We consider a promising application in Vehicular Cyber-Physical Systems (VCPS) called On-road Ad Delivery (OAD), where targeted advertisements are delivered via roadside APs to attract commuters to nearby shops. Different from most existing works on VANETs which only focused on a single technical area, this work on OAD involves technical elements from human factors, cyber systems and transportation systems since a commuter's shopping decision depends on e.g. the attractiveness of the ads, the induced detour, and traffic conditions on different routes. In this paper, we address a new optimization problem in OAD whose goal is to schedule ad messages and allocate a limited amount of AP bandwidth so as to maximize the system-wide performance in terms of total realized utilities (TRU) of the delivered ads. A number of efficient heuristics are proposed to deal with ad message scheduling and AP bandwidth allocation. Besides largescale simulations, we also present a case study in a more realistic scenario utilizing real traces collected from taxis in the city of Shanghai. In addition, we use a commercial traffic simulator (PARAMICS) to show that our proposed solutions are also useful for traffic management in terms of balancing vehicular traffic and alleviating congestion.

Vehicular Cyber-Physical Systems (VCPS) provide human drivers with various services related to road safety, and on-road infotainments. Since a service (message) delivery includes service transmission, service display and driver processing, many challenges arise due to limited network resources, possible pre-emption and contention between services for the display and non-negligible driver processing delay. In this paper, we address a new Driver-centric Service Delivery Problem (DSDP) from a cross-disciplinary resource allocation standpoint. Our goal is to deliver a number of services to a set of intended drivers in a given time period so as to maximize the system-wide performance in terms of total utility income (TUI) to drivers. We show that DSDP differs from all existing problems and is NP-Complete. A number of efficient heuristics are proposed to address several issues, including wireless transmission failure as well as distributed implementation of the multi-sender systems. Utilizing real traces collected from taxis in the city of Shanghai, we also present a case study in a more realistic scenario and conduct comprehensive simulations providing numerical results.

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.