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.

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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

Several multicast mechanisms, known as small group multicast, were proposed that scale better with the number of multicast sessions than traditional multicast does. We propose a new approach, simple explicit multicast (SEM), which uses an efficient method to construct multicast trees and deliver multicast packets. SEM is original because it adopts the source-specific channel address allocation, reduces forwarding states in non-branching node routers and implements data distribution using unicast trees.

SEM: a new small group multicast routing protocol

Recently several multicast mechanisms were proposed that scale better with the number of multicast groups than traditional multicast. These proposals are known as small group multicast (SCM) or explicit multicast (Xcast). Explicit multicast protocols, such as the Xcast protocol, encode the list of group members in the Xcast header of every packet. If the number of members in a group increases, routers may need to fragment an Xcast packet. Fragmented packets may not be identified as Xcast packets by routers. In this paper, we show that the Xcast protocol does not support the IP fragmentation. We show also that avoiding fragmentation limits the group size that can be handled by the Xcast protocol. First, we describe the Xcast protocol, the Xcast+ protocol (which is an extension of Xcast) and we compare these two protocols with traditional multicast protocols. We propose then a generalized version of the Xcast protocol, called GXcast, intended to permit the Xcast packets fragmentation and to support the increasing number of members in a multicast group. The behavior of the GXcast protocol is analyzed according to several criteria. Finally, we present and evaluate with simulations an improvement to GXcast and we conclude that GXcast is a feasible and promising protocol.

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GXcast: generalized explicit multicast routing protocol

Redundant sensing capabilities are often required in sensor network applications due to various reasons, e.g. robustness, fault tolerance, or increased accuracy. At the same time high sensor redundancy offers the possibility of increasing network lifetime by scheduling sleep intervals for some sensors and still providing continuous service with help of the remaining active sensors. In this paper centralized and distributed algorithms are proposed to solve the k-coverage sensing problem and maximize network lifetime. When physically possible, the proposed robust controlled greedy sleep algorithm provides guaranteed service independently of node and communication errors in the network. The performance of the algorithm is illustrated and compared to results of a random solution by simulation examples.

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Dependable k-coverage algorithms for sensor networks

In Wireless Sensor Networks (WSN), a tradeoff between the network lifetime (limited by energy of sensors) and redundancy of actively sensing and communicating sensors (induced by coverage requirements for the measured area) has to be established, typically in an over-deployed environment. This is achieved by scheduling algorithms which periodically alternate the state of sensors between "asleep" and "awake". Obviously, the period length of the periodical synchronized scheduling affects the network performance and lifetime. Controlled Greedy Sleep algorithm is a quasi-optimal synchronized sensor scheduling algorithm which increases network lifetime while maintaining correct functionality, based on local decisions of sensors. This paper investigates the optimization of the period length of this algorithm and highlights best practices with simulations. Studies have been performed within a ring topology and in random square topology.

/pdf/optimal-period-length-for-the-cgs-sensor-network-scheduling-2aiiqija79.pdf

Optimal Period Length for the CGS Sensor Network Scheduling Algorithm

As we know, the member-only algorithm in provides the best links stress and wavelength usage for the construction of multicast light-trees in WDM networks with sparse splitting. However, the diameter of tree is too big and the average delay is also too large, which are intolerant for QoS required multimedia applications. In this paper, a distance priority based algorithm is proposed to build light-trees for multicast routing, where the Candidate Destinations and the Candidate Connectors are introduced. Simulations show the proposed algorithm is able to greatly reduce the diameter and average delay of the multicast tree (up to 51% and 50% respectively), while keep the same or get a slightly better link stress as well as the wavelength usage than the famous Member-Only algorithm.

/pdf/distance-priority-based-multicast-routing-in-wdm-networks-jyt9lznk69.pdf

Bernard Cousin

Papers

SEM: a new small group multicast routing protocol

GXcast: generalized explicit multicast routing protocol

Dependable k-coverage algorithms for sensor networks

Optimal Period Length for the CGS Sensor Network Scheduling Algorithm

Distance priority based multicast routing in WDM networks considering sparse light splitting