In wireless sensor networks, congestion not only leads to packet loss, but also increases delays and lowers network throughput with a lot of energy wastage due to retransmissions. Therefore an effective solution should be proposed to mitigate congestion to increase energy efficiency and prolong the lifetime of network. Traditional solutions work in an open-loop fashion. In this paper, we propose a novel decentralized and weighted fairness guaranteed congestion control protocol (WFCC). WFCC introduces node weight to reflect the importance of each node, and uses the ratio of packet inter-arrival time to packet service time as congestion metric. Based on node weight and congestion metric, WFCC divides time axis into period sequences, and uses closed-loop control method to mitigate congestion by adjusting the incoming data rate of each node periodically. Importantly, WFCC introduces a weighted fairness metric and gives its lower bound for the first time, i.e., 1 - (10c/9)2where 0 <; c <; 0.2 is a constant. The results of simulation show that the weighted fairness of WFCC achieves 95% on the average, which is much better than the existing rate-based congestion control protocol. Moreover, WFCC achieves 50% and 19% gains in network throughput and weighted fairness on the average, compared with PCCP that is the state-of-art rate control based congestion control protocol.

The problem of aggregation scheduling in wireless sensor networks is widely studied in order to minimize aggregation latency and prevent collisions. Many aggregation scheduling algorithms have been designed for time-critical applications. However, all of the existing work on aggregation scheduling suffers from packet losses due to the lossy links in wireless sensor networks. We observe that high data loss rates of over 90% often occur using existing scheduling algorithms when the link qualities are poor. This makes the aggregation scheduling algorithms impractical in real applications. This paper makes aggregation scheduling usable by proposing an opportunistic approach to improve the delivery rate by elaborately selecting opportunistic links for sensor nodes. Simulation results verify the efficiency of our approach. One of our methods can achieve delivery rates of close to 100% in various network conditions.

Data aggregation is an essential operation in wireless sensor networks and aggregation time is critical in many sensor network applications. This paper focuses on the minimum-time aggregation scheduling problem in multi-sink sensor networks (MS-MTAS problem). To the best of our knowledge, this is the first work on the MS-MTAS problem, which is NP-hard. In this paper, we first present a lower bound for MS-MTAS. Then, we design two approximation algorithms for MS-MTAS based on the maximal independent sets. The upper bounds of the aggregation time are, respectively, (min{26k-2, 2Δ})(maxki=1{Ri}-l)+2Δ and 23 maxki=1{Ri}+2Δ-23, where k is the number of sinks, A is the maximum vertex degree of the network graph and Riis the radius of the induced subgraph G[V(Ti)], with respect to sink Si. Here Tirefers to the tree generated by the corresponding algorithm and V(Ti) is the vertex set of Ti. We prove that the second algorithm is a nearly constant approximation. This demonstrates that nearly constant approximation can be achieved for MS-MTAS, which is the same as MTAS. Both algorithms are implemented by simulation and the simulation results verify the effectiveness of the proposed algorithms.

Data aggregation is an essential operation in wireless sensor network applications. This paper focuses on the data aggregation scheduling problem. Based on maximal independent sets, a distributed algorithm to generate a collision-free schedule for data aggregation in wireless sensor networks is proposed. The time latency of the aggregation schedule generated by the proposed algorithm is minimized using a greedy strategy. The latency bound of the schedule is 24D + 6 Delta + 16, where D is the network diameter and Delta is the maximum node degree. The previous data aggregation algorithm with least latency has the latency bound (Delta- Delta 1)R, where R is the network radius. Thus in our algorithm Delta contributes to an additive factor instead of a multiplicative factor, which is a significant improvement. To the best of our knowledge, the proposed algorithm is the first distributed algorithm for data aggregation scheduling. This paper also proposes an adaptive strategy for updating the schedule when nodes fail or new nodes join in a network. The analysis and simulation results show that the proposed algorithm outperforms other aggregation scheduling algorithms.