Honghai Zhang

On deriving the upper bound of α-lifetime for large sensor networks

In this paper, we explore the fundamental limits of sensor network lifetime that all algorithms can possibly achieve. Specifically, under the assumptions that nodes are deployed as a Poisson point process with density λ in a square region with side length <i>l</i> and each sensor can cover a unit-area disk, we first derive the necessary and sufficient condition of the node density in order to maintain complete <i>k</i>-coverage with probability approaching 1. With this result, we obtain that if #955; = log <i>l</i>² + (<i>k</i>+2)log log <i>l</i>² + <i>c</i>(<i>l</i>), <i>c</i>(<i>l</i>) → -∞, as <i>l</i> → +∞, the sensor network lifetime (for maintaining complete coverage) is upper bounded by <i>kT</i> with probability approaching 1 as <i>l</i> → +∞, where <i>T</i> is the lifetime of each sensor. Second, we derive, given a fixed node density in a finite (but reasonably large) region, the upper bounds of lifetime when only α-portion of the region is required to be covered at any time. We also carry out simulations to validate the derived results. Simulation results indicate that the derived upper bounds apply not only to networks of large areas but also to small-area networks.

J-Sim: a simulation and emulation environment for wireless sensor networks

Wireless sensor networks have gained considerable attention in the past few years. They have found application domains in battlefield communication, homeland security, pollution sensing, and traffic monitoring. As such, there has been an increasing need to define and develop simulation frameworks for carrying out high-fidelity WSN simulation. In this article we present a modeling, simulation, and emulation framework for WSNs in J-Sim - an open source, component-based compositional network simulation environment developed entirely in Java. This framework is built on the autonomous component architecture and extensible internetworking framework of J-Sim, and provides an object-oriented definition of target, sensor, and sink nodes, sensor and wireless communication channels, and physical media such as seismic channels, mobility models, and power models (both energy-producing and energy-consuming components). Application-specific models can be defined by subclassing classes in the simulation framework and customizing their behaviors. We also include in J-Sim a set of classes and mechanisms to realize network emulation. We demonstrate the use of the proposed WSN simulation framework by implementing several well-known localization, geographic routing, and directed diffusion protocols, and perform performance comparisons (in terms of the execution time incurred and memory used) in simulating WSN scenarios in J-Sim and ns-2. The simulation study indicates the WSN framework in J-Sim is much more scalable than ns-2 (especially in memory usage). We also demonstrate the use of the WSN framework in carrying out real-life full-fledged Future Combat System (FCS) simulation and emulation

J-Sim: A Simulation Environment for Wireless Sensor Networks

Wireless sensor networks (WSNs) have gained considerable attention in the past few years. As such, there has been an increasing need for defining and developing simulation frameworks for carrying out high-fidelity WSN simulation. In this paper, the authors presented a modeling and simulation framework for WSNs in J-Sim - an open-source, component-based compositional network simulation environment that is developed entirely in Java. This framework is built upon the autonomous component architecture (ACA) and the extensible internetworking framework (INET) of J-Sim, and provides an object-oriented definition of (i) target, sensor and sink nodes, (ii) sensor and wireless communication channels, and (iii) physical media such as seismic channels, mobility model and power model (both energy-producing and energy-consuming components). Application-specific models can be defined by sub-classing classes in the simulation framework and customizing their behaviors. The use of the proposed WSN simulation framework was demonstrated by implementing several well-known localization, geographic routing, and directed diffusion protocols. In addition, performance comparisons were performed (in terms of execution time incurred, and the memory used) in simulating several typical WSN scenarios in J-Sim and ns-2. The simulation study indicates that the proposed WSN simulation framework in J-Sim is much more scalable than ns-2 (especially in memory usage).

Is Deterministic Deployment Worse than Random Deployment for Wireless Sensor Networks

Before a sensor network is deployed, it is important to determine how many sensors are required to achieve a certain coverage degree. The number of sensor required for maintaining k-coverage depends on the area of the monitored region, the probability that a node fails or powers off (to save energy), and the deployment strategy. In this paper, we derive the density required to maintain k-coverage under three deployment strategies: (i) nodes are deployed as a Poisson point process, (ii) nodes are uniformly randomly distributed, (iii) nodes are deployed on regular grids. Our results show that under most circumstances, grid deployment renders asymptotically lower node density than random deployment. These results override a previous conclusion that grid deployment may render higher node density than random node distributions.

On the upper bound of α-lifetime for large sensor networks

In this article, we explore the fundamental limits of sensor network lifetime that all algorithms can possibly achieve. Specifically, under the assumptions that nodes are deployed as a Poisson point process with density λ in a square region with side length ℓ and each sensor can cover a unit-area disk, we first derive the necessary and sufficient condition of the node density in order to maintain complete <i>k</i>-coverage with probability approaching 1. With this result, we obtain that if λ = log ℓ² + (<i>k</i> + 2)loglog ℓ² + <i>c</i>(ℓ), <i>c</i>(ℓ) → −∞, as ℓ → + ∞, the sensor network lifetime (for maintaining complete coverage) is upper bounded by <i>kT</i> with probability approaching 1 as ℓ → + ∞, where <i>T</i> is the lifetime of a single sensor. Second, we derive, given a fixed node density in a finite (but reasonably large) region, the upper bounds of lifetime when only α-portion of the region is covered at any time. We also carry out several sets of experiments to validate the derived theoretical results. Numerical results indicate that the derived upper bounds apply not only to networks of large sizes and homogeneous nodal distributions but also to small size networks with clustering nodal distributions.

