Road Transportation is a crucial component of today's society, which drives several facets of our lives. The goal of intelligent transportation systems (ITS) is to improve the effectiveness, efficiency, and safety of the transportation system. Traffic signals are an elementary component of all road transportation systems. In order to maximize the productivity of a city, traffic signals must be able to efficiently control the flow of vehicles. Traditionally, current traffic signal optimization is based on traffic arrival rates, either estimated or forecasted. In this paper, we illustrate that arrival time based solutions can outperform arrival rate based approaches. To the best of our knowledge, this is the first work that exploits arrival times of vehicles to improve traffic signal efficiency in order to reduce stopped delays and fuel consumptions, thus in turn reducing greenhouse gases and emissions. We show that arrival time knowledge can be utilized in realizing drastic gains in sparse load scenarios and significant gains in moderate load scenarios. The performance improvement translates to reducing stopped delays by over 40,000 hours daily and in reducing fuel consumption by over 650 gallons/signal/day.

A buffer overflow at a network node occurs if the sum of the amount of data it generates and the amount of data it receives from upstream nodes exceeds its transmission throughput. Densely connected wireless sensor networks (WSNs) with intermittently available sinks (such as mobile sinks) strongly inherit such a buffer overflow problem as sensor nodes in WSNs have limited buffering capacity, and the intermittent nature of these sinks can further influence the network throughput. We propose distribution and caching of sensor-generated data at sensor nodes that belong to any of the minimum cost paths leading to a mobile sink path. We further propose to store the data at each source node for a certain period of time instead of immediately forwarding them to the downstream nodes so as to reduce unnecessary network congestion. Both mathematical and simulation evaluation show that these schemes significantly reduce packet loss probability at network of any size. Consequently, the amount of information lost in the system due to packet overflow/retransmission will be significantly reduced.

Information system security policies have grown in complexity and the emerging collaborative nature of business has created new challenges in creating and managing such policies. These policies address several domains ranging from access control to disaster recovery and depend not only on the business itself but on socio-political/legal requirements as well. Events like collaborative work or project-based organisational units result in the need to create a new information system security policy for the specific work/project, while maintaining status quo of existing policies. This requires identification and evaluation of existing policies to enable creating the new policy in line with the existing ones with acceptable deviations based on informed decisions. This paper provides a framework for capturing and converting security policies in terms of an XML format and further into alloy language format. Policies are converted to alloy format for performing further policy consistency analysis using Alloy Analyser.

A statistical multiplexer is a basic model used in the design and the dimensioning of communication networks. The multiplexer model consists of a single server queue with constant service time and a more or less complicated arrival process. The aim is to determine the packet loss probability as a function of the capacity of the buffer. In this paper, we show how rational approximation techniques may be applied to compute the packet loss efficiently. The approach is based on the knowledge of a limited number of sample values, together with the decay rate of the probability distribution function. A strategy is proposed where the sample points are chosen automatically. The accuracy of the approach is validated by comparison with both analytical results obtained using a matrix-analytic method and simulation results.

Closed Markovian networks of queues with multiclass customers and having a product form equilibrium state probability distribution are useful in the performance evaluation and design of computer and telecommunication systems. Therefore, the efficient computation of the normalizing function, the key element of the solution in product form, has attracted considerable effort. We consider a network that consists of one infinite-server (IS) station and one processor-sharing (PS) or FCFS single-server station. We use multivariate Newton-Pade approximants computed from data for small numbers of customers in each class, to estimate the normalizing function for a larger population in the network. The effectiveness and tremendous gain in computing time of this procedure are illustrated through various numerical experiments.