Traffic condition estimation and distinguishing from real‐time video cameras are essential research in traffic information systems (ITSs), providing accurate and live traffic information to commuters and traffic management, as video‐based traffic data are available ubiquitously. However, many challenges need to be resolved, including the complexity of video data with multiple vehicle types in chaos traffic environments, such as those in developing countries like Vietnam, the lack of specific training datasets, and the appropriate selection or modification of pretrained deep learning (DL) models for video data processing. This paper proposes a novel traffic congestion prediction method based on real‐time traffic video analysis utilizing appropriate DL models. The proposed approach using YOLOv10, YOLOv8, and Faster R‐CNN to detect and classify vehicles and region of interest (ROI) to calculate the occupied their area, which resulted in a prototype system for real‐world applications consisting of three main stages: (i) traffic video data in a chaos traffic environment, specifically in Hanoi, Vietnam, are collected, preprocessed, and annotated for traffic conditions; (ii) various pretrained DL models for video data analysis specified to traffic condition estimations are studied to apply to the above traffic video data; and (iii) thorough evaluations using the implemented prototype with real‐time video traffic data to confirm the effectiveness and the efficiency of the proposed method have been analyzed. The results indicate that the proposed method achieves up to 94% accuracy in vehicle detection and processes at a speed of 27 frames per second. The implemented prototype also provides a visual presentation of traffic density and makes reliable congestion predictions to commuters and management. The proposed approach not only supports traffic operation and management in regulating traffic flows but also paves the way for applying technology to address complex urban traffic challenges, especially in developing countries.

Smart contracts are Turing-complete programs that execute on the infrastructure of the blockchain, which often manage valuable digital assets. Solidity is one of the most popular programming languages for writing smart contracts on the Ethereum platform. Like traditional programs, smart contracts may contain vulnerabilities. Unlike traditional programs, smart contracts cannot be easily patched once they are deployed. It is thus important that smart contracts are tested thoroughly before deployment. In this work, we present an adaptive fuzzer for smart contracts on the Ethereum platform called sFuzz. Compared to existing Solidity fuzzers, sFuzz combines the strategy in the AFL fuzzer and an efficient lightweight multi-objective adaptive strategy targeting those hard-to-cover branches. sFuzz has been applied to more than 4 thousand smart contracts and the experimental results show that (1) sFuzz is efficient, e.g., two orders of magnitude faster than state-of-the-art tools; (2) sFuzz is effective in achieving high code coverage and discovering vulnerabilities; and (3) the different fuzzing strategies in sFuzz complement each other.

Mobile core networks have seen serious network resource contention among a huge amount of delay‐sensitive and delay‐insensitive traffic leading to network congestion or even failures. Delay‐insensitive traffic which is commonly generated by background applications or the operating systems to update the system's status can be postponed to yield network resources for delay‐sensitive traffic, especially in the peak times. This study proposes an efficient approach to delay‐insensitive traffic control leveraging software defined network (SDN) techniques. The study devises an effective data structure, namely flow tree , to resolve the flow aggregation, an essential issue in SDN‐based traffic control, especially when dealing with a huge number of small delay‐insensitive traffic flows. The proposed approach significantly reduces the inherent communication cost between the controller and Openflow switches and the storage cost in switches' expensive memories. This approach is significantly robust in controlling a huge number of small flows which are common in the modern mobile core networks and Internet of things environments. Experimental results from both simulated and real datasets reveal the effectiveness and efficiency of the proposed scheme.

A huge amount of smart devices which have capacity of computing, storage, and communication to each other brings forth fog computing paradigm. Fog computing is a model in which the system tries to push data processing from cloud servers to “near” IoT devices in order to reduce latency time. The execution orderings and the deployed places of services make significant effect on the overall response time of an application. Beside new research directions in fog computing, e.g., fog-cloud collaboration, service scalability, fog scalability, mobile fog computing, fog federation, trade-off between energy consumption and communication efficiency, duration of storing data locally, storage security and communication security, and semantic-aware fog computing, the service deployment problem is one of the attractive research fields of fog computing. The service deployment is a multiobjective optimization problem; there are so many proposed solutions for various targets, such as response time, communication cost, and energy consumption. In this paper, we focus on the optimization problem which minimizes the overall response time of an application with awareness of network usage and server usage. Then, we have conducted experiments on two service deployment strategies, called cloudy and foggy strategies. We analyze numerically the overall response time, network usage, and server usage of those two strategies in order to prove the effectiveness of our proposed foggy service deployment strategy.

