Date of Conference: 04-07 August 2025. Conference Location: Tokyo, Japan

The rapid evolution of wireless technologies has intensified interest in open and fully programmable radio access networks for whole-stack research, innovation, and evaluation of emerging solutions. Large-scale wireless living labs, such as ARA, equipped with real-world infrastructure play a vital role in this evolution by enabling researchers to prototype and evaluate advanced algorithms for next-generation wireless systems in outdoor and over-the-air environments benefiting from real-world fidelity and end-to-end programmability. However, at the core of this innovation is the performance in terms of coverage and reliability of these wireless living labs. For instance, interfacing power amplifiers and low noise amplifiers with software-defined radios (SDRs) for experimenting outdoors introduces issues in random access procedure—a process crucial in establishing connectivity between user equipment (UE) and the core network in 5G and 6G systems. Therefore, to ensure seamless connectivity and reliable communications in open-source 5G software stacks such as OpenAirInterface (OAI), we propose a slot-based approach to the 5G random access procedure leveraging full downlink (DL) and uplink (UL) slots instead of using special or mixed slots. We highlight how this approach achieves reliable 5G connectivity over 1 mile—the longest communication range that has been achieved so far in real-world settings using open-source 5G software stacks and the Universal Software Radio Peripheral (USRP) SDRs. We also demonstrate that, in a highly obstructed environment such as an industrial setting, we can increase the probability of a successful random access procedure to 90%–100% when we use at least 9 OFDM symbols to transmit msg2 and msg3.

Wireless innovation can significantly benefit from having access to real-world, over-the-air (OTA) living labs for open-source prototyping and field evaluation of emerging, state-of-the-art solutions. However, the existing open-source 5G testbeds are either confined to controlled indoor environments, or they use commercial-off-the-shelf (COTS) user equipment (UEs) only without supporting software-defined-radio (SDR) UEs, thus lacking real-world fidelity or end-to-end programmability from UEs to gNBs and core networks. To fill the gap, we develop and deploy AraSDR that, as an integral element of the ARA Platform for Advanced Wireless Research (PAWR) on rural broadband, serves as a first-of-its-kind outdoor living lab supporting end-to-end, fully-programmable 5G experiments with SDR UEs and base stations (BSes) in real-world rural settings. AraSDR deploys in agriculture farms and rural cities NI N320 and B210 as the BS and UE SDRs respectively, and it employs low-cost, performant custom RF front-ends with power amplifiers (PAs) and low-noise amplifiers (LNAs) to boost the transmit and receive signals for extended cellular coverage. To enable real-world SDR-based experiments with open-source 5G stand-alone (SA) TDD cellular operations, we address the challenges of reliable control signaling, precision timing of the transmission/reception mode of RF front-ends, as well as transmission and reception gain control. We develop the software control framework to support remote experiments with streamlined workflows and to enable container-based experiment portability and reproducibility. Using OpenAirInterface (OAI) as an example open-source 5G software platform, we demonstrate the capability of AraSDR in supporting real-world, OTA 5G experiments.

ARA is an at-scale Platform for Advanced Wireless Research (PAWR), specifically tailored to the unique community, application, and economic context of rural regions. It features the first-of-its-kind real-world implementation of long-distance, high-capacity wireless backhaul and access systems spanning over 30 km in diameter. Leveraging both software-defined radios and programmable Commercial Off-The-Shelf (COTS) systems, ARA orchestrates the wireless resources alongside the networking and compute resources for enabling end-to-end experiments involving user equipment, base stations, edge computing, and cloud infrastructure. Such an integration facilitates the coevolution of rural-focused wireless innovation and applications, while helping to advance the frontiers of advanced Next-G wireless systems such as Open RAN. As of summer 2024, ARA is publicly accessible with 7 base stations (BSes) and over 30 user equipment (UEs). In this demo, we share advanced wireless research experiments enabled by ARA, involving MU-MIMO in TV White Space (TVWS) bands, long-range mmWave and microwave backhaul communications, and open-source 5G NR protocol stacks such as srsRAN and OpenAirInterface (OAI).

5G-and-beyond cellular networks are set to enable ultra-reliable, low-latency communications (URLLC), catering to a wide range of applications such as real-time control and extended reality (XR). For these URLLC applications, it is crucial to ensure per-packet communication reliability and high throughput. To this end, we propose a novel joint scheduling and power control approach, denoted by PktR, that ensures application-specific per-packet communication reliability as well as high channel spatial reuse and high network throughput. PktR is designed as a close-loop system, incorporating Gain-Ratio-K (GRK) interference modeling, optimization, and transmit power control mechanisms. PktR ensures predictable interference control for receivers and fine-tunes transmit power at transmitters in a highly agile manner. Our measurement studies demonstrate for the first time the feasibility of ensuring per-packet communication reliability in live cellular systems, by showing that PktR ensures high per-packet communication SINR (e.g., 20dB) and high success probability (e.g., 0.9) across diverse network and environmental settings. Through local, distributed coordination, PktR also outperforms state-of-the-art solutions significantly. For instance, besides ensuring predictable guarantee of required per-packet communication reliability in scenarios where existing solutions are unable to provide such guarantees for up to 31.01 % of the network links, PktR improves the network throughput by a factor up to 1.596.

Time synchronization is a critical component in network operation and management, and it is also required by Ultra-Reliable, Low-Latency Communications (URLLC) in next-generation wireless systems such as those of 5G, 6G, and Open RAN. In this context, we design and implement AraSync as an end-to-end time synchronization system in the ARA wireless living lab to enable advanced wireless experiments and applications involving stringent time constraints. We make use of Precision Time Protocol (PTP) at different levels to achieve synchronization accuracy in the order of nanoseconds. Along with fiber networks, AraSync enables time synchronization across the AraHaul wireless x-haul network consisting of long-range, high-capacity mmWave and microwave links. In this paper, we present the detailed design and implementation of AraSync, including its hardware and software components and the PTP network topology. Further, we experimentally characterize the performance of AraSync from spatial and temporal dimensions. Our measurement and analysis of the clock offset and mean path delay show the impact of the wireless channel and weather conditions on the PTP synchronization accuracy.

