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Cache-enabled wireless networks have gained increasing popularity in recent years for facilitating the delivery of content. Most existing works assumed that the network is static without the change of users and content, which is not the case in the real world. In this paper, we study the content delivery in cache-enabled wireless networks, where the network evolves in terms of users and content. We adopt the affiliation network model to reveal the network evolution process, where new users and content are added into the network randomly and connected with existing nodes via the preferential attachment. Users with strong social relations tend to request the same content objects, thus content popularity is sharply concentrated. Therefore, caching technique is more effective. To maximize the content delivery rate, we formulate the optimization problem of caching replication jointly with routing strategy. We show that network evolution can greatly improve the content delivery rate. In particular, when per-user cache capacity is in the order of $\Theta (1)$ , per-user delivery rate can achieve constant, i.e., the network can scale. Finally, theoretical results are validated based on a real-world data set from Facebook. [ABSTRACT FROM AUTHOR]

This paper studies the problem of optimizing multicast energy consumption in delay-constrained mobile wireless networks, where information from the source needs to be delivered to all the $k$ destinations within an imposed delay constraint. Most existing works simply focus on deriving transmission schemes with the minimum transmitting energy, overlooking the energy consumption at the receiver side. Therefore, in this paper, we propose ConMap, a novel and general framework for efficient transmission scheme design that jointly optimizes both the transmitting and receiving energy. In doing so, we formulate our problem of designing minimum energy transmission scheme, called DeMEM, as a combinatorial optimization one, and prove that the approximation ratio of any polynomial time algorithm for DeMEM cannot be better than ({1}/{4})\ln k . Aiming to provide more efficient approximation schemes, the proposed ConMap first converts DeMEM into an equivalent directed Steiner tree problem through creating auxiliary graph gadgets to capture energy consumption, then maps the computed tree back into a transmission scheme. The advantages of ConMap are threefolded: 1) Generality– ConMap exhibits strong applicability to a wide range of energy models; 2) Flexibility– Any algorithm designed for the problem of directed Steiner tree can be embedded into our ConMap framework to achieve different performance guarantees and complexities; 3) Efficiency– ConMap preserves the approximation ratio of the embedded Steiner tree algorithm, to which only slight overhead will be incurred. The three features are then empirically validated, with ConMap also yielding near-optimal transmission schemes compared to a brute-force exact algorithm. To our best knowledge, this is the first work that jointly considers both the transmitting and receiving energy in the design of multicast transmission schemes in mobile wireless networks. [ABSTRACT FROM PUBLISHER]

The widespread of smart phones has brought new social-aware features to wireless networks. Users prefer to forward traffic to their social contacts in social-aware networks, which is highly different from the traditional uniform traffic pattern. To this end, we analyze a social-aware wireless network, which is modeled by the social contacts between nodes, and explore its impact on network throughput capacity. We present that it can refine social contacts, thus improving the network capacity. Moreover, directional antennas are applied to further enhance the capacity performance, which can reduce the interference brought by other simultaneous communications. We derive the throughput capacity for social-aware networks with single-beam and multi-beam directional antenna, respectively. For single-beam directional antenna, we prove that when wireless networks are dominated by traffics of short-distance social contact, the throughput capacity can be promoted from order of (\Theta (1/\theta ^2n\,\text log\,n)^1/2) to \Theta (1/\theta ^2 \text log\,n) . In addition, when the beamwidth \theta is at order of \Omega ({1}/{({\log {n}})^{1/2}}) , the throughput capacity is a constant. Thus, the wireless network is scalable. For multi-beam antennas, we prove that when sidelobe gain G_{s} is at order of o(Gm) , where Gm is main lobe gain, compared with the single-beam case, the network capacity can achieve the order of \Theta (1/\theta ^2\text log\,n) . [ABSTRACT FROM AUTHOR]

Since it was found that real mobility processes exhibit significant degree of correlation (correlated mobility) and nodes are often heterogeneously distributed in clustered networks (cluster scalability), there has been a great interest in studying their impact on network performance, such as throughput and delay. However, limited works have been done to investigate their impact jointly, which may due to the challenges in capturing both features under a unified network model. In this paper, we focus on their impact on asymptotic connectivity and propose correlated mobile $k$ -hop clustered network model. Two connectivity metrics are considered. One is network connectivity with probability (w.p.). The other is connectivity almost surely (a.s.), which requires a stronger condition than connectivity with probability. With mobility correlation and cluster scalability vary, we show that there are three distinct states for network connectivity, i.e., cluster-sparse, cluster-dense state, and cluster-inferior dense state, respectively. We first prove the exact value of the critical transmission range for each state, respectively, and then further generalize the three states into a unified one, which we call it cluster mixed state. The critical transmission range for connectivity almost surely is \sqrt {2} times the range for connectivity with probability. Our main contribution lies in how to group correlated nodes into independent ones in various settings, and reveals the interrelated relationship between correlated mobility and cluster scalability through state transitions. [ABSTRACT FROM AUTHOR]