Fog computing is one of the promising technologies for realizing global-scale Internet of Things (IoT) applications as it allows moving compute and storage resources closer to IoT devices, where data is generated, in order to solve the limitations in cloud-based technologies such as communication delay, network load, energy consumption, and operational cost. However, this technology is still in its infancy stage containing essential research challenges. For instance, what is a suitable fog computing scheme where effective service provision models can be deployed is still an open question. This paper proposes a novel multitier fog computing architecture that supports IoT service provisioning. Concretely, a solid service placement mechanism that optimizes service decentralization on fog landscape leveraging context-aware information such as location, response time, and resource consumption of services has been devised. The proposed approach optimally utilizes virtual resources available on the network edges to improve the performance of IoT services in terms of response time, energy, and cost reduction. The experimental results from both simulated data and use cases from service deployments in real-world applications, namely, the intelligent transportation system (ITS) in Ho Chi Minh City, show the effectiveness of the proposed solution in terms of maximizing fog device utilization while reducing latency, energy consumption, network load, and operational cost. The results confirm the robustness of the proposed scheme revealing its capability to maximize the IoT potential.

In Delay Tolerant Networks, there is no guarantee that a fully connected path between source and destination exists any time. In this context, the limited hop count scheme which allows a message to travel at most k hops is considered as a basic routing scheme. However, there are no significant works offering an efficient method to identify a proper value for k. In this paper, we propose a new approach, named Adaptive Spray (AS-scheme). Instead of identifying a strict value k, it heuristically identifies a real-value coefficient to improve the network performance while eliminating the computational complexity. By leveraging only local knowledge like messages remaining time and nodes meeting rate, AS-scheme is highly scalable. Simulation results reveal that the proposed approach can quickly identify an appropriate coefficient and outperforms Epidemic algorithm in terms of increasing delivery rate, reducing overhead ratio and average latency, especially in dense networks.

Core networks have to contend with a huge amount of traffic, making the networks heavy, congested or even fail, specifically at peak times. However, not all traffic has to be processed immediately. Background (BG) traffic can be delayed to yield network resources for foreground (FG) traffic when needed. This paper proposes an efficient approach to BG and FG traffic control leveraging software defined network (SDN) techniques. We devised an effective data structure, namely a flow tree, for flow aggregation, to reduce the inherent communication costs (between the Controller and Openflow switches) and the storage cost in the switch's expensive memory. Experimental results from both simulated and real datasets reveal the effectiveness and efficiency of the proposed scheme, confirming the viability of the proposed SDN-based BG and FG traffic control approach.

Background (BG) traffic generated automatically by applications or the operating system on devices is delay-insensitive traffic as it does not immediately affect user experiences. However, this traffic may significantly contend network resources with foreground (FG) traffic which conveys data/content to users. This contention directly affects the QoS and QoE of network services, specifically at the peak times. This paper proposes an integration approach to which delay-insensitive traffic is quickly detected and effectively transferred in accordance with the network conditions hence the best resources are yielded to FG traffic. For the traffic transferring, we propose two methods namely the router centric (RC) and the SDN-based approaches. In the RC method, the edge routers analyze network conditions, compute the suggested sending rates for individual traffic flows and send these values back to clients where the adjustment mechanisms are implemented. In the SDN-based method the course of actions for each traffic flow is computed by a remote controller. Experimental results reveal the effectiveness and the feasibility of the proposed approaches.

Catastrophic disasters can destroy large regions and, in the process, leave many victims isolated from the rest of the world. Recovering the communication infrastructure is typically slow and expensive, which is not suitable for emergency response. Multihop wireless access networks have the potential to quickly provide Internet connectivity to victims, but so far no simple and practical solution has been proposed to help people configure these networks easily. We are pursuing the approach of utilizing wireless virtualization techniques to establish wireless access networks on-the-fly using on-site mobile devices. While our previous work has demonstrated proof-of-concept solutions, it lacked fundamental communication abstractions, a rigorous design, and a thorough analysis on the effectiveness of these solutions. The main new contributions of this article are: (1) the wireless multihop communication abstraction (WMCA) as a fundamental communication concept for a practical tree-based disaster recovery access network (TDRAN), (2) the complete design and implementation details of TDRAN, and (3) a comprehensive analysis of the effectiveness of the proposed approach based on field experiments, both in indoor and outdoor settings, at different sites in Japan. The results demonstrate the effectiveness of the proposed solution for on-site configuration of wireless access networks, as it can easily extend to 20 hops by 15 m-distance and 16 hops by 30 m-distance networks, which result in 300 m and 480 m (respectively) in radius or about 1 km in diameter. This work also confirms that our approach is ready for realization as a real disaster recovery solution.

