André Riker

Video quality estimator for wireless mesh networks

As Wireless Mesh Networks (WMNs) have been increasingly deployed, where users can share, create and access videos with different characteristics, the need for new quality estimator mechanisms has become important because operators want to control the quality of video delivery and optimize their network resources, while increasing the user satisfaction. However, the development of in-service Quality of Experience (QoE) estimation schemes for Internet videos (e.g., real-time streaming and gaming) with different complexities, motions, Group of Picture (GoP) sizes and contents remains a significant challenge and is crucial for the success of wireless multimedia systems. To address this challenge, we propose a real-time quality estimator approach, HyQoE, for real-time multimedia applications. The performance evaluation in a WMN scenario demonstrates the high accuracy of HyQoE in estimating the Mean Opinion Score (MOS). Moreover, the results highlight the lack of performance of the well-known objective methods and the Pseudo-Subjective Quality Assessment (PSQA) approach.

A quality of experience handover system for heterogeneous multimedia wireless networks

The convergence of emerging real-time multimedia services, the increasing coverage of wireless networks and the ever-growing popularity of mobile devices, are leading to an era of user-centric multimedia wireless services. In this scenario, heterogeneous communications will co-exist and ensure that the end-user is always best connected. However, the Quality of Experience (QoE) support for emerging video applications in multi-operator environments remains a significant challenge and is crucial for the success of wireless multimedia systems. This paper presents a Quality of Experience Handover Architecture for Converged Heterogeneous Wireless Networks, called QoEHand. QoEHand allows users of multimedia content to be always best connected in IEEE 802.11e and IEEE 802.16e environments. Simulation results show the impact and benefit of the proposed solution in multi-access and multi-operator wireless scenarios by using objective and subjective QoE metrics.

A Two-Tier Adaptive Data Aggregation Approach for M2M Group-Communication

Network lifetime is the time interval in which the nodes are operational. Considering that machine-to-machine (M2M) devices have limited energy resources, an important challenge in M2M communications is to prolong the network lifetime. The constrained application protocol (CoAP) supports multi-target monitoring applications in M2M communications, allowing the creation and maintenance of groups, as well as their periodic communication. It is essential to aggregate the CoAP group-communication over the paths to increase the network lifetime of low-power M2M devices, since data aggregation reduces the use of energy-consuming hardware (e.g., central processing unit and wireless interface). However, the current data aggregation solutions do not specify how to support data aggregation with multiple CoAP-based groups in multi-target monitoring applications. In this paper, the proposed approach, called two-tier aggregation for multi-target applications (TTAMAs), aggregates the data originated from nodes belonging to either the same or different CoAP groups. Furthermore, TTAMA is an adaptive solution because it performs the data aggregation in accordance with the CoAP configurations, such as communication periodicity and data aggregation functions. We compare TTAMA with current data aggregation approaches that use minimum spanning tree and shortest path tree. The results show that TTAMA outperforms the related works in terms of network lifetime and energy consumption.

Data Aggregation for group communication in Machine-to-Machine environments

The energy resources of Machine-to-Machine (M2M) devices need to last as much as possible. Data aggregation is a suitable solution to prolong the network lifetime, since it allows the devices to reduce the amount of data traffic. In M2M systems, the M2M platform and the Constrained Application Protocol (CoAP) enable multiple entities to send concurrent data-requests to the same capillary network. For example, in a Smart Metering scenario, there are devices measuring the electricity consumption of an entire building. The supplier company requests all devices to send the data updates every 1800 seconds (i.e., 30 minutes). On the other hand, a resident requests his/her devices to communicate every 600 seconds (i.e., 10 minutes). These concurrent data-requests create heterogeneous groups over the same capillary network, since each group might be able to execute different in-network functions and to have a unique temporal-frequency of communication. However, the traditional data aggregation solutions designed for periodic monitoring assume the execution of a single static data-request during all network lifetime. This makes the traditional data aggregation solutions not suitable for M2M environments. To fill this gap, this paper presents Data Aggregation for Multiple Groups (DAMiG), which is designed to provide Data Aggregation for heterogeneous and concurrent sets of CoAP data-requests. DAMiG explores the group communication periodicity to perform internal and external-group traffic aggregation. To achieve that, DAMiG computes a suitable aggregation structure and applies statistical and merger aggregation functions along the path. DAMiG is able to reduce the energy consumption in scenarios with single or several concurrent CoAP data-requests. Moreover, the selection of internal and external-group paths takes into account the residual energy of the nodes, avoiding the paths with low residual energy.

A mobile QoE Architecture for Heterogeneous Multimedia Wireless Networks

One of the main requirements in this emerging wireless multimedia era is the Quality of Experience (QoE) assurance for 2D or 3D video applications in heterogeneous multi-operator environments. Therefore, this paper proposes a QoE Architecture for Heterogeneous Multimedia Wireless Networks, called QoEHand. QoEHand extends the Media Independent Handover (MIH)/IEEE 802.21 proposal with QoE-awareness, seamless mobility, dynamic class of service mapping and a set of content adaptation schemes. The proposed solution allows the best connection and considers the QoE needs of mobile clients and available wireless resources in IEEE 802.11e and IEEE 802.16e service classes. Simulation experiments were carried out to show the impact and benefits of QoEHand on the users perception, by using objective and subjective QoE metrics.

