Mobile Agents Research Articles

Neural Robust Control for a Mobile Agent Leader–Follower System

A controller employing a combined new strategy of output feedback linearization and a recurrent high-order neural network (RHONN) adaptive approach for a mobile agent leader–follower system is presented. The controller structure is based on feedback linearization; then, a scheme of lumping uncertainties which are estimated via the RHONN is incorporated; with this estimate, the controller is able to produce a robust control action for mobile agents so they track a prescribed reference trajectory. Moreover, the nonlinear system part is transformed into a linearizable one; then, a specific function lumps all the nonlinearities, uncertain parameters, and unmodeled dynamics of the system; this overall function is estimated via the RHONN. Thus, both parametric uncertainties and unmodeled dynamics between agents can be compensated via the controller, and, subsequently, follower agents track the reference provided by the leader. The obtained controller is such that the estimation scheme is not based on high-gain controllers. Here, it is underlined that the main contribution consists of designing a nonlinear controller and combining it with an RHONN to estimate the nonlinear uncertainties in the leader–follower system. This control action includes robust features provided by the online recurrence and the nonlinear base of the neural network in which not general but specific parametric disturbances and unmodeled discrepancies are identified or compensated. For this control scheme, only nominal values of the system parameters are required, as well as the velocities of the agents. Numeric simulation of the model and designed tracking control are carried out in which the control law is applied to a two-wheeled differential mobile robot model, obtaining satisfactory results for tracking angular velocities of the wheels.

Noblewomen, Court Service, and Crossing Borders: England <em>c</em>. 1500-1550

There are significant difficulties in defining aristocracy when it comes to women in sixteenth-century England, because women were so often agents of social mobility and might in theory move between social classes by dint of multiple marriages. Exactly who “counted” as a member of an aristocratic dynasty could get complex extremely quickly. This issue is further complicated when considering court service, because intimacy with and proximity to the monarch was not usually defined by aristocratic title. Moreover as princesses moved across borders to marry, they brought women with them as ladies-in-waiting who often then married into the aristocracy of their new country, necessitating a “meshing” of systems of nobility that might suggest that actual titles were less significant than sometimes thought.

Distributed Cooperative Quantum Learning for Discrete-Time Multiagent Source Exploration With Information Prompts.

In light of optimization theory and swarm evolutionary schemes, under multiple single-integrator mobile agents equipped with sensors and prompters, this article addresses a discrete-time multiagent source exploration problem with information prompts. Regarding information prompts as constraints on the unknown target, by virtue of penalty function skills (PFSs) and sequential unconstrained minimization techniques (SUMTs), the agents are driven toward the source under the guidance of the control strategy. In two cases of available and unavailable gradient information, a quantum potential well, an average optimal position estimator (AOPE), and a global optimal position estimator (GOPE) are introduced into swarm evolutionary schemes with a periodically oscillating weight, such that distributed cooperative quantum learning (DCQL) policy is proposed as a control strategy under communication restrictions, where AOPE and GOPE are developed relying on distributed consensus theory. In particular, when the gradient is unavailable, we put forth an adaptive generalized Bernstein neural network (AGBNN) to replace it based on excellent properties of Bernstein polynomials and adaptive approaches. Further, a performance analysis for the proposed policy is executed on the convergence and computational complexity, which ensures the accuracy and efficiency of the source exploration in theory. Ultimately, a simulation test is carried out, and the results validate the practicability and effectiveness of the offered method.

A Novel Framework for Cloud Data Security with Blockchain Technology and Distributed Virtual Machine Agents

Even though cloud computing has advanced over the years, the two major challenges currently faced by cloud computing are data security and trusted computing. In cloud computing applications, blockchain technology can enhance data security and trusted computing when paired with virtual machine agents and mobile agent technologies. A decentralized new framework for distributed computing is blockchain. A potential area of study is the integration of blockchain technology with cloud computing, leveraging the former's security mechanisms to enhance the latter's secure computing and storage capabilities. The usage of the proposed framework builds on the inherent security features of blockchain technology in an effort to secure data and information integrity, confidentiality and authentication in cloud environments. Through the introduction of distributed virtual machine agents, the framework increases the scalability and effectiveness of data protection mechanisms for virtual data environments that can be used to store information protected by cloud security measures. Thus, the proposed study substantiates the applicability and efficiency of the existing and emerging problems of cloud data security and this approach develops satisfactory data protection solutions.

