Search

With the advancement of artificial intelligence technologies, mobile agents are becoming more commonly used in a variety of industries that require high reliability from their control systems. In an uncertain environment, if the mobile agent control system's state transition includes only one plan, the system will enter the fault state immediately after the plan fails. Therefore, multiple alternative plans can be provided during the system design process to improve system reliability. First, this paper studies and describes the factors associated with the proposed multiple alternative plans, namely the success rate and plan implementation cost. Second, a Policy Generation Algorithm for identifying an appropriate execution sequence of those alternative plans is proposed. Furthermore, we propose a formal method-based pipeline framework for verifying the reliability of a mobile agent control system equipped with multiple alternative plans: we invoke the probabilistic model checking technique to create a Discrete-Time Markov Chain formal model of the mobile agent control system, convert the required properties into Probabilistic Computation Tree Logic formulae, and verify the model using the advanced probabilistic model checker PRISM. A case study is provided to demonstrate the applicability of the suggested methodological framework. The experimental results demonstrate that the proposed mobile agent control system with multiple alternative plans can improve system reliability while also meeting the least expected operational cost under the alternative plan set. [ABSTRACT FROM AUTHOR]

The routing performance of the In Mobile Ad-hoc Networks (MANETs) depends on the individual nodes that represent the network. In this paper, a new protocol based on self-monitoring (agent-based) and following the Ad Hoc on Demand Distance Vector (AODV) Routing Algorithm is presented. This protocol may be called Agent-Based Ad Hoc on Demand Distance Vector Routing Algorithm (AB-AODV) Protocol for MANETs. The main objective of the proposed method is to manage trusted information locally with minimal overhead in terms of extra messages and time delay. This objective is achieved through installing in each participating node in the network a multi-agent system. Multi-agent system consists of two types of agents: monitoring agent and routing agent. A new mathematical and more practical objective model for measuring the trust value is introduced. This model is weighted by both the number and size of routed packets to react to the selective forwarding behavior of a node. The performance evaluation via simulation using NS-3 shows that the proposed protocol is better than standard AODV. The simulation is done under a variety of environmental conditions such as the number of malicious nodes, host density, and movement rates. [ABSTRACT FROM AUTHOR]

In the classic Symmetric Rendezvous problem on a Line (SR-Line), two robots at known distance 2 but unknown direction execute the same randomized algorithm trying to minimize the expected rendezvous time. A long standing conjecture is that the best possible rendezvous time is 4.25 with known upper and lower bounds being very close to that value. We introduce and study a geometric variation of SR-Line that we call Symmetric Rendezvous in a Disk (SR-Disk) where two robots at distance 2 have a common reference point at distance ρ. We show that even when ρ is not too small, the two robots can meet in expected time that is less than 4.25. Part of our contribution is that we demonstrate how to adjust known, even simple and provably non-optimal, algorithms for SR-Line , effectively improving their performance in the presence of a reference point. Special to our algorithms for SR-Disk is that, unlike in SR-Line , for every fixed ρ the worst case distance traveled in our algorithms is finite. In particular, we show that the worst case distance of our algorithms is O ρ 2 , while we also explore average–worst case tradeoffs, concluding that one may be efficient both with respect to average and worst case, with only a minor compromise on the optimal termination time. • Introduction of a variant of a rendezvous problem on mobile agent computing. • First paper to consider worst-case/average-case efficiency trade-offs. • Algorithmic reduction designs for the new rendezvous problem. • Worst-case analysis of algorithmic families with optimal average-case performance. [ABSTRACT FROM AUTHOR]

Edge computing in Internet of Things enhances application execution by retrieving cloud resources to the close proximity of resource-constrained end devices at the edge and by enabling task offloading from these devices to the edge. In this paper, edge computing platforms are extended into the data producing end devices, including wireless sensor network nodes and smartphones, with mobile agents. Mobile agents operate, as a multi-agent system, on the opportunistic network of heterogeneous end devices. The benefits include autonomous, asynchronous and adaptive execution and relocation of application-specific computational tasks, while taking into account the local resource availability. In addition to the vertical edge connectivity, mobile agents enable horizontal sharing of information between these devices. Use cases are presented where mobile agents address challenges in current edge computing platforms. An edge application is evaluated where mobile agents as a multi-agent system process sensor data in a heterogeneous set of end devices, control the operation of the devices and share their tasks results in the system. The mobile agents operate atop a REST-compliant software agent framework that relies on embedded Web services for interoperability. A real-world evaluation and large-scale simulations show that energy consumption is reduced significantly, up to 60%, in the edge application execution. [ABSTRACT FROM AUTHOR]

Abstract We study several problems of clearing subgraphs by mobile agents in digraphs. The agents can move only along directed walks of a digraph and, depending on the variant, their initial positions may be pre-specified. In general, for a given subset S of vertices of a digraph D and a positive integer k , the objective is to determine whether there is a subgraph H = (V H , A H) of D such that (a) S ⊆ V H , (b) H is the union of k directed walks in D , and (c) the underlying graph of H includes a Steiner tree for S in D. Since a directed walk is a not necessarily a simple directed path, the problem is actually on covering with paths. We provide several results on the polynomial time tractability, hardness, and parameterized complexity of the problem. Our main fixed-parameter algorithm is randomized. [ABSTRACT FROM AUTHOR]

