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This letter addresses the problem of wireless indoor positioning and proposes the concept of personal mobility map (PMM). As a graph describing an individual's typical mobility pattern over a physical or abstract space, PMM can be used as the building block for crowd-sourcing based simultaneous localization and mapping (SLAM). PMM can also enable new kinds of location-based services (LBSs). To construct a PMM, we conduct intra-sequence clustering on the sequences of Wi-Fi received signal strength (RSS) measurements to cluster the measurements into different location points (LPs), followed by personal intersequence LP assembling, which aligns these LPs with the concept of the Smith-Waterman algorithm originally used for protein sequencing. Experiments with Wi-Fi RSS measurements collected in the university campus prove that the PMM performs well in indoor mapping and path estimation.

Path tracking in wireless and mobile environments is a fundamental technology for ubiquitous location-based services (LBSs). In particular, it is very challenging to develop highly accurate and cost-efficient tracking systems applied to the anonymous areas where the floor plans are not available for security and privacy reasons. This paper proposes a novel path tracking approach for large Wi-Fi areas based on the time-stamped unlabeled mobility map which is constructed from Smith-Waterman received signal strength (RSS) measurement matching. Instead of conventional location fingerprinting, we construct mobility map with the technique of dimension reduction from the raw measurement space into a low-dimensional embedded manifold. The feasibility of our proposed approach is verified by the real-world experiments in the HKUST campus Wi-Fi networks, sMobileNet. The experimental results prove that our approach is adaptive and capable of achieving an adequate precision level in path tracking.

Many wireless networks are subject to frequent changes in a combination of network topology, traffic demand, and link capacity, such that nonstationary/transient conditions always exist in packet-level network behavior. Although there are extensive studies on the steady-state performance of wireless networks, little work exists on the systematic study of their packet-level time varying behavior. However, it is increasingly noted that wireless networks must not only perform well in steady state, but must also have acceptable performance under nonstationary/transient conditions. Furthermore, numerous applications in today's wireless networks are very critical to the real-time performance of delay, packet delivery ratio, etc, such as safety applications in vehicular networks and military applications in mobile ad hoc networks. Thus, there exists a need for techniques to analyze the time dependent performance of wireless networks. In this dissertation, we develop a performance modeling framework incorporating queuing and stochastic modeling techniques to efficiently evaluate packet-level time dependent performance of vehicular networks (single-hop) and mobile ad hoc networks (multi-hop). For vehicular networks, we consider the dynamic behavior of IEEE 802.11p MAC protocol due to node mobility and model the network hearability as a time varying adjacency matrix. For mobile ad hoc networks, we focus on the dynamic behavior of network layer performance due to rerouting and model the network connectivity as a time varying adjacency matrix. In both types of networks, node queues are modeled by the same fluid flow technique, which follows flow conservation principle to construct differential equations from a pointwise mapping of the steady-state queueing relationships. Numerical results confirm that fluid-flow based performance models are able to respond to the ongoing nonstationary/transient conditions of wireless networks promptly and accurately. Moreover, compared to the c

This paper studies the statistical errors for the fingerprint-based RADAR neighbor matching localization with the linearly calibrated reference points (RPs) in logarithmic received signal strength (RSS) varying Wi-Fi environment. To the best of our knowledge, little comprehensive analysis work has appeared on the error performance of neighbor matching localization with respect to the deployment of RPs. However, in order to achieve the efficient and reliable location-based services (LBSs) as well as the ubiquitous context-awareness in Wi-Fi environment, much attention has to be paid to the highly accurate and cost-efficient localization systems. To this end, the statistical errors by the widely used neighbor matching localization are significantly discussed in this paper to examine the inherent mathematical relations between the localization errors and the locations of RPs by using a basic linear logarithmic strength varying model. Furthermore, based on the mathematical demonstrations and some testing results, the closed-form solutions to the statistical errors by RADAR neighbor matching localization can be an effective tool to explore alternative deployment of fingerprint-based neighbor matching localization systems in the future. © 2014 Mu Zhou et al.

IEEE 802.15.4 Low Rate Wireless Personal Area Network (LR-WPAN) intends to provide low-power ubiquitous communications between devices. However, energy utilization by devices (or nodes) in the network often impacts the application functionality and lifetime due to limited battery capacity. For instance, in the typical multihop LR-WPAN used for data detection and monitoring, the traffic flow often converges to the data sink and such a many-to-one pattern typically results in energy imbalance. Previous research has proposed many approaches to solve this issue. However, a common limitation is about using the idealized energy model, such as 'first order radio model', which is the idealized estimation for RF transmission energy cost of the node. In this paper, we develop a realistic and representative LR-WPAN RF transceiver energy model from the measured data on Chipcon CC2420 and corresponding power control policy. Further, with this energy model, we formulate an optimized energy balanced chain (OEBC) model to maximize the network lifetime. Finally, the OEBC model is applied to develop the network deployment strategy by optimizing node placement, node density and traffic flow distribution. © 2013 IEEE.

P2P live streaming systems have been widely adopted nowadays. In such systems, flash crowds still remains a big challenge, which often occur when an enormous number of users suddenly arrive to view a newly released program. In a flash crowd scenario, users often suffer from a long startup delay and a high failure rate. In this paper, we propose a topology-construction-based algorithm to alleviate the flash crowd. Specifically, first the tracker server constructs a multiple tree topology with total new peers. Then according to the topology, all new peers join the current P2P system in form of multiple trees. When constructing the topology, the tracker server puts new peers with higher bandwidth and longer waiting time more closer to the root in each tree, in order to reduce the average waiting time of new peers. Moreover, a new analytical model is also devised to evaluate our algorithm. Model analysis and simulation indicate that our method can enhance the joining process of new peers and improve their startup delay and failure rate. © 2013 IEEE.