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Research in vehicular analytics has explored two different approaches to infer properties about a vehicle's surroundings: (a) using sensors on smartphone devices to infer properties about surroundings, or (b) to use in-vehicle sensors. The latter approach was less studied. In this article, we take a first step beyond research to understand, using a pilot study, how to design vehicular analytics at scale. Our pilot prototype NORA (Network Oriented Road Applications) contains novel algorithms to detect roadside phenomena (such as potholes, rough road, and slippery surfaces). These leverage multiple in-vehicle sensors to disambiguate these phenomena from other conflating factors. NORA reliably detects, on a cloud service, roadside events from multiple individual in-vehicle detections. To do this, it uses careful clustering techniques to assess the spatial scale of the event, and belief decay techniques to match event duration. It also employs aggressive fleetwide suppression of detections to minimize communication cost. Through a 50-vehicle deployment of NORA pilot over 9 months, it is shown that the results obtained from NORA in-vehicle detection methods match very well with ground truth measurements, and NORA cloud is effective at aggregating road events in an accurate and efficient fashion.

Federated learning (FL) has been facilitating privacy-preserving deep learning in many walks of life such as medical image classification, network intrusion detection, and so forth. Whereas it necessitates a central parameter server for model aggregation, which brings about delayed model communication and vulnerability to adversarial attacks. A fully decentralized architecture like Swarm Learning allows peer-to-peer communication among distributed nodes, without the central server. One of the most challenging issues in decentralized deep learning is that data owned by each node are usually non-independent and identically distributed (non-IID), causing time-consuming convergence of model training. To this end, we propose a decentralized learning model called Homogeneous Learning (HL) for tackling non-IID data with a self-attention mechanism. In HL, training performs on each round’s selected node, and the trained model of a node is sent to the next selected node at the end of each round. Notably, for the selection, the self-attention mechanism leverages reinforcement learning to observe a node’s inner state and its surrounding environment’s state, and find out which node should be selected to optimize the training. We evaluate our method with various scenarios for two different image classification tasks. The result suggests that HL can achieve a better performance compared with standalone learning and greatly reduce both the total training rounds by 50.8% and the communication cost by 74.6% for decentralized learning with non-IID data.

For small and medium-sized enterprises and large enterprises alike, crowdsourcing innovation has become an important element of a product’s whole life cycle. It is the open call process of soliciting consumers to harvest and evaluate ideas or other intellectual assets. The previous proposed taxonomic framework for charactering this process is mainly for general crowdsourcing process and summarized by empirical study. The purpose of this paper is to propose a conceptual model for crowdsourcing innovation from a cybernetic and knowledge management perspective by normative research. The authors performed a normative study and deduced five systemic characteristics from the general laws of control system that guarantee ongoing efficiency for the innovation process. The normative research results provide two key contributions. Firstly, general control laws deduce five indispensable characteristics, and they reveal the intrinsic mechanism of crowdsourcing innovation: the knowledge flow controls, which are also the connotation of open innovation. Secondly, the authors have analyzed a five-characteristics system model and tested the model in several classical cases to show the design tricks of cases. This study provides a new conceptual framework that integrates the theory of open innovation and cybernetics to provide a new view of crowdsourcing innovation process design. In practice, this framework guides managers through the design criteria needed to implement a success crowdsourcing process.

Distributed intelligence is a well-known approach for optimizing interactions among numerous smart devices that interconnect and operate together as Internet of Things (IoT) systems. A modern form of human-machine collective intelligence emerges when humans interact with IoT systems in sociotechnical environments such as smart homes. Fifth-generation (5G) communication networks are designed for high-speed reliable wireless connectivity and expected to boost IoT and (distributed) collective intelligence by revolutionizing human–device–human interactions. In this paper, we contribute a comprehensive review of state-of-the-art sociotechnical environments that exhibit collective intelligence, supported by 5G-enabled IoT. We discuss the latest developments in 5G and their implications for collective intelligence. Further, we explain the key challenges for using 5G to support collective intelligence, e.g., data processing, security, and radio resource management. Finally, we describe four practical applications of collective intelligence to sociotechnical environments—road traffic control, unmanned aerial vehicles, electrical load demand response, and augmented democracy.

