Online adaptive compression in delay sensitive wireless sensor networks

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.