In digital twin-enabled smart cities, the delivery of various urban services based on urban digital twins is expected. These services comprise various microservice-like artificial-intelligence-empowered atomic tasks that use dynamic spatial information through the digital twin. To maintain urban service quality amid fluctuating demand, effective resource utilization is essential. In this paper, we propose a system model for provisioning urban services composed of atomic tasks, along with an adaptive task and resource allocation method based on the monitor, analyze, plan, execute, and knowledge (MAPE-K) loop framework. Performance evaluation demonstrates that combining task quality and resource allocation adjustments enables effective adaptation to demand fluctuations.

There will be an increase in situations where images taken in specific locations are transmitted through networks for various services. However, this trend can lead to significant communication loads due to simultaneous transmission of images from multiple locations. Therefore, it is important to reduce the amount of network traffic in image transmission. While traditional compression methods focus on minimizing information loss in images, some applications only require the retention of semantic information, suggesting potential improvements in communication efficiency. This paper proposes an image generation-based transmission method for highly-efficient communications exploiting composition and descriptive information. The proposed method extracts specific information from an image to decrease the amount of data transmission, and reconstructs the image using an image-generative model by a receiver. In addition, image compression and reconstruction in the proposed method are demonstrated through an experiment. The experimental results indicate a need for a method to evaluate the output and a method for image reconstruction based on this evaluation.

We propose a user mobility-driven federated learning method, which integrates learning models from different regions, leveraging user mobility. This method aims to improve performance of learning models in specific regions by merging them with models from other areas. In regions with less user mobility, our method creates unique regional models, while in areas with high mobility, it integrates models for enhanced performance. Evaluation results indicate that accuracy improved with additional training, although it temporarily decreased after model integration.

Wireless point cloud streaming is a key technology in digital twin-enabled smart cities for maintaining city-scale spatial information. Various point cloud coding and compression algorithms have been studied to facilitate efficient transmission. However, real-field experiments are required to reveal the characteristics using small devices, which support the city from the edges. In this paper, we conducted field experiments to analyze the characteristics of wireless point cloud streaming. First, we proposed a middleware to mediate between the robot operating system and the network interface to easily configure the transmission protocol. Thereafter, we implemented the middleware on edge devices and analyzed the performance using IEEE 802.11ax based on the following perspectives: processing time versus transmission frequency, point cloud and packet transmission success rates versus distance between nodes, and point cloud transmission success rate versus mobility of nodes. The first experiment revealed that small edge devices with the proposed middleware could handle high-frequency streaming without exceeding the maximum time of every period. The second experiment revealed the relationship between data rate and transmission success rates of point clouds and packets. Finally, the third experiment revealed that the mobility of the nodes caused variations in the transmission success rate.

Concurrent transmission (CT) is a promising technology for achieving efficient data transmission based on constructive interference of wireless broadcasts. However, it fails to transmit packets because of the destructive interference. Opportunistic routing (OR) avoids transmission failure by using path diversity based on flexible forwarder selection. However, it fundamentally imposes transmission delay owing to the packet-level forwarder selection. In this paper, we propose backoff-based OR with CT, which efficiently forwards data packets using CT, and then supports them based on OR. Finally, we compared the performance of the proposed method with that of traditional approaches using a simple lattice topology. The simulation results revealed that the proposed method achieved both a high packet transmission success rate and a small transmission delay while suppressing an increase in the network load.

In anticipation of environments where Internet of things (IoT) is extensively utilized, various methods for understanding and managing the behavior of IoT devices based on traffic analysis have been proposed to tackle security issues such as the difficulties in replacing devices, updating their firmware, and comprehending their behavior. We have previously studied an IoT traffic analysis and device identification in smart home environments with applying two-stage clustering. This method realizes extracting time-series characteristics of IoT traffic based on unsupervised machine learning, k-means clustering, and identifying 21 IoT devices with a high accuracy. Nevertheless, we have not yet examined whether k-means clustering is appropriate to the two-stage clustering for IoT traffic analysis and device identification. In this paper, we investigate various clustering methods for two-stage clustering and then compare and discuss the characteristics of the methods for IoT traffic analysis and device identification. The results confirm that the k-means clustering or Ward’s method is more suitable for the performance of feature extraction and the computational loads.

