Haisheng Tan

Distributed multiple-message broadcast in wireless ad hoc networks under the SINR model

In a multiple-message broadcast, an arbitrary number of messages originate at arbitrary nodes in the network at arbitrary times. The problem is to disseminate all these messages to the whole network. This paper gives the first randomized distributed multiple-message broadcast algorithm with worst-case performance guarantee in wireless ad hoc networks employing the SINR interference model which takes interferences from all the nodes in the network into account. The network model used in this paper also considers the harsh characteristics of wireless ad hoc networks: there is no prior structure, and nodes cannot perform collision detection and have little knowledge of the network topology. Under all these restrictions, our proposed randomized distributed multiple-message broadcast protocol can deliver any message m to all nodes in the network in O ( D + k + log 2 ? n ) timeslots with high probability, where D is the network diameter, k is the number of messages whose broadcasts overlap with m, and n is the number of nodes in the network. We also study the lower bound for randomized distributed multiple-message broadcast protocols. In particular, we prove that any uniform randomized algorithm needs ? ( D + k + log 2 ? n log ? log ? log ? n ) timeslots to disseminate k messages initially stored at k nodes.

Distributed multiple-message broadcast in wireless ad-hoc networks under the SINR model

In a multiple-message broadcast, an arbitrary number of messages originate at arbitrary nodes in the network at arbitrary times. The problem is to disseminate all these messages to the whole network. This paper gives the first randomized distributed multiple-message broadcast algorithm with worst-case performance guarantee in wireless ad-hoc networks employing the SINR interference model which takes interferences from all the nodes in the network into account. The network model used in this paper also considers the harsh characteristics of wireless ad-hoc networks: there is no prior structure, and nodes cannot perform collision detection and have little knowledge of the network topology. Under all these restrictions, our proposed randomized distributed multiple-message broadcast protocol can deliver any message m to all nodes in the network in O(D+k+log2n) timeslots with high probability, where D is the network diameter, k is the number of messages whose broadcasts overlap with m, and n is the number of nodes in the network. We also study the lower bound for randomized distributed multiple-message broadcast protocols. In particular, we prove that any uniform randomized algorithm needs

Arbitrary obstacles constrained full coverage in wireless sensor networks

\Omega(D+k+\frac{\log^2n}{\log\log\log n})

A Generic Pigment Model for Digital Painting

timeslots to deliver k messages initially stored at k nodes to all nodes in the network.

Online job dispatching and scheduling in edge-clouds

Coverage is critical for wireless sensor networks to monitor a region of interest and to provide a good quality of service. In many application scenarios, full coverage is required, which means every point inside the region (excluding the obstacles) must be covered by at least one sensor. The problem of using the minimum number of sensors to achieve full coverage for an arbitrary region with obstacles is NP-hard. Most existing coverage methods, such as contour-based ones, simply place sensors along the boundaries to cover the holes that are near the obstacles and the region boundary. These methods are inefficient especially when the obstacles or the region are irregular. In this paper, based on computational geometry, we design a full coverage method, which accurately finds the uncovered holes and places sensors efficiently for both the regular and irregular obstacles and regions. Specifically, we show that the more irregular the obstacles and the region are, the more sensors our method can save.

Minimizing average interference through topology control

We propose a generic pigment model suitable for digital painting in a wide range of genres including traditional Chinese painting and water‐based painting. The model embodies a simulation of the pigment‐water solution and its interaction with the brush and the paper at the level of pigment particles; such a level of detail is needed for achieving highly intricate effects by the artist. The simulation covers pigment diffusion and sorption processes at the paper surface, and aspects of pigment particle deposition on the paper. We follow rules and formulations from quantitative studies of adsorption and diffusion processes in surface chemistry and the textile industry. The result is a pigment model that spans a continuum from the very wet to the very dry brush stroke effects. We also propose a new pigment mixing method based on machine learning techniques to emulate pigment mixing in real life as well as to support the creation of new artificial pigments. To experiment with the proposed model, we embedded the model in a sophisticated digital brush system. The combined system exhibits interactive speed on a modest PC platform. http://www.cs.hku.hk/~songhua/pigment provides supplementary materials for this paper.

