Falls are the second leading cause of accidental or unintentional injuries/deaths worldwide. Accurate pose estimation using commodity mobile devices will help early detection and injury assessment of falls, which are essential for the first aid of elderly falls. By following the definition of fall, we propose a P ervasive P ose Est imation scheme for fall detection ( P \( ^2 \) Est ), which measures changes in tilt angle and height of the human body. For the tilt measurement, P \( ^2 \) Est leverages the pointing of the mobile device, e.g., the smartphone, when unlocking to associate the Device coordinate system with the World coordinate system. For the height measurement, P \( ^2 \) Est exploits the fact that the person’s height remains unchanged while walking to calibrate the pressure difference between the device and the floor. We have prototyped and tested P \( ^2 \) Est in various situations and environments. Our extensive experimental results have demonstrated that P \( ^2 \) Est can track the body orientation irrespective of which pocket the phone is placed in. More importantly, it enables the phone’s barometer to detect falls in various environments with decimeter-level accuracy.

A smart shelving system can visualize stock data in real time by leveraging item-level RFID tagging so that we can minimize out-of-stock and reduce warehousing and labor costs. The key issue of smart shelving is to locate RFID tags at any time, especially after misplacing tags. The detection of misplaced tags on stationary shelved items is very challenging due to position ambiguity, phase wrapping, device diversity, and phase ambiguity. Using a combination of theoretical analysis, simulation-based prediction and experimental verification, we propose an effective way of detecting misplaced tags, called FINDS, that integrates Particle Swarm Optimization (PSO), Synthetic Minority Over-sampling TEchnique (SMOTE) and Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithms to make theoretical and measured phases consistent with each other, and observe the phase shifts caused by misplaced tags. FINDS requires neither antenna movement nor external disturbances. We have implemented a prototype of FINDS with 20 tags and evaluated its performance, demonstrating FINDS's accuracy to be higher than 0.92 in the case of 2 stationary antennas.

A smart shelving system can visualize stock data in real time by leveraging item-level RFID tagging. The detection of misplaced tags on stationary shelved items is very challenging due to position ambiguity, phase wrapping, device diversity, and phase ambiguity. We propose an effective way of detecting misplaced tags, called FINDS, that requires neither antenna movement nor external disturbances.

Many RFID tags store valuable information that can easily be subject to unauthorized reading, leading to system security and privacy risks. The detection methods existed are not only complex and impractical, but also unable to extract more information about the abnormal signal. In this paper, we propose a lightweight detection approach for the unauthorized reading without affecting the operation of RFID systems. Such an approach contains three parts: RF signal scanner, signalevent model construction and abnormal feature extraction. In particular, we design and implement a RF scanner to acquire RF signals and measure RSSI values. After that, we build a signal-event model to analyze how the RSSI value is related to the RFID event. The detection of unauthorized reading is to investigate the deviation of observed RSSI values from their expected values. Finally, we extract and separate abnormal RSSI values to estimate the risk of unauthorized reading. The primary experimental results show that our approach can achieve high prediction accuracy in detecting unauthorized reading and make better performance in extracting abnormal features.

Recently, there has been growing interest in indoor localization, because numerous applications depend on the rapid and accurate position estimation of tagged objects. While RFID-based indoor localization is attractive, the need for a large-scale and high-density deployment of readers and reference tags is costly. Being the range-free localization, our schemes depend solely on mobile readers without reference tags or other devices, and it avoids the need of distance estimation according to RSSI or phase difference. We propose two novel algorithms, continuous scanning and category-based scheduling, for locating single and multiple tagged objects, respectively. Our primary experimental results show that the system can achieve high time efficiency and localization accuracy.

Cloning attacks seriously impede the security of radio-frequency identification (RFID) applications. This paper tackles deterministic clone detection for anonymous RFID systems without tag identifiers (IDs) as a priori. Existing clone detection protocols either cannot apply to anonymous RFID systems due to necessitating the knowledge of tag IDs or achieve only probabilistic detection with a few clones tolerated. This paper proposes three protocols—BASE, DeClone, and DeClone+—toward fast and deterministic clone detection for large anonymous RFID systems. BASE leverages the observation that clone tags make tag cardinality exceed ID cardinality. DeClone is built on a recent finding that clone tags cause collisions that are hardly reconciled through rearbitration. For DeClone to achieve detection certainty, this paper designs breadth first tree traversal toward quickly verifying unreconciled collisions and hence the cloning attack. DeClone+ further incorporates optimization techniques that promise faster clone detection when clone ratio is relatively high. The performance of the proposed protocols is validated through analysis and simulation. This paper also suggests feasible extensions to enrich their applicability to distributed design.