APEX: affordance-based plan executor for indoor robotic navigation

ABSTRACT The concept of affordance is commonly described as the set of possibilities an environment offers an animal to select and execute actions. In the context of autonomous robot navigation, determining which affordances are available to a mobile agent provides crucial information to perform navigation tasks such as executing navigation plans or exploring new environments. In this work, we provide a robot with the capability of detecting changes in the set of navigation affordances that it has available as it moves through indoor spaces. Specifically, given a navigation plan represented as a sequence of high-level behaviors such as ‘turn left’ or ‘follow a corridor’, we propose the Affordance-Based Plan Executor (APEX) as a new learning-based method to execute this plan based on the identification of navigation affordances. As a relevant fact, the proposed method does not require a map of the environment at execution time. To the best of our knowledge, our work is one of the first to explicitly consider the identification of navigation affordances as a central element to implement the execution of a behavior-based navigation plan in an indoor environment. Our experiments using the Gibson simulator and environments from the Stanford 2D-3D-S dataset suggest that our approach performs well in previously seen environments in comparison to baselines, exceeding alternative approaches' success rate by up to 43%. Additionally, our approach shows promise at generalizing to previously unseen environments, exceeding the success rate of alternative approaches by up to 26.3%. Finally, our experiments confirm that explicit reasoning about affordances is key to APEX's performance.

A systematic strategy for the architecture design of collaborative and reconfigurable assembly lines

Current manufacturing environments have to face a competitive and evolving framework characterised by high-mix and low-volume demands. To properly deal with new requests coming from the market, Reconfigurable Assembly Systems (RASs) have to be designed. In this paper, we propose a methodology for the design of the architecture of a RAS, featuring both human and mobile robotic agents, and dealing with multi-product assembly of medium-light-weight products, originally assembled manually. In particular, a systematic methodology is proposed to define the number of workstations, specifying which set of operations must be performed in each one, and to design the system layout guaranteeing reconfigurability. To this end, the concepts of operation, datasheet of an operation, assembly tree, and macro-operation are introduced and formalised. Guidelines to automatise the required operations in a flexible way are also proposed. The method is applied to a use case where we define the proper number of workstations of a RAS to assemble several types of motorbike brakes, showing the reconfigurability of the system.

Assessing the efficacy of a novel adaptive fuzzy c-means (AFCM) based clustering algorithm for mobile agent itinerary planning in wireless sensor networks using validity indices

Assessing the efficacy of a novel adaptive fuzzy c-means (AFCM) based clustering algorithm for mobile agent itinerary planning in wireless sensor networks using validity indices

Meter‐Scale Distance Manipulation of Diverse Objects with Jet‐Induced Airflow Field

In nature, wind can transport objects of diverse shapes and materials over long distances spanning meters or beyond. In contrast, most noncontact manipulation methods are effective only over centimeter‐scale distances and require the manipulated objects to have specific magnetic, electrical, or other material properties and shapes. Herein, a meter‐scale distance manipulation method is presented for controlling the motion of objects of diverse shapes and materials on a plane surface using a jet‐induced airflow field. The airflow field is varied by controlling the direction of a single air jet projected onto a surface based on the positions of objects to steer the objects to follow desired trajectories up to 2.7 m away. A wide variety of objects can be automatically manipulated, from regularly shaped polystyrene hemispheres and sticks to irregularly shaped cotton wads and face masks as well as deformable tissue papers and plastic bags. The method is also robust, effectively manipulating objects under challenging conditions, e.g., external airflow disturbances, surface obstacles, and air–water interface. Finally, three application cases are demonstrated: collecting diverse objects into a target receptacle, hooking and retrieving heavy objects with a tethered mobile agent, and closing an electrical circuit with an untethered soft agent.

Is there a dark side to financial inclusion? Understanding the relationship between financial inclusion and market risk

This study delves into the connection between market risk and financial inclusion. Market risk is gauged through primary risk metrics commonly used in industry and academia, such as Standard Deviation (SD), Value at Risk (VaR), Expected Shortfall (ES), and Expected Loss (EL). In exploring financial inclusion, we consider twelve specific indicators that span three crucial dimensions: penetration, availability, and usage. Our analysis covers data from 49 countries spanning the period from 2004 to 2021, encompassing G20 nations and markets hosting major global stock exchanges. Our findings reveal a positive correlation between various indicators of financial inclusion and certain risk characteristics. Notably, distinctions emerge between underdeveloped and developed countries. In the case of developed nations, for instance, the quantity of active mobile money accounts and mobile money agent outlets exhibits a robust correlation with market risk. Through quantile correlation analysis, we discern that the relationship between risk and financial inclusion undergoes shifts based on the level of inclusion. Therefore, despite the documented advantages of financial inclusion, our study sheds light on its potential adverse implications.

Quantum strategies for rendezvous and domination tasks on graphs with mobile agents

Quantum strategies for rendezvous and domination tasks on graphs with mobile agents

DESIGN WIRELESS SENSOR NETWORKS USING MOBILE AGENTS

The issues of building wireless sensor networks with the use of stochastic mobile agents for information exchange between nodes are considered. The basic requirements, features and limitations of algorithms for processing and transmitting measured data in a sensor network are analyzed. It is shown that the actual task of collecting data from sensor networks with dense coverage of the territory is to optimize traffic by decoupling, i.e., eliminating information redundancy. A comparative assessment of energy consumption and average delay of data delivery from the source to the collection point for a conventional client-server structure and a sensor network with mobile agents is given.