Abstract This paper studies the well-known problem of gathering multiple mobile agents moving in a graph, but unlike previous results, we consider the problem in the presence of an adversarial mobile entity which we call the malicious agent. The malicious entity can occupy any empty node and prevent honest mobile agents from entering this node. This new adversarial model is interesting as it models transient mobile faults that can appear anywhere in a network. Moreover, our model lies between the less powerful delay-fault model , where the adversary can block an agent for only a finite time, and the more powerful but static fault model of black holes that can even destroy the agents. We study the problem for ring networks and we provide a complete characterization of the solvability of gathering, depending on the size n of the ring and the number of agents k. We consider both oriented or unoriented rings with either synchronous or asynchronous agents. We prove that in an unoriented ring network with asynchronous agents the problem is not solvable when k is even, while for synchronous agents the problem is unsolvable when both n is odd and k is even. We then present algorithms that solve gathering for all the remaining cases, thus completely solving the problem. Finally, we provide a proof-of-concept implementation of the synchronous algorithms using programmable Lego Mindstorms EV3 robots. [ABSTRACT FROM AUTHOR]

This paper considers the distributed control problem of nonholonomic mobile agents with input delay to track a target. In contrast to the existing works in which some controllers have been designed for ideal conditions, the destructive factor of input delay is considered in the dynamics of the agents in this work. At first, a distributed controller is suggested for each agent to track the target in the absence of input delay. In this case, each nonholonomic mobile agent is divided into two subsystems and two terminal sliding mode controllers are designed for the two subsystems. Based on the proposed controllers, a switching control strategy is obtained to guarantee the finite time tracking control of nonholonomic mobile agents. Then, this controller is extended based on a future state estimator for tracking control in the presence of input delay. The stability analysis of distributed controllers and estimator is also provided. Simulation results illustrate the effectiveness of the suggested algorithms. [ABSTRACT FROM AUTHOR]

Threats in wireless network are common these days and when it comes to security threats their consequences are countless. Some of the most commonly witnessed security threats are route manipulation and jamming. There are several researches that are proposed to even more enhance the efficiency of these attacks against their counterfeit. The recently proposed and the most challenging threat is the cross-layer attack. Though we have solutions for single-layer attacks there are very few or no satisfactory methods to detect and counter-attack the cross-layer attacks. With this as the main goal for this paper we proposed a novel framework that secures the wireless network from cross-layer attacks. This proposal concentrates on both detecting and mitigating the attack. The former one is based on Bayesian learning detecting scheme and the later on is constructed to enhance security and performance of the network. The composed protocol is tested on a framework with cross layer attack that utilizes jamming. [ABSTRACT FROM AUTHOR]

Abstract A pair of agents (robots) are moving in a graph with the goal of meeting at the same node or while traversing the same edge. An asynchronous adversary knows the prescribed walks of the two agents and is in complete control of the speed of each agent during its walk. We provide a complete characterization of pairs of walks that enforce rendezvous against an asynchronous adversary after traversing a given number of edges. The characterization is efficient in that it can be checked in polynomial time. We argue that the certificate of rendezvous enforcement that is produced by the checking algorithm contains a wealth of information on why rendezvous is enforced. [ABSTRACT FROM AUTHOR]

To enable mobile agents signing securely on potentially malicious hosts in electronic commerce and other applications, we proposed the definition and security notion of identity-based undetachable digital signature schemes. More importantly, we proposed a concrete identity-based undetachable digital signature scheme with provable security. In the scheme, mobile agents need not carry the private key when they generate digital signatures on behalf of the original signer, so the private key will not be compromised. The encrypted function is combined with the original signer’s requirement, so misuse of the signing algorithm can be prevented. Moreover, because the scheme is identity-based, verification of the signatures generated by mobile agents does not require either verification of the entire certificate path or communication with the certification authority. Therefore, compared with existing undetachable signature schemes, the cost of verification is reduced and even the dependence on a stable network connection is weakened. [ABSTRACT FROM AUTHOR]

This paper presents a new immune inspired algorithm, the Clonal Selection Algorithm for Multi-agent Itinerary Planning (CSA-MIP), to solve the MIP problem in wireless sensor networks. CSA-MIP has two computational stages called Stage I and Stage II. When only Stage I is activated in CSA-MIP, in the obtained solutions, the difference between the maximum and minimum numbers of sensor nodes, visited by each mobile agent, is relatively large. When only Stage II is activated in CSA-MIP, in the obtained solutions, the number of mobile agents is relatively small. When both stages are activated in CSA-MIP, in the obtained solutions, the diversity in the number of mobile agents can be achieved and the unbalance in the number of sensor nodes visited by each mobile agent can be reduced, which indicate a higher possibility to obtain solutions of better quality. Moreover, according to the simulation results and analysis of computational complexity, CSA-MIP is shown to perform better than GA-MIP in terms of solution quality and computational efficiency. [ABSTRACT FROM AUTHOR]

A pivotal cause for the boom of Mobile Agent paradigm relies on the competence to ward off security attacks. This article surveys the prevalent attacks on the mobile agents and the agent platforms; the existing countermeasures and their curbs, catering threefold benefaction: First, to acquaint the researchers with numerous security requirements and the objectives. Second, to analyze the different detection and prevention mechanisms mitigating the security bottlenecks in Mobile Agents System. Third, to address the future security challenges. In a nutshell, this survey hands over a key to researchers who primarily target the security concerns of the mobile agent-based applications. [ABSTRACT FROM AUTHOR]

