Finite Horizon Online Lazy Scheduling with Energy Harvesting Transmitters over Fading Channels

Lazy scheduling, i.e. setting transmit power and rate in response to data traffic as low as possible so as to satisfy delay constraints, is a known method for energy efficient transmission.This paper addresses an online lazy scheduling problem over finite time-slotted transmission window and introduces low-complexity heuristics which attain near-optimal performance.Particularly, this paper generalizes lazy scheduling problem for energy harvesting systems to deal with packet arrival, energy harvesting and time-varying channel processes simultaneously. The time-slotted formulation of the problem and depiction of its offline optimal solution provide explicit expressions allowing to derive good online policies and algorithms

Real-time scheduling for energy harvesting sensor nodes

Energy harvesting has recently emerged as a feasible option to increase the operating time of sensor networks. If each node of the network, however, is powered by a fluctuating energy source, common power management solutions have to be reconceived. This holds in particular if real-time responsiveness of a given application has to be guaranteed. Task scheduling at the single nodes should account for the properties of the energy source, capacity of the energy storage as well as deadlines of the single tasks. We show that conventional scheduling algorithms (like e.g. EDF) are not suitable for this scenario. Based on this motivation, we have constructed optimal scheduling algorithms that jointly handle constraints from both energy and time domain. Further we present an admittance test that decides for arbitrary task sets, whether they can be scheduled without deadline violations. To this end, we introduce the concept of energy variability characterization curves (EVCC) which nicely captures the dynamics of various energy sources. Simulation results show that our algorithms allow significant reductions of the battery size compared to Earliest Deadline First schedulin

Optimizing Transmission and Shutdown for Energy-Efficient Packet Scheduling in Sensor Networks

Energy-efficiency is imperative to enable the deployment of sensor networks with satisfactory lifetime. Conventional power management in radio communication primarily focuses independently on the physical layer, medium access control (MAC) or routing and approaches differ depending on the levels of abstraction. At the physical layer, the fundamental trade-off that exists between transmission rate and energy is exploited. This leads to the lazy scheduling approach, which consists of transmitting with the lowest power over the longest feasible duration. At MAC level, power reduction techniques tend to keep the transmission as short as possible to maximize the radio\u27s power-off interval. Those two approaches seem conflicting and it is not clear which one is the most appropriate for a given network scenario. In this paper, we propose a transmission strategy that combines both techniques optimally. We present a cross-layer solution to determine the best transmission strategy taking into account the transceiver power consumption characteristics, the system load and the scenario constraints. Based on this approach, we derive a low complexity, on-line scheduling algorithm that can be used to optimally organize the forwarding of the sensed information from cluster heads to the data sink (uplink) in a hierarchical sensor network. Results, considering Coded Frequency Shift Keying (FSK) modulation, show that depending on the scenario, a 50% extra power reduction is achieved in a realistic uplink data gathering context, compared to the case where only transmission rate scaling or shutdown is considered

NETEMBED: A Network Resource Mapping Service for Distributed Applications

Emerging configurable infrastructures such as large-scale overlays and grids, distributed testbeds, and sensor networks comprise diverse sets of available computing resources (e.g., CPU and OS capabilities and memory constraints) and network conditions (e.g., link delay, bandwidth, loss rate, and jitter) whose characteristics are both complex and time-varying. At the same time, distributed applications to be deployed on these infrastructures exhibit increasingly complex constraints and requirements on resources they wish to utilize. Examples include selecting nodes and links to schedule an overlay multicast file transfer across the Grid, or embedding a network experiment with specific resource constraints in a distributed testbed such as PlanetLab. Thus, a common problem facing the efficient deployment of distributed applications on these infrastructures is that of "mapping" application-level requirements onto the network in such a manner that the requirements of the application are realized, assuming that the underlying characteristics of the network are known. We refer to this problem as the network embedding problem. In this paper, we propose a new approach to tackle this combinatorially-hard problem. Thanks to a number of heuristics, our approach greatly improves performance and scalability over previously existing techniques. It does so by pruning large portions of the search space without overlooking any valid embedding. We present a construction that allows a compact representation of candidate embeddings, which is maintained by carefully controlling the order via which candidate mappings are inserted and invalid mappings are removed. We present an implementation of our proposed technique, which we call NETEMBED – a service that identify feasible mappings of a virtual network configuration (the query network) to an existing real infrastructure or testbed (the hosting network). We present results of extensive performance evaluation experiments of NETEMBED using several combinations of real and synthetic network topologies. Our results show that our NETEMBED service is quite effective in identifying one (or all) possible embeddings for quite sizable queries and hosting networks – much larger than what any of the existing techniques or services are able to handle.National Science Foundation (CNS Cybertrust 0524477, NSF CNS NeTS 0520166, NSF CNS ITR 0205294, EIA RI 0202067

