Distributed Computation of Connected Dominating Set for Multi-Hop Wireless Networks

AbstractIn large wireless multi-hop networks, routing is a main issue as they include many nodes that span over relatively a large area. In such a scenario, finding smallest set of dominant nodes for forwarding packets would be a good approach for better communication. Connected dominating set (CDS) computation is one of the method to find important nodes in the network. As CDS computation is an NP problem, several approximation algorithms are available but these algorithms have high message complexity. This paper discusses the design and implementation of a distributed algorithm to compute connected dominating sets in a wireless network with the help of network spectral properties. Based on local neighborhood, each node in the network finds its ego centric network. To identify dominant nodes, it uses bridge centrality value of ego centric network. A distributed algorithm is proposed to find nodes to connect dominant nodes which approximates CDS. The algorithm has been applied on networks with different network sizes and varying edge probability distributions. The algorithm outputs 40% important nodes in the network to form back haul communication links with an approximation ratio ≤ 0.04 * ∂ + 1, where ∂ is the maximum node degree. The results confirm that the algorithm contributes to a better performance with reduced message complexity

Connectivity, Coverage and Placement in Wireless Sensor Networks

Wireless communication between sensors allows the formation of flexible sensor networks, which can be deployed rapidly over wide or inaccessible areas. However, the need to gather data from all sensors in the network imposes constraints on the distances between sensors. This survey describes the state of the art in techniques for determining the minimum density and optimal locations of relay nodes and ordinary sensors to ensure connectivity, subject to various degrees of uncertainty in the locations of the nodes

Locating Battery Charging Stations to Facilitate Almost Shortest Paths

We study a facility location problem motivated by requirements pertaining to the distribution of charging stations for electric vehicles: Place a minimum number of battery charging stations at a subset of nodes of a network, so that battery-powered electric vehicles will be able to move between destinations using "t-spanning" routes, of lengths within a factor t > 1 of the length of a shortest path, while having sufficient charging stations along the way. We give constant-factor approximation algorithms for minimizing the number of charging stations, subject to the t-spanning constraint. We study two versions of the problem, one in which the stations are required to support a single ride (to a single destination), and one in which the stations are to support multiple rides through a sequence of destinations, where the destinations are revealed one at a time

Data Collection and Capacity Analysis in Large-scale Wireless Sensor Networks

In this dissertation, we study data collection and its achievable network capacity in Wireless Sensor Networks (WSNs). Firstly, we investigate the data collection issue in dual-radio multi-channel WSNs under the protocol interference model. We propose a multi-path scheduling algorithm for snapshot data collection, which has a tighter capacity bound than the existing best result, and a novel continuous data collection algorithm with comprehensive capacity analysis. Secondly, considering most existing works for the capacity issue are based on the ideal deterministic network model, we study the data collection problem for practical probabilistic WSNs. We design a cell-based path scheduling algorithm and a zone-based pipeline scheduling algorithm for snapshot and continuous data collection in probabilistic WSNs, respectively. By analysis, we show that the proposed algorithms have competitive capacity performance compared with existing works. Thirdly, most of the existing works studying the data collection capacity issue are for centralized synchronous WSNs. However, wireless networks are more likely to be distributed asynchronous systems. Therefore, we investigate the achievable data collection capacity of realistic distributed asynchronous WSNs and propose a data collection algorithm with fairness consideration. Theoretical analysis of the proposed algorithm shows that its achievable network capacity is order-optimal as centralized and synchronized algorithms do and independent of network size. Finally, for completeness, we study the data aggregation issue for realistic probabilistic WSNs. We propose order-optimal scheduling algorithms for snapshot and continuous data aggregation under the physical interference model

Acceleration of High-Fidelity Wireless Network Simulations

Network simulation with bit-accurate modeling of modulation, coding and channel properties is typically computationally intensive. Simple link-layer models that are frequently used in network simulations sacrifice accuracy to decrease simulation time. We investigate the performance and simulation time of link models that use analytical bounds on link performance and bit-accurate link models executed in Graphical Processing Units (GPUs). We show that properly chosen analytical bounds on link performance can result in simulation results close to those using bit-level simulation while providing a significant reduction in simulation time. We also show that bit-accurate decoding in link models can be expedited using parallel processing in GPUs without compromising accuracy and decreasing the overall simulation time

Near Optimal Broadcast with Network Coding in Large Homogeneous Wireless Networks

We propose an efficient broadcast algorithm for wireless sensor networks, based on network coding: we introduce a simple rate selection and analyze its performance (through computation of \emph{min-cut}). By broadcast, we mean sending data from one source to all the other nodes in the network, and our metric for efficiency is the number of transmissions necessary to transmit one packet from the source to every destination. We address this problem, in some special cases of wireless ``homogeneous'' sensor networks contained of the plane: wireless lattice networks, and dense unit disk networks. Our results are based on the simple principle of Increased Rate for Exceptional Nodes, Identical Rate for Other Nodes (IREN/IRON), for setting rates on the nodes (wireless links) of the network. With this rate selection, we give a value of the maximum broadcast rate of the source: our central result is a proof of the value of the min-cut for such networks

Wireless Broadcast with Network Coding: Energy Efficiency, Optimality and Coding Gain in Lossless Wireless Networks

We consider broadcasting in multi-hop wireless networks, in which one source transmits information to all the nodes in the networks. We focus on energy efficiency, or minimizing the total number of transmissions. Our main result is the proof that, from the energy-efficiency perspective, network coding may essentially operate in an optimal way in the core of the network for uniform wireless networks in Euclidean spaces with idealized communication. In such networks, one corollary is that network coding is expected to outperform routing. We prove that the asymptotic network coding gain is comprised between 1.642 and 1.684 for networks of the plane, and comprised between 1.432 and 2.035 for networks in 3-dimensional space