Cellular communication standards have been established to ensure connectivity across most urban environments, complemented by deployment hardware and facilities tailored for city life. At the same time, numerous initiatives seek to broaden connectivity to rural and developing areas. However, with nearly half the global population still offline, there is an urgent need to drive research toward enhancing connectivity in areas and conditions that deviate from the norm. This article delves into innovative communication solutions not only for hard-to-reach and extreme environments but also introduces “hard-to-serve” areas as a crucial, yet underexplored, category within the broader spectrum of connectivity challenges.We explore the latest advancements in communication systems designed for environments subject to extreme temperatures, harsh weather, excessive dust, or even disasters such as fires. Our exploration spans the entire communication stack, covering communications on isolated islands, sparsely populated regions, mountainous terrains, and even underwater and underground settings. We highlight system architectures, hardware, materials, algorithms, and other pivotal technologies that promise to connect these challenging areas. Through case studies, we explore the application of 5G for innovative research, long range (LoRa) for audio messages and emails, LoRa wireless connections, free-space optics, communications in underwater and underground scenarios, delay-tolerant networks, satellite links, and the strategic use of shared spectrum and TV white space (TVWS) to improve mobile connectivity in secluded and remote regions. These studies also touch on prevalent challenges such as power outages, regulatory gaps, technological availability, and human resource constraints, where we introduce the concept of peri-urban hard-to-serve areas where populations might struggle with affordability or lack the skills for traditional connectivity solutions. This article provides an exhaustive summary of our research, showcasing how 6G and future networks will play a crucial role in delivering connectivity to areas that are hard-to-reach, hard-to-serve, or subject to extreme conditions (ECs).

As the Platform for Advanced Wireless Research (PAWR) in rural broadband, the ARA wireless living lab features the deployment of first-of-its-kind wireless access and backhaul platforms in real-world agriculture and rural settings, and preliminary experiments have demonstrated very promising results, e.g., up to 3.2 Gbps wireless access throughput and more than 10 Gbps throughput across a wireless backhaul link of over 10 km. ARA is expected to be publicly released for broad community use starting in September 2023. Through this demo, we plan to share, for the first time, with the wireless research community the transformative research experiments enabled by ARA. To stimulate discussion and community participation, we will demonstrate a few example experiments ranging from MU-MIMO in TV White Space (TVWS) bands to long-range mmWave and microwave backhaul communications, as well as open-source 5G NR protocol stacks such as srsRAN and OpenAirInterface.

Thanks to the availability of large bandwidth and high-gain directional antennas at the millimeter-wave (mmWave) bands, mmWave communications have been considered as one of the primary solutions to meet the high data rates needs in vehicular networks. Unicast in mmWave vehicle-to-vehicle (V2V) communications has been well-studied, but much less attention has been paid to V2V broadcast which is required by many V2V applications such as active safety. To fill the gap, this paper systematically investigates mmWave V2V broadcast by considering the unique properties of mmWave signal propagation in V2V environments as well as the impacts of directional antennas and interference, aiming to provide unique insight into mmWave V2V broadcast and to shed light on designing effective V2V broadcast protocols. Based on widely-accepted, high-fidelity system models, we mathematically analyze the receiver-side signal-to-interference-plus-noise-ratio (SINR) and broadcast coverage, and we study the impacts of blockage, inter-vehicle distance, vehicle density and beam pattern. Through comprehensive numerical analysis, we find out that, instead of a single unique optimal beamwidth, there exists an optimal range of beamwidth, in which the beamwidths have similar performance and can maximize the coverage. We also find out that the selection of carrier sensing range plays an important role as it highly influences the performance of the whole vehicular networks.

Operating at lower frequencies than systems such as Wi-Fi, TVWS wireless communication can enable long-range communication in rural communities and can more easily penetrate obstacles (vegetation, terrains). Thus, it is appealing to scenarios where line-of-sight is not always guaranteed. In particular, TVWS communication is a good candidate for supporting precision agriculture such as camera-based plant phenotyping and sensor-based analysis of plant behaviour. Yet there lacks in-depth real-world measurement data on the behavior of TVWS wireless channels in agriculture farms. To fill this gap, we use the field-deployed TVWS network of CyNet to measure TVWS channel behaviour in the Curtiss Research Farm in Ames, Iowa, where the landscape is predominantly composed of soybean and corn fields. We investigate the impact that crop diversity (soybean vs. corn), height and density of corn fields, antennas’ placement and variations of temperature and humidity have on the spatiotemporal behaviour of TVWS channels. This study also helps identify path loss models that best reflect radio propagation characteristics of TVWS systems in corn farms for different antenna heights.

Radio access network (RAN) virtualization is gaining more and more ground and expected to re-architect the next-generation cellular networks. Existing RAN virtualization studies and solutions have mostly focused on sharing communication capacity and tend to require the use of the same PHY and MAC layers across network slices. This approach has not considered the scenarios where different slices require different PHY and MAC layers, for instance, for radically different services and for whole-stack research in wireless living labs where novel PHY and MAC layers need to be deployed concurrently with existing ones on the same physical infrastructure. To enable whole-stack slicing where different PHY and MAC layers may be deployed in different slices, we develop PV-RAN, the first open-source virtual RAN platform that enables the sharing of the same SDR physical resources across multiple slices. Through API Remoting, PV-RAN enables running paravirtualized instances of OpenAirInterface (OAI) at different slices without requiring modifying OAI source code. PV-RAN effectively leverages the inter-domain communication mechanisms of Xen to transport time-sensitive I/Q samples via shared memory, making the virtualization overhead in communication almost negligible. We conduct detailed performance benchmarking of PV-RAN and demonstrate its low overhead and high efficiency. We also integrate PV-RAN with the CyNet wireless living lab for smart agriculture and transportation.

