A fully decentralized trust management and cooperation incentives framework for wireless user-centric networks

This special issue is devoted to the new research addressing challenges in the areas of wireless computing and networking. Wireless computing and networking has enabled heterogeneous access technologies for providing services to ubiquitous users. The demanding networking environment of wireless communications imposes many challenges, such as channel estimation, network protocol design, resource management, and systematic design. Due to nonuniform spectrum allocation, various radio resource management policies, economic concerns, scarcity of radio resources, and user mobility, overcoming these issues becomes more demanding. This special issue includes ten research articles from five countries including the United States, China, South Korea, Norway, and India which covers various research issues in the areas of wireless computing and networking. These articles can be broadly classified into three categories: (1) data management and query processing in wireless networks; (2) backbone management in wireless networks; and (3) routing in wireless networks. A detailed description of the corresponding works in each category is given below.

While worms and their propagation have been a major security threat over the past years, causing major financial losses and down times for many enterprises connected to the Internet, we will argue in this paper that valuable lessons can be learned from them and that network management, which is the activity supposed to prevent them, can actually benefit from their use. We focus on five lessons learned from current malware that can benefit to the network management community. For each topic, we analyse how it is been addressed in standard management frameworks, we identify their limits and describe how current malware already provides efficient solutions to these limits. We illustrate our claim through a case study on a realistic application of worm based network management, which is currently developed in our group.

On the optimal formulation of resource management in wireless networks by Jihui Zhang thesis 2005 xvi, 176 leaves : ill. ; 30 cm Resource management is of great importance in wireless networks, as it can improve the scarce wireless spectrum…Read more ›

We propose a framework for adaptive policy-based quality of service (QoS) management in wireless networks. The proposed framework is a multi-layer agent system where agents cooperate to dynamically adapt network behavior to maintain the delivered QoS level to the user. QoS prediction agents utilize user information such as the user profile, location and terminal characteristics, to predict future changes in the supplied QoS. These changes are then reported to a set of QoS adaptation agents, which dynamically adapt the network policies to meet the user's requirements. The proposed adaptive approach assembles policies at run-time given sets of imposed constraints and goals to be satisfied. The new approach gives more flexibility to users and applications to dynamically change their QoS requirements while maintaining a smooth delivery of QoS.

Below the Media Access Control (MAC) layer is the Physical (PHY) layer which deals with the actual transmission of the bits received from the MAC layer above into electromagnetic signals. This layer is optimized to implore power management in wireless networks. Power management is a crucial issue in wireless and mobile networks. In this paper, we propose an Adaptive Context-Aware Rate Selection (ACARS) algorithm to handle the issue of power consumption in wireless networks. This algorithm is implemented by optimizing the PHY layer to transmit efficiently as the number of nodes changes and we estimate the Signal-to-Noise Ratio (SNR) to the PHY layer. Results show that by using the appropriate power management technique, ACARS is reliable and efficient for power consumption in wireless networks which is a high demand for vehicular networks.

Effective resource management plays a pivotal role in wireless networks, which, unfortunately, typically results in challenging mixed-integer nonlinear programming (MINLP) problems. Machine learning-based methods have recently emerged as a disruptive way to obtain near-optimal performance for MINLPs with affordable computational complexity. There have been some attempts in applying such methods to resource management in wireless networks, but these attempts require huge amounts of training samples and lack the capability to handle constrained problems. Furthermore, they suffer from severe performance deterioration when the network parameters change, which commonly happens and is referred to as the task mismatch problem. In this paper, to reduce the sample complexity and address the feasibility issue, we propose a framework of Learning to Optimize for Resource Management (LORM). In contrast to the end-to-end learning approach adopted in previous studies, LORM learns the optimal pruning policy in the branch-and-bound algorithm for MINLPs via a sample-efficient method, namely, imitation learning . To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, named LORM-TL , which can quickly adapt a pre-trained machine learning model to the new task with only a few additional unlabeled training samples. Numerical simulations demonstrate that LORM outperforms specialized state-of-the-art algorithms and achieves near-optimal performance, while providing significant speedup compared with the branch-and-bound algorithm. Moreover, LORM-TL, by relying on a few unlabeled samples, achieves comparable performance with the model trained from scratch with sufficient labeled samples.

