Critical-Path Aware Scheduling for Latency Efficient Broadcast in Duty-Cycled Wireless Sensor Networks

Minimum latency scheduling has arisen as one of the most crucial problems for broadcasting in duty-cycled Wireless Sensor Networks (WSNs). Typical solutions for the broadcast scheduling iteratively search for nodes able to transmit a message simultaneously. Other nodes are prevented from transmissions to ensure that there is no collision in a network. Such collision-preventions result in extra delays for a broadcast and may increase overall latency if the delays occur along critical paths of the network. To facilitate the broadcast latency minimization, we propose a novel approach, critical-path aware scheduling (CAS), which schedules transmissions with a preference of nodes in critical paths of a duty-cycled WSN. This paper presents two schemes employing CAS which produce collision-free and collision-tolerant broadcast schedules, respectively. The collision-free CAS scheme guarantees an approximation ratio of in terms of latency, where denotes the maximum node degree in a network. By allowing collision at noncritical nodes, the collision-tolerant CAS scheme reduces up to 10.2 percent broadcast latency compared with the collision-free ones while requiring additional transmissions for the noncritical nodes experiencing collisions. Simulation results show that broadcast latencies of the two proposed schemes are significantly shorter than those of the existing methods

Low Power, Low Delay: Opportunistic Routing meets Duty Cycling

Traditionally, routing in wireless sensor networks consists of two steps: First, the routing protocol selects a next hop, and, second, the MAC protocol waits for the intended destination to wake up and receive the data. This design makes it difficult to adapt to link dynamics and introduces delays while waiting for the next hop to wake up. In this paper we introduce ORW, a practical opportunistic routing scheme for wireless sensor networks. In a dutycycled setting, packets are addressed to sets of potential receivers and forwarded by the neighbor that wakes up first and successfully receives the packet. This reduces delay and energy consumption by utilizing all neighbors as potential forwarders. Furthermore, this increases resilience to wireless link dynamics by exploiting spatial diversity. Our results show that ORW reduces radio duty-cycles on average by 50% (up to 90% on individual nodes) and delays by 30% to 90% when compared to the state of the art

Collaborative Communication And Storage In Energy-Synchronized Sensor Networks

In a battery-less sensor network, all the operation of sensor nodes are strictly constrained by and synchronized with the fluctuations of harvested energy, causing nodes to be disruptive from network and hence unstable network connectivity. Such wireless sensor network is named as energy-synchronized sensor networks. The unpredictable network disruptions and challenging communication environments make the traditional communication protocols inefficient and require a new paradigm-shift in design. In this thesis, I propose a set of algorithms on collaborative data communication and storage for energy-synchronized sensor networks. The solutions are based on erasure codes and probabilistic network codings. The proposed set of algorithms significantly improve the data communication throughput and persistency, and they are inherently amenable to probabilistic nature of transmission in wireless networks. The technical contributions explore collaborative communication with both no coding and network coding methods. First, I propose a collaborative data delivery protocol to exploit the optimal performance of multiple energy-synchronized paths without network coding, i.e. a new max-flow min-variance algorithm. In consort with this data delivery protocol, a localized TDMA MAC protocol is designed to synchronize nodes\u27 duty-cycles and mitigate media access contentions. However, the energy supply can change dynamically over time, making determined duty cycles synchronization difficult in practice. A probabilistic approach is investigated. Therefore, I present Opportunistic Network Erasure Coding protocol (ONEC), to collaboratively collect data. ONEC derives the probability distribution of coding degree in each node and enable opportunistic in-network recoding, and guarantee the recovery of original sensor data can be achieved with high probability upon receiving any sufficient amount of encoded packets. Next, OnCode, an opportunistic in-network data coding and delivery protocol is proposed to further improve data communication under the constraints of energy synchronization. It is resilient to packet loss and network disruptions, and does not require explicit end-to-end feedback message. Moreover, I present a network Erasure Coding with randomized Power Control (ECPC) mechanism for collaborative data storage in disruptive sensor networks. ECPC only requires each node to perform a single broadcast at each of its several randomly selected power levels. Thus it incurs very low communication overhead. Finally, I propose an integrated algorithm and middleware (Ravine Stream) to improve data delivery throughput as well as data persistency in energy-synchronized sensor network

Dependable k-coverage algorithms for sensor networks

Redundant sensing capabilities are often required in sensor network applications due to various reasons, e.g. robustness, fault tolerance, or increased accuracy. At the same time high sensor redundancy offers the possibility of increasing network lifetime by scheduling sleep intervals for some sensors and still providing continuous service with help of the remaining active sensors. In this paper centralized and distributed algorithms are proposed to solve the k-coverage sensing problem and maximize network lifetime. When physically possible, the proposed robust Controlled Greedy Sleep Algorithm provides guaranteed service independently of node and communication errors in the network. The performance of the algorithm is illustrated and compared to results of a random solution by simulation examples