Pseudo-scheduling: A New Approach to the Broadcast Scheduling Problem

The broadcast scheduling problem asks how a multihop network of broadcast transceivers operating on a shared medium may share the medium in such a way that communication over the entire network is possible. This can be naturally modeled as a graph coloring problem via distance-2 coloring (L(1,1)-labeling, strict scheduling). This coloring is difficult to compute and may require a number of colors quadratic in the graph degree. This paper introduces pseudo-scheduling, a relaxation of distance-2 coloring. Centralized and decentralized algorithms that compute pseudo-schedules with colors linear in the graph degree are given and proved.Comment: 8th International Symposium on Algorithms for Sensor Systems, Wireless Ad Hoc Networks and Autonomous Mobile Entities (ALGOSENSORS 2012), 13-14 September 2012, Ljubljana, Slovenia. 12 page

Effective scheduling algorithm for on-demand XML data broadcasts in wireless environments

The organization of data on wireless channels, which aims to reduce the access time of mobile clients, is a key problem in data broadcasts. Many scheduling algorithms have been designed to organize flat data on air. However, how to effectively schedule semi-structured information such as XML data on wireless channels is still a challenge. In this paper, we firstly propose a novel method to greatly reduce the tuning time by splitting query results into XML snippets and to achieve better access efficiency by combining similar ones. Then we analyze the data broadcast scheduling problem of on-demand XML data broadcasts and define the efficiency of a data item. Based on the definition, a Least Efficient Last (LEL) scheduling algorithm is also devised to effectively organize XML data on wireless channels. Finally, we study the performance of our algorithms through extensive experiments. The results show that our scheduling algorithms can reduce both access time and tuning time signifcantly when compared with existing work

Towards Optimal Distributed Node Scheduling in a Multihop Wireless Network through Local Voting

In a multihop wireless network, it is crucial but challenging to schedule transmissions in an efficient and fair manner. In this paper, a novel distributed node scheduling algorithm, called Local Voting, is proposed. This algorithm tries to semi-equalize the load (defined as the ratio of the queue length over the number of allocated slots) through slot reallocation based on local information exchange. The algorithm stems from the finding that the shortest delivery time or delay is obtained when the load is semi-equalized throughout the network. In addition, we prove that, with Local Voting, the network system converges asymptotically towards the optimal scheduling. Moreover, through extensive simulations, the performance of Local Voting is further investigated in comparison with several representative scheduling algorithms from the literature. Simulation results show that the proposed algorithm achieves better performance than the other distributed algorithms in terms of average delay, maximum delay, and fairness. Despite being distributed, the performance of Local Voting is also found to be very close to a centralized algorithm that is deemed to have the optimal performance

A note on on-line broadcast scheduling with deadlines

In this paper, we study an on-line broadcast scheduling problem with deadlines, in which the requests asking for the same page can be satisfied simultaneously by broadcasting this page, and every request is associated with a release time, deadline and a required page with a unit size. The objective is to maximize the number of requests satisfied by the schedule. In this paper, we focus on an important special case where all the requests have their spans (the difference between release time and deadline) less than 2. We give an optimal online algorithm, i.e., its competitive ratio matches the lower bound of the problem.postprin

PSA: The Packet Scheduling Algorithm for Wireless Sensor Networks

The main cause of wasted energy consumption in wireless sensor networks is packet collision. The packet scheduling algorithm is therefore introduced to solve this problem. Some packet scheduling algorithms can also influence and delay the data transmitting in the real-time wireless sensor networks. This paper presents the packet scheduling algorithm (PSA) in order to reduce the packet congestion in MAC layer leading to reduce the overall of packet collision in the system The PSA is compared with the simple CSMA/CA and other approaches using network topology benchmarks in mathematical method. The performances of our PSA are better than the standard (CSMA/CA). The PSA produces better throughput than other algorithms. On other hand, the average delay of PSA is higher than previous works. However, the PSA utilizes the channel better than all algorithms

Broadcast Scheduling Problem in TDMA Ad Hoc Networks using Immune Genetic Algorithm

