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___ Time synchronization is a critical piece of infrastructure for any distributed system. Wireless sensor networks have emerged as an important and promising research area in the recent years. Time synchronization is important for many sensor network applications that require very precise mapping of gathered sensor data with the time of the events, for example, in tracking and vehicular surveillance. It also plays an important role in energy conservation in MAC layer protocols. The paper studies different existing methods, protocols, significant time parameters (clock drift, clock speed, synchronization errors, and topologies) to achieve accurate synchronization in a sensor network. The studied Synchronization protocols include conventional time sync protocols (RBS, Timing-sync Protocol for Sensor Networks-TPSN, FTSP), and other application specific approaches such as all node-based approach, a diffusion-based method and group sync approaches aiming at providing network-wide time. The goal for writing this paper is to study most common existing time synchronization approaches and stress the need of a new class of secure-time synchronization protocol that is scalable, topology independent, fast convergent, energy efficient, less latent and less application dependent in a heterogeneous hostile environment. Our survey provides a valuable framework by which protocol designers can compare new and existing synchronization protocols from various metric discussed in the paper. So, we are hopeful that this paper will serve a complete one-stop investigation to study the characteristics of existing time synchronization protocols and its implementation mechanism in a Sensor network environment.

A weapon system consisting of a swarm of air vehicles whose mission is to search for, classify, attack, and perform battle damage assessment, is considered. It is assumed that the target field information is communicated to all the elements of the swarm as it becomes available. A network flow optimization problem is posed whose readily obtained solution yields the optimum resource allocation among the air vehicles in the swarm. Hence, the periodic reapplication of the centralized optimization algorithm yields the benefit of cooperative feedback control.

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In vehicle routing problems with time windows (VRPTW), a set of vehicles with limits on capacity and travel time are available to service a set of customers with demands and earliest and latest time for servicing. The objective is to minimize the number of vehicles and the distance traveled for servicing the set of customers without being tardy or exceeding the capacity or travel time of the vehicles. As finding a feasible solution to the problem is NP-hard, szarch methods based upon heuristics are most promising for problems of practical size. In this paper we describe GIDEON, a genetic algorithm system for solving the VRPTW. On a standard set of 56 VRPTW problems obtained from the literature, GIDEON did better than the alternate methods on 41 of them, with an average reduction of 3.9% in fleet size and 4.4% in distance traveled for the 56 problems. AI TOPIC: Genetic Algorithms. DOMAIN AREA: Vehicle Routing Problems with Time Windows. LANGUAGED'OOL: C Language/GENESIS. STATUS: Implemented. EFFORT: Two and a half person years.IMPACT: Genetic algorithms used as a meta-level search strategy in routing and scheduling problems can obtain near optimal solutions for dynamic environments in real time. IntroductionThe problem we address is the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW involves routing a fleet of vehicles, of limited capacity and travel time, from a central depot to a set of geographically dispersed customers with known demands within specified time windows. The time windows are two-sided, meaning a customer must be serviced at or after its earliest time and before its latest time. If a vehicle reaches a customer before the earliest time it results in idle or waiting time. A vehicle that reaches a customer after the latest time is tardy. A service time is also associated with servicing each customer. The route cost of a vehicle is the total of the traveling time (proportional to the distance), waiting time and service time taken to visit a set of customers.The VRPTW arises in a wide array of practical decision making problems. Instances of the VRPTW occur in retail distribution, school bus routing, mail and newspaper delivery, municipal waste collection, fuel oil delivery, dial-a-ride service, airline and railway fleet routing and scheduling. Efficient routing and scheduling of vehicles can potentially save govemment and industry many millions of dollars a year. The current status of vehicle routing research is available in [3] and [l]. Solomon and Desrosiers[l9] provide an excellent survey on vehicle routing problems with time windows.Savelsbergh[l6] has shown that finding a feasible solution for a VRPTW using a fixed fleet size is NP-hard. Due to the intrinsic difficulty of the problem, search methods based upon heuristics are most promising for solving practical size problems [21,[18] and [20].In this paper we describe GIDEON, a genetic algorithm system to heuristically solve the VRPTW. GIDEON consists of two distinct modules, a global clustering module that assigns customers to vehi...