Mission-critical IoT applications such as wirelessnetworked industrial control require reliable wireless communication. Due to co-channel interference and wireless channel dynamics (e.g., multi-path fading), however, wireless communication is inherently dynamic and subject to complex uncertainties. Joint scheduling and power control has been explored for reliable wireless communication, but existing solutions are mostly centralized or do not consider real-world challenges such as fast channel fading. Towards a foundation for mission-critical IoT communication, we develop a distributed, field-deployable approach to joint scheduling and power control that adaptively regulates cochannel interference and ensures predictable IoT communication reliability in the presence of wireless communication dynamics and uncertainties. Our approach effectively leverages the Perron-Frobenius theory, physical-ratio- K (PRK) interference model, and feedback control for PRK model adaptation and transmission power update. Through simulation analysis, we have shown that our approach improves concurrency by 70% than state-of-art fixed scheduling while ensuring successful SINR tracking over time. To the best of our knowledge, our approach is the first distributed scheduling and power control scheme that ensures predictable wireless communication reliability while considering real-world challenges such as fast channel fading, and it is expected to serve as a foundation for real-world deployment of mission- critical IoT systems.

The mission-critical nature of wireless networked sensing and control (WSC) systems, such as the control of industrial plants, requires stringent real-time delivery of packets. Due to inherent dynamics and uncertainties in wireless communication, real-time communication guarantees are probabilistic in nature. In this paper, a probabilistic framework is therefore proposed for per-packet real-time delivery guarantee. The notion of real-time in this paper differs from the existing work in the sense that it ensures, in an execution history of arbitrary length, every packet is successfully delivered before its deadline with a probability no less than a user-specified threshold (e.g., 99%). The framework has several novel building blocks: First, “R3 (requirement-reliability-resource) mapping” translates the upper layer probabilistic real-time communication requirement, and the lower layer links reliability into the resource (i.e., optimal number of transmission opportunities) reserved for each packet. Second, “EDF (earliest deadline first) based real-time scheduling” as well as the “admission test” and “traffic load optimization” maximize system utility while satisfying per-packet real-time communication requirements. The proposed admission test is proved to be both sufficient and necessary, and the simulation results show that the proposed framework ensures probabilistic per-packet real-time communication.

In wireless networked control systems, ensuring predictable communication link reliabilities among sensors, controllers, and actuators is critical. In such scenarios, different data gathered at the application layer of each sender require different packet delivery ratios (i.e., reliabilities). The lower layers try to accommodate these requests by first mapping each of them into a service level and then deliver the associated data packets to the receiver at the mapped service level. Due to resource constraints and maintenance overhead, the number of supported service levels is usually limited. An important question is then how to determine the set of service levels to maintain and how to map each request to an appropriate service level, such that the requested reliabilities are guaranteed and the total cost of mapping is minimized? We formally formulate this as an optimal request clustering problem since each service level acts as a cluster and can host multiple requests. In particular, we formulate the Migratory Clustering Problem and the Non-Migratory Clustering Problem, depending on whether a request can migrate from one service level to another after its initial assignment. We propose two optimal algorithms to solve both problems.

Predictable link reliability is required for wireless networked control, yet co-channel interference remains a major source of uncertainty in wireless link reliability. Formulated specifically for distributed predictable control of co-channel interference, the physical-ratio-K (PRK) interference model integrates the protocol model's locality and the physical model's high fidelity while addressing their weaknesses, and it transforms interference control in arbitrary networks to a problem involving coordination between close-by nodes only. To apply the PRK model in real-world settings, we design protocol PRKS that addresses the challenges of model instantiation and protocol signaling in PRK-based scheduling. In particular, PRKS uses a control-theoretic approach to instantiate the PRK model in dynamic uncertain networks, uses local signal maps to address the challenges of large interference range and anisotropic asymmetric wireless communication, and leverages the different timescales of PRK model adaptation and data transmission to decouple protocol signaling from data transmission. Through testbed-based measurement study, we show that, unlike existing scheduling protocols where link reliability is unpredictable and the ratio of links whose reliability meets application requirements can be as low as 0%, PRKS enables predictably high link reliability (e.g., 95%) for all the links in different network and environmental conditions without a priori knowledge of these conditions. Through local distributed coordination, PRKS also achieves a channel spatial reuse very close to what is enabled by the state-of-the-art centralized scheduler while ensuring the required link reliability. By ensuring the required link reliability in scheduling, PRKS also enables a lower communication delay and a higher network throughput than existing scheduling protocols.

Wireless sensor networks (WSNs) are increasingly deployed for mission-critical applications such as emergency management and health care, which impose stringent requirements on the communication performance of WSNs. To support these applications, it is crucial to model and measure the effect of wireless interference, which is the major factor that limits WSN performance. Accurate modeling and measurement of interference faces two key challenges. First, as shown in our experimental results, interference yields considerable spatial and temporal variations of WSN performance, which poses a major challenge for measurement at rum-time. Second, in the unlicensed band, the communication of WSN is interfered by coexisting wireless devices such as smartphones and laptops equipped with 802.11 radios, which lead to cross-technology interference that are difficult to characterize due to the heterogeneous PHY. To tackle these challenges, this paper presents a novel accuracy-aware approach to interference modeling and measurement for WSNs. First, we propose a new regression-based interference model and analytically characterize its accuracy based on statistics theory. Second, we develop a novel protocol called accuracy-aware interference measurement for measuring the proposed interference model with assured accuracy at run time. Third, building on interference modeling, we propose an algorithm that accurately forecasts the performance of WSNs in the presence of cross-technology interference. Our extensive experiments on a testbed of 17 TelosB motes show that the proposed approaches achieve high accuracy of interference modeling and WSN performance forecasting with significantly lower overhead than state-of-the-art approaches.

