Power Aware Routing in Mobile Ad Hoc Networks

To support bursty traffic on the Internet (and especially WWW) efficiently, optical burst switching (OBS) is proposed as a way to streamline both protocols and hardware in building the future gener...

Optical burst switching (OBS) - a new paradigm for an optical Internet

Computer communications

In the fifth-generation (5G) mobile communication system, various service requirements of different communication environments are expected to be satisfied. As a new evolution network structure, heterogeneous network (HetNet) has been studied in recent years. Compared with homogeneous networks, HetNets can increase the opportunity in the spatial resource reuse and improve users’ quality of service by developing small cells into the coverage of macrocells. Since there is mutual interference among different users and the limited spectrum resource in HetNets, however, efficient resource allocation (RA) algorithms are vitally important to reduce the mutual interference and achieve spectrum sharing. In this article, we provide a comprehensive survey on RA in HetNets for 5G communications. Specifically, we first introduce the definition and different network scenarios of HetNets. Second, RA models are discussed. Then, we present a classification to analyze current RA algorithms for the existing works. Finally, some challenging issues and future research trends are discussed. Accordingly, we provide two potential structures for 6G communications to solve the RA problems of the next-generation HetNets, such as a learning-based RA structure and a control-based RA structure. The goal of this article is to provide important information on HetNets, which could be used to guide the development of more efficient techniques in this research area.

/pdf/a-survey-on-resource-allocation-for-5g-heterogeneous-4gmdtz8zbn.pdf

A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges

Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks.

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Although many multicast routing algorithms have been proposed in order to reduce the total cost in WDM optical networks, the link stress and delay are two parameters which are not always taken into consideration. This paper proposes a novel wavelength routing algorithm, which tries to avoid the multicast incapable branching nodes (MIB, branching nodes without splitting capability) to diminish the link stress for the shortest path based multicast tree and maintains good parts of the shortest path tree to reduce the end-to-end delay. Firstly a DijkstraPro algorithm with priority assignment and node adoption is introduced to produce a shortest path tree with up to 38% fewer MIB nodes, and then critical articulation and deepest branch heuristics are used to process the MIB nodes. Finally distance based reconnection algorithm is proposed to create the multicast tree or forest.

/pdf/avoidance-of-multicast-incapable-branching-nodes-for-4bh4fijdiz.pdf

Avoidance of multicast incapable branching nodes for multicast routing in WDM networks

In this paper, we seek to jointly minimize the network power consumption and the user transmission delays in green wireless access networks. We recently studied the case of a WLAN, where we evaluated the tradeoffs between these two minimization objectives using a Mixed Integer Linear Programming (MILP) formulation. However, the MILP formulation could not deliver solutions in a reasonable amount of time due to computational complexity issues. As a result, we propose here a heuristic algorithm for the power-delay minimization problem. The proposed heuristic aims to compute the transmit power level of the Access Points (APs) deployed in the network and associate users with these APs in a way that jointly minimizes the total network power and the total network delay. The simulation results show that the proposed algorithm has a low computational complexity, which makes it advantageous compared with the optimal scheme, particularly in dense networks. Moreover, the heuristic algorithm performs close to optimally and provides power savings of up to 45% compared with legacy networks.

/pdf/a-heuristic-algorithm-for-joint-power-delay-minimization-in-3rrf1219io.pdf

A heuristic algorithm for joint power-delay minimization in green wireless access networks

Given a multicast session in Wavelength Division Multiplexing (WDM) networks, we try to find the multicast light-trees with the minimum power budget while taking into account the optical power loss as light splitting loss, node tapping loss and light attenuation loss. Although light splitting causes non-linear power relationship, we succeed to formulate this problem as a Mixed-Integer Linear Programming (MILP) by developing a set of equivalent linear equations to replace the non-linear ones. The distribution of power loss is analyzed by simulations, which suggests to bound the combined power loss ratio of the node tapping loss and the light attenuation in each source-destination path, and make power-symmetric light-trees by properly using light-splitters in order to minimize the overall power loss.

/pdf/power-optimal-design-of-multicast-light-trees-in-wdm-bg3x7ri19p.pdf

Power Optimal Design of Multicast Light-Trees in WDM Networks

https://www.irisa.fr/prive/Bernard.Cousin/Articles/josn_final.2006.pdf

Optimized design of survivable MPLS over optical transport networks

Today, reducing global greenhouse gas emissions has become a crucial issue for protecting the earth's environment. This requires the reduction of energy consumption in as many sectors as possible, including home networks. Indeed, in the home environment, networked devices consume a large proportion of household energy. There are three reasons that can explain the high energy consumption of home network devices. Firstly, devices are in idle state for hours when they are not in operation. Secondly, they cannot go to an ultra-low power consumption state when they are not needed. And last but not least, there is an increasing number of home network devices with soaring power consumption in our homes. These home network devices have a long switching time from idle state to sleeping state. Moreover, explicit user commands are required to switch the device from idle state to soft-off state. We can certainly gain energy if the device stays in sleeping state or soft-off state instead of idle state. We propose an Overlay Energy Control Network (OECN) which can switch devices from idle state to sleeping state much more quickly and from idle state to soft-off state automatically. The Overlay Energy Control Network (OECN) is formed by at least one overlay energy control node connected to each home network device. The OECN power management coordinates the power states of all home network devices. The overlay energy control nodes can exchange energy control messages. The devices can be turned on or turned off, or can return to their power states when they receive the OECN messages. So that the OECN can be adaptive to our home network devices, the OECN is developed in two ways: (i) all overlay energy control nodes in the home network are ZigBee nodes. This is a ZigBee Mandatory OECN Solution (ZMS); (ii) one or more devices become the overlay energy control nodes where there are no ZigBee modules on that device. This is a ZigBee Optional OECN Solution (ZOS). In our simulation, we will evaluate our overlay energy-saving solutions with a self-controlled energy solution in three metrics: energy consumption, delay and cost. In the self-controlled solution, the device controls its own power state. The proposed Overlay Energy Control Network provides an efficient energy-saving solution for home network devices. In our use case, the ZigBee Mandatory Solution can gain 21.79% energy on average compared to the self-controlled solution. It is an efficient energy-saving solution, but it has a relatively high delay compared to the ZigBee Optional Solution and the self-controlled solution.

https://hal.archives-ouvertes.fr/hal-01012038/document

Bernard Cousin

Papers

Avoidance of multicast incapable branching nodes for multicast routing in WDM networks

A heuristic algorithm for joint power-delay minimization in green wireless access networks

Power Optimal Design of Multicast Light-Trees in WDM Networks

Optimized design of survivable MPLS over optical transport networks

Green Home Network based on an Overlay Energy Control Network