6th-generation (6G)mobile networks are envisioned as AI-native systems, integrating learning and inference across the entire protocol stack. Although 5G’s 3GPP Network Data Analytics Function (NWDAF) introduced analytics-driven automation, it lacks standardised support for model lifecycle control, Data Analytics as a Service (DAaaS), closed-loop feedback, and largescale interoperability. To address these gaps, we propose a protocol-based framework for AI-as-a-Service (AIaaS) management for 6 G, centered on an enhanced NWDAF architecture with four components: Model Lifecycle Orchestrator, Model Registry & Validator, Distributed Execution Engine, and Feedback Aggregator. It introduces two lightweight, service-based interfaces: Model Training and Creation Protocol (MTCP) for intent-based model training and publication, and Model Execution Protocol (MEP) for on-box inference and metric feedback. We validate the framework via formal verification under message loss using a reproducible TLA+ model with three NFs and two model versions. Results show that NWDAF can evolve into a feasible AI lifecycle manager, enabling scalable and stable AI-native deployments in 6G. Complexity modelling confirms linear resource scaling up to 128 network functions (theoretical), with the public TLA+ specification configured for 3 NFs.

The advent of fifth-generation mobile networks (5G) has brought transformative improvements in data transfer speeds, latency reduction, and device connectivity, enabled by the softwarization, virtualization, and disaggregation of network functions. Open-source projects have been instrumental in facilitating software-driven 5G deployments on standard hardware, accelerating the development of private 5G networks, test environments, and advancing research and innovation in the domain. Despite these advancements, the current maturity of open-source solutions remains inadequate for high-performance, large-scale 5G deployments. Existing 5G performance testing approaches primarily adopt a black-box perspective, which fails to capture the intricate interactions among core 5G components, thereby hindering the identification of performance bottlenecks. Additionally, testing is often limited to small-scale environments, and publicly available datasets from real-world deployments are scarce. To overcome these challenges, this paper introduces an observability-driven methodology for testing$5G$core network performance, offering deeper insights into the internal interactions of network components. We contribute to research and innovation in 5G and beyond field by (i) enabling the simulation of varying user arrival rates, (ii) releasing a public dataset capturing data of 5G core components interactions, logs, and performance metrics, and (iii) providing a comparative analysis of two popular open-source 5G core implementations based on our observability-oriented methodology.

The fifth generation of mobile networks (5G) has introduced significant advancements in data transfer rates, latency, and device density, empowered by softwarization, virtualization, and disaggregation of network functions. Open-source initiatives have played a crucial role in enabling software-based 5G network deployments on commodity hardware, driving the growth of private 5G networks and testbeds, and fostering research and innovation in the field. However, the maturity level of such open-source technologies is still insufficient for highly performant large-scale deployments. Current 5G performance testing methodologies rely predominantly on a black-box approach, which overlooks the complexity of interactions between 5G core components, limiting the ability to explain performance bottlenecks. To address this limitation, this paper proposes a new observability-driven methodology for 5G core network performance testing, which improves visibility into internal component interactions. We contribute to research and innovation in 5G and beyond field by (i) implementing testing workloads that are able to simulate varying user arrival rates and (ii) integrate a set of cloud-native observability tools to allow white-box performance evaluation of the 5G core.

This work presents N-Deployment, a flexible strategy for deploying fixed infrastructure in vehicular networks (Roadside Units - RSUs), designed to meet heterogeneous service requirements in urban environments. The proposed approach seeks to identify a minimal yet effective deployment configuration that maximizes coverage efficiency while supporting multiple service types over a unified urban network. N-Deployment incorporates key elements from previous studies, including infrastructure minimization, geometric positioning, budget constraints, service continuity, data delivery requirements, and AI-based mobility pattern detection. A multiobjective optimization model is formulated to ensure that a given fraction of the vehicle fleet connects to the infrastructure within a predefined time window, traversing at least N covered zones along their routes. NSGA-II metaheuristic is employed to address large-scale urban scenarios. Experimental results using real mobility traces from Cologne, Germany, show that N-Deployment significantly outperforms traditional strategies, achieving greater vehicle coverage with fewer RSUs while supporting differentiated service levels. To our knowledge, this is the first unified strategy to simultaneously address service heterogeneity, infrastructure cost, and real-time connectivity in vehicular network deployments.