Cloning attacks seriously impede the security of Radio-Frequency Identification (RFID) applications. In this paper, we tackle deterministic clone detection for anonymous RFID systems without tag identifiers (IDs) as a priori. Existing clone detection protocols either cannot apply to anonymous RFID systems due to necessitating the knowledge of tag IDs or achieve only probabilistic detection with a few clones tolerated. We propose two protocols, BASE and DeClone, toward fast and deterministic clone detection for large anonymous RFID systems. BASE leverages the observation that clone tags make tag cardinality exceed ID cardinality. DeClone is built on a recent finding that clone tags cause collisions that are hardly reconciled through re-arbitration. For DeClone to achieve detection certainty, we design breadth first tree traversal toward quickly verifying unreconciled collisions and hence the cloning attack. We validate their detection performance through analysis and simulation. The results show that BASE delivers faster detection for small systems while DeClone for large ones especially when clone ratio increases.

BitTorrent (BT) is one of the most common Peer-to-Peer (P2P) file sharing protocols. Rather than downloading a file from a single source, the protocol allows users to join a swarm of peers to download and upload from each other simultaneously. Worms exploiting information from BT servers or trackers can cause serious damage to participating peers, which unfortunately has been neglected previously. In this article, we first present a new worm, called Adaptive BitTorrent worm (A-BT worm), which finds new victims and propagates sending forged requests to trackers. To reduce its abnormal behavior, the worm estimates the ratio of infected peers and adaptively adjusts its propagation speed. We then build a hybrid model to precisely characterize the propagation behavior of the worm. We also propose a statistical method to automatically detect the worm from the tracker by estimating the variance of the time intervals of requests. To slow down the worm propagation, we design a safe strategy in which the tracker returns secured peers when receives a request. Finally, we evaluate the accuracy of the hybrid model, and the effectiveness of our detection method and containment strategy through simulations.

In many RFID applications, we conduct a RFID reader network to monitor a large number of tagged items. We also want to utilize this network to trace un-tagged items. In this paper, we propose ARNS-Adaptive RFID Network Scheduling scheme for device-free tracking. In particular, we firstly deploy a set of readers and reference tags into the monitored area. Then, we divide the tags within the read region of a reader into two groups, one in reader-to-reader collision area while the other not. We monitor the area by normal sampling model in which we need two phases to separately query the two groups. When an un-tagged target appears in the monitored area, we adopt tracking sampling model. In tracking sampling mode, the reader who detects the tags' frequency variations will adaptively query all tags in its read range to obtain the target's full trace. Meanwhile, we fill the missing path using shortest-path algorithm to recovery the trajectory. Experimental results show that ARNS achieves high tracking accuracy for certain applications.

In this paper, we consider the RFID reader coverage problem in three-dimensional space. We propose a cube-based model to describe both the reading region of readers and the area covered by reading regions. In our cube-based model, we place a reader into a large cube, which is equally cut into n small cubes. Then, we can use a mathematical three-dimensional matrix to model the reading region and covered area. Finally, we modify the particle swarm optimization (PSO) algorithm to maximize the coverage rate and minimize the overlap rate. Our simulation results show that the proposed model and algorithm are efficient for solving the RFID coverage problem in three-dimensional space.

Cloning attacks threaten radio-frequency identification (RFID) applications but are hard to prevent. Existing cloning attack detection methods are enslaved to the knowledge of tag identifiers (IDs). Tag IDs, however, should be protected to enable and secure privacy-sensitive applications in anonymous RFID systems. In a first step, this paper tackles cloning attack detection in anonymous RFID systems without requiring tag IDs as a priori. To this end, we leverage unreconciled collisions to uncover cloning attacks. An unreconciled collision is probably due to responses from multiple tags with the same ID, exactly the evidence of cloning attacks. This insight inspires GREAT, our pioneer protocol for cloning attack detection in anonymous RFID systems. We evaluate the performance of GREAT through theoretical analysis and extensive simulations. The results show that GREAT can detect cloning attacks in anonymous RFID systems fairly fast with required accuracy. For example, when only six out of 50,000 tags are cloned, GREAT can detect the cloning attack in 75.5 s with a probability of at least 0.99.

