Albert Sunny

Delay modelling for a single-hop wireless mesh network under light aggregate traffic

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 λ. 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.

A Framework for Designing Multihop Energy Harvesting Sensor Networks

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 ϵ-optimal solution to the original problem. Finally, we present numerical evaluations to validate the correctness of the proposed algorithm.

Distributed greedy scheduling for multihop wireless networks

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.

Joint Scheduling and Sensing Allocation in Energy Harvesting Sensor Networks With Fusion Centers

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).

Link dependence probabilities in IEEE 802.11 infrastructure WLANs

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.

Beating resource constrained eavesdroppers: A physical layer security study

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.

Secure Transmission in Cooperative Networks with Weak Eavesdroppers

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.

Reduced-complexity delay-efficient throughput-optimal distributed scheduling with heterogeneously delayed network-state information

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

A generic controller for managing TCP transfers in IEEE 802.11 infrastructure WLANs

Abstract In this paper, we present a generic controller that ensures fair and efficient operation of IEEE 802.11 infrastructure wireless local area networks (WLANs) with multiple co-channel access points. Our controller addresses performance issues of long-lived TCP transfers in multi-AP WLANs, by overlaying a coarse time-slicing scheduler on top of a cascaded fair queuing scheduler. The time slices and queue weights, used in our controller, are obtained from the solution of a constrained utility optimization formulation. A study of the impact of coarse time-slicing on TCP is also presented in this paper. We also present a methodology to improve the performance of co-existing short-lived and interactive TCP flows. Finally, we report the results of experiments performed on a real testbed, demonstrating the efficacy of our controller.

Incentivized Campaigning in Social Networks

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.