This paper proposes a novel approach to separating background (BG) and foreground (FG) traffic based on periodicity analysis. As BG traffic is commonly periodically generated by applications, this trait is leveraged to effectively detect BG traffic. Concretely, the Period Candidate Array (PCA) approach is proposed to extract only necessary information from long and sparse traffic flows, hence quickly detects the flows' periodicity with low computational cost. The PCA works directly with "on-site'' traffic without depending on historical data as in machine learning methods. As a result, the proposed approach can be immediately applied to the real world traffic management systems. In addition, the PCA properly works with latency-included traffic affected by network delays. Experimental results reveal the effectiveness and efficiency of the PCA compared to the conventional methods in terms of computational cost, memory usage, and independence to historical data.

Wireless access network is an appropriate approach providing Internet connection in disasters where communication infrastructures might completely be damaged. This paper clarifies essential requirements for a resilient disaster recovery multihop access network (DRAN). Based on those requirements, which could not be satisfied by existing access network technologies, a novel approach, thereby multihop access networks can be established quickly using commodity mobile devices (laptops, tablets, smart phones), has been proposed. Users can connect to the proposed wireless access network as easily as connecting to conventional access points (APs) and unconsciously contribute to the network extension. As a result, the network is extended incrementally providing more Internet access opportunities to far apart nodes. Experimental evaluations reveal the feasibility as well as the scalability of the proposed approach.

Fast recovering backbone networks from failures caused by unexpected disasters is critical to enhance the Internet resilience. The major challenge is fast switchover traffic from a faulty path to an alternative one. In this paper, we present an approach toward achieving a zero switchover time (i.e., optimal) by using two emerging technologies: Multipath TCP (MPTCP) and Software-Defined Networking (SDN). The conceptual idea is keeping multiple concurrent paths in an end-to-end communication, which is continuously alive unless all the paths get failure. Besides that, when a disaster occurs, traffic engineering is adopted to quickly balance the traffic on available paths. In order to establishes the concurrent paths, MPTCP divides an application's byte stream into multiple TCP subflows, each of which is forwarded in an end-to-end route. The routes are normally determined by IP routing protocols, such as OSPF or BGP, which have two disadvantages. The first one is the protocols always take long time to reestablish new routes in disasters (i.e., long convergence time). The second one is they are originally designed without traffic engineering. On the other hand, SDN enables the use of central controllers with global view of dynamic network state. In addition, the controllers can remotely controls both direction and capacity of traffic flows in many flexible ways (i.e., using OpenFlow). Therefore, SDN potentially solves the disadvantages of IP routing protocols.

The current technology of Internet provides an acceptable level of resilience in normal operation. However, the Internet may get catastrophic impacts when unexpected disasters such as earthquakes, tsunami, etc. happen. Specifically, the Internet infrastructure including Wide Area Networks (WANs) face many challenges to normal operation including power outage, link, device failures, rerouting packets, traffic engineering, etc. In this paper, we follow a systematic approach for realizing disaster-resilient WANs using Software-Defined Networking (SDN) technology. SDN enables the network control plane to be decoupled from the network forwarding hardware, and moves the control plane to a programmable component, i.e., the network controller. The network management and operation therefore increase flexibility. To confirm the feasibility of SDN-based resilient network towards fast disaster recovery, we have constructed two evaluations under the real large scale network topology. One is to investigate the latencies between controllers and switches in order to find the appropriate number and locations of controllers. Another one is to simulate a reactive switch-over from a faulty link to an alternative link, assuming a realistic scenario. The results show that the switch-over time depends on the latencies between networking devices and a controller. Additionally, even those latency values equal the worst-case latency, the fast rerouting of TCP traffic is achievable.

Disasters may destroy everything including communication infrastructures isolating people in the disaster-stricken areas. Recovery of these infrastructures is often prolonged which is not suitable for disastrous fast-responses. This paper proposes a wireless multihop access network virtualization (WMANV) approach thereby users/victims are provided Internet access transparently. Concretely, users are provided Internet connection via multihop wireless access networks as if they are connecting to conventional access points (APs) using their commodity mobile devices. A tree-based architecture was proposed to realize the concept of WMANV. This approach allows participating mobile nodes (MNs) to contribute on growing up access networks, thus provide the Internet connection means to further disconnected MNs. The feasibility and the scalability of the proposed approach have been verified by real deployments and experimental evaluations.