A vehicular fog network is an emerging paradigm adopted to facilitate delay-sensitive and innovative applications. Since vehicular environments are inherently dynamic, it becomes a challenge to effectively utilize all available resources. Often a centralized resource distribution model is adopted for effective resource utilization but this comes with significant overhead. Thus, a distributed model seems more suited for highly dynamic vehicular environments; however, a distributed model without collective intelligence can end up in uneven workload distribution. In this paper, we propose a distributed non-cooperative task offloading framework for efficient resource utilization. Here, vehicles with heterogeneous task requirements interact with one another without directly influencing the actions of the neighboring vehicles. That is, the framework allows vehicles to communicate with neighbors to gather contextual information to revisit their offloading decisions. The shared information includes resource type, task residence time, system cost, and offloading inference. The effectiveness of the framework is evaluated against baseline schemes in terms of service delay, transmission delay, system cost, delivery rate, and system efficiency. The results demonstrate significantly reduced task residence times by 50%, highest throughput at 83 Mbits/s, which in turn contributes to an improved system utilization up to 70% across the resource-sharing network.

Mobile crowdsensing (MCS) is a well-established paradigm that leverages mobile devices’ ubiquitous nature and processing capabilities for large-scale data collection to monitor phenomena of common interest. Crowd-powered data collection is significantly faster and more cost-effective than traditional methods. However, it poses challenges in assessing the accuracy and extracting information from large volumes of user-generated data. SmartRoadSense (SRS) is an MCS technology that utilises sensors embedded in mobile phones to monitor the quality of road surfaces by computing a crowdsensed road roughness index (referred to as $P_{PE}$ ). The present work performs statistical modelling of $P_{PE}$ to analyse its distribution across the road network and elucidate how it can be efficiently analysed and interpreted. Joint statistical analysis of open datasets is then carried out to investigate the effect of both internal and external road features on $P_{PE}$ . Several road properties affecting $P_{PE}$ as predicted are identified, providing evidence that SRS can be effectively applied to assess road quality conditions. Finally, the effect of road category and the speed limit on the mean and standard deviation of $P_{PE}$ is evaluated, incorporating previous results on the relationship between vehicle speed and $P_{PE}$ . These results enable more effective and confident use of the SRS platform and its data to help inform road construction and renovation decisions, especially where a lack of resources limits the use of conventional approaches. The work also exemplifies how crowdsensing technologies can benefit from open data integration and highlights the importance of making coherent, comprehensive, and well-structured open datasets available to the public.

Following a new innovation strategy, software vendors move their software onto their software service platforms and open up their platforms to third-party software service vendors. Although many studies state that enlarging the scope of software service offerings is the goal of the platform providers, only a few studies have focused on the roles that the platform providers take on to achieve the goal. These studies identified that the platform providers not only manage the platform but also promote and regulate third-party software. In this article, we extend this research by analyzing the activities of how platform providers promote innovation. For the analysis, we use empirical data about software services gathered from AppExchange of Salesforce. The analysis identifies the clusters and positions of software services of Salesforce and third-party vendors in the software service network. Our analysis results show that Salesforce promotes innovation by provisioning software services that are in core positions or that are bridges between service clusters. Third-party vendors release software services that are complementary to those of Salesforce. Overall, the results suggest that platform providers need to position strategically their software services to build successful software ecosystems, and that research on innovation needs to analyze the roles and efforts of the platform providers in detail. [ABSTRACT FROM AUTHOR]

The advantage of human-machine collective intelligence for decision support systems is the ability to make better decisions due to the mitigation of human biases in the generation of potential solutions and their evaluation. So far, the potential of human-machine collective intelligence was used only in few decision support systems, however, teamwork between humans and machines has not been achieved. This is partly due to the lack of interoperability in these systems. In earlier works, the authors proposed the apparatus of multi-aspect ontologies implying the integration of multiple domain ontologies to provide interoperability between humans and machines and coordinate interrelated processes going on in the systems of the considered type. Such ontologies have proved efficient for systems that require intensive information and knowledge exchange between loosely-related dynamic autonomous domains (e.g., enterprise knowledge management, product lifecycle management, or human-machine collective intelligence systems). However, existing ontology development methodologies fail to recommend a process that would support cross-domain knowledge integration during the multi-aspect ontology development. Moreover, the structure of the multi-aspect ontology imposes some restrictions on the integration approach. The paper proposes such a methodology for the multi-aspect ontology development that incorporates the aspect integration approach at multiple levels. The methodology is applied to develop a multi-aspect ontology for decision support based on human-machine collective intelligence. An example from the “e-tourism” domain demonstrates the applicability of the proposed methodology as well as the usage of the multi-aspect ontology for a human-machine environment aimed at solving real-world problems. The proposed methodology can facilitate the development of ontologies for complex knowledge-based systems that operate with knowledge from multiple loosely-connected domains.

