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Collision avoidance is one of the most promising applications for vehicular networks, dramatically improving the safety of the vehicles that support it. In this paper, we investigate how it can be extended to benefit vulnerable users, e.g., pedestrians and bicycles, equipped with a smartphone. We argue that, owing to the reduced capabilities of sma...
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... the following, we describe the reference scenario considered for our performance evaluation, the simulation tools we employ, and the metrics (key performance indicators, KPIs) we evaluate. Our reference topology, depicted in Figure 4, is an urban area composed of three roads, crossing at two intersections, a pedestrian lane and three pedestrian crossings. Vehicles and pedestrians move throughout the topology, and all of them are covered by the cellular infrastructure (namely, an LTE eNB located at the center of the topology). ...
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Collision avoidance is one of the most promising applications for vehicular networks, dramatically improving the safety of the vehicles that support it. In this paper, we investigate how it can be extended to benefit vulnerable users, e.g., pedestrians and bicycles, equipped with a smartphone. We argue that, owing to the reduced capabilities of sma...
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... From amongst the solutions proposed to solve the problem of VRU detection and collision avoidance, few considered impact on battery life [29]. The issue of optimizing energy efficiency has led many researchers [111], [112], [113], [114], [115] to come up with offloading schemes so that some/all calculations are off loaded to nearby infrastructure, unlike [24] and [78] in which all computations are assumed to be done locally. ...
... The authors investigated this relationship and designed a curb detection module to improve collision detection accuracy at the moment the pedestrian crosses the curb. The tradeoff between latency and energy consumption with regards to context information exchange was investigated in [29], [112], [113] which used Multi-access Edge Computing (MEC) to improve energy efficiency by computational offloading to cloud servers in less critical situations. These efforts facilitate the use of a smartphone as an OBU without overexerting its limited battery capabilities. ...
The accelerated rise of new technologies has reshaped the manufacturing industry of contemporary vehicles. Numerous technologies and applications have completely revolutionized the driving experience in terms of both safety and convenience. Although vehicles are now connected and equipped with a multitude of sensors and radars for collision avoidance, millions of people suffer serious accidents on the road, and unfortunately, the death rate is still on the rise. Collisions are still a dire reality for vehicles and pedestrians alike, which is why the improvement of collision prevention mechanisms is an ongoing necessity. Collision prevention mechanisms have evolved from vision-based systems like radars to systems that transcend the driver’s line of sight. These latter systems depend on vehicular ad hoc networks (VANETs) to employ bidirectional communication between vehicles and other vehicles (V2V) as well as between vehicles and road infrastructure (V2I). Recently, research has expanded to include a new communication system between vehicles and pedestrians (V2P) through the latter’s smartphones. In this paper, we provide an extensive survey of existing V2P projects, categorize different parameters that influence V2P system design, compare different communication technologies used in V2P systems and present an overview of recent trends that solve problems in V2P systems like network congestion, pedestrian localization, and context information exchange. The main contribution of our work is to pave the road for future research by providing a comprehensive view of projects, challenges and recent trends in the emerging and rapidly growing field of V2P system design.
... This projection depends on the status of the blinking lights; (ii) Collision Avoidance Strategy (CAS), which notifies the vehicles potentially involved in a collision regarding the action needed to avoid it. In [7], the collision avoidance application for vehicular networks is extended to benefit vulnerable users, e.g., pedestrians and bicycles, equipped with a smartphone. The authors proposed a MEC-based collision avoidance system. ...
Collision detection and avoidance between vehicles is one of the key services envisioned in the Internet of Vehicles. Such services are usually deployed at the multi-access edge computing (MEC) to ensure low-latency communication and thus guarantee real-time reactions to avoid collisions between vehicles. In order to maximize the coverage of the road and ensure that all vehicles are connected to an optimal MEC host (in terms of geographical location), the collision avoidance application needs to be instantiated on all the MEC hosts. This may add a burden on the computing resources available at the latter. In this article, we propose an AI-empowered framework that aims to optimize the computing resources at the MEC hosts. Our framework uses deep learning to predict the vehicle density to be served by a MEC host and derive the exact computing resources required by the collision detection application to run optimally. We evaluate the proposed framework using a real dataset representing vehicle mobility in a big city. Obtained results show the accuracy of our prediction model, and hence the efficiency of our resources assignment framework to exactly deduce the optimal computing resources needed by each instance of the application.
... According to the ETSI 102.941 (2019) standard, connected vehicles periodically (e.g., every 100 ms) broadcast cooperative awareness messages (CAMs), including their location, speed, and heading. Such information is then used by other vehicles and/or the network infrastructure for several safety and convenience services, e.g., [9]. Upon detecting a situation warranting action, decentralized environmental notification messages (DENMs) are sent to the affected vehicles, so that their actuator is triggered and/or their driver is warned. ...
