Guoming Tang

Divisional perimeter routing for GPSR based on left and right hand rules

The routing void emerges in wireless sensor networks, when the density of sensor node is low or special geography factors appear. To handle the routing void problem, many methods were proposed under different routing protocol, while most of them are complex or demanding for sensor nodes. After analyzing the shortages of former methods, this paper improved the perimeter forwarding strategy in Greedy Perimeter Stateless Routing (GPSR), and proposed a divisional perimeter routing based on right and left hand rules. The results of simulation indicate that, compared to GPSR, the improved protocol can increase the packet delivery success rate, shorten the routing path, and advance the real-time capability of the greedy geographic routing protocol.

An Adaptive Grid Division algorithm for target location in wireless sensor networks

Target location and tracking have been an important fundamental technology in wireless sensor networks. Distance irrelevant location algorithm is one of the most popular methods in target location application. This kind of algorithm mostly locates the target by getting the target approximate area and calculating its centroid. Because of the large calculating quantity and bad real-time capability of the traditional methods in solving the centroid of irregular approximate area, a universal Adaptive grId Division (AID) method was proposed. By grid dividing and scanning towards the WSNs deploying area, a target approximate area was got and the location precision was adaptively adjusted according to the grid granularity. The results of experiment show that AID has a higher real-time capability under similar precision with traditional methods.

A Prediction-based Energy-conserving Approximate Storage and Query Processing Schema in Object-Tracking Sensor Networks

Energy efficiency is one of the most critical issues in the design of wireless sensor networks. In object-tracking sensor networks, the data storage and query processing should be energy-conserving by decrease the message complexity. In this paper, by figuring out the shortcomings of EASE, a Prediction-based Energy-conserving Approximate StoragE (P-EASE) is proposed, which reduce the query error of EASE by changing its approximate area and adopting predicting model without increasing the cost. The theoretical analyses illuminate the correctness and efficiency of the P-EASE. And simulation experiments are conducted under semi-random walk and random waypoint mobility which compares the query error, message complexity, total energy consuming and hotspot energy consuming to validate that P-EASE is more energy-conserving than EASE and has less query error as well.

A Fault-Tolerant Target-Tracking Strategy Based on Unreliable Sensing in Wireless Sensor Networks

Focusing on the unreliable sensing phenomenon in wireless sensor networks and its impact on target-tracking accuracy, this paper first analyzes the uncertain area and its boundaries. Then the monitor area can be divided into faces by these uncertain boundaries and each face has an identical signature vector. On the other hand, for each target localization, any pair-wise nodes RSS is ordinal or flipped can be determined by multiple grouping samplings and the sampling vector is built. Hence, the Fault-Tolerant Target-Tracking (FTTT) strategy is proposed, which transforms the tracking problem into a vector matching process in order to improve the tracking flexibility, increase the tracking accuracy and reduce the influence of in-the-filed factors. In addition, a heuristic matching algorithm is introduced to reduce the computational complexity. Results have shown that FTTT is more flexible and has higher tracking accuracy than congenerous methods.

A new approach of double-level grid-based target localization in wireless sensor networks

Target localization is one of the most important and fundamental issues in wireless sensor networks (WSNs). Because of its special topology structure, grid-based wireless sensor networks have a great advantage in target localization. In this paper, firstly, we analyze the uncertain area founded by the topology structure of single-level grid-deployed networks. A double-level grid-based localization method is proposed, which is theoretical and practical. We consider the computational complexity of the algorithm and define its localization error in both single-level and double-level grid-deployed networks. Simulation results reveal the general rule between different factors and their influences on location accuracy, which is meaningful to the real application of our method. The results prove that double-level grid-based localization method can not only keep location precision to a certain extent, but is also real-time and practical.

Rethinking of the Uncertainty: A Fault-Tolerant Target-Tracking Strategy Based on Unreliable Sensing in Wireless Sensor Networks

Uncertainty is ubiquitous in target tracking wireless sensor networks due to environmental noise, randomness of target mobility and other factors. Sensing results are always unreliable. This paper considers unreliability as it occurs in wireless sensor networks and its impact on target-tracking accuracy. Firstly, we map intersection pairwise sensors’ uncertain boundaries, which divides the monitor area into faces. Each face has a unique signature vector. For each target localization, a sampling vector is built after multiple grouping samplings determine whether the RSS (Received Signal Strength) for a pairwise nodes’ is ordinal or flipped. A Fault-Tolerant Target-Tracking (FTTT) strategy is proposed, which transforms the tracking problem into a vector matching process that increases the tracking flexibility and accuracy while reducing the influence of in-the-filed factors. In addition, a heuristic matching algorithm is introduced to reduce the computational complexity. The fault tolerance of FTTT is also discussed. An extension of FTTT is then proposed by quantifying the pairwise uncertainty to further enhance robustness. Results show FTTT is more flexible, more robust and more accurate than parallel approaches.

UFO: A United Framework for Target Localization in Wireless Sensor Networks

Recently, various range-free based target localization applications and systems are developed in Wireless Sensor Networks. Nevertheless, few of them can be reused or grafted by others, which has led to serious waste. In this paper, a Unified Framework for target lOcalization (UFO) is proposed and an adaptive grids dividing algorithm is addressed within the framework. Based on the common ground of current typical range-free localization methods, UFO is constructed by four major modules to achieve candidate area acquisition and target location determination: sensor deployment and signal process module, candidate area acquisition module, target localization module and parameter setting module. Not only can UFO construct a localization framework quickly, the computing complexity of loaded localization methods is expected to be decreased by bringing in adaptive grids dividing. In our simulations, the UFO assembled with schemes from node sequences based localization method is tested, and the results from our framework are compared with the original methods. Performance evaluation shows us both UFO and original schemes perform well at localizing accuracy, while the former has a big advantage at real-time capacity.

Target Tracking with Pairwise Uncertainty in Wireless Sensor Networks: Qualitatively and Quantitatively

Ubiquitous uncertainty has great impacts on target tracking accuracy in wireless sensor networks (WSNs). This paper focuses on the uncertainty of pair wise sensor nodes, and analyzes it on unreliable samplings of sensors as well as the uncertain relationship between location points and expected sensing results, qualitatively and quantitatively. The sampling vector, which reflects sensing information, is constructed by multiple grouping samplings, while the tracking area can be divided into patches which are assigned to specific signature vectors. Two target tracking strategies are proposed by matching the two vectors, from the qualitative and quantitative aspect, respectively. Results show that both methods can achieve high tracking reliability and satisfy tracking accuracy and robustness for the uncertainty caused by in-the-field factors.

A Prediction-based Energy-conserving Approximate Storage and query processing schema in object-tracking sensor networks

Energy efficiency is one of the most critical issues in the design of wireless sensor networks. In object-tracking sensor networks, the data storage and query processing should be energy-conserving by decrease the message complexity. In this paper, by figuring out the shortcomings of EASE, a Prediction-based Energy-conserving Approximate StoragE (P-EASE) is proposed, which reduce the query error of EASE by changing its approximate area and adopting predicting model without increasing the cost. The theoretical analyses illuminate the correctness and efficiency of the P-EASE. And simulation experiments are conducted under semi-random walk and random waypoint mobility which compares the query error, message complexity, total energy consuming and hotspot energy consuming to validate that P-EASE is more energy-conserving than EASE and has less query error as well.