By James Horey, Eric Nelson, Arthur B. Maccabe (auth.), Rajmohan Rajaraman, Thomas Moscibroda, Adam Dunkels, Anna Scaglione (eds.)
The e-book constitutes the refereed court cases of the sixth foreign convention on disbursed Computing in Sensor platforms, DCOSS 2010, held in Santa Barbara, CA, united states, in June 2010. The 28 revised complete papers offered have been rigorously reviewed and chosen from seventy six submissions. The examine contributions during this complaints span vital elements of sensor platforms, together with strength administration; conversation; insurance and monitoring; time synchronization and scheduling; key institution and authentication; compression; medium entry keep an eye on; code replace; and mobility.
Read Online or Download Distributed Computing in Sensor Systems: 6th IEEE International Conference, DCOSS 2010, Santa Barbara, CA, USA, June 21-23, 2010. Proceedings PDF
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Extra resources for Distributed Computing in Sensor Systems: 6th IEEE International Conference, DCOSS 2010, Santa Barbara, CA, USA, June 21-23, 2010. Proceedings
We estimate the residence times in the diﬀerent protocol states using the sleep interval Ts , the number of received Crx and sent messages Ctx , and the number of transmitted preamble packets Cp : Ttx = Cp · Tp + Ctx · Tmsg , Trx = Crx · Tmsg , Tcp = (Tep − Ttx − Trx ) · Tcs /(Ts + Tcs ) , (5) where Tp , Tcs , and Tmsg are constants speciﬁc to the radio device and the MAC protocol as illustrated in Figure 2. The time spent for sending messages depends on the number of transmitted preamble packets and the number of sent data packets.
1% (high data rate). Our energy estimation model is general enough to accommodate other MAC protocols as well. , RI-MAC ), we have to replace the preamble counter Cp in Equation 5 with a counter that keeps track of the number of time intervals (with length Tp ) a node waits for an announcement from the receiver. 4 System Integration As for the routing, we assume many-to-one data traﬃc that ﬂows toward a common sink node. , hop count or ETX ). Our approach works on any mesh and tree topology.
Ch Abstract. Sensor network MAC protocols are typically conﬁgured for an intended deployment scenario once and for all at compile time. This approach, however, leads to suboptimal performance if the network conditions deviate from the expectations. We present ZeroCal, a distributed algorithm that allows nodes to dynamically adapt to variations in trafﬁc volume. Using ZeroCal, each node autonomously conﬁgures its MAC protocol at runtime, thereby trying to reduce the maximum energy consumption among all nodes.