In this paper, we consider the uniform deployment problem of mobile agents in asynchronous unidirectional rings, which requires the agents to uniformly spread in the ring. The uniform deployment problem is in striking contrast to the rendezvous problem which requires the agents to meet at the same node. While rendezvous aims to break the symmetry, uniform deployment aims to attain the symmetry. It is well known that the symmetry breaking is difficult in distributed systems and the rendezvous problem cannot be solved from some initial configurations. Hence, we are interested in clarifying what difference the uniform deployment problem has on the solvability and the number of agent moves compared to the rendezvous problem. We consider two problem settings, with knowledge of k (or n ) and without knowledge of k or n where k is the number of agents and n is the number of nodes. First, we consider agents with knowledge of k (or n since k and n can be easily obtained if one of them is given). In this case, we propose two algorithms. The first algorithm solves the uniform deployment problem with termination detection. This algorithm requires O ( k log n ) memory space per agent, O ( n ) time, and O ( k n ) total moves. The second algorithm also solves the uniform deployment problem with termination detection. This algorithm reduces the memory space per agent to O ( log n ) , but uses O ( n log k ) time, and requires O ( k n ) total moves. Both algorithms are asymptotically optimal in terms of total moves since there are some initial configurations such that agents require Ω ( k n ) total moves to solve the problem. Next, we consider agents with no knowledge of k or n . In this case, we show that, when termination detection is required, there exists no algorithm to solve the uniform deployment problem. For this reason, we consider the relaxed uniform deployment problem that does not require termination detection, and we propose an algorithm to solve the relaxed uniform deployment problem. This algorithm requires O ( ( k ∕ l ) log ( n ∕ l ) ) memory space per agent, O ( n ∕ l ) time, and O ( k n ∕ l ) total moves when the initial configuration has symmetry degree l . This means that the algorithm can solve the problem more efficiently when the initial configuration has higher symmetric degree (i.e., is closer to uniform deployment). Note that all the proposed algorithms achieve uniform deployment from any initial configuration, which is a striking difference from the rendezvous problem because the rendezvous problem is not solvable from some initial configurations. [ABSTRACT FROM AUTHOR]

Mobile Agent systems are prevalent in Distributed Environment due to the autonomy and adaptability in diversified situations. Mobile Agents are capable of movement from one platform to another and hence it is a need to ensure the safety of the Mobile Agent during the phase of transit. Since Mobile Agents are capable of deciding their itinerary dynamically, a decision support system which helps to ensure the trustworthiness of a platform would complement the decision. A Trust Scoring System is therefore proposed to measure the trustworthiness of a platform based on a metric termed Trust Score. Trust Score varies dynamically with respect to time and is based on a function comprising of five parameters namely Persistence, Competence, Reputation, Credibility and Integrity. In order to reduce the computational latency, another metric named Trust Rank for platforms, based on Trustability co-efficient of variation is introduced. The experimentation is done in cloud environment with platforms located at different geographic regions. The performance is evaluated in terms of response time and accuracy of decision. From the experimental results, it is evident that the Trust Ranking mechanism consumes less response time and improves the accuracy of decision of Mobile Agents during their itineraries, compared to that of the prototype system that uses Trust Score alone as a measure of decision-making. [ABSTRACT FROM AUTHOR]

Data aggregation represents one of the most challenging and well-studied subjects in the Wireless Sensor Networks (WSN) literature. The energy constraints of sensor nodes call for energy-efficient data aggregation methods so as to prolong network lifetime. Among other approaches, Mobile Agents (MAs) have been proposed to improve the performance of data aggregation in WSNs. In such approaches, the itineraries followed by travelling agents largely determine the overall performance of the data aggregation tasks. Along this line, several heuristics have been proposed to perform efficient itinerary planning for MAs. However, a direct comparison of the proposed algorithms is not straightforward, as they are typically performed on the ground of different parameter instances and assumptions about the underlying network and nodes capabilities. This article provides a critical review and qualitative evaluation of the most prominent itinerary planning algorithms. More importantly, having implemented and simulated a set of eleven (11) itinerary planning algorithms, we compare their performance upon a common parameter space, making realistic network-level assumptions. [ABSTRACT FROM AUTHOR]

The typical business processes in building material equipment manufacturing enterprises are analyzed, and the framework and functional models of the tracking system for building material equipment manufacturing enterprises in the order lifecycle are presented. Based on the model, using a mobile agent and XML technology, the order tracking system’s framework is built for building material equipment manufacturing enterprises based on a mobile agent in the Aglet platform. The key technologies in the process of tracking the order lifecycle are introduced in detail. Finally, a prototype system is developed and used in an enterprise successfully, verifying the feasibility and availability of the system. [ABSTRACT FROM AUTHOR]

Two mobile agents, starting at arbitrary, possibly different times from arbitrary locations in the plane, have to meet. Agents are modeled as discs of diameter 1, and meeting occurs when these discs touch. Agents have different labels which are positive integers. Each agent knows its own label, but not the label of the other agent. Agents are equipped with compasses and have synchronized clocks. They make a series of moves. Each move specifies the direction and the duration of moving. This includes a null move which consists in staying inert for some time, or forever. In a non-null move agents travel at the same constant speed, normalized to 1. We assume that agents have sensors enabling them to estimate the distance from the other agent (defined as the distance between centers of discs), but not the direction towards it. We consider two models of estimation. In both models an agent reads its sensor at the moment of its appearance in the plane and then at the end of each move. This reading (together with the previous ones) determines the decision concerning the next move. In both models the reading of the sensor tells the agent if the other agent is already present. Moreover, in the monotone model, each agent can find out, for any two readings in moments t1 and t2, whether the distance from the other agent at time t1 was smaller, equal or larger than at time t2. In the weaker binary model, each agent can find out, at any reading, whether it is at distance less than ρ or at distance at least ρ from the other agent, for some real ρ > 1 unknown to them. Such distance estimation mechanism can be implemented, e.g., using chemical sensors. Each agent emits some chemical substance (scent), and the sensor of the other agent detects it, i.e., sniffs. The intensity of the scent decreases with the distance. In the monotone model it is assumed that the sensor is ideally accurate and can measure any change of intensity. In the binary model it is only assumed that the sensor can detect the scent below some distance (without being able to measure intensity) above which the scent is too weak to be detected. We show the impact of the two ways of sensing on the cost of meeting, defined as the total distance travelled by both agents until the meeting. For the monotone model we show an algorithm achieving meeting at cost O(D), where D is the initial distance between the agents. This complexity is optimal. For the binary model we show that, if agents start at distance smaller than ρ (i.e., when they sense each other initially) then meeting can be guaranteed at cost O(ρ log λ), where λ is the larger label, and that this cost cannot be improved in general. Finally we observe that, if agents start at distance αρ, for some constant α > 1 in the binary model, then sniffing does not help, i.e., the worst-case optimal meeting cost is of the same order of magnitude as without any sniffing ability. [ABSTRACT FROM AUTHOR]

