Computing systems now monitor, coordinate, control, integrate and facilitate many physical processes from vehicle management and crisis response to managing data centers. Such systems, termed Cyber-Physical Systems (CPS), can consist of three major components—i) human inhabitants, ii) physical environment and iii) computing entities. Trustworthiness of the CPSs depends on how safe the physical environment is and how survivable the human inhabitants are in the environment. Safety and survivability require pro-active operations in the CPSs so that the conditions violating these properties are predicted and avoided. Pro-activity, however, generally causes undesirable resource consumption overhead. Further, the complex interactions among the physical environment and computing entities can cause additional overhead and uncertainty in pro-actively ensuring the safety and survivability. Thus, a synergistic design of pro-activity is required which considers such complex interactions. In this regard, synergistic planning and preparedness of crisis response, which is cyber-physical in nature, is performed to pro-actively avoid life losses during crises. To this effect, crisis response is modeled as a state-based, real-time stochastic system capturing the uncertainties due to human interactions. The research outcomes include a crisis preparedness tool using the industry standard Architecture Analysis and Design Language (AADL) to specify and analyze the proposed stochastic model. Further, to

Many time-critical applications for Mobile Ad hoc NETworks (MANETs), such as the military applications and disaster response, call for proactive link and route maintenance to ensure low latency for reliable data delivery. The goal of this paper is to minimize the energy overhead due to the high control traffic caused by the periodic route and link maintenance operations in the proactive routing protocols for MANETs. This paper — i) categorizes the proactive protocols based on the maintenance operations performed; ii) derives analytical estimates of the optimum route and link update periods for the different protocol classes by considering a) the data traffic intensity, b) link dynamics, c) target reliability, measured in terms of Packet Delivery Ratio (PDR), and d) the network size; and iii) proposes a network layer dynamic Optimization of Periodic Timers (OPT) method based on the analytical estimates to locally vary the update periods in the distributed nodes. Simulation results show that DSDV-Opt, a variation of DSDV protocol using OPT, — i) achieves the target PDR with 98.7 % accuracy while minimizing the overhead energy; ii) improves the protocol scalability; and iii) reduces the control traffic for low data traffic intensity.

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