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

Hidden Terminal-Aware Contention Resolution with an Optimal Distribution

Achieving low-power operation in wireless sensor networks with high data load or bursty traffic is challenging. The hidden terminal problem is aggravated with increased amounts of data in which traditional backoff-based contention resolution mechanisms fail or induce high latency and energy costs. We analyze and optimize Strawman, a receiver-initiated contention resolution mechanism that copes with hidden terminals. We propose new techniques to boost the performance of Strawman while keeping the resolution overhead small. We finally validate our improved mechanism via experiments

Optimal scaling of the ADMM algorithm for distributed quadratic programming

This paper presents optimal scaling of the alternating directions method of multipliers (ADMM) algorithm for a class of distributed quadratic programming problems. The scaling corresponds to the ADMM step-size and relaxation parameter, as well as the edge-weights of the underlying communication graph. We optimize these parameters to yield the smallest convergence factor of the algorithm. Explicit expressions are derived for the step-size and relaxation parameter, as well as for the corresponding convergence factor. Numerical simulations justify our results and highlight the benefits of optimally scaling the ADMM algorithm.Comment: Submitted to the IEEE Transactions on Signal Processing. Prior work was presented at the 52nd IEEE Conference on Decision and Control, 201

Sparsity-promoting iterative learning control for resource-constrained control systems

We propose novel iterative learning control algorithms to track a reference trajectory in resource-constrained control systems. In many applications, there are constraints on the number of control actions, delivered to the actuator from the controller, due to the limited bandwidth of communication channels or battery-operated sensors and actuators. We devise iterative learning techniques that create sparse control sequences with reduced communication and actuation instances while providing sensible reference tracking precision. Numerical simulations are provided to demonstrate the effectiveness of the proposed control method.</p

The ADMM algorithm for distributed averaging : convergence rates and optimal parameter selection

We derive the optimal step-size and overrelaxationparameter that minimizes the convergence time oftwo ADMM-based algorithms for distributed averaging. Ourstudy shows that the convergence times for given step-size andover-relaxation parameters depend on the spectral propertiesof the normalized Laplacian of the underlying communicationgraph. Motivated by this, we optimize the edge-weights of thecommunication graph to improve the convergence speed evenfurther. The performance of the ADMM algorithms with ourparameter selection are compared with alternatives from theliterature in extensive numerical simulations on random graphs.QC 20150511</p

A metric for opportunistic routing in duty cycled wireless sensor networks

Opportunistic routing is widely known to have substantially better performance than traditional unicast routing in wireless networks with lossy links. However, wireless sensor networks are heavily duty-cycled, i.e. they frequently enter deep sleep states to ensure long network life-time. This renders existing opportunistic routing schemes impractical, as they assume that nodes are always awake and can overhear other transmissions. In this paper, we introduce a novel opportunistic routing metric that takes duty cycling into account. By analytical performance modeling and simulations, we show that our routing scheme results in significantly reduced delay and improved energy efficiency compared to traditional unicast routing. The method is based on a new metric, EDC, that reflects the expected number of duty cycled wakeups that are required to successfully deliver a packet from source to destination. We devise distributed algorithms that find the EDC-optimal forwarding, i.e. the optimal subset of neighbors that each node should permit to forward its packets. We compare the performance of the new routing with ETX-optimal single path routing in both simulations and testbed-based experiments

Optimal control of linear systems with limited control actions: Threshold-based event-triggered control

We consider a finite-horizon linear-quadratic optimal control problem where only a limited number of control messages are allowed for sending from the controller to the actuator. To restrict the number of control actions computed and transmitted by the controller, we employ a threshold-based event-triggering mechanism that decides whether or not a control message needs to be calculated and delivered. Due to the nature of threshold-based event-triggering algorithms, finding the optimal control sequence requires minimizing a quadratic cost function over a nonconvex domain. In this paper, we first provide an exact solution to this nonconvex problem by solving an exponential number of quadratic programs. To reduce computational complexity, we then propose two efficient heuristic algorithms based on greedy search and the alternating direction method of multipliers technique. Later, we consider a receding horizon control strategy for linear systems controlled by event-triggered controllers, and we further provide a complete stability analysis of receding horizon control that uses finite-horizon optimization in the proposed class. Numerical examples testify to the viability of the presented design technique.</p