A Classification and Performance Comparison of Mobility Models for Ad Hoc Networks

Abstract. In mobile ad hoc network research, simulation plays an important role in determining the network characteristics and measuring performance. On the other hand, unrealistic simulation conditions may be misleading, instead of being explanatory. For this reason, constructing simulation models closer to the real circumstances is very significant. Movement behavior of mobile entities is one of the most important concepts for the realistic simulation scenarios in mo-bile ad hoc networks. In this study, we first provide a survey and a new hybrid classification of existing mobility models in the literature. We implemented the random direction and boundless simulation area models on Scalable Wireless Ad Hoc Network Simulator (SWANS) and conducted simulations of Ad Hoc On-Demand Distance Vector (AODV) protocol for these as well as the random walk and random waypoint models. Our comparative results for the mobility models are discussed on a variety of simulation settings and parameters.

In-network processing of nearest neigbor queries for wireless sensor networks

Abstract. Wireless sensor networks have been widely used for civilian and military applications, such as environmental monitoring and vehicle tracking. The sensor nodes in the network have the abilities to sense, store, compute and communicate. To enable object tracking applications, spatial queries such as nearest neighbor queries are to be supported in these networks. The queries can be injected by the user at any sensor node. Due to the limited power supply for sensor nodes, energy efficiency is the major concern in query processing. Centralized data storage and query processing schemes do not favor energy efficiency. In this paper, we propose a distributed scheme called DNN for in-network processing of nearest neighbor queries. A cost model is built to analyze the performance of DNN. Experimental results show that DNN outperforms the centralized scheme significantly in terms of energy consumption and network lifetime.

A Study of a Wireless Smart Sensor Platform for Practical Training

[[abstract]]In order to overcome the obstacles in traditional experimenting and practical training courses, as well as in enhancing the functions of the present e-learning system, the study took sensor network technology as the foundation in developing a web services system. The system will be able to make presentations of the students ‘operations and results on an immediate basis, allowing the students to be guided adequately as they face problems during experiment and practical training.[[booktype]]紙

Flow Monitoring in Wireless Mesh Networks

The original publication is available at www.springerlink.comInternational audienceWe present a dynamic and self-organized flow monitoring framework in Wireless Mesh Networks. An algorithmic mechanism that allows for an autonomic organization of the probes is investigated, with the goal of monitoring all the flows in the backbone of the mesh network accurately and robustly, while minimizing the overhead introduced by the monitoring architecture. The architecture of the system is presented, the probe organization protocol is explained and the performance of the monitoring framework is evaluated by simulation

Fault-Tolerant Aggregation: Flow-Updating Meets Mass-Distribution

Flow-Updating (FU) is a fault-tolerant technique that has proved to be efficient in practice for the distributed computation of aggregate functions in communication networks where individual processors do not have access to global information. Previous distributed aggregation protocols, based on repeated sharing of input values (or mass) among processors, sometimes called Mass-Distribution (MD) protocols, are not resilient to communication failures (or message loss) because such failures yield a loss of mass. In this paper, we present a protocol which we call Mass-Distribution with Flow-Updating (MDFU). We obtain MDFU by applying FU techniques to classic MD. We analyze the convergence time of MDFU showing that stochastic message loss produces low overhead. This is the first convergence proof of an FU-based algorithm. We evaluate MDFU experimentally, comparing it with previous MD and FU protocols, and verifying the behavior predicted by the analysis. Finally, given that MDFU incurs a fixed deviation proportional to the message-loss rate, we adjust the accuracy of MDFU heuristically in a new protocol called MDFU with Linear Prediction (MDFU-LP). The evaluation shows that both MDFU and MDFU-LP behave very well in practice, even under high rates of message loss and even changing the input values dynamically.Comment: 18 pages, 5 figures, To appear in OPODIS 201