This paper presents and evaluates two principles for designing robust, reliable, and efficient collection protocols. These principles allow a protocol to benefit from accurate and agile link estimators by handling the dynamism such estimators introduce to routing tables. The first is datapath validation: a protocol can use data traffic as active topology probes, quickly discovering and fixing routing loops. The second is adaptive beaconing: by extending the Trickle code propagation algorithm to routing control traffic, a protocol sends fewer beacons while simultaneously reducing its route repair latency. We study these mechanisms in an implementation called Collection Tree Protocol (CTP) and evaluate their contributions to its performance. We evaluate CTP on 12 different testbeds ranging in size from 20-310 nodes and comprising 7 hardware platforms, on 6 different link layers, and on interference-free and interference-prone channels. In all cases, CTP delivers > 90% of packets. Many experiments achieve 99.9%. Compared to standard beaconing, CTP sends 73% fewer beacons while reducing topology repair latency by 99.8%. Finally, when using low-power link layers, CTP has duty cycles of 3% while supporting aggregate loads of 30 packets/minute.

Network congestion occurs when offered traffic load exceeds available capacity at any point in a network. In wireless sensor networks, congestion causes overall channel quality to degrade and loss rates to rise, leads to buffer drops and increased delays (as in wired networks), and tends to be grossly unfair toward nodes whose data has to traverse a larger number of radio hops. Congestion control in wired networks is usually done using end-to-end and network-layer mechanisms acting in concert. However, this approach does not solve the problem in wireless networks because concurrent radio transmissions on different "links" interact with and affect each other, and because radio channel quality shows high variability over multiple timescales. We examine three techniques that span different layers of the traditional protocol stack: hop-by-hop flow control, rate limiting source traffic when transit traffic is present, and a prioritized medium access control (MAC) protocol. We implement these techniques and present experimental results from a 55-node in-building wireless sensor network. We demonstrate that the combination of these techniques, Fusion, can improve network efficiency by a factor of three under realistic workloads.

Abstract-Recent research in sensor networks, wireless location systems, and power-saving in ad hoc networks suggests that some applications' wireless traffic be modeled as an event-driven workload: a workload where many nodes send traffic at the time of an event, not all reports of the event are needed by higher level protocols and applications, and events occur infrequently relative to the time needed to deliver all required event reports. We identify several applications that motivate the event-driven workload and propose a protocol that is optimal for this workload.Our proposed protocol, named CSMA , is nonpersistent carrier sense multiple access (CSMA) with a carefully chosen nonuniform probability distribution that nodes use to randomly select contention slots. We show that CSMA is optimal in the sense that is the unique probability distribution that minimizes collisions between contending stations. CSMA has knowledge of . We conclude with an exploration of how could be used to build a more practical medium access control protocol via a probability distribution with no knowledge of that approximates .Index Terms-Carrier sense multiple access (CSMA), medium access control (MAC), nonpersistent, performance, poisson process, sensor networks.

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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 Vigil's 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 user's query.

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User demand for increasing amounts of wireless capacity continues to outpace supply, and so to meet this demand, significant progress has been made in new MIMO wireless physical layer techniques. Higher-performance systems now remain impractical largely only because their algorithms are extremely computationally demanding. For optimal performance, an amount of computation that increases at an exponential rate both with the number of users and with the data rate of each user is often required. The base station's computational capacity is thus becoming one of the key limiting factors on wireless capacity. QuAMax is the first large MIMO centralized radio access network design to address this issue by leveraging quantum annealing on the problem. We have implemented QuAMax on the 2,031 qubit D-Wave 2000Q quantum annealer, the state-of-the-art in the field. Our experimental results evaluate that implementation on real and synthetic MIMO channel traces, showing that 10 µs of compute time on the 2000Q can enable 48 user, 48 AP antenna BPSK communication at 20 dB SNR with a bit error rate of 10 −6 and a 1,500 byte frame error rate of 10 −4 .

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.