Network coding (NC) based opportunistic routing has been well studied, but the impact of routing diversity on the performance of NC-based routing remains largely unexplored. Towards understanding the importance of routing diversity in NC-based routing, we study the problems of estimating and minimizing the data delivery cost in NC-based routing. In particular, we propose an analytical framework for estimating the total number of packet transmissions for NC-based routing in arbitrary topologies. We design a greedy algorithm that minimizes the total transmission cost of NC-based routing and determines the corresponding forwarder set for each node. We prove the optimality of this algorithm and show that 1) nodes on the shortest path may not always be favored when selecting forwarders for NC-based routing and 2)the minimal cost of NC-based routing is upper-bounded by the cost of shortest path routing. Based on the greedy, optimal algorithm, we design and implement ONCR, a distributed minimal cost NC-based routing protocol. Using the NetEye sensor testbed, we comparatively study the performance of ONCR and existing approaches such as the single path routing protocol CTP and the NC-based opportunistic routing protocols MORE and CodeOR. Results show that ONCR achieves close to 100% delivery reliability while having the lowest delivery cost among all the protocols and 25-28% less than the second best protocol CTP. This low delivery cost also enables ONCR to achieve the highest network goodput, i.e., about two-fold improvement over MORE and CodeOR. Our findings demonstrate the significance of optimizing data forwarding diversity in NC-based routing for data delivery reliability, efficiency, and goodput.

Predictable link reliability is required for wireless networked control, yet co-channel interference remains a major source of uncertainty in wireless link reliability. Integrating the protocol model's locality and the physical model's high fidelity, the physical-ratio-K (PRK) interference model has the potential to enable distributed, predictable control of co-channel interference and thus predictable link reliability. To realize the potential of the PRK model, we design protocol PRKS that addresses the challenge of instantiating the PRK model in the presence of network and environmental uncertainties. Formulating the PRK-model-instantiation problem as a minimum-variance regulation control problem, in particular, PRKS uses a control-theoretic approach to instantiating the PRK model on the fly. Through testbed-based measurement study, we show that, unlike existing scheduling protocols where link reliability is unpredictable and the ratio of links whose reliability meets application requirements can be as low as 0%, PRKS enables predictably high link reliability (e.g., 95%) for all the links in different network and environmental conditions without a priori knowledge of these conditions. Through local, distributed coordination, PRKS also achieves a channel spatial reuse very close to what is enabled by the state-of-the-art centralized scheduler while ensuring the required link reliability. By ensuring the required link reliability in scheduling, PRKS also enables a lower communication delay and a higher network throughput than existing scheduling protocols.

Optimizing vehicle-centered mobility experience requires leveraging the heterogeneous wireless connectivities (e.g., cellular, WiFi, and VANET) between vehicles and the Internet. Towards a foundation for heterogeneous vehicular wireless networking, we investigate the mathematical formulation of the problem, and we analyze the impact of bandwidth aggregation on the problem formulation as well as the computational complexity of the problem. Our analysis shows that the problem can be solved in polynomial time if bandwidth aggregation is employed, but the problem becomes NP-complete and cannot be solved optimally in general if bandwidth aggregation is not employed. Our analysis is constructive such that it suggests efficient approaches to solving the problem in an optimal manner when bandwidth aggregation is employed, and it suggests both optimal and approximate approaches to solving the problem when bandwidth aggregation is not employed. The above analytical insight serves as a guidance on choosing the system architecture and algorithms for heterogeneous vehicular wireless networking.

Packet loss in block erasure channels creates a randomly switching networked system that impacts control performance significantly. This paper employs safety of highway vehicle platoons as a platform to study such an impact. By autonomous intervehicle coordination, a platoon can potentially enhance safety, improve highway utility, increase fuel economy, and reduce emission. By comparing different information structures that utilize radar distance sensors and wireless communication channels, we characterize some intrinsic relationships between communication resources and control performance. The findings of this paper provide useful guidelines in communication resource allocations and vehicle coordination in vehicle safety problems.

Packet loss in block erasure channels creates a randomly switching networked system that impacts control performance significantly. This paper employs safety of highway vehicle platoons as a platform to study such impact. By autonomous inter-vehicle coordination, a platoon can potentially enhance safety, improve highway utility, increase fuel economy, and reduce emission. By comparing different information structures which utilize radar distance sensors and wireless communication channels, we are able to characterize some intrinsic relationships between communication resources and control performance. The findings of this paper provide useful guidelines on communication resource allocations and vehicle coordinations in vehicle safety problems.

Interference model is the basis of MAC protocol design in wireless networked sensing and control, and it directly affects the efficiency and predictability of wireless messaging. To exploit the strengths of both the physical and the protocol interference models, we analyze how network traffic, link length, and wireless signal attenuation affect the optimal instantiation of the protocol model. We also identify the inherent trade-off between reliability and throughput in the model instantiation. Our analysis sheds light on the open problem of efficiently optimizing the protocol model instantiation. Based on the analytical results, we propose the physical-ratio-K (PRK) interference model as a reliability-oriented instantiation of the protocol model. Via analysis, simulation, and testbed-based measurement, we show that PRK-based scheduling achieves a network throughput very close to (e.g., 95%) what is enabled by physical-model-based scheduling while ensuring the required packet delivery reliability. The PRK model inherits both the high fidelity of the physical model and the locality of the protocol model, thus it is expected to be suitable for distributed protocol design. These findings shed new light on wireless interference models; they also suggest new approaches to MAC protocol design in the presence of uncertainties in network and environmental conditions as well as application QoS requirements.

Scheduling channel access for interference control is a basic building block of wireless networking. Despite much work in this area, the existing algorithms did not explicitly address the impact of link ordering (i.e., the order in which links are added to the schedule of a time slot) on receiver-side interference accumulation and thus on optimal scheduling. Towards understanding the importance of considering the ordering effect, we formulate the concept of interference budget, and, by modeling the scheduling problem as a knapsack problem, we propose the scheduling algorithm iOrder that maximizes the schedulability of future channel access when scheduling concurrent transmissions. When selecting concurrent transmitters for a time slot, more specifically, iOrder tries to maximize the additional interference that can be tolerated by all the receivers while satisfying the application requirement on link reliability. We analyze the approximation ratio of iOrder, and, through extensive simulation and testbed-based measurement, we observe that addressing the ordering effect can improve the performance of existing algorithms by a significant margin in the case of both backlogged and online traffic, for instance, improving the throughput and reducing the latency of the well-known algorithm LQF by a factor up to 2 and 24, respectively. Thus our study demonstrates the importance of explicitly addressing the ordering effect in wireless scheduling, which opens up new avenues for future research and for optimizing wireless network performance.

