## 1 Introduction  
A wireless sensor network (WSN) is an ad-hoc network technology that may consist of thousands of autonomic and self-organizing nodes. To achieve low cost, WSN nodes use components that impose severe resource and power constraints. A typical WSN node has only a couple of Millions of Instructions Per Second (MIPS) processing speed, tens of kilobytes program and data memories, and error prone low power radio connectivity. As a result, a WSN node combines environmental sensing, data processing, and wireless networking in constantly changing environment with extremely low energy and cost. The applications for sensor networks range from home and industrial environments to military uses. Due to the _ad-hoc_ nature of the network, WSN nodes are battery powered, which makes the network lifetime the primary concern [1]. The highest energy efficiency can be achieved with low duty cycle operation, in which a node saves energy in a low power state and is active only a fraction of time. This is the normal method in most proposals and standards in WSNs [2; 3; 4]. Although the low duty cycle operation reduces capacity and increases delays, it is deemed suitable for WSNs as traffic load is usually light and network lifetime is a key factor. WSNs are deployed for different uses, each requiring unique Quality of Service (QoS). In legacy communication networks, QoS is commonly expressed and managed by throughput, latency, jitter, and reliability. However, due to the unique characteristics, especially dynamic wireless multihop routing and resource constraints, these are not feasible for WSNs as such. QoS varies as network load, number of nodes, topology, and operating conditions change. Problems occurring on a single link propagate via established routes to other parts of network. Still, a sensor reading may indicate a potentially life-threatening situation, thus requiring timely delivery. The scope of this paper is detailed in Figure 1. In this paper, we analyze how QoS is affected by low duty cycling, synchronization, unreliable wireless communications causing packet errors, and buffer space limitations due to the resource constraints. For energy, we assume the trade-off between performance and battery life to be a result of duty cycling. To save more energy, lower duty cycle is applied. We have concluded two methods to measure QoS in low duty cycle WSNs. The first is availability, which expresses the probability to receive a new measurement from a node within a certain waiting period. The second is reliability, which means the probability to transfer a single measurement through the network within certain latency. Buffer space control, duty cycle control, and access cycle length adjustment are methods for managing the availability and reliability, and used in the evaluations to present factors for resulting performance. Based on the results, we propose a data forwarding algorithm suitable for resource constrained WSNs to guarantee end-to-end reliability. Figure 1: Factors affecting availability and reliability evaluated in this paper. For realistic results, availability and reliability are evaluated in two low power, multihop WSNs: ZigBee [5] and TUTWSN. These network technologies are not compared to each other, but represent different approaches to channel access and routing. ZigBee technology is emerging as a _de-facto_ standard for WSNs, and represents a general contention-based radio link technology. TUTWSN, on the other hand, is a university proposal which represents a general contention-free link technology using Time Division Multiple Access (TDMA) without carrier sensing. To our knowledge, this paper is the first to evaluate the effect of duty cycling to availability and reliability in WSNs, and to present such analysis on ZigBee and a TDMA-based large scale WSNs. The rest of the paper is organized as follows. Availability and reliability in WSNs discussed Section 2, including analysis of related research. Section 3 presents the ZigBee and TUTWSN technologies.