Journal of Interconnection Networks

Over the past decade, energy efficiency has consistently been a critical research topic in the field of wireless sensor networks. In wireless networks, signal interference often leads to power waste in a sensor node. Several SINR-based routing algorithms designed for energy efficiency or interference avoidance had been proposed. However, they are either too complex to be useful in practices or may slow in routing computation speed. In this paper, two energy efficient geographic routing algorithms (EEGRA) for wireless sensor network are proposed to address the power consumption issue while considering the routing computation speed. The first algorithm take the value of interference into the routing cost function, and uses it in the routing decision. The second algorithm transforms the problem into a constrained optimization problem, and solves it by searching the optimal discretized interference level. We adopt four geographic routing algorithms: GOAFR+, Face Routing, GPSR, and RandHT, in EEGRA algorithms and compare them with three other routing methods in terms of power consumption and computation cost for the grid and irregular sensor topologies. The experimental result shows that the EEGRA algorithms reduce energy consumption by 30–50% comparing to geographic routing methods. In addition, the time complexity of EEGRA algorithms is similar to the geographic greedy routing methods, which is much faster than the optimal SINR-based algorithm.

This paper presents a family of minimal-diameter, four-regular nonbipartite circulants on 2a² vertices, where a is odd. If a ≡ 3 mod (4), then the step sizes are 1 and (a − 1)², and if a ≡ 1 mod (4), then the step sizes are 1 and (a + 1)². Each graph is obtainable from the 2a × a rectangular twisted torus by appropriately trading a total of 3a − 1 edges for as many new edges. Further, each admits an almost-square L-shaped embedding in the two-dimensional Cartesian plane, facilitating a plane tessellation that leads to an efficient dimension-order routing algorithm.

Combining multiple information retrieval (IR) systems has been shown to improve performance over individual systems. However, it remains a challenging problem to determine when and how a set of individual systems should to be combined. In this paper, we investigate these issues using combinatorial fusion analysis and five data sets provide by TREC 2, 3, 4, 5, and 6. In particular, we compare the performance of combining six IR systems selected by random choice vs. by performance measurement from these five TREC data sets. Two experiments are conducted, which include: (1) combination of two systems and their performance outcome in terms of performance ratio and cognitive diversity, and (2) combinatorial fusion of t-systems, t = 2 to 6, using both score and rank combinations and exploration of the effect of diversity on the performance outcome. It is demonstrated in both experiments that combination of two or more systems improves the performance more significantly when the systems are selected by performance evaluation than those selected by random choice. Our work provides a distinctive method of system selection for the combination of multiple retrieval systems.

In a network, each arc is assigned a weight. The weight of a path or a cycle is defined as the minimum weight of its arcs. The maximum of weights of all paths between two nodes is defined as the strength of connectedness between the nodes. In network applications, the reduction in the strength of connectedness is more relevant than the total disconnection of the graph. A graph is totally weighted if both node set and arc set are weighted. A precisely weighted graph is a totally weighted complete graph with weight functions σ : V → ℜ⁺ and μ : E → ℜ⁺ such that μ(x,y) = σ(x) ∧ σ(y) for any pair of nodes x,y of G where ∧ represents the minimum. Different types of arcs and the existence of partial bridges in precisely weighted complete graphs are studied. The minimum and maximum strong degrees of such a graph are also obtained. Also the concept of node strength sequence is introduced and relations between node strength sequence and strong degrees are obtained.

Statistical multiplexing in telecommunications packet networks allows more connections to be carried by a router or trunk of a given bandwidth than could be carried using time division multiplexing. We provide a simple model of statistical multiplexing which can be used to quickly estimate the effect of such changes as using a higher bandwidth router or trunk.