Patrolling Mechanisms for Disconnected Targets in Wireless Mobile Data Mules Networks

[[abstract]]This paper considers the target patrolling problem which asks a set of mobile data mules to efficiently patrol a set of given targets. Since the time interval (also referred to visiting interval) for consecutively visiting to each target reflects the monitoring quality of this target, the goal of this research is to minimize the maximal visiting interval. This paper firstly proposes a basic algorithm, called Basic (B-TCTP), which aims at constructing an efficient patrolling route for a number of given data mules such that the visiting intervals of all target points can be minimized. For the scenario containing weighted target points, a Weighted-TCTP (W-TCTP) algorithm is further proposed to satisfy the demand that targets with higher weights have higher data collection frequencies. By considering the energy constraint of each data mule, this paper additionally proposes a RW-TCTP algorithm which treats energy recharge station as a weighted target and arranges the data mules visiting the recharge station before exhausting their energies. Performance study demonstrates that the proposed algorithms outperform existing approaches in terms of visiting intervals of the given targets and length of patrolling path.[[conferencetype]]國際[[conferencedate]]20110913~20110916[[conferencelocation]]Taiwa

An Energy Efficient Evo-Fuzzy Sleep Scheduling Protocol for Stationary Target Coverage in Wireless Sensor Networks

Target coverage is a fundamental problem that needs to be addressed in sensor networks for a variety of applications such as environment monitoring and surveillance purposes. A typical approach to prolong network lifetime would entail the partitioning of the sensors capable of sensing the targets, in a network for target monitoring into several disjoint subsets such that each subset can cover all the targets. Thus, each time only the sensors in one of such subsets are activated. In this paper, we have proposed a novel sleep scheduling protocol, abbreviated as EEFSSP, based on this concept which incorporates three novel features. Firstly, it paves way for an equitable distribution of nodes while forming cover sets through the proposed CSGH heuristic. Secondly, it schedules the cover sets using an evolutionary approach with the objective being to optimize the maximum breach interval. Thirdly, the EEFSSP introduces a novel routing protocol abbreviated as DFPRP to establish routes to transfer data packets to the Base Station, with the objective being to ensure energy-efficiency and minimize the number of packet drops. We finally conduct experiments by simulation to evaluate the performance of the proposed scheme under various conditions, and compare its performance with other relevant protocols. The experimental results show that the proposed scheme clearly outperforms its peers by delivering a much longer network lifetime and minimizing the number of packet drops

Target Tracking Using Wireless Sensor Networks

Tracking of targets in remote inaccessible areas is an important application of Wireless Sensor Networks (WSNs). The use of wired networks for detecting and tracking of intruders is not feasible in hard-to-reach areas. An alternate approach is the use of WSNs to detect and track targets. Furthermore, the requirements of the tracking problem may not necessarily be known at the time of deployment. However, issues such as low onboard power, lack of established network topology, and the inability to handle node failures have limited the use of WSNs in these applications. In this dissertation, the performance of WSNs in remote surveillance type of applications will be addressed through the development of distributed tracking algorithms. The algorithm will focus on identifying a minimal set of nodes to detect and track targets, estimating target location in the presence of measurement noise and uncertainty, and improving the performance of the WSN through distributed learning.The selection of a set of sensor nodes to detect and track a target is first studied. Inactive nodes are forced into `sleeping' mode to conserve power, and activated only when required to sense the target. The relative distance and angle of the target from sensor nodes are used to determine which of the sensors are needed to track the target.The effect of noisy measurements on the estimation of the position of the target is addressed through the implementation of a Kalman filter. Contrary to centralized Kalman filter implementations reported in the literature, implementation of the distributed Kalman filter is considered in the proposed solution.Distributed learning is implemented by passing on the knowledge of the target, i.e. the filter state and covariance matrix onto the subsequent node running the filter. The problem is mathematically formulated, and the stability and tracking error of the proposed strategy are rigorously examined. Numerical examples are then used to demonstrate the utility of the proposed technique.It will be shown by mathematical proofs and numerical simulation in this dissertation that distributed detection and tracking using a limited number of nodes can result in efficient tracking in the presence of measurement noise. Furthermore, minimizing the number of active sensors will reduce communication overhead and power consumption in networks, improve tracking efficiency, and increase the useful life span of WSNs

Co-Optimization of Communication, Motion and Sensing in Mobile Robotic Operations

In recent years, there has been considerable interest in wireless sensor networks and networked robotic systems. In order to achieve the full potential of such systems, integrative approaches that design the communication, navigation and sensing aspects of the systems simultaneously are needed. However, most of the existing work in the control and robotic communities uses over-simplified disk models or path-loss-only models to characterize the communication in the network, while most of the work in networkingand communication communities does not fully explore the benefits of motion.This dissertation thus focuses on co-optimizing these three aspects simultaneously in realistic communication environments that experience path loss, shadowing and multi-path fading. We show how to integrate the probabilistic channel prediction framework, which allows the robots to predict the channel quality at unvisited locations, into the co-optimization design. In particular, we consider four different scenarios: 1) robotic routerformation, 2) communication and motion energy co-optimization along a pre-defined trajectory, 3) communication and motion energy co-optimization with trajectory planning, and 4) clustering and path planning strategies for robotic data collection. Our theoretical, simulation and experimental results show that the proposed framework considerably outperforms the cases where the communication, motion and sensing aspects of the system are optimized separately, indicating the necessity of co-optimization. They furthershow the significant benefits of using realistic channel models, as compared to the case of using over-simplified disk models

Biologically inspired, self organizing communication networks.

PhDThe problem of energy-efficient, reliable, accurate and self-organized target tracking in Wireless Sensor Networks (WSNs) is considered for sensor nodes with limited physical resources and abrupt manoeuvring mobile targets. A biologically inspired, adaptive multi-sensor scheme is proposed for collaborative Single Target Tracking (STT) and Multi-Target Tracking (MTT). Behavioural data obtained while tracking the targets including the targets’ previous locations is recorded as metadata to compute the target sampling interval, target importance and local monitoring interval so that tracking continuity and energy-efficiency are improved. The subsequent sensor groups that track the targets are selected proactively according to the information associated with the predicted target location probability such that the overall tracking performance is optimized or nearly-optimized. One sensor node from each of the selected groups is elected as a main node for management operations so that energy efficiency and load balancing are improved. A decision algorithm is proposed to allow the “conflict” nodes that are located in the sensing areas of more than one target at the same time to decide their preferred target according to the target importance and the distance to the target. A tracking recovery mechanism is developed to provide the tracking reliability in the event of target loss. The problem of task mapping and scheduling in WSNs is also considered. A Biological Independent Task Allocation (BITA) algorithm and a Biological Task Mapping and Scheduling (BTMS) algorithm are developed to execute an application using a group of sensor nodes. BITA, BTMS and the functional specialization of the sensor groups in target tracking are all inspired from biological behaviours of differentiation in zygote formation. Simulation results show that compared with other well-known schemes, the proposed tracking, task mapping and scheduling schemes can provide a significant improvement in energy-efficiency and computational time, whilst maintaining acceptable accuracy and seamless tracking, even with abrupt manoeuvring targets.Queen Mary university of London full Scholarshi

Coverage for Target Localization in Wireless Sensor Networks

10.1145/1127777.1127799Proceedings of the Fifth International Conference on Information Processing in Sensor Networks, IPSN '062006118-12

Coverage for target localization in wireless sensor networks

10.1109/TWC.2008.060611IEEE Transactions on Wireless Communications72667-67