Ahstract-Various methods focusing on either spatial or temporal utilization, such as path diversity or link utilization, have been studied to realize efficient ad hoc networks. Furthermore, a method that integrates these methods has been proposed; however, it has only been evaluated theoretically and partially. Hence, achieving efficiency from both viewpoints as a protocol is challenging. This study proposes and defines block transmission-enabled opportunistic routing, which can achieve spatiotemporal efficiency as a protocol. The proposed method was evaluated by comparing its performance with that of two conventional methods that focus on either spatial or temporal efficiency, through computer simulations.

Machine learning (ML) is predominantly performed in the cloud or other computing facilities. While this computing method allows for the benefits of ML to be leveraged in urban settings, it may also lead to unfair sharing of the environment owing to the heat generated by servers in areas housing computing infrastructure. In this study, we developed a green distributed machine leaning (DML) concept-CoopL-to calculate the local consumption of computational resources based on DML, thereby mitigating the environmental burden. Moreover, we analyzed the impact of long-distance communication by incrementally raising the communication latency. Furthermore, the power consumption was examined by considering the hop count of the router.

This paper presents a flexible group voice conversation system that utilizes the location and direction of speakers to help them choose their listeners. The proposed system comprises a group construction mechanism for peer-to-peer voice conversation and a listener selection mechanism in the conversation group. To reveal the effectiveness in real environments, we implemented the proposed system as a smartphone application. Finally, we conducted a real-world experiment to perform subjective evaluations using the proposed application to capture and understand the user experience and application usability.

To collect and aggregate the enormous amounts of spatial information needed to maintain virtual three-dimensional (3D) spaces, such as digital twins, in real time, significant resources are required. Recently, light detection and ranging (LiDAR)-equipped mobile devices have become increasingly popular. Therefore, mobile sensing using such devices is a feasible solution for maintaining and updating virtual 3D city spaces by motivating people. In this study, we propose and develop a mobile sensing system based on an augmented reality coloring game using LiDAR-equipped mobile devices. Next, we verify the fundamental operation and effectiveness of the proposed system.

Backoff-based opportunistic routing can adaptively select a forwarder among receivers based on a random backoff time. However, it cannot correctly work in an environment with asymmetric links where the communication quality differs in the directions. To solve this problem, a confirmation method for link symmetry and destination reachability was previously proposed. However, the method can only confirm link symmetry with one neighbor and destination reachability for a single terminal in a single packet transmission. This paper proposes a fast confirmation method to verify the link symmetry of multiple terminals in single packet transmissions. The performance of the proposed method is clarified with simulations.

The authors have been proposed a wireless multihop routing method inspired by the behavior of the slime mold. The previous methods iteratively calculate bandwidth allocation for each link based on the maximum bandwidth, the transmission delay, and the required communication volume by the source node. However, the appropriate number of calculation iterations and the actual calculation time for bandwidth allocation vary depending on various factors. In addition, the allocation results cannot be reflected until the calculation is completed, thus the allocation results may not be suitable for the network conditions at the end of the calculation. In this paper, the authors propose an adaptive wireless multi-hop routing with reduced impact of processing delay by sequentially using values that are convergence processes as indicators for route selection.

In mobile ad-hoc networks (MANET), multi-hop communication relies on a give-and-take relationship among users. For stimulating users to be more cooperative in MANET, an incentive mechanism that offers electric power in exchange for relaying packets by using wireless power transmission (WPT) technology has been proposed. To evaluate the availability of MANET and this incentive mechanism, it is important to reveal factors affecting to the user's willingness to relay packets. In this paper, we investigate and analyze user's willingness to relay through qualitative evaluations. On the basis of the analysis, we model user's willingness to relay packets depending on the residual battery level and user's characteristics. Finally, we also confirm that the electric incentive mechanism can stimulate reciprocal cooperation among users in MANET.

This paper proposes an adaptive peer search method that considers the dynamic users’ mobility in location-based peer-to-peer (P2P) networks. In the previous study, the neighbor peer search is periodically executed, and the search area is constantly fixed as a circle regardless of the peers’ moving speed. Therefore, it is difficult to properly acquire on neighbor peers, especially in the situation when peers are moving at high speed. In this paper, we suggest a method to determine the peer search interval and the search area size adaptively depending on the peers’ locations and moving speed. Simulation results show that the proposed method enables peers to search neighbor peers at appropriate intervals and to obtain nearby information efficiently.

