In situations involving teams of diverse robots, assigning appropriate roles to each robot and evaluating their performance is crucial. These roles define the specific characteristics of a robot within a given context. The stream of actions exhibited by a robot based on its assigned role are referred to as the process role. Our research addresses the depiction of process roles using a multivariate probabilistic function. The main aim of this study is to develop a role engine for collaborative multirobot systems and optimize the behavior of the robots. The role engine is designed to assign suitable roles to each robot, generate approximately optimal process roles, update them on time, and identify instances of robot malfunction or trigger replanning when necessary. The environment considered is dynamic, involving obstacles and other agents. The role engine operates hybrid, with central initiation and decentralized action, and assigns unlabeled roles to agents. We employ the Gaussian process (GP) inference method to optimize process roles based on local constraints and constraints related to other agents. Furthermore, we propose an innovative approach that utilizes the environment’s skeleton to address initialization and feasibility evaluation challenges. We successfully demonstrated the proposed approach’s feasibility, and efficiency through simulation studies and real-world experiments involving diverse mobile robots.

Trust evaluation and trust establishment play crucial roles in the management of trust within a multi-agent system. When it comes to collaboration systems, trust becomes directly linked to the specific roles performed by agents. The Role-Based Collaboration (RBC) methodology serves as a framework for assigning roles that facilitate agent collaboration. Within this context, the behavior of an agent with respect to a role is referred to as a process role. This research paper introduces a role engine that incorporates a trust establishment algorithm aimed at identifying optimal and reliable process roles. In our study, we define trust as a continuous value ranging from 0 to 1. To optimize trustworthy process roles, we have developed a consensus-based Gaussian Process Factor Graph (GPFG) tool. Our simulations and experiments validate the feasibility and efficiency of our proposed approach with autonomous robots in unsignalized intersections and narrow hallways.

In safety-critical applications, where several mobile robots and autonomous agents are being utilized for a mission, a fault-resilience behavior of the system is necessary. The fault resilience mechanism mostly uses the robot’s redundancy and tasks reassignment to recover malfunctioning and increase operating efficiency. The E-CARGO (Environments - Classes, Agents, Roles, Groups, and Objects) model designed for the Role-Based Collaboration (RBC) approach has been used successfully on cooperative Multi-Robot Systems (MRSs). Role-based characteristics of E-CARGO will facilitate cooperative decision-making and simplify handling failure. This paper develops an extended E-CARGO model for a fault resilience role engine. Agents use factor graphs to update the process role and manage the potential failure in each time step. We apply hybrid control in this paper. By “hybrid” we mean that evaluating and assigning initial roles are centralized, and role-playing is decentralized based on the local observations. The RBC life cycle and a Bayesian consensus will maintain fault resilience behaviors. Potential failure can be identified in a Bayesian way by updating agents’ reliability and calling the central unit to assign new process roles to guarantee robustness. Simulation experiments show that the proposed role engine can increase performance and tolerate failures in multi-robot path planning scenarios.

In Extended Target Tracking, where estimating the shape is essential as kinematic, exploiting the dependencies between targets is often an excellent way to enhance performance. In a group of dependent targets, sampled features tend to have spatially and temporally correlations inside and between frames. Gaussian process regression has been used as a powerful Bayesian semi-supervised method to describe functions’ spatial and temporal correlation. This paper exploits and models the dependency between extended targets using Gaussian Process. We propose a novel recursive approach called Multi-Output Spatio-Temporal Gaussian Process Kalman Filter (MO-STGP-KF) to estimate and track multiple dependent extended targets that have possibly been degraded or covered with clutter. We used this method for detecting and tracking the group of connected lane markings called “lane-lines”. For detection and clustering, we propose a new Kernel-based Joint Probabilistic Data Association Coupled Filter (K-JPDACF) to cluster point features belonging to each lane-line. Compared to recently published model-based multi-lane tracking, semi-supervised, and fully supervised lane detection methods, our method shows 13 percent 34 percent and 20 percent improvement in accuracy, respectively.

Network slicing is a crucial part of the 5G networks that communication service providers (CSPs) seek to deploy. By exploiting three main enabling technologies, namely, software-defined networking (SDN), network function virtualization (NFV), and network slicing, communication services can be served to the end-users in an efficient, scalable, and flexible manner. To adopt these technologies, what is highly important is how to allocate the resources and admit the customers of the CSPs based on the predefined criteria and available resources. In this regard, we propose a novel joint resource and admission management algorithm for slice-enabled networks. In the proposed algorithm, our target is to minimize the network cost of the CSP subject to the slice requests received from the tenants corresponding to the virtual machines and virtual links constraints. Our performance evaluation of the proposed method shows its efficiency in managing CSP’s resources.

