Analytic modeling of detection latency in mobile sensor networks

Detection latency, which is defined as the time from the target arrival to the time of the first detection, is an important metric for the performance of sensor networks carrying out target detection, especially when the target is malicious or hostile. It characterizes the efficiency of detecting the presence of a target in a region of interest. Traditionally, stationary sensor networks are used to perform such sensing tasks. Consequently, nearly all research literature for the target detection problem has focused on stationary sensor networks. This paper addresses the problem of detecting the presence/absence of a target using a mobile sensor network. An analytic method is proposed to model the detection latency based on a collaborative sensing architecture. Detection latency for different node mobility models is presented. The accuracy of the analytic model is verified by simulations. This paper also compares the performance of mobile and stationary sensor networks. The comparison shows that if the target is present at the worst possible location in a given deployment, then detection latency of mobile sensor networks is considerably shorter as compared to that of stationary networks with the same number of nodes.

The collaboration between wireless sensor networks and the distributed robotics has prompted the making of mobile sensor networks. However, there has been a growing enthusiasm in developing mobile sensor networks, which are the favoured family of wireless sensor networks in which autonomous movement assumes a key part in implementing its application. By introducing mobility to nodes in wireless sensor networks, the capability and flexibility of mobile sensor networks can be enhanced to support multiple mansions, and to address the previously stated issues. The reduction in costs of mobile sensor networks and their expanding capacities makes mobile sensor networks conceivable and useful. Today, many types of research are focused on the making of mobile wireless sensor networks due to their favourable advantage and applications. Allowing the sensors to be mobile will boost the utilization of mobile wireless sensor networks beyond that of static wireless sensor networks. Sensors can be mounted on, or implanted in animals to monitor their movements for examinations, but they can also be deployed in unmanned airborne vehicles for surveillance or environmental mapping. Mobile wireless sensor networks and robotics play a crucial role if it integrated with static nodes to become a Mobile Robot, which can enhance the capabilities, and enables their new applications. Mobile robots provide a means of exploring and interacting with the environment in more dynamic and decentralised ways. In addition, this new system of networked sensors and robots allowed the development of fresh solutions to classical problems such as localization and navigation beyond that. This article presents an overview of mobile sensor network issues, sensor networks in robotics and the application of robotic sensor networks.

Improving network lifetime with mobile wireless sensor networks

The mobile wireless sensor network is a promising technology having a wide number of applications. The sensor nodes are mobile and able to communicate with each other in an ad hoc manner. Due to mobility, it outperforms the static wireless sensor network as MWSN increases the throughput, network lifetime, and reduces energy consumption. Mobile sensor network has better ability to monitor the target area than static sensor network. However, the routing protocol in mobile environment is complex in resource constraints MWSN. So, it is required to develop an energy-efficient routing protocol to improve network performance. In this paper, a robust energy-efficient cluster-based routing protocol is proposed. The energy-rich node is selected as cluster head with minimum velocity for maximum connectivity among the cluster members. Selection of reliable forwarder improves network performance. Extensive simulation study is carried out to evaluate the performance of the proposed routing protocol with respect to delay, throughput, PDR, and total energy consumption.

Localization problem in mobile wireless sensor networks has drawn widely attention in recent years due to the rapid advances in mobile computing and improvements of wireless communication technologies. Indeed, mobile sensor networks become a valuable asset for many applications, since sensors can be mobile or easily tethered to mobile entities such as robots, vehicles or human. Thereby, it is crucial to support mobility without compromising the quality of applications that require wireless communications. Although, numerous research activities have successfully addressed the localization problem in static networks, few studies deal with the localization issue in mobile sensor networks. In this paper, we present a survey on existing work in this area and we address the gaps for actual deployment of mobile wireless sensor networks. Following our analysis, we propose a classification of the most important algorithms and schemes that outcome from recent literature. We discuss their basic principles, characteristics and concepts. Finally, we point out open research issues, their challenges and some future directions for localization in mobile wireless sensor networks.