Piece-related algorithms, including piece revelation, selection, and queuing, play a crucial role in the BitTorrent (BT) protocol, because the BT system can be viewed as a market where peers trade their pieces with one another. During the piece exchanging, a peer selects some pieces revealed by neighbors, and queues them up for downloading. In this paper, we provide a deep understanding of these algorithms, and also propose some improvements to them. Previous study has shown that the piece revelation strategy is vulnerable to under-reporting. We provide a game-theoretic analysis for this selfish gaming, and propose a distributed credit method to prevent it. Existing piece selection strategies, though long believed to be good enough, may fail to balance piece supply and demand. We propose a unified strategy to shorten the download time of peers by applying utility theory. The design of the piece queuing algorithm has a conflict with that of piece selection strategy, because it is not possible to assume that the queued requests for a selected piece can always be available on multiple neighbors. We give a possible fix to address the conflict by allowing peers to dynamically manage their unfulfilled requests. To evaluate the performance of the proposed algorithms, we run several experiments in a live swarm. Our primary results show that they can achieve fast individual and system-wide download time.

With the development of various wireless personal area networks (WPANs), the issue of security has become a crucial problem for their applications. Jamming is one of the most important methods of attack to deprive or reduce the communication service of WPANs. Most existing jamming attacks can cause negative interference, but the attack strategies are usually not adjusted against the countermeasures that are currently taken. This paper proposes an effective dynamic jamming attack (EDJam) in an 802.15.4-compliant WPAN. In this attack, a jammer who is aware of a change in the network defense strategy, e.g. the use of a dynamic retransmission mechanism, may choose a better strategy to make more damage to the network with less cost. Similarly, a well-protected network can change its defense strategy against the EDJam. This procedure of competition between the EDJam attacker and defending networks is modeled and formulated as a Stackelberg game, and a unique Nash Equilibrium point is derived in analytical format. Based on an equilibrium analysis, we discuss the condition under which a defense strategy will increase the utility of the network and a dynamic retransmission mechanism defense strategy is proposed accordingly. The simulation results show that EDjam can be more cost-efficient than continuous, random and fixed-period jamming.

Bootstrapping is critical in any P2P network, since, on initial startup, a peer must bootstrap and find at least one neighbor. Existing P2P networks simply rely on centralized servers or static peers for bootstrapping, which may become a single point of failure. Recently, the shutdown of BT web-sites in China has caused a serious problem in BT bootstrapping. A decentralized way to bootstrap P2P networks is to clone existing social networks. In particular, a new peer obtains addresses of potential neighbors by sniffing instant messaging packets (e.g., MSN or QQ packets). With such addresses, the peer can bootstrap neighbors and join the network without the help of any centralized server.

Worm propagation analysis, including exploring mechanisms of worm propagation and formulating effects of network/worm parameters, has great importance for worm containment and host protection in P2P networks. Previous work only focuses on topological worm propagation where worms search a hosts neighbor-list to find new victims. In BitTorrent (BT) networks, the information from servers or trackers, however, could be fully exploited to design effective worms. In this paper, we propose a new approach for worm propagation in BT-like P2P networks. The worm, called Dynamic Hit-List (DHL) worm, locates new victims and propagates itself by requesting a tracker to build a dynamic hit list, which is a self-stopping BT worm to be stealthy. We construct an analytical model to study the propagation of such a worm: breadth-first propagation and depth-first propagation. The analytical results provide insights of the worm design into choosing parameters that enable the worm to stop itself after compromising a large fraction of vulnerable peers in a P2P network. We finally evaluate the performance of DHL worm through simulations. The simulation results verify the correctness of our model and show the effectiveness of the worm by comparing it with the topological worm.

This paper deals with the problem of guiding mobile sensors (or robots) to a phenomenon across a region covered by static sensors. We present a distributed, reliable and energy-efficient algorithm to construct a smoothed moving trajectory for a mobile robot. The reliable trajectory is realized by first constructing among static sensors a distributed hop count based artificial potential field (DH-APF) with only one local minimum near the phenomenon, and then navigating the robot to that minimum by an attractive force following the reversed gradient of the constructed field. Besides the attractive force towards the phenomenon, our algorithm adopts an additional repulsive force to push the robot away from obstacles, exploiting the fast sensing devices carried by the robot. Compared with previous navigation algorithms that guide the robot along a planned path, our algorithm can (1) tolerate the potential deviation from a planned path, since the DH-APF covers the entire deployment region; (2) mitigate the trajectory oscillation problem; (3) avoid the potential collision with obstacles; (4) save the precious energy of static sensors by configuring a large moving step size, which is not possible for algorithms neglecting the issue of navigation reliability. Our theoretical analysis of the above features considers practical sensor network issues including radio irregularity, packet loss and radio conflict. We implement the proposed algorithm over TinyOS and test its performance on the simulation platform with a high fidelity provided by TOSSIM and Tython. Simulation results verify the reliability and energy efficiency of the proposed mobile sensor navigation algorithm.