Real-time routing is a basic element of closed-loop, real-time sensing and control, but it is challenging due to dynamic, uncertain link/path delays. The probabilistic nature of link/path delays makes the basic problem of computing the probabilistic distribution of path delays NP-hard, yet quantifying probabilistic path delays is a basic element of real-time routing and may well have to be executed by resource-constrained devices in a distributed manner; the highly-varying nature of link/path delays makes it necessary to adapt to in-situ delay conditions in real-time routing, but it has been observed that delay-based routing can lead to instability, estimation error, and low data delivery performance in general. To address these challenges, we propose the Multi-Timescale Estimation (MTE) method; by accurately estimating the mean and variance of per-packet transmission time and by adapting to fast-varying queueing in an accurate, agile manner, MTE enables accurate, agile, and efficient estimation of probabilistic path delay bounds in a distributed manner. Based on MTE, we propose the Multi-Timescale Adaptation (MTA) routing protocol; MTA integrates the stability of an ETX-based directed-acyclic-graph (DAG) with the agility of spatiotemporal data flow control within the DAG to ensure real-time data delivery in the presence of dynamics and uncertainties. We also address the challenges of implementing MTE and MTA in resource-constrained devices such as TelosB motes. We evaluate the performance of MTA using the NetEye and Indriya sensor network testbeds. We find that MTA significantly outperforms existing protocols, e.g., improving deadline success ratio by 89% and reducing transmission cost by a factor of 9.7.

Wireless Sensor Networks (WSNs) are increasingly available for mission-critical applications such as emergency management and health care. To meet the stringent requirements on communication performance, it is crucial to understand the complex wireless interference among sensor nodes. Recent empirical studies suggest that the packet-level interference model, also referred to as the packet reception ratio (PRR) versus SINR model or PRR-SINR model, offers significantly improved realism than other simplistic models such as the disc model. However, as shown in our experimental results, the PRR-SINR model yields considerable spatial and temporal variations in reality, which poses a major challenge for accurate measurement at run time. This paper presents a novel accuracy-aware approach to interference modeling and measurement for WSNs. First, we propose a new regression-based PRR-SINR model and analytically characterize its accuracy based on statistics theory. Second, we develop a novel protocol called accuracy-aware interference measurement (AIM) for measuring the proposed PRR-SINR model with assured accuracy at run time. AIM also adopts new clock calibration and in-network aggregation techniques to reduce the overhead of interference measurement. Our extensive experiments on a 17-node testbed of TelosB motes show that AIM achieves high accuracy of PRR-SINR modeling with significantly lower overhead than state of the art approaches.

Co-channel interference is a limiting factor to the predictability and performance of wireless networks, thus interference-oriented scheduling of channel access has become a basic building block of wireless networking. Despite much work in this area, the existing algorithms did not address the limiting impact of interference when optimizing transmission scheduling. Towards understanding the importance of considering the limiting impact of interference, we formulate the concept of interference budget, and we propose the scheduling algorithm iOrder that maximizes the schedulability of future channel access when scheduling concurrent transmissions. When selecting concurrent transmitters for a time slot, more specifically, iOrder tries to maximize the additional interference that can be tolerated by all the receivers while satisfying the application requirement on link reliability. We analyze the approximation ratio of iOrder, and, through extensive simulation and testbed-based measurement, we observe that addressing the limiting impact of interference can improve the performance of existing algorithms by a significant margin, for instance, improving the throughput of the well-known algorithm LQF by a factor up to 2. Thus our study demonstrates the importance of explicitly addressing the limiting impact of interference, which opens up new avenues for future research and for optimizing wireless network performance.

As sensornets are increasingly being deployed in mission-critical applications, it becomes imperative that we consider application QoS requirements in in-network processing (INP). Toward understanding the complexity of joint QoS and INP optimization, we study the problem of jointly optimizing packet packing (i.e., aggregating shorter packets into longer ones) and the timeliness of data delivery. We identify the conditions under which the problem is strong NP-hard, and we find that the problem complexity heavily depends on aggregation constraints (in particular, maximum packet size and reaggregation tolerance) instead of network and traffic properties. For cases when the problem is NP-hard, we show that there is no polynomial-time approximation scheme (PTAS); for cases when the problem can be solved in polynomial time, we design polynomial time, offline algorithms for finding the optimal packet packing schemes. To understand the impact of joint QoS and INP optimization on sensornet performance, we design a distributed, online protocol tPack that schedules packet transmissions to maximize the local utility of packet packing at each node. Using a testbed of 130 TelosB motes, we experimentally evaluate the properties of tPack. We find that jointly optimizing data delivery timeliness and packet packing and considering real-world aggregation constraints significantly improve network performance. Our findings shed light on the challenges, benefits, and solutions of joint QoS and INP optimization, and they also suggest open problems for future research.

Interference modeling is crucial for the performance of numerous WSN protocols such as congestion control, link/channel scheduling, and reliable routing. In particular, understanding and mitigating interference becomes increasingly important for Wireless Sensor Networks (WSNs) as they are being deployed for many data-intensive applications such as structural health monitoring. However, previous works have widely adopted simplistic interference models that fail to capture the wireless realities such as probabilistic packet reception performance. Recent studies suggested that the physical interference model (i.e., PRR-SINR model) is significantly more accurate than existing interference models. However, existing approaches to physical interference modeling exclusively rely on the use of active measurement packets, which imposes prohibitively high overhead to bandwidth-limited WSNs. In this paper, we propose the passive interference measurement (PIM) approach to tackle the complexity of accurate physical interference characterization. PIM exploits the spatiotemporal diversity of data traffic for radio performance profiling and only needs to gather a small amount of statistics about the network. We evaluate the efficiency of PIM through extensive experiments on both a 13-node and a 40-node testbeds of TelosB motes. Our results show that PIM can achieve high accuracy of PRR-SINR modeling with significantly lower overhead compared with the active measurement approach.

