Huadong Ma

Opportunities in Mobile Crowd Sensing

Mobile crowd sensing is a new paradigm that takes advantage of pervasive mobile devices to efficiently collect data, enabling numerous largescale applications. Human involvement is one of the most important features, and human mobility offers unprecedented opportunities for both sensing coverage and data transmission. In this article, we investigate the opportunistic characteristics of human mobility from the perspectives of both sensing and transmission, and discuss how to exploit these opportunities to collect data efficiently and effectively. We also outline various open issues brought by human involvement in this emerging research area.

Physarum Optimization: A Biology-Inspired Algorithm for the Steiner Tree Problem in Networks

Using insights from biological processes could help to design new optimization techniques for long-standing computational problems. This paper exploits a cellular computing model in the slime mold physarum polycephalum to solve the Steiner tree problem which is an important NP-hard problem in various applications, especially in network design. Inspired by the path-finding and network formation capability of physarum, we develop a new optimization algorithm, named as the physarum optimization, with low complexity and high parallelism. To validate and evaluate ourproposed models and algorithm, we furtherapplythe physarum optimization to the minimal exposure problem which is a fundamental problem corresponding to the worst-case coverage in wireless sensor networks. Complexity analysis and simulation results show that our proposed algorithm could achieve good performance with low complexity. Moreover, the core mechanism of our physarum optimization also may provide a useful starting point to develop some practical distributed algorithms for network design.

Energy-Efficient Opportunistic Routing in Wireless Sensor Networks

Opportunistic routing, has been shown to improve the network throughput, by allowing nodes that overhear the transmission and closer to the destination to participate in forwarding packets, i.e., in forwarder list. The nodes in forwarder list are prioritized and the lower priority forwarder will discard the packet if the packet has been forwarded by a higher priority forwarder. One challenging problem is to select and prioritize forwarder list such that a certain network performance is optimized. In this paper, we focus on selecting and prioritizing forwarder list to minimize energy consumption by all nodes. We study both cases where the transmission power of each node is fixed or dynamically adjustable. We present an energy-efficient opportunistic routing strategy, denoted as EEOR. Our extensive simulations in TOSSIM show that our protocol EEOR performs better than the well-known ExOR protocol (when adapted in sensor networks) in terms of the energy consumption, the packet loss ratio, and the average delivery delay.

Multicast Video-on-Demand services

The servers storage I/O and network I/O bandwidths are the main bottleneck of VoD service. Multicast offers an efficient means of distributing a video program to multiple clients, thus greatly improving the VoD performance. However, there are many problems to overcome before development of multicast VoD systems. This paper critically evaluates and discusses the recent progress in developing multicast VoD systems. We first present the concept and architecture of multicast VoD, and then introduce the techniques used in multicast VoD systems. We also analyze and evaluate problems related to multicast VoD service. Finally, we present open issues on multicast VoD as possible future research directions.

On coverage problems of directional sensor networks

In conventional sensor networks, the sensors often are based on omni-sensing model. However, directional sensing range and sensors are great application chances, typically in video sensor networks. Thus, the directional sensor network also demands novel solutions, especially for deployment policy and sensor’s scheduling. Toward this end, this paper evaluates the requirements of deploying directional sensors for a given coverage probability. Moreover, the paper proposes how to solve the connectivity problem for randomly deployed sensors under the directional communication model. The paper proposes a method for checking and repairing the connectivity of directional sensor networks for two typical cases. We design efficient protocols to implement our idea. A set of experiments are also performed to prove the effectivity of our solution. The results of this paper can be also used to solve the coverage problem of traditional sensor networks as a special case.

A Biology-Based Algorithm to Minimal Exposure Problem of Wireless Sensor Networks

The Minimal Exposure Problem (MEP), which corresponds to the quality of coverage, is a fundamental problem in wireless sensor networks. This paper exploits a biological model of physarum to design a novel biology-inspired optimization algorithm for MEP. We first formulate MEP and the related models, and then convert MEP into the Steiner problem by discretizing the monitoring field to a large-scale weighted grid. Inspired by the path-finding capability of physarum, we develop a biological optimization solution to find the minimal exposure road-network among multiple points of interest, and present a Physarum Optimization Algorithm (POA). Furthermore, POA can be used for solving the general Steiner problem. Extensive simulations demonstrate that our proposed models and algorithm are effective for finding the road-network with minimal exposure and feasible for the Steiner problem.

Some problems of directional sensor networks

Wireless sensor networks are often based on omni-sensing and communication models. In contrast, in this paper, we investigate sensor networks with directional sensing and communication capability. Due to the distinct characteristics and potential effects on coverage and connectivity of the network, novel analysis and solutions are demanded. Towards this end, this paper analyses deployment strategies for satisfying given coverage probability requirements with directional sensing models. Moreover, for sensors with directional communication model, we propose methods for checking and repairing the connectivity of the network. We design efficient protocols to implement our idea. A set of experiments are also performed to prove the effectiveness of our solution.

A Coverage-Enhancing Method for 3D Directional Sensor Networks

In conventional directional sensor networks, coverage control for each sensor is based on a 2D directional sensing model. However, 2D directional sensing model cannot accurately characterize the actual application scene of image/video sensor networks. To remedy this deficiency, we propose a 3D directional sensor coverage-control model with tunable orientations. In order to improve the efficiency of target-detecting, we develop a virtual potential-field based coverage-enhancing scheme to improve the coverage performance. Furthermore, we apply the simulated annealing algorithm for objective optimization. The extensive simulations show the effectiveness of our proposed 3D sensing model and coverage enhancing method.

Correlation based video processing in video sensor networks

While conventional sensor networks have revealed their vast potentials, simple and special purposed data doubtlessly limits their expansion into information hungry domains. Video sensor networks present an unprecedented opportunity to annex rich information with distributed sensor networks. However, directional sensing range and high data rate unique to video sensors also demands novel solutions, especially for reducing data amount and individual sensors workload. Toward this end, this paper proposes a method for cooperative video processing in video sensor networks bused on sensor correlations. By pairing up highly correlated nodes and divide sensing tasks among them, each sensor only needs to cover a fraction of the targeted area. Compared to the non-cooperative approach, video sensors image processing and transmission workload is significantly reduced. We design efficient algorithms to calculate the correlations, partition sensing areas, and fuse partial images. A set of experiments are also performed to prove the concept.

Coverage-Enhancing Algorithm for Directional Sensor Networks

Adequate coverage is very important for sensor networks to fulfill the issued sensing tasks. In traditional sensor networks, the sensors are based on omni-sensing model. However, directional sensing sensors are with great application chances, typically in video sensor networks. Toward this end, this paper addresses the problem of enhancing coverage in a directional sensor network. First, based on a rotatable directional sensing model, we present a method to deterministically estimate the amount of directional nodes for a given coverage rate. We also employ Sensing Connected Sub-graph (SCSG) to divide a directional sensor network into several parts in a distributed manner, in order to decrease time complexity. Moreover, the concept of convex hull is introduced to model each sensing connected sub-graph. According to the characteristic of adjustable sensing directions of directional nodes, we study a coverage-enhancing algorithm to minimize the overlapping sensing area of directional sensors only with local topology information. Extensive simulation is conducted to verify the effectiveness of our solution and we give detailed discussions on the effects of different system parameters.