Probabilistic time geography considers the encounter of moving agents to be random; therefore, a quantitative time geography analysis must consider the actual encounter probability. The existing algorithm of encounter probability is oriented over a discrete space and is sensitive to the unit definition of a virtual grid; thus, it is not suitable for continuous space. For this reason, a new method is presented in this paper for the encounter of two moving agents in continuous space. When the encounters are less than a specified distance threshold apart, an encounter event occurs based on the probability of the product, which is calculated by their respective probability density functions over their respective potential location areas. This probability provides a quantitative basis for predicting the likelihood of two agents meeting, as well as the location of this meeting point. Finally, the validity of the proposed model is verified by an experiment, which uses tracking data to calculate the encounter probabilities of three zebras and analyse the distribution characteristics of these probabilities over time and space. [ABSTRACT FROM AUTHOR]

Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to reason about the behaviours of other agents, by constructing models which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research. [ABSTRACT FROM AUTHOR]

Multi-agent applications that include teams of mobile sensing agents are challenging since they are inherently dynamic and a single movement of a mobile sensor can change the problem that the entire team is facing. A variation of the Distributed Constraint Optimization model for Mobile Sensor Teams (DCOP_MST) was previously adjusted to represent such problems along with local search algorithms that were enhanced with exploration methods. This paper considers the use of the Max-sum algorithm for solving problems of deploying a mobile sensor team in an unknown environment to track and monitor points of interest (targets), represented by the DCOP_MST model. The DCOP_MST model allows the representation of different functions for aggregating the joint coverage of targets by multiple sensors. The use of different functions has a dramatic effect on the complexity of the Max-sum algorithm. When using cardinality functions, Max-sum can be performed efficiently regardless of the arity of constraints. When Max-sum is used to solve applications that require other (more complex) aggregation functions, its complexity is exponential in the arity of the constraints and thus, its usefulness is limited. In this paper we investigate the performance of the Max-sum algorithm on two implementations of the DCOP_MST model. Each implementation considers a different joint credibility function for determining the coverage for each target, with respect to the locations and the credibility of agents. In the first, the coverage is calculated according to the number of agents that are located within sensing range from the target. This function can be calculated efficiently. The second takes the angle between the lines of sight of different agents to a target into consideration. The larger the difference in the angle between the lines of sight, the higher the coverage efficiency. We analyze the challenges in adjusting the Max-sum algorithm in both scenarios and propose enhancements of the algorithm that make it more efficient. We provide empirical evidence of the advantages resulting from these enhancements in comparison to the naive algorithm. [ABSTRACT FROM AUTHOR]

As a consequence of their practical implications for communications networks, traffic dynamics on complex networks have recently captivated researchers. Previous routing strategies for improving transport efficiency have paid little attention to the orders in which the packets should be forwarded, just simply used first-in-first-out queue discipline. Here, we apply a priority queuing discipline and propose a shortest-distance-first routing strategy on networks of mobile agents. Numerical experiments reveal that the proposed scheme remarkably improves both the network throughput and the packet arrival rate and reduces both the average traveling time and the rate of waiting time to traveling time. Moreover, we find that the network capacity increases with an increase in both the communication radius and the number of agents. Our work may be helpful for the design of routing strategies on networks of mobile agents. [ABSTRACT FROM AUTHOR]

In this paper, we consider the partial gathering problem of mobile agents in asynchronous tree networks. The partial gathering problem is a generalization of the classical gathering problem, which requires that all the agents meet at the same node. The partial gathering problem requires, for a given positive integer g , that each agent should move to a node and terminate so that at least g agents should meet at each of the nodes they terminate at. The requirement for the partial gathering problem is weaker than that for the (well-investigated) classical gathering problem, and thus, we clarify the difference on the move complexity between them. We consider two multiplicity detection models: weak multiplicity detection and strong multiplicity detection models. In the weak multiplicity detection model, each agent can detect whether another agent exists at the current node or not but cannot count the exact number of the agents. In the strong multiplicity detection model, each agent can count the number of agents at the current node. In addition, we consider two token models: non-token model and removable token model. In the non-token model, agents cannot mark the nodes or the edges in any way. In the removable-token model, each agent initially leaves a token on its initial node, and agents can remove the tokens. Our contribution is as follows. First, we show that for the non-token model agents require Ω ( k n ) total moves to solve the partial gathering problem, where n is the number of nodes and k is the number of agents. Second, we consider the weak multiplicity detection and non-token model. In this model, for asymmetric trees, by a previous result agents can achieve the partial gathering in O ( k n ) total moves, which is asymptotically optimal in terms of total moves. In addition, for symmetric trees we show that there exist no algorithms to solve the partial gathering problem. Third, we consider the strong multiplicity detection and non-token model. In this model, for any trees we propose an algorithm to achieve the partial gathering in O ( k n ) total moves, which is asymptotically optimal in terms of total moves. At last, we consider the weak multiplicity detection and removable-token model. In this model, we propose an algorithm to achieve the partial gathering in O ( g n ) total moves. Note that in this model, agents require Ω ( g n ) total moves to solve the partial gathering problem. Hence, the second proposed algorithm is also asymptotically optimal in terms of total moves. [ABSTRACT FROM AUTHOR]