To exploit the strengths of both the physical and the protocol interference models and to understand the varying observations on the relative goodness of scheduling based on the two models in literature, we analyze how network traffic, link length, and wireless signal attenuation affect the optimal instantiation of the protocol model. We also identify the inherent tradeoff between reliability and throughput in the model instantiation. Our analysis explains the seemingly inconsistent observations in literature and sheds light on the open problem of efficiently optimizing the protocol model instantiation. Based on the analytical results, we propose the physical-ratio-K (PRK) interference model as a reliability-oriented instantiation of the protocol model. Via analysis, simulation, and testbed-based measurement, we show that PRK-based scheduling achieves a network throughput very close to (e.g., 95%) what is enabled by physical-model-based scheduling while ensuring the required packet delivery reliability. The PRK model inherits both the high fidelity of the physical model and the locality of the protocol model, thus it is expected to be suitable for distributed protocol design. These findings shed new light on wireless interference models; they also suggest new approaches to MAC protocol design in the presence of uncertainties in traffic and application properties.

Link estimation is a basic element of routing in low-power wireless networks, and data-driven link estimation using unicast MAC feedback has been shown to outperform broadcast-beacon-based link estimation. Nonetheless, little is known about how different data-driven link estimation methods affect routing behaviors. To address this issue, we classify existing data-driven link estimation methods into two broad categories: L-NT that uses aggregate information about unicast and L-ETX that uses information about the individual unicast-physical-transmissions. Through mathematical analysis and experimental measurement in a testbed of 98 XSM motes (an enhanced version of MICA2 motes), we examine the accuracy and stability of L-NT and L-ETX in estimating the ETX routing metric. We also experimentally study the routing performance of L-NT and L-ETX. We discover that these two representative, seemingly similar methods of data-driven link estimation differ significantly in routing behaviors: L-ETX is much more accurate and stable than L-NT in estimating the ETX metric, and accordingly, L-ETX achieves a higher data delivery reliability and energy efficiency than L-NT (for instance, by 25.18 percent and a factor of 3.75, respectively, in our testbed). These findings provide new insight into the subtle design issues in data-driven link estimation that significantly impact the reliability, stability, and efficiency of wireless routing, thus shedding light on how to design link estimation methods for mission-critical wireless networks which pose stringent requirements on reliability and predictability.

Link asymmetry is one of the unique challenges that wireless sensor networks pose in the design of network protocols. We observe, based on testbed experiments, that a substantial percentage of links are asymmetric, many are even unidirectional. We also find that the reliability of synchronous acknowledgments is considerably higher than that of asynchronous messages. Thus the norm of estimating link quality bidirectionally via asynchronous beacons underestimates the link reliability of asymmetric links. This leads us to investigate how to exploit asymmetric links in order to improve network functions such as convergecast routing in sensor networks via one-way link estimation. We propose a new one-way link metric ETF (for the expected number of transmissions over forward links ) and present a local procedure for its estimation. We use ETF to identify reliable forward links, and we use dynamic retransmission thresholding for error control. Via experiments on testbeds of CC1000 radios and CC2420 radios (an IEEE 802.15.4-compliant radio), we quantify the performance improvement in ETF as compared with ETX. We also study the performance improvement of ETF over ETX when no special mechanism is employed to discover asymmetric links or to control retransmissions.

As sensornets are increasingly being deployed in mission-critical applications, it becomes imperative that we consider application QoS requirements in in-network processing (INP). Towards understanding the complexity of joint QoS and INP optimization, we study the problem of jointly optimizing packet packing (i.e., aggregating shorter packets into longer ones) and the timeliness of data delivery. We identify the conditions under which the problem is strong NP-hard, and we find that the problem complexity heavily depends on aggregation constraints (in particular, maximum packet size and re-aggregation tolerance) instead of network and traffic properties. For cases when the problem is NP-hard, we show that there is no polynomial-time approximation scheme (PTAS); for cases when the problem can be solved in polynomial time, we design polynomial time, offline algorithms for finding the optimal packet packing schemes. To understand the impact of joint QoS and INP optimization on sensornet performance, we design a distributed, online protocol \emph{tPack} that schedules packet transmissions to maximize the local utility of packet packing at each node. Using a testbed of 130 TelosB motes, we experimentally evaluate the properties of tPack. We find that jointly optimizing data delivery timeliness and packet packing significantly improve network performance. Our findings shed light on the challenges, benefits, and solutions of joint QoS and INP optimization, and they also suggest open problems for future research.

Link estimation is a basic element of routing in low-power wireless networks, and data-driven link estimation using unicast MAC feedback has been shown to outperform broadcast- beacon based link estimation. Nonetheless, little is known about the impact that different data-driven link estimation methods have on routing behaviors. To address this issue, we classify existing data- driven link estimation methods into two broad categories: L-NT that uses aggregate information about unicast and L-ETX that uses information about the individual unicast-physical transmissions. Through mathematical analysis and experimental measurement in a testbed of 98 XSM motes (an enhanced version of MICA2 motes), we examine the accuracy and stability of L-NT and L-ETX in estimating the ETX routing metric. We also experimentally study the routing performance of L-NT and L-ETX. We discover that these two representative, seemingly similar methods of data-driven link estimation differ significantly in routing behaviors: L-ETX is much more accurate and stable than L-NT in estimating the ETX metric, and, accordingly, L-ETX achieves a higher data delivery reliability and energy efficiency than L-NT (for instance, by 25.18% and a factor of 3.75 respectively in our testbed). These findings provide new insight into the subtle design issues in data-driven link estimation that significantly impact the reliability, stability, and efficiency of wireless routing, thus shedding light on how to design link estimation methods for mission-critical wireless networks which pose stringent requirements on reliability and predictability.