This work introduces a strategy to identify vehicular traffic entry points — critical locations for deploying Roadside Units (RSUs) that collect real-time data, enabling effective traffic management and the efficient operation of vehicular networks in urban environments. An Integer Linear Programming (ILP) model is formulated to minimize the number of urban cells that demand coverage while ensuring that a minimum percentage of vehicles pass through at least one of these areas within a predefined time window. To address scalability in large urban scenarios, a metaheuristic based on the Greedy Randomized Adaptive Search Procedure (GRASP) is used. Experimental results using realistic mobility traces from Cologne, Germany, show that the proposed Primary Deployment strategy significantly outperforms the baseline approach of placing RSUs in high-traffic areas, achieving up to 302% more vehicle coverage under specific configurations. To our knowledge, this is the first study to map traffic entry points based on the origin of vehicle journeys, using a minimal RSU deployment strategy tailored for urban vehicular networks.

Beyond 5G (B5G) and 6G networks must offer network slicing as a service to support disruptive applications using mobile network infrastructures. Moreover, network slicing as a service should enable the orchestration and management of disaggregated radio access networks (RAN), i.e., it allows the automation and abstraction of network configurations composed of physical and virtualized components such as defined by the 3rd Generation Partnership Project (3GPP) and Open RAN (O-RAN) Alliance. Network slicing must reach the part of the network between the radio units and the distribution units, i.e., the fronthaul network. Fronthaul is essential and diverse in 5G and B5G networks and can be composed of point-to-point or multipoint connections. In this context, the literature presents several works investigating the problems of network slicing and disaggregated networks. However, no survey explores current works integrating network slicing in disaggregated networks, specifically in the fronthaul network. This article surveys network slicing on disaggregated networks and its potential use in point-to-point and multipoint fronthaul infrastructures. We cover the state-of-the-art by analyzing four fundamental research questions and discuss existing solutions, open challenges, and research opportunities in B5G and 6G networks.

In this work, we overview a novel strategy that can support the decision making of systems dealing with combinatorial problems. We are particularly interested in supporting decision making in nearly real time systems. To target this goal, the proposed strategy must have a configurable time threshold to output the solution. Obviously, the solution presented will not be the optimal one because we are dealing with NP-complete problems. However, the evolution of this research may lead to an interesting class of solutions specifically designed for the real time scenario, where machines have to learn fast how to solve a problem, and take the appropriate measures to avoid a catastrophic situation (such as car crashes, course corrections, and general automation systems). The initial experiments show interesting results, with high gains when compared to similar approaches in the literature.

The use of roadside units (RSUs) in vehicular networks can increase the efficiency of the network by providing hot spots for data dissemination between vehicles and an infrastructure-based networking integrating specialized systems. To maximize the effectiveness of the network, the locations where roadside units are deployed require special attention. In this work, we introduce a novel strategy for the deployment of roadside units in vehicular networks to maximize the number of vehicle-to-infrastructure contact opportunities. By using this strategy, we intend to provide a larger time window, increasing the vehicular communication and the QoS provided by the underlying network. As baselines, we consider the FPF and KP strategies. FPF projects the flow of vehicles in a Markovian approach, while KP is a heuristic for the 0-1 Knapsack Problem and does not consider any mobility information. Simulation results based on a realistic scenario demonstrate that the proposed method can find locations of roadside units more efficiently in comparison to FPF and KP methods in terms of the average number of satisfied vehicles (5.31% more than FPF and 35.64% more than KP).

Device-to-Device (D2D) communication has gained attention due to its potential to reduce data traffic in mobile operator networks. Several models and algorithms for efficient D2D communication have been proposed recently. However, despite the availability of D2D communication technologies in most current devices, there are few real-world performance evaluations using such communication model. Aiming to fill this gap, this paper presents an experimental evaluation of D2D communication using off-the-shelf devices. To achieve this goal, we propose ShareFile, a tool to measure the performance of content sharing through Wi-Fi Direct in Android devices. We deployed a testbed to compare the performance of D2D communication against centralized or non direct solutions. The results demonstrate that even small distances degrade D2D communication performance. The time to find nearby devices ranges from 400ms to 1.5s, while time to establish a connection can reach 6s and the flow rate reaches up to 6.5MBps, which is close to the 7.9MBps achieved by cloud solutions.

Mobile devices are equipped with limited processing power and battery charge. A mobile computation offloading framework is a software that provides better user experience in terms of computation time and energy consumption, also taking profit from edge computing facilities. This article presents User-Level Online Offloading Framework (ULOOF), a lightweight and efficient framework for mobile computation offloading. ULOOF is equipped with a decision engine that minimizes remote execution overhead, while not requiring any modification in the device’s operating system. By means of real experiments with Android systems and simulations using large-scale data from a major cellular network provider, we show that ULOOF can offload up to 73 percent of computations, and improve the execution time by 50 percent while at the same time significantly reducing the energy consumption of mobile devices.

