Communication Primitives in Cognitive Radio Networks

Cognitive radio networks are a new type of multi-channel wireless network in which different nodes can have access to different sets of channels. By providing multiple channels, they improve the efficiency and reliability of wireless communication. However, the heterogeneous nature of cognitive radio networks also brings new challenges to the design and analysis of distributed algorithms. In this paper, we focus on two fundamental problems in cognitive radio networks: neighbor discovery, and global broadcast. We consider a network containing $n$ nodes, each of which has access to $c$ channels. We assume the network has diameter $D$, and each pair of neighbors have at least $k\geq 1$, and at most $k_{max}\leq c$, shared channels. We also assume each node has at most $\Delta$ neighbors. For the neighbor discovery problem, we design a randomized algorithm CSeek which has time complexity $\tilde{O}((c^2/k)+(k_{max}/k)\cdot\Delta)$. CSeek is flexible and robust, which allows us to use it as a generic "filter" to find "well-connected" neighbors with an even shorter running time. We then move on to the global broadcast problem, and propose CGCast, a randomized algorithm which takes $\tilde{O}((c^2/k)+(k_{max}/k)\cdot\Delta+D\cdot\Delta)$ time. CGCast uses CSeek to achieve communication among neighbors, and uses edge coloring to establish an efficient schedule for fast message dissemination. Towards the end of the paper, we give lower bounds for solving the two problems. These lower bounds demonstrate that in many situations, CSeek and CGCast are near optimal

Approximate Neighbor Counting in Radio Networks

For many distributed algorithms, neighborhood size is an important parameter. In radio networks, however, obtaining this information can be difficult due to ad hoc deployments and communication that occurs on a collision-prone shared channel. This paper conducts a comprehensive survey of the approximate neighbor counting problem, which requires nodes to obtain a constant factor approximation of the size of their network neighborhood. We produce new lower and upper bounds for three main variations of this problem in the radio network model: (a) the network is single-hop and every node must obtain an estimate of its neighborhood size; (b) the network is multi-hop and only a designated node must obtain an estimate of its neighborhood size; and (c) the network is multi-hop and every node must obtain an estimate of its neighborhood size. In studying these problem variations, we consider solutions with and without collision detection, and with both constant and high success probability. Some of our results are extensions of existing strategies, while others require technical innovations. We argue this collection of results provides insight into the nature of this well-motivated problem (including how it differs from related symmetry breaking tasks in radio networks), and provides a useful toolbox for algorithm designers tackling higher level problems that might benefit from neighborhood size estimates

Almost Optimal Algorithms for Token Collision in Anonymous Networks

In distributed systems, situations often arise where some nodes each holds a collection of tokens, and all nodes collectively need to determine whether all tokens are distinct. For example, if each token represents a logged-in user, the problem corresponds to checking whether there are duplicate logins. Similarly, if each token represents a data object or a timestamp, the problem corresponds to checking whether there are conflicting operations in distributed databases. In distributed computing theory, unique identifiers generation is also related to this problem: each node generates one token, which is its identifier, then a verification phase is needed to ensure that all identifiers are unique. In this paper, we formalize and initiate the study of token collision. In this problem, a collection of k tokens, each represented by some length-L bit string, are distributed to n nodes of an anonymous CONGEST network in an arbitrary manner. The nodes need to determine whether there are tokens with an identical value. We present near optimal deterministic algorithms for the token collision problem with Õ(D+k⋅L/log n) round complexity, where D denotes the network diameter. Besides high efficiency, the prior knowledge required by our algorithms is also limited. For completeness, we further present a near optimal randomized algorithm for token collision

Blocking Nuclear Factor-Kappa B Protects against Diet-Induced Hepatic Steatosis and Insulin Resistance in Mice

Inflammation critically contributes to the development of various metabolic diseases. However, the effects of inhibiting inflammatory signaling on hepatic steatosis and insulin resistance, as well as the underlying mechanisms remain obscure. In the current study, male C57BL/6J mice were fed a chow diet or high-fat diet (HFD) for 8 weeks. HFD-fed mice were respectively treated with p65 siRNA, non-silence control siRNA or vehicle every 4th day for the last 4 weeks. Vehicle-treated (HF) and non-silence siRNA-treated (HFNS) mice displayed overt inflammation, hepatic steatosis and insulin resistance compared with chow-diet-fed (NC) mice. Upon treatment with NF-κB p65 siRNA, HFD-fed (HFPS) mice were protected from hepatic steatosis and insulin resistance. Furthermore, Atg7 and Beclin1 expressions and p-AMPK were increased while p-mTOR was decreased in livers of HFPS mice in relative to HF and HFNS mice. These results suggest a crosslink between NF-κB signaling pathway and liver AMPK/mTOR/autophagy axis in the context of hepatic steatosis and insulin resistance