The Internet of Things is now part of everyday life and there has been a wide range of novel IoT applications collecting cyber-physical data and providing information on the environment. As it is expected that the IoT traffic will count for a major part of the Internet traffic, it is essential to characterize the IoT traffic and to identify each device, and especially in the case of cyberattacks. In this paper, we present a new method to identify IoT devices based on traffic entropy. We compute the entropy values of traffic features and we rely on Machine Learning algorithms to classify the traffic. Our method succeeds in identifying devices under various network conditions with performances up to 94% in all cases. Our method is also robust to unpredictable network behavior with anomalies spreading into the network.

The Internet of Things is expected to count for a large part of the overall Internet traffic but its main characteristics are still widely unknown. In this work, we focus on IoT Traffic and we present a novel IoT Traffic Generator. As an use-case, we generate IoT traffic within a Smart-Home Environment. Our generator allows studying the IoT traffic and can serve for describing the main properties of the IoT traffic. It is a first step toward the detection of security threats and anomalies for IoT Traffic.

The Internet of Things (IoT) is expected to count for a large part of the Internet traffic and its impact on the network is still widely unknown. It is therefore essential to study the IoT Traffic in order to characterize its properties and evaluate its performances. In this paper, we propose a novel IoT traffic generator called IoTTGen. We model the IoT traffic and we generate synthetic traffic for smart home and bio-medical IoT environments. We also extracted anomalous IoT traffic from a real dataset and study the IoT traffic properties by computing the entropy value of traffic parameters. Our generator succeeds in capturing the characteristics of the IoT traffic, which can be visually observed on Behavior Shape graphs. Our generator can also serve to describe the main IoT traffic properties and also to detect IoT traffic anomalies.

This paper proposes a classification method for peer-to-peer video streaming (P2PTV) traffic using machine learning. Since the user terminals (peers) share video data in P2PTV, P2PTV traffic is difficult to control and manage statically as both the number of peers sharing the same video data and the throughput vary with respect to contents. Although there exists a conventional method to classify and model P2PTV traffic by focusing on the number of peers and throughput, problems on the classification criteria and reproducibility remain in this method. In this paper, we use a clustering method that is considered as one of the machine learning methods and try to classify P2PTV traffic data into some categories. We extracted 18 features by analyzing P2PTV traffic of popular P2PTV applications: PPStream and PPTV; and then classified the traffic. The classification results show that about 400 traffic data sets were categorized into four clusters.

In mobile ad hoc networks, radio interference and mobility of nodes may degrade the packet arrival rate due to the dynamic topological change. Then, traditional reactive routing protocols may cause huge network resource consumption due to the route request flooding for discovering the destination. To solve the above issue, flooding area restriction methods can reduce the unnecessary control messages to narrow the flooding area based on the location information. However, each node must share location information before sending route requests via control messages. Besides, it is also difficult to share the correct location information of nodes due to the mobility of nodes. This paper proposes a flooding area restriction method to reduce the unnecessary control messages to determine the flooding area by sharing the location information via a mobile network. In addition, computer simulations reveal the effectiveness of the proposed method in comparison with a traditional routing protocol.

This paper proposes an ant-inspred backoff-based opportunistic routing protocol which optimizes the forwarding area based on the concept of ant colony optimization. In the traditional backoff-based opportunistic routing protocols, they may increase unnecessary packet forwarding to expand the forwarding area since a receiver of data packet autonomously participates the packet forwarding. Hence, to deal with the forwarding area expansion, the proposed method suppresses the potential forwarders to adjust the backoff time based on their pheromone levels. Finally, this paper clarifies the performance of the proposed method in comparison with the conventional method using computer simulations. The results show that the proposed method can reduce the network resource consumptions and the end-to-end delay while keeping higher packet transmission success rate.

