Online Adaptive Compression in Delay Sensitive Wireless Sensor Networks

NANO SCIENTIFIC RESEARCH CENTRE PVT.LTD.,  AMEERPET, HYD

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DOT NET PROJECTS LIST--2013

DOT NET 2013 IEEE PAPERS

Online Adaptive Compression in Delay

Sensitive Wireless Sensor Networks

ABSTRACT:

                Compression, as a popular technique to reduce data size by exploiting data redundancy, can be used in delay sensitive wireless sensor networks (WSNs) to reduce end-to-end packet delay as it can reduce packet transmission time and contention on the wireless channel. However, the limited computing resources at sensor nodes make the processing time of compression a nontrivial factor in the total delay a packet experiences and must be carefully examined when adopting compression. In this paper, we first study the effect of compression on data gathering in WSNs under a practical compression algorithm. We observe that compression does not always reduce packet delay in a WSN as commonly perceived, whereas its effect is jointly determined by the network configuration and hardware configuration. Based on this observation, we then design an adaptive algorithm to make online decisions such that compression is only performed when it can benefit the overall performance. We implement the algorithm in a completely distributed manner that utilizes only local information of individual sensor nodes. Our extensive experimental results show that the algorithm demonstrates good adaptiveness to network dynamics and maximizes compression benefit.

EXISTING SYSTEM:

            Delay sensitive wireless sensor networks (WSNs) require real-time delivery of sensing data to the data sink. Such networks are widely adopted in various real-time applications including traffic monitoring, hazard detection and battlefield surveillance, where decisions should be made promptly once the emergent events occur. Compared to general WSNs where energy efficiency is the primary design concern, delay sensitive WSNs demand more on minimizing the communication delay during data delivery.

            In WSNs, compression reduces the data amount by exploiting the redundancy resided in sensing data. The reduction can be measured by the compression ratio, defined as the original data size divided by the compressed data size. A higher compression ratio indicates larger reduction on the data amount and results in shorter communication delay. Thus, much work in the literature has been endeavored to achieve better compression ratio for sensing data. However, from the implementation perspective, most of the compression algorithms are complex and time consuming procedures running on sensor nodes which are very resource constrained. As the processing time of compression could not be simply neglected in such nodes, the effect of compression on the total delay during data delivery becomes a trade-off between the reduced communication delay and the increased processing time. As a result, compression may increase rather than decrease the total delay when the processing time is relatively long.

PROPOSED SYSTEM:

            We approach this problem from a different and orthogonal angle by considering the effect of data compression. Compression was initially adopted as an effective approach to saving energy in WSNs. In fact, it can also be used to reduce the communication delay in delay sensitive WSNs. we will first analyze this effect in a typical data gathering scheme in WSNs where each sensor collects data continuously and delivers all the packets to a data sink. Then, we will design an online adaptive algorithm that performs compression only when compression can actually reduce the total delay to guarantee the network to achieve the shortest total delay under all conditions. To analyze the effect of compression, we need to first obtain the processing time of compression, which depends on several factors, including the compression algorithm, processor architecture, CPU frequency, and the compression data) as an example, which is a lossless compression algorithm suitable for sensor nodes. Our experiments on typical sensing data reveal that the numerous compression algorithms, in such a system is comparable to the transmission time of packets, thus cannot be simply ignored. To support the study in large scale WSNs, we utilize a software estimation approach to providing runtime measurement of the algorithm execution time in the NS-2 simulator Our simulation results show that compression may lead to several times longer overall delay under light traffic loads, while it can significantly reduce the delay under heavy traffic loads and increase maximum throughput. Since the effect of compression varies heavily with network traffic and hardware configurations, we design an

online adaptive algorithm that dynamically makes compression  decisions to accommodate the changing state of WSNs. In the algorithm, we adopt a queuing model to estimate the queuing behavior of sensors with the assistance of only local information of each sensor node. Based on the queuing model, the algorithm predicts the compression effect on the average packet delay and performs compression only when it can reduce packet delay. By conducting extensive simulations on the NS-2 simulator, we show that the adaptive algorithm can make decisions properly and yield near optimal performance under various network configurations.

HARDWARE REQUIREMENTS

Processor: Pentium IV.

Operating System: Windows XP (Service Pack3)

RAM: 1GB

Working Environment: Visual Studio 2010

Language: C#

MODULES:

·         Server Module

·         Compression Module

·         Decompression Module