It is essential to consider drivers' perceptions and reactions when building Vehicular Cyber-Physical Systems (VCPS) since the effectiveness and efficiency of VCPS will largely depend on how drivers could benefit from such a system. This paper considers, for the first time, novel service scheduling problems from a Human Factors (HF) standpoint by taking into consideration the following fact: a driver may not be able to receive more than one service in a short period of time, even if multiple services can be transmitted to the driver from the conventional communications and networking standpoint. We study a family of the HF-aware Service Scheduling (HFSS) Problems, where the goal is to deliver up to n services, each having a time-dependent (and possibly decreasing) utility to a subset of intended drivers so as to minimize the system-wide total utility loss due to unsuccessful delivery of some services. We show that such problems are different from all existing problems. We formulate the basic HFSS problem (BHFSSP) using Integer Linear Programming (ILP) and prove its NP-Completeness. We then propose efficient heuristics for BHFSSP and its more general versions, and present numerical results from large-scale test cases. We also address several practical issues related to wireless transmission failures, distributed implementation in the multisender scenario, and other HF related considerations.

It is essential to consider drivers' perceptions and reactions when building Vehicular Cyber-Physical Systems (VCPS) since the effectiveness and efficiency of VCPS will largely depend on how drivers could benefit from such a system. This paper considers, for the first time, novel service scheduling problems from Human Factors (HF) standpoint by taking into consideration the following fact: a driver may not be able to receive more than one service in a short period of time, even if the VCPS can somehow transmit multiple services to the driver from the conventional communications and networking standpoint. We study a family of the HF-aware Service Scheduling (HFSS) Problems, where the goal is to deliver up to n services, each having a time-dependent (and non-increasing) utility to a subset of intended drivers so as to minimize the system-wide total utility loss due to unsuccessful delivery of some services. We show that such problems are different from all existing problems. We formulate the basic HFSS problem (BHFSSP) using Integer Linear Programming (ILP) and prove it and other more general problems to be NP-Complete. We also propose efficient heuristics and present numerical results from large-scale test cases.

Order-k Markov Model can be used in many fields such as Natural Language Understanding, Coding, Mobile Path Prediction and so on to make prediction and then control. But the model has to face the problem of state space expansion. Taking the mobile path prediction as the research background, the paper firstly proposes a Step-k Markov model and validates its feasibility. Secondly, a hybrid Markov predictor model is put forward based on the Step-k Markov model. The complexity of the Hybrid Markov Model is O(N) while the Order-k Markov model is O(N2). And the memory demand of the hybrid Markov model is O(N2) while Order-k Markov model is O(N3). Finally, it is proved that the hybrid Markov predictor can get close performance with Order-k Markov Predictor at much lower expense by conditional entropy analysis and user mobility data analysis. Also, it can alleviate the zero probability problem in Order-k Markov model to some extent. The hybrid Markov predictor is more practical than Order-k Markov predictor under WLAN.

Path prediction is an important issue in QoS of wireless networks. The paper points out problems in some existed path prediction schemes, especially the state space expansion problem in order-k Markov predictor. And it firstly proposes a step-k Markov model and validates its feasibility. Secondly, a hybrid Markov predictor model and its improved models are put forward based on the step-k Markov model. Because of the order-2 Markov model's best performance in order-k Markov models, the Hybrid Markov model takes the order-2 Markov model as its target. The state space's complexity of the Hybrid Markov Model is 0(N) while the order-2 Markov model is O(N2). And the memory demand of the hybrid Markov model is O(N2) while Order-2 Markov model is O(N3). Finally, it is proved that the hybrid Markov predictor can get close performance with order-2 Markov predictor at much lower expense by conditional entropy analysis and user mobility data analysis. Also it can alleviate the zero probability problem in order-k Markov model to some extent. The hybrid Markov predictor is more practical than order-k Markov predictors under WLAN.