An approximation algorithm for conflict-aware broadcast scheduling in wireless ad hoc networks

Broadcast is a fundamental operation in wireless sensor networks and plays an important role in the communication protocol design. This paper investigates the Minimum Latency Broadcast Scheduling in Duty Cycle (MLBSDC) problem in wireless sensor networks, which is known to be NP-hard. Recent studies in this area focus on giving collision-free scheduling and finding the upper bound of broadcast latency. The previously best-known algorithm for MLBSDC, OTAB algorithm, has an approximation ratio of 17|T| where |T| denotes the number of time slots in a working period. In this paper, we present an improved algorithm combining more transmissions into one working period, called Latency Aware Broadcast Scheduling (LABS) algorithm, to reduce the broadcast latency. Theoretical analysis proves that the LABS achieves the approximation ratio of 12|T| while keeping the same complexity with OTAB. We also conduct extensive simulations to show that our proposed algorithm achieves up to 34% and 40% performance improvement over existing schemes in terms of delay time and number of transmissions, respectively.

In order to effectively utilize the broadcast bandwidth, it is necessary to have efficient on-line scheduling algorithms that can balance individual and overall performance and can scale in terms of database sizes and client populations. Moreover, the scheduling algorithm should be applicable to a heterogeneous environment where data items have different sizes. We address these issues in this paper. As stretch is widely adopted as a performance metric for variable-size data requests, we propose a broadcast scheduling algorithm to optimize the system performance in terms of stretch. We show by analysis that the proposed algorithm can indeed achieve the optimal performance in terms of stretch. Moreover, the proposed scheduling algorithm has very low decision overhead, which makes it a practical solution for on-demand broadcast scheduling. Simulation results demonstrate that our algorithm significantly outperforms existing scheduling algorithms under various scenarios.

In the classical broadcast scheduling problem , there are n pages stored at a server, and requests for these pages arrive over time. Whenever a page is broadcast, it satisfies all outstanding requests for that page. The objective is to minimize average flow time of the requests. For any ϵ > 0, we give a (1+ϵ)-speed O (1/ϵ 3 )-competitive online algorithm for broadcast scheduling. This improves over the recent breakthrough result of Im and Moseley [2010], where they obtained a (1+ϵ)-speed O (1/ϵ 11 )-competitive algorithm. Our algorithm and analysis are considerably simpler than Im and Moseley [2010]. More importantly, our techniques also extend to the general setting of nonuniform page sizes and dependent requests . This is the first scalable algorithm for broadcast scheduling with varying size pages and resolves the main open question from Im and Moseley [2010].

One of the two main approaches of data broadcasting is pull-based data delivery. In this paper, we focus on the problem of scheduling data items to broadcast in such a pull-based environment. Previous work has shown that the Longest Wait First heuristic has the best performance results compared to all other broadcast scheduling algorithms, however the decision overhead avoids its practical implementation. Observing this fact, we propose an efficient broadcast scheduling algorithm which is based on an approximate version of the Longest Wait First heuristic. We also compare the performance of the proposed algorithm against well-known broadcast scheduling algorithms.

The first main goal of this paper is to present Sketch-it!, a framework aiming to facilitate development and experimental evaluation of new scheduling algorithms. It comprises many helpful data-structures, a graphical interface with several components and a library with implementations of selected scheduling algorithms. Every scheduling problem covered by the classification-scheme originally proposed by Graham et al. [] can easily be integrated into the framework.One of the more recent enhancements of this scheme, the so called broadcast scheduling problem, was chosen for an extensive case study of Sketch-it!, yielding very interesting experimental results that represent the second main contribution of this paper. In broadcast scheduling many clients listen to a high bandwidth channel on which a server can transmit documents of a given set. Over time the clients request certain documents. In the pull-based setting each client has access to a slow bandwidth channel whereon it notifies the server about its requests. In the push-based setting no such channel exists. Instead it is assumed that requests for certain documents arrive randomly with probabilities known to the server. The goal in both settings is to generate broadcast schedules for these documents which minimize the average time a client has to wait until a request is answered.We conduct experiments with several algorithms on generated data. We distinguish scenarios for which a slow feedback channel is very advantageous, and others where its benefits are negligible, answering the question posed in the title.

