Sensor networks that carry heterogeneous traffics and are responsible for reporting very time-critical important events necessitate an efficient and robust data dissemination framework. Designing such a framework, that can achieve both the reliability and delay guarantee while preserving the energy efficiency, namely multi-constrained QoS (MCQoS), is a challenging problem. Although there have been many research works on QoS routing for sensor networks, to the best of our knowledge, no one addresses the above three service parameters all together. In this paper, we propose a new aggregate routing model and a distributed aggregate routing algorithm (DARA) that implements the model for achieving MCQoS. DARA is designed for multi-sink, multipath and location aware network architecture. We develop probabilistic models for multipath reliability constraint, sojourn time of a packet at an intermediary node and node energy consumption. Delay-differentiated multi-speed packet forwarding and in-node packet scheduling mechanisms are also incorporated with DARA. The results of the simulations demonstrate that DARA effectively improves the reliability, delay guarantee and energy efficiency.

For successful data collection in wireless sensor networks, it is important to ensure that the required delivery ratio is maintained while keeping a fair rate for every sensor. Furthermore, emerging high-rate applications might require complete reliability and the transfer of large volume of data, where persistent congestion might occur. These requirements demand a complete but efficient solution for data transport in sensor networks which reliably transports data from many sources to one or more sinks, avoids congestion and maintains fairness. In this paper, we propose congestion-aware and rate-controlled reliable transport (CRRT), an efficient and low-overhead data transport mechanism for sensor networks. CRRT uses efficient MAC retransmission to increase one-hop reliability and end-to-end retransmission for loss recovery. It also controls the total rate of the sources centrally, avoids the congestion in the bottleneck based on congestion notifications from intermediate nodes and centrally assigns the rate to the sources based on rate assignment policy of the applications. Performance of CRRT is evaluated in NS-2 and simulation results demonstrate the effectiveness of CRRT.

Congestion in WSN increases the energy dissipation rates of sensor nodes as well as the loss of packets and thereby hinders fair and reliable event detection. We find that one of the key reasons of congestion in WSN is allowing sensing nodes to transfer as many packets as possible. This is due to the use of CSMA/CA that gives opportunistic medium access control. In this paper, we propose an energy efficient congestion avoidance protocol that includes source count based hierarchical and load adaptive medium access control and weighted round robin packet forwarding. We also propose in-node fair packet scheduling to achieve fair event detection. The results of simulation show our scheme exhibits more than 90% delivery ratio even under bursty traffic condition which is good enough for reliable event perception.

Broadband wireless access networks are promising technology for providing better end user services. For such networks, designing a scheduling algorithm that fairly allocates the available bandwidth to the end users and maximizes the overall network throughput is a challenging task. In this paper, we develop a centralized fair scheduling algorithm for IEEE 802.16 mesh networks that exploits the spatio-temporal bandwidth reuse to further enhance the network throughput. The proposed mechanism reduces the length of a transmission round by increasing the number of non-contending links that can be scheduled simultaneously. We also propose a greedy algorithm that runs in polynomial time. Performance of the proposed algorithms is evaluated by extensive simulations. Results show that our algorithms achieve higher throughput than that of the existing ones and reduce the computational complexity.