PSR: Practical synchronous rendezvous in low-duty-cycle wireless networks

Abstract

Abstract-Low-duty-cycle radio operations have been proposed for wireless networks facing severe energy constraints. Despite energy savings, duty-cycling the radio creates transient-available wireless links, making communication rendezvous a challenging task under the practical issue of clock drift. To overcome limitations of prior work, this paper presents PSR, a practical design for synchronous rendezvous in low-duty-cycle wireless networks. The key idea behind PSR is to extract timing information naturally embedded in the pattern of radio duty-cycling, so that normal traffic in the network can be utilized as a "free" input for drift detection, which helps reduce (or even eliminate) the overhead of traditional time-stamp exchange with dedicated packets or bits. To prevent an overuse of such free information, leading to energy waste, an energy-driven adaptive mechanism is developed for clock calibration to balance between energy efficiency and rendezvous accuracy. PSR is evaluated with both test-bed experiments and extensive simulations, by augmenting and comparing with four different MAC protocols. Results show that PSR is practical and effective under different levels of traffic load, and can be fused with those MAC protocols to improve their energy efficiency without major change of the original designs. I. INTRODUCTIONIn wireless networks with severe energy constraints, e.g., wireless sensor networks [1], low-duty-cycle radio operations have been proposed as one of the major techniques to elongate the network lifetime [5], since the radio can be a main source of energy consumption [13]. Basically, the RF module of a node stays active only for a small percentage of time during each duty-cycle period (e.g., 1%), while keeps in low-energy sleep/off mode for the rest of the time [5][28]. Low-duty-cycle radio activity has been favorable in applications such as environment monitoring (e.g., Redwood [3], GreenOrbs [9]), animal observation (e.g., Great Duck Island [10]), civil structure surveillance (e.g., Mine [11]), etc. In all those applications, low-duty-cycle networking provides a nice trade-off between service quality and energy cost; however, it also brings about transient-available radio links that are essentially at odd with highly efficient communication. This is because in low-dutycycle networks, two nodes located within each other's radio range can communicate only when both of them are active simultaneously for transmitting (TX) and receiving (RX) [12]. A problem called communication rendezvous [17].Many smart ideas have been proposed for the rendezvous task in low-duty-cycle wireless networks. They usually function at the MAC layer and can be categorized into two general classes: (i) asynchronous [5]

LearJam: An Energy-Efficient Learning-Based Jamming Attack against Low-Duty-Cycle Networks

“…It can be observed that the transmission periods are randomly distributed within the interval [30.28s, 30.35s]. Moreover, the distribution can be approximated by a Gaussian distribution [7], which is also validated by our experimental results ( Fig.3(b)). 10 20 Such considerable variation of transmission period is mainly caused by two reasons.…”

“…In these works, the transmission period, i.e., the duration between each two consecutive transmission instances of one node, is considered as a constant. However, owing to the clock drift (illustrated in Transmission Stage in Fig.2) as well as the uncertainties during the sensing, computation and communication processes [7], the practical transmission periods turn out to be dynamic and random values which possibly deteriorate the effects of many existing attacking strategies. For example, setting the frame length as 30s, we conducted experiments over the MicaZ node which was equipped with sensor and set to transmit packets about the temporal temperature under the transmission rate of 250kbps.…”

“…Specifically, even if each node may be set to transmit according to a given duty cycle, the transmission period of node will still deviate from its setting one in a stochastic way due to the clock drift and various uncertainties within sensing, computation and communication processes [7]. For example, according to our empirical measurement on MicaZ nodes, after 100 cycles, the largest deviation of transmission period reaches as much as 30 ms in the 30s period, which may reduce one ideal periodic jammer's [16] effectiveness (number of successful attacks under given energy budget) by 87%.…”

Free Side-Channel Cross-Technology Communication in Wireless Networks

“…Duty cycling is a critical technique to enable long-term operations [11], [28], [36] for battery-powered devices, where radios turned off for the majority of the time. FreeBee messages are continuously transferred over the air without overwhelming the channel, thereby safely reaching mobile and/or duty cycled receivers without the need for complex rendezvous techniques [17], [22]. Moreover, interval multiplexing enables (iv) concurrent many-to-many communication to further enhance the practicality under today's crowded wireless devices.…”

Gradient Descent Algorithm Inspired Adaptive Time Synchronization in Wireless Sensor Networks

“…For any message, the time that passes from the start of broadcast attempt until the recipient node receives it is referred as transmission delay. Based on central limit theory and empirical observations [10], we model the transmission delays as a Gaussian distributed random variable, denoted by T ∼ N (0, σ 2 d ). The logical clock l u () of node u can be modeled as…”