Chaos

Abstract

An important building block for low-power wireless systems is to efficiently share and process data among all devices in a network. However, current approaches typically split such all-to-all interactions into sequential collection, processing, and dissemination phases, thus handling them inefficiently.We introduce Chaos, the first primitive that natively supports all-to-all data sharing in low-power wireless networks. Different from current approaches, Chaos embeds programmable in-network processing into a communication support based on synchronous transmissions. We show that this design enables a variety of common all-to-all interactions, including network-wide agreement and data aggregation. Results from three testbeds and simulations demonstrate that Chaos scales efficiently to networks consisting of hundreds of nodes, achieving severalfold improvements over LWB and CTP/Drip in radio duty cycle and latency with almost 100 % reliability across all scenarios we tested. For example, Chaos computes simple aggregates, such as the maximum, in a 100-node multi-hop network within less than 90 milliseconds.

Collision-Free Advertisement Scheduling for IEEE 802.15.4-TSCH Networks

“…Our simulations took place on an ad-hoc simulator (The code is available through the following link: ), which we developed in Python. In order to provide realistic results, our simulator implements the general site path loss model recommended by ITU-R P.1238-9 [24] and takes into account the capture effect as described in the literature [25]. The general parameters of our simulations are presented in Table 2.…”

Bandit Learning with Concurrent Transmissions for Energy-Efficient Flooding in Sensor Networks

“…The capture effect is a phenomenon, where the receiver can lock on to and correctly demodulate the signal when a received signal is approximately 3 -4 dB stronger than the sum of all the other received signals [2,11]. Besides, in IEEE 802.15.4 wireless networks, the strongest signal must arrive no later than 160 µs after the weaker signals [4] in order to be properly recognized and decoded by the receiver.…”

“…On the one hand, reducing the number of absorbing nodes increases the concurrent transmitters, consequently strengthening the concurrent transmissions in the network. Based on the results in [4], however, the reliability (i.e., packet delivery ratio) degrades greatly with the number of synchronous transmitters. On the other hand, maintaining too many absorbing nodes might lead to a fragile network with a higher probability that nodes get disconnected while the environment dynamically changes.…”

“…LWB centrally schedules the data communication to support one-to-many, many-toone, and many-to-many traffic patterns in WSNs. On the contrary, Chaos [4] builds on Glossy to achieve fast all-to-all data sharing in a distributed manner. Chaos further combines programmable in-network processing with concurrent transmissions in WSNs.…”

“…For example, Flash Flooding [1] and the Glossy protocol [2] (referred to as "Glossy" authors in [4,5], the probability of receiving a packet due to the capture effect drops notably, as the number of synchronous transmitters increases. To overcome these problems, Chaos [4] exploits in-network processing together with concurrent transmissions: While each node receives a packet, it spends a fixed period of time (processing time) to process the data, and then makes a decision whether it is necessary to forward the received packet. In this case, it is able to appropriately decrease the number of concurrent transmitters and maintain a best-effort performance even in high-density WSNs.…”