In this paper, a new efficient immune genetic algorithm (IGA) is proposed for broadcast scheduling problem in TDMA Ad hoc network. Broadcast scheduling is a primary issue in wireless ad hoc networks. The objective of a broadcast schedule is to deliver a message from a given source to all other nodes in a minimum amount of time. Broadcast scheduling avoids packet collisions by allowing the nodes transmission that does not make interference of a time division multiple access (TDMA) ad hoc network. It also improves the transmission utilization by assigning one transmission time slot to one or more non-conflicting nodes such a way that every node transmits at least once in each TDMA frame. An optimum transmission schedule could minimize the length of a TDMA frame while maximizing the total number of transmissions. The aim of this paper is to increase the number of transmissions in fixed Ad hoc network with time division multiple access (TDMA) method, with in a reduced time slot. The results of IGA are compared to the recently reported algorithms. The simulation result indicates that IGA performs better even for a larger network

Centralized broadcast scheduling in packet radio networks via genetic-fix algorithms

An important, yet difficult, problem in the design of a packet radio network is the determination of a conflict-free broadcast schedule at a minimum cycle length. In this letter, we first formulate the problem via a within-two-hop connectivity matrix and then, by assuming a known cycle length, determine a conflict-free scheduling pattern using a centralized approach that exploits the structure of the problem via a modified genetic algorithm. This algorithm, called genetic-fix, generates and manipulates individuals with fixed size (i.e., in binary representation, the number of ones is fixed) and therefore, can reduce the search space substantially. We also propose a method to find a reasonable cycle length and shorten it gradually to obtain a near-optimal one. Simulations on three benchmark problems showed that our approach could achieve 100% convergence to solutions with optimal cycle length within reasonable time.published_or_final_versio

Data broadcast scheduling: Models, algorithms, and analysis

Inherent in the field of data broadcasting is a communication problem in which a server is to transmit a subset of data items in response to requests received from clients. The intent of the server is to optimize metrics quantifying the quality of service the system provides. This method of data dissemination has proved to be an efficient means of delivering information in asymmetric environments demanding massive scalability. of critical importance in such a system is the algorithm used by the server to construct a schedule of item broadcasts.;Due to the real-time nature of this problem, performances of heuristics designed to construct such schedules are heavily dependent on request instances. Thus it is challenging to establish the quality of one algorithm over another. Though several scheduling methods have been developed, these algorithms have been studied with a reliance on probabilistic assumptions and little emphasis on analytical results.;In contrast, we provide a formal treatment of the data broadcast scheduling problem in which analytical methods are applied, complemented by simulation experiments. Utilizing a worst-case technique known as competitive analysis, we establish bounds on the performance of various algorithms in the context of several different broadcast models. We describe results in three different settings.;Minimizing the total wait time of all requests with a single channel and multiple database items we establish the competitive ratios for two well-known algorithms, First Come First Served (FCFS) and Most Requests First (MRF) to be equal, and provide a general lower bound for all algorithms in this context. We describe simulation results that indicate the superior performance of MRF over FCFS on average. Minimizing two conflicting metrics, the total wait time and total broadcast cost, with a single channel and single database item we develop two on-line algorithms, establish their competitive ratios, and provide an optimal off-line algorithm used to simulate the impact of various parameters on the performance of both on-line heuristics. Finally, we extend the previous model by including multiple database items and establish a lower bound to a greedy algorithm for this context

Contemporary Methods for Graph Coloring as an Example of Discrete Optimization

This paper provides an insight into graph coloringapplication of the contemporary heuristic methods. It discusses avariety of algorithmic solutions for The Graph Coloring Problem(GCP) and makes recommendations for implementation. TheGCP is the NP-hard problem, which aims at finding the minimumnumber of colors for vertices in such a way, that none of twoadjacent vertices are marked with the same color.With the adventof multicore processing technology, the metaheuristic approachto solving GCP reemerged as means of discrete optimization. Toexplain the phenomenon of these methods, the author makes athorough survey of AI-based algorithms for GCP, while pointingout the main differences between all these techniques