ShuttleBus: Dense Packet Assembling With QUIC Stream Multiplexing for Massive IoT

Congestion control mechanism is solely responsible for maintaining the performance of streaming data. However, when there is no congestion, a regular delivery window update is followed as a step by step process. The process can be improved by individual window update along with acknowledgement (ACK) as feedback to the server even in the absence of congestion. To achieve maximum throughput and minimum delay, we have suggested modification in the existing handshaking mechanism of quick UDP internet connections (QUIC) protocol. This paper presents an investigation of QUIC protocol performance and proposes a modified QUIC (ModQUIC): a modification in existing handshaking mechanism of QUIC to reduce control overhead. Chromium server-client model testbed setup results, show that the proposed technique gives stable output and improves performance in terms of overall throughput and delay over QUIC and transmission control protocol (TCP). Performance has been tested for limited (2 Mbps) and sufficient (10 Mbps) link bandwidth in presence of loss. The validation of results has been carried out with the help of linear regression model. The result show a throughput improvement of 35.66% and 51.93% over QUIC and TCP respectively and also the delay is reduced by 3% to 5% over QUIC and TCP.

Quick UDP Internet connection(QUIC) protocol and bottleneck bandwidth and round-trip propagation time (BBR) based congestion control algorithm are two major contributions out of Google's persistent attempts to make the Internet faster. QUIC is an user level reliable protocol running on top of UDP to enable evolution of transport mechanisms, while BBR is designed for better TCP congestion control. Although in two different directions, they also converge with BBR support in QUIC. Many researches have proved the performance of QUIC and BBR in different network scenarios, but the evaluation in satellite Internet with long propagation delays and relatively high error rates is still missing.In this paper, we have a preliminary evaluation of the performance of QUIC with BBR via GEO satellite Internet access on dedicated network emulation testbeds. The results and our analysis confirms that for satellite Internet QUIC with the new BBR congestion control has improvements compared with the classic CUBIC.

Constrained Application Protocol (CoAP) and Message Queue Telemetry Transport (MQTT) are two IoT application layer protocols that are seeing increased attention and industry deployment. CoAP uses a request-response model and runs over UDP, while MQTT follows a publish-subscribe model running over TCP. For more constrained IoT devices, MQTTSensor Networks (MQTT-SN) provides a UDP-based transport between the sensor and an MQTT-SN gateway, while using TCP between that gateway and the MQTT broker. Quick UDP Internet Connections (QUIC) is a new protocol and although not originally designed for IoT devices, some design features such as reduced connection establishment time may be useful in an IoT environment. Each of these protocols seeks optimizations in features and implementation complexity based on application domains rather than having the full flexibility and adaptability of traditional transport protocols such as TCP. We investigate and analyze four protocols, namely, CoAP, MQTT, MQTT-SN and QUIC, to understand the overhead of obtaining data from an IoT device at a sink to potentially disseminate this data downstream. These constrained IoT devices often operate under challenging, varying network conditions, and it is important to understand the limitations of the protocols in such conditions. Thus, we evaluate the performance of these protocols under varying loss, delay and disruption conditions to identify the most effective environment for their operation and understand the limitations of their dynamic range. Results show that with non-confirmable CoAP a more adaptive wait timer is required; and a more streamlined QUIC protocol may be a potential alternative IoT protocol.

Congestion control is a fundamental technology to balance the traffic load and the network. The Internet Engineering Task Force (IETF) Quick UDP Internet Connection (QUIC) protocol has flexible congestion control and at the same time possesses the advantages of high efficiency, low latency, and easy deployment at the application layer. Bottleneck bandwidth and round‐trip propagation time (BBR) is an optional congestion control algorithm adopted by QUIC. BBR can significantly increase throughput and reduce latency, in particular over long‐haul paths. However, BBR results in high packet loss in low bandwidth and low fairness in multi‐stream scenarios. In this article, we propose the enhanced BBR congestion control (eBCC) algorithm, which improves the BBR algorithm in two aspects: (1) 10.87% higher throughput and 74.58% lower packet loss rate in the low‐bandwidth scenario and (2) 8.39% higher fairness in the multi‐stream scenario. This improvement makes eBCC very suitable for IoT communications to provide better QoS services.