With the increasing popularity of portable wireless computers, mechanisms to efficiently transmit information to such clients are of significant interest. The environment under consideration is asymmetric in that the information server has much more bandwidth available, as compared to the clients. It has been proposed that in such systems the server should broadcast the information periodically. A broadcast schedule determines what is broadcast by the server and when. This paper makes the simple, yet useful, observation that the problem of broadcast scheduling is closely related to the problem of fair queueing. Based on this observation, we present a log-time algorithm for scheduling broadcast, based on an existing fair queueing algorithm. This algorithm significantly improves the time-complexity over previously proposed broadcast scheduling algorithms. Also, for environments where different users may be listening to different number of broadcast channels, we present an algorithm to coordinate broadcasts over different channels. Simulation results are presented for proposed algorithms.

Transitional Region Phenomenon leads to the existence of lossy links in wireless networks, which results in a transmission between two users who are theoretically connected under the Deterministic Network Model cannot be guaranteed. Therefore, we focus on a more practical network model - Probabilistic Network Model (PNM) which can better characterize the lossy links in wireless networks. To be specific, we focus on the investigation of accelerating data aggregation process in probabilistic wireless networks with the cognitive radio technology. By involving cognitive radio technology, users in the wireless networks can seek extra transmission opportunity if other spectrum resource is available. Otherwise, the data aggregation process still can be done on the default working spectrum. Particularly, we are interested in the time efficient data aggregation scheduling problem. In this work, a two phase scheduling algorithm is proposed. The first phase is finding an efficient routing structure considering the speciality of the network model under investigation. In the second phase, a dynamic scheduling algorithm is introduced. Theoretical analysis is provided to estimate the lower latency bound for the scheduling algorithm, followed by the experimental simulation verification.

This paper addresses time-efficient broadcast scheduling problem in Cognitive Radio (CR) Networks. Cognitive Radio is a promising technology that enables the use of unused spectrum in an opportunistic manner. Because of the unique characteristics of CR technology, the broadcast scheduling problem in CR networks needs unique solutions. Even for single channel wireless networks, finding a minimum-length broadcast schedule is an NP-hard problem. In addition, the multi-channel nature of the CR networks, especially the non-uniform channel availability, makes it a more complex problem to solve. In this paper, we first present an Integer Linear Programming formulation (ILP) to determine the minimum broadcast schedule length for a CR network. We then present two heuristics to construct minimal length broadcast schedules. Comparison of optimal results (found by solving the ILP formulation) with the result of the heuristics through simulation shows that both heuristics produce schedules of either optimal or very closer to optimal lengths.Keywordscognitive radio networkstime efficient broadcastingbroadcast scheduling

We consider two scheduling problems in the broadcast setting. The first is that of minimizing the average response time of requests. For the offline version of this problem we give an algorithm with an approximation ratio of O(log2 (n)/ log log(n)), where n is the total number of pages. This substantially improves the previously best known approximation factor of O(√n) for the problem [3]. Our second result is for the profit maximization version of the broadcast scheduling problem. Here each request has a deadline and a profit which is obtained if the request is satisfied before its deadline. The goal is to maximize the total profit. We give an algorithm with an approximation ratio of 5/6, which improves the previously best known approximation guarantee of 3/4 for the problem [13].

Parallel transmission algorithms CLBA and DAS greatly improved the speed of transferring data files, but they cause a large overhead of storage space and network traffic for deployment of duplications of large files, in addition to their poor feasibility of deploying large complete replicas on nodes with limited storage space. In view of Google and Hadoop distributed file systems all adopted block storage mechanism, a parallel data transmission algorithm based on block storage strategy is proposed, but the interrelation between block storage and parallel transmission was not properly taken into consideration and so the nodes of slower bandwidth greatly affected the overall transmission performance. Therefore, we proposed in this paper block and ratio storage strategies for files of massive data in distributed systems and parallel transmission algorithms PTBM and PTRM for the two storage strategies. Experimental results indicate the proposed algorithms are better than CLBA, and close to DAS, and they save more than 50% storage space, and can adapt to the deployment of large data files.

