Abstract-This paper presents SYNAPSE++, a system for over the air reprogramming of wireless sensor networks (WSNs). In contrast to previous solutions, which implement plain negative acknowledgment-based ARQ strategies, SYNAPSE++ adopts a more sophisticated error recovery approach exploiting rateless fountain codes (FCs). This allows it to scale considerably better in dense networks and to better cope with noisy environments. In order to speed up the decoding process and decrease its computational complexity, we engineered the FC encoding distribution through an original genetic optimization approach. Furthermore, novel channel access and pipelining techniques have been jointly designed so as to fully exploit the benefits of fountain codes, mitigate the hidden terminal problem and reduce the number of collisions. All of this makes it possible for SYNAPSE++ to recover data over multiple hops through overhearing by limiting, as much as possible, the number of explicit retransmissions. We finally created new bootloader and memory management modules so that SYNAPSE++ could disseminate and load program images written using any language. At the end of this paper, the effectiveness of SYNAPSE++ is demonstrated through experimental results over actual multihop deployments, and its performance is compared with that of Deluge, the de facto standard protocol for code dissemination in WSNs. The TinyOS 2 code of SYNAPSE++ is available at http://dgt.dei.unipd.it/download.

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Wireless reprogramming is a key functionality in Wireless Sensor Networks (WSNs). In fact, the requirements for the network may change in time, or new parameters might have to be loaded to change the behavior of a given protocol. In large scale WSNs it makes economical as well as practical sense to upload the code with the needed functionalities without human intervention, i.e., by means of efficient over the air reprogramming. This poses several challenges as wireless links are affected by errors, data dissemination has to be 100% reliable, and data transmission and recovery schemes are often called to work with a large number of receivers. State-of-the-art protocols, such as Deluge, implement error recovery through the adaptation of standard Automatic Repeat reQuest (ARQ) techniques. These, however, do not scale well in the presence of channel errors and multiple receivers. In this paper, we present an original reprogramming system for WSNs called SYNAPSE, which we designed to improve the efficiency of the error recovery phase. SYNAPSE features a hybrid ARQ (HARQ) solution where data are encoded prior to transmission and incremental redundancy is used to recover from losses, thus considerably reducing the transmission overhead. For the coding, digital Fountain Codes were selected as they are rateless and allow for lightweight implementations. In this paper, we design special Fountain Codes and use them at the heart of SYNAPSE to provide high performance while meeting the requirements of WSNs. Moreover, we present our implementation of SYNAPSE for the Tmote Sky sensor platform and show experimental results, where we compare the performance of SYNAPSE with that of state of the art protocols.

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SUMMARYThis paper studies networked estimation over a communication channel shared by a contention-based medium access protocol. A collection of N identical and physically decoupled scalar systems are sampled without sensor noise and transmitted over a common channel, using a contention-based medium access mechanism. We first carry out a calculation of the average distortion in estimation with irregular samples. Given the rate of packet generation at sensors, we characterize the traffic characteristics of the some contention-based MAC schemes. This lets us derive the statistics of inter-arrival times which in turn allows us to compute the packet loss rates and also the statistics of delay within a sample period. Using these results, we track the estimation performance as the sample generation rate and the number of contending nodes are varied. We provide a heuristic rule-of-thumb for choosing the sampling interval which minimizes the average distortion. By combining the network traffic characterization with that of the estimation performance, we show this rule performs pretty well. Carrying along the same lines, we are able to compute the scaling limits of estimation performance with respect to the number of contending nodes.