Sharing the cost of multicast transmissions in wireless networks

AbstractA crucial issue in non-cooperative wireless networks is that of sharing the cost of multicast transmissions to different users residing at the stations of the network. Each station acts as a selfish agent that may misreport its utility (i.e., the maximum cost it is willing to incur to receive the service, in terms of power consumption) in order to maximize its individual welfare, defined as the difference between its true utility and its charged cost. A provider can discourage such deceptions by using a strategyproof cost sharing mechanism, that is a particular public algorithm that, by forcing the agents to truthfully reveal their utility, starting from the reported utilities, decides who gets the service (the receivers) and at what price. A mechanism is said budget balanced (BB) if the receivers pay exactly the (possibly minimum) cost of the transmission, and β-approximate budget balanced (β-BB) if the total cost charged to the receivers covers the overall cost and is at most β times the optimal one, while it is efficient if it maximizes the sum of the receivers’ utilities minus the total cost over all receivers’ sets. In this paper, we first investigate cost sharing strategyproof mechanisms for symmetric wireless networks, in which the powers necessary for exchanging messages between stations may be arbitrary and we provide mechanisms that are either efficient or BB when the power assignments are induced by a fixed universal spanning tree, or (3ln(k+1))-BB (k is the number of receivers), otherwise. Then we consider the case in which the stations lay in a d-dimensional Euclidean space and the powers fall as 1/dα, and provide strategyproof mechanisms that are either 1-BB or efficient for α=1 or d=1. Finally, we show the existence of 2(3d-1)-BB strategyproof mechanisms in any d-dimensional space for every α⩾d. For the special case of d=2 such a result can be improved to achieve 12-BB mechanisms

Localized Minimum Spanning Tree Based Multicast Routing with Energy-Efficient Guaranteed Delivery in Ad Hoc and Sensor Networks

We present a minimum spanning tree based energy aware multicast protocol (MSTEAM), which is a localized geographic multicast routing scheme designed for ad hoc and sensor networks. It uses locally-built minimum spanning trees (MST) as an efficient approximation of the optimal multicasting backbone. Using a MST is highly relevant in the context of dynamic wireless networks since its computation has a low time complexity (O(n log n)). Moreover, our protocol is fully localized and requires nodes to gather information only on 1-hop neighbors, which is common assumption in existing work. In MSTEAM, a message split occurs when the MST over the current node and the set of destinations has multiple edges originated at the current node. Destinations spanned by each of these edges are grouped together, and for each of these subsets the best neighbor is selected as the next hop. This selection is based on a cost over progress metric, where the progress is approximated by subtracting the weight of the MST over a given neighbor and the subset of destinations to the weight of the MST over the current node and the subset of destinations. Since such greedy localized scheme may lead the message to a void area (i.e., there is no neighbor providing positive progress toward the destinations), we also propose a completely new multicast generalization of the well-know face recovery mechanism. We provide a theoretical analysis proving that MSTEAM is loop-free and always achieves delivery of the multicast message, as long as a path exists between the source node and the destinations. Our experimental results demonstrate that MSTEAM is highly energy-efficient, outperforms the best existing localized multicast scheme and is almost as efficient as a centralized scheme in high densities

Power assignment problems in wireless communication

A fundamental class of problems in wireless communication is concerned with the assignment of suitable transmission powers to wireless devices/stations such that the resulting communication graph satisfies certain desired properties and the overall energy consumed is minimized. Many concrete communication tasks in a wireless network like broadcast, multicast, point-to-point routing, creation of a communication backbone, etc. can be regarded as such a power assignment problem. This paper considers several problems of that kind; for example one problem studied before in (Vittorio Bil{\`o} et al: Geometric Clustering to Minimize the Sum of Cluster Sizes, ESA 2005) and (Helmut Alt et al.: Minimum-cost coverage of point sets by disks, SCG 2006) aims to select and assign powers to $k$ of the stations such that all other stations are within reach of at least one of the selected stations. We improve the running time for obtaining a $(1+\epsilon)$-approximate solution for this problem from $n^{((\alpha/\epsilon)^{O(d)})}$ as reported by Bil{\`o} et al. (see Vittorio Bil{\`o} et al: Geometric Clustering to Minimize the Sum of Cluster Sizes, ESA 2005) to $O\left( n+ {\left(\frac{k^{2d+1}}{\epsilon^d}\right)}^{ \min{\{\; 2k,\;\; (\alpha/\epsilon)^{O(d)} \;\}} } \right)$ that is, we obtain a running time that is \emph{linear} in the network size. Further results include a constant approximation algorithm for the TSP problem under squared (non-metric!) edge costs, which can be employed to implement a novel data aggregation protocol, as well as efficient schemes to perform $k$-hop multicasts

A Kind of New Multicast Routing Algorithm for Application of Internet of Things

Wireless Sensor Networks (WSN) is widely used as an effective medium to integrate physical world and information world of Internet of Things (IOT). While keeping energy consumption at a minimal level, WSN requires reliable communication. Multicasting is a general operation performed by the Base Station, where data is to be transmitted to a set of destination nodes. Generally, the packets are routed in a multi-hop approach, where some intermediate nodes are also used for packet forwarding. This problem can be reduced to the well-known Steiner tree problem, which has proven to be NP-complete for deterministic link descriptors and cost functions. In this paper, we propose a novel multicast protocol, named heuristic algorithms for the solution of the Quality of Service (QoS) constrained multicast routing problem, with incomplete information in Wireless Sensor Networks (WSN). As information aggregation or randomly fluctuating traffic loads, link measures are considered to be random variables. Simulation results show that the Hop Neural Networks (HNN) based heuristics with a properly chosen additive measures can yield to a good solution for this traditionally NP complex problem, when compared to the best multicast algorithms known

A cluster based communication architecture for distributed applications in mobile ad hoc networks

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2006Includes bibliographical references (leaves: 63-69)Text in English; Abstract: Turkish and Englishx, 85 leavesIn this thesis, we aim to design and implement three protocols on a hierarchical architecture to solve the balanced clustering, backbone formation and distributed mutual exclusion problems for mobile ad hoc network(MANET)s. Our ¯rst goal is to cluster the MANET into balanced partitions. Clustering is a widely used approach to ease implemen-tation of various problems such as routing and resource management in MANETs. We propose the Merging Clustering Algorithm(MCA) for clustering in MANETs that merges clusters to form higher level of clusters by increasing their levels. Secondly, we aim to con-struct a directed ring topology across clusterheads which were selected by MCA. Lastly, we implement the distributed mutual exclusion algorithm based on Ricart-Agrawala algo-rithm for MANETs(Mobile RA). Each cluster is represented by a coordinator node on the ring which implements distributed mutual exclusion algorithm on behalf of any member in the cluster it represents. We show the operations of the algorithms, analyze their time and message complexities and provide results in the simulation environment of ns2