The growing importance of Internet of Things (IoT) calls for tools able to provide users with secure systems. Traditional approaches to analyze distributed systems are not expressive enough to address this challenge. As a solution, we present a framework to analyze networked embedded systems. Our key insight is to look at a distributed system as a single body, and not as separate programs that exchange messages. We then can crosscheck information inferred from different nodes. To construct this global view of a distributed system, we introduce a novel algorithm that discovers inter-program links efficiently. Such links lets us build an inter-program view, a knowledge that we can thus forward to a traditional static analysis tool. We prove that our algorithm always terminates and that it correctly models the semantics of a distributed system. We have implemented our solution on top of the LLVM compiler, and have used it to secure six ContikiOS applications against buffer overflow attacks. Our solution produces code that is as safe as code secured by more traditional analyses; however, our binaries are on average 18% more energy-efficient.

An ever-growing number of embedded devices supports different kinds of applications, such as healthcare, surveillance, gas monitoring, and others, that require an elevated level of communication reliability. However, the expected high density of those embedded devices increases the competition for frequency spectrum, making it difficult to achieve a reliable machine-to-machine (M2M) communication. To overcome these difficulties, the use of link quality estimators (LQE) is crucial to provide a solid communication. In order to provide robust and faster communication under harsh conditions, this paper proposes a new LQE, called PRR2, which uses two metrics and two levels of PRR (Packet Received Ratio). The use of two PRR sliding windows captures link quality variations in the short term and also considers the long-term. PRR2is compared against the state of the art on a prototype using USRPs, and the results show that the proposal reduces the number of retransmissions and increases the delivery rate, which are two important metrics for link layer reliability.

Vehicular Ad Hoc Networks (VANETs) are mobile networks that extend over vast areas and have intense node mobility. These characteristics lead to frequent delays and disruptions. A solution is to employ the Delay Tolerant Network (DTN) paradigm. However, the frequent disruptions as well as the delay and reliability constraints of certain VANET applications hinder the employment of both conventional and DTN-based management architectures. This paper tackles monitoring, one of the tasks of network management. We describe a hierarchical architecture that copes with near real-time as well as non real-time monitoring tasks. The proposed solution is evaluated using simulations, where we measure the delay and delivery rates of the monitoring data. The results show that the proposed solution reduces the delivery delay and increases the chances that a notification will be delivered on time to its destination.

The use of mobile devices for work and for entertainment is growing. However, some applications may consume too much battery or be too CPU intensive for those devices. One alternative is to dynamically offload parts of the computation of those applications to the Cloud, reducing the battery usage of the devices and improve the responsiveness of the applications. One of the biggest challenges in offloading is to decide whether offloading a certain computation should be performed or not, since the time and energy required to upload the code as well as the input parameters may be less advantageous than executing it locally. This paper proposes a location aware decision engine for mobile offloading. The proposed decision engine can be easily plugged into any offloading framework. Further, we propose a black-box approach to estimate a method's execution time using simple code annotations. An experimental evaluation indicates that location-aware offloading can reduce the energy usage by 50%, with a CPU overhead of approximately 10%.

The Internet of Things (IoT) is increasingly more relevant. This growing importance calls for tools able to provide users with correct, reliable and secure systems. In this paper, we claim that traditional approaches to analyze distributed systems are not expressive enough to address this challenge. As a solution to this problem, we present SIoT, a framework to analyze networked systems. SIoT's key insight is to look at a distributed system as a single body, and not as separate programs that exchange messages. By doing so, we can crosscheck information inferred from different nodes. This crosschecking increases the precision of traditional static analyses. To construct this global view of a distributed system we introduce a novel algorithm that discovers inter-program links efficiently. Such links lets us build a holistic view of the entire network, a knowledge that we can thus forward to a traditional tool. We prove that our algorithm always terminates and that it correctly models the semantics of a distributed system. To validate our solution, we have implemented SIoT on top of the LLVM compiler, and have used one instance of it to secure 6 ContikiOS applications against buffer overflow attacks. This instance of SIoT produces code that is as safe as code secured by more traditional analyses; however, our binaries are on average 18% more energy-efficient.

In the recent years, the research community proposed several protocols and applications for opportunistic networking. A common assumption is that all nodes have pro social behavior and are willing to cooperate with the network. However, in opportunistic networking applications, this assumption can lead to degradations in the network performance. People can be selfish and this behavior affects the operation of the network. In this work, we propose an incentive mechanism to improve routing, called MINEIRO, which aims to detect and avoid selfish nodes based on the source of the messages. We demonstrate under which constraints our algorithm leads to Bayesian equilibrium. Moreover, we show that without an incentive mechanism the network supports up to 60% of nodes with selfish behavior without performance degradation in a random mobility scenario. Meanwhile, in a scenario with social-based mobility, the performance decreases linearly for more than 20% of selfish nodes. Our proposal, on the other hand, improves the performance with any amount of selfish nodes by encouraging users to relay messages from third-parties.