We establish a classification of decision problems that are to be solved by mobile agents operating in unlabeled graphs, using a deterministic protocol. The classification is with respect to the ability of a team of agents to solve decision problems, possibly with the aid of additional information. In particular, our focus is on studying differences between the decidability of a decision problem by agents and its verifiability when a certificate for a positive answer is provided to the agents (the latter is to the former what NP is to P in the framework of sequential computing). We show that the class MAV of mobile agents verifiable problems is much wider than the class MAD of mobile agents decidable problems. Our main result shows that there exist natural MAV-complete problems: the most difficult problems in this class, to which all problems in MAV are reducible via a natural mobile computing reduction. Beyond the class MAV we show that, for a single agent, three natural oracles yield a strictly increasing chain of relative decidability classes. [ABSTRACT FROM AUTHOR]

Sensors are increasingly used in mobile environments with wireless network connections. Multiple sensor types measure distinct aspects of the same event. Their measurements are then combined to produce integrated, reliable results. As the number of sensors in networks increases, low energy requirements and changing network connections complicate event detection and measurement. We present a data fusion scheme for use in mobile wireless sensor networks with high energy efficiency and low network delays, that still produces reliable results. In the first phase, we used a network simulation where mobile agents dynamically select the next hop migration node based on the stability parameter of the link, and perform the data fusion at the migration node. Agents use the fusion results to decide if it should return the fusion results to the processing center or continue to collect more data. In the second phase. The feasibility of data fusion at the node level is confirmed by an experimental design where fused data from color sensors show near-identical results to actual physical temperatures. These results are potentially important for new large-scale sensor network applications. [ABSTRACT FROM AUTHOR]

Mobile agents environment is a new application paradigm with unique features such as mobility and autonomy. Traditional deadlock detection algorithms in distributed computing systems do not work well in mobile agent systems due to the unique feature property of the mobile agent. Existing deadlock detection and resolution algorithms in mobile agent systems have limitations such as performance inefficiency and duplicate detection/resolution when multiple mobile agents simultaneously detect/resolve the same deadlock. To address these problems, we propose an improved deadlock detection and resolution algorithm that adopts priority-based technique and lazy reaction strategy. The priority-based technique aims to ensure that there is only one instance of deadlock detection and resolution, and it also helps reduce mobile agent movement and data traffic together with the lazy reaction strategy. The liveness and safety properties of the proposed algorithm are proved in this paper. Theoretical analysis and experimental results show that the proposed algorithm provides better performance in terms of agent movement, data traffic, and execution time. [ABSTRACT FROM AUTHOR]

The difference between the speed of the actions of different processes is typically considered as an obstacle that makes the achievement of cooperative goals more difficult. In this work, we aim to highlight potential benefits of such asynchrony phenomena to tasks involving symmetry breaking. Specifically, in this paper, identical (except for their speeds) mobile agents are placed at arbitrary locations on a (continuous) cycle of length n and use their speed difference in order to rendezvous fast. We normalize the speed of the slower agent to be 1, and fix the speed of the faster agent to be some c > 1 . (An agent does not know whether it is the slower agent or the faster one.) The straightforward distributed-race ( DR ) algorithm is the one in which both agents simply start walking until rendezvous is achieved. It is easy to show that, in the worst case, the rendezvous time of DR is n / ( c − 1 ) . Note that in the interesting case, where c is very close to 1 (e.g., c = 1 + 1 / n k ), this bound becomes huge. Our first result is a lower bound showing that, up to a multiplicative factor of 2, this bound is unavoidable, even in a model that allows agents to leave arbitrary marks (the white board model), even assuming sense of direction, and even assuming n and c are known to agents. That is, we show that under such assumptions, the rendezvous time of any algorithm is at least n 2 ( c − 1 ) if c ⩽ 3 and slightly larger (specifically, n c + 1 ) if c > 3 . We then manage to construct an algorithm that precisely matches the lower bound for the case c ⩽ 2 , and almost matches it when c > 2 . Moreover, our algorithm performs under weaker assumptions than those stated above, as it does not assume sense of direction, and it allows agents to leave only a single mark (a pebble) and only at the place where they start the execution. Finally, we investigate the setting in which no marks can be used at all, and show tight bounds for c ⩽ 2 , and almost tight bounds for c > 2 . [ABSTRACT FROM AUTHOR]

We consider the population protocol (PP) model used to represent a collection of finite-state mobile agents that interact with each other to accomplish a common task. Motivated by the well-known BZ reaction , we address the problem of autonomously generating an oscillatory execution from any initial configuration (i.e., in a self-stabilizing manner). For deterministic PPs under a deterministic scheduler, we show that the self-stabilizing leader election (SS-LE) problem and the self-stabilizing oscillation (SS-OS) problem are equivalent, in the sense that an SS-OS protocol is constructible from a given SS-LE protocol, and vice versa, which unfortunately implies that (1) resorting to a leader is inevitable (although we seek a decentralized solution), (2) n states are necessary to create oscillations of amplitude n , where n is the number of agents (although we seek a memory-efficient solution). Aiming at reducing the space complexity, we present some deterministic oscillatory PPs under a uniform random scheduler. [ABSTRACT FROM AUTHOR]