An Approximate Approach for Area Coverage in Wireless Sensor Networks

In edge-cloud computing, a set of edge servers are deployed near the mobile devices such that these devices can offload jobs to the servers with low latency. One fundamental and critical problem in edge-cloud systems is how to dispatch and schedule the jobs so that the job response time (defined as the interval between the release of a job and the arrival of the computation result at its device) is minimized. In this paper, we propose a general model for this problem, where the jobs are generated in arbitrary order and times at the mobile devices and offloaded to servers with both upload and download delays. Our goal is to minimize the total weighted response time over all the jobs. The weight is set based on how latency sensitive the job is. We derive the first online job dispatching and scheduling algorithm in edge-clouds, called OnDisc, which is scalable in the speed augmentation model; that is, OnDisc is (1 + ε)-speed O(1/ε)-competitive for any constant ε ∊ (0,1). Moreover, OnDisc can be easily implemented in distributed systems. Extensive simulations on a real-world data-trace from Google show that OnDisc can reduce the total weighted response time dramatically compared with heuristic algorithms.

Complexity of Connectivity in Cognitive Radio Networks through Spectrum Assignment

Reducing interference is one of the main challenges in wireless communication. To minimize interference through topology control in wireless sensor networks is a well-known open algorithmic problem. In this paper, we answer the question of how to minimize the average interference when a node is receiving a message. We assume the receiver-centric interference model where the interference on a node is equal to the number of the other nodes whose transmission ranges cover the node. For one-dimensional (1D) networks, we propose a fast polynomial exact algorithm that can minimize the average interference. For two-dimensional (2D) networks, we give a proof that the maximum interference can be bounded while minimizing the average interference. The bound is only related to the distances between nodes but not the network size. Based on the bound, we propose the first exact algorithm to compute the minimum average interference in 2D networks. Optimal topologies with the minimum average interference can be constructed through traceback in both 1D and 2D networks.

On the complexity of connectivity in cognitive radio networks through spectrum assignment

Abstract In Wireless Sensor Networks (WSNs), coverage is a critical issue that has a major bearing on the quality of sensing over the target region. In this paper, we study the coverage of a region P with a transparent boundary and transparent obstacles. A transparent obstacle is an area in which a sensor cannot be deployed but through which sensing signals can pass. For cost-effectiveness, our problem is to deploy the minimum number of sensors to cover P excluding the obstacles. This problem is challenging mainly due to the fact that the target region is continuous. A straight-forward idea is to sample a finite set of crucial coverage points in P, thus making the coverage space discrete. Most existing approaches, however, tend to either require too many sampled points, which leads to increased running time, or have an inferior coverage of the region. We propose a discretization approach which converts the area coverage problem into the problem of Minimum Geometric Disk Cover with Candidate Positions (MGDCCP) which is proved to be strongly NP-hard. We present a polynomial-time approximation scheme (PTAS) based on the shifting strategy for the MGDCCP problem. Specifically, our approach guarantees covering a (1−ɛ) fraction of the region with probability no less than (1−(ɛ/h)) using at most (1−(1/l)2)h sensors, where h is the theoretical minimal number of sensors needed to cover the region P, l is a positive integer parameter in the shifting strategy, and ɛ (0, 1) is the covering tolerance. Furthermore, we show that our proposed approach is output-sensitive with time complexity that is polynomial in the input size and the optimal solution size. Therefore, for any fixed parameter l and ɛ, the coverage accuracy, the running time, the approximation ratio and the success probability are all bounded.

Maximizing network lifetime online by localized probabilistic load balancing

Cognitive Radio Networks (CRNs) are considered as a promising solution to the spectrum shortage problem in wireless communication. In this paper, we address the algorithmic complexity of the connectivity problem in CRNs through spectrum assignment. We model the network of secondary users (SUs) as a potential graph, where if two nodes have an edge between them, they are connected as long as they choose a common available channel. In the general case, where the potential graph is arbitrary and SUs may have different number of antennae, we prove that it is NP-complete to determine whether the network is connectable even if there are only two channels. For the special case when the number of channels is constant and all the SUs have the same number of antennae, which is more than one but less than the number of channels, the problem is also NP-complete. For special cases that the potential graph is complete or a tree, we prove the problem is NP-complete and fixed-parameter tractable (FPT) when parameterized by the number of channels. Furthermore, exact algorithms are derived to determine the connectivity.