The Quick UDP Internet Connection (QUIC) protocol has been utilized in traditional cloud computing environments. However, in consumer electronic devices under edge cloud scenarios, QUIC may struggle to take advantage of its benefits due to its unique network characteristics and resource constraints. This problem arises when the network quality fluctuates or resources are insufficient to handle incoming data, causing rapid buffer expansion, significant delays, and potential packet loss. This study proposes a queue management algorithm inspired by the classical control theory of the Proportional-Integral-Differential (PID) control, which aims to support QUIC data flows to further enhance delay control and improve goodput. The algorithm adds differential operations to the traditional PI control to predict the error trend to respond to queue changes in advance. Combining expert experience in integral separation and queue error management, it makes the control strategy more relevant to the specific needs of real application scenarios. Simulation results demonstrate that the PID-Delay algorithm achieves an average goodput improvement of 1.98 times and reduces the standard deviation by 26.1% than the classical algorithm. In comparison to other delay algorithms, it exhibits an average 1.81 times increase in goodput and an 18.5% reduction in standard deviation.

Internet traffic is experiencing rapid growth, with the majority of traffic generated from video steaming, web data services and Internet of Things. As these services include the transmission of small data, such as web pages, video chunks and sensing data, data latency affects the quality of experience rather than the throughput. Therefore, this study aims to decrease latency to improve the quality of the user experience. To this end, we measure the web service delay and throughput in mobile networks. The results indicate a low quality experience for mobile users, even though mobile networks support a large throughput. We therefore propose a light-weight latency reduction scheme for the Quick UDP Internet Connections (QUIC) protocol. The proposed scheme calculates the average congestion window, which is utilized as the initial congestion window when a new connection is established. The proposed scheme is evaluated through experiments on a testbed. The results show that our scheme reduces latency significantly. The results of this study can help improve user experiences of video streaming and web data services.

IoT devices have been gaining popularity and become integral to our daily life. These devices are prone to be compromised as well as any computing system, but lack computing capabilities for cybersecurity software. An important measure for protecting attacks on IoT devices is through isolation of such devices by restriction of communications to the device from firewall/gateway. To this end identification of the IoT device is valuable for network administration and security. In this paper, we introduce a system for automated classification of device characteristics, called System IDentifier (SysID), based on their network traffic. SysID uses any single packet that is originated from the device to detect its kind. We use genetic algorithm (GA) to determine relevant features in different protocol headers and then deploy various machine learning (ML) algorithms (i.e., DecisionTable, J48 Decision Trees, OneR, and PART) to classify host device types by analyzing features selected by GA. GA helps reduce classification complexity and increases its accuracy by eliminating noisy features from the data. SysID allows the ability to have a completely automated way of classifying IoT devices using their TCP/IP packets without expert input for classification. In an experimental study with 23 IoT devices, SysID identified the device type from a single packet with over 95% accuracy.

IoT devices have been widely deployed in many applications to facilitate smart technology, increased portability, and seamless connectivity. Despite being widely adopted, security in IoT devices is often considered an afterthought due to resource and cost constraints. Among multiple security threats, malware attacks are observed to be a pivotal threat to IoT devices. Considering the spread of IoT devices and the threats they experience over time, deploying a static malware detector trained offline seems ineffective. On the other hand, on-device learning is an expensive or infeasible option due to the limited available resources on IoT devices. To overcome these challenges, this work employs 'Federated Learning' (FL) which enables timely updates to the malware detection models for increased security while mitigating the high communication or data storage overhead of centralized cloud approaches. Federated learning allows training machine learning models with decentralized data while preserving its privacy by design. However, one of the challenges with the FL is that the on-device models are required to be homogeneous, which may not be true in the case of networked IoT systems. As a panacea, we introduce a methodology to unify the models in the cloud with minimal overheads and an impact on on-device malware detection. We evaluate the proposed technique against homogeneous models in networked IoT systems encompassing Raspberry Pi devices. The experimental results and system efficiency analysis indicate that end-to-end training time is just 1.12× higher than traditional FL, testing latency is 1.63× faster, and malware detection performance is improved by 7% to 13% for resource-constrained IoT devices.

