Adaptive Fault Tolerant QoS Control Algorithms for Maximizing System Lifetime of Query-Based Wireless Sensor Networks

Adaptive Fault Tolerant QoS Control Algorithms for Maximizing System Lifetime of Query-Based Wireless Sensor Networks

Abstract

Data sensing and retrieval in wireless sensor systems have a widespread application in areas such as security and surveillance monitoring, and command and control in battlefields. In query-based wireless sensor systems, a user would issue a query and expect a response to be returned within the deadline. While the use of fault tolerance mechanisms through redundancy improves query reliability in the presence of unreliable wireless communication and sensor faults, it could cause the energy of the system to be quickly depleted. Therefore, there is an inherent tradeoff between query reliability vs. energy consumption in query-based wireless sensor systems. In this paper, we develop adaptive fault tolerant quality of service (QoS) control algorithms based on hop-by-hop data delivery utilizing “source” and “path” redundancy, with the goal to satisfy application QoS requirements while prolonging the lifetime of the sensor system. We develop a mathematical model for the lifetime of the sensor system as a function of system parameters including the “source” and “path” redundancy levels utilized. We discover that there exists optimal “source” and “path” redundancy under which the lifetime of the system is maximized while satisfying application QoS requirements. Numerical data are presented and validated through extensive simulation, with physical interpretations given, to demonstrate the feasibility of our algorithm design.

Architecture

Architecture of WSN

Algorithm

1. Adaptive fault tolerant QoS control (AFTQC) algorithm:

Algorithm developed in this paper takes two forms of redundancy. The first form is path redundancy. That is, instead of using a single path to connect a source cluster to the processing center, mp disjoint paths may be used. The second is source redundancy. That is, instead of having one sensor node in a source cluster return requested sensor data, ms sensor nodes may be used to return readings to cope with data transmission and/or sensor faults. The above architecture illustrates a scenario in which mp = 2 (two paths going from the CH to the processing center) and ms = 5 (five SNs returning sensor readings to the CH).

2. Clustering Algorithm:

A clustering algorithm that aims to fairly rotate SNs to take the role of CHs has been used to organize sensors into clusters for energy conservation purposes. The function of a CH is to manage the network within the cluster, gather sensor reading data from the SNs within the cluster, and relay data in response to a query. clustering algorithm is executed during the system lifetime.

• Aggregation of readings

• Each cluster has a CH

• Users issue queries through any CH.

• CH that receives the query is called the Processing Center (PC)

• Each non-CH node selects the CH candidate with the highest residual energy, sends it a cluster join message (includes the non-CH node’s location). The CH will acknowledge this message.

• Randomly rotates role of CH among nodes -> nodes consume their energy evenly

Existing System:

Existing research efforts related to applying redundancy to satisfy QoS requirements in query-based WSNs fall into three categories: traditional end-to-end QoS, reliability assurance, and application specific QoS . Traditional end-to-end QoS solutions are based on the concept of end-to-end QoS requirements. The problem is that it may not be feasible to implement end-to-end QoS in WSNs due to the complexity and high cost of the protocols for resource constrained sensors.

This method does not consider the reliability issue.

Disadvantages:

1. Complexity and high cost of the protocols for resource constrained sensors

2. Does not consider the reliability issue.

3. Does not consider energy issues.

4. Data delivery such as reliability and timelines are not considered.

Proposed System:

In this paper, we develop adaptive fault tolerant quality of service (QoS) control algorithms based on hop-by-hop data delivery utilizing “source” and “path” redundancy, with the goal to satisfy application QoS requirements while prolonging the lifetime of the sensor system. We develop a mathematical model for the lifetime of the sensor system as a function of system parameters including the “source” and “path” redundancy levels utilized. We discover that there exists optimal “source” and “path” redundancy under which the lifetime of the system is maximized while satisfying application QoS requirements.

Advantages:

1. To applying redundancy to satisfy application specified reliability and timeliness requirements for query-based WSNs.

2. We develop the notion of “path” and “source” level redundancy

3. Lifetime of the system is maximized.

4. Timeliness, Multiple data delivery speed options.

5. Reliability, Multi-path forwarding.

Modules:

1. General Approach

In this paper we are also interested in applying redundancy to satisfy application specified reliability and timeliness requirements for query-based WSNs. Moreover, we aim to determine the optimal redundancy level that could satisfy QoS requirements while prolonging the lifetime of the WSN. Specifically, we develop the notion of “path” and “source” level redundancy. When given QoS requirements of a query, we identify optimal path and source redundancy such that not only QoS requirements are satisfied, but also the lifetime of the system is maximized. We develop adaptive fault tolerant QoS control (AFTQC) algorithms based on hop-by-hop data delivery to achieve the desired level of redundancy and to eliminate energy expended for maintaining routing paths in the WSN.

2. Software Fault

For source redundancy, ms SNs are used for returning sensor readings. If we consider both hardware and software failures of SNs, the system will fail if the majority of SNs does not return sensor readings (due to hardware failure), or if the majority of SNs returns sensor readings incorrectly (due to software failure). Assume that all SNs have the same software failure probability, denoted by qs. Also assume that all sensors that sense a given event make the same measurements. The probability that the majority of ms SNs failing to return sensor readings due to hardware failure, and the second expression is the probability that the majority of ms SNs returning sensor readings but no majority of them agrees on the same sensor reading as the output because of software failure.

3. Data Aggregation

The analysis performed thus far assumes that a source CH does not aggregate data. The CH may receive up to ms redundant sensor readings due to source redundancy but will just forward the first one received to the PC. Thus, the data packet size is the same. For more sophisticated scenarios, conceivably the CH could also aggregate data for query processing and the size of the aggregate packet may be larger than the average data packet size. We extend the analysis to deal with data aggregation in two ways. The first is to set a larger size for the aggregated packet that would be transmitted from a source CH to the PC. This will have the effect of favoring the use of a smaller number of redundant paths (i.e., mp) because more energy would be expended to transmit aggregate packets from the source CH to the PC. The second is for the CH to collect a majority of sensor readings from its sensors before data are aggregated and transmitted to the PC.

4. Forward Traffic

The analysis performed in the paper considers only the reserve traffic for response propagation from SNs to the PC but neglects the forward traffic for query dissemination from the sink to the CH and SNs. The reliability and energy consumption of the forward traffic due to hop-by-hop query delivery can be calculated by following a similar analysis as for the reverse traffic. The success probability (Rq) would be adjusted by considering the forward traffic and reverse traffic together as a series system. The energy consumption of a query (Eq) would be used to calculate the maximum number of queries the system can possibly process.

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENTS:

· System : Pentium IV 2.4 GHz.

· Hard Disk : 40 GB.

· Floppy Drive : 1.44 Mb.

· Monitor : 15 VGA Color.

· Mouse : Logitech.

· Ram : 512 MB.

SOFTWARE REQUIREMENTS:

· Operating system : Windows XP Professional.

· Coding Language : C#.NET