In this paper, we present a wireless networks management system based on a mobile terminal detector.The system is designed on C/S architecture, consisting of client and server software, a centralized database and a data sharing in-sync mechanism.It helps to detect wireless access point devices automatically.The detailed information of wireless access point devices, such as precise location, will be stored in a shared centralized database.By using the data sharing in-sync mechanism, the data is consistent, even when multiple terminals access the database at the same time.The system makes wireless network safer, and network management work easier.

As SDN technology is becoming popular in the field of computer networks management, the need for evolvable SDN-based network management systems arises. SDN facilitates introducing abstractions to the network through programmability and control separation. In this paper, we propose an abstraction in the SDN management layer through adopting a concept of separation of network concerns. We present a management framework, termed NWManager, that provides an abstraction to the network administrator through policies based on network management functions, like behaviour analysis and monitoring, quality of service preservation and security. And based on network concerns as follows, application, communication, resources concern and common concern representing the area of intersection between basic concerns. NWManager provides various network management functions in one system. Our conducted network simulation scenario by mininet demonstrates that this abstraction and separation of concerns in management layer facilitates and enhances the management of the network. As well, it extends the network control parameters provided to the network administrator.

A network management system can be divided into the management function and management intelligence logically according to the coordination concept. We represent a cooperative network management (CNM) framework using a reactive intelligent agent. The agent includes two types of objects. The event manager agent communication object (ACO) and business object repository compose the management function. Management intelligence is embodied in an engine which analyzes the received events from the event manager and messages from the ACO, then decide to invoke the business object's method based on action rules. The CNM framework is comprised of a management console, cooperative intelligent agents and access adapters. Using this framework, the administrator can decompose the management task into several subtasks and delegate these subtasks to cooperative intelligent agents. The management framework enables the administrator to modify the intelligent agent's subtask without interrupting the system's running.

Resource management in UAV-assisted wireless networks: An optimization perspective

Cooperative communication is a promising technique for future wireless networks. It can be used in improving communication reliability, and enhancing power and spectrum efficiency. However, its performance gain degrades in the presence of co-channel interference which makes it essential to propose interference mitigation schemes. In this paper, we introduce two efficient cooperative communication schemes with interference management for multi-user cooperative wireless networks that are based on best relay and user selection (BRUS) technique. BRUS maximizes the received signal to noise ratio (SNR) while minimizing the interference by an optimal time slot allocation for the users. The first introduced scheme is using BRUS technique only if the user demands cooperation, and the second scheme is always using BRUS technique to enhance the system performance. We derive exact closed form expressions for the outage probability, ergodic capacity, average consumed power, and average bit error probability for the introduced cooperative schemes. Simulations are used to validate the analytical results and an agreement is observed. The results confirm the advantage of the introduced cooperation schemes in enhancing the system performance and improving the interference management.

Rapid advances in wireless networking technologies have enabled mobile devices to be connected anywhere and anytime. While roaming, applications on these devices dynamically discover hosts and services with whom interactions can be started. However, the fear of exposure to risky transactions with potentially unknown entities may seriously hinder collaboration. To minimise this risk, an engineering approach to the development of trust-based collaborations is necessary. This paper introduces hTrust, a human trust management model and framework that facilitates the construction of trust-aware mobile systems and applications. In particular, hTrust supports: reasoning about trust (trust formation), dissemination of trust information in the network (trust dissemination), and derivation of new trust relationships from previously formed ones (trust evolution). The framework views each mobile host as a self-contained unit, carrying along a portfolio of credentials that are used to prove its trustworthiness to other hosts in an ad-hoc mobile environment. Customising functions are defined to capture the natural disposition to trust of the user of the device inside our trust management framework.

Over the past few years, wireless mesh networks (WMNs) have emerged to become a ubiquitous solution for providing wireless access at lower cost, greater flexibility, higher reliability and performance compared to conventional wireless local area network (WLANs). As the demand for delay sensitive real-time applications such as VOIP and streaming media grows, mobility management is becoming a critical function of WMN. Although Mobility management schemes designed for Mobile Ad Hoc Networks (MANETs) and traditional fixed networks might be useful for WMNs, mobility management in WMNs is still an active research issue because of the special properties of WMNs. This chapter focuses on the mobility management in wireless mesh networks, which takes into account the unreliability of wireless mesh links and other special properties of WMNs. The chapter began by discussing the special properties of WMNs and the general concepts of mobility management, and then gives an overview of the mobility management problems in wireless mesh networks (layer 2 handoff, vertical handoff and cross layer design, etc.) with some potential solutions.

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes—a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., $k$ -nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner.

Optimizing compatible sets in wireless networks through integer programming