In deduplication, index-lookup disk bottleneck is a major obstacle which limits the throughput of backup processes. One way to minimize the effect of this issue and boost speed is to use very high course-grained chunks for deduplication at a cost of low storage saving and limited scalability. Another way is to distribute the deduplication process among multiple nodes but this approach introduces storage node island effect and also incurs high communication cost. In this paper, we explore dCACH, a content-aware clustered and hierarchical deduplication system, which implements a hybrid of inline course grained and offline fine-grained distributed deduplication where routing decisions are made for a set of files instead of single files. It utilizes bloom filters for detecting similarity between a data stream and previous data streams and performs stateful routing which solves the storage node island problem. Moreover, it exploits the negligibly small amount of content shared among chunks from different file types to create groups of files and deduplicate each group in their own fingerprint index space. It implements hierarchical deduplication to reduce the size of fingerprint indexes at the global level, where only files and big sized segments are deduplicated. Locality is created and exploited first using the big sized segments deduplicated at the global level and second by routing a set of consecutive files together to one storage node. Furthermore, the use of bloom filter for similarity detection between streams has low communication and computation cost while it enables to achieve duplicate elimination performance comparable to single node deduplication. dCACH is evaluated using a prototype deployed on a server environment distributed over four separate machines. It is shown to have 10× the speed of Extreme_Binn with a minimal communication overhead, while its duplicate elimination effectiveness is on a par with a single node deduplication system.

Tree-structured computations are relatively easy to process in parallel. As leaf processes are recursively spawned they can be assigned to independent processors in a multicomputer network. However, to achieve good performance the on-line mapping algorithm must maintain load balance, i.e., distribute processes equitably among processors. Additionally, the algorithm itself must be distributed in nature, and process allocation must be completed via message-passing with minimal communication overhead. This paper investigates bounds on the performance of deterministic and randomized algorithms for on-line tree embeddings. In particular, we study trade-offs between computation overhead (load imbalance) and communication overhead (message congestion). We give a simple technique to derive lower bounds on the congestion that any on-line allocation algorithm must incur in order to guarantee load balance. This technique works for both randomized and deterministic algorithms. We prove that the advantage of randomization is limited. Optimal bounds are achieved for several networks, including multidimensional grids and butterflies.

The key concerns to enhance the lifetime of IoT-enabled Underwater Wireless Sensor Networks (IoT-UWSNs) are energy-efficiency and reliable data delivery under constrained resource. Traditional transmission approaches increase the communication overhead, which results in congestion and affect the reliable data delivery. Currently, many routing protocols have been proposed for UWSNs to ensure reliable data delivery and to conserve the node’s battery with minimum communication overhead (by avoiding void holes in the network). In this paper, adaptive energy-efficient routing protocols are proposed to tackle the aforementioned problems using the Shortest Path First (SPF) with least number of active nodes strategy. These novel protocols have been developed by integrating the prominent features of Forward Layered Multi-path Power Control One (FLMPC-One) routing protocol, which uses 2-hop neighbor information, Forward Layered Multi-path Power Control Two (FLMPC-Two) routing protocol, which uses 3-hop neighbor information and ’Dijkstra’ algorithm (for shortest path selection). Different Packet Sizes (PSs) with different Data Rates (DRs) are also taken into consideration to check the dynamicity of the proposed protocols. The achieved outcomes clearly validate the proposed protocols, namely: Shortest Path First using 3-hop neighbors information (SPF-Three) and Breadth First Search with Shortest Path First using 3-hop neighbors information (BFS-SPF-Three). Simulation results show the effectiveness of the proposed protocols in terms of minimum Energy Consumption (EC) and Required Packet Error Rate (RPER) with a minimum number of active nodes at the cost of affordable delay.

