Autonomous vehicles (AVs) are an exciting emerging technology that reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, increasing driving quality and safety. Validation testing of AVs provides the assurance of quality and safety, and is also an integral part of new AV research and development. Following in suite of their predecessor, i.e. non-autonomous vehicles, scenario-based validation testing is a safe, time and cost effective means of testing these complex, costly, and safety critical systems. In this paper we propose an open-source implementation of a scenario-based AV validation testing approach for AVs in traffic environments with pedestrians and intersections. Furthermore, we utilize the approach to implement scenarios of previously unimplementable AV scenarios from the Atomic Blocks dataset.

Validation and Verification (V&V) of complex Autonomous Vehicle (AV) systems is currently an unsolved problem. In response, PolyVerif, an open-source AV V&V project, was released with the objective of accelerating research in this important area. PolyVerif provided a full open-source stack consisting of an AV engine, symbolic test generation, simulations at various levels of fidelity, a digital twin creation environment, and a flow from simulation tests to construct physical tests. This demonstration will present a series of “Design for Test” modules focused on the major pieces of an AV stack - Detection, Location Services, Control, and Path Planning. Taken together, these modules build a framework to validate complex AV stacks.

This demonstration paper presents a study on the development of a simulation testbed for meteorological data collection using crowdsensing, implemented by agent-based modeling and simulation. In addition to the data collection, the testbed integrates the collected data into a separate simulation model to determine the best hybrid aerial-terrestrial routes in a city for user mobility. The testbed allows fast and user friendly implementation of any strategy. The demonstration explores the design and implementation of the testbed, and discusses the potential benefits and challenges of this integrated approach.

Autonomous vehicle (AV) technology is positioned to have a significant impact on various industries. Hence, artificial intelligence powered AVs and modern vehicles with advanced driver-assistance systems have been operated in street networks for real-life testing. As these tests become more frequent, accidents have been inevitable and there have been reported crashes. The data from these accidents are invaluable for generating edge case test scenarios and understanding accident-time behavior. In this paper, we use the existing AV accident data and identify the atomic blocks within each accident, which are modular and measurable scenario units. Our approach formulates each accident scenario using these atomic blocks and defines them in the Measurable Scenario Description Language (M-SDL). This approach produces modular scenario units with coverage analysis, provides a method to assist in the measurable analysis of accident-time AV behavior, identifies edge scenarios using AV assessment metrics.

Environmental monitoring applications for wireless sensor and actor networks rely on position estimation in order to process or evaluate the observed data. The absence of efficient positioning techniques for sensor nodes operating in harsh environments calls for novel approaches. While monitoring the Amazon river, unprecedented characteristics of the river and its surroundings challenge the node communications and drifting of the nodes makes it difficult to use the existing positioning methods. To address these challenges, we propose a multi-hop localization technique that takes advantage of sensor mobility with local information exchange. The collected information is used to enrich the environmental data with location information. The maximum hop distance for actor affiliation is also adapted according to network characteristics to improve energy consumption behavior. The motion of the sensor nodes follows the advection of the fluid parcels in the river, which is modeled as a combination of a central streamline with a meandering motion around the rough surface. This translates into a stretching topology with correlated motion for sensor nodes. Through extensive simulations, we show that the nodes can be efficiently positioned using the proposed approach, as our technique is compliant with the movement patterns of the sensor nodes in the realistic mobility model of the river.

Realistic human mobility modeling is critical for accurate performance evaluation of mobile wireless networks. Movements of visitors in theme parks affect the performance of systems which are designed for various purposes including urban sensing and crowd management. Previously proposed human mobility models are mostly generic while some of them focus on daily movements of people in urban areas. Theme parks, however, have unique characteristics in terms of very limited use of vehicles, crowd's social behavior, and attractions. Human mobility is strongly tied to the locations of attractions and is synchronized with major entertainment events. Hence, realistic human mobility models must be developed with the specific scenario in mind. In this paper, we present a novel model for human mobility in theme parks. In our model, the nondeterminism of movement decisions of visitors is combined with deterministic behavior of attractions in a theme park. The attractions are categorized as rides, restaurants, and live shows. The time spent at these attractions are computed using queueing-theoretic models. The realism of the model is evaluated through extensive simulations and compared with the mobility models SLAW, RWP and the GPS traces of theme park visitors. The results show that our proposed model provides a better match to the real-world data compared to the existing models.