Maximum likelihood localization of a network of moving agents from ranges, bearings and velocity measurements

Localization is a fundamental enabler technology for many applications, like vehicular networks, IoT, and even medicine. While Global Navigation Satellite Systems solutions offer great performance, they are unavailable in scenarios like indoor or underwater environments, and, for large networks, the instrumentation cost is prohibitive. We develop a localization algorithm from ranges and bearings, suitable for generic mobile networks. Our algorithm is built on a tight convex relaxation of the Maximum Likelihood position estimator. To serve positioning to mobile agents, a horizon-based version is developed accounting for velocity measurements at each agent. To solve the convex problem, a distributed gradient-based method is provided. This constitutes an advantage over centralized approaches, which usually exhibit high latency for large networks and present a single point of failure. Additionally, the algorithm estimates all required parameters and effectively becomes parameter-free. Our solution to the dynamic network localization problem is theoretically well-founded and still easy to understand. We obtain a parameter-free, outlier-robust and trajectory-agnostic algorithm, with nearly constant positioning error regardless of the trajectories of agents and anchors, achieving better or comparable performance to state-of-the-art methods, as our simulations show. Furthermore, the method is distributed, convex and does not require any particular anchor configuration.

Bearing-Based Formation Tracking Control of Nonholonomic Mobile Agents With a Persistently Exciting Leader

This paper investigates the leader-following formation control problem of nonholonomic agents with a persistently exciting leader based on bearing measurements. A follower needs to maintain a desired relative position to the leader in its local coordinate frame using bearing measurements. An integrated method consisting of a bearing-based estimator and a tracking controller is proposed to achieve the leader-following control objective. The estimator is designed to estimate the relative position to the leader in the follower's local coordinate frame. Based on the estimated relative position, the controller is proposed to drive the follower to achieve the desired formation. Different from existing integrated methods, our method does not require any conditions on the relative bearing between the leader and the follower in the global frame, which are difficult to guarantee. We characterize the conditions locally on the leader only, namely, conditions on the leader's linear and angular velocities with persistently exciting steering speed, such that the overall system is asymptotically stable with our integrated method. Besides, the proposed method is extended to the multi-agent formation control problem. A reasonable solution is proposed to the multi-agent case with a minimal leader follower structure. Simulations prove the effectiveness of the proposed methods in both cases.

Empowering Village Communities Through Mutual Cooperation Mobilization Agents in Kalimontang Village in Overcoming Stunting

Stunting is a world problem because of its implications for determining the future of the nation. Indonesia, as a developing country, still has a high stunting prevalence rate. Based on data from the 2022 National Nutrition Status Survey (SSGI), the prevalence of stunting in Indonesia is 21.6%. This figure is still more than the target figure set by the World Health Organization (WHO), the stunting rate should be no more than 20%. There were obstacles in implementing the stunting reduction target, one of which was a lack of important intake such as animal protein and vegetable protein. This research was conducted with the aim of analyzing stunting prevention by empowering village communities through mutual cooperation agents in Kalimantong Village, West Sumbawa Regency. The research method uses a descriptive qualitative approach. The research results show that Kalimantong Village has a relatively low prevalence rate compared to other villages in Brang Ene District, West Sumbawa Regency. Therefore, to support the target of reducing stunting, human empowerment is needed through mutual cooperation agents to prevent stunting and increase human resources in Indonesia.

Gridless Underdetermined DOA Estimation for Mobile Agents with Limited Snapshots Based on Deep Convolutional Generative Adversarial Network

Deep learning techniques have made certain breakthroughs in direction-of-arrival (DOA) estimation in recent years. However, most of the current deep-learning-based DOA estimation methods view the direction finding problem as a grid-based multi-label classification task and require multiple samplings with a uniform linear array (ULA), which leads to grid mismatch issues and difficulty in ensuring accurate DOA estimation with insufficient sampling and in underdetermined scenarios. In order to solve these challenges, we propose a new DOA estimation method based on a deep convolutional generative adversarial network (DCGAN) with a coprime array. By employing virtual interpolation, the difference co-array derived from the coprime array is extended to a virtual ULA with more degrees of freedom (DOFs). Then, combining with the Hermitian and Toeplitz prior knowledge, the covariance matrix is retrieved by the DCGAN. A backtracking method is employed to ensure that the reconstructed covariance matrix has a low-rank characteristic. We performed DOA estimation using the MUSIC algorithm. Simulation results demonstrate that the proposed method can not only distinguish more sources than the number of physical sensors but can also quickly and accurately solve DOA, especially with limited snapshots, which is suitable for fast estimation in mobile agent localization.

