-
[show abstract]
[hide abstract]
ABSTRACT: Sensor scheduling is essential to collaborative target tracking in wireless sensor networks (WSNs). In the existing works
for target tracking in WSNs, such as the information-driven sensor query (IDSQ), the tasking sensors are scheduled to maximize
the information gain while minimizing the resource cost based on the uniform sampling intervals, ignoring the changing of
the target dynamics and the specific desirable tracking goals. This paper proposes a novel energy-efficient adaptive sensor
scheduling approach that jointly selects tasking sensors and determines their associated sampling intervals according to the
predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduled in alternative tracking
mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenance mode with larger sampling
interval, according to a specified tracking error threshold. The approach employs an extended Kalman filter (EKF)-based estimation
technique to predict the tracking accuracy and adopts an energy consumption model to predict the energy cost. Simulation results
demonstrate that, compared to a non-adaptive sensor scheduling approach, the proposed approach can save energy cost significantly
without degrading the tracking accuracy.
Journal of Control Theory and Applications 04/2012; 8(1):86-92.
-
[show abstract]
[hide abstract]
ABSTRACT: Due to limited network resources for sensing, communication and computation, information quality (IQ) in a wireless sensor network (WSN) depends on the algorithms and protocols for managing such resources. In this paper, for target tracking application in WSNs consisting of active sensors (such as ultrasonic sensors) in which normally a sensor senses the environment actively by emitting energy and measuring the reflected energy, we present a novel collaborative sensing scheme to improve the IQ using joint sensing and adaptive sensor scheduling. With multiple sensors participating in a single sensing operation initiated by an emitting sensor, joint sensing can increase the sensing region of an individual emitting sensor and generate multiple sensor measurements simultaneously. By adaptive sensor scheduling, the emitting sensor for the next time step can be selected adaptively according to the predicted target location and the detection probability of the emitting sensor. Extended Kalman filter (EKF) is employed to estimate the target state (i.e., the target location and velocity) using sensor measurements and to predict the target location. A Monte Carlo method is presented to calculate the detection probability of an emitting sensor. It is demonstrated by simulation experiments that collaborative sensing can significantly improve the IQ, and hence the tracking accuracy, as compared to individual sensing.
Pervasive Computing and Communications Workshops (PERCOM Workshops), 2010 8th IEEE International Conference on; 05/2010
-
Eigth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2010, March 29 - April 2, 2010, Mannheim, Germany, Workshop Proceedings; 01/2010
-
Proceedings of the IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2010, 26-29 September 2010, Istanbul, Turkey; 01/2010
-
11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, Singapore, 7-10 December 2010, Proceedings; 01/2010
-
Eigth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2010, March 29 - April 2, 2010, Mannheim, Germany, Workshop Proceedings; 01/2010
-
INFOCOM 2010. 29th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies, 15-19 March 2010, San Diego, CA, USA; 01/2010
-
[show abstract]
[hide abstract]
ABSTRACT: In cooperative retransmissions, nodes with better channel qualities help other nodes in retransmitting a failed packet to its intended destination. In this paper, we propose a cooperative retransmission scheme where each node makes local decision to cooperate or not to cooperate at what transmission power using a Markov decision process with reinforcement learning. With the reinforcement learning, the proposed scheme avoids solving an Markov decision process with a large number of states. Through simulations, we show that the proposed scheme is robust to collisions, is scalable with regard to the network size, and can provide significant cooperative diversity.
Personal, Indoor and Mobile Radio Communications, 2009 IEEE 20th International Symposium on; 10/2009
-
[show abstract]
[hide abstract]
ABSTRACT: Wireless community networks, where users share wireless bandwidth is attracting tremendous interest from academia and industry. Companies such as FON have been successful in attracting large communities of users. However, solutions such as FON either require users to buy specialized FON routers or firmware modifications to existing routers. In this paper we propose a solution which requires no such sophisticated hardware. An alternative is to provide a solution which requires users to download a client software on to their PCs. While the solution appears simple it raises several issues of incentivizing users to share their bandwidth and also issues of preventing users from cheating behaviors which give them an unfair advantage. In this paper, we propose a system and solution which (i) requires only software downloads on PCs, (ii) is robust to tampering of the software, and intermittent monitoring of an access point by the owner, (iii) a credit based mechanism whereby users earn credits for sharing bandwidth and punishment and pricing mechanism whereby users are charged at a higher price whenever they are caught misbehaving. By making simple but plausible assumptions about user behavior, we show via analysis and extensive simulations that the system converges to a Pareto optimal Nash equilibrium. We further validate our system model, by running trace driven simulations on real world data. We believe that the solution provided by Wi-Sh is an attractive and more credible alternative to solutions such as FON.