This paper proposes an SCTP optimal path selection mechanism which is inspired by the Ant Colony Optimization probabilistic equation. The proposed mechanism uses three parameters, which are the path delay, RSSI level at the mobile node and the RSSI level at the corresponding node, as the decision criteria. A cross-layer architecture is proposed to obtain the value from the Physical layer. In addition, a new parameter for the SCTP Heartbeat chunk is introduced in order to share the RSSI information between the nodes in the SCTP association. The proposed work was evaluated using two types of scenarios. Then the result was translated into the R-factor using the E-Model recommended by ITU-T. The results show that the mechanism was able to find the optimal path for the user.

Low penetration rate is one of the essential issues in the mobile phone based traffic state estimation model. This paper proposes an appropriate genetic algorithm (GA) mechanism to optimize the traffic state estimation model even in cases of low penetration rate. This mechanism also reduces the critical penetration rate, thus improves the error-tolerance as well as the scalability of the traffic state estimation system. The paper also investigates the ANN-based prediction model to overcome the weakness of the GA-based traffic state estimation approach when the penetration rate becomes unacceptably low. In addition, the effect of different level related road segments on the prediction effectiveness is thoroughly discussed. Consequently, this study provides practically useful instructions in verifying the data missing rate at different level related road segments to ensure the prediction accuracy. The experimental evaluations reveal the effectiveness and the robustness of the proposed solutions.

This paper evaluates the proposed SCTP optimal path selection mechanism which implements the Ant Colony Optimization probabilistic equation. The proposed mechanism uses three parameters, which are the path delay, RSSI level at the mobile node and the RSSI level at the corresponding node, as the decision criteria. A cross-layer architecture is proposed to obtain the value from the Physical layer. In addition, a new parameter for the SCTP Heartbeat chunk is introduced in order to share the RSSI information between the nodes in the SCTP association. A brief comparison between using two parameters (path delay and RSSI at mobile node only) and three parameters (plus RSSI at the corresponding node) is also included. The experimental evaluations show that the proposed mechanism is able to select the optimal path.

This paper proposes a practical quantification model for mobile phone based traffic state estimation systems (M-TES). The low penetration rate issue, an inherent issue impeding the realization of a mobile phone based application such as the M-TES, is thoroughly discussed. A notable solution framework, namely the intelligent context-aware velocity-density inference circuit (ICIC), is proposed to effectively resolve the low penetration rate issue. In the ICIC model, velocities and densities calculated directly from the sensed data and inferred by using different inference models such as the Greeshields or the moving average model are appropriately integrated. In addition, appropriate contexts extracted from data reported by mobile devices are utilized to identify the optimal estimation parameters leading to the optimal estimation effectiveness. The experimental evaluations reveal the effectiveness and the robustness of the proposed solutions.

This paper analyzes the effect of penetration rate to the estimation error in mobile phone based traffic state estimation systems. More concretely, the error-tolerance is analyzed based upon the penetration rate of participating mobile phones. In addition, a hybrid model by which not only real-time data but also the historical data utilized under a suitable data mining technique is introduced. This work also introduces an effective method for dynamically creating the historical dataset which is especially adequate for the aforementioned data mining model. This approach improves not only the effectiveness, robustness and the accuracy but also the scalability of the system. The evaluation reveals that the estimation error is sensitive to the penetration rate while the existing work did not mention.

This paper investigates the effect of the penetration rate on the effectiveness of the mobile phone-based traffic state estimation. As a result, the acceptable penetration rate is identified. This recognition is useful for the investigating to bring the traffic state estimation using mobile phones as traffic probes into the real world application. In addition, an adaptive velocity-density estimation model, namely the velocity-density inference circuit, is proposed to improve the accuracy of the average velocity and the density estimations in cases of low penetration rate. Furthermore, a neural network-based prediction model is introduced to assure the effectiveness of the velocity/density estimation when the penetration rate degrades to zero. The experimental evaluations reveal the effectiveness as well as the robustness of the proposed solutions.

This paper proposes a novel method for detecting traffic congestions, qualifying and quantifying congestion levels using mobile phones as traffic probes. The system provides a robust mechanism for granularly comparing the seriousness of different congested areas. Congested areas are detected in a detailed manner by which exact congested positions are reported. Moreover, congestions can be detected even though no complete traffic trace due to the traffic jam is collected. This feature is quite different from, and makes the system more robust compared to the previous ones. This project also consists of a reasonable vehicle classification method based on only GPS data. This mechanism improves not only the effectiveness and the accuracy but also the scalability, thus the system is flexibly applicable for any traffic system structure, especially in developing countries where a lot of motorbikes are travelling on the roads. The evaluation reveals that the proposed ideas are novel which are not discussed in the existing work.