Kyle Jamieson

Span: An energy-efficient coordination algorithm for topology maintenance in Ad Hoc wireless networks

This paper presents Span, a power saving technique for multi-hop ad hoc wireless networks that reduces energy consumption without significantly diminishing the capacity or connectivity of the network. Span builds on the observation that when a region of a shared-channel wireless network bag a sufficient density of nodes, only a small number of them need be on at any time to forward traffic for active connections. Span is a distributed, randomized algorithm where nodes make local decisions on whether to sleep, or to join a forwarding backbone as a coordinator. Each node bases its decision on an estimate of how many of its neighbors will benefit from it being awake, and the amount of energy available to it. We give a randomized algorithm where coordinators rotate with time, demonstrating how localized node decisions lead to a connected, capacity-preserving global topology. Improvement in system lifetime due to Span increases as the ratio of idle-to-sleep energy consumption increases, and increases as the density of the network increases. For example, our simulations show that with a practical energy model, system lifetime of an 802.11 network in power saving mode with Span is a factor of two better than without. Span integrates nicely with 802.11—when run in conjunction with the 802.11 power saving mode, Span improves communication latency, capacity, and system lifetime.

Span: an energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks

This paper presents Span, a power saving technique for multi-hop ad hoc wireless networks that reduces energy consumption without significantly diminishing the capacity or connectivity of the network. Span builds on the observation that when a region of a shared-channel wireless network has a sufficient density of nodes, only a small number of them need be on at any time to forward traffic for active connections. Span is a distributed, randomized algorithm where nodes make local decisions on whether to sleep, or to join a forwarding backbone as a coordinator. Each node bases its decision on an estimate of how many of its neighbors will benefit from it being awake, and the amount of energy available to it. We give a randomized algorithm where coordinators rotate with time, demonstrating how localized node decisions lead to a connected, capacity-preserving global topology. Improvement in system lifetime due to Span increases as the ratio of idle-to-sleep energy consumption increases. Our simulations show that with a practical energy model, system lifetime of an 802.11 network in power saving mode with Span is a factor of two better than without. Additionally, Span also improves communication latency and capacity.

Cross-layer wireless bit rate adaptation

This paper presents SoftRate, a wireless bit rate adaptation protocol that is responsive to rapidly varying channel conditions. Unlike previous work that uses either frame receptions or signal-to-noise ratio (SNR) estimates to select bit rates, SoftRate uses confidence information calculated by the physical layer and exported to higher layers via the SoftPHY interface to estimate the prevailing channel bit error rate (BER). Senders use this BER estimate, calculated over each received packet (even when the packet has no bit errors), to pick good bit rates. SoftRates novel BER computation works across different wireless environments and hardware without requiring any retraining. SoftRate also uses abrupt changes in the BER estimate to identify interference, enabling it to reduce the bit rate only in response to channel errors caused by attenuation or fading. Our experiments conducted using a software radio prototype show that SoftRate achieves 2X higher throughput than popular frame-level protocols such as SampleRate and RRAA. It also achieves 20% more throughput than an SNR-based protocol trained on the operating environment, and up to 4X higher throughput than an untrained SNR-based protocol. The throughput gains using SoftRate stem from its ability to react to channel variations within a single packet-time and its robustness to collision losses.

PPR: partial packet recovery for wireless networks

Bit errors occur in wireless communication when interference or noise overcomes the coded and modulated transmission. Current wireless protocols may use forward error correction (FEC) to correct some small number of bit errors, but generally retransmit the whole packet if the FEC is insufficient. We observe that current wireless mesh network protocols retransmit a number of packets and that most of these retransmissions end up sending bits that have already been received multiple times, wasting network capacity. To overcome this inefficiency, we develop, implement, and evaluate a partial packet recovery (PPR) system. PPR incorporates two new ideas: (1) SoftPHY, an expanded physical layer (PHY) interface that provides PHY-independent hints to higher layers about the PHYs confidence in each bit it decodes, and (2) a postamble scheme to recover data even when a packet preamble is corrupted and not decodable at the receiver. Finally, we present PP-ARQ, an asynchronous link-layer ARQ protocol built on PPR that allows a receiver to compactly encode a request for retransmission of only those bits in a packet that are likely in error. Our experimental results from a 31-node Zigbee (802.15.4) testbed that includes Telos motes with 2.4 GHz Chipcon radios and GNU Radio nodes implementing the 802.15.4 standard show that PP-ARQ increases end-to-end capacity by a factor of 2x under moderate load.

Understanding the real-world performance of carrier sense

Carrier sense is a fundamental part of most wireless networking stacks in wireless local area- and sensor networks. As increasing numbers of users and more demanding applications push wireless networks to their capacity limits, the efficacy of the carrier sense mechanism becomes a key factor in determining wireless network capacity.We describe how carrier sense works, point out its limitations, and advocate an experimental approach to studying carrier sense. We describe our current testbed setup, and then present preliminary experimental results from both a 60-node sensor network deployment and a small-scale 802.11 deployment. Our preliminary results evaluate how well carrier sense works and expose its limitations.