A Graph-Based Algorithm for Robust Sequential Localization Exploiting Multipath for Obstructed-LOS-Bias Mitigation

This paper presents a factor graph formulation and particle-based sum-product algorithm (SPA) for robust sequential localization in multipath-prone environments. The proposed algorithm jointly performs data association, sequential estimation of a mobile agent position, and adapts all relevant model parameters. We derive a novel non-uniform false alarm (FA) model that captures the delay and amplitude statistics of the multipath radio channel. This model enables the algorithm to indirectly exploit position-related information contained in the multipath components (MPCs) for the estimation of the agent position without using any prior information such as floorplan information or training data. Using simulated and real measurements in different channel conditions, we demonstrate that the algorithm can provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations and show that the performance of our algorithm constantly attains the posterior Cramér-Rao lower bound (P-CRLB), facilitating the additional information contained in the presented FA model. The algorithm is shown to provide robust estimates in both, dense multipath channels as well as channels showing specular, resolved MPCs, significantly outperforming state-of-the-art radio-based localization methods.

Trust aware angle based secure routing approach for wireless sensor network

Security in wireless sensor network (WSN) is an important approach in the present context as data breaching is becoming more. The data to be routed from source to destination needs more security as WSN has no specific security approach by default. This paper proposes trust based security in WSN using approach. The secure line is drawn from head node to its cluster end point called as angle to provide the security to the nodes which are transferring the data to the head node. Secure line becomes the trust worth line where mobile agent migrates to all the corresponding nodes which are along or near to the secure lines, collects the data and encrypt them. Finally, the data is sent to sink node from head node using a secure path. The agent paradigm is responsible for creating the angle from head node to cluster boundary. Multiple angles can be created if numbers of nodes are more and deployed at different locations. The result shows that the security provided is much better to combat the intruder involvement to breach data along with better network lifetime and minimum delay than compare to conventional techniques.

Digital financial services adoption: a retrospective time-to-event analysis approach

Expanding digital financial services (DFS) such as mobile money has become a key policy intervention for many developing and emerging countries as they seek to fast-track financial and economic inclusion. To date, adults in these markets have had more mobile money accounts than traditional bank accounts. Numerous studies have used binary approaches to understand the factors explaining DFS adoption. However, there is a dearth of studies, that investigate the time to DFS adoption and factors that predict time to adoption. To close this gap, this study used a time-to-event analysis approach to estimate the time to DFS adoption and investigate the factors that explain the variation in time to adoption. Using a sample of 1800 survey respondents from Zimbabwe, the study found that it took 4.4 and 8.5 years, respectively, for urban and rural residents to adopt DFS. Overall, the findings show that individuals who are significantly more likely to adopt DFS faster are those residing in urban areas, near mobile money agents, banked, with high levels of education and financial literacy, middle-aged, belonging to social groups, and self-employed. In addition, an expansionary macroeconomic environment was associated with greater DFS adoption intensities. The findings also show that gender and income level do not predict the time to DFS adoption in the studied market. This study provides policy and practitioner recommendations for possible actions to accelerate the adoption of DFS.

A security framework for mobile agent systems

A security framework for mobile agent systems

Effect of Cashless Financial Services on Economic Growth in Kigali, Rwanda

The research aimed at assessing the effect of cashless financial services on economic Growth in Kigali, Rwanda. The study was conducted in Kigali Rwanda. The time was limited in the period of 2014-2022.Thestudy was guided by the following specific objectives upon which also the following research questions were derived: To evaluate the effect of internet banking on Economic Growth in Kigali, Rwanda. To establish the impact of mobile cards on Economic Growth in Kigali, Rwanda. To assess the effect of mobile wallets on Economic Growth in Kigali, Rwanda. To evaluate the effect on mobile money on Economic Growth in Kigali, Rwanda. The research employed the use of Focus Group as one of interview methods to collect data from respondents. Rwanda still faces less privacy, greater exposure to hacking, technological dependency, magnifying economic inequality, and more. The current study therefore sought to find out the effect of cashless financial services on economic growth in Kigali, Rwanda amidst of the highlighted challenges associated with the cashless society plan. The researcher collected data from employees of MTN Rwanda, Eco bank Rwanda, BK, BNR and beneficiaries of selected institutions. The research design included: questionnaires; interviews and documentation research techniques. The sample size of the study is210 participants from Bank of Kigali, Eco bank, telecommunication companies (MTN) including mobile money agents by means of purposive sampling. The researcher requested the selected institutions to allow their members to participate in the interview and an authority letter for data collection from the University was presented to selected Institutions. Confidentiality and privacy were observed during the interview process. The researcher allowed the respondents to be free when collecting the data, mentioning names was avoided as it could be considered as coercing the respondents. Data collected was analyzed using SPSS.