The wireless network community has become increasingly aware of the benefits of data-driven link estimation and routing as compared with beacon-based approaches, but the issue of Biased Link Sampling (BLS) estimation has not been well studied even though it affects routing convergence in the presence of network and environment dynamics. Focusing on traffic-induced dynamics, we examine the open, unexplored question of how serious the BLS issue is and how to effectively address it when the routing metric ETX is used. For a wide range of traffic patterns and network topologies and using both node-oriented and network-wide analysis and experimentation, we discover that the optimal routing structure remains quite stable even though the properties of individual links and routes vary significantly as traffic pattern changes. In cases where the optimal routing structure does change, data-driven link estimation and routing is either guaranteed to converge to the optimal structure or empirically shown to converge to a close-to-optimal structure. These findings provide the foundation for addressing the BLS issue in the presence of traffic-induced dynamics and suggest approaches other than existing ones. These findings also demonstrate that it is possible to maintain an optimal, stable routing structure despite the fact that the properties of individual links and paths vary in response to network dynamics.

In the context of IEEE 802.11b network testbeds, we examine the differences between unicast and broadcast link properties, and we show the inherent difficulties in precisely estimating unicast link properties via those of broadcast beacons even if we make the length and transmission rate of beacons be the same as those of data packets. To circumvent the difficulties in link estimation, we propose to estimate unicast link properties directly via data traffic itself without using periodic beacons. To this end, we design a data-driven routing protocol Learn-on-the-Fly (LOF). LOF chooses routes based on ETX/ETT-type metrics, but the metrics are estimated via MAC feedback for unicast data transmission instead of broadcast beacons. Using a realistic sensor network traffic trace and an 802.11b testbed of ~195 Stargates, we experimentally compare the performance of LOF with that of beacon-based protocols, represented by the geography-unaware ETX and the geography-based PRD. We find that LOF reduces end-to-end MAC latency by a factor of 3, enhances energy efficiency by a factor up to 2.37, and improves network throughput by a factor up to 7.78, which demonstrate the feasibility and the potential benefits of data-driven link estimation and routing.

A substantial percentage of links in wireless networks, especially low-power ones, is asymmetric. For the low-quality direction of asymmetric links, we observe based on testbed experiments that the reliability of synchronous acknowledgments is considerably higher than that of asynchronous messages. Thus the norm of estimating link quality in both directions via asynchronous beacons such as in ETX-based routing potentially underestimates the link reliability of asymmetric links. This leads us to investigate how to exploit asymmetric links in order to improve network functions such as convergecast routing in sensor networks via one-way link estimation. We propose a new one-way link metric ETF (for the expected number of transmissions over forward links) and present a local procedure for its estimation. We use ETF to identify high reliability forward links and use dynamic retransmission thresholding for error control and observe an improvement inconvergecast routing over ETX. This is quantified with experimental testbed results with respect to reliability, number of transmissions per packet, latency, duplicates and average hops. We also study the comparative performance improvement of ETF over ETX when no special mechanism is employed to discover asymmetric links.

We present Sprinkler, a reliable data dissemination service for wireless embedded devices which are constrained in energy, processing speed, and memory. Sprinkler embeds a virtual grid over the network whereby it can locally compute a connected dominating set of the devices to avoid redundant transmissions and a transmission schedule to avoid collisions. Sprinkler transmits O(1) times the optimum number of packets in O(1) of the optimum latency; its time complexity is O(1). Sprinkler is tolerant to fail-stop and state corruption faults. Thus, Sprinkler is suitable for resource-constrained wireless embedded devices. We evaluate the performance of Sprinkler in terms of the number of packet transmissions and the latency, both in an outdoor and indoor environment. The outdoor evaluation is based on data from project ExScal, which deployed 203 extreme scale stargazer (XSS). Our indoor evaluation is based on an implementation in the Kansei testbed, which houses 210 XSSs whose transmission power is controllable to even low ranges. We compare Sprinkler with the existing reliable data dissemination services, analytically or using simulations also. Our evaluations show that Sprinkler is not only energy efficient as compared to existing schemes, but also has less latency. Further, the energy consumption of nodes and the latency grows linearly as a function of newly added nodes as the network grows larger.

In the context of IEEE 802.11b network testbeds, we examine the differences between unicast and broadcast link properties, and we show the inherent difficulties in precisely estimating unicast link properties via those of broadcast beacons even if we make the length and transmission rate of beacons be the same as those of data packets. To circumvent the difficulties in link estimation, we propose to estimate unicast link properties directly via data traffic itself without using periodic beacons. To this end, we design a data-driven routing protocol Learn on the Fly (LOF). LOF estimates link quality based on data traffic, and it chooses routes by way of a locally measurable metric ELD, the expected MAC latency per unit-distance to the destination. Using a realistic sensor network traffic trace and an 802.11b testbed of 195 Stargates, we experimentally compare the performance of LOF with that of existing protocols, represented by the geography-unaware ETX and the geography-based PRD. We find that LOF reduces endto-end MAC latency by a factor of 3, enhances energy efficiency by a factor up to 2.37, improves route stability by 2 orders of magnitude, and improves network throughput by a factor up to 7.78. The results demonstrate the feasibility as well as potential benefits of data-driven link estimation and routing. Keywords—sensor network, beacon-free geographic routing, data-driven link quality estimation, MAC latency, IEEE 802.11b, real time, energy, reliability

The Kansei testbed at the Ohio State University is designed to facilitate research on networked sensing applications at scale. Kansei embodies a unique combination of characteristics as a result of its design focus on sensing and scaling: (i) Heterogeneous hardware infrastructure with dedicated node resources for local computation, storage, data exfiltration and back-channel communication, to support complex experimentation, (ii) Time accurate hybrid simulation engine for simulating substantially larger arrays using testbed hardware resources, (iii) High fidelity sensor data generation and real-time data and event injection, (iv) Software components and associated job control language to support complex multi-tier experiments utilizing real hardware resources and data generation and simulation engines. In this paper, we present the elements of Kansei testbed architecture, including its hardware and software platforms as well as its hybrid simulation and sensor data generation engines.

