Despite recent advances in localization technology for mobile devices, to provide real-time position information to people indoors is still a big challenge; usually there is a trade-off between localization accuracy and infrastructural costs (e.g., dense anchor deployment). A possible solution would be employing cooperative approaches which utilize estimated positions of surrounding mobile nodes to complement a small number of anchors. However, it often results in poor estimation accuracy since a temporary large position error due to node mobility easily propagates to neighbor nodes. This paper presents a novel cooperative localization algorithm that addresses this problem by focusing on “stop-and-go behavior” of indoor pedestrians. The key idea is to collaboratively find movement state (moving or static) of each node based on peer-to-peer distance measurement which is inherently necessary for cooperative localization, and use only static nodes as reference points for localization to avoid potential accuracy deterioration. Also, nodes in static state can reduce localization frequency to conserve battery power, keeping the tracking quality. Through extensive simulations, we have demonstrated the performance of our method in terms of accuracy and energy efficiency. The effectiveness in a real application scenario has been also confirmed using a measurement-based sensor model and real mobility traces.

We propose an opportunistic ad hoc localization algorithm called Urban Pedestrians Localization (UPL), for estimating locations of mobile nodes in urban districts. The design principles of UPL are twofold. First, we assume that location landmarks are deployed sparsely due to deployment-cost constraints. Thus, most mobile nodes cannot expect to meet these location landmarks frequently. Each mobile node in UPL relies on location information received from its neighboring mobile nodes instead in order to estimate its area of presence in which the node is expected to exist. Although the area of presence of each mobile node becomes inexact as it moves, it can be used to reduce the areas of presence of the others. Second, we employ information about obstacles such as walls, and present an algorithm to calculate the movable areas of mobile nodes considering obstacles for predicting the area of presence of mobile nodes accurately under mobility. This also helps to reduce each node's area of presence. The experimental results have shown that UPL could be limited to 0.7r positioning error in average, where r denotes the radio range by the above two ideas.

This paper presents a cooperative localization approach for mobile nodes using wireless and ranging devices. We consider scenarios where node mobility follows stop-and-go behavior; we can then utilize the different movement states of nodes as an input to our localization approach. In the proposed method, each node autonomously finds among its surrounding nodes the ones that do not seem to move, and treats them as static nodes. Only nodes that are deemed static are then used as reference points for position estimation. Furthermore, each node adjusts its localization frequency automatically according to its estimated velocity. Performance evaluation results based on a realistic sensor model and actual mobility traces show that our method could achieve sufficient accuracy and efficiency for an exhibition scenario where people need to be tracked.

This paper presents a vehicular positioning system in which multiple vehicles cooperatively calibrate their positions and recognize surrounding vehicles with their GPS receivers and ranging sensors. The proposed system operates in a distributed manner and works even if all vehicles nearby do not or cannot participate in the system. Each vehicle acquires various pieces of positioning information with different degrees of accuracies depending on the sources and recency of information, and compiles them based on likelihood derived from estimated accuracies to minimize estimation errors. A simulation based performance evaluation given in the paper shows that the proposed system improves the estimation accuracy by 85% on average with respect to the standalone GPS receiver, and recognizes about 70% surrounding vehicles with an error of 1m.

In this paper, we propose an algorithm to estimate 2D shapes and positions of obstacles such as buildings using GPS and wireless communication history of mobile nodes. Our algorithm enables quick recognition of geography, which is required in broader types of activities such as rescue activities in emergency situations. Nevertheless, detailed building maps might not be immediately available in private regions such as large factories, warehouses and universities, or prepared maps might not be effective due to collapse of buildings or roads in disaster situations. Some methodologies adopt range measurement sensors like infra-red and laser sensors or cameras. However, they require dedicated hardware and actions for the measurement. Meanwhile, the proposed method can create a rough 2D view of buildings and roads using only wireless communication history between mobile nodes and position history from GPS receivers. The results from the experiment conducted in 150m×190m region on our university campus assuming rescue and treatment actions by 15 members have shown that our method could generate a local map with 85% accuracy within 350 seconds. We have also validated the performance of our algorithm by simulations with various settings.

In this paper, we propose a new trajectory estimation method named TRADE (TRAjectory estimation in DEcentralized way). TRADE is a range-free localization algorithm in fully decentralized mobile ad hoc networks. In TRADE, each mobile node periodically transmits messages containing its estimated trajectory information, and re-computes its own trajectory using those from its neighbors. This information exchange considerably contributes to improvement of the position accuracy. Furthermore, we give the optimal design of the protocol based on the analysis of the algorithm property. Through the analysis, we consider how much trajectory information should be exchanged among nodes to estimate the position within a certain error range in the protocol design. We have evaluated the position accuracy under various settings, and have shown the effectiveness of the protocol in the real world through two realistic application examples.

In this paper, we propose an off-line algorithm called TRACKIE to estimate trajectories of mobile nodes based on encounter information. This method only assumes reasonable number of landmarks and ad-hoc wireless communication facility of mobile nodes, and does not rely on multi-hop ad-hoc networks nor global positioning system. The method achieves low-cost estimation of trajectories and provides accurate solution (the average estimation error was less than 40% of the wireless range in simulations). We have evaluated TRACKIE with Micaz Mote and shown that estimation error is about 2 m in real environments where wireless range is about 3 m.

In this paper, we present a range-free ad-hoc localization algorithm called UPL (Urban Pedestrians Localization), for positioning mobile nodes in urban district. The design principle of UPL is two-fold. (1) We assume that location seeds are deployed sparsely due to deployment-cost constraints. Thus most mobile nodes cannot expect to meet these location seeds frequently. Therefore, each mobile node in UPL relies on location information received from its neighboring mobile nodes in order to estimate its area of presence. The area of presence of each mobile node becomes inexact as it moves, but it is helpful to reduce the areas of presence of the other mobile nodes. (2) To predict the area of presence of mobile nodes accurately under mobility, we employ information about obstacles such as walls, and present an algorithm to calculate the movable areas of mobile nodes considering obstacles. This also helps to reduce each node's area of presence. The experimental results have shown that by the above two ideas UPL could achieve 8mpositioning error in average with 10mof radio range.