Data aggregation for machine-to-machine communication with energy harvesting

Machine-to-Machine (M2M) communications have emerged as a new concept for the next generation of sensing and actuating systems. With the recent emergence of energy harvesting technologies, the current communication solutions have addressed the problem of controlling the data communication to regulate efficiently the energy consumption, aiming to avoid under and overuse of energy. However, these solutions do not consider data aggregation as means of controlling the network traffic. To fill this gap, this paper proposes the Data Aggregation for energy harVesting NETworks (DAV-NET), which regulates the energy consumption in accordance with the residual energy stored in the batteries, exploiting the data aggregation capabilities to control the network traffic. The performed simulations show that the proposed solution is able to regulate the energy consumption in case of abundant or scarce energy, controlling the aggregation level in a distributed fashion.

Efficient and secure M2M communications for smart metering

Machine-to-Machine technology supports several application scenarios, such as smart metering, automotive, healthcare and city monitoring. Smart metering applications have attracted the interest of companies and governments since these applications bring many benefits (e.g. costs reduction and increased reliability) for production, monitoring and distribution of utilities, such as gas, water and electricity. Multi-hop wireless communication is a cost-effective technology for smart metering applications because it extends the wireless range and enables fast deployment. Smart metering data communicated via wireless multi-hop approaches needs mechanisms that makes the communication less vulnerable to security threats and saves the device resources. Data encryption and data aggregation mechanisms emerge as potential solutions to fulfill these requirements. However, the simultaneous execution of data encryption and data aggregation mechanisms is not a trivial task. This is because the data encryption prevents the data aggregation mechanism to summarize the data along the path. Another challenge is to manage both mechanisms according to the concurrent Machine-to-Machine (M2M) applications interests. In this context, we present sMeter, which is a framework that deals with multiple applications interests, avoiding interest conflicts of concurrent users and supporting the management of data aggregation and data encryption. sMeter is implemented using low-cost hardware in an indoor environment. The communication is performed via a wireless multi-hop technology, and the performance of this communication is evaluated in terms of delay, data reception ratio and received signal strength indication.

Group Communication in Machine-to-Machine Environments

M2M systems bring new horizons to the current concept of smart environments, since M2M enables a new set of services and applications. One of the main M2M features is the large number of resource-constrained devices that usually perform collective communication. This characteristic requires the design of network solutions that support the Data Aggregation (DA) of groups of Low Duty Cycling (LDC) devices. If LDC and DA are not designed jointly, the intermittent periods caused by Low Duty Cycling make the execution of Data Aggregation impracticable or with low performance. To address this problem, this book chapter describes the Group Communication Architecture for M2M Environments (GoCAME). This architecture enables the joint execution of DA and LDC, taking into account two-way latency tolerance, and multiple data-types. GoCAME also assures the concurrent execution of data requests, managing groups of nodes to provide the best strategy to reply to each data request.

QoE-based packet drop control for 3D-video streaming over wireless networks

Currently, the Internet is experiencing a revolution in the provisioning and demand of multimedia services. In this context, 3D-video is envisioned to attract more and more the multimedia market with the perspective for enhanced applications (video surveillance, mission critical control, entertainment, etc.). However, 3D-video content delivery places increased bandwidth demands, as well as have rigorous Quality of Service (QoS) and Quality of Experience (QoE) requirements for efficient support. This paper proposes a novel QoE-aware packet controller mechanism, which aims at connecting real-time 3D-video streaming applications over congested wireless networks with acceptable levels of QoS and QoE over the time. Simulation experiments were carried out to show the impact and benefit of our proposal. Subjective and objective QoE metrics were used for benchmarking, showing that in congestion periods our solution improves the quality level of real-time 3D-video sequences from the user point-of-view, as well as, optimizes the usage of wireless network resources.

Energy‐efficient multigroup communication

Wireless devices are widely used to monitor and control multiple groups within the context of machine-to-machine applications. The Constrained Application Protocol provides communication capabilities for applications that demand periodic monitoring of multiple groups. Because of the energy constraints of the devices used, a key challenge is to extend the network lifetime. Data aggregation solutions have been proposed to reduce the amount of network traffic and increase energy efficiency. However, for periodic monitoring of multiple groups, current data aggregation solutions do not exploit the potential of combining multiple payloads in a single message. In addition, solutions in literature are unable to take advantage of the communication interactions that occur when there is traffic originating from different groups. To fill this gap, this paper focuses on a nontraditional data aggregation approach, named two-tier aggregation, that applies the idea of inserting many payloads in 1 message to efficiently gather data from multiple groups, introducing novelty on how the messages are assembled. An integer linear programming model is proposed to maximize the network lifetime in multiple group scenarios. The proposed formulation guarantees the energy efficiency of two-tier aggregation and defines an upper bound for the heuristics. The evaluation shows the lifetime upper bound obtained by the proposed integer linear programming model on different network sizes and compares it to state-of-the-art heuristic solutions.