Sift: a MAC protocol for event-driven wireless sensor networks

Nodes in sensor networks often encounter spatially-correlated contention, where multiple nodes in the same neighborhood all sense an event they need to transmit information about. Furthermore, in many sensor network applications, it is sufficient if a subset of the nodes that observe the same event report it. We show that traditional carrier-sense multiple access (CSMA) protocols for sensor networks do not handle the first constraint adequately, and do not take advantage of the second property, leading to degraded latency as the network scales in size. We present Sift, a medium access control (MAC) protocol for wireless sensor networks designed with the above observations in mind. We show using simulations that as the size of the sensor network scales up to 500 nodes, Sift can offer up to a 7-fold latency reduction compared to other protocols, while maintaining competitive throughput.

CTP: An efficient, robust, and reliable collection tree protocol for wireless sensor networks

We describe CTP, a collection routing protocol for wireless sensor networks. CTP uses three techniques to provide efficient, robust, and reliable routing in highly dynamic network conditions. CTPs link estimator accurately estimates link qualities by using feedback from both the data and control planes, using information from multiple layers through narrow, platform-independent interfaces. Second, CTP uses the Trickle algorithm to time the control traffic, sending few beacons in stable topologies yet quickly adapting to changes. Finally, CTP actively probes the topology with data traffic, quickly discovering and fixing routing failures. Through experiments on 13 different testbeds, encompassing seven platforms, six link layers, and multiple densities and frequencies, and detailed observations of a long-running sensor network application that uses CTP, we study how these three techniques contribute to CTPs overall performance.

ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization

Indoor localization of mobile devices and tags has received much attention recently, with encouraging fine-grained localization results available with enough line-of-sight coverage and hardware infrastructure. Some of the most promising techniques analyze the time-of-arrival of incoming signals, but the limited bandwidth available to most wireless transmissions fundamentally constrains their resolution. Frequency-agile wireless networks utilize bandwidths of varying sizes and locations in a wireless band to efficiently share the wireless medium between users. ToneTrack is an indoor location system that achieves sub-meter accuracy with minimal hardware and antennas, by leveraging frequency-agile wireless networks to increase the effective bandwidth. Our novel signal combination algorithm combines time-of-arrival data from different transmissions as a mobile device hops across different channels, approaching time resolutions previously not possible with a single narrowband channel. ToneTracks novel channel combination and spectrum identification algorithms together with the triangle inequality scheme yield superior results even in non-line-of-sight scenarios with one to two walls separating client and APs and also in the case where the direct path from mobile client to an AP is completely blocked. We implement ToneTrack on the WARP hardware radio platform and use six of them served as APs to localize Wi-Fi clients in an indoor testbed over one floor of an office building. Experimental results show that ToneTrack can achieve a median 90 cm accuracy when 20 MHz bandwidth APs overhear three packets from adjacent channels.

Phaser: enabling phased array signal processing on commodity WiFi access points

Signal processing on antenna arrays has received much recent attention in the mobile and wireless networking research communities, with array signal processing approaches addressing the problems of human movement detection, indoor mobile device localization, and wireless network security. However, there are two important challenges inherent in the design of these systems that must be overcome if they are to be of practical use on commodity hardware. First, phase differences between the radio oscillators behind each antenna can make readings unusable, and so must be corrected in order for most techniques to yield high-fidelity results. Second, while the number of antennas on commodity access points is usually limited, most array processing increases in fidelity with more antennas. These issues work in synergistic opposition to array processing: without phase offset correction, no phase-difference array processing is possible, and with fewer antennas, automatic correction of these phase offsets becomes even more challenging. We present Phaser, a system that solves these intertwined problems to make phased array signal processing truly practical on the many WiFi access points deployed in the real world. Our experimental results on three- and five-antenna 802.11-based hardware show that 802.11 NICs can be calibrated and synchronized to a 20° median phase error, enabling inexpensive deployment of numerous phase-difference based spectral analysis techniques previously only available on costly, special-purpose hardware.

The Design and Implementation of a Wireless Video Surveillance System

Internet-enabled cameras pervade daily life, generating a huge amount of data, but most of the video they generate is transmitted over wires and analyzed offline with a human in the loop. The ubiquity of cameras limits the amount of video that can be sent to the cloud, especially on wireless networks where capacity is at a premium. In this paper, we present Vigil, a real-time distributed wireless surveillance system that leverages edge computing to support real-time tracking and surveillance in enterprise campuses, retail stores, and across smart cities. Vigil intelligently partitions video processing between edge computing nodes co-located with cameras and the cloud to save wireless capacity, which can then be dedicated to Wi-Fi hotspots, offsetting their cost. Novel video frame prioritization and traffic scheduling algorithms further optimize Vigils bandwidth utilization. We have deployed Vigil across three sites in both whitespace and Wi-Fi networks. Depending on the level of activity in the scene, experimental results show that Vigil allows a video surveillance system to support a geographical area of coverage between five and 200 times greater than an approach that simply streams video over the wireless network. For a fixed region of coverage and bandwidth, Vigil outperforms the default equal throughput allocation strategy of Wi-Fi by delivering up to 25% more objects relevant to a users query.