A lightweight framework for source-to-sink data transfer in wireless sensor networks

Lightweight protocols that are both bandwidth and power thrifty are desirable for sensor networks. In addition, for many sensor network applications, timeliness of data delivery at a sink that collects and interprets raw sensor data is of great importance. In this work, we propose a lightweight framework for source-to-sink data transfer in a wireless sensor network that is geared towards achieving the above two objectives. Our integrated framework consists of three elements: 1) simple labels that eliminate complex addressing requirements, 2) implicit routing that provides an inherent robustness during sleep/wake schedules, and 3) MAC layer anycast to support routing. Our framework, in addition, facilitates the self-organization of sensor nodes into a network that efficiently relays information from the sources to the sink. The key idea of our framework is to associate each sensor node with a hierarchical level with respect to a sink and using MAC layer anycast to simply further packets to higher levels towards the sink. There are no explicit route tables created or maintained; this eliminates the overhead due to route queries or updates, the need for complex processing and the memory requirements for caching routing information. Furthermore, with our framework, the energy costs of data transmission are evenly distributed across the nodes, thereby improving the longevity of the network. Our MAC layer anycast mechanism not only facilitates routing, but also reduces the number of MAC layer back-offs incurred and, consequently, the waiting times for data transmission. This in turn, improves the timeliness of data delivery at the sink. To summarize, our framework is a) energy efficient, b) inherently robust, and c) conceptually simple. We qualitatively assess our scheme to show its efficiency in terms of power consumption, robustness to failure, ease of setup. The results from our simulations and assessments demonstrate the aforementioned benefits and the viability and potential of using our framework.