Federated Learning (FL) enables collaborative training of machine learning models for edge devices (e.g., mobile phones) over a network without revealing raw data of the participants. The existing FL benchmarks mostly assume static distribution of local data over time failing to capture the behavior of real-world applications with space-time varying data (e.g., autonomous cars). Our framework addresses this limitation by leveraging popular open source physics simulator (CARLA) and FL framework (OpenFL) to allow collection of streaming data from the mobile agents and feeding them to a practically deployable FL engine for online collaborative training. It also provides the FL researchers with the ability to model data heterogeneity, annotate data with practically zero cost, and perform reproducible continual FL experiments. We believe that this is one of the first attempts to demonstrate online FL on realistic streaming datasets from a virtual world. The demo showcases this framework using a popular object detection use case.

Sensor nodes are prone to failures due to their limited hardware capabilities, and software uncertainties stemming from erroneous logic or configuration. Such failures as well as wireless channel dynamics can degrade network performance, potentially creating network partitions. Existing troubleshooting tools either only diagnose a few problems or suffer from high overhead due to periodic transmission of control packets. In this paper, we propose Feluda, a system that exploits provenance, i.e., forwarding path of data packets, for automatic localization of problematic nodes and packets. Unlike existing methods, Feluda extracts necessary network performance metrics from packet headers and stores them into node flash storage, thereby reducing out-of-band packet transmissions. Once problematic nodes and corresponding packets are identified at the base station (BS), Feluda provides efficient querying mechanisms to retrieve packet headers of interest from specific nodes. Packet header analysis reveals the root cause of the problem. We implement Feluda using Java and ContikiOS on the BS and sensor nodes, respectively. Testbed experiments and COOJA simulations demonstrate the effectiveness of Feluda compared to the state-of the-art.

A smartphone application that displays a live view of road traffic can provide drivers with real-time information on traffic jams, flash floods or accidents along their planned routes. This information aids them in avoiding delays and uncertainties on the road, enhancing their navigation experience. Several US departments of transportation provide information from street cameras in the form of a snapshot of a particular location on a Google map. However, it is difficult for drivers to manually select cameras on the map to obtain consolidated information about road conditions along a particular route. In this paper, we design an energy-efficient mobile service that provides drivers with a convenient interface to observe live camera coverage along a route. Our proposed solution comprises an Android application and a cloud-based proxy service between smartphones and traffic cameras. The proxy provides an abstraction layer over different communication protocols adopted by traffic cameras, and transfers camera images for a specified route to the smartphone as a batch, thereby reducing communication overhead and improving user response time. By employing an in-memory cache, the proxy server maintains the most recently accessed camera images and reduces camera polling. We integrate our solution with traffic cameras from cities in Massachusetts, New York, and Washington DC, and demonstrate its reduced energy consumption and reduced response time.

Sensor nodes are inherently unreliable and prone to hardware or software faults. Thus, they may report untrustwor- thy or inconsistent data. Assessing the trustworthiness of sensor data items can allow reliable sensing or monitoring of physical phenomena. A provenance-based trust framework can evaluate the trustworthiness of data items and sensor nodes based on the intuition that two data items with similar data values but with different provenance (i.e., forwarding path) can be considered more trustworthy. Forwarding paths of data items generated from redundantly deployed sensors should consist of trustworthy nodes and remain dissimilar. Unfortunately, operating many sensors with dissimilar paths consumes significant energy. In this paper, we formulate an optimization problem to identify a set of sensor nodes and their corresponding paths toward the base station that achieve a certain trustworthiness threshold, while keeping the energy consumption of the network minimal. We prove the NP-hardness of this problem and propose ERUPT, a simulated annealing solution. Testbed and simulation results show that ERUPT achieves high trustworthiness, while reducing total energy consumption by 32-50% with respect to current approaches.

Rapid flooding is necessary for code updates and routing tree formation in wireless sensor networks. Link correlation-aware collective flooding (CF) is a recently proposed technique that provides a substrate for efficiently disseminating a single packet. Applying CF to multiple packet dissemination poses several challenges, such as reliability degradation, redundant transmissions, and increased contention among node transmissions. The varying link correlation observed in real networks makes the problem harder. In this paper, we propose a multi-packet flooding protocol, SYREN, that exploits the synergy among link correlation and network coding. In particular, SYREN exploits link correlation to eliminate the overhead of explicit control packets in networks with high correlation, and uses network coding to pipeline transmission of multiple packets via a novel, single yet scalable timer per node. SYREN reduces the number of redundant transmissions while achieving near-perfect reliability, especially in networks with low link correlation. Test bed experiments and simulations show that SYREN reduces the average number of transmissions by 30% and dissemination delay by more than 60% while achieving the same reliability as state-of-the-art protocols.

Congestion control in multi-hop infrastructure wireless mesh networks is both an important and a unique problem. It is unique because it has two prominent causes of failed transmissions which are difficult to tease apart - lossy nature of wireless medium and high extent of congestion around gateways in the network. The concurrent presence of these two causes limits applicability of already available congestion control mechanisms, proposed for wireless networks. Prior mechanisms mainly focus on the former cause, ignoring the latter one. Therefore, we address this issue to design an end-to-end congestion control mechanism for infrastructure wireless mesh networks in this paper. We formulate the congestion control problem and map that to the restless multi-armed bandit problem, a well-known decision problem in the literature. Then, we propose three myopic policies to achieve a near-optimal solution for the mapped problem since no optimal solution is known to this problem. We perform comparative evaluation through ns-2 simulation and a real testbed experiment with a wireline TCP variant and a wireless TCP protocol. The evaluation reveals that our proposed mechanism can achieve up to 52% increased network throughput and 34% decreased average energy consumption per transmitted bit in comparison to the other end-to-end congestion control variants.

