A mobile transaction model that captures both the data and movement behavior
Mobile Networks and Applications
Optimal dynamic mobility management for PCS networks
IEEE/ACM Transactions on Networking (TON)
Mobile Networks and Applications
The impact of mobility on cellular network configuration
Wireless Networks
Mobility and performance modeling in cellular communication networks
ACM SIGMOBILE Mobile Computing and Communications Review
The lookahead strategy for distance-based location tracking in wireless cellular networks
ACM SIGMOBILE Mobile Computing and Communications Review
Mobility modeling in wireless networks: categorization, smooth movement, and border effects
ACM SIGMOBILE Mobile Computing and Communications Review
A New Analytic Framework for Dynamic Mobility Management of PCS Networks
IEEE Transactions on Mobile Computing
User Mobility Pattern Scheme for Location Update and Paging in Wireless Systems
IEEE Transactions on Mobile Computing
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
User mobility modeling and characterization of mobility patterns
IEEE Journal on Selected Areas in Communications
A new random walk model for PCS networks
IEEE Journal on Selected Areas in Communications
WiGriMMA: A Wireless Grid Monitoring Model Using Agents
Journal of Grid Computing
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Model-based movement patterns play a crucial role in evaluating the performance of mobility-dependent Personal Communication Service (PCS) strategies. This study proposes a new normal walk model to represent more closely the daily movement patterns of a mobile station (MS) in PCS networks than a conventional random walk model. The proposed walk model uses a drift angle θ to determine the direction in which an MS leaves a hexagonal cell in the next one step. The angle θ is assumed to approach the normal distribution with the parameters: μ = 0° and σ is on the interval [5°; 90°], based on the concept that most trips follow the shortest path, to make the movement patterns more realistic. A compact classification that types cells with side indices is further developed to partition an n-layer cluster of PCS networks into 12 mirror regions; consequently, the number of states can be reduced significantly, and the computational complexity is also reduced in the probability derivation. Two metrics are formulated in experiments to measure the expected and average numbers of steps taken by an MS to move out of an n-layer cluster. Experimental results confirm that for σ = 10°, 30°, 60°, and 90°, the discrepancies between the analytical computations and the simulated values are all within ±0.46%, and most are even within ±0.35%. Moreover, when σ is set to 71°, a normal walk can almost represent, and even replace, a conventional random walk, since the discrepancies between them are all within ±0.71%.