This paper presents a mobile tracking scheme that exploits the predictability of user mobility patterns in wireless PCS networks. In this scheme, a mobile's future location is predicted by the network, based on the information gathered from the mobile's recent report of location and velocity. When a call is made, the network pages the destination mobile around the predicted location. A mobile makes the same location prediction as the network does; it inspects its own location periodically and reports the new location when the distance between the predicted and the actual locations exceeds a threshold. To more realistically represent the various degrees of velocity correlation in time, a Gauss-Markov mobility model is used. For practical systems where the mobility pattern varies over time, we propose a dynamic Gauss-Markov parameter estimator that provides the mobility parameters to the prediction algorithm. Based on the Gauss-Markov model, we describe an analytical framework to evaluate the cost of mobility management for the proposed scheme. We also present an approximation method that reduces the computational complexity of the cost evaluation for multi-dimensional systems. We then compare the cost of predictive mobility management against that of the regular, non-predictive distance-based scheme, for both the case with ideal Gauss-Markov mobility pattern and the case with time-varying mobility pattern. The performance advantage of the proposed scheme is demonstrated under various mobility patterns, call patterns, location inspection cost, location updating cost, mobile paging cost, and frequencies of mobile location inspections. As a point of reference, prediction can reduce the mobility management cost by more than 50% for all systems, where a the mobile users have moderate mean velocity and where performing a single location update is as least as expensive as paging a mobile in one cell. Index Terms-predictive mobility management, Gauss-Markov model, distance-based location management, mobility pattern, random walk, fluid flow, dynamic parameter estimation, wireless networking
It may not be feasible for sensor networks monitoring nature and inaccessible geographical regions to include powered sinks with Internet connections. We consider the scenario where sinks are not present in large-scale sensor networks, and unreliable sensors have to collectively resort to storing sensed data over time on themselves. At a time of convenience, such cached data from a small subset of live sensors may be collected by a centralized (possibly mobile) collector. In this paper, we propose a decentralized algorithm using fountain codes to guarantee the persistence and reliability of cached data on unreliable sensors. With fountain codes, the collector is able to recover all data as long as a sufficient number of sensors are alive.We use random walks to disseminate data from a sensor to a random subset of sensors in the network. Our algorithms take advantage of the low decoding complexity of fountain codes, as well as the scalability of the dissemination process via random walks. We have proposed two algorithms based on random walks. Our theoretical analysis and simulation-based studies have shown that, the first algorithm maintains the same level of fault tolerance as the original centralized fountain code, while introducing lower overhead than naive random-walk based implementation in the dissemination process. Our second algorithm has lower level of fault tolerance than the original centralized fountain code, but consumes much lower dissemination cost.
Abstract-This paper presents a mobile tracking scheme that exploits the predictability of user mobility patterns in wireless PCS networks. In this scheme, a mobile's future location is predicted by the network, based on the information gathered from the mobile's recent report of location and velocity. When a call is made, the network pages the destination mobile around the predicted location. A mobile makes the same location prediction as the network does; it inspects its own location periodically and reports the new location when the distance between the predicted and the actual locations exceeds a threshold. To more realistically represent the various degrees of velocity correlation in time, a Gauss-Markov mobility model is used. For practical systems where the mobility pattern varies over time, we propose a dynamic Gauss-Markov parameter estimator that provides the mobility parameters to the prediction algorithm.Based on the Gauss-Markov model, we describe an analytical framework to evaluate the cost of mobility management for the proposed scheme. We also present an approximation method that reduces the computational complexity of the cost evaluation for multi-dimensional systems. We then compare the cost of predictive mobility management against that of the regular, nonpredictive distance-based scheme, for both the case with ideal Gauss-Markov mobility pattern and the case with time-varying mobility pattern.The performance advantage of the proposed scheme is demonstrated under various mobility patterns, call patterns, location inspection cost, location updating cost, mobile paging cost, and frequencies of mobile location inspections. As a point of reference, prediction can reduce the mobility management cost by more than 50% for all systems, where a the mobile users have moderate mean velocity and where performing a single location update is as least as expensive as paging a mobile in one cell.Index Terms-predictive mobility management, GaussMarkov model, distance-based location management, mobility pattern, random walk, fluid flow, dynamic parameter estimation, wireless networking
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