GHz.
  • Hard Disk           : 40 GB.
  • Floppy Drive : 44 Mb.
  • Monitor : 15 VGA Colour.
  • Mouse :
  • Ram : 512 Mb.

 SOFTWARE REQUIREMENTS:

  • Operating system : Windows XP/7/LINUX.
  • Implementation : NS2
  • NS2 Version : 2.28
  • Front End : OTCL (Object Oriented Tool Command Language)
  • Tool : Cygwin (To simulate in Windows OS)

REFERENCE:

Jen-Feng Huang, Guey-Yun Chang, and Gen-Huey Chen, “A Historical-Beacon-Aided Localization Algorithm for Mobile Sensor Networks”, IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 14, NO. 6, JUNE 2015.

ieee projects for cse 2015 with abstract and base paper

IEEE Projects for CSE /IT / ECE / EEE 2015 with Abstracts and Base papers

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A Rank Correlation Based Detection against Distributed Reflection DoS Attacks

A Rank Correlation Based Detection against Distributed Reflection DoS Attacks

ABSTRACT:

DDoS presents a serious threat to the Internet since its inception, where lots of controlled hosts flood the victim site with massive packets. Moreover, in Distributed Reflection DoS (DRDoS), attackers fool innocent servers (reflectors) into flushing packets to the victim. But most of current DRDoS detection mechanisms are associated with specific protocols and cannot be used for unknown protocols. It is found that because of being stimulated by the same attacking flow, the responsive flows from reflectors have inherent relations: the packet rate of one converged responsive flow may have linear relationships with another. Based on this observation, the Rank Correlation based Detection (RCD) algorithm is proposed. The preliminary simulations indicate that RCD can differentiate reflection flows from legitimate ones efficiently and effectively, thus can be used as a useable indicator for DRDoS.

PROJECT OUTPUT VIDEO: (Click the below link to see the project output video):

EXISTING SYSTEM:

There have been some packet-level defense methods. Filtering all incoming response packets, which is of low cost, will result in no general access to the remote server. Inspecting packet content and tracking protocol status maybe helpful, but need a lot of computation which is also vulnerable to attacks. Along with more protocols being exploited to launch DRDoS, countermeasures must consider a list of possible protocols with each one treated specifically, and the list needs to be updated in time. So we urgently expect some protocol independent methods to help detecting most kinds of DRDoS.

DISADVANTAGES OF EXISTING SYSTEM:

PROPOSED SYSTEM:

We investigate the basic traffic pattern introduced near the victim under DRDoS, and propose a general detection method: the Rank Correlation based Detection (RCD). RCD is protocol independent and its computation cost is not affected by network throughput. In RCD, once an attack alarm rises, upstream routers will sample and test rank correlation of suspicious flows and use the correlation value for further detection. Correlation has been successfully used in DDoS detection, e.g., correlation coefficient has been successfully employed to discriminate DDoS attacks from flash crowds. As we know, it is the first time that DRDoS is analyzed and detected using correlation.

ADVANTAGES OF PROPOSED SYSTEM:

The preliminary simulations indicate that RCD can differentiate reflection flows from legitimate ones efficiently and effectively, thus can be used as a useable indicator for DRDoS.

ALGORITHM USED:

Spearman’s Rank Correlation

The well-known Pearson’s correlation coefficient is suitable for describing the linear relationship. However, due to the background traffic and delay, the linearity may not be obvious. And Pearson’s correlation is sensitive to outliers introduced by traffic bursts. Through experimental comparisons, Spearman’s rank correlation coefficient (Spearman’s rho) is more suitable for detection, where a raw value is converted to a ranked value and then Pearson’s correlation is applied. For a given value, its ranked value is the average of its position(s) in the ascending order of all values.

SYSTEM CONFIGURATION:-

HARDWARE REQUIREMENTS:-

ü Processor                  –        Pentium –IV

ü Speed                        –        1.1 Ghz

ü RAM                         –        512 MB(min)

ü Hard Disk                 –        40 GB

ü Key Board                –        Standard Windows Keyboard

ü Mouse                       –        Two or Three Button Mouse

ü Monitor                     –        LCD/LED

 

SOFTWARE REQUIREMENTS:-

v   Operating System          : LINUX

v   Tool                               : Network Simulator-2

v   Front End                      : OTCL (Object Oriented Tool Command  Language)

REFERENCE:

 

Wei Wei, Feng Chen, Yingjie Xia, and Guang Jin, “A Rank Correlation Based Detection against Distributed Reflection DoS Attacks”, IEEE COMMUNICATIONS LETTERS, VOL. 17, NO. 1, JANUARY 2013