Kai-Wei Fan

CMAC: An energy-efficient MAC layer protocol using convergent packet forwarding for wireless sensor networks

Low duty cycle operation is critical to conserve energy in wireless sensor networks. Traditional wake-up scheduling approaches either require periodic synchronization messages or incur high packet delivery latency due to the lack of any synchronization. In this paper, we present the design of a new low duty-cycle MAC layer protocol called Convergent MAC (CMAC). CMAC avoids synchronization overhead while supporting low latency. By using zero communication when there is no traffic, CMAC allows operation at very low duty cycles. When carrying traffic, CMAC first uses any cast to wake up forwarding nodes, and then converges from route-suboptimal any cast with unsynchronized duty cycling to route-optimal unicast with synchronized scheduling. To validate our design and provide a usable module for the community, we implement CMAC in TinyOS and evaluate it on the Kansei testbed consisting of 105 XSM nodes. The results show that CMAC at 1% duty cycle significantly outperforms BMAC at 1% in terms of latency, throughput and energy efficiency. We also compare CMAC with other protocols using simulations. The results show for 1% duty cycle, CMAC exhibits similar throughput and latency as CSMA/CA using much less energy, and outperforms SMAC and GeRaF in all aspects.

Structure-Free Data Aggregation in Sensor Networks

Data aggregation protocols can reduce the communication cost, thereby extending the lifetime of sensor networks. Prior works on data aggregation protocols have focused on tree-based or cluster-based structured approaches. Although structured approaches are suited for data gathering applications, they incur high maintenance overhead in dynamic scenarios for event-based applications. The goal of our work is to design techniques and protocols that lead to efficient data aggregation without explicit maintenance of a structure. As packets need to converge spatially and temporally for data aggregation, we propose two corresponding mechanisms - data-aware anycast at the MAC layer and randomized waiting at the application layer. We model the performance of the combined protocol that uses both the approaches and show that our analysis matches with the simulations. Using extensive simulations and experiments on a testbed with implementation in TinyOS, we study the performance and potential of structure-free data aggregation.

Steady and fair rate allocation for rechargeable sensors in perpetual sensor networks

Renewable energy enables sensor networks with the capability to recharge and provide perpetual data services. Due to low recharging rates and the dynamics of renewable energy such as solar and wind power, providing services without interruptions caused by battery runouts is non-trivial. Most environment monitoring applications require data collection from all nodes at a steady rate. The objective of this paper is to design a solution for fair and high throughput data extraction from all nodes in presence of renewable energy sources. Specifically, we seek to compute the lexicographically maximum data collection rate for each node, such that no node will ever run out of energy. We propose a centralized algorithm and an asynchronous distributed algorithm that can compute the optimal lexicographic rate assignment for all nodes. The centralized algorithm jointly computes the optimal data collection rate for all nodes along with the flows on each link, while the distributed algorithm computes the optimal rate when the routes are pre-determined. We prove the optimality for both the centralized and the distributed algorithms, and use a testbed with 155 sensor nodes to validate the distributed algorithm.

On the Potential of Structure-Free Data Aggregation in Sensor Networks

Data aggregation protocols can reduce the cost of communication, thereby extending the lifetime of sensor networks. Prior work on data aggregation protocols has focused on tree-based or cluster-based structured approaches. Although structured approaches are suited for data gathering applications, they incur high maintenance overhead in dynamic scenarios for event-based applications. The goal of our work is to design techniques and protocols that lead to efficient data aggregation without explicit maintenance of a structure. As packets need to converge spatially and temporally for data aggregation, we propose two corresponding mechanisms Data-Aware Anycast at the MAC layer and Randomized Waiting at the application layer. We model the performance of the combined protocol that uses both the approaches and show that our analysis matches with the simulations. Using extensive simulations and experiments on a testbed with implementation in TinyOS, we study the performance and potential of structure-free data aggregation.