Aerial wireless sensor and actor networks are composed of multiple unmanned aerial vehicles. An actor node in the network has the capabilities of both acting on the environment and also performing networking functionalities for sensor nodes. Thus, positioning of actors is critical for the efficient data collection. In this paper, we propose an actor positioning strategy, which utilizes a hybrid antenna model that combines the complimentary features of an isotropic omni radio and directional antennas. We present a distributed algorithm for fast neighbor discovery with the hybrid antenna. The omni module of the hybrid antenna is used to form a self organizing network and the directional module is used for reliable data transmission. Extensive simulations show that our protocol improves the packet reception ratio by up to 50% compared to omnidirectional antenna. Moreover, the network reorganization delay is also reduced. The tradeoff between coverage and reorganization delay is also illustrated.

Social networking websites have been increasingly popular in the recent years. The users create and maintain their social networks by themselves in these websites by establishing or removing the connections to friends and sites of interests. The smart phones not only create a high availability for social network applications, but also serve for all forms of digital communication such as voice or video calls, e-mails and texts, which are also the ways to form or maintain our social network. In this paper, we deal with the problem of automatically generating and organizing social networks by analyzing and assessing mobile phone usage and interaction data. We assign weights to the different types of interactions. The interactions among users are then evaluated based on these weight values for certain periods of time. We use these values to rank the friends of users by a sports ranking algorithm, which recognizes the changes in the collected data over time.

SUMMARY Location estimation of sensor nodes is an essential part of most applications for wireless sensor and actor networks. The ambiguous location information often makes the collected data useless in these applications. Environmental monitoring relies on an accurate position estimation to process or evaluate the collected data. In this paper, we present a novel and scalable approach for positioning of mobile sensor nodes with the goal of monitoring the Amazon river. The actors in the scenario are stationary and positioned at reachable spots on the land alongside the river whereas sensor nodes are thrown into the river to collect data such as water temperature, depth, and geographical features. The actors are not equipped with positioning adaptors, and they are only aware of their distances from the other actors. The sensor nodes collect data and forward it to the actors. While floating in the river, sensor nodes are often multiple hops away from the actors, which makes it challenging to apply traditional positioning techniques. Through extensive simulations, we show that the nodes can be efficiently positioned using a multi‐hop approach with local information exchange only. The introduced approach is also applied to a scenario, where monkey swarm monitoring is simulated, to test the generalizability of the algorithm. Copyright © 2011 John Wiley & Sons, Ltd.

Advances in unmanned aerial vehicle (UAV) technology and wireless sensor and actor networks (WSAN) made it possible to equip small UAVs with sensors and deploy aerial sensor and actor networks. Aerial sensor networks enable high quality observation of events while reducing the number of requirements. Positioning of UAVs with actor nodes is critical in these systems for effective data collection. In this paper we propose an actor positioning strategy for aerial WSANs considering the scenario of toxic plume observation after a volcanic eruption. The positioning algorithm utilizes the Valence Shell Electron Pair Repulsion (VSEPR) theory of chemistry, which is based on the correlation between molecular geometry and the number of atoms in a molecule. The limitations of the basic VSEPR theory are eliminated by extending the approach for multiple central data collectors. The simulations show that the proposed system provides high connectivity and coverage for the aerial sensor network.

Realistic modeling of the movement of people in an environment is critical for evaluating the performance of mobile wireless systems such as urban sensing or mobile sensor networks. Existing human movement models are either fully synthetic or rely on traces of actual human movement. There are many situations where we cannot perform an accurate simulation without taking into account what the people are actually doing. For instance, in theme parks, the movement of people is strongly tied to the locations of the attractions and is synchronized with major external events. For these situations, we need to develop scenario specific models. In this paper, we present a model of the movement of visitors in a theme park. The nondeterministic behavior of the human walking pattern is combined with the deterministic behavior of attractions in the theme park. The attractions are divided into groups of rides, restaurants and live shows. The time spent by visitors at different attractions is calculated using specialized queuing-theoretic models. We compare the realism of the model by comparing its simulations to the statistics of the theme parks and to real-world GPS traces of visitor movement. We found that our model provides a better match to the real-world data compared to current state-of-the-art movement models.

Wild life monitoring requires a sophisticated process of planning, installation, execution, data collection, and data interpretation. The effort and time spent increases tremendously with the area and the number of observed objects as well as with the time frame of the observations. However, the application of wireless sensor nodes enables a scalable sampling method and fine granularity of data difficult to obtain otherwise. By including resource rich actor nodes, the data collection and evaluation are further optimized. In this paper, a wireless sensor and actor network (WSAN) protocol is designed for capturing and monitoring the social interactions of the complex social network of gorillas. The nodes are intended to be attached on the apes forming a mobile network. The local interaction patterns among the nodes are analyzed throughout the network life time. The protocol then determines the social roles of the gorillas based on previous research findings about their social structure, and builds a profile for each ape, which demonstrates the status, kinship and role played within the society. As a result, the proposed protocol generates a social directory of the ape troop under observation, namely fAPEbook. The contribution of this paper is to evaluate the efficiency of the proposed algorithm by its ability to capture the different characteristics in the society with the established mobility models. The results suggest the applicability of the algorithm in field tests with wild life.