A Randomized Greedy Algorithm for Near-Optimal Sensor Scheduling in Large-Scale Sensor Networks

We study the problem of scheduling sensors in a resource-constrained linear dynamical system, where the objective is to select a small subset of sensors from a large network to perform the state estimation task. We formulate this problem as the maximization of a monotone set function under a matroid constraint. We propose a randomized greedy algorithm that is significantly faster than state-of-the-art methods. By introducing the notion of curvature which quantifies how close a function is to being submodular, we analyze the performance of the proposed algorithm and find a bound on the expected mean square error (MSE) of the estimator that uses the selected sensors in terms of the optimal MSE. Moreover, we derive a probabilistic bound on the curvature for the scenario where{\color{black}{ the measurements are i.i.d. random vectors with bounded $\ell_2$ norm.}} Simulation results demonstrate efficacy of the randomized greedy algorithm in a comparison with greedy and semidefinite programming relaxation methods

Eligible earliest deadline first:Server-based scheduling for master-slave industrial wireless networks

Industrial automation and control systems are increasingly deployed using wireless networks in master-slave, star-type configurations that employ a slotted timeline schedule. In this paper, the scheduling of (re)transmissions to meet real-time constraints in the presence of non-uniform interference in such networks is considered. As packet losses often occur in correlated bursts, it is often useful to insert gaps before attempting retransmissions. In this paper, a quantum Earliest Deadline First (EDF) scheduling framework entitled ‘Eligible EDF’ is suggested for assigning (re)transmissions to available timeline slots by the master node. A simple but effective server strategy is introduced to reclaim unused channel utilization and replenish failed slave transmissions, a strategy which prevents cascading failures and naturally introduces retransmission gaps. Analysis and examples illustrate the effectiveness of the proposed method. Specifically, the proposed framework gives a timely throughput of 99.81% of the timely throughput that is optimally achievable using a clairvoyant scheduler

A Randomized Greedy Algorithm for Near-Optimal Sensor Scheduling in Large-Scale Sensor Networks

We study the problem of scheduling sensors in a resource-constrained linear dynamical system, where the objective is to select a small subset of sensors from a large network to perform the state estimation task. We formulate this problem as the maximization of a monotone set function under a matroid constraint. We propose a randomized greedy algorithm that is significantly faster than state-of-the-art methods. By introducing the notion of curvature which quantifies how close a function is to being submodular, we analyze the performance of the proposed algorithm and find a bound on the expected mean square error (MSE) of the estimator that uses the selected sensors in terms of the optimal MSE. Moreover, we derive a probabilistic bound on the curvature for the scenario where{\color{black}{ the measurements are i.i.d. random vectors with bounded $\ell_2$ norm.}} Simulation results demonstrate efficacy of the randomized greedy algorithm in a comparison with greedy and semidefinite programming relaxation methods

Optimizing Transmission and Shutdown for Energy-Efficient Real-Time Packet Scheduling in Clustered Ad Hoc Networks

Energy efficiency is imperative to enable the deployment of ad hoc networks. Conventional power management focuses independently on the physical orMAC layer and approaches differ depending on the abstraction level. At the physical layer, the fundamental tradeoff between transmission rate and energy is exploited, which leads to transmit as slow as possible. At MAC level, power reduction techniques aim to transmit as fast as possible to maximize the radios power-off interval. The two approaches seem conflicting and it is not obvious which one is the most appropriate.We propose a transmission strategy that optimally mixes both techniques in a multiuser context.We present a cross-layer solution considering the transceiver power characteristics, the varying system load, and the dynamic channel constraints. Based on this, we derive a low-complexity online scheduling algorithm. Results considering an M-ary quadrature amplitude modulation radio show that for a range of scenarios a large power reduction is achieved, compared to the case where only scaling or shutdown is considered