The major challenge in mobile agent path planning, within an uncertain environment, is effectively determining an optimal control model to discover the target location as quickly as possible and evaluating the control system's reliability. To address this challenge, we introduce a learning-verification integrated mobile agent path planning method to achieve both the effectiveness and the reliability. More specifically, we first propose a modified Q-learning algorithm (a popular reinforcement learning algorithm), called Q E A − l e a r n i n g algorithm, to find the best Q-table in the environment. We then determine the location transition probability matrix, and establish a probability model using the assumption that the agent selects a location with a higher Q-value. Secondly, the learnt behaviour of the mobile agent based on Q E A − l e a r n i n g algorithm, is formalized as a Discrete-time Markov Chain (DTMC) model. Thirdly, the required reliability requirements of the mobile agent control system are specified using Probabilistic Computation Tree Logic (PCTL). In addition, the DTMC model and the specified properties are taken as the input of the Probabilistic Model Checker PRISM for automatic verification. This is preformed to evaluate and verify the control system's reliability. Finally, a case study of a mobile agent walking in a grids map is used to illustrate the proposed learning algorithm. Here we have a special focus on the modelling approach demonstrating how PRISM can be used to analyse and evaluate the reliability of the mobile agent control system learnt via the proposed algorithm. The results show that the path identified using the proposed integrated method yields the largest expected reward. [ABSTRACT FROM AUTHOR]

Motivated by area coverage optimization problems with time-varying risk densities, in this paper we propose a decentralized control law for a team of autonomous mobile agents in a 2-D area such that their asymptotic configurations optimize a generalized non-autonomous coverage metric. We emphasize that the generalized non-autonomous coverage metric explicitly depends on a nonuniform time-varying measurable scalar field that is defined by the trajectories of a set of mobile targets (distinct from the agents). The time-varying density that we consider here is not directly controllable by agents. We show that under certain conditions on the density defined on a closed bounded region of operation, the agents configure themselves asymptotically to optimize a related generalized non-autonomous coverage metric. A set of simulations illustrates the proposed control. [ABSTRACT FROM AUTHOR]

Mobile agents start at different nodes of an n -node network. The agents synchronously move along the network edges in a collision-free way, i.e., in no round two agents may occupy the same node. An agent has no knowledge of the number and initial positions of other agents. We are looking for the shortest time required to reach a configuration in which each agent has visited all nodes and returned to its starting location. In the scenario when each mobile agent knows the map of the network, we provide tight (up to a constant factor) lower and upper bounds on the collision-free exploration time in arbitrary graphs, and the exact bound for the trees. In the second scenario, where the network is unknown to the agents, we propose collision-free exploration strategies running in O ( n 2 ) rounds in tree networks and in O ( n 5 log ⁡ n ) rounds in networks with an arbitrary topology. [ABSTRACT FROM AUTHOR]

Mobile agent (MA), a part of the mobile computing paradigm, was recently proposed for data gathering in Wireless Sensor Networks (WSNs). The MA-based approach employs two algorithms: Single-agent Itinerary Planning (SIP) and Multi-mobile agent Itinerary Planning (MIP) for energy-efficient data gathering. The MIP was proposed to outperform the weakness of SIP by introducing distributed multi MAs to perform the data gathering task. Despite the advantages of MIP, finding the optimal number of distributed MAs and their itineraries are still regarded as critical issues. The existing MIP algorithms assume that the itinerary of the MA has to start and return back to the sink node. Moreover, each distributed MA has to carry the processing code (data aggregation code) to collect the sensory data and return back to the sink with the accumulated data. However, these assumptions have resulted in an increase in the number of MA's migration hops, which subsequently leads to an increase in energy and time consumption. In this paper, a spawn multi-mobile agent itinerary planning (SMIP) approach is proposed to mitigate the substantial increase in cost of energy and time used in the data gathering processes. The proposed approach is based on the agent spawning such that the main MA is able to spawn other MAs with different tasks assigned from the main MA. Extensive simulation experiments have been conducted to test the performance of the proposed approach against some selected MIP algorithms. The results show that the proposed SMIP outperforms the counterpart algorithms in terms of energy consumption and task delay (time), and improves the integrated energy-delay performance. [ABSTRACT FROM AUTHOR]

Mobile agent (MA)-based middleware has been thoroughly investigated in the past few years as a means to address the efficiency, scalability, and reliability issues of data fusion applications on wireless sensor networks. Deriving an efficient itinerary for each MA to follow is of high importance, because itineraries determine to a large extent the overall performance of data fusion tasks. In this article, we present a novel algorithmic approach for efficient itinerary planning of MA objects undertaking data fusion tasks. We adopt a method based on iterated local search to construct the itineraries (ie, visiting sequences of source nodes) assigned to multiple traveling MAs. We apply alternative optimization criteria which aim either at minimizing the overall energy expenditure over all derived MA itineraries or prolonging the network lifetime. Furthermore, we propose algorithmic solutions for 2 realistic settings which have not been investigated in the past: firstly, the employment of multiple sinks that share the responsibility of MA-based data fusion tasks across the sensor field, and secondly, the consideration of heterogeneous sensor networks comprising nodes powerful enough to host the runtime environment required to execute MA code as well as 'ordinary' nodes which lack these resources. Simulation tests verify the performance gain attained by our algorithmic methods against alternative itinerary planning approaches which involve multiple MAs. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the business process of reengineering, one of the main problems consists in the organizational reintegration of the employee. New instruments for the automatic identification of stakeholders among existing staff evaluation are needed. It is difficult to identify a stakeholder, especially if we refer to science stakeholders, and the proper evaluation of a researcher's value and position at international level is needed. There are large contradictions concerning the validity of various types of indexing services and other techniques used to classify the researchers. This paper proposes the use of corporate Human Resource Management (HRM) specific instruments when it comes to the researcher as informational stakeholder identification. The main idea behind the method is to use some form of information retrieval techniques over a set of research papers which contain, mostly, the same key words and to extract the hierarchy of the researchers involved/referenced over that set. A distributed approach was used due to its inherent scalability. The use of an inference engine was also deemed necessary due to the complexity of the problem. The system may be used in the automatic assessment of research performance in public or private institutions. It can be easily modified to support complex sets of rules depending on each user's needs in personnel evaluation. The main research field of this papers concerns distributed artificial intelligence. [ABSTRACT FROM AUTHOR]