Monitoring is an issue of primary concern in current and next generation networked systems. For ex, the objective of sensor networks is to monitor their surroundings for a variety of different applications like atmospheric conditions, wildlife behavior, and troop movements among others. Similarly, monitoring in data networks is critical not only for accounting and management, but also for detecting anomalies and attacks. Such monitoring applications are inherently continuous and distributed, and must be designed to minimize the communication overhead that they introduce. In this context we introduce and study a fundamental class of problems called where we must return the aggregate frequency count of an event that is continuously monitored by distributed nodes with a user-specified accuracy whenever the actual count exceeds a given threshold value.In this paper we propose to address the problem of thresholded counts by setting local thresholds at each monitoring node and initiating communication only when the locally observed data exceeds these local thresholds. We explore algorithms in two categories: static and adaptive thresholds. In the static case, we consider thresholds based on a linear combination of two alternate strategies, and show that there exists an optimal blend of the two strategies that results in minimum communication overhead. We further show that this optimal blend can be found using a steepest descent search. In the adaptive case, we propose algorithms that adjust the local thresholds based on the observed distributions of updated information. We use extensive simulations not only to verify the accuracy of our algorithms and validate our theoretical results, but also to evaluate the performance of our algorithms. We find that both approaches yield significant savings over the naive approach of centralized processing.

The smart water monitoring system (SWMS) integrated with IoT provides improved services to the society with minimal effort. At the same time, IoT connectivity brings various active, passive, and physical security threats to the system. The conventional cryptosystems are infeasible for resource-constrained IoT devices and susceptible to physical attacks. Physically unclonable function (PUF) emerged as a low-cost, convenient hardware security primitive. In this work, we have proposed a secure (against active, passive, and physical attacks) authentication and key sharing protocol for the SWMS with low overhead. The extensive security analysis illustrates the durability of the protocol. The performance analysis shows the proposed protocol's minimal computation, communication overhead, and resource utilization. Using a mathematical PUF model (i.e., soft model) reduces the server-side burden of storing PUF challenge-response pairs. Moreover, the protocol deals with the noisy PUF responses to maintain the correctness and accuracy of the authentication process. Finally, a comparison with the existing works establishes the proposed protocol's superiority in the SWMS.

In this paper, we consider the problem of thresholded monitoring in distributed data streams, that is, given multiple distributed data streams observed by multiple monitors during a certain period, finding the items whose global frequencies over all data streams exceeding a given threshold. We first derive a lower bound of communication overhead for any deterministic algorithm for this problem. Then, we propose two different schemas, i.e., Low-threshold Cascaded Cuckoo Filter (L-CCF) for low-threshold monitoring and High-threshold Cascaded Cuckoo Filter (H-CCF) for high-threshold monitoring. L-CCF and H-CCF can identify items whose frequencies are more than the given threshold while a desired false negative rate (FNR) is achieved and communication overhead is optimized. The key idea is to compress the communication overhead caused by transferring the ID and frequency information at the same time. First, to reduce the communication overhead of transferring IDs, we propose to encode the IDs into separate tiny parts and store these tiny parts in L-CCF or H-CCF. Second, to reduce the communication overhead of transferring frequencies, we adopt a carry-in counter technique in L-CCF and multiple sampling technique in H-CCF. We evaluated L-CCF and H-CCF on two real-world traces and compared their performance with two prior adapted algorithms. Our experimental results show that on average, L-CCF and H-CCF achieve FNRs with 55% and 65% better than that of comparison algorithms while FPRs is maintained at the level of 2%.

Spatial and temporal data such as multiple data streams often have concept drift problems, which refers to changes of the data distributions over time. Once concept drift occurs, a stationary machine learning predictor will probably be invalid because the testing data have a different data distribution from that of the training data. Recent studies in data streams aim to address this issue by concept drift adaptation techniques. Concept drift adaptation methods update the predictor by time and have been validated to provide accurate real-time prediction for a single data stream. However, handling multiple relevant data streams is still an unsolved challenge in this area, considering many real-world applications generate multiple data streams that are simultaneously evolving. To fill this gap, we here present a predicting network for multiple data streams, named MuNet. MuNet leverages the dependency between data streams to efficiently lower down the computational cost of real-time predictions for multiple data streams. In MuNet, an online-learned Bayesian neural network (BNN) is designed as a connector between streams. The BNN-connector can continuously use the real-time information from only one base stream to correct the stationary predictor for the other streams, which avoids the high cost caused by repeated adaptation.