Time synchronization is an important aspect of sensor network operation. However, it is well known that synchro- nization error accumulates over multiple hops. This presents a challenge for large-scale, multi-hop sensor networks with a large number of nodes distributed over wide areas. In this work, we present a protocol that uses spatial averaging to reduce error accumulation in large-scale networks. We provide an analysis to quantify the synchronization improvement achieved using spatial averaging and find that in a basic cooperative network, the skew and offset variance decrease approximately as 1 ¯ N where ¯ N is the number of cooperating nodes. For general networks, simulation results and a comparison to basic cooperative network results are used to illustrate the improvement in synchronization performance.
The convergence result is clearly desirable for synchronization in networks and in @cite_0 theoretical and simulation results suggested that such a technique could be adapted to communication and sensor networks. Experimental validation for the ideas of @cite_14 was obtained in @cite_18 where the authors implemented the Reachback Firefly Algorithm (RFA) on TinyOS-based motes.
@cite_0 @cite_18 @cite_14
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Synchronization is considered a particularly difficult task in wireless sensor networks due to its decentralized structure. Interestingly, synchrony has often been observed in networks of biological agents (e.g., synchronously flashing fireflies, or spiking of neurons). In this paper, we propose a bio-inspired network synchronization protocol for large scale sensor networks that emulates the simple strategies adopted by the biological agents. The strategy synchronizes pulsing devices that are led to emit their pulses periodically and simultaneously. The convergence to synchrony of our strategy follows from the theory of Mirollo and Strogatz, 1990, while the scalability is evident from the many examples existing in the natural world. When the nodes are within a single broadcast range, our key observation is that the dependence of the synchronization time on the number of nodes N is subject to a phase transition: for values of N beyond a specific threshold, the synchronization is nearly immediate; while for smaller N, the synchronization time decreases smoothly with respect to N. Interestingly, a tradeoff is observed between the total energy consumption and the time necessary to reach synchrony. We obtain an optimum operating point at the local minimum of the energy consumption curve that is associated to the phase transition phenomenon mentioned before. The proposed synchronization protocol is directly applied to the cooperative reach-back communications problem. The main advantages of the proposed method are its scalability and low complexity. Synchronicity is a useful abstraction in many sensor network applications. Communication scheduling, coordinated duty cycling, and time synchronization can make use of a synchronicity primitive that achieves a tight alignment of individual nodes' firing phases. In this paper we present the Reachback Firefly Algorithm (RFA), a decentralized synchronicity algorithm implemented on TinyOS-based motes. Our algorithm is based on a mathematical model that describes how fireflies and neurons spontaneously synchronize. Previous work has assumed idealized nodes and not considered realistic effects of sensor network communication, such as message delays and loss. Our algorithm accounts for these effects by allowing nodes to use delayed information from the past to adjust the future firing phase. We present an evaluation of RFA that proceeds on three fronts. First, we prove the convergence of our algorithm in simple cases and predict the effect of parameter choices. Second, we leverage the TinyOS simulator to investigate the effects of varying parameter choice and network topology. Finally, we present results obtained on an indoor sensor network testbed demonstrating that our algorithm can synchronize sensor network devices to within 100 μsec on a real multi-hop topology with links of varying quality. A simple model for synchronous firing of biological oscillators based on Peskin's model of the cardiac pacemaker (Mathematical aspects of heart physiology, Courant Institute of Mathematical Sciences, New York University, New York, 1975, pp. 268-278) is studied. The model consists of a population of identical integrate-and-fire oscillators. The coupling between oscillators is pulsatile: when a given oscillator fires, it pulls the others up by a fixed amount, or brings them to the firing threshold, whichever is less. The main result is that for almost all initial conditions, the population evolves to a state in which all the oscillators are firing synchronously. The relationship between the model and real communities of biological oscillators is discussed; examples include populations of synchronously flashing fireflies, crickets that chirp in unison, electrically synchronous pacemaker cells, and groups of women whose menstrual cycles become mutually synchronized.