The effort and time spent in wild life monitoring increases with the area and the number of objects to observe. Deployment of wireless sensor nodes enables a scalable sampling method and fine granularity of data collection. When we include resource rich actor nodes the data collection and evaluation are further optimized. However realistic mobility data is missing for various animal species to develop prototypes of wireless network based monitoring systems. In this paper, the problem of the absence of realistic data is considered from a social network perspective. Node deployment and mobility algorithms are provided to model a complete system of an animal swarm to be used for animal social life monitoring. A novel spatial cut-off preferential attachment model and center of mass concept are used and extended for the models according to the characteristics of the animal swarms. In the application scenario of a gorilla swarm, each gorilla is equipped with a sensor node for the monitoring of the social system. The local interaction patterns among the mobile nodes are also monitored throughout the network life time to observe the social interactions among animals and to determine the role of each animal in the society. The performance of the monitoring protocol and the applicability of the deployment and mobility models are presented through extensive simulations.

The node mobility is a natural element of many wireless sensor and actor network (WSAN) applications. Recent advances in the development of small unmanned aerial vehicles (UAVs) with built in sensors made it possible to deploy aerial sensor and actor networks. An aerial network composed of small UAVs enables high quality observation for events while reducing the number of personnel and the risk for the operators. In order to have an effective data collection, the positioning of actors plays a critical role in aerial WSANs. In this paper we propose an actor positioning strategy for aerial WSANs considering the scenario of toxic plume observation after a volcanic eruption, which is one of the emerging applications of aerial UAV networks. Measuring the composition of volcanic plumes allows the computation of volcanogenic fluxes and provides insights into volatile degassing mechanisms. The actors in the proposed approach use a lightweight and distributed algorithm to form a self organizing network around the central UAV, which has the role of the sink in the WSAN. Our algorithm makes use of the Valence Shell Electron Pair (VSEPR) theory of chemistry, which is based on the correlation between molecular geometry and the number of atoms in a molecule. The performance of the proposed practical positioning algorithm is presented through extensive simulations.

Wireless sensor and actor networks (WSANs) can be used for monitoring physical environments and acting according to the observations. In order to differentiate the actions based on the sensed information, WSANs comprise of various applications with different quality of service (QoS) requirements. QoS solutions for WSANs are challenging compared to traditional networks because of the limited resource capabilities of sensor nodes. In terms of QoS requirements, WSANs also differ from WSNs since actors and sensors have distinct resource constraints. In this paper we present LRP-QS, lightweight routing protocol with QoS support for WSANs. Our protocol provides QoS by differentiating the rates among different types of applications with dynamic packet tagging at the sensor nodes and per flow management at the actor nodes. Through extensive simulations we observe a greater packet delivery ratio and a better memory consumption rate in comparison with the related mechanisms.

Wireless sensor and actor networks (WSAN) have become increasingly popular in recent years. The cooperative operation between sensor and actor nodes results in a major advantage against pure sensor networks and extends the range of possible application scenarios. One emerging application is the Amazon scenario in which stationary actors are deployed at accessible points in a thick forest structure and sensor nodes are thrown in a river flowing through the forest to gather observations from unreachable areas of the forest. This unprecedented and unique setting exposes two important challenges: (a) the dynamics of the river forms a continuously varying topology of sensor nodes requiring a highly adaptive network organization and (b) the inherent features of sensor and actor nodes, combined with rapid changes in the link structure of the network require efficient bandwidth utilization and data transmission. In this paper, we address these challenges by introducing SOFROP, a novel self-organizing and fair routing protocol for WSANs. The extensive simulations that are carried out for evaluation point out two highlights of SOFROP. These are the lightweight and efficient routing that is optimized for fairness and the locally acting adaptive overlay network formation.

Location estimation of sensor nodes is an essential part of most applications for wireless sensor and actor networks (WSAN). The ambiguous location information often makes the collected data useless in these applications. Environmental monitoring in particular, relies on an accurate position estimation in order to process or evaluate the collected data. In this paper, we present a novel and scalable approach for positioning of mobile sensor nodes with the goal of monitoring the Amazon river. The actors in the scenario are stationary and positioned at reachable spots on the land alongside the river whereas sensor nodes are thrown into the river to collect data such as water temperature, depth and geographical features. The actors are not equipped with positioning adaptors and they are only aware of their distances from the other actors. The sensor nodes collect data and forward it to the actors. While floating in the river, sensor nodes are often multiple hops away from the actor nodes, which makes it challenging to apply traditional positioning techniques. Through extensive simulations, we show that the positioning of the nodes is feasible using a multi-hop approach with local information exchange only.