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.
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.
Abstract-Data generated in wireless sensor networks may not all be alike: some data may be more important than others and hence may have different delivery requirements. In this paper, we address differentiated data delivery in the presence of congestion in wireless sensor networks. We propose a class of algorithms that enforce differentiated routing based on the congested areas of a network and data priority. The basic protocol, called Congestion-Aware Routing (CAR), discovers the congested zone of the network that exists between high-priority data sources and the data sink and, using simple forwarding rules, dedicates this portion of the network to forwarding primarily high-priority traffic. Since CAR requires some overhead for establishing the high-priority routing zone, it is unsuitable for highly mobile data sources. To accommodate these, we define MAC-Enhanced CAR (MCAR), which includes MAC-layer enhancements and a protocol for forming high-priority paths on the fly for each burst of data. MCAR effectively handles the mobility of high-priority data sources, at the expense of degrading the performance of low-priority traffic. We present extensive simulation results for CAR and MCAR, and an implementation of MCAR on a 48-node testbed.
Abstract-One of the key features of high speed WLAN such as 802.11n is the use of MIMO (Multiple Input Multiple Output) antenna technology. The MIMO channel is described with fine granularity by Channel State Information (CSI) that can be utilized in various ways to maximize the network performance. Many complex parameters of a MIMO system require numerous samples to obtain CSI for all possible channel configurations. As a result, measuring the complete CSI space requires excessive sampling overhead and thus degrades the network performance. We propose CSI-SF (CSI with Sampling & Fusion), a method for estimating CSI using a small number of frame transmissions and extrapolating data to settings that have not been sampled. For instance, we predict CSI of multiple stream settings using CSI obtained only from single stream packets. We evaluate the effectiveness of CSI-SF on various network scenarios using our 802.11n testbed and show that CSI-SF provides an accurate, complete knowledge of the MIMO channel with reduced overhead from traditional sampling. We also show that CSI-SF can be applied to network algorithms such as rate adaptation, antenna selection and association control to significantly improve their performance and efficiency.The new IEEE 802.11n [1] and the emerging IEEE 802.11ac [2] standards aim to provide very high throughput WLAN to meet this growing demand of applications and services. Some of the key enhancements used for increasing the throughput are using wider, bonded channels (40 MHz in 802.11n and up to 160 MHz in 802.11ac), frame aggregation and block acknowledgments, a short guard interval, and MIMO (Multiple Input Multiple Output) antennas [3], [4]. MIMO is a popular technology in wireless communications (e.g., 802.11n, WiMax, 3GPP LTE, etc.) to increase link throughput and distance. 802.11n devices in the current market support up to three MIMO spatial streams.Algorithms and protocols for WLAN need to consider the new features offered by multiple antennas; for instance, rate adaptation is not only selecting modulation and coding rate but also the number of concurrent spatial data streams transmitted. In order to achieve optimal WLAN performance, we require a detailed knowledge of the wireless link, which can be acquired through the Channel State Information (CSI). CSI represents the current condition of the channel, and consists of the attenuation and phase shift experienced by each spatial stream to each receive antenna in each of the OFDM subcarriers. CSI is provided in the 802.11n hardware by analyzing received packets using training sequences in the packet headers. For network algorithms such as rate selection, AP association, channel assignment, etc., to make a timely, optimal decision, accurate CSI estimates under various settings (e.g., different number of spatial streams, transmission antennas used, transmission powers, etc.) must be known. However, some of these settings might not have been sampled in recently received packets and additional frame transmissions are required to obt...
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