A Protocol to Recover the Unused Time Slot Assignments in Transmission Scheduling Protocols for Channel Access in Ad Hoc Networks

In mobile ad hoc networks without centralized control distributed transmission scheduling protocols for channel access are of interest. Many scheduling-based MAC protocols have been proposed to provide contention-free transmissions and to guarantee certain levels of performance. However, one of the major drawbacks of these protocols is that once a slot is assigned to a particular node if the node does not have a packet to transmit, then the slot is not utilized. This leads to a poor network performance. In our proposed protocol these assigned but un-utilized slots are recovered by other nodes. We use custom computer simulations to compare our new protocol against two ap-proaches that do not recover wasted slots. The simulation models the performance of the physical and link layers and includes a limited network layer that supports end-to-end for-warding of traffic. Through investigations of random networks with varying densities we conclude that our new approach results in an increase in the capacity of the network

A Channel-Access Framework for Scheduling Transmission Assignments in Ad Hoc Networks with Rate Adaptive Radios

In mobile ad hoc networks transmission-scheduling channel-access protocols are of interest because they can ensure collision free transmissions and provide fair access to the channel. The time taken to gain access to the channel is deterministic and hence these types of protocols can also guarantee a certain quality of service. However, these protocols suffer from two major drawbacks. The first issue is poor utilization of the channel due to fixed slot assignments. Once the slot assignments are decided they are held constant for a period of time. As a result the node to which a slot is assigned may not always have a packet to transmit in its assigned slot. This results in wasted slots and leads to poor utilization of the channel. The second issue is that there is no support for networks with rate adaptive radios. In this work a combined solution to both of these shortcomings is presented. In order to make transmission-scheduling channel-access protocols support networks with rate adaptive radios, a process called slot-packing is developed. The design of slot-packing ensures that it works with any transmission-scheduling channel-access protocol. Using slot-packing, we design and investigate a new protocol called adaptive recovering mini-slot transmission scheduling (RMTS-a) that tackles both the shortcomings and improves the performance of the network significantly. A key feature of our RMTS-a protocol is that if a radio assigned to a transmission opportunity is unable to utilize all of the time slot, other radios in the local neighborhood are given the opportunity to transmit in the remaining time. Additionally, because multiple radios within communication range of a transmitter are likely to be able to decode the payload, packets to multiple neighbors can be packed within a single transmission

Managing On-air Ad Inventory in Broadcast Television

This is the author's accepted manuscript. The original publication is available at http://dx.doi.org/10.1080/07408170802323026.Motivated by the experiences of the National Broadcasting Company (NBC), we present an analytical model for managing on-air ad inventory in broadcast television. The ad inventory in this industry is priced based on rating points or the number of viewers that watch a commercial. The rating points during a broadcast year are sold through two distinct processes: the Upfront, which occurs before the broadcast season, and the Scatter, which occurs during the broadcast season. A firm needs to allocate its total rating points inventory to these two markets before knowing either the performance rating of its shows or the Scatter market price, both of which are random. The networks offer ratings (performance) guarantees on the inventory that is sold in the Upfront market while such guarantees are seldom offered in the Scatter market. We propose an optimization model for the networks to manage their rating points inventory. Our model explicitly incorporates the performance uncertainty of the television shows as well as the revenue uncertainty of the Scatter market. We derive conditions for feasibility of the problem and characterize the optimal amount of rating points to sell in the Upfront market. Our model explains the current practice of selling around 60-80% of the total rating points for the season during the Upfront market and analyzes other common strategies used by the firms. In addition to providing key managerial insights, our work introduces quantitative methodologies to television networks in planning their Upfront markets

Adaptive Consensus: A network pruning approach for decentralized optimization

We consider network-based decentralized optimization problems, where each node in the network possesses a local function and the objective is to collectively attain a consensus solution that minimizes the sum of all the local functions. A major challenge in decentralized optimization is the reliance on communication which remains a considerable bottleneck in many applications. To address this challenge, we propose an adaptive randomized communication-efficient algorithmic framework that reduces the volume of communication by periodically tracking the disagreement error and judiciously selecting the most influential and effective edges at each node for communication. Within this framework, we present two algorithms: Adaptive Consensus (AC) to solve the consensus problem and Adaptive Consensus based Gradient Tracking (AC-GT) to solve smooth strongly convex decentralized optimization problems. We establish strong theoretical convergence guarantees for the proposed algorithms and quantify their performance in terms of various algorithmic parameters under standard assumptions. Finally, numerical experiments showcase the effectiveness of the framework in significantly reducing the information exchange required to achieve a consensus solution.Comment: 35 pages, 3 figure