Gil Zussman

Maximizing throughput in wireless networks via gossiping

A major challenge in the design of wireless networks is the need for distributed scheduling algorithms that will efficiently share the common spectrum. Recently, a few distributed algorithms for networks in which a node can converse with at most a single neighbor at a time have been presented. These algorithms guarantee 50% of the maximum possible throughput. We present the first distributed scheduling framework that guarantees maximum throughput. It is based on a combination of a distributed matching algorithm and an algorithm that compares and merges successive matching solutions. The comparison can be done by a deterministic algorithm or by randomized gossip algorithms. In the latter case, the comparison may be inaccurate. Yet, we show that if the matching and gossip algorithms satisfy simple conditions related to their performance and to the inaccuracy of the comparison (respectively), the framework attains the desired throughput.It is shown that the complexities of our algorithms, that achieve nearly 100% throughput, are comparable to those of the algorithms that achieve 50% throughput. Finally, we discuss extensions to general interference models. Even for such models, the framework provides a simple distributed throughput optimal algorithm.

Enabling distributed throughput maximization in wireless mesh networks: a partitioning approach

This paper considers the interaction between channel assignment and distributed scheduling in multi-channel multiradio Wireless Mesh Networks (WMNs). Recently, a number of distributed scheduling algorithms for wireless networks have emerged. Due to their distributed operation, these algorithms can achieve only a fraction of the maximum possible throughput. As an alternative to increasing the throughput fraction by designing new algorithms, in this paper we present a novel approach that takes advantage of the inherent multi-radio capability of WMNs. We show that this capability can enable partitioning of the network into subnetworks in which simple distributed scheduling algorithms can achieve 100% throughput. The partitioning is based on the recently introduced notion of Local Pooling. Using this notion, we characterize topologies in which 100% throughput can be achieved distributedly. These topologies are used in order to develop a number of channel assignment algorithms that are based on a matroid intersection algorithm. These algorithms partition a network in a manner that not only expands the capacity regions of the subnetworks but also allows distributed algorithms to achieve these capacity regions. Finally, we evaluate the performance of the algorithms via simulation and show that they significantly increase the distributedly achievable capacity region.

Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms

Recent advances in energy harvesting materials and ultra-low-power communications will soon enable the realization of networks composed of energy harvesting devices. These devices will operate using very low ambient energy, such as energy harvested from indoor lights. We focus on characterizing the light energy availability in indoor environments and on developing energy allocation algorithms for energy harvesting devices. First, we present results of our long-term indoor radiant energy measurements, which provide important inputs required for algorithm and system design (e.g., determining the required battery sizes). Then, we focus on algorithm development, which requires nontraditional approaches, since energy harvesting shifts the nature of energy-aware protocols from minimizing energy expenditure to optimizing it. Moreover, in many cases, different energy storage types (rechargeable battery and a capacitor) require different algorithms. We develop algorithms for calculating time fair energy allocation in systems with deterministic energy inputs, as well as in systems where energy inputs are stochastic.

Networking low-power energy harvesting devices: Measurements and algorithms

Recent advances in energy harvesting materials and ultra-low-power communications will soon enable the realization of networks composed of energy harvesting devices. These devices will operate using very low ambient energy, such as energy harvested from indoor lights. We focus on characterizing the light energy availability in indoor environments and on developing energy allocation algorithms for energy harvesting devices. First, we present results of our long-term indoor radiant energy measurements, which provide important inputs required for algorithm and system design (e.g., determining the required battery sizes). Then, we focus on algorithm development, which requires nontraditional approaches, since energy harvesting shifts the nature of energy-aware protocols from minimizing energy expenditure to optimizing it. Moreover, in many cases, different energy storage types (rechargeable battery and a capacitor) require different algorithms. We develop algorithms for calculating time fair energy allocation in systems with deterministic energy inputs, as well as in systems where energy inputs are stochastic.

Power Grid Vulnerability to Geographically Correlated Failures - Analysis and Control Implications

We consider line outages in the transmission network of the power grid, and specifically those caused by natural disasters or large-scale physical attacks. In such networks, an outage of a line may lead to overload on other lines, thereby leading to their outage. Such a cascade may have devastating effects not only on the power grid but also on the interconnected communication networks. We study a model of such failures and show that it differs from other models used to analyze cascades (e.g., epidemic/percolation-based models). Inspired by methods developed for network-survivability analysis, we show how to identify the most vulnerable locations in the network. We also perform extensive numerical experiments with real grid data to estimate the effects of geographically correlated outages and briefly discuss mitigation methods. The developed techniques can indicate potential locations for grid monitoring, and hence, will have impact on the deployment of the smart-grid networking infrastructure.

Distributed throughput maximization in wireless mesh networks via pre-partitioning

This paper considers the interaction between channel assignment and distributed scheduling in multi-channel multi-radio Wireless Mesh Networks (WMNs). Recently, a number of distributed scheduling algorithms for wireless networks have emerged. Due to their distributed operation, these algorithms can achieve only a fraction of the maximum possible throughput. As an alternative to increasing the throughput fraction by designing new algorithms, we present a novel approach that takes advantage of the inherent multi-radio capability of WMNs. We show that this capability can enable partitioning of the network into subnetworks in which simple distributed scheduling algorithms can achieve 100% throughput. The partitioning is based on the notion of Local Pooling. Using this notion, we characterize topologies in which 100% throughput can be achieved distributedly. These topologies are used in order to develop a number of centralized channel assignment algorithms that are based on a matroid intersection algorithm. These algorithms pre-partition a network in a manner that not only expands the capacity regions of the subnetworks but also allows distributed algorithms to achieve these capacity regions. We evaluate the performance of the algorithms via simulation and show that they significantly increase the distributedly achievable capacity region. We note that while the identified topologies are of general interference graphs, the partitioning algorithms are designed for networks with primary interference constraints.

