5G and Beyond 5G (B5G) networks are regarded as effective solutions for serving numerous emerging applications through 5G Vehicle-to-Everything (V2X) communications, that is, 5G V2X. Vehicles experience several messages such as safety messages like Decentralized Environmental Notification Messages (DENMs) and non-safety messages like entertainment data packets having varied requirements. In this work, we consider the co-existence of DENMs along with background data traffic and explore the impact of DENMs on such low-priority background data traffic. We explore the multicast and broadcast techniques for robust 5G V2X communications. We consider the delivery of DENMs using the multicast of gNodeBs (gNBs) as Single-Cell Point-to-Multipoint (SC-PTM) over Vehicle-to-Infrastructure (V2I) links as well as the broadcast of the vehicles over Vehicle-to-Vehicle (V2V) links. User-specific data traffic is unicasted over a high-capacity V2I link. Next, we propose a joint V2I resource allocation algorithm for the two traffic classes. We mathematically model average DENM delivery delay, average DENM packet reception ratio, and average throughput of background data traffic. Our mathematical analysis is further validated by extensive simulations. Through these simulations, it is shown that our proposed scheme outperforms the existing schemes in terms of average DENM delivery delay.

Non-Terrestrial Networks (NTNs) based on emerging 5G New Radio (NR) technology stand as an effective solution to complement terrestrial networks in service provisioning, owing to their ability to satisfy requests for anywhere and anytime connection. This work concentrates on the power saving and battery lifetime of a User Equipment (UE) communicating with NTN. We consider NTN to support Narrowband Internet-of-Things (NB-IoT) applications in 5G networks. We model an NTN UE's Discontinuous Reception (DRX) as a Semi-Markovbased model. We assess the model's performance in terms of UE power consumption and average delay. We validate the proposed model through extensive simulations. The reduction of the number of signaling steps can also reduce the power consumption of the UE. In this line, we also compare the random access performance obtained with 3GPP compliant configuration and satellite configuration with the aim of studying the power consumption of the UE.

Software-Defined Vehicle (SDV) is a new player towards autonomous vehicles in smart road networks. An SDV is constructed by a software platform like a cloud-native system like Kubernetes and has its internal network. To facilitate the easy and efficient configuration of networks in the SDV, an intent-based management is an appropriate direction. In this paper, we introduce a framework of intent-based management for networks and applications in SDVs so that they can communicate with other SDVs and infrastructure nodes for safe driving and infotainment services in the road networks. The proposed framework can also be extended to configure security policies for SDVs. We show a preliminary implementation of the proposed framework and suggest several research issues and challenges when designing such a framework.

Interface to Network Security Functions (I2NSF) Working Group within Internet Engineering Task Force (IETF) has developed a framework and its interfaces with YANG data models for configuring Network Security Functions (NSF). These models include a high-level security policy (i.e., an overview of configuration) and a low-level security policy (i.e., a detailed and specific configuration) to facilitate the configuration of NSFs. In this paper, a Security Policy Translator (SPT) is proposed to translate high-level security policies created by users into the corresponding low-level security policies. It leverages the design of I2NSF YANG data models to accurately translate security policies. The SPT performs a translation by extracting the high-level security principles using Deterministic Finite Automaton (DFA) construction from the high-level YANG data model. It converts the extracted information to a low-level form by utilizing a mapping model created by comparing the two YANG data models, such as the Consumer-Facing Interface (CFI) and NSF-Facing Interface (NFI) YANG data models. It selects the optimal NSFs based on the security policies to provide maximum security performance. It generates low-level security policies for the NSFs to deploy the security services. The proposed approach allows security policy translation for the I2NSF framework with high accuracy and speed.