Indoor air quality is important. It influences human productivity and health. Personal pollution exposure can be measured using stationary or mobile sensor networks, but each of these approaches has drawbacks. Stationary sensor network accuracy suffers because it is difficult to place a sensor in every location people might visit. In mobile sensor networks, accuracy and drift resistance are generally sacrificed for the sake of mobility and economy. We propose a hybrid sensor network architecture, which contains both stationary sensors (for accurate readings and calibration) and mobile sensors (for coverage). Our technique uses indoor pollutant concentration prediction models to determine the structure of the hybrid sensor network. In this work, we have (1) developed a predictive model for pollutant concentration that minimizes prediction error; (2) developed algorithms for hybrid sensor network construction; and (3) deployed a sensor network to gather data on the airflow in a building, which are later used to evaluate the prediction model and hybrid sensor network synthesis algorithm. Our modeling technique reduces sensor network error by 40.4% on average relative to a technique that does not explicitly consider the inaccuracies of individual sensors. Our hybrid sensor network synthesis technique improves personal exposure measurement accuracy by 35.8% on average compared with a stationary sensor network architecture.

Severe energy limitations, and a paucity of computation pose a set of difficult design challenges for sensor networks. Recent progress in two seemingly disparate research areas namely, distributed robotics and low power embedded systems has led to the creation of mobile (or robotic) sensor networks. Autonomous node mobility brings with it its own challenges, but also alleviates some of the traditional problems associated with static sensor networks. We illustrate this by presenting the design of the robomote, a robot platform that functions as a single mobile node in a mobile sensor network. We briefly describe two case studies where the robomote has been used for table top experiments with a mobile sensor network.

The focus of this thesis is on design and evaluation of one-shot data aggregation protocols for static and mobile wireless sensor networks (WSNs). The goal in one-shot data aggregation is to compute a statistical summary of sensor data such as max, average, sum, count and min, when initiated by a special node such as the base station. WSNs have wide range of applications in both static and mobile/dynamic systems. Static sensor networks are especially useful when monitoring is required in harsh, inaccessible environments and when the region to be monitored is really large. Examples of static sensor network applications include environmental monitoring systems, monitoring of industrial control systems, monitoring of degradation in slagging gasifiers, distributed object detection and tracking. Example of mobile applications include vehicular ad-hoc networks and networks of personal radios used in emergency dispatch and battlefields.;For data aggregation in static networks with stable links, structured approaches such as spanning trees are generally preferred. This is because, once a data aggregation structure has been established, link topologies remain fixed and there is minimal need to actively maintain and change the routing structures. In this thesis, one such tree based data aggregation protocol has been designed and evaluated using simulations in networks ranging from 100-1000 nodes. The protocol has also been implemented at a smaller scale in the context of a smart refractory environment, where slag penetration in gasifiers is remotely monitored using smart bricks that are embedded with sensors. In mobile networks and networks with frequent link changes, topology driven structures are likely to be unstable and to incur a high communication overhead. Therefore, self-repelling random walks have been recently proposed as an attractive alternative for data aggregation in mobile systems. In this thesis, a brief overview of random walk based data aggregation has been presented and systematic evaluation of tree based and random walk based data aggregation protocols in networks ranging from 100-1000 nodes under varying degrees of node mobility has been done. The conditions under which unstructured protocols become more attractive in terms of convergence time and messaging efficiency as compared to tree based structured approaches have been quantified.