CMAC: An Energy Efficient MAC Layer Protocol Using Convergent Packet Forwarding for Wireless Sensor Networks

Low duty cycle operation is critical to conserve energy in wireless sensor networks. Traditional wake-up scheduling approaches either require periodic synchronization messages or incur high packet delivery latency due to the lack of any synchronization. In this paper, we present the design of a new low duty-cycle MAC layer protocol called Convergent MAC (CMAC). CMAC avoids synchronization overhead while supporting low latency. By using zero communication when there is no traffic, CMAC allows operation at very low duty cycles. When carrying traffic, CMAC first uses any cast to wake up forwarding nodes, and then converges from route-suboptimal any cast with unsynchronized duty cycling to route-optimal unicast with synchronized scheduling. To validate our design and provide a usable module for the community, we implement CMAC in TinyOS and evaluate it on the Kansei testbed consisting of 105 XSM nodes. The results show that CMAC at 1% duty cycle significantly outperforms BMAC at 1% in terms of latency, throughput and energy efficiency. We also compare CMAC with other protocols using simulations. The results show for 1% duty cycle, CMAC exhibits similar throughput and latency as CSMA/CA using much less energy, and outperforms SMAC and GeRaF in all aspects.

Scalable data aggregation for dynamic events in sensor networks

Computing and maintaining network structures for efficient data aggregation incurs high overhead for dynamic events where the set of nodes sensing an event changes with time. Moreover, structured approaches are sensitive to the waiting-time which is used by nodes to wait for packets from their children before forwarding the packet to the sink. Although structureless approaches can address these issues, the performance does not scale well with the network size. We propose a semi-structured approach that uses a structureless technique locally followed by Dynamic Forwarding on an implicitly constructed packet forwarding structure to support network scalability. The structure, ToD, is composed of multiple shortest path trees. After performing local aggregation, nodes dynamically decide the forwarding tree based on the location of the sources. The key principle behind ToD is that adjacent nodes in a graph will have low stretch in one of these trees in ToD, thus resulting in early aggregation of packets. Based on simulations on a 2000 nodes network and real experiments on a 105 nodes Mica2-based network, we conclude that efficient aggregation in large scale networks can be achieved by our semi-structured approach.

Dynamic Forwarding over Tree-on-DAG for Scalable Data Aggregation in Sensor Networks

Computing and maintaining network structures for efficient data aggregation incurs high overhead for dynamic events where the set of nodes sensing an event changes with time. Moreover, structured approaches are sensitive to the waiting time that is used by nodes to wait for packets from their children before forwarding the packet to the sink. Although structureless approaches can address these issues, the performance does not scale well with the network size. We propose tree on DAG (ToD), a semistructured approach that uses dynamic forwarding on an implicitly constructed structure composed of multiple shortest path trees to support network scalability. The key principle behind ToD is that adjacent nodes in a graph will have low stretch in one of these trees in ToD, thus resulting in early aggregation of packets. Based on simulations on a 2,000-node network and real experiments on a 105-node Mica2-based network, we conclude that efficient aggregation in large-scale networks can be achieved by our semistructured approach.

Distributed online data aggregation for large scale sensor networks

To benefit from data aggregation in large scale sensor networks, an aggregation point, i.e. the place where data are aggregated, must be close to sources. In event triggered sensor networks, this can be achieved by dynamically constructing a tree connecting the sources rooted at a nearby node. However, this incurs high control and maintenance overhead. With static trees, the distance (Delta) between sources and the aggregation point can be as high as O(n) where n is the number of nodes in the network. This diminishes the benefit of data aggregation, thereby limiting the scalability of static trees. In this paper we propose AFT (alternative forwarding tree), a structure with multi-level overlapping clusters. Packet forwarding decisions on AFT are made on the fly when packets are being forwarded and it bounds the distance between the aggregation point and sources by O(delta) irrespective of network size, where delta is the diameter of the event. This guarantees that packets can be aggregated near sources without the overhead of constructing a dynamic structure and therefore is scalable. We prove that in the worst case, AFT guarantees aggregation at a node that is at most 2(1 + radic13)delta away from the sources.

Distributed roadmap aided routing in sensor networks

Communication between arbitrary pairs of nodes has become critical to support in emerging sensor networking applications. Traditional routing techniques for multi-hop wireless networks either require high control overhead in computing and maintaining routes, or may lead to unbounded route-stretch. In order to bound the route-stretch, we propose a distributed shortest-path roadmap based routing paradigm that embodies two ideas: routing hole approximation that summaries the critical information about hole boundaries and controlled advertisement that advertises the boundary information of each hole within limited neighborhoods. We show that our approach makes a desired tradeoff between the worst case route-stretch and the message overhead through both analysis and simulations.