Protocol), a key management protocol for sensor networks that is designed to support in-network processing, while at the same time restricting the security impact of a node compromise to the immediate network neighborhood of the compromised node. The design of the protocol is motivated

applications include fixed-income pricing models, with a role for intensity-based models of default, as well as a wide range of option-pricing applications. An illustrative example examines the implications of stochastic volatility and jumps for option valuation. This example highlights the impact on option

Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our

We evaluate the effect of processor speed, network characteristics, and software overhead on the performance of release-consistent software distributed shared memory. We examine five different protocols for implementing release consistency: eager update, eager invalidate, lazy update, lazy

propose a query algorithm based on multiple random walks that resolves queries almost as quickly as gnutella's flooding method while reducing the network traffic by two orders of mag-nitude in many cases. We also present a distributed replication strategy that yields close-to-optimal performance

Sensor networks are distributed event-based systems that differ from traditional communication networks in several ways: sensor networks have severe energy constraints, redundant low-rate data, and many-to-one flows. Datacentric mechanisms that perform in-network aggregation of data are needed

Much of the comment on Budget 2005 has focused on whether it lived up to its advance billing as a “socially inclusive budget ” and on supposed contrasts between the distributive impact of Budget 2005 and earlier budgets. Typically these comments are based on small numbers of examples,1 and on a

March 23—26, 1983, Dorado Beach, Puerto Rico. The authors would like to thank Beth Van Zuinmeren for her splendid and exhausting research assistance. Without her extraordinary work, the paper would not have been possible. We have also greatly benefited from the extremely efficient and capable assistance of Steve Galatis and

This chapter discusses our proposed methods for characterizing distributional impacts of the UST cleanup program, including environmental justice impacts, risk tradeoffs, economic impacts (including employment and economicgrowtheffects), and promotion of brownfields redevelopment. We also provide a

-layer congestion control will be impacted if the network can only use an imperfect (and potentially distributed) scheduling component that is easier to implement. We study both the case when the number of users in the system is fixed and the case with dynamic arrivals and departures of the users, and we establish