Decision making is one of the most frequently performed processes nowadays. Our future or other people’s future may depend on the decision we make, therefore we try to take it as good as possible. The rapid development of technology facilitates it for us, as we are able to store, process and analyse more and more data. However, the power of human knowledge extended by experience and intuition still overperforms computer systems in solving such problems. The power of the combined knowledge of a group of people is known as a collective intelligence. In this paper, an analysis of one of the properties describing the consensus is presented, id est the influence between its members. The work is based on graph theory and a multi-agent system approach to formalise the problem. The new measures describing the collective and the accuracy of the final prediction were proposed. Experimental evaluations, based on the developed environment, followed by the statistical analysis of the obtained results allowed to observed four relationships between the considered measures describing the collective and the accuracy of collective prediction. The independence of the collective members plays a crucial role in the decision-making, therefore it should be carefully considered when selecting people for the collective, especially the large ones.

Collective intelligence is manifested when multiple agents coherently work in observation, interaction, decision-making and action. In this paper, we define and quantify the intelligence level of heterogeneous agents group organized in a flat structure without explicit leadership by the improved Anytime Universal Intelligence Test (AUIT), an extension of the existing evaluation of homogeneous agents group. The relationship of intelligence level with agents' composition, group size, spatial complexity and testing time is analyzed. The intelligence level of heterogeneous agents groups is compared with that for the homogeneous groups as well, so as to demonstrate the effects of heterogeneity on collective intelligence. Our work will contribute to understand the essence of collective intelligence more deeply and reveal the effect of various key factors on group intelligence level.

Nowadays, to solve a problem, people/systems typically use knowledge from different sources. A binary vector is a useful structure to represent knowledge states, and determining the consensus for a binary vector collective is helpful in many areas. However, determining a consensus that satisfies postulate 2-Optimality is an NP-hard problem; therefore, many heuristic algorithms have been proposed. The basic heuristic algorithm is the fastest in the literature, and most widely used to solve this problem. The computational complexity of the basic heuristic algorithm is O(m2n). In this study, we propose a quick algorithm (called QADC) to determine the 2-Optimality consensus. The QADC algorithm is developed based on a new approach for calculating the distances from a candidate consensus to the collective members. The computational complexity of the QADC algorithm has been reduced to O(mn), and the consensus quality of QADC algorithm and the basic heuristic algorithm is the same.

Collective intelligence projects based on citizen participation are gaining momentum in today's society. Citizen science applies crowdsourcing techniques to produce reliable data, quickly and easily. These projects allow getting new knowledge and help professional scientists to come to real conclusions. This paper proposes that the use of reputation systems improves the results obtained in citizen science projects. To prove this hypothesis, a reputation system is applied to a real experiment and the results are analyzed. The goal of the experiment is to calculate the real-time solar activity, known as the Wolf number, using the infrastructure and user community of the GLORIA project (a set of professional robotic telescopes running since 2013). The sample size of the study are 196 end-users and 2,108 executions of the experiment. The key findings presented in the paper are: 1) the online experiment with volunteers correctly reproduces the traditional method of the year 1848 performed by astronomers or advanced amateurs, 2) the model is contrasted and validated with the values published by the official organization, and 3) the reputation system reduces the error in calculations by more than half, discarding the contributions of the users with lowest karma.

