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 many ways to improve 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 network performance. We propose CSI-SF (CSI Sampling & Fusion), a method for estimating CSI for every MIMO configuration by sampling a small number of frames transmitted with different settings and extrapolating data for the remaining settings. For instance, we predict CSI of multi-stream settings using CSI obtained only from single stream packets. We evaluate the effectiveness of CSI-SF in various 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.

poster Share on LoadingZones: leveraging street parking to enable vehicular internet access Authors: Riccardo Crepaldi University of Illinois, Urbana-Champaign, IL, USA University of Illinois, Urbana-Champaign, IL, USAView Profile , Ryan Beavers University of Illinois, Urbana-Champaign, IL, USA University of Illinois, Urbana-Champaign, IL, USAView Profile , Braden Ehrat University of Illinois, Urbana-Champaign, IL, USA University of Illinois, Urbana-Champaign, IL, USAView Profile , Jason Sze University of Illinois, Urbana-Champaign, IL, USA University of Illinois, Urbana-Champaign, IL, USAView Profile , Matt Jaeger University of Illinois, Urbana-Champaign, IL, USA University of Illinois, Urbana-Champaign, IL, USAView Profile , Steven Biersteker University of Illinois, Urbana-Champaign, IL, USA University of Illinois, Urbana-Champaign, IL, USAView Profile , Robin Kravets University of Illinois, Urbana-Champaign, IL, USA University of Illinois, Urbana-Champaign, IL, USAView Profile Authors Info & Claims Mobicom '12: Proceedings of the 18th annual international conference on Mobile computing and networkingAugust 2012Pages 415–418https://doi.org/10.1145/2348543.2348598Published:22 August 2012Publication History 1citation235DownloadsMetricsTotal Citations1Total Downloads235Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Publisher SiteGet Access

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We present an experimental performance evaluation study of WiFi links in an open-space outdoor environment. We consider a large scale wireless sensor network scenario of seismic data collection from sensors that are buried in ground and a set of access points (APs) form the hierarchical aggregation layer and the backbone of the network. We conduct two different link characterization studies. First, we evaluate the links between the sensor nodes and a wireless AP using IEEE 802.11a/b/g. We construct the path loss model and investigate the reachability distance of this link for different protocols and different sensor node antenna heights. We then characterize the long distance wireless backhaul links between the APs. We use 802.11n and high gain directional antenna for high throughput and long distance. We evaluate how different PHY and MAC layer enhancements of 802.11n impacts its performance in an open outdoor environment. We observed up to 148 Mb/s throughput at 800 meter line-of-sight links without sophisticated tuning of antenna orientation. We believe our findings can be a benchmark for WiFi based outdoor network deployment, especially for high throughput long distance links.

Wireless sensor networks typically conserve energy by following a periodic wakeup-sleep schedule: nodes minimize idle time and spend most of their time in a low power sleep state. In order to communicate and exchange data in such a network, the current duty-cycling MAC protocols either require tight synchronization between the neighbor wakeup schedules or spend a significant amount of energy in signaling the sleeping nodes. In contrast, this paper presents Neighborhood-based Power Management (NPM), an energy efficient asynchronous MAC protocol that minimizes signaling overhead through opportunistically gained knowledge about neighbor wakeup schedules. Unlike the synchronization-based MAC protocols, NPM does not require a priori knowledge of the wakeup schedules. Using only a minimal exchange of schedule information, NPM reduces the signaling overhead by shortening the wakeup signal. Furthermore, NPM uses its wakeup signal to awaken all receivers in the neighborhood of the sender, enabling all sender-receiver pairs in that neighborhood to communicate. Our extensive evaluations show that NPM outperforms popular B-MAC, X-MAC and SCP protocols under all network conditions. NPM reduces the signaling overhead, with up to 74% savings in energy and 80% reduction in delay.

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.

This paper presents Pirrus, a replica management system that addresses the problem of providing data availability on a wireless sensor network. Pirrus uses probabilistic failure models (e.g., derived from environmental conditions and estimation of available energy) to adaptively create and maintain a number of replicas of the data. Replica management is formulated as an energy optimization problem, then solved with a greedy heuristic that only uses information gathered from neighbors. Intuitively, Pirrus trades off the energy saved by limiting the number of replicas when the network health is good to extend the lifetime when more replicas are needed. Our simulation results show how the solution provided by Pirrus achieves good performance with a sustainable computational cost. Compared to the performance of a fixed number of replicas, Pirrus extends the network lifetime by more than 20%.

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.

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.

Wireless sensor network technologies enable a wide variety of applications (e.g., environmental monitoring). Such sensor networks are often deployed in regions that make it difficult to collect and redistribute the nodes for maintenance. However, there is often a need to reprogram all of the nodes in the network, either during application test phases on deployed networks, or to support software upgrades. Therefore, a reliable method of sending a relatively large amount of data to each node in the network is required to support these functions.

No abstract available.