An analysis of the lifetime of OLSR networks

The Optimized Link State Routing (OLSR) protocol is a well-known route discovery protocol for ad-hoc networks. OLSR optimizes the flooding of link state information through the network using multipoint relays (MPRs). Only nodes selected as MPRs are responsible for forwarding control traffic. Many research papers aim to optimize the selection of MPRs with a specific purpose in mind: e.g., to minimize their number, to keep paths with high Quality of Service or to maximize the network lifetime (the time until the first node runs out of energy). In such analyzes often the effects of the network structure on the MPR selection are not taken into account. In this paper we show that the structure of the network can have a large impact on the MPR selection. In highly regular structures (such as grids) there is even no variation in the MPR sets that result from various MPR selection mechanisms. Furthermore, we study the influence of the network structure on the network lifetime problem in a setting where at regular intervals messages are broadcasted using MPRs. We introduce the ’maximum forcedness ratio’, as a key parameter of the network to describe how much variation there is in the lifetime results of various MPR selection heuristics. Although we focus our attention to OLSR, being a widely implemented protocol, on a more abstract level our results describe the structure of connected sets dominating the 2-hop neighborhood of a node

Mesh based and Hybrid Multicast routing protocols for MANETs: Current State of the art

This paper discusses various multicast routing protocols which are proposed in the recent past each having its own unique characteristic, with a motive of providing a complete understanding of these multicast routing protocols and present the scope of future research in this field. Further, the paper specifically discusses the current development in the development of mesh based and hybrid multicasting routing protocols. The study of this paper addresses the solution of most difficult task in Multicast routing protocols for MANETs under host mobility which causes multi-hop routing which is even more severe with bandwidth limitations. The Multicast routing plays a substantial part in MANETs

FAR: Face-Aware Routing for Mobicast in Large-Scale Sensor Networks

This paper presents FAR, a Face-Aware Routing protocol for mobicast, a spatiotemporal variant of multicast tailored for sensor networks with environmental mobility. FAR features face-routing and timed-forwarding for delivering a message to a mobile delivery zone. Both analytical and statistical results show that, FAR achieves reliable and just-in-time mes-sage delivery with only moderate communication and memory overhead. This paper also presents a novel distributed algorithm for spatial neighborhood discovery for FAR boot-strapping. The spatiotemporal performance and reliability of FAR are demonstrated via ns-2 simulations

Approximating optimal Broadcast in Wireless Mesh Networks with Machine Learning

With the growth of IoT, efficient broadcast is required for many applications. Yet, current protocols use primitive mechanisms based on heuristics. Multi-agent reinforcement learning is applied to approximate optimal broadcast in Wireless Mesh Networks. One of the proposed fully distributed algorithms, using Bayesian Neural Networks, outperforms MORE multicast and BATMAN, improving airtime up to 20%, e2e delay up to 30%, and satisfying timeout constraints in over the 97% of the cases

Cross-Layer Optimal Rate Allocation for Heterogeneous Wireless Multicast

Heterogeneous multicast is an efficient communication scheme especially for multimedia applications running over multihop networks. The term heterogeneous refers to the phenomenon when multicast receivers in the same session require service at different rates commensurate with their capabilities. In this paper, we address the problem of resource allocation for a set of heterogeneous multicast sessions over multihop wireless networks. We propose an iterative algorithm that achieves the optimal rates for a set of heterogeneous multicast sessions such that the aggregate utility for all sessions is maximized. We present the formulation of the multicast resource allocation problem as a nonlinear optimization model and highlight the cross-layer framework that can solve this problem in a distributed ad hoc network environment with asynchronous computations. Our simulations show that the algorithm achieves optimal resource utilization, guarantees fairness among multicast sessions, provides flexibility in allocating rates over different parts of the multicast sessions, and adapts to changing conditions such as dynamic channel capacity and node mobility. Our results show that the proposed algorithm not only provides flexibility in allocating resources across multicast sessions, but also increases the aggregate system utility and improves the overall system throughput by almost 30% compared to homogeneous multicast

Reliable Mobicast via Face-Aware Routing

This paper presents a novel protocol for a spatiotemporal variant of multicast called mobicast, designed to support message delivery in sensor and mobile ad hoc networks. The spatiotemporal character of mobicast relates to the obligation to deliver a message to all the nodes that will be present at time t in some geographic zone Z, where both the location and shape of the delivery zone are a function of time over some interval (tstart, tend). The protocol, called Face-Aware Routing (FAR), exploits ideas adapted from existing applications of face routing to achieve reliable mobicast delivery. The key features of the protocol are a routing strategy, which uses information confined solely to a node’s immediate spatial neighborhood, and a forwarding schedule, which employs only local topological information. Statistical results showing that, in uniformly distributed random disk graphs, the spatial neighborhood size is usually less than 20 suggest that FAR is likely to exhibit a low average memory cost. An estimation formula for the average size of the spatial neighborhood in random network is another analytical result reported in this paper. This paper also includes a novel and low cost distributed algorithm for spatial neighborhood discovery