Search

We consider the problem of link scheduling in multihop wireless networks subject to interference constraints. We consider a graph-based representation of wireless networks where scheduled links adhere to the $K$ -hop link interference model. For this scheduling problem, we develop an asynchronous and distributed greedy heuristic. Furthermore, we show that this distributed heuristic computes the exact same schedule as the well-known centralized greedy link scheduling algorithm.

We consider the problem of distributed scheduling in wireless communication networks where heterogeneously delayed queue lengths and channel states of all links are available at all the transmitters. In an earlier work (by Reddy et al. in Queueing Systems, 2012), a throughput-optimal scheduling policy (which we refer to henceforth as the R policy) for this setting was proposed. We study the R policy, and examine its two drawbacks - (i) its huge computational complexity, and (ii) its non-optimal average per-packet queueing delay. We show that the R policy unnecessarily constrains itself to work with information that is more delayed than that afforded by the system. We propose a new distributed scheduling policy that fully exploits the common state information available to all transmitters, thereby greatly improving upon the computational complexity and the delay performance relative to those of the R policy. We also establish the throughput optimality of our policy analytically. We evaluate the performance of the proposed policy and validate our analytical results through extensive numerical simulation. Thus, our work enlarges the ambit of networks for which throughput-optimal scheduling is practicable. (C) 2018 Elsevier B.V. All rights reserved.

Due to intermittent connectivity among nodes in mobile opportunistic networks, the direct packet delivery strategy often experiences large delays. To alleviate this problem, nodes often pass a copy of their message to relay nodes, which in turn could deliver it to the destination. In this setting, we consider the problem of relay selection that minimizes the mean delivery delay. Applying ideas from a renewal theory , we derive a closed-form expression for the mean delivery delay in a heterogeneous network under any probabilistic two-hop relay selection policy. Utilizing this expression, a policy that assures the least mean delivery delay can be obtained as a solution of a linear program (the time complexity of $\mathcal {O}(n^{3})$ ). However, exploiting the structure of the mean delay minimization problem, we compute an optimal solution using an algorithm with linearithmic ( $\mathcal {O}(n \log n)$ ) time complexity.

International audience; Video streaming over cellular network has become extremely popular in 4G and will be an integral part of future cellular networks. While most modern-day video clients continually adapt quality of the video stream, they neither coordinate with the network elements nor among each other. Consequently, a streaming client may quickly overload the cellular network, leading to poor Quality of Experience (QoE) for the users in the network. Motivated by this problem, we present D-VIEWS-a scheduling paradigm that assures video bitrate stability of adaptive video streams while ensuring better system utilization. D-VIEWS only needs to be aware of the set of video bitrates and requires no changes to the streaming client and other network functions. We also study, through simulations, the performance of proportional fairness scheduler and D-VIEWS in the presence of user arrival and departure events.

International audience; Dynamic Adaptive Streaming over HTTP (DASH) has become the standard choice for live events and on-demand video services. In fact, by performing bitrate adaptation at the client side, DASH operates to deliver the highest possible Quality of Experience (QoE) under given network conditions. In cellular networks, in particular, video streaming services are affected by mobility and cell load variation. In this context, DASH video clients continually adapt the streaming quality to cope with channel variability. However, since they operate in a greedy manner, adaptive video clients can overload cellular network resources, degrading the QoE of other users and suffer persistent bitrate oscillations. In this paper, we tackle this problem using a new eNB scheduler, named Shadow-Enforcer, which ensures minimal number of quality switches as well as efficient and fair utilization of network resources. Our scheduler works well under dynamic scenarios and mobility, and requires minimal information, i.e., just the set of video bitrates supported by DASH video clients. It consists of the cascade of a virtual scheduler, Shadow, and the actual scheduler, Enforcer, piloted by the virtual one. Extensive simulations demonstrate the efficiency, fairness and the smooth response to channel variations of the proposed solution.

Energy harvesting wireless networks have become a reality in recent years. Designing policies that store and utilize the harvested energy, for achieving the desired network performance, remains one of the key challenges in such networks. To this end, based on the quality of monitoring , we formulate a long-term time-averaged joint scheduling and sensing allocation problem in wireless sensor networks with finite energy and data buffers, subject to certain data and battery quality-of-service constraints. Relaxing the finite energy buffer assumption, using virtual queues and techniques from Lyapunov optimization, we obtain the JSSA algorithm. We show that by appropriately choosing the control parameter of the JSSA algorithm, we can achieve a performance gap that decays inversely proportional to the battery capacity. The implementation overhead of the JSSA algorithm scales as $O(n)$ . Therefore, we also present a low-complexity distributed version of this algorithm whose implementation overhead scales as $O(\log n)$ .