Capacity of wireless ad-hoc networks under ultra wide band with power constraint

In this paper, we study how the achievable throughput scales in a wireless network with randomly located nodes as the number of nodes increases, under a communication model where (i) each node has a maximum transmission power W/sub O/ and is capable of utilizing B Hz of bandwidth and (ii) each link can obtain a channel throughput according to the Shannon capacity. Under the limit case that B tends to infinity, we show that each node can obtain a throughput of /spl theta/(n/sup (/spl alpha/-1)/2/) where n is the density of the nodes and /spl alpha/ > 1 is the path loss exponent. Both the upper bound and lower bound are derived through percolation theory. In order to derive the capacity bounds, we have also derived an important result on random geometric graphs: if the distance between two points in a Poisson point process with density n is non-diminishing, the minimum power route requires a power rate at least /spl Omega/(n/sup (1-/spl alpha/)/2/). Our results show that the most promising approach to improving the capacity bounds in wireless ad hoc networks is to employ unlimited bandwidth resources, such as the ultra wide band (UWB).

Maximising α-lifetime for wireless sensor networks

In energy constrained wireless sensor networks, it is very important to conserve energy and prolong network lifetime while ensuring proper operations of the network. In this paper, we investigate how to maximise the α-lifetime of wireless sensor networks, where α-lifetime is defined as the time duration during which at least α portion of the surveillance region is covered. We first present, given node locations, two upper bounds of the α-lifetime. We then design, based on the derived upper bound, an algorithm that sub-optimally schedules node activities to maximise the α-lifetime of a sensor network. We carry out simulations to validate the derived results and evaluate the designed algorithm. Simulation results show that the proposed algorithm achieves around 90% of the derived upper bound. This implies that the derived upper bounds are rather tight and the proposed algorithm is close to optimal. Finally, we draw from our study several useful conclusions on sensor network deployment and design.

On the critical total power for asymptotic k-connectivity in wireless networks

In this paper, we investigate the minimum total power (termed as critical total power) required to ensure asymptotic k-connectivity in heterogeneous wireless networks where nodes may transmit using different levels of power. We show that under the assumption that wireless nodes form a homogeneous Poisson point process with density /spl lambda/ on a unit square region [0, 1]/sup 2/ and the Toroidal model [M.D. Penrose, 1997], the critical total power required for maintaining k-connectivity is /spl theta/((/spl Gamma/(e/2+k))/((k-1)l)/spl lambda//sup 1-e/2/) with probability approaching one as /spl lambda/ goes to infinity, where e is the path loss exponent. Compared with the results that all nodes use a common critical transmission power for maintaining k-connectivity [M.D. Penrose, 1999], [P.-J. Wan and C. Yi, 2004], we show that the critical total power can be reduced by an order of (log /spl lambda/)e/2 by allowing nodes to optimally choose different levels of transmission power. This result is not subject to any specific power/topology control algorithm, but rather a fundamental property in wireless networks.

A Bluetooth loop scatternet formation algorithm

Bluetooth is a promising new wireless technology that enables portable devices to form short-range wireless ad hoc networks. In this paper, we present a new, distributed Bluetooth scatternet formation algorithm, called loop scatternet formation, which forms scatternets with slave/slave bridges only. In addition to meeting the criteria of maintaining connectivity, minimizing the number of piconets and the maximum degree of devices, the proposed algorithm formalizes the notion of network diameter and node contention. The loop scatternet thus formed incurs a much smaller network diameter and the number of node pairs for which a device has to serve, as a relay node is significantly smaller than that in the other types of scatternets. To validate the design, we derive the bounds of the number of piconets, the network diameter, and the maximum node contention. We also conduct ns-2 simulation to evaluate the performance of loop scatternets. Both analytical and simulation results validate the desirable features of loop scatternets.

A scheduling algorithm for transporting variable rate coded voice in bluetooth networks

In the current Bluetooth specification voice traffic is transmitted using synchronous connection-oriented (SCO) links. Only uncompressed voice connections with Pulse Code Modulation (PCM) are supported, and 64Kbps bandwidth is assigned to each voice connection. However, with the use of speech coding techniques that produce compressed voice and/or detect silent periods, it is expected that high quality voice data can be transmitted at or below 4Kbps, and that the Bluetooth specification will support voice transmission at two or more distinct rates. A key issue that should be resolved in order to support variable rate coded voice transmission in Bluetooth is how to meet the temporal service requirements of various variable rate coded voices.In this paper, we propose, in compliance with the Bluetooth specification, a distance constrained scheduling (DCS)-based, intra-piconet scheduling algorithm to support admission control and scheduling of variable rate coded voice traffic, with the objective of providing the temporal quality of service requirement of each master-slave pair. Further, in the case that requests to establish voice connections exceed the system capacity, we present two admission control algorithms that select, based on utility functions defined, optimal subsets from an otherwise unschedulable request set. We also conduct simulations to evaluate the performance of the proposed algorithms in terms of schedulability, capability of delay jitter control, and effectiveness of admission control.