... w ← arg max w∈W : κ(w)=k δ w (ω k ) 10: w ← arg min w∈W : κ(w)=k max qi∈w (µ i − λ i ) 11: if does improve(k , w , w , φ) then 12: (Line 5); if no such flow exists (Line 6), then FA is extended to include all flows in K (Line 7). The flow k to act upon is chosen in Line 8: considering the min-max nature of objective (9), k is the flow for which the summation in (9) is largest. For the same reason, the path w to remove vehicles from is chosen as the most loaded one, hence, the one associated with the largest term in (9). ...
... The flow k to act upon is chosen in Line 8: considering the min-max nature of objective (9), k is the flow for which the summation in (9) is largest. For the same reason, the path w to remove vehicles from is chosen as the most loaded one, hence, the one associated with the largest term in (9). Similarly, the path w to add traffic to is selected (Line 10) as the least-loaded one among those used by k ; note that this implies choosing a non-critical path if such paths exist. ...
Automotive services for connected vehicles are one of the main fields of application for new-generation mobile networks as well as for the edge computing paradigm. In this paper, we investigate a system architecture that integrates the distributed vehicular network with the network edge, with the aim to optimize the vehicle travel times. We then present a queue-based system model that permits the optimization of the vehicle flows, and we show its applicability to two relevant services, namely, lane change/merge (representative of cooperative assisted driving) and navigation. Furthermore, we introduce an efficient algorithm called Bottleneck Hunting (BH), able to formulate high-quality flow policies in linear time. We assess the performance of the proposed system architecture and of BH through a comprehensive and realistic simulation framework, combining ns-3 and SUMO. The results, derived under real-world scenarios, show that our solution provides much shorter travel times than when decisions are made by individual vehicles.
... According to the ETSI 102.941 (2019) standard, connected vehicles periodically (e.g., every 100 ms) broadcast cooperative awareness messages (CAMs), including their location, speed, and heading. Such information is then used by other vehicles and/or the network infrastructure for several safety and convenience services, e.g., [9]. Upon detecting a situation warranting action, decentralized environmental notification messages (DENMs) are sent to the affected vehicles, so that their actuator is triggered and/or their driver is warned. ...
... w ← arg max w∈W : κ(w)=k δ w (ω k ) 10: w ← arg min w∈W : κ(w)=k max qi∈w (µ i − λ i ) 11: if does improve(k , w , w , φ) then 12: (Line 5); if no such flow exists (Line 6), then FA is extended to include all flows in K (Line 7). The flow k to act upon is chosen in Line 8: considering the min-max nature of objective (9), k is the flow for which the summation in (9) is largest. For the same reason, the path w to remove vehicles from is chosen as the most loaded one, hence, the one associated with the largest term in (9). ...
... The flow k to act upon is chosen in Line 8: considering the min-max nature of objective (9), k is the flow for which the summation in (9) is largest. For the same reason, the path w to remove vehicles from is chosen as the most loaded one, hence, the one associated with the largest term in (9). Similarly, the path w to add traffic to is selected (Line 10) as the least-loaded one among those used by k ; note that this implies choosing a non-critical path if such paths exist. ...
Automotive services for connected vehicles are one of the main fields of application for new-generation mobile networks as well as for the edge computing paradigm. In this paper, we investigate a system architecture that integrates the distributed vehicular network with the network edge, with the aim to optimize the vehicle travel times. We then present a queue-based system model that permits the optimization of the vehicle flows, and we show its applicability to two relevant services, namely, lane change/merge (representative of cooperative assisted driving) and navigation. Furthermore, we introduce an efficient algorithm called Bottleneck Hunting (BH), able to formulate high-quality flow policies in linear time. We assess the performance of the proposed system architecture and of BH through a comprehensive and realistic simulation framework, combining ns-3 and SUMO. The results, derived under real-world scenarios, show that our solution provides much shorter travel times than when decisions are made by individual vehicles.
... Among these, collision avoidance is one of the most prominent, due to the high impact that road fatalities have in our daily life [6], [13]- [16]. The CAA algorithm used in this work, was deployed in [17]- [19], but under different scenarios (e.g., including pedestrians) or in the presence of a distributed architectures. A different CAA algorithm involving vulnerable users is presented in [14], where however the focus is on smartphone power consumption. ...
... An overview of the eCA service architecture is shown in Figure 2. The eCA draws on the Collision Avoidance Algorithm (CAA) introduced in [17]- [19]. The general idea of the CAA is to exploit the information contained in CAMs, such as position, velocity, acceleration, and heading, to predict the future trajectory of the vehicle. ...