A connected network of automated vehicles on roads can increase the driving safety of driverless vehicles (i.e., autonomous vehicles). The critical level of dangerous situations on the road while driving can be increased by the speed, orientation, and traffic density of the vehicles involved. Therefore, there is a need for a maneuvering mechanism that handles both the current driving vehicle and the oncoming vehicles headed toward an emergency zone (e.g., road hazard and road accident spot). In this paper, we present a context-aware navigation protocol (CNP) that enhances the safety of vehicles driving in urban roads. Firstly, CNP includes a collision avoidance module that builds on both vehicular networks and on-board sensors to track vehicles’ behaviors, and this module analyzes the driving risks to determine the necessary maneuvers in dangerous situations. Secondly, CNP establishes a collision mitigation strategy that limits the severity of collision damages in hazardous road during non-maneuverable scenarios. We conducted a theoretical analysis as well as extensive simulations to prove and evaluate the effectiveness of CNP. The results show that CNP can reduce communication overhead from a baseline scheme by up to 60% while the risk of road collisions is less than 5%.

Abstract A device‐free human counting (DFC) algorithm that uses fine‐grained subcarrier information from WiFi devices, called channel state information (CSI), to count the number of people in indoor environments is proposed. The DFC algorithm extracts the features of average attenuation and variation of CSI amplitudes caused by human motions, and puts the features into a training process to improve the counting accuracy. Through a bootstrapping process, the DFC can estimate the number of people standing in the middle of a WiFi link by constructing a probability model with the CSI signals at a receiver side. With this human counting capability, the DFC can support the efficient monitoring and automatic control of electrical devices (e.g. air conditioner, heater, bulb, and beam projector) indoors. Through a real implementation and experiments, it is shown that the DFC algorithm outperforms the state‐of‐the‐art DFC algorithm based on RSSI in indoor environments with human mobility. For a dynamic‐target case in a meeting room, for example, DFC can predict the number of people in an indoor space with an accuracy about 98% at best.

This study proposes an effective delay‐aware packet forwarding (DAPF) for driving safety and efficiency in vehicular networks. Vehicular ad hoc networks have been an emerging technology for vehicular communication for the last few decades, but still, it has many challenging issues such as on‐time dissemination of message at an emergency situation (e.g. accident and obstacle) to the vehicles having the same route to their destinations. This on‐time dissemination can prevent further collision of vehicles and road traffic congestion. In this study, the authors propose an effective way of selecting the processing position of a message among a cluster head, road‐side unit (RSU), and vehicular cloud, on the basis of total delivery time and cost. They further show that this effective selection and on‐time dissemination helps the upcoming vehicles to select an appropriate route to their destinations. Through simulation results, it is shown that their DAPF outperforms other schemes in terms of packet delivery time.

For long-distance flying, drones often need to charge their battery at quick battery-charging machines (QCMs) because of their limited battery capacity. If a drone individually chooses a QCM without any coordination, a drone network may experience QCM congestion when multiple drones select the same QCM. This QCM congestion may lead to an increasing drone traffic delay. In order to solve this problem, we propose a cloud-based drone navigation (CBDN) system for efficient drone battery charging in drone networks. In order to achieve this goal, the CBDN gathers drone traffic information and determines efficient drone routes so as to minimize the overall QCM congestion level for drone battery charging using cloud-based management. Our key idea is to find globally coordinated drone routes so as to minimize the total traffic delay in a drone network by reducing the overall QCM congestion level. In order to demonstrate the effectiveness of the proposed system, we evaluated the performance of CBDN by simulating a drone network under various network conditions. The simulation results show that CBDN is more efficient than the existing shortest-path-based drone route planning algorithms in terms of end-to-end traffic delay and QCM average utilization.

In this paper, we propose a spatio-temporal coordination-based media access control (STMAC) protocol for efficiently sharing driving safety information in urban vehicular networks. STMAC exploits a unique spatio-temporal feature characterized from a geometric relation among vehicles to form a line-of-collision graph, which shows the relationship among vehicles that may collide with each other. Based on this graph, we propose a contention-free channel access scheme to exchange safety messages simultaneously by employing directional antenna and transmission power control. Based on an urban road layout, we propose an optimized contention period schedule by considering the arrival rate of vehicles at an intersection in the communication range of a road-side unit to reduce vehicle registration time. Using theoretical analysis and extensive simulations, it is shown that STMAC outperforms legacy MAC protocols especially in a traffic congestion scenario. In the congestion case, STMAC can reduce the average superframe duration by 66.7%, packet end-to-end delay by 68.3%, and packet loss ratio by 88% in comparison with the existing MAC protocol for vehicle-to-infrastructure communication, based on the IEEE 802.11p.