The proliferation of malicious entities in the distributed environment poses various serious threats to the protection of Mobile Agent Platform (MAP). Numerous researches have been proposed to ward off the inherent security risks, though these solutions are not enough to identify and remove all the vulnerabilities. In this paper, a self-adaptive IV-Phase MAP Security Architecture is proposed, which is inspired by the Biological Immune System, with the prime objective of detecting unknown malicious mobile agents. In this context, data mining methods are studied for the detection of unknown malicious executable. In particular, Boyer Moore pattern matching algorithm and N-gram feature analysis of mobile agent using a k-Nearest Neighbor Classifier, facilitate the discovery of known and unknown malicious content from incoming mobile agent in the proposed architecture, and protects against the Man In The Middle (MITM) attack, the Masquerading Attack, the Replay attack, the Repudiation attack and the Unauthorized Access Attack. The architecture is designed and implemented in IBM Aglets. A comprehensive 5-fold cross validation scheme on a large collection of malicious and non-malicious files is performed while performing Classification technique involving Feature Selection Method. The propitious experimental outcomes express that the performance (time and security) and accuracy of proposed architecture outperform the earlier known related schemes and makes the proposed architecture suitable for MAP protection in the Mobile Agent Environment (MAE). Above all, these findings exhibit wide-ranging newness, since the concept of machine learning has never been employed so far in the sphere of Mobile Agents System (MAS). Hence the proposed work is likely to be of great interest to the researchers who particularly deal with MAS security. [ABSTRACT FROM AUTHOR]

Security is a significant concern for mobile agent-based computing as mobile agents are vulnerable to malicious hosts and rogue agents. Existing models developed to secure agents, were not essentially evaluated to guarantee the desired level of security. Hence, this work aimed at implementing a cybersecurity approach for evaluating mobile agents using a web-based penetration testing tool called Lens. [ABSTRACT FROM AUTHOR]

This paper considers current situation and development of software for group of mobile agents applicable on group of unmanned aerial vehicles. Generally, unmanned aerial vehicles are used for transfer of information, mass and energy and their group work enhances their success rate in comparison with the success rate of the use of a single unmanned aerial vehicle. Despite the constant daily use, their potential is realized only in a small portion. It is argued that software development is the natural further step in achieving considerably larger portion of realizations of their potential of groups of unmanned aerial vehicles. Starting requirements that such software must fulfill are rudimentariness of the code, openness regarding number of group members and closeness regarding information exchange. Prospective directions of development of that software are analyzed. [ABSTRACT FROM AUTHOR]

In this paper, we investigate consensus and disturbance attenuation in a chain of mobile agents, which include non-autonomous agents, semi-autonomous agents and autonomous agents. In particular, the nonlinear dynamics of non-autonomous agents is given and cannot be designed, while the dynamics of semi-autonomous and autonomous agents can be partially and fully designed, respectively. To improve the robustness of multi-agent chains against disturbances, we propose a nonlinear control framework for semi-autonomous and autonomous agents such that they mimic the behavior of non-autonomous agents for compatibility while also exploiting long-range connections with distant agents. This framework ensures the existence of a unique consensus equilibrium, which is independent of the network size, connectivity topologies, control gains and information delays. Robustness of multi-agent chains against disturbances is investigated by evaluating the frequency response at the nonlinear level. For infinitely long multi-agent chains with recurrent patterns, we also derive a condition that ensures the disturbance attenuation but only requires the analysis of the linearized model. A case study is conducted for a connected vehicle system where numerical simulations are used to validate the analytical results. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Mobile agent data aggregation routing forwards mobile agents in wireless sensor network to collect and aggregate data. The key objective of data aggregation routing is to maximise the number of collected data samples at the same time as minimising network resource consumption and data collection delay. This paper proposes a mobile agent routing protocol, called zone-based mobile agent aggregation. This protocol utilises a bottom-up mobile agent migration scheme in which the mobile agents start their journeys from the centre of the event regions to the sink aiming to reduce the MA itinerary cost and delay and increase data aggregation routing accuracy. In addition, the proposed protocol reduces the impact of network architecture, event source distribution model and/or data heterogeneity on the performance of data aggregation routing. [ABSTRACT FROM AUTHOR]

The rapid development of modern society continually transforms the social structure which leads to an increasingly distinct dual structure of higher population density in urban areas and lower density in rural areas. Such structure may induce distinctive spreading behavior of epidemics which does not happen in a single type structure. In this paper, we study the epidemic spreading of mobile agents on dual structure networks based on SIRS model. First, beyond the well known epidemic threshold for generic epidemic model that when the infection rate is below the threshold a pertinent infectious disease will die out, we find the other epidemic threshold which appears when the infection rate of a disease is relatively high. This feature of two thresholds for the SIRS model may lead to the elimination of infectious disease when social network has either high population density or low population density. Interestingly, however, we find that when a high density area is connected to a low density may cause persistent spreading of the infectious disease, even though the same disease will die out when it spreads in each single area. This phenomenon indicates the critical role of the connection between the two areas which could radically change the behavior of spreading dynamics. Our findings, therefore, provide new understanding of epidemiology pertinent to the characteristic modern social structure and have potential to develop controlling strategies accordingly. [ABSTRACT FROM AUTHOR]