Assessing the trustworthiness of sensor data and transmitters of this data is critical for quality assurance. Trust evaluation frameworks utilize data provenance along with the sensed data values to compute the trustworthiness of each data item. However, in a sizeable multi-hop sensor network, provenance information requires a large and variable number of bits in each packet, resulting in high energy dissipation due to the extended period of radio communication, and making trust systems unusable. We propose energy-efficient provenance encoding and construction schemes, which we refer to as Probabilistic Provenance Flow (PPF). To the best of our knowledge, ours is the first work to make the Probabilistic Packet Marking (PPM) approach for IP traceback feasible for sensor networks. We design two bit-efficient provenance encoding schemes along with a complementary vanilla scheme. Depending on the network size and bit budget, we select the best method using mathematical approximations and numerical analysis. Our TOSSIM simulations demonstrate that the encoding schemes of PPF have identical performance with a low bit budget (~ 32-bit), requiring 33% fewer packets and 30% less energy than PPM variants to construct provenance. With a two-fold increase in bit budget, PPF with the selected encoding scheme reduces the energy consumption by 60%1.

Correlated packet reception can be advantageous for sensor network broadcast protocols. By exploiting link correlation information, researchers have devised efficient single packet flooding protocols. In this work, we use testbed experiments to gain insight into the behavior of link correlation-aware broadcast protocols. We observe that, in the presence of varying link correlation, traditional link correlation-aware flooding mechanisms do not perform well in disseminating multiple packets due to reliability requirements and redundant transmissions. We conduct simulations to compare existing link correlation-aware flooding protocols with two versions of a multi-packet dissemination protocol, where one uses network coding and the other exploits both link correlation and network coding. Simulation results indicate the potential of the latter approach to be used as a reliable multi-packet dissemination protocol in practical scenarios. We also compare this protocol with existing multi-packet dissemination protocols, and reveal cases when certain protocols perform better than others.

Large-scale sensor-based decision support systems are being widely deployed. Assessing the trustworthiness of sensor data and the owners of this data is critical for quality assurance of decision making in these systems. Trust evaluation frameworks use data provenance along with the sensed data values to compute the trustworthiness of each data item. However, in a sizeable multi-hop sensor network, provenance information requires a large and variable number of bits in each packet, which, in turn, results in high energy dissipation with extended period of radio communication, making trust systems unusable. We propose an energy-efficient provenance transmission and construction scheme, which we refer to as Probabilistic Provenance Flow (PPF). To the best of our knowledge, ours is the first approach to make the Probabilistic Packet Marking (PPM) approach of IP traceback feasible for sensor networks. We propose two bit-efficient complementary provenance encoding and construction methods, and combine them to handle topological changes in the network. Our TOSSIM simulations demonstrate that PPF requires at least 33% fewer packets and consumes 30% less energy than PPM-based approaches to construct provenance, yet still provides high accuracy in trust score calculation.1

Contention among multiple nearby WLANs in urban areas may cause severe TCP unfairness, where some TCP flows can achieve very high throughput at the expense of starving others. This unfairness results from the fact that different physical nodes conveying TCP flows at a wireless bottleneck may have different channel observations and consequently they may provide inconsistent feedbacks to the TCP sources. Existing solutions to this problem try to synchronize channel observations of contending nodes by exchanging control messages among them. They rely on the assumption that these nodes are within each other''s transmission range, which however may not always hold. In this paper, we propose a new approach, called Wireless Probabilistic Drop (WPD), to improve TCP fairness without requiring direct communication among nodes. In WPD, when a node detects congestion, it probabilistically chooses to either drop some packets to resolve the congestion, or aggressively spread the congestion signal to other contending nodes. Each node makes the choice with a probability that is proportional to its flow rate. Henceforth, high-rate flows tend to perform rate reduction more often, and low-rate flows are more likely to increase their flow rates. Eventually, all flows passing the bottleneck are expected to get a fair share of the channel bandwidth. Extensive simulations in ns-2 demonstrate that WPD can significantly improve fairness among TCP flows across multiple contending WLANs.

New generations of video, voice, high-performance computing and social networking applications have continuously driven the development of novel routing technologies for higher packet forwarding speeds to meet the future Internet demand. One of the fundamental design issues for core routers is fast routing table lookup, which is a key problem at the network layer of the Internet protocol suite. It is difficult to scale the current TCAM-based or trie-based solutions for future routing tables due to increasing table size, longer prefix length, and demands for higher throughput. This paper focuses on hash-based lookup solutions that have the potential of offering high throughput at one memory access per packet. We design the first deterministic multi-hashing scheme with small indexing overhead, which evenly distributes address prefixes to hash buckets for routing-information storage. We minimize both the size of each bucket and the number of buckets that need to be fetched to the network processor for packet forwarding. Consequently, near-optimal routing throughput is achieved. Performance evaluations demonstrate that the proposed deterministic multi-hashing scheme can maintain a constant lookup rate of over 250 million packets per second with today's commodity SRAM, which is much faster than the existing hashing schemes.