Floating content (FC) is a decentralized on-demand information sharing method with ad hoc communication among terminals. In FC, all contents are anchored to specific locations and shared among mobile terminals inside a predefined sharing area, which is referred to as an anchor zone (AZ), as if the contents float in the geographical area. However, contents may be vanished if there is no terminal holding the contents inside an AZ because the sharing procedure depends on ad hoc communication and infrastructure-based supports are not available. Thus, terminal density in the AZ significantly affects the content lifetime since there are less communication opportunities among terminals in sparse environments and disseminating contents are difficult. To address this issue, a method that adaptivel adjusts the effective radius of an AZ, namely the area in which information is shared with FC, has been proposed. The principle of the method is to expand the area according to the terminal density in the AZ. Although expanding the effective radius easily prolongs the lifetime of contents, unregulated expansion may lead to excessive and unnecessary dissemination. This paper proposes an effective radius adjustment and transmission timing control method to realise efficient and effective information sharing with FC. The proposed method employs terminal density and terminal encounter rate to estimate further communication opportunities. The simulation results show that the proposed method could achieve longer content lifetime with less overhead.

In ad hoc networks, backoff-based OR (opportunistic routing) protocols, which autonomously selects a forwarder among the potential forwarders based on a random backoff time, have been proposed. However, each potential forwarder must wait for the backoff time to avoid packet collisions among receivers. In addition, the terminal density strongly affects the performance since the backoff-based OR protocols improve the performance by using multiple terminals. In this paper, we propose a novel OR protocol called PRIOR (prioritized forwarding for opportunistic routing). In PRIOR, a terminal called prioritized forwarder, which forwards packets without using a backoff time, is selected among neighbours. In addition, we propose a hop-by-hop retransmission control that performs the retransmission terminal selection on the basis of a neighbour relation with the PFs. Moreover, we introduce an explicit acknowledgement mechanism on the basis of the difference of hop counts to alleviate the bad effect of the retransmission control in dense environments. Finally, we evaluate PRIOR in comparison with conventional backoff-based OR protocols in computer simulation.

In ad hoc networks, radio interference and terminal' mobility can cause frame losses. When the frame losses occur continuously, general datalink layer protocols regard it as a link failure and then notify it to network layer protocols. In conventional routing protocols, a source terminal initiates route discovery process to recover from the link failures on receiving the notifications even if the source terminal still has a valid route to a destination terminal. However, the route discovery requires a route request flooding and the flooding may consume limited network resources of ad hoc networks and that also degrades transmission efficiency. For solving the problems, several retransmission and relay control methods have been proposed. In these methods, neighbour terminals of a transmission route overhear transmitted frames and retransmit lost frames autonomously by using the overheard frames. However, the conventional retransmission methods have a disadvantage that they degrade the transmission efficiency under bursty frame loss interval. Moreover, conventional relay control methods suffer from a processing overhead and a slow adaptation problem under a sparse frame loss. In this paper, we propose an efficient and lightweight method that neighbour terminals adaptively retransmit lost frames considering a frequency of the losses. Computer simulations clarify the performances and the effectiveness of the proposed method.

Recognizing a target in synthetic-aperture radar (SAR) images is an important, yet challenging, application of the model-based vision technique. This paper describes a model-based SAR recognition system based on invariant histograms and deformable template matching techniques. An invariant histogram is a histogram of invariant values defined by geometric features such as points and lines in SAR images. Although a few invariants are sufficient to recognize a target, we use a histogram of all invariant values given by all possible target feature pairs. This redundant histogram enables robust recognition under severe occlusions typical in SAR recognition scenarios. Multi-step deformable template matching examines the existence of an object by superimposing templates over potential energy field generated from images or primitive features. It determines the template configuration which has the minimum deformation and the best alignment of the template with features. The deformability of the template absorbs the instability of SAR features. We have implemented the system and evaluated the system performance using hybrid SAR images, generated from synthesized model signatures and real SAR background signatures.

A coupled tendon-driven manipulator, CT arm, is presented. In its tendon traction force transmission mechanism, the pair of tendons driving a joint is pulled by base actuators via pulleys mounted on the base-side joints. The mechanism uses coupled drive function of the tendon traction forces and thus exhibits a large payload capability for such a light-weight manipulator arm. CT arm has a solid structure and can be manufactured inexpensively because of its mechanical simplicity. Through experimental simulation, it is known that CT arm has the structure of a variable compliance arm and has an unusually large payload capacity.>