This paper proposes an evolved Self-Adaptive Interactive Navigation Tool (SAINT+) to reduce the delivery time of emergency services and to improve navigation efficiency for the vehicles influenced by accidents. To the best of our knowledge, SAINT+ is the first attempt to optimize the delivery of emergency services as well as the navigation routes of vehicles around accident areas. Based on the congestion contribution model of SAINT and aggregated information from vehicles in the vehicular cloud, we propose a virtual path reservation strategy for emergency vehicles to guarantee a fast emergency service delivery. We also develop an accident area protection scheme based on an adjusted congestion contribution matrix and protection zones to evacuate vehicles in the accident area. To further reduce travel delay of neighbor vehicles in the accident area, we also present a dynamic traffic flow control model. Through extensive simulations with a real-world map, SAINT+ outperforms other state-of-the-art schemes for the travel delay of emergency vehicles. In scenarios with a high vehicle density, SAINT+ reduces the travel delay of emergency vehicles by 42.2%.

In this paper, we exploit the capture-effect for channel allocation. We experimentally show the characteristics of capture-effect across different channels, over time, and in different network densities. Then, we introduce CACA, an effective channel assignment protocol for wireless sensor networks. Traditional channel assignment protocols utilize all available channels to minimize interferences between any adjacent links. However, their performances are often not much better than the case of using single channel only. This is mainly due to an assumption that all channels are independent and quality of all channels are similar. However, this is a false assumption. In fact, there are only a few channels that show very good quality at any given time. The CACA avoids this problem by utilizing a few good channels and reuse these channels. When the channels are reused it relies on the capture-effect to ensure at least one of the contending nodes to transmit successfully. Maximizing this capture probability is a main objective of the CACA whenever the channels need to be reused. We evaluate the CACA on a 140-node wireless sensor network testbed and compare its performance with another benchmark protocol. Our results indicate that the CACA can improve the packet reception ratio of every link. As a result of this improvement, end-to-end throughput increases upto 100% in the case of a wireless sensor network with bursty traffic.

The increasing mobile traffic is becoming a serious concern for mobile network providers. To address the traffic explosion problem, there have been a lot of efforts to offload the traffic from cellular networks to other networks, such as WiFi hotspots and femtocells. In this paper, we explore the potential benefits of vehicular networks for data offloading and propose a Data Offloading framework through Vehicular nEtworks (DOVE), which reduces the cellular traffic for in-vehicle data services in a cost effective way. DOVE exploits vehicle trajectories for offloading purposes so that content files requested by vehicles can be delivered via vehicular networks rather than via cellular networks for economical purposes. We formulate the problem of selecting offloading positions as a spatio-temporal set-covering problem, and propose a time-prediction based set-covering algorithm using vehicle trajectories. Simulation results show that our DOVE framework can significantly reduce 57% of cellular link usage by performing data offloading through vehicular networks.

This paper proposes Trajectory-based Statistical Forwarding (TSF) scheme, tailored for the multihop data delivery from infrastructure nodes (e.g., Internet access points) to moving vehicles in vehicular ad hoc networks. To our knowledge, this paper presents the first attempt to investigate how to effectively utilize the packet destination vehicle's trajectory for such an infrastructure-to-vehicle data delivery. This data delivery is performed through the computation of a target point based on the destination vehicle's trajectory that is an optimal rendezvous point of the packet and the destination vehicle. TSF forwards packets over multihop to a selected target point where the vehicle is expected to pass by. Such a target point is selected optimally to minimize the packet delivery delay while satisfying the required packet delivery probability. The optimality is achieved analytically by utilizing the packet's delivery delay distribution and the destination vehicle's travel delay distribution. Through theoretical analysis and extensive simulation, it is shown that our design provides an efficient data forwarding under a variety of vehicular traffic conditions.

