Cognitive Radio Networks: Realistic or Not?

A large volume of research has been conducted in the cognitive radio (CR) area the last decade. However, the deployment of a commercial CR network is yet to emerge. A large portion of the existing literature does not build on real world scenarios, hence, neglecting various important interactions of the research with commercial telecommunication networks. For instance, a lot of attention has been paid to spectrum sensing as the front line functionality that needs to be completed in an efficient and accurate manner to enable an opportunistic CR network architecture. This is necessary to detect the existence of spectrum holes without which no other procedure can be fulfilled. However, simply sensing (cooperatively or not) the energy received from a primary transmitter cannot enable correct dynamic spectrum access. For example, the low strength of a primary transmitter's signal does not assure that there will be no interference to a nearby primary receiver. In addition, the presence of a primary transmitter's signal does not mean that CR network users cannot access the spectrum since there might not be any primary receiver in the vicinity. Despite the existing elegant and clever solutions to the DSA problem no robust, implementable scheme has emerged. In this paper, we challenge the basic premises of the proposed schemes. We further argue that addressing the technical challenges we face in deploying robust CR networks can only be achieved if we radically change the way we design their basic functionalities. In support of our argument, we present a set of real-world scenarios, inspired by realistic settings in commercial telecommunications networks, focusing on spectrum sensing as a basic and critical functionality in the deployment of CRs. We use these scenarios to show why existing DSA paradigms are not amenable to realistic deployment in complex wireless environments.Comment: Work in progres

A Search Strategy of Level-Based Flooding for the Internet of Things

This paper deals with the query problem in the Internet of Things (IoT). Flooding is an important query strategy. However, original flooding is prone to cause heavy network loads. To address this problem, we propose a variant of flooding, called Level-Based Flooding (LBF). With LBF, the whole network is divided into several levels according to the distances (i.e., hops) between the sensor nodes and the sink node. The sink node knows the level information of each node. Query packets are broadcast in the network according to the levels of nodes. Upon receiving a query packet, sensor nodes decide how to process it according to the percentage of neighbors that have processed it. When the target node receives the query packet, it sends its data back to the sink node via random walk. We show by extensive simulations that the performance of LBF in terms of cost and latency is much better than that of original flooding, and LBF can be used in IoT of different scales

The Traveling Salesman Problem Under Squared Euclidean Distances

Let $P$ be a set of points in $\mathbb{R}^d$, and let $\alpha \ge 1$ be a real number. We define the distance between two points $p,q\in P$ as $|pq|^{\alpha}$, where $|pq|$ denotes the standard Euclidean distance between $p$ and $q$. We denote the traveling salesman problem under this distance function by TSP($d,\alpha$). We design a 5-approximation algorithm for TSP(2,2) and generalize this result to obtain an approximation factor of $3^{\alpha-1}+\sqrt{6}^{\alpha}/3$ for $d=2$ and all $\alpha\ge2$. We also study the variant Rev-TSP of the problem where the traveling salesman is allowed to revisit points. We present a polynomial-time approximation scheme for Rev-TSP$(2,\alpha)$ with $\alpha\ge2$, and we show that Rev-TSP$(d, \alpha)$ is APX-hard if $d\ge3$ and $\alpha>1$. The APX-hardness proof carries over to TSP$(d, \alpha)$ for the same parameter ranges.Comment: 12 pages, 4 figures. (v2) Minor linguistic change

Stopping ongoing broadcasts in large MANETs

Broadcast is a communication primitive building block widely used in mobile ad-hoc networks (MANETs) for the exchange of control packets and resource location for upper level services such as routing and management protocols. Flooding is the most simple broadcast algorithm, but it wastes a lot of energy and bandwidth, as flooding leads to many redundant radio transmissions. An optimization to flooding is to contain it, once the resource has been found. In this paper, we compare the impact on the latency and power consumption of four competing approaches for flooding containment. The results show that stopping ongoing broadcasts can achieve promising performance increases over other flooding base techniques, when applied in large scale MANETs with scarce power resources. In addition, results show that both network topology and the number of copies of the resource influence differently the performance of each searching approach.(undefined

An Overview of Query-Broadcasting Techniques in Ad Hoc Networks

This chapter presents query-broadcasting techniques used to minimize expenses of the route discovery in ad hoc networks. A broad variety of such techniques have been proposed that improved the effectiveness and efficiency in various aspects of route discovery considering time and energy. Time-to-live based broadcast is the most common controlled flooding scheme widely used in routing protocols. One category of such techniques leveraged the routing history, while other category used broadcast repealing strategy to cancel the query-broadcast after successful route discovery

Search in Power-Law Networks

Many communication and social networks have power-law link distributions, containing a few nodes which have a very high degree and many with low degree. The high connectivity nodes play the important role of hubs in communication and networking, a fact which can be exploited when designing efficient search algorithms. We introduce a number of local search strategies which utilize high degree nodes in power-law graphs and which have costs which scale sub-linearly with the size of the graph. We also demonstrate the utility of these strategies on the Gnutella peer-to-peer network.Comment: 17 pages, 14 figure