Efficient resource planning in energy harvesting wireless sensor networks (WSNs) is of immense practical significance. However, it is encumbered by the sheer diversity of parameters that affect the performance of such WSNs. In this paper, we propose a unified framework that can be leveraged to efficiently design and deploy such large-scale networks. To this end, we propose an intuitive utility function to quantify the Quality of Monitoring (QoM). Based on this QoM, we formulate a long-term time-averaged joint resource allocation problem, whose optimal solution serves as a metric to compare different deployment scenarios. This resource allocation problem is subject to energy and capacity constraints inherent to WSNs. The capacity constraints, though very intuitive, require us to enumerate all the maximal independent sets in the network—a well known NP-hard problem. Therefore, for computational tractability, we replace the maximal independent set constraints with clique constraints. We also prove the sufficiency of the clique constraints, and present an algorithm that obtains an $\epsilon$ -optimal solution to the original problem. Finally, we present numerical evaluations to validate the correctness of the proposed algorithm.

In this letter, we propose a scheme to improve the secrecy rate of cooperative networks using Analog Network Coding (ANC). ANC mixes the signals in the air; the desired signal is then separated out, from the mixed signals, at the legitimate receiver using techniques like self interference subtraction and signal nulling, thereby achieving better secrecy rates. Assuming global channel state information, memoryless adversaries and the decode-and-forward strategy, we seek to maximize the average secrecy rate between the source and the destination, subject to an overall power budget. Then, exploiting the structure of the optimization problem, we compute its optimal solution. Finally, we use numerical evaluations to compare our scheme with the conventional approaches.

Campaigners, advertisers, and activists are increasingly turning to social recommendation mechanisms, provided by social media, for promoting their products, services, brands, and even ideas. However, many a time, such social network-based campaigns perform poorly in practice, because the intensity of recommendations drastically reduces beyond a few hops from the source. A natural strategy for maintaining the intensity is to provide incentives. In this paper, we address the problem of minimizing the cost incurred by the campaigner for incentivizing a fraction of individuals in the social network, while ensuring that the campaign message reaches a given expected fraction of individuals. We also address the dual problem of maximizing the campaign penetration for a resource constrained campaigner. To help us understand and solve the above-mentioned problems, we use percolation theory to formally state them as optimization problems. These problems are not amenable to traditional approaches because of a fixed point equation that needs to be solved numerically. However, we use results from reliability theory to establish some key properties of the fixed point, which in turn enables us to solve these problems using algorithms that are linearithmic in maximum node degree. Furthermore, we evaluate the efficacy of the analytical solutions by performing simulations on real-world networks.

In this paper, by augmenting beamforming with signal nulling, we present a scheme to improve the equivocation in wireless relay networks. Assuming global channel state information, memoryless adversaries, and the decode-and-forward relaying strategy, we seek to maximize the average achievable secrecy rate between the source and the legitimate destination, subject to an overall power budget per message. Then, exploiting the structure of the optimization problem, we present an online sequential approach to compute an achievable average rate. Finally, we use numerical evaluations to compare our method with the conventional schemes.

Interference management (RF management) remains one of the main challenges facing the design and deployment of large-scale WLAN. RF management involves the detection, estimation and control of power level, channel allocations and link schedules, to improve the performance of the wireless network. Among interfering links, one can find different types of dependencies. While some of these types can be predicted with relatively low overheads, observing and inferring other types can be a challenging task. In this paper, we ask the question — in WLANs, what is the probability of different types of pair-wise link dependencies? We answer this question by deriving analytical expressions for various types of link dependencies seen in IEEE 802.11 WLANs, numerically evaluating them, and comparing them against simulations.

In this paper, we consider the problem of modelling the average delay experienced by an application packets of variable length in a single cell IEEE 802.11 DCF wireless local area network. The packet arrival process at each node i is assumed to be a stationary and independent increment random process with mean a i and second moment a i (2). The packet lengths at node i are assumed to be i.i.d random variables P i with finite mean and second moment. A closed form expression has been derived for the same. We assume the input arrival process across queues to be uncorrelated Poison processes. As the nodes share a single channel, they have to contend with one another for a successful transmission. The mean delay for a packet has been approximated by modelling the system as a 1-limited Random Polling system with zero switchover times. Extensive simulations are conducted to verify the analytical results.

In this paper, we consider the problem of modelling the average delay in an IEEE 802.11 DCF wireless mesh network with a single root node under light traffic. We derive expression for mean delay for a co-located wireless mesh network, when packet generation is homogeneous Poisson process with rate \lambda. We also show how our analysis can be extended for non-homogeneous Poisson packet generation. We model mean delay by decoupling queues into independent M/M/1 queues. Extensive simulations are conducted to verify the analytical results.

In this paper, we consider the problem of scheduling in multihop wireless networks subject to interference constraints. We consider a graph based representation of wireless networks, where scheduled links adhere to the K-hop link interference model. We develop a distributed greedy heuristic for this scheduling problem. Further, we show that this distributed greedy heuristic computes the exact same schedule as the centralized greedy heuristic.