Social characteristics have been observed to significantly affect the communications in wireless social networks, especially that within a group following the multicast manner. To model the multicast group in wireless social networks, we should incorporate two important social characteristics, i.e., social relationship and group size. In most existing works, the modeling of social relationship only considers geographic factor. However, such models fail to well characterize wireless social networks, since unlike that in traditional wireless networks, geographic distance is no longer the major factor that affects people’s communications and some non-geographic factors, such as user popularity, become more and more important. Moreover, group size is always assumed to be known a priori in previous works, which cannot fully meet the realistic condition. Therefore, in this paper, we model the multicast group from a combination of geographic and non-geographic (GN) perspectives. Specifically, we propose the GN Model to characterize social relationship and the independently-selected model to characterize group size. In addition to the geographic distance considered in the modeling of social relationship, we also introduce user popularity which reflects the influence of each user on others. Then, we assume that the source transmits the data packet to all his friends following the multicast manner. Based on the proposed model, we calculate transmission distance and network traffic load, and then discuss how they are influenced by both geographic and GN factors. Moreover, our proposed models are verified through experimental measurements based on real datasets. [ABSTRACT FROM PUBLISHER]

This paper investigates the problem of optimally determining source-destination connectivity in random networks. Viewing the network as a random graph, we start by investigating the Erdos–Renyi (ER) graph, as well as a structured graph where, interesting, the problem appears to be open. The problem examined is that of determining whether a given pair of nodes, a source $S$ , and a destination D$ are connected by a path. Assuming that at each step one edge can be tested to see if it exists or not, we determine an optimal policy that minimizes the total expected number of steps. The optimal policy has several interesting features. In order to establish the connectivity of S$ and D$ , a policy needs to check all edges on some path to see if they all exist, but to establish the disconnectivity it has to check all edges on some cut to see if none of them exists. The optimal policy has the following form. At each step, it examines the condensation multigraph formed by contracting each known connected component to a single node, and then checks an edge that is simultaneously on a shortest S$ – D$ path as well as in a minimum S$ – D$ cut. Among such edges, it chooses that which lead to the most opportunities for connection. Interestingly, for an ER graph with n$ nodes, where there is an edge between two nodes with probability p operations to determine which edge to test next. The result also extends to some more general graphs and, meanwhile, provide useful insights into the connectivity determination in random networks. [ABSTRACT FROM AUTHOR]

Due to the nature of a wireless medium, a wireless channel is susceptible to malicious nodes, which encourages the consideration of secrecy in many applications such as satellite networks, social networking services, etc. Motivated by the social characteristic that makes nodes more likely to communicate with nearby neighbors, we study the secrecy capacity of large-scale wireless networks with social relationships. We consider two distributions of legitimate nodes: They are homogeneously placed based on a Poisson point process (PPP), or they are inhomogeneously distributed as multiclustering topology. In particular, we consider two scenarios: 1) the noncolluding case, where each eavesdropper decodes its receiving message independently; and 2) the colluding case, where eavesdroppers can cooperate to decode the message. We adopt a rank-based model to describe the social characteristic and investigate its impact on the secrecy capacity using the technique of self-interference cancelation. We employ a passive attack model, where eavesdroppers can only listen to transmissions among legitimate nodes. Our important findings include the following: 1) In the noncolluding case, secrecy capacity is not influenced by eavesdroppers in order sense, but it varies with the density of eavesdroppers and social impacts in the colluding case. 2) The proposed scheme can achieve optimal secrecy throughput neglecting polylogarithmic factor in homogeneous wireless networks. [ABSTRACT FROM PUBLISHER]

Multicast tree is a key structure for data dissemination from one source to multiple receivers in wireless networks. Minimum length multica modeled as the Steiner tree problem, and is proven to be NP-hard. In this paper, we explore how to efficiently generate minimum length multi wireless sensor networks (WSNs), where only limited knowledge of network topology is available at each node. We design and analyze a simple algorithm, which we call toward source tree (TST), to build multicast trees in WSNs. We show three metrics of TST algorithm, i.e., running and energy efficiency. We prove that its running time is O(\sqrt n\log n) , the best among all existing solutions to our best knowledge. We prove that TST tree length is in the same order as Steiner tree, which give a theoretical upper bound and use simulations to show the ratio be only 1.114 when nodes are uniformly distributed. We evaluate energy efficiency in terms of message complexity and the number of forwarding prove that they are both order-optimal. We give an efficient way to construct multicast tree in support of transmission of voluminous data. [ABSTRACT FROM PUBLISHER]