In this work, we present a monitor and rescue system utilizing hybrid networks which is a integration of stationary sensor networks and mobile sensor networks: stationary sensor networks comprised of large numbers of small, simple, and inexpensive wireless sensors, and the mobile sensor network contains a set of mobile sensors (robots). The static sensors in our network have “monitoring” ability, i.e., any activated static sensor can detect the event as long as its sensing range intersects the event region. And the mobile sensors have “moving” and “rescuing” ability, e.g., they can move toward the event region with limited speed and further perform certain rescuing/processing operations on the event. We can consider the event as a hazard, e.g., wild fire, and the mobile sensors as fireman robots. As soon as the fire is detected by the static sensors, the fireman robots are expected to move from its initial location to the hazard region within minimum latency. We define the reaction delay of the system as the delay from the occurrence of event till at least one mobile sensor reaches the event. In order to satisfy certain reaction delay requirement while minimizing the total cost, we propose a number of deployment strategies for the stationary sensor network and mobile sensor network respectively. We further design a random wake-up scheduling for the static sensors for the sake of energy efficiency. Finally, we propose a pure distributed motion strategy for mobile sensors without reliance on localization services such as GPS, focusing on simple algorithms for distributed decision making and information propagation. We demonstrate the efficacy of our system in simulation, providing empirical results.

최근 Robomote, Robotic Sensor Agents(RSAs)와 같은 이동 센서의 등장으로 인해 이동 센서네트워크(MSN: Mobile Sensor Network)에 대한 연구가 활발히 진행되고 있다. 하지만 기존의 이동 센서네트워크에 대한 연구는 주로 기존의 고정 센서네트워크(SSN: Stationary Sensor Network)에서 발생하는 문제점인 Coverage Hole을 해결하는데 초점을 맞추고 있다. 이러한 연구들에서는 이동 센서들에게 부여된 이동 능력을 최대한 활용하지 못하는 단점을 안고 있다. 이를 해결하기 위해 이동 센서에게 지속적인 이동성을 부여함으로써 고정 센서네트워크에 비해 더 넓은 영역을 센싱하도록 제안한 연구가 있으나, 그 연구가 아직 초기 단계로써 이동 센서의 지속적인 이동으로 인한 싱크 노드로의 통신 경로선정 및 데이터 전송 문제에 대해서는 논하고 있지 않다. 이에 본 논문에서는 지속적인 이동성을 갖는 이동 센서로 구성된 이동 센서네트워크 환경에서 효율적으로 경로 설정 및 데이터 전송을 가능하게 하는 통신 프로토콜을 제안한다. 제안하는 프로토콜에서는 이동 센서와 함께 고정센서를 배치함으로써 고정 센서가 이동 센서를 대신하여 싱크 노드로 센싱 데이터를 전송하도록 한다. 시뮬레이션을 이용한 성능 평가를 통해 제안한 통신 프로토콜이 기존의 고정 센서네트워크에 비해 네트워크 커버리지 면에서 최대 40%, 트래픽 오버헤드 부분에서는 최대 76%의 성능을 향상시킴을 보인다. Mobile Sensor Network(MSN) is actively studied due to the advent of mobile sensors such as Robomote and Robotic Sensor Agents(RSAs), However, existing studies on MSN have mainly focused on coverage hole problem which occurs in Stationary Sensor Network(SSN). To address coverage hole problem, these studies make mobile sensors move temporarily so that they do not make the best use of the mobility of mobile sensors, Thus, a mechanism utilizing the continuous movement of mobile sensors is proposed to improve the network coverage performance. However, this mechanism is presently immature and does not explain how to make routing path and send data from mobile sensors to a sink node, Therefore, to efficiently make routing path and send data from mobile sensors to a sink node, we propose a communication protocol for mobile sensor network where mobile sensors continuously move. The proposed protocol deploys not only mobile sensors but also stationary sensors which send sensing data to a sink node instead of mobile sensors. Simulation results show that the proposed protocol improves the performance in terms of network coverage and traffic overhead, compared to conventional SSN protocols.