Vehicular ad hoc networks (VANETs) represent promising technologies for improving driving safety and efficiency. Due to the highly dynamic driving patterns of vehicles, it has been a challenging research problem to achieve effective and time-sensitive data forwarding in vehicular networks. In this paper, a Shared-Trajectory-based Data Forwarding Scheme (STDFS) is proposed, which utilizes shared vehicle trajectory information to address this problem. With access points sparsely deployed to disseminate vehicles' trajectory information, the encounters between vehicles can be predicted by the vehicle that has data to send, and an encounter graph is then constructed to aid packet forwarding. This paper focuses on the specific issues of STDFS such as encounter prediction, encounter graph construction, forwarding sequence optimization and the data forwarding process. Simulation results demonstrate the effectiveness of the proposed scheme.

Road networks are one of important surveillance areas in military scenarios. In these road networks, sensors will be sparsely deployed (hundreds of meters apart) for the cost-effective deployment. This makes the existing localization solutions based on the ranging ineffective. To address this issue, this paper introduces a novel approach based on the passive vehicular traffic measurement, called Autonomous Passive Localization (APL). Our work is inspired by the fact that vehicles move along routes with a known map. Using binary vehicle-detection time stamps, we can obtain distance estimates between any pair of sensors on roadways to construct a virtual graph composed of sensor identifications (i.e., vertices) and distance estimates (i.e., edges). The virtual graph is then matched with the topology of the road map, in order to identify where sensors are located on roadways. We evaluate our design outdoors on Minnesota roadways and show that our distance estimate method works well despite traffic noises. In addition, we show that our localization scheme is effective in a road network with 18 intersections, where we found no location matching error, even with a maximum sensor time synchronization error of 0.07 sec and a vehicle speed deviation of 10 km/h.

This paper proposes a Trajectory-Based Data (TBD) Forwarding scheme, tailored for the data forwarding for roadside reports in light-traffic vehicular ad hoc networks. State-of-the-art schemes have demonstrated the effectiveness of their data forwarding strategies by exploiting known vehicular traffic statistics (e.g., densities and speeds). These results are encouraging, however, further improvements can be made by taking advantage of the growing popularity of GPS-based navigation systems. This paper presents the first attempt to effectively utilize vehicles' trajectory information in a privacy-preserving manner. In our design, such trajectory information is combined with the vehicular traffic statistics for a better performance. In a distributed way, each individual vehicle computes its end-to-end expected delivery delay to the Internet access points based on its position on its vehicle trajectory and exchanges this delay with neighboring vehicles to determine the best next-hop vehicle. For the accurate end-to-end delay computation, this paper also proposes a link delay model to estimate the packet forwarding delay on a road segment. Through theoretical analysis and extensive simulation, it is shown that our link delay model provides the accurate link delay estimation and our forwarding design outperforms the existing scheme in terms of both the data delivery delay and packet delivery ratio.

This paper proposes a data forwarding scheme called Trajectory-based Statistical Forwarding (TSF), tailored for the data delivery from infrastructure nodes (e.g., Internet access points) to moving vehicles in vehicular networks. To our knowledge, this paper presents the first attempt to investigate how to effectively utilize the packet destination vehicle's trajectory for such an infrastructure-to-vehicle data delivery. This data delivery is performed through the computation of a target point based on the destination vehicle's trajectory that is an optimal rendezvous point of the packet and the destination vehicle. TSF forwards packets over multi-hop to a selected target point where the vehicle is expected to pass by. Such a target point is selected optimally to minimize the packet delivery delay while satisfying the required packet delivery probability. The optimality is achieved analytically by utilizing the packet's delivery delay distribution and the destination vehicle's travel delay distribution. Through theoretical analysis and extensive simulation, it is shown that our design provides an efficient data forwarding under a variety of vehicular traffic conditions.

This paper proposes a VIrtual Scanning Algorithm (VISA), tailored and optimized for road network surveillance. Our design uniquely leverages upon the facts that 1) the movement of targets (e.g., vehicles) is confined within roadways and 2) the road network maps are normally known. We guarantee the detection of moving targets before they reach designated protection points (such as temporary base camps), while maximizing the lifetime of the sensor network. The main idea of this work is virtual scan-waves of sensing activities scheduled for road network protection. We provide design-space analysis on the performance of virtual scan in terms of lifetime and average detection delay. Importantly, to our knowledge, this is the first work to study how to guarantee target detection while sensor network deteriorates, using a novel hole handling technique. Through theoretical analysis and extensive simulation, it is shown that a surveillance system, using our design, sustains orders-of-magnitude longer lifetime than full coverage algorithms, and as much as 10 times longer than legacy duty cycling algorithms.