Summary The highly dynamic nature of the current network traffics makes the network managers to exploit the flexibility of the state‐of‐the‐art paradigm called SDN. In this way, there has been an increasing interest in hybrid networks of SDN‐MPLS. In this paper, a new traffic engineering architecture for SDN‐MPLS network is proposed. To this end, OpenFlow‐enabled switches are applied over the edge of the network to improve flow‐level management flexibility while MPLS routers are considered as the core of the network to make the scheme applicable for existing MPLS networks. The proposed scheme re‐assigns flows to the Label‐Switched Paths (LSPs) to highly utilize the network resources. In the cases that the flow‐level re‐routing is insufficient, the proposed scheme re‐computes and re‐creates the undergoing LSPs. To this end, we mathematically formulate two optimization problems, ie, i) flow re‐routing and ii) LSP re‐creation, and propose a heuristic algorithm to improve the performance of the scheme. Our experimental results show the efficiency of the proposed hybrid SDN‐MPLS architecture in traffic engineering superiors traditionally deployed MPLS networks.

Summary Middleboxes have become a vital part of modern networks by providing services such as load balancing, optimization of network traffic, and content filtering. A sequence of middleboxes comprising a logical service is called a Service Function Chain (SFC). In this context, the main issues are to maintain an acceptable level of network path survivability and a fair allocation of the resource between different demands in the event of faults or failures. In this paper, we focus on the problems of traffic engineering, failure recovery, fault prevention, and SFC with reliability and energy consumption constraints in Software Defined Networks (SDN). These types of deployments use Fog computing as an emerging paradigm to manage the distributed small‐size traffic flows passing through the SDN‐enabled switches (possibly Fog Nodes). The main aim of this integration is to support service delivery in real‐time failure recovery in an SFC context. First, we present an architecture for Failure Recovery called FRFP; this is a multi‐tier structure in which the real‐time traffic flows pass through SDN‐enabled switches to jointly decrease the network side‐effects of flow rerouting and energy consumption of the Fog Nodes. We then mathematically formulate an optimization problem called the Optimal Fast Failure Recovery algorithm (OFFR) and propose a near‐optimal heuristic called Heuristic HFFR to solve the corresponding problem in polynomial time. In this way, the reliability of the selected paths are optimized, while the network congestion is minimized.

Service function chaining (SFC) allows the forwarding of traffic flows along a chain of virtual network functions (VNFs). Software defined networking (SDN) solutions can be used to support SFC to reduce both the management complexity and the operational costs. One of the most critical issues for the service and network providers is the reduction of energy consumption, which should be achieved without impacting the Quality of Service. In this paper, we propose a novel resource allocation architecture which enables energy-aware SFC for SDN-based networks, considering also constraints on delay, link utilization, server utilization. To this end, we formulate the problems of VNF placement, allocation of VNFs to flows, and flow routing as integer linear programming (ILP) optimization problems. Since the formulated problems cannot be solved (using ILP solvers) in acceptable timescales for realistic problem dimensions, we design a set of heuristic to find near-optimal solutions in timescales suitable for practical applications. We numerically evaluate the performance of the proposed algorithms over a real-world topology under various network traffic patterns. Our results confirm that the proposed heuristic algorithms provide near-optimal solutions (at most 14% optimality-gap) while their execution time makes them usable for real-life networks.

ABSTRACT A tremendous number of low‐level alerts reported by information security systems clearly reflect the need for an advanced alert correlation system to reduce alert redundancy, correlate security alerts, detect attack strategies, and take appropriate actions against upcoming attacks. Up to now, a variety of alert correlation methods have been suggested. However, most of them rely on a priori and hard‐coded domain expert knowledge that leads to their difficult implementation and limited capabilities of detecting new attack strategies. To overcome the drawbacks of these approaches, the recent trend of research in alert correlation has gone towards extracting attack strategies through automatic analysis of intrusion alerts. In line with the recent researches, in this paper, we present new algorithms to automatically mine attack behavior patterns from historical alerts as accurately and efficiently as possible. Our system is composed of two main components. The first offline component automatically generates correlation rules by analyzing the previously observed alerts using a Bayesian causality analysis mechanism. Then, in the online alert correlation component, alerts are correlated using a hierarchical scheme and based on the extracted rules. Our experimental results clearly show efficiency of the proposed method in learning new attack strategies. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper we propose a rate-efficient peer-to-peer architecture for video multicasting over the Internet. The limited capacity of the Internet hosts and the heterogeneous property of their access links are the main challenges of the peer-to-peer video multicasting over the Internet. Although, the rate-optimized overlay tree construction is a NP-hard problem, we propose a number of distributed and efficient protocols for rate-efficient overlay tree construction. Our proposed protocols include efficient join, improvement, overlay tree refinement and an optimum rate allocation protocol. The simulation results show the efficiency of the proposed protocols in rate-efficient overlay tree construction. We show that through combination of the efficient join protocol, tree refinement operations and an optimum rate allocation algorithm we can achieve a suboptimum overlay tree.