When the wireless communication network is interfered, the communication effect will be affected. In order to improve the interference signal processing effect and the identification accuracy of the interference signal, a wireless communication network interference suppression algorithm based on joint estimation is proposed. Using the deep learning method to identify the interference signal, obtain the effective interference signal of wireless communication network, improve the accuracy of interference signal identification, and track and parameter modulation the identified signal; The node model of wireless communication network is established, and the joint estimation method is used to suppress the interference signal for the nodes in the model. The interference suppression of wireless communication network is realized through the state estimation of single tone interference and narrowband interference. The experimental results show that the proposed algorithm has a high accuracy of interference signal recognition, the highest value reaches 98%, and the wireless communication data packet loss rate is low, the highest value is only 0.37, which verifies its interference suppression effect.

A new multi-rat scheduling algorithm for heterogeneous wireless networks

Due to the mutual interference and sharing of wireless links in TDMA ad hoc networks, conflicts will occur when data message are transmitting between nodes. The broadcast scheduling problem (BSP) is aim to schedule each node in different slot of fixed length frame at least once, and the objective of BSP is to seek for the optimal feasible solution, which has the shortest length of frame slots as well as the maximum node transmission. In this paper, we propose a two stage hybrid algorithm based on fuzzy Hopfield neural network to solve this BSP in ad hoc. In the first stage, we use a modified sequential vertex coloring algorithm to obtain a minimal TDMA frame length. Experimental results, obtained from the running on three benchmark graph, show that our algorithm can achieve better performance with shorter frame length and higher channel utilizing ratio than other exit BSP solutions.

Fractional Link scheduling is one of the most fundamental problems in wireless networks. The prevailing approach for shortest fractional link scheduling is based on a reduction to the maximum-weighted independent set problem, which itself may not admit efficient approximation algorithms. In addition, except for the wireless networks under the protocol interference model, none of the existing scheduling algorithms can produce a link schedule with explicit upper bounds on its length in terms of the link demands. As the result, the polynomial approximate capacity regions in these networks remain blank. This paper develops a purely combinatorial paradigm for fractional link scheduling in wireless networks. In addition to the superior efficiency, it is able to provide explicit upper bounds on the lengths of the produced link schedule. By exploiting these upper bounds, polynomial approximate capacity regions are derived. The effectiveness of this new paradigm is demonstrated by its applications in wireless networks under the physical interference model and wireless MIMO networks under the protocol interference model.

This paper presents several online scheduling algorithms for two related performance metrics, namely maximum response time and maximum delay-factor, and also their weighted versions. The delay factor metric is new (introduced in Chang et al. (SODA'08)), while special cases of maximum weighted response time have been considered before. We study both the standard scheduling model where each arriving job requires its own processing, as well as the broadcast scheduling model where multiple requests/jobs can be simultaneously satisfied. Motivated by strong lower bounds, we consider the resource augmentation model introduced in Kalyanasundaram and Pruhs (JACM'95) where the algorithm is given a machine with faster speed than the adversary. We give scalable algorithms; that is, algorithms that when given $(1+\epsilon)$-speed are $O(\mbox{poly}(1/\epsilon))$-competitive for any fixed $\epsilon > 0$. Our main contributions are for broadcast scheduling. Along the way we also show that the FIFO (first-in-first-out) algorithm is $2$-competitive for broadcast scheduling even when pages have non-uniform sizes. We complement our algorithmic results by showing that a natural greedy algorithm modeled after LWF (Longest-Wait-First) is, for any $s \ge 1$, not constant competitive for minimizing maximum delay factor when given an $s$-speed machine. The lower bound holds even in the restricted setting of standard scheduling of jobs with uniform size, and demonstrates the importance of the trade-off made in our algorithms.