ACOR is a well-established ant colony optimization algorithm for continuous-domain optimization. We present an approach for the dynamic adaptation of the ACOR algorithm's controlling parameters, focusing on the search width parameter, based on using several pre-specified parameter configurations, which we call personalities. Before an ant starts to generate a candidate solution, it stochastically adopts a personality based on the relative past success of different personalities. The success of a personality is measured, in turn, by the survival rate of the previous solutions generated by the ants adopting that personality. The premise of our approach is that some personalities will be more appropriate than others for different phases of the search. In addition, our adaptive approach can accommodate solution recombination, the use of which within ACOR was recently explored in the previous work. It allows the frequency of applying recombination and the type of recombination operator to be dynamically adapted, by having one or more recombination personalities among the competing personalities. We evaluate these proposals experimentally on two applications: 1) training feedforward neural networks for classification using 65 benchmark datasets from the University of California Irvine repository and 2) optimizing several popular synthetic benchmark continuous-domain functions. Our experimental results indicate that our proposals perform better than the standard ACOR on both applications, to a statistically significant extent.

Collective intelligence (CI) refers to the intelligence that emerges at the macro-level of a collection and transcends that of the individuals. CI is a continuously popular research topic that is studied by researchers in different areas, such as sociology, economics, biology, and artificial intelligence. In this survey, we summarize the works of CI in various fields. First, according to the existence of interactions between individuals and the feedback mechanism in the aggregation process, we establish CI taxonomy that includes three paradigms: isolation, collaboration and feedback. We then conduct statistical literature analysis to explain the differences among three paradigms and their development in recent years. Second, we elaborate the types of CI under each paradigm and discuss the generation mechanism or theoretical basis of the different types of CI. Third, we describe certain CI-related applications in 2019, which can be appropriately categorized by our proposed taxonomy. Finally, we summarize the future research directions of CI under each paradigm. We hope that this survey helps researchers understand the current conditions of CI and clears the directions of future research.

Significant attention has been paid to the bipartite consensus (BC) problem of multiagent systems (MASs) with antagonistic interactions. In many practical applications, the convergence speed is very important. In this article, we focus on the finite-time BC (FTBC) problems of MASs with detail-balanced antagonistic interactions. First, two FTBC protocols are designed and expressed in a unified form. Then, by taking advantage of some recent findings on network stability, we obtain theoretical results to guarantee that the states of all agents reach agreement in finite time under our proposed protocols. Finally, the effectiveness and validity of the main theoretical results are demonstrated via two numerical examples. [ABSTRACT FROM AUTHOR]

This paper is concerned with the topological structure, reachability, and stabilization for Boolean control networks (BCNs) with state constraints via event-triggered control (ETC) scheme. In the first part, the topological structure of BCNs with state constraints is studied. Under the framework of state constrain, the definitions of fixed point and cycle are first defined. A novel phenomenon is that there may exist two kinds constrained fixed points, which are, respectively, named as the livelock and deadlock ones. It is different with the traditional fixed point. Accordingly, a formula is presented to calculate the number of constrained fixed points and constrained cycles. In the second part, the constrained reachability and stabilization problem of the event-triggered controlled BCNs are investigated. Two necessary and sufficient criteria are, respectively, obtained. Furthermore, an algorithm is developed to design all feasible controllers. Finally, a reduced model of the lac operon in the Escherichia coli is shown to illustrate the efficiency of the obtained results. [ABSTRACT FROM AUTHOR]

This paper investigates the resource allocation problem for a group of agents communicating over a strongly connected directed graph, where the total objective function of the problem is composted of the sum of the local objective functions incurred by the agents. With local convex sets, we first design a continuous-time projection algorithm over a strongly connected and weight-balanced directed graph. Our convergence analysis indicates that when the local objective functions are strongly convex, the output state of the projection algorithm could asymptotically converge to the optimal solution of the resource allocation problem. In particular, when the projection operation is not involved, we show the exponential convergence at the equilibrium point of the algorithm. Second, we propose an adaptive continuous-time gradient algorithm over a strongly connected and weight-unbalanced directed graph for the reduced case without local convex sets. In this case, we prove that the adaptive algorithm converges exponentially to the optimal solution of the considered problem, where the local objective functions and their gradients satisfy strong convexity and Lipachitz conditions, respectively. Numerical simulations illustrate the performance of our algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates a distributed static and dynamic self-triggered impulsive control for nonlinear multiagent systems (MASs) where the impulsive gains follow a normal distribution, respectively. By integrating the distributed self-triggered control scheme with the impulsive control approach, a novel distributed impulsive controller is developed. The goal of the consensus of MASs can be realized using the proposed methods and several consensus criteria are obtained. Our schemes have some distinct superiorities, including the impulsive gains obeying a normal distribution, avoiding the continuous communication, and reducing the sampling frequency. Hence, compared with the existing literature, the conservativeness coming from the limitation of impulse gain and the sampling frequency is degraded, and it effectively extends the generality of the method in the practical application. Finally, the effectiveness of the theoretical results is demonstrated by two simulations. [ABSTRACT FROM AUTHOR]