With the increasing number of users subscribing to mobile Internet such as 3G and 4G networks, Wireless Internet Services providers (WISP) aim to provide a good service for customers while elevating the number of clients. Several proposals to offload the traffic of 3G networks were made in the last few years, including the use of femtocells, WiFi offloading and more recently mobile-to-mobile opportunistic offloading. In this paper, we propose a multi-criteria decision-making framework to manage the offload of data from 3G networks using mobile-to-mobile opportunistic communications. Primarily, we focus on building a decision framework that employs only user knowledge to select which users should handover from infrastructure to mobile-to-mobile network, avoiding changes in the infrastructure. Next, we evaluate our proposal and demonstrate its feasibility through trace-driven simulations, achieving 6% of data offload when there is no delay tolerance in the application and up to 36% when application can tolerate 20 minutes of delay.

Vehicular Disruption Tolerant Networks appear due to the search of information by drivers, which build mobile ad hoc networks that may suffer from long interruptions. This paper proposes a certification mechanism by means of social networks, enabling cars to share keys through direct contacts between two acquaintances that warrant their identity, so they sign a reciprocal certificate. Certificates signed by a third party can be validated if the user's public key is available to the other party. Further, the reputation mechanism can identify certificates of trusted users. The evaluation shows the behavior of a vehicular network that uses certificates in social networks as cryptographic security mechanism to establish a degree of trust between users.

Several applications of Wireless Sensor Networks (WSN) demand information only from sub-regions of the monitored area. The user establishes the region of interest (RoI) and requires the WSN to collect data only inside this region. This kind of query is called spatial query. The state of the art of spatial query processing considers, in general, that nodes do not fail. However, nodes can be destroyed, interference can deny the communication and nodes can go to sleep mode (turn off the radio in duty cycles) in order to save energy. This work proposes a fault-tolerant energy efficient in-network spatial query processing mechanism for WSN. The proposed mechanism employs failure detection algorithms in order to avoid failed neighbors when forwarding and processing spatial queries. Our contributions are lightweight and resilient because they operate on demand and avoid the use of routing tables.

The extreme dynamics of wireless multi-hop networks (WMN) - those that use multiple wireless links to forward data - require self-configuration solutions in every communication layer. Self-configuration demands mechanisms for information sharing, as well as specific decision-making algorithms for each service or application. Self-configuration must also be embedded in the communication layers in order to cope with changes in the wireless environment. This article is a digest of the three contributions of, which investigated the self-configuration tasks of programmability, sharing of information and specialized control loops for WMN. First, we designed a middleware, based on the Information Plane (InP) concept, to ease the programmability and sharing of information among nodes. Next, we proposed two InP-based control loops for WMN: one for data rate and transmission power aware routing, and another for mobile context-aware peer-to-peer services.

The Simple Network Management Protocol (SNMP), proposed almost 20 years ago, still remains as the de facto standard for TCP/IP networks managing. However, there are few studies that present the real usage of SNMP in production networks. This work aims at presenting a characterization study that was performed over a sample of the management traffic from the Brazilian Education and Research Network (RNP). The achieved results may help both academy and industry in the development of new technologies for managing networks, in order to better meet the needs of the production networks users and administrators.

Nowadays, the number of disasters occurred and the people affected have been increasing. Everyday, newspapers report new disasters around the world. Immediately after the occurrence of a disaster, the first responders go to the affected region to rescue people and solve eventual problems. These areas offer many dangers to the rescue team. Furthermore, the communication network infrastructure has usually been destroyed. For these reasons, it is important to create a structure to sense environmental data to detect hazards. This structure needs to be independent, easy deployed and adapted to different situations. This paper presents a energy efficient mechanism for processing spatial queries on wireless sensor networks to detect dangers in disaster situations. This is different from other studies found in literature, that only process spatial queries with rectangular regions of interest. The proposed mechanism manipulates queries with regions of interest that have irregular shapes and reduce the overhearing consumption. In the scenarios analyzed, this mechanism decreased by about 60% of energy consumption, with an inaccuracy of approximately 14% in information collected, if compared with the network without improvements.

Wireless sensor networks are subjected to several types of attacks specially attacks of denial of service types (DoS). Several mechanisms and techniques were proposed to provide security to wireless sensor networks, like cryptographic process, key management protocols, intrusion detection systems, node revocation schemas, secure routing, and secure data fusion. A recent work proposes a security management framework to dynamically configure and reconfigure security components in sensor networks according to management information collected by sensor nodes and sent to decision-maker management entities. It turns on or off security components only when they are necessary, saving power and extend network lifetime. The architecture is policy based, what enable rules configuration specific for each application. We evaluate that security management framework, showing possibilities to save power and how that work can contribute to extend network lifetime. We propose some scenarios to evaluate the performance of the security management framework and estimate the cost of security components.