This paper proposes a trajectory-based data forwarding (TBD) scheme, tailored for the data forwarding in light- traffic vehicular ad-hoc networks. State-of-the-art schemes have demonstrated the effectiveness of their data forwarding strategies by exploiting known vehicular traffic statistics (e.g., densities and speeds) in these vehicular networks. These results are encouraging, however, further improvements can be made by taking advantage of the growing popularity of GPS-based navigation systems. This paper presents the first attempt to investigate how to effectively utilize vehicles" trajectory information in a privacy-preserving manner. In our design, the trajectory information is combined with the traffic statistics to improve the performance of data forwarding in road networks. Through theoretical analysis and extensive simulation, it is shown that our design outperforms the existing scheme.

This paper proposes a virtual scanning algorithm (VISA), tailored and optimized for road network surveillance. Our design uniquely leverages upon the facts that (i) the movement of targets (e.g., vehicles) is confined within roadways and (ii) the road network maps are normally known. We guarantee the detection of moving targets before they reach designated protection points (such as temporary base camps), while maximizing the lifetime of the sensor network. The main idea of this work is virtual scan - waves of sensing activities scheduled for road network protection. We provide design-space analysis on the performance of virtual scan in terms of lifetime and average detection delay. Importantly, to our knowledge, this is the first work to study how to guarantee target detection while sensor network deteriorates, using a novel hole handling technique. Through theoretical analysis and extensive simulation, it is shown that a surveillance system, using our design, sustains orders-of-magnitude longer lifetime than full coverage algorithms, and as much as ten times longer than legacy duty cycling algorithms.

In road networks, sensors are deployed sparsely (hundreds of meters apart) to save costs. This makes the existing localization solutions based on the ranging be ineffective. To address this issue, this paper introduces an autonomous passive localization scheme, called APL. Our work is inspired by the fact that vehicles move along routes with a known map. Using binary vehicle-detection timestamps, we can obtain distance estimates between any pair of sensors on roadways to construct a virtual graph composed of sensor identifications (i.e., vertices) and distance estimates (i.e., edges). The virtual graph is then matched with the topology of road map, in order to identify where sensors are located in roadways. We evaluate our design outdoor in Minnesota roadways and show that our distance estimate method works well despite of traffic noises. In addition, we show that our localization scheme is effective in a road network with eighteen intersections, where we found no location matching error, even with a maximum sensor time synchronization error of 0.3 sec and the vehicle speed deviation of 10 km/h.

This paper proposes a minimal contour tracking algorithm (MCTA) that reduces energy consumption for tracking mobile targets in wireless sensor networks in terms of sensing and communication energy consumption. MCTA conserves energy by letting only a minimum number of sensor nodes participate in communication and perform sensing for target tracking. MCTA uses the minimal tracking area based on the vehicular kinematics. The modeling of target's kinematics allows for pruning out part of the tracking area that cannot be mechanically visited by the mobile target within scheduled time. So, MCTA sends the tracking area information to only the sensor nodes within minimal tracking area and wakes them up. Compared to the legacy scheme which uses circle-based tracking area, our proposed scheme uses less number of sensors for tracking in both communication and sensing without target missing. Through simulation, we show that MCTA outperforms the circle-based scheme with about 60% energy saving under certain ideal situations.

This paper suggests an architecture of DNS service system for IPv6 mobile ad-hoc network connected to the Internet. The resolution of DNS names of mobile nodes within mobile ad-hoc network is performed by multicast DNS and that of DNS names of nodes in the Internet is performed through DNS autoconfiguration of recursive DNS server. In the former, each mobile node plays a role of DNS name server for the DNS resource records associated with DNS name of which authority it has. The latter allows mobile node to receive the global Internet service, such as Web service, in IPv6 mobile ad-hoc network that is connected to the Internet. These two kinds of DNS name resolution are processed automatically without the intervention of users in mobile ad-hoc network.