We address the challenges of bursty convergecast in multi-hop wireless sensor networks, where a large burst of packets from different locations needs to be transported reliably and in real-time to a base station. Via experiments on a 49 MICA2 mote sensor network using a realistic traffic trace, we determine the primary issues in bursty convergecast, and accordingly design a protocol, RBC (for Reliable Bursty Convergecast), to address these issues: To improve channel utilization and to reduce ack-loss, we design a window-less block acknowledgment scheme that guarantees continuous packet forwarding and replicates the acknowledgment for a packet; to alleviate retransmission-incurred channel contention, we introduce differentiated contention control. Moreover, we design mechanisms to handle varying ack-delay and to reduce delay in timer-based re-transmissions. We evaluate RBC, again via experiments, and show that compared to a commonly used implicit-ack scheme, RBC doubles packet delivery ratio and reduces end-to-end delay by an order of magnitude, as a result of which RBC achieves a close-to-optimal goodput.

No abstract available.

Project ExScal (for extreme scale) fielded a 1000+ node wireless sensor network and a 200+ node peer-to-peer ad hoc network of 802.11 devices in a 13km by 300m remote area in Florida, USA during December 2004. In comparison with previous deployments, the ExScal application is relatively complex and its networks are the largest ones of either type fielded to date. In this paper, we overview the key requirements of ExScal, the corresponding design of the hardware/software platform and application, and some results of our experiments.

We present Sprinkler, a reliable data dissemination service for wireless embedded devices which are constrained in energy, processing speed, and memory. Sprinkler embeds a virtual grid over the network whereby it can locally compute a connected dominating set of the devices to avoid redundant transmissions, and a transmission schedule to avoid collisions. Sprinkler transmits O(1) times the optimum number of packets in O(1) of the optimum latency; its time complexity is O(1). Thus, Sprinkler is suitable for resource-constrained wireless embedded devices. We evaluate the performance of Sprinkler in terms of the number of packet transmissions and the latency, both in an outdoor and an indoor environment. Our indoor evaluation is based on an implementation in the Kansei testbed that houses 210 XSSs whose transmission power is controllable to even low ranges. We compare Sprinkler with the existing reliable data dissemination services, analytically or using simulations also. Our evaluations show that Sprinkler is not only energy efficient as compared to existing schemes but also have less latency. Further, the energy consumption of nodes and the latency grows linearly as a function of newly added nodes as network grows larger

This paper shows that the elimination of fault-agnostic instability, the instability caused by fault-agnostic distributed control, substantially improves BGP convergence speed. To this end, we first classify BGP convergence instability into two categories: fault-agnostic instability and distribution-inherent instability; secondly, we prove the impossibility of eliminating all distribution-inherent instability in distributed routing protocols; thirdly, we design the grapevine border gateway protocol (G-BGP) to show that all fault-agnostic instability can be eliminated. G-BGP eliminates all fault-agnostic instability under different fault and routing policy scenarios by (i) piggybacking onto BGP UPDATE messages fine-grained information about faults to the nodes affected by the faults, (ii) quickly resolving the uncertainty between link and node failure as well as the uncertainty of whether a node has changed route, and (iii) rejecting obsolete fault information. We have evaluated G-BGP by both analysis and simulation. Analytically, we prove that, by eliminating fault-agnostic instability, G-BGP achieves optimal convergence speed in several scenarios where BGP convergence is severely delayed (e.g., when a node or a link fail-stops), and when the shortest-path-first policy is used, G-BGP asymptotically improves BGP convergence speed except in scenarios where BGP convergence speed is already optimal (e.g., when a node or a link joins). By simulating networks with up to 115 autonomous systems, we observe that G-BGP improves BGP convergence stability and speed by an order of magnitude.

We formulate a notion of local stabilization, by which a system self-stabilizes in time proportional to the size of any perturbation that changes the network topology or the state of nodes. The notion implies that the part of the network involved in the includes at most the nodes whose distance from the perturbed nodes is proportional to the perturbation size. Also, we present LSRP, a protocol for local in shortest path routing. LSRP achieves local via two techniques. First, it layers system computation into three diffusing waves each having a different propagation speed, i.e., stabilization with the lowest speed, with intermediate speed, and with the highest speed. The containment wave contains the mistakenly initiated wave, the super-containment wave contains the mistakenly initiated containment wave, and the super-containment wave self-stabilizes itself locally. Second, LSRP avoids forming loops during stabilization, and it removes all transient loops within small constant time. To the best of our knowledge, LSRP is the first protocol that achieves local in shortest path routing.

As the Internet and network technologies evolve, the IP over WDM solution has been envisioned as the most promising solution for the next generation Internet architecture. So survivability in IP/WDM networks becomes critical for the success of the next generation Internet architecture. Considerable research efforts have been dedicated to studying the survivability in IP/GMPLS and WDM network respectively, but still remains the need for a better understanding of the interworking, coordination and functionality partitioning in survivability between IP and WDM. We explore the necessity, methods and advantages to coordinate multi-layer survivability in IP/WDM network. We especially focus on the study of the escalation method, multi-layer network spare capacity design and function partitioning. We study the use of differentiated survivability policies combined with a multi-layer survivability scheme for IP/WDM networks.

We present GS3, a distributed, scalable, self-configuration and self-healing algorithm for multi-hop wireless networks. The algorithm enables network nodes in a 2D plane to configure themselves into a cellular hexagonal structure such that cells have tightly bounded geographic radius and low overlap between neighboring cells. The structure is self-healing under various perturbations, such as node joins, leaves, deaths, movements, and state corruptions. For instance, it slides as a whole if nodes in many cells die at the same rate. Moreover, its configuration and healing are scalable in three respects: first, local knowledge enables each node to maintain only limited information with respect to a constant number of nearby nodes; second, local healing guarantees that all perturbations are contained within a tightly bounded region with respect to the perturbed area and dealt with in a one-way message diffusion time across the region; third, only local coordination is needed in both configuration and self-healing.