Abstract Resource selection on current brokers still presents challenges for achieving the best solution in the decision‐making process, especially when considering many factors. We approach this problem considering user preference for specific resource selection objectives such as expected performance for an application execution, resource access restriction, execution application cost, and resource reliability. For each objective, we employ different techniques and combine them in a decision theory model. By considering performance in the selection process, we use the case‐based reasoning technique based on similar past job executions to predict a new job time execution. With the resource access restriction as a selection factor, we develop a fine‐grain policy‐based model for distributed resource access verification. Unlike a global access policy, which applies to all resources in a virtual organization, a fine‐grain policy establishes rules for specific resources and users. In this case, a previous access restriction verification prevents a resource selection, which may deny access to a requisition, resulting in an unsuccessful submission. The decision model uses the multi‐attribute utility theory, which relates the important objectives above and allows different proportions of user preferences for each objective. The complete solution is distributed and implemented using a multi‐agent system, acting as a resource broker. All models of this paper are analyzed in a real environment, presenting appropriate functional behaviors. Results show that our prediction model is accurate and efficient in the prediction process and our distributed model runs faster than centralized approaches and considers access restriction heterogeneities. Copyright © 2008 John Wiley & Sons, Ltd.

Due to the emergence of multimedia context-rich applications and services over wireless networks, networking protocols and services are becoming more and more integrated, thus relying on context and application information to support their operation. Further, wireless protocols and services now employ information from several network layers and the environment, breaking the layering paradigm. In order to cope with this increasing reliance on information, we have proposed MANIP, a middleware for MANETs that instantiates a new networking plane. The Information Plane (InP) is a distributed entity to store and disseminate information concerning the network, its services and the environment, orchestrating the collaboration among cross-layer protocols, autonomic management solutions and context-aware services. We use MANIP to support the autonomic reconfiguration of a P2P network over MANETs. Simulation results show that the MANIP-enabled solutions reduce the response time and increase the number of solved P2P queries when compared to classic, cross-layer implementations of the same protocols.

Communication is usually the most energy-consuming event in wireless sensor networks (WSNs). The lifetime of these networks is determined by the capacity of batteries and one of the biggest challenges is to reduce the energy consumed in the communication. Several techniques to reduce the energy consumption have been proposed in routing protocols: diminish the traffic of data, optimize the routes or the control of the topology. However, the management and operation of the node’s radio is responsibility of the medium access control (MAC) protocol. This thesis presents four new transmission power control (TPC) techniques for MAC protocols in WSNs. These techniques are based on the interaction between sensor nodes and take into account the limitations of resources such as processing, memory and energy in the calculation of the minimum of transmission power. To evaluate these techniques, four MAC protocols had been developed: Iterative, Attenuation, AEWMA and Hybrid. The Iterative was the first MAC protocol with TPC developed exclusively for WSN. These protocols have been experimented in the Mica Motes2 platform, in diverse scenarios varying parameters such as internal and external environment, the distance among the communicating nodes, the occurrence of simultaneous transmissions, the use of multi-hop transmissions and node mobility. Results have shown that TPC protocols reduce the energy consumption by up to 57% in comparison to the protocols with fixed transmission power. Moreover, the protocols with TPC increase the throughput of the network while maintaining a very high delivery rate.

In mobile wireless ad hoc networks (MANETs), layering is frequently broken to cope with changes on the medium. Layering is also violated in order to implement autonomic behavior, which depends on correlating data from various layers to identify relevant events. This paper proposes a networking plane, called MANET knowledge plane (MANKOP), that stores information concerning all protocol layers. This plane improves network performance, as protocols may employ a broader range of inputs on their algorithms. Moreover, being an information repository, MANKOP eases the deployment of self-optimizing and self-configuring mechanisms. To showcase our solution, we use MANKOP to build transmission power control aware protocols, which increases the throughput of the network by up to 280%.

This paper proposes a security management framework to dynamically configure and reconfigure security components in sensor networks according to management information collected by sensor nodes and sent to decision-maker management entities. The idea is to give sensor networks tools to become self-managed and more autonomic. The security management model includes the definition of security levels, management information base, protocol messages and events.

Grid technology allows resource sharing among several entities, but selecting the best resource to run a specific job remains one of its main problems. The challenges for the best resource selection involve analysis of several factors such as prediction time to run a job, access restriction to resources, and cost to use resources. This paper presents a resource selection model using decision theory for combining these influential factors in the resource selection process. The model is based on a multi-attribute utility function that maximizes the user preference. This model forms part of a multi-agent system deploying distributed and parallel processing for job execution prediction, access policy verification, and resource selection models. All models of this paper were verified through experiments using real machines. They have presented appropriate functional behaviors and positive performance results.

