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In this paper we study the broadcast capacity of multihop wireless networks which we define as the maximum rate at which broadcast packets can be generated in the network such that all nodes receive the packets successfully in a limited time. We employ the Protocol Model for successful packet reception usually adopted in network capacity studies and provide novel upper and lower bounds for the broadcast capacity for arbitrary connected networks. In a homogeneous dense network these bounds simplify to Θ(W/ max(1, ∆ d )) where W is the wireless channel capacity, ∆ the interference parameter, and d the number of dimensions of space in which the network lies. Interestingly, we show that the broadcast capacity does not change by more than a constant factor when we vary the number of nodes, the radio range, the area of the network, and even the node mobility. To address the achievability of capacity, we demonstrate that any broadcast scheme based on a backbone of size proportional to the Minimum Connected Dominating Set guarantees a throughput within a constant factor of the broadcast capacity. Finally, we demonstrate that broadcast capacity, in stark contrast to unicast capacity, does not depend on the choice of source nodes or the dimension of the network.

Existing work on the capacity of wireless networks predominantly considers homogeneous random networks with random work load. The most relevant bounds on the network capacity, e.g., take into account only the number of nodes and the area of the network. However, these bounds can significantly overestimate the achievable capacity in real world situations where network topology or traffic patterns often deviate from these simplistic assumptions. To provide analytically tractable yet asymptotically tight approximations of network capacity we propose a novel space-based approach. At the heart of our methodology lie simple functions which indicate the presence of active transmissions near any given location in the network and which constitute a tool well suited to untangle the interactions of simultaneous transmissions. We are able to provide capacity bounds which are tighter than the traditional ones and which involve topology and traffic patterns explicitly, e.g., through the length of Euclidean Minimum Spanning Tree, or through traffic demands between clusters of nodes. As an additional novelty our results cover unicast, multicast and broadcast and are asymptotically tight. Notably, our capacity bounds are simple enough to require only knowledge of node location, and there is no need for solving or optimizing multi-variable equations in our approach.

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Abstract-This paper develops a novel color-based broadcast scheme for wireless ad hoc networks where each forwarding of the broadcast message is assigned a color from a given pool of colors. A node only forwards the message if it can assign it a color from the pool which it has not already overheard after a random time. In the closely related counter-based broadcast scheme a node simply counts the number of broadcasts not the colors overheard. The forwarding nodes form a so-called backbone, which is determined by the random timers and, thus, is random itself. Notably, any counter-generated backbone could result from pruning a color-generated backbone; the typical color-generated backbone, however, exhibits a connectivity graph richer than the counter-based ones. As a particular advantage, the colors reveal simple geometric properties of the backbones which we exploit to prove that the size of both, color-and counter-generated backbones are within a small constant factor of the optimum. We also propose two techniques, boosting and edge-growing, that improve the performance of color-and counter-based broadcast in terms of reachability and number of rebroadcasts. Experiments reveal that the powerful boosting method is considerably more effective with the color-based schemes. I. MOTIVATION AND BACKGROUNDIn ad hoc networks, broadcast plays a crucial role, relaying a message generated by one node to all other nodes. Several unicast routing protocols such Dynamic Source Routing (DSR), Ad Hoc On Demand Distance Vector (AODV), Zone Routing Protocol (ZRP), and Location Aided Routing (LAR), as well as multicast protocols employ broadcasting to detect and maintain routes in an ever changing environment.The simplest approach for broadcasting is flooding, where each node rebroadcasts a message as soon as it receives it for the first time. While ensuring a high success rate in reaching all nodes, flooding produces redundant broadcast messages. This redundancy can become overwhelming in dense wireless networks, leading to a loss of precious bandwidth and battery power and to a dramatic degradation of performance, a situation called "broadcast storm" [1].Several broadcast schemes have been developed that avoid broadcast storms. The performance of these schemes is measured in terms of reachability, that is the fraction of the total nodes that receive the broadcast message, the number of rebroadcasts, that is the number of nodes that forward the message, and the latency, that is the time between the first and last instant that the broadcast message is transmitted. The set of nodes which forward the broadcast message form the socalled backbone. Good broadcast schemes ensure reachability close to 1 and simultaneously a small backbone.Broadcast Probabilistic schemes, in contrast, rebuild a backbone from scratch during each broadcast. Nodes make instantaneous local decisions about whether to broadcast a message or not using information derived only from overheard broadcast messages. Consequently these schemes incur a smaller overhead ...

In this paper we introduce a novel linear network coding scheme, namely "rotate-and-add coding", that possesses low encoding complexity and operates fundamentally different from the traditional network codes. This scheme can operate on a small field (e.g. F2), thereby, it alleviates the computational complexities due to multiplication and addition operations in large finite fields. The key idea is to function on a vector of symbols instead of working with a single symbol of a large field. Each node encodes its received vectors by simply rotationally shifting the vectors and then adding them, i.e., here the addition is done in vector form and the multiplication is replaced by rotation. We verify that the new scheme requires lower computation and overhead than the existing schemes. However, as the cost of reducing the complexity, it provides slightly smaller throughput.

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