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 nontrivial. 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 the presence of renewable energy sources. Specifically, we seek to compute the lexicographically maximum data collection rate and routing paths for each node such that no node will ever run out of energy. We propose a centralized algorithm and two distributed algorithms. The centralized algorithm jointly computes the optimal data collection rate for all nodes along with the flows on each link, the first distributed algorithm computes the optimal rate when the routing structure is a given tree, and the second distributed algorithm, although heuristic, jointly computes a routing structure and a high lexicographic rate assignment that is nearly optimum. We prove the optimality for the centralized and the first distributed algorithm, and use real test-bed experiments and extensive simulations to evaluate both of the distributed algorithms.

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. To simultaneously achieve the seemingly contradictory goals of energy efficiency and low latency, the design of a new low duty-cycle MAC layer protocol called Convergent MAC (CMAC) is presented. CMAC avoids synchronization overhead while supporting low latency. By using zero communication when there is no traffic, CMAC allows sensor nodes to operate at very low duty cycles. When carrying traffic, CMAC first uses anycast to wake up forwarding nodes, and then converges gradually from route-suboptimal anycast with unsynchronized duty cycling to route-optimal unicast with synchronized scheduling. To validate our design and provide a usable module for the research community, CMAC has been implemented in TinyOS and evaluated 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. The performance of CMAC is also compared with other protocols using simulations, in which the results show for 1% and lower duty cycles, CMAC exhibits similar throughput and latency as CSMA/CA using much less energy, and outperforms SMAC, DMAC and GeRaF in almost all aspects.

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.

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.

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.

In wireless sensor networks, the many-to-one data communication pattern induces high collision losses as multiple transmissions cause contention and interference along the paths from sources to the sink. This paper proposes a low-overhead MAC layer solution to address the high contention problem to improve system throughput and reduce energy consumption. Periods of bursty transmissions with reduced contention from neighboring nodes are exploited to efficiently clear up back- logged queues and improve the performance of CSMA. Through analytical modeling we characterize the expected performance improvement and show that it conforms to the simulation results. Using extensive simulations onns-2and experiments on the 49 node sensor network testbed (Kansei) running TinyOS, we evaluate the performance of our solution.

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.

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 Anycastat the MAC layer andRandomized Waitingat 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.

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.

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.