Hop-by-hop authentication in wireless sensor networks (WSNs) prevents outside intruders from taking part in the network and launching attacks. Due to resource constraints of WSNs, standard key distribution schemes are not applicable. Such networks require resilience when intruders compromise a key. This paper presents NEKAP - neighborhood-based key agreement protocol, a key distribution protocol for WSNs. NEKAP is intruder resilient: an intruder that compromises a key has access to the links of a limited neighborhood, and not to those in other regions of the network. NEKAP is energy-efficient in that only a few messages are exchanged in small neighborhoods to establish the keys. The paper presents the protocol and algorithms, make a security analysis, compare it with other protocols in the literature and shows one implementation for the TinySec platform.

There are many approaches that can be applied to the management of wireless sensor networks (WSN) and nowadays the research in this field is only beginning. A management approach determines how the management monitor and control functions are performed by the network and network elements. Since a wireless sensor network presents severe resource constraints, a certain management approach must be chosen properly in order to optimize resource usage and at the same time satisfy the application requirements. In this paper we try to answer the following question: what is the tradeoff between to migrate management services or to adopt traditional approaches for WSN management? We have concluded from simulated and experimental results that the choice of the best approach to be adopted must consider the size of the network, the configuration of the nodes, the application goals and the management complexity, in terms of managed objects. We have also noticed that, despite the service migration approach being theoretically interesting for WSN management, for the sensor networks and sensor nodes hardware current technological situation, the approach would be applied with some negative impact on the performance of the network.

Abstract One of the main focuses of Grid computing is solving resource‐sharing problems in multi‐institutional virtual organizations. In such heterogeneous and distributed environments, selecting the best resource to run a job is a complex task. The solutions currently employed still present numerous challenges and one of them is how to let users know when a job will finish. Consequently, reserve in advance remains unavailable. This article presents a new approach, which makes predictions for job execution time in Grid by applying the case‐based reasoning paradigm. The work includes the development of a new case retrieval algorithm involving relevance sequence and similarity degree calculations. The prediction model is part of a multi‐agent system that selects the best resource of a computational Grid to run a job. Agents representing candidate resources for job execution make predictions in a distributed and parallel manner. The technique presented here can be used in Grid environments at operation time to assist users with batch job submissions. Experimental results validate the prediction accuracy of the proposed mechanisms, and the performance of our case retrieval algorithm. Copyright © 2006 John Wiley & Sons, Ltd.

Wireless sensor networks (WSNs) are characterized by having scarce resources. The usual way of designing network functions is to consider them isolatedly, a strategy which may not guarantee the correct and efficient operation of WSNs. For this reason, in this paper we propose an integrated design of network functions. We take two important WSN functions - density control and routing - as an example and present two approaches to integrate them. In particular, we present two solutions, named RDC-sync and RDC-integrated, which integrate a geographical density control algorithm with tree routing. The simulations experiments performed prove that the integrated design improves the network performance, especially when density control and routing are fully integrated

The goal of a wireless sensor network (WSN) management solution is to promote network resources productivity and quality of services. This paper presents a comparative study of client/server (CS) and mobile agent (MA) approaches used in distributed self-management solutions for a hierarchical heterogeneous WSN, in which the managers are embedded into the cluster head nodes. These approaches were simulated and evaluated according to energy consumption. Considering energy consumption with transmission, the results reveal that the MA approach is more scalable than the CS one, tending to be more useful when the number of network elements and managed objects increases. On the other hand, the CS approach presents more interesting results regarding energy consumption with processing. Besides, the simulations performed also show that the MA size is an important parameter that impacts directly on the results

Wireless sensor networks are ad hoc networks with severe resource constraints. These constraints preclude the use of traditional ad hoc protocols, and demand optimizations that incur in solutions specific to a class of applications. This article presents PROC, a protocol designed for continuous data dissemination networks, that interacts with the application to establish routes, allowing the application to reconfigure PROC on runtime. A performance evaluation in topologies varying from 50 to 200 nodes showed that PROC increases network lifetime around 7% to 12%, and has higher throughput than EAD and TinyOS Beaconing. Furthermore, PROC presents a softer performance degradation when the number of nodes in the network increases.

