Users roaming cellular signal coverage with their mobile devices essentially form a mobile cyber-physical system (CPS). By modeling cyber human mentality and physical signal coverage, as well as their interplay, user mobility can be leveraged to improve users' mobile experience with limited wireless bandwidth. Through a real-world case study, we observed that numerous “null zones” and “hot zones” exist in cellular signal coverage areas, where mobile devices cannot obtain sufficiently high data rates for delay-sensitive applications. Over one third of the locations in a crowded area could have weak signal coverage and low bandwidth shares, resulting in poor mobile connectivity experience. This paper considers the practicality of a mobile CPS called Oasis, which guides users to leave those zones and move to nearby locations with better mobile experience. To realize the system, we model and maximize a user's willingness to travel to another location, where the willingness involves the compound impact of the travel distance and the improved perceptual quality. We also develop a prototype system that creates a feedback control loop to allow self-adaptation to users' needs. To evaluate the efficacy, we conducted a series of experiments based on the real data collected in downtown Taipei, Taiwan. The results demonstrate that our mobile CPS can further reduce the average distance per unit of quality improvement achieved with OpenSignalMaps by about 80%, and motivate further research.

Mobile devices, which are inherently of limited computing capabilities, face a growing demand to support increasingly complex applications. Computation offloading addresses this issue by enabling mobile devices to offload computations to a remote server. Advancing on previous work, this paper presents a cloud-based offloading service, which models an optimization problem with the objective of minimizing the operation cost of the service provider while achieving the agreed quality of service (QoS) for subscribers. The problem is shown to be NP-hard. We propose a pseudo-polynomial-time optimal algorithm for the offline scenario, as well as an efficient online algorithm that has a provable QoS guarantee and allows practical implementations. To evaluate our algorithms, we synthesize remotable tasks and conduct extensive simulations based on real mobile user traces and application workload patterns. Our results demonstrate that our online algorithm could achieve comparable performance to the optimal offline algorithm, in terms of both the required cloud cost and the provided user benefit.

Reducing the communication energy is essential to facilitate the growth of emerging mobile applications. In this paper, we introduce signal strength into location-based applications to reduce the energy consumption of mobile devices for data reception. First, we model the problem of data fetch scheduling, with the objective of minimizing the energy required to fetch location-based information without impacting the application's semantics adversely. To solve the fundamental problem, we propose a dynamic-programming algorithm and prove its optimality in terms of energy savings. Then, we perform postoptimal analysis to explore the tolerance of the algorithm to signal strength fluctuations. Finally, based on the algorithm, we consider implementation issues. We have also developed a virtual tour system integrated with existing Web applications to validate the practicability of the proposed concept. The results of experiments conducted based on real-world case studies are very encouraging and demonstrate the applicability of the proposed algorithm toward signal strength fluctuations .

Reducing the communication energy is essential to facilitate the growth of emerging mobile applications. In this paper, we introduce signal strength into location-based applications to reduce the energy consumption of mobile devices for data reception. First, we model the problem of data fetch scheduling, with the objective of minimizing the energy required to fetch location-based information without adversely impacting user experience. Then, we propose a dynamic-programming algorithm to solve the fundamental problem and prove its optimality in terms of energy savings. We also provide an optimality condition with respect to signal strength fluctuations. Finally, based on the algorithm, we consider implementation issues. We have also developed a virtual tour system integrated with existing web applications to validate the practicability of the proposed concept. The results of experiments conducted based on real-world case studies are very encouraging.

The effective call admission control (CAC) scheme provides preferential treatments on call requests to achieve efficient utilization of system resources. The Markov Decision Process (MDP) is one of the optimization schemes used to derive the optimal CAC policy for pre-determined goals. However, the MDP-based approaches suffer from the Curse of Dimensionality problem, where the size of state space grows exponentially with the increase of the number of call types and the number of channels. The Curse of Dimensionality problem makes the MDP-based approach difficult to be implemented in practice. In this paper, we consider a heterogeneous telecommunication system with multiple service priority classes. We propose a suboptimal MDP-based CAC scheme, designed based on a two-phase reduced dimension structure to substantially reduce the overall computational complexity from the order of O(C12) to the order of O(C4), where C denotes number of system capacity. Our proposed MDP-based CAC scheme is evaluated via an event driven simulator and the results are compared with the conventional complete sharing scheme and different guard channel schemes under various traffic loads.