... The target of this work is to evaluate our eCA system under different scenarios, in which we varied the speed of the vehicles, their spatial density, the reaction time associated to a DENM reception, and the reaction strategy (with SE-CAS, or with ME-CAS). Unlike previous works [17]- [19], which evaluated the service by only analysing the DENMs received at the vehicles, we perform a study on the possible actions to be taken upon the reception of a DENM, and on how different approaches may impact the system performance, from both the network and the application point of view. ...
In this paper, we present an enhanced Collision Avoidance (eCA) service that leverages vehicle connectivity through a cellular network to avoid vehicle collisions and increase road safety at intersections. The eCA service is assumed to be deployed at the edge of the network, thus curbing the latency incurred by the communication process. The core of the eCA service is composed of a Collision Avoidance Algorithm (CAA), and a Collision Avoidance Strategy (CAS). The former predicts the vehicle’s future trajectory through the positional information advertised by periodic beacons and detects if two vehicles are on a collision course. The latter decides which of the vehicles potentially involved in a collision should yield. The vehicles are then notified of both the impending danger and of the actions needed to avoid it. We have simulated our solution using SUMO (Simulation of Urban MObility) and ns-3 (network simulator 3) with the LENA (LTE-EPC Network simulAtor) framework on a Manhattan-grid road topology, and observed its good performance in terms of avoided collisions percentage as a function of vehicle speed and different vehicles densities.
One of the core elements for the upcoming generation of wireless cellular networks is the availability of network service access continuity in addition to high-speed internet and low latency. The forthcoming fifth generation (5G) greatly improves users’ demand in terms of faster download rates, exceptional system availability, superb end to end coverage with exceptionally low latency and ultra reliability. One of the solutions to provide end to end low latency is the utilization of Mobile Edge Computing (MEC) in the network. MEC provides cloud advantages to users by setting up a small cloud server in the edge node (i.e. close to the end-user), which decreases the amount of latency in network connections, in this regard, service migration has required as users migrate to the new location. Optimal migration decisions are challenging because they depend on the cloud environment, or edge nodes belong to different orchestrators, and security issues in the migration process must also be resolved in order to prevent unreliable requests. This study provides different approaches to address these challenges by identifying the security implications of migration methods based on the blockchain integration.
In this paper, some collision avoidance systems based on MEC in a VANET environment are proposed and investigated. Micro services at edge are considered to support service continuity in vehicle communication and advertising. This considered system makes use of cloud and edge computing, allowing to switch communication from edge to cloud server and vice versa when possible, trying to guarantee the required constraints and balancing the communication among the servers. Simulation results were used to evaluate the performance of three considered mechanisms: the first one considering only edge with load balancing, the second one using edge/cloud switching and the third one using edge with load balancing and collision avoidance advertising.
The increasing number of heterogeneous devices connected to the Internet, together with tight 5G requirements have generated new challenges for designing network infrastructures. Industrial verticals such as automotive, smart city and eHealthcare (among others) need secure, low latency and reliable communications. To meet these stringent requirements, computing resources have to be moved closer to the user, from the core to the edge of the network. In this context, ETSI standardized Multi-Access Edge Computing (MEC). However, due to the cost of resources, MEC provisioning has to be carefully designed and evaluated. This survey firstly overviews standards, with particular emphasis on 5G and virtualization of network functions, then it addresses flexibility of MEC smart resource deployment and its migration capabilities. This survey explores how the MEC is used and how it will enable industrial verticals.
Network function virtualization is one of the most relevant enabling technologies for 5G: conceiving a vertical service as a set of virtual network functions (VNFs) makes it possible to implement a service at the network edge in a fast and flexible manner, using computing, networking, and storage resources available therein. However, while deploying the VNFs composing a service, it is critical (i) to meet the key performance indices required by the vertical and (ii) to use the available resources in the most efficient manner so as to both cope with possible resource shortage and minimize the overall deployment cost.
To successfully address the aforementioned issues, several works have tackled the problem of VNF placement at the network edge and, more recently, the problem of VNF sharing, i.e. of reusing the same VNF for multiple concurrent services.
In this article, we first introduce VNF placement in 5G cellular networks and review existing solutions to this aspect. Then we focus on the problem of how to efficiently reuse VNFs and how to adapt the resources assigned to the virtual machines where such VNFs run, so that that the vertical key performance indicators (KPIs) are still satisfied. Importantly, it has been shown that carefully prioritizing the traffic of the services sharing a set of VNFs can lead to a significant cost reduction while still meeting the services KPIs. Such priorities can be set through an efficient algorithm, which provides a solution within a constant factor from the optimum, in polynomial time.