Medium access control (MAC) protocols manage energy consumption on the network element during communication, which is the most energy-consuming event on Wireless Sensor Networks (WSNs). One method to mitigate energy consumption is to adjust transmission power. This paper presents two approaches to adjust transmission power in WSNs. The first approach employs dynamic adjustments by exchange of information among nodes, and the second one calculates the ideal transmission power according to signal attenuation in the link. The proposed algorithms were implemented and evaluated with experiments, comparing their results with B-MAC, the standard MAC protocol in the Mica Motes 2 platform. Results show that transmission power control is an effective method to decrease energy consumption, and incurs in a negligible loss in packet delivery rates. For node distances of 5m, the proposed transmission power control techniques decrease energy consumption by 27% over B-MAC.

Both mobile ad hoc networks (MANETs) and peer-to-peer (P2P) networks are decentralized and self-organizing networks with dynamic topology and are responsible for routing queries in a distributed environment. Because MANETs are composed of resource-constrained devices susceptible to faults, whereas P2P networks are fault-tolerant, P2P networks are the ideal data sharing system for MANETs. We have conducted an evaluation of two approaches for P2P content discovery running over a MANET. The first, based on unstructured P2P networks, relies on controlled flooding, while the second, based on structured P2P networks, uses distributed indexing to optimize searches. We use simulations to evaluate the effect of network size, mobility, channel error rates, network workload, and application dynamics in the performance of P2P protocols over MANETs. Results show that unstructured protocols are the most resilient, although at higher energy and delay costs. Structured protocols, conversely, consume less energy and are more appropriate for MANETs where topology is mostly static.

In this paper, a new data dissemination algorithm for wireless sensor networks is presented. The key idea of the proposed solution is to combine concepts presented in trajectory-based forwarding with the information provided by the energy map of the network to determine routes in a dynamic fashion, according to the energy level of the sensor nodes. This is an important feature of an autonomic system, which must have the capacity of adapting its behavior according to its available resources. Simulation results revealed that the energy spent with the data dissemination activity can be concentrated on nodes with high-energy reserves, whereas low-energy nodes can use their energy only to perform sensing activity or to receive information addressed to them. In this manner, partitions of the network due to nodes that ran out of energy can be significantly delayed and the network lifetime extended.

Wireless Sensor Networks (WSNs) have emerged as a new monitoring and control solution for a variety of applications. Although the behavior of a WSN is characterized by the type of its application, a common element exist: faults are a normal fact, not isolated events as in traditional networks. Thus, in order to guarantee the network quality of service it is essential for the WSN to be able to detect failures and perform something akin to healing, recovering from events that might cause some of its parts to malfunction. In this work we propose and evaluate a failure detection scheme using a management architecture for WSNs, called MANNA. We take a deep look at its fault management capabilities supposing the existence of an event-driven WSN. This is a challenging and attractive kind of WSN and we show how the use of automatic management services defined by MANNA can provide self-configuration, self-diagnostic, and self-healing (some of the self-managing capabilities). We also show that the management solution promote the resources productivity without incurring a high cost to the network.

The management of huge, heterogeneous and highly distributed networks is a very complex task due to their low degree of integration. Such a network is that of Telemar, the largest telecommunications company of Brazil. The plant is composed of a large variety of switching equipments and transmission systems, differing not only by their vendors but also by their technology, age and way of monitoring and control. In order to solve that management problem, a distributed management platform called SIS (system for integration of supervision) was developed. This platform is fully operational and implemented in a real telecommunications plant. SIS has been evolving since the mid 1990s, when it was initially deployed. The platform was conceived to allow the management of new systems and showed to be a key factor for the company as its business focus also evolves (e.g., the management of data communication networks of other companies such as a bank, an airline company, and a large retailer). We discuss the development and use of the SIS platform, its design and main modules.

A wireless sensor network aims to collect data and, sometimes, control an environment. This kind of network is composed of hundreds to thousands of devices that have the capability of sensing, processing and wireless communicating, called sensor nodes. The sensor nodes are projected with small dimensions (cm/sup 3/ or mm/sup 3/) and this size limitation ends up restraining the node resources, like energy, processor and transceiver capacity. The task of building and deploying management systems in environments where there will be tens of thousand of network elements with particular features and organization is very complex. This task becomes worse due to the physical restrictions of these unattended sensor nodes. In this paper we have implemented and evaluated some automatic services of configuration and performance management, proposed by a WSN management architecture called MANNA. This architecture is based on the paradigm of self-management, which contains the automatic functions and services of management using a minimum of human interference. This work aims to evaluate different WSN configurations considering an application of continuous data sensing and dissemination, and the effects of the management solution proposed for this network. The built application does temperature and carbon monoxide concentration level monitoring, in an urban area. The results show the cost-benefit relations of the different organizations and demonstrates that management can promote the productivity of the resources and control the quality of the provided services.