Multihop Local Pooling for Distributed Throughput Maximization in Wireless Networks

Efficient operation of wireless networks requires distributed routing and scheduling algorithms that take into account interference constraints. Recently, a few algorithms for networks with primary- or secondary-interference constraints have been developed. Due to their distributed operation, these algorithms can achieve only a guaranteed fraction of the maximum possible throughput. It was also recently shown that if a set of conditions (known as Local Pooling) is satisfied, simple distributed scheduling algorithms achieve 100% throughput. However, previous work regarding Local Pooling focused mostly on obtaining abstract conditions and on networks with single-hop interference or single-hop traffic. In this paper, we identify several graph classes that satisfy the Local Pooling conditions, thereby enabling the use of such graphs in network design algorithms. Then, we study the multihop implications of Local Pooling. We show that in many cases, as the interference degree increases, the Local Pooling conditions are more likely to hold. Consequently, although increased interference reduces the maximum achievable throughput of the network, it tends to enable distributed algorithms to achieve 100% of this throughput. Regarding multihop traffic, we show that if the network satisfies only the single-hop Local Pooling conditions, distributed joint routing and scheduling algorithms are not guaranteed to achieve maximum throughput. Therefore, we present new conditions for Multihop Local Pooling, under which distributed algorithms achieve 100% throughout. Finally, we identify network topologies in which the conditions hold and discuss the algorithmic implications of the results.

Challenge: ultra-low-power energy-harvesting active networked tags (EnHANTs)

This paper presents the design challenges posed by a new class of ultra-low-power devices referred to as Energy-Harvesting Active Networked Tags (EnHANTs). EnHANTs are small, flexible, and self-reliant (in terms of energy devices that can be attached to objects that are traditionally not networked (e.g., books, clothing, and produce), thereby providing the infrastructure for various novel tracking applications. Examples of these applications include locating misplaced items, continuous monitoring of objects (items in a store, boxes in transit), and determining locations of disaster survivors. Recent advances in ultra-low-power wireless communications, ultra-wideband (UWB) circuit design, and organic electronic harvesting techniques will enable the realization of EnHANTs in the near future. In order for EnHANTs to rely on harvested energy, they have to spend significantly less energy than Bluetooth, Zigbee, and IEEE 802.15.4a devices. Moreover, the harvesting components and the ultra-low-power physical layer have special characteristics whose implications on the higher layers have yet to be studied (e.g., when using ultra-low-power circuits, the energy required to receive a bit is an order of magnitude higher than the energy required to transmit a bit). These special characteristics pose several new cross-layer research problems. In this paper, we describe the design challenges at the layers above the physical layer, point out relevant research directions, and outline possible starting points for solutions.

Energy harvesting active networked tags (EnHANTs) for ubiquitous object networking

This article presents the design challenges posed by a new class of ultra-low-power devices referred to as Energy-Harvesting Active Networked Tags (EnHANTs). EnHANTs are small, flexible, and self-reliant (in terms of energy) devices that can be attached to objects that are traditionally not networked (e.g., books, furniture, walls, doors, toys, keys, produce, and clothing). EnHANTs will enable the Internet of Things by providing the infrastructure for various novel tracking applications. Examples of these applications include locating misplaced items, continuous monitoring of objects, and determining locations of disaster survivors. Recent advances in ultra-low-power circuit design, ultra-wideband (UWB) wireless communications, and organic energy harvesting techniques will enable the realization of EnHANTs in the near future. The harvesting components and the ultra-low-power physical layer have special characteristics whose implications on the higher layers have yet to be studied (e.g., when using UWB communications, the energy required to receive a bit is significantly higher than the energy required to transmit a bit). In this article, we describe paradigm shifts associated with technologies that enable EnHANTs and demonstrate their implications on higher-layer protocols. Moreover, we describe some of the components we have designed for EnHANTs. Finally, we briefly discuss our indoor light measurements and their implications on the design of higher-layer protocols.

Mobile backbone networks --: construction and maintenance

We study a novel hierarchical wireless networking approach in which some of the nodes are more capable than others.In such networks,the more capable nodes can serve as Mobile Backbone Nodes and provide a backbone over which end-to-end communication can take place. Our approac consists of controlling the mobility of the Backbone Nodes in order to maintain connectivity. We formulate the problem of minimizing the number of backbone nodes and refer to it as the Connected Disk Cover problem.We show that it can be decomposed into the Geometric Disk Cover (GDC)problem and the Steiner Tree Problem wit Minimum Number of Steiner Points (STP-MSP). We prove that if these sub-problems are solved separately by γ- and δ- approximation algorithms, the approximation ratio of t e joint solution is γ + δ. Then, we focus on the two subproblems and present a number of distributed approximation algorithms that maintain a solution to the GDC problem under mobility A new approach to the solution of the STP-MSP is also described. We show that this approach can be extended in order to obtain a joint approximate solution to the Connected Disk Cover problem. Finally, we evaluate the performance of the algorithms via simulation and show that the proposed GDC algorithms perform very well under mobility and that the new approac for the joint solution can significantly reduce the number of required Mobile Backbone Nodes.