N-time Division Multiple Access Dynamic Slot Allocation Protocol Based on I-Sequential Vertex Coloring Algorithm

针对移动自组网中介质访问控制层协议的特性,结合实际工程需求,基于I-序列顶点着色算法,提出一种N-时分多址动态时隙分配协议。对协议的帧结构、交互过程及动态时隙分配算法进行分析。在NS2软件上的仿真结果表明,该协议的时隙利用率、吞吐量、平均时延、公平性等均优于IEEE802.11和时分多址协议。According to the characteristics of Medium Access Control(MAC) layer protocol in mobile Ad Hoc network,combining with the actual engineering requirements,N-time Division Multiple Access(TDMA) dynamic slot allocation protocol based on I-sequential Vertex Coloring(SVC) algorithm is proposed.The protocol frame structure,interaction and dynamic slot allocation algorithm are specifically analysed and described.This protocol is simulated under NS2 Jesuits show that the algorithm has better performance on high slot utilization throughput,average latency,fairness and other parameters than IEEE802.11 and Time Division Multiple Access(TDMA) protocols.厦门大学海西通信工程技术中心开放基金资助项目(HXCXJJ2014-012

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

Multi-channel TDMA scheduling in wireless sensor networks

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent Univ., 2013.Thesis (Master's) -- Bilkent University, 2013.Includes bibliographical references leaves 55-61.The Multiple Instance Learning (MIL) paradigm arises to be useful in many application domains, whereas it is particularly suitable for computer vision problems due to the difficulty of obtaining manual labeling. Multiple Instance Learning methods have large applicability to a variety of challenging learning problems in computer vision, including object recognition and detection, tracking, image classification, scene classification and more. As opposed to working with single instances as in standard supervised learning, Multiple Instance Learning operates over bags of instances. A bag is labeled as positive if it is known to contain at least one positive instance; otherwise it is labeled as negative. The overall learning task is to learn a model for some concept using a training set that is formed of bags. A vital component of using Multiple Instance Learning in computer vision is its design for abstracting the visual problem to multi-instance representation, which involves determining what the bag is and what are the instances in the bag. In this context, we consider three different computer vision problems and propose solutions for each of them via novel representations. The first problem is image retrieval and re-ranking; we propose a method that automatically constructs multiple candidate Multi-instance bags, which are likely to contain relevant images. The second problem we look into is recognizing actions from still images, where we extract several candidate object regions and approach the problem of identifying related objects from a weakly supervised point of view. Finally, we address the recognition of human interactions in videos within a MIL framework. In human interaction recognition, videos may be composed of frames of different activities, and the task is to identify the interaction in spite of irrelevant activities that are scattered through the video. To overcome this problem, we use the idea of Multiple Instance Learning to tackle irrelevant actions in the whole video sequence classification. Each of the outlined problems are tested on benchmark datasets of the problems and compared with the state-of-the-art. The experimental results verify the advantages of the proposed MIL approaches to these vision problems.Uyanık, ÖzgeM.S

Industrial Wireless Sensor Networks

Wireless sensor networks are penetrating our daily lives, and they are starting to be deployed even in an industrial environment. The research on such industrial wireless sensor networks (IWSNs) considers more stringent requirements of robustness, reliability, and timeliness in each network layer. This Special Issue presents the recent research result on industrial wireless sensor networks. Each paper in this Special Issue has unique contributions in the advancements of industrial wireless sensor network research and we expect each paper to promote the relevant research and the deployment of IWSNs

Fair TDMA scheduling in wireless multihop networks

In wireless multihop networks, communication between two end-nodes is carried out by hopping over multiple wireless links. However, the fact that each node has to transmit not only its own traffic, but also traffic on behalf of other nodes, leads to unfairness among the communication rates of the nodes. Traditional Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) based media access control does not work satisfactory in a multihop scenario, since an intended target of a communication may be subject to mutual interference imposed by concurrent transmissions from nodes, which cannot directly sense each other, thus causing unfair throughput allocation. Although Time Division Multiple Access (TDMA) seems to be a more promising solution, careful transmission scheduling is needed in order to achieve error-free communication and fairness. Several algorithms may be found in the literature for scheduling TDMA transmissions in wireless multihop networks. Their main goal is to determine the optimal scheduling, in order to increase the capacity and reduce the delay for a given network topology, though they do not consider the traffic requirements of the active flows of the multihop network or fairness issues. In this paper, we propose a joint TDMA scheduling/load balancing algorithm, called Load-Balanced-Fair Flow Vector Scheduling Algorithm (LB-FFVSA). This algorithm schedules the transmissions in a fair manner, in terms of throughput per connection, taking into account the communication requirements of the active flows of the network. Simulation results show that the proposed algorithm achieves improved performance compared to other solutions, not only in terms of fairness, but also in terms of throughput. Moreover, it was proved that when a load balancing technique is used, the performance of the scheduling algorithm is further improved