Peer-to-peer ridesharing enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers. It is a prominent collective intelligence application producing significant benefits both for individuals (reduced costs) and for the entire community (reduced pollution and traffic). Despite these very promising potential advantages, the percentage of users who currently adopt ridesharing solutions is very low, well below the adoption rate required to achieve said benefits. One of the reasons of this insufficient engagement by the public is the lack of effective incentive policies by regulatory authorities, who are not able to estimate the costs and the benefits of a given ridesharing adoption policy. Here we address these issues by (i) developing a novel algorithm that makes large-scale, real-time peer-to-peer ridesharing technologically feasible; and (ii) exhaustively quantifying the impact of different ridesharing scenarios in terms of environmental benefits (i.e., reduction of CO2 emissions, noise pollution, and traffic congestion) and quality of service for the users. Our analysis on a real-world dataset shows that major societal benefits are expected from deploying peer-to-peer ridesharing depending on the trade-off between environmental benefits and quality of service. Results on a real-world dataset show that our approach can produce reductions up to a 70.78% in CO2 emissions and up to 80.08% in traffic congestion. [ABSTRACT FROM AUTHOR]

In this paper, a continuous-time multiagent system is proposed for solving optimal resource allocation problems with local allocation feasible constraints. In the system, all the primal agents are divided into different groups. We use dual variables which describe the dual agents to represent the groups of the original agents. The groups of dual agents are used to communicate with others on behalf of the primal agents to reduce communication costs. That is to say, primal agents aim to seek their own optimal solutions by using local information. And dual agents represent primal agents to communicate with other agents in different groups by using the whole group information. The two kinds of agents cooperate to find the optimal solution of the problem. In this way, we only need to know the connections of dual agents to design the multiagent network, and do not need to consider the connections of the primal agents. So the communication cost and the amount of variables will be largely reduced especially for large-scale problem. Furthermore, it is proved that the multiagent system can reach consensus with respect to the dual variables. At the same time, the primal variables are convergent to the optimal solutions of the optimization problem under some certain assumptions on the communication network. For large-scale problem if we take the groups as areas, then the system is suitable for multiarea problem. Simulation results are presented to demonstrate the performance of the proposed multiagent system. [ABSTRACT FROM AUTHOR]

A multitude of autonomous robotic platforms collectively organized as a swarm attracts increasing attention for remote sensing and exploration tasks. A navigation system is essential for the swarm to collectively localize itself as well as external sources. In this article, we propose a self-aware swarm navigation system that is conscious of the causality between its position and the localization uncertainty. This knowledge allows the swarm to move in a way to not only account for external mission objectives but also enhance position information. Position information for classical navigation systems has already been studied with the Fisher information (FI) and Bayesian information (BI) theories. We show how to extend these theories to a self-aware swarm navigation system, particularly emphasizing the collective performance. In this respect, fundamental limits and geometric interpretations of localization with generic observation models are discussed. We further propose a general concept of FI and BI based information seeking swarm control. The weighted position Cramér–Rao bound (CRB) and posterior CRB (PCRB) are employed flexibly as either a control cost function or constraints according to different mission criteria. As a result, the swarm actively adapts its position to enrich position information with different emerging collective behaviors. The proposed concept is illustrated by a case study of a swarm mission for gas exploration on Mars. [ABSTRACT FROM AUTHOR]