INFOCOM 2009, IEEE; 05/2009
-
[show abstract]
[hide abstract]
ABSTRACT: The challenging problem of cooperative retransmission in the wireless networks is investigated in this paper. This paper introduces the centralized and distributed Markov decision process (MDP) frameworks in the context of cooperative retransmission. Specifically, a MDP model with the global channel information is first constructed for the cooperation problem in the MAC layer. It is shown that this global MDP is able to perform optimally, where the objective is to minimize the total number of required transmissions for a successful packet delivery to the destination. When the global information is unavailable, we show that the suitable distributed MDP models can replace the global model for a near-optimal performance. Furthermore, the reinforcement learning methods are investigated when the MDP model is unavailable. Interestingly, simulation results confirm that the learning methods also provide an acceptable performance despite their simplicity and low overhead.
Wireless Communications and Networking Conference, 2009. WCNC 2009. IEEE; 05/2009
-
[show abstract]
[hide abstract]
ABSTRACT: This paper introduces methods for allocating computational resources to track multiple targets in a sensor network of cameras, with the aim of maximizing tracking accuracy, an aspect of information quality (IQ). Particle filters are used where an independent filter is created for each target track. The particle filter's strength to track complex probability models comes at the cost of a higher demand for computational resources. As there is a direct correlation between the number of particles used and the computational costs, a means to meet the computational resource constraints is to limit the total number of particles. This motivates an online distribution of available particles to available tracks to maximise tracking accuracy. Restricting the number of particles may result in ineffective particle filters that diverge from the true target position. This issue has not been addressed in any known work of allocating particles. Our solution is a particle allocation scheme that allocates available particles to existing tracks based on both filter uncertainty and the effectiveness of the particle sample set. A method to allocate assigned particles in a track to overcome suspected divergence is also applied. Experimental results, from a multi-target tracking simulation, show that given the same number of particles to allocate, our scheme outperforms two other methods, which are (a) an equal (fixed) allocation of particles to each filter and (b) an allocation of particles solely based on filter uncertainty.
Pervasive Computing and Communications, 2009. PerCom 2009. IEEE International Conference on; 04/2009
-
IEEE 6th International Conference on Mobile Adhoc and Sensor Systems, MASS 2009, 12-15 October 2009, Macau (S.A.R.), China; 01/2009
-
Seventh Annual IEEE International Conference on Pervasive Computing and Communications - Workshops (PerCom Workshops 2009), 9-13 March 2009, Galveston, TX, USA; 01/2009
-
Proceedings of the IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2009, 13-16 September 2009, Tokyo, Japan; 01/2009
-
Proceedings of IEEE International Conference on Communications, ICC 2009, Dresden, Germany, 14-18 June 2009; 01/2009
-
INFOCOM 2009. 28th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies, 19-25 April 2009, Rio de Janeiro, Brazil; 01/2009
-
Seventh Annual IEEE International Conference on Pervasive Computing and Communications - Workshops (PerCom Workshops 2009), 9-13 March 2009, Galveston, TX, USA; 01/2009
-
[show abstract]
[hide abstract]
ABSTRACT: This paper introduces a two-stage approach to the detection of people eating and/or drinking for the purposes of surveillance of daily life. With the sole use of wearable accelerometer sensor attached to somebody's (man or a woman) wrists, this two-stage approach consists of feature extraction followed by classification. At the first stage, based on the limb's three dimensional kinematics movement model and the Extended Kalman Filter (EKF), the realtime arm movement features described by Euler angles are extracted from the raw accelerometer measurement data. In the latter stage, the Hierarchical Temporal Memory (HTM) network is adopted to classify the extracted features of the eating/drinking activities based on the space and time varying property of the features, by making use of the powerful modelling capability of HTM network on dynamic signals which is varying with both space and time. The proposed approach is tested through the real eating and drinking activities using the three dimensional accelerometers. Experimental results show that the EKF and HTM based two-stage approach can perform the activity detection successfully with very high accuracy.
Sensors 01/2009; 9(3):1499-517. · 1.74 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: We propose a novel routing scheme for providing Quality of Service (QoS) for multi-target tracking in multi-sink wireless sensor networks (WSNs). We first introduce the concept of event ordering, by virtue of which a priority-based buffer management scheme is applied to achieve QoS. We also propose a directional QoS-aware routing protocol (DQRP) for the dissemination of the event ordering list. The buffer management scheme works in conjunction with the DQRP to ensure accurate as well as energy-efficient detection in the presence of multiple targets. The novelty of our network architecture is that a distributed admission control scheme is implemented on each node based on a geographic routing algorithm.
Communication Systems, 2008. ICCS 2008. 11th IEEE Singapore International Conference on; 12/2008
-
[show abstract]
[hide abstract]
ABSTRACT: Most of the target tracking algorithms proposed for wireless sensor networks (WSNs) so far have been relying on sensors of single modality. To integrate multiple sensing modalities (e.g., by using the proximity sensors and ranging sensors together) to improve the tracking performance, the tracking algorithm shall be capable to deal with non-linear and non-Gaussian nature of the tracking problem. In this paper, we present a particle filter algorithm for multi-modality target tracking in WSNs. Simulation results show that the proposed algorithm can provide a good balance among sensor costs and tracking accuracy.
Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on; 11/2008