This paper studies the throughput capacity of wireless networks with social characteristics. We propose a simple model to reflect both the social relations between nodes and power-law node degree distribution, and then examine their impact on capacity. We show the fact that two features above lead to traffic locality and improve capacity. Moreover, multicasting may be employed to further enhance performance when information is desired to be published from the source to all its contacts, of which the number follows power-law distribution. In addition, we propose the corresponding capacity-achieving communication schemes, which optimally exploit the underlying structure. Our study is an attempt to understand how social relations may impact on network capacity from a theoretical perspective, and provides fundamental insight on the design and analysis of real wireless networks. [ABSTRACT FROM PUBLISHER]

Since wireless channel is vulnerable to eavesdroppers, the secrecy during message delivery is a major concern in many applications such as commercial, governmental, and military networks. This paper investigates information-theoretic secrecy in large-scale networks and studies how capacity is affected by the secrecy constraint where the locations and channel state information (CSI) of eavesdroppers are both unknown. We consider two scenarios: 1) noncolluding case where eavesdroppers can only decode messages individually; and 2) colluding case where eavesdroppers can collude to decode a message. For the noncolluding case, we show that the network secrecy capacity is not affected in order-sense by the presence of eavesdroppers. For the colluding case, the per-node secrecy capacity of \Theta(1 \over \sqrtn) can be achieved when the eavesdropper density \psi_e(n) is O(n^-\beta), for any constant \beta > 0 and decreases monotonously as the density of eavesdroppers increases. The upper bounds on network secrecy capacity are derived for both cases and shown to be achievable by our scheme when \psi_e(n)=O(n^-\beta) or \psi_e(n)=\Omega(\log^\alpha-2 \over \alphan), where \alpha is the path-loss gain. We show that there is a clear tradeoff between the security constraints and the achievable capacity. Furthermore, we also investigate the impact of secrecy constraint on the capacity of dense network, the impact of active attacks and other traffic patterns, as well as mobility models in the context. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of multicast scaling performance in multichannel multiradio (MC-MR) networks. Under the assumption of both limited bandwidth and node tunability, a total fixed bandwidth (W) is equally split into (c) channels with (0

A cognitive network refers to the one where two overlaid structures, called primary and secondary networks coexist. The primary network consists of primary nodes who are licensed spectrum users while the secondary network comprises unauthorized users that have to access the licensed spectrum opportunistically. In this paper, we study the percolation degree of the secondary network to achieve k-percolation in large scale cognitive radio networks. The percolation degree is defined as the number of nearest neighbors for each secondary user when there are at least k vertex-disjoint paths existing between any two secondary relays in the percolated cluster. The percolated cluster is formed when there are an infinite number of mutually connected secondary users spanning the whole network. Each user in the cluster is possibly connected to several neighbors, inducing more communication links between any two of them. Since nodes located near the boundary have fewer neighbors, the boundary effect becomes a bottleneck in determining the percolation degree. For cognitive networks, when the primary node density becomes considerably large, the boundary effect spreads inside the network. The transmission area of most secondary users who are located near the primary nodes decreases due to the restriction of the primary network. Therefore, to ensure k-connectivity in the percolated cluster, each secondary user must be connected to more neighbors, and the percolation degree of the secondary network yields a function of the primary node density. We specify the relationship into three regimes regarding the topology variation of the cognitive network. A closed-form expression of the percolation degree under different primary node densities is presented. The expression characterizes the connectivity strength in the secondary percolated cluster, therefore providing analytical insight on fault tolerance improvement in cognitive networks. [ABSTRACT FROM AUTHOR]

In this paper, we study a unified mobility model for mobile multicast (MotionCast) with n nodes, and k destinations for each multicast session. This model considers nodes which can either serve in a local region or move around globally, with a restricted speed R. In other words, there are two particular forms: Local-based Speed-Restricted Model (LSRM) and Global-based Speed-Restricted Model (GSRM). We find that there is a special turning point when mobility speed varies from zero to the scale of network. For LSRM, as R increases, the delay-capacity trade-off ratio decreases iff R is greater than the turning point \Theta (\sqrt1\over k ); For GSRM, as R increases, the trade-off ratio decreases iff R is smaller than the turning point, where the turning point is located at \Theta (k^0.25\over \sqrtn ) when k=o(n^2\over 3 ), and at \Theta (k\over n ) when k=\omega (n^2\over 3 ). As k increases from 1 to n-1, the region that mobility can improve delay-capacity trade-off is enlarged. When R=\Theta (1), the optimal delay-capacity trade-off ratio is achieved. This paper presents a general approach to study the performance of wireless networks under more flexible mobility models. [ABSTRACT FROM AUTHOR]