In this paper, some efficient criteria for finite-time consensus of a class of nonsmooth opinion dynamics over a digraph are established. The lower and upper bounds on the finite settling time are obtained based respectively on the maximal and minimal cut capacity of the digraph. By using tools of the nonsmooth theory and algebraic graph theory, the Carathéodory and Filippov solutions of nonsmooth opinion dynamics are analyzed and compared in detail. In the sense of Filippov solutions, the dynamic consensus is demonstrated without a leader and the finite-time bipartite consensus is also investigated in a signed digraph correspondingly. To achieve a predetermined consensus, a leader agent is introduced to the considered agent networks. As an application, the nonsmooth compartmental dynamics in the presence of a leader is embedded in the proposed continuous-time protocol to solve the distributed optimization problems over an unbalanced digraph. The convergence to the optimal solution by using the proposed distributed algorithm is guaranteed with appropriately selected parameters. To verify the effectiveness of the proposed protocols, three numerical examples are performed. [ABSTRACT FROM AUTHOR]

This paper studies pinning controllers for activation output tracking (AOT) of Boolean network under one-bit perturbation, based on the semitensor product of matrices. First, the definition of AOT with respect to an activation number is presented, where the activation number means the number of active outputs whose logical variables are 1 s. Then, several criteria are established for AOT issue. Further, the impact of one-bit perturbation on AOT is studied, where one-bit perturbation means that only one logical function has one-bit change of its truth table by flipping the value from 1 to 0 or 0 to 1. In addition, if a one-bit perturbation is a valid perturbation on AOT, an output feedback pinning control is designed to recover AOT. The obtained results are effectively illustrated by a D. melanogaster segmentation polarity gene network and a reduced signal transduction network. [ABSTRACT FROM AUTHOR]

This paper presents a collaborative route discovery method that leverages the experience and preferences of taxi drivers in urban areas. The proposed method is mainly comprised of two phases: collaborative preference discovery (CPD) and intelligent driver network generation (IDNG). In the first phase, given an origin-destination (O-D) pair and provided that the cluster is a road segment set within a time-reachable range, we propose CPD which involves cluster-to-cluster retrieval to capture the top-k routes that are not only frequently traversed by taxis but also neighboring to the O-D pair. In the second phase, to support route computation, an IDNG algorithm is devised to generate an experiential graph for each specific O-D pair. In empirical studies, using the period-based experiential route database, sensitivity analysis is employed to select optimal parameters of intelligent driver networks. The results demonstrate that the routes recommended by our collaborative method are much more reliable than those of the shortest-path method with respect to the variance of travel time. Moreover, the recommended routes are traversed more frequently than those of the fastest-path and the shortest-path methods, while the travel time and route lengths of our routes are approximately equal to those of the conventional methods. [ABSTRACT FROM AUTHOR]

Many techniques have been developed to enhance learning experience with computer technology. A particularly great influence of technology on learning came with the emergence of the web and adaptive educational hypermedia systems. While the web enables users to interact and collaborate with each other to create, organize, and share knowledge via user-generated content, majority of e-learning systems do not utilize the power of their users to create high quality educational content and provide data for adaptive algorithms. In this paper, we introduce a novel social learning framework that allows anybody to author educational content in a form of mini-lessons, learn lessons by following adaptive learning pathways as well as interact with their peers as in any social network. The proposed approach combines concepts of crowdsourcing, online social networks, and complex adaptive systems to engage users in efficient learning through teaching process. We first describe the main idea behind the framework and how users interact with it, and then we describe SALT system that implements the framework. We also performed evaluation of the SALT system via several classroom studies. Our results show that collective learning experiences can be efficiently utilized in adaptive social learning. We found that students tend to form stable clusters that survive very high similarity threshold. Meanwhile, our learning pathway analysis showed that almost all students have their own unique best pathway. Experiments with various recommendation algorithms showed that most algorithms obtain very small penalty in all classes. [ABSTRACT FROM PUBLISHER]

In the 21st century, where automated systems and artificial intelligence are replacing arduous manual labor by supporting data-intensive tasks, many problems still require human intelligence. Over the last decade, by tapping into human intelligence through microtasks, crowdsourcing has found remarkable applications in a wide range of domains. In this article, the authors discuss the growth of crowdsourcing systems since the term was coined by columnist Jeff Howe in 2006. They shed light on the evolution of crowdsourced microtasks in recent times. Next, they discuss a main challenge that hinders the quality of crowdsourced results: the prevalence of malicious behavior. They reflect on crowdsourcing's advantages and disadvantages. Finally, they leave the reader with interesting avenues for future research. [ABSTRACT FROM PUBLISHER]