A wireless sensor network is a special kind of ad-hoc network with distributed sensing and processing capability that can be used in a wide range of applications, such as environmental monitoring, industrial applications and precision agriculture. Despite their potential applications, such networks have particular features imposed by resource restrictions, such as low computational power, reduced bandwidth and specially limited power source. In case of a network with a high density of sensor nodes, some problems may arise such as the intersection of sensing area, redundant data, communication interference, and energy waste. A management application is necessary to make the most of network resources. On the other hand, a high-density network can introduce a fault-tolerant mechanism, increase precision, and provide multi-resolution data. The network density control depends on the application. In this paper, we propose a method to set up which nodes should be turned off or on. The management may take the sensor node out of service temporally. Our design uses a Voronoi diagram, which decomposes the space into regions around each node. That schema could be used in management architecture for a wireless sensor network.

One aspect that differentiates telecommunications and network services providers are the efficient use of their information. The current telecommunications environments are composed of a vast quantity of information largely disconnected and disentangled. Organizing such information so that it can be used in agreement with the value it possesses doesn't constitute a trivial task. This paper discusses some questions concerning analysis of management data originated from telecommunication's environments. First, some concepts related to service management and personnel performance are discussed. Second, the design and implementation of a system for event analysis is presented. The system is able to process historical data from a management database and to deliver timely, concise, well organized and Web-based information about two aspects of a telecommunication plant: the events that come from the managed resources and the performance and behavior of the management work force. The system has features like summarization of data, independence of management platform, flexible operation interface, possibility of viewing data in different levels of detail and grouping, besides the possibility of a compared analysis of performance and behavior of managed elements. In the sequence, a case of use of the developed system and the results analysed are presented. The management platform used was the System for Integration of Supervision (SIS), system that motivated this work.

In this paper we report our experience of introducing Web interfaces into a real distributed management platform that provides a consistent and uniform way of accessing services such as fault management, performance management, business management, call center, and service management. The use of Web interfaces that allow to access these services was greatly felt by the organization and improved enormously the quality of service provided to the clients.

A model for alarm correlation in a telecommunication network is proposed, which contributes to the identification of possible causes for the alarms received, as well as the prediction of possible effects of some known faults. The model allows the partition of the telecommunication network into several smaller sub-networks, while preserving some basic information on the propagation of the effect of faults among sub-networks. The breakdown of the problem into smaller sub-problems facilitates its solution and allows the use, in each sub-network, of the correlation technique most suitable to their peculiarities. It also allows the execution of several correlation processes in parallel, increasing the speed of the overall correlation process and reducing the complexity of the problem. This approach named "multi-focal correlation", may be recursively utilized in each sub-network until the network element level is reached.

In heterogeneous telecommunications networks with a variety of legacy systems of many kinds, the activity of network supervision is a difficult task and involves a large number of operators with a diversity of skills. In such environments, the time to diagnose problems and correct faults usually is high and subject to many errors. This paper discusses these problems and some techniques and methods for automated fault management. Next, it presents the design and implementation of a system that automatically corrects faults collected by a fault management system. Its utilization greatly reduces mean time to repair faults, therefore increasing the quality of service perceived by clients. The system also reduces the cost of fault management, as it reduces the required work for supervision. The main characteristics of the system enforces its generality: it can correct faults of any network element type and it is a case-based reasoning system, so enabling the inclusion of new cases to be corrected without interfering in the cases already being treated.

This paper presents a proposal of a general model for telecommunication networks based on distributing sets of sub-networks (or sub-network classes) into layers, according to functional affinity criteria. Based on the definition of some rules for the utilization of the communication services among sub-networks, a telecommunication network is modeled as a directed acyclic graph, where each node represents a sub-network or a sub-network class, and an edge indicates a functional dependence among sub-networks. The proposed model has several applications in the integrated management of telecommunication networks and, specially, in the correlation of the alarms generated in these networks.

This paper presents SIS (System for the Integration of Supervision), a system that has established a new paradigm for the management of telecommunication networks. The SIS approach is based on the gradual integration of the existing management systems-which are often proprietary-to a reference integrating platform, while concurrently pursuing the conformance of the platform to the ITU-T network management standard, that is, TMN. The paper presents, besides the platform itself, the methodology adopted in the design of the whole system. It includes the statement of the original problem, requirements, functionalities, and restrictions. The functional, hardware, software and data architectures are also presented, as well as the data distribution schemes and the man-machine interface. The problems found and the solutions adopted throughout the development and deployment phases are also summarized. The platform has been developed in Brazil by the Federal University of Minas Gerais (UFMG) jointly and funded by the Minas Gerais State Telecommunications Company (TELEMIG). Today the system is totally deployed, distributed over a large area, collecting data from thousands of points and executing actions upon hundreds of devices.