Two mobile agents, starting at arbitrary, possibly different times from arbitrary nodes of an unknown network, have to meet at some node. Agents move in synchronous rounds: in each round an agent can either stay at the current node or move to one of its neighbors. Agents have different labels which are positive integers. Each agent knows its own label, but not the label of the other agent. In traditional formulations of the rendezvous problem, meeting is accomplished when the agents get to the same node in the same round. We want to achieve a more demanding goal, called rendezvous with detection: agents must become aware that the meeting is accomplished, simultaneously declare this and stop. This awareness depends on how an agent can communicate to the other agent its presence at a node. We use two variations of the arguably weakest model of communication, called the beeping model, introduced in [8]. In each round an agent can either listen or beep. In the local beeping model, an agent hears a beep in a round if it listens in this round and if the other agent is at the same node and beeps. In the global beeping model, an agent hears a loud beep in a round if it listens in this round and if the other agent is at the same node and beeps, and it hears a soft beep in a round if it listens in this round and if the other agent is at some other node and beeps. We first present a deterministic algorithm of rendezvous with detection working, even for the local beeping model, in an arbitrary unknown network in time polynomial in the size of the network and in the length of the smaller label (i.e., in the logarithm of this label). However, in this algorithm, agents spend a lot of energy: the number of moves that an agent must make, is proportional to the time of rendezvous. It is thus natural to ask if bounded-energy agents, i.e., agents that can make at most c moves, for some integer c, can always achieve rendezvous with detection as well. This is impossible for some networks of unbounded size. Hence we rephrase the question: Can bounded-energy agents always achieve rendezvous with detection in bounded-size networks? We prove that the answer to this question is positive, even in the local beeping model but, perhaps surprisingly, this ability comes at a steep price of time: the meeting time of bounded-energy agents is exponentially larger than that of unrestricted agents. By contrast, we show an algorithm for rendezvous with detection in the global beeping model that works for bounded-energy agents (in bounded-size networks) as fast as for unrestricted agents. [ABSTRACT FROM AUTHOR]

This paper investigates an open problem introduced in Dieudonné et al. (ACM Trans Algorithms 11(1):1, 2014). Two or more mobile agents start from nodes of a network and have to accomplish the task of gathering which consists in getting all together at the same node at the same time. An adversary chooses the initial nodes of the agents and assigns a different positive integer (called label) to each of them. Initially, each agent knows its label but does not know the labels of the other agents or their positions relative to its own. Agents move in synchronous rounds and can communicate with each other only when located at the same node. Up to f of the agents are Byzantine. A Byzantine agent can choose an arbitrary port when it moves, can convey arbitrary information to other agents and can change its label in every round, in particular by forging the label of another agent or by creating a completely new one. What is the minimum number $${\mathcal {M}}$$ of good agents that guarantees deterministic gathering of all of them, with termination? We provide exact answers to this open problem by considering the case when the agents initially know the size of the network and the case when they do not. In the former case, we prove $${\mathcal {M}}=f+1$$ while in the latter, we prove $${\mathcal {M}}=f+2$$ . More precisely, for networks of known size, we design a deterministic algorithm gathering all good agents in any network provided that the number of good agents is at least $$f+1$$ . For networks of unknown size, we also design a deterministic algorithm ensuring the gathering of all good agents in any network but provided that the number of good agents is at least $$f+2$$ . Both of our algorithms are optimal in terms of required number of good agents, as each of them perfectly matches the respective lower bound on $${\mathcal {M}}$$ shown in Dieudonné et al. (2014), which is of $$f+1$$ when the size of the network is known and of $$f+2$$ when it is unknown. Perhaps surprisingly, our results highlight an interesting feature when put in perspective with known results concerning a relaxed variant of this problem in which the Byzantine agents cannot change their initial labels. Indeed under this variant $${\mathcal {M}}=1$$ for networks of known size and $${\mathcal {M}}=f+2$$ for networks of unknown size. Following this perspective, it turns out that when the size of the network is known, the ability for the Byzantine agents to change their labels significantly impacts the value of $${\mathcal {M}}$$ . However, the relevance for $${\mathcal {M}}$$ of such an ability completely disappears in the most general case where the size of the network is unknown, as $${\mathcal {M}}=f+2$$ regardless of whether Byzantine agents can change their labels or not. [ABSTRACT FROM AUTHOR]

We consider the problem of exploration of networks, some of whose edges are faulty. A mobile agent, situated at a starting node and unaware of which edges are faulty, has to explore the connected fault-free component of this node by visiting all of its nodes. The cost of the exploration is the number of edge traversals. For a given network and given starting node, the overhead of an exploration algorithm is the worst-case ratio (taken over all fault configurations) of its cost to the cost of an optimal algorithm which knows where faults are situated. An exploration algorithm, for a given network and given starting node, is called perfectly competitive if its overhead is the smallest among all exploration algorithms not knowing the location of faults. We design a perfectly competitive exploration algorithm for any ring, and show that, for networks modeled by hamiltonian graphs, the overhead of any DFS exploration is at most 10/9 times larger than that of a perfectly competitive algorithm. Moreover, for hamiltonian graphs of size at least 24, this overhead is less than 6% larger than that of a perfectly competitive algorithm. [ABSTRACT FROM AUTHOR]