An efficient top-k query processing framework in mobile sensor networks

Most of the MAC protocols proposed for the wireless sensor networks (WSN) assume sensor nodes to be static and therefore they usually fail or provide very bad network performance in mobile sensor networks. Since WSN mobile applications have become popular nowadays, there is a need for MAC protocols that consider mobility. In this paper, we propose a mobility-aware MAC protocol for WSN that can work with satisfactory performance in both stationary and mobile sensor networks. Furthermore, most of the WSN mobile applications are considered critical ones (e.g. a patient assistance system which monitors patients' health via wearable bio-sensors). Such applications require very quick responses. So, in addition to handling mobility, the proposed MAC protocol considers the problem of latency as well. In summary, this paper proposes a WSN MAC protocol (MD-SMAC) that is considered to be mobility-aware, delay-sensitive and provides satisfactory level of energy efficiency. In addition, we study the performance of the proposed MD-SMAC protocol by simulating it using the NS-2 simulator and comparing it to other WSN MAC protocols. The results show that the MD-SMAC protocol outperforms other existing WSN MAC protocols in terms of mobility-handling, delay-reduction, and energy-efficiency in scenarios involving mobile sensors.

Wireless sensor networks is an important component of Internet of everything, and can be deployed in many applications, such as search and rescue, border patrols, environmental monitoring, and combat scenarios. In these applications, target tracking is a crucial difficulty. Compared with the traditional static wireless sensor networks (WSN), the mobile sensor networks (MSN) has the advantages of strong robustness, flexibility, energy saving, etc., and has been widely deployed. For target tracking applications in mobile wireless sensor networks, this paper investigates an extended Kalman filter (EKF) algorithm in a dynamic scenario, and proposes a low-power, high-accuracy sensor scheduling strategy based on the extend kalman filter algorithm. The properly sensors selection and path planning at each sample time of target tracking can make the EKF algorithm in dynamic scenarios complete target trajectory prediction more efficiently. Simulation results show that the proposed sensor scheduling strategies have better performances in power consumption and tracking accuracy, compared with the static network extend Kalman filter algorithm.

Sensor networks possess the inherent potential to detect the presence of a target in a monitored region. Although a stationary sensor network is often adequate to meet application requirements, it is not suited to many situations, for example, a huge number of nodes are required to monitor a large region. In such situations, mobile sensor networks can be used to resolve the communication and sensing coverage problems. This paper addresses the problem of detecting a target using mobile sensor networks. One of the fundamental issues in target detection problems is exposure, which measures how the region is covered by the sensor network. While traditional studies focus on stationary sensor networks, this paper formally defines and evaluates exposure in mobile sensor networks with the presence of obstacles and noise. To conform with practical situations, detection is conducted without presuming the target's activities and moving directions. As there is no fixed layout of node positions, a time expansion technique is developed to evaluate exposure. Since determining exposure can be computationally expensive, algorithms to calculate the upper and lower bounds on exposure are developed. Simulation results are also presented to illustrate the effectiveness of the algorithms

Although advance network planning and dense node deployment, wireless sensor networks (WSNs) may achieve the required performance, it still face the fundamental challenge of meeting stringent power and time requirements using nodes with limited sensing capacities. To better cope with the power consumption problem, mobile sensor nodes can be introduced to dynamically reconfigure the sensor network capacity in an on-demand manner. Through data gathering and relaying, mobile nodes can reduce the amount of data transmitting between the static nodes then conserve the power of these nodes to prolong the lifetime of network. In this paper we describe the DataTruck, a new open-source sensing platform specifically designed to support our experimental research in mobile sensor networks, which is used to collect or relay data from static sensors. The DataTruck node is designed around the S3C2440A ARM920T RISC microprocessor and the IEEE 802.15.4 compliant CC2431 radio from Chipcon. Mobility is enabled with an additional accessory board that allows the node to drive its 4 linear motion actuators. To reduce power consumption, a long term sleep mode is supported through different power supplying methods for main board and clock. Furthermore, we integrated a smart antenna system to gather the data from multiple static nodes concurrently which transmitting data using the same frequency of channel. The experiments show that DataTruck collects data efficiently to reduce the average data transmission delay by using SDMA technology.