An adaptive wakeup scheme to support fast routing in sensor networks.
ABSTRACT We propose a novel routing asynchronous wakeup scheme - Adaptive Wakeup Schedule Function (AWSF), which adapts to deployment topography to support fast routing in sensor networks. Unlike most other wakeup schemes such as the random and cyclic designs, AWSF guarantees hard delay bounds, have better average delays and smaller delay variances, and eliminates the "Lonely Node" problem where nodes wakeup to find no communicable neighbours. Since AWSF assumes complete radio turn-off in the "sleep" mode, it is largely different from most data-centric wakeup schemes where the radio modules are only put to "idle" mode for "data snooping" where real energy savings cannot be achieved. We provide simulation results to support our claims and have implemented our solution on real Crossbow Mica2 motes.
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ABSTRACT: Future sensor networks will be composed of a large number of densely deployed sensors/actuators. A key feature of such networks is that their nodes are untethered and unattended. Consequently, energy efficiency is an important design consideration for these networks. Motivated by the fact that sensor network queries may often be geographical, we design and evaluate an energy efficient routing algorithm that propagates a query to the appropriate geographical region, without flooding. The proposed Geographic and Energy Aware Routing (GEAR) algorithm uses energy aware neighbor selection to route a packet towards the target region and Recursive Geographic Forwarding or Restricted Flooding algorithm to disseminate the packet inside the destination region. We evaluate the GEAR protocol using simulation. We find that, especially for non-uniform traffic distribution, GEAR exhibits noticeably longer network lifetime than non-energyaware geographic routing algorithms. 110/2001;
Conference Proceeding: Simulating the power consumption of large-scale sensor network applications.[show abstract] [hide abstract]
ABSTRACT: Developing sensor network applications demands a new set of tools to aid programmers. A number of simulation environments have been de- veloped that provide varying degrees of scalability, realism, and detail for understanding the behavior of sensor networks. To date, however, none of these tools have addressed one of the most important aspects of sensor application design: that of power consumption.While simple approximations of overall power usage can be derived from estimates of node duty cycle and communication rates, these techniques often fail to capture the detailed, low-level energy requirements of the CPU, radio, sensors, and other peripherals. In this paper, we present PowerTOSSIM, a scalable simulation en- vironment for wireless sensor networks that provides an accurate, per- node estimate of power consumption. PowerTOSSIM is an extension to TOSSIM, an event-driven simulation environment for TinyOS ap- plications. In PowerTOSSIM, TinyOS components corresponding to specific hardware peripherals (such as the radio, EEPROM, LEDs, and so forth) are instrumented to obtain a trace of each device's activ- ity during the simulation run. PowerTOSSIM employs a novel code- transformation technique to estimate the number of CPU cycles exe- cuted by each node, eliminating the need for expensive instruction-level simulation of sensor nodes. PowerTOSSIM includes a detailed model of hardware energy consumption based on the Mica2 sensor node plat- form. Through instrumentation of actual sensor nodes, we demonstrate that PowerTOSSIM provides accurate estimation of power consump- tion for a range of applications and scales to support very large simula- tions.Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, SenSys 2004, Baltimore, MD, USA, November 3-5, 2004; 01/2004
Conference Proceeding: Random Asynchronous Wakeup Protocol for Sensor Networks.[show abstract] [hide abstract]
ABSTRACT: This paper presents a random asynchronous wakeup (RAW), a power saving technique for sensor networks that reduces energy consumption without significantly affecting the latency or connectivity of the network. RAW builds on the observation that when a region of a shared-channel wireless network has a sufficient density of nodes, only a small number of them need be active at any time to forward the traffic for active connections. RAW is a distributed, randomized algorithm where nodes make local decisions on whether to sleep, or to be active. Each node is awake for a randomly chosen fixed interval per time frame. High node density results in existence of several paths between two given nodes whose path length and delay characteristics are similar to the shortest path. Thus, a packet can be forwarded to any of several nodes in order to be delivered to the destination without affecting much the path length and delay experienced by the packet as compared to forwarding the packet through the shortest path. The improvement in system lifetime, due to RAW, increases as the ratio of idle-to-sleep energy consumption increases, and as the density of the network increases. Through analytical and experimental evaluations, we show that RAW improves communication latency and system lifetime compared to current schemes.1st International Conference on Broadband Networks (BROADNETS 2004), 25-29 October 2004, San Jose, CA, USA; 01/2004