CNS: a new energy efficient transmission scheme for wireless sensor networks

We present in this paper a new energy-efficient communication scheme called CNS (Compression with Null Symbol) that combines the power of data compression and communication through silent symbol. The concept of communication through silent symbol is borrowed from the energy efficient schemes proposed in Sinha (Proceedings of 6th IEEE consumer communications and networking conference (CCNC), Las Vegas, pp. 1–5, 2009), Ghosh et al. (Proceedings of 27th IEEE international performance computing and communications conference (IPCCC), USA, pp. 85–92, 2008), and Sinha and Sinha (Proceedings of international conference on distributed computing and internet technologies (ICDCIT), LNCS, pp. 139–144, 2008). We show that the average theoretical energy saving at the transmitter by CNS is 62.5%, assuming an ideal channel and for equal likelihood of all possible binary strings of a given length. Next, we propose a transceiver design that uses a hybrid modulation scheme utilizing FSK and ASK so as to keep the cost/complexity of the radio devices low. Considering an additive white gaussian noise (AWGN) channel and a non-coherent detection based receiver, CNS shows a saving in transmitter energy by 30% when compared to binary FSK, for equal likelihood of all possible binary strings of a given length. Simultaneously, there is a saving of 50% at the receiver for all types of data modulation due to halving of the transmitted data duration, compared to binary encoding. In contrast, RBNSiZeComm proposed in Sinha (Proceedings of 6th IEEE consumer communications and networking conference (CCNC), Las Vegas, pp. 1–5, 2009), TSS proposed in Ghosh et al. (Proceedings of 27th IEEE international performance computing and communications conference (IPCCC), USA, pp. 85–92, 2008) and RZE proposed in Sinha and Sinha (Proceedings of international conference on distributed computing and internet technologies (ICDCIT), LNCS, pp. 139–144, 2008) generate average transmitter energy savings of about 41, 20, and 35.2%, respectively. Also, at the receiver side, while RBNSiZeComm does not generate any saving, TSS and RZE produce about 36.9 and 12.5% savings on an average, respectively. Considering certain data types that may occur in the context of some wireless sensor networks (WSN) based applications (e.g., remote healthcare, agricultural WSNs, etc.), our simulation results demonstrate that for AWGN noisy channels, the transmitter side savings vary from about 33–50% on an average, while for RBNSiZeComm, this saving is about 33–61% on the same data set (Sinha in Proceedings of 6th IEEE consumer communications and networking conference (CCNC), Las Vegas, pp. 1–5, 2009). Thus, taking into account the low cost/complexity of the proposed transceiver, these results clearly establish that CNS can be a suitable candidate for communication in low power wireless sensor networks, such as in remote healthcare applications, body area networks, home automation, WSNs for agriculture and many others.