Scalable Adaptive Multitarget Tracking Using Multiple Sensors

The large-scale mobile Underwater Wireless Sensor Network (UWSN) is a useful networking paradigm to explore underwater environments. The characteristics of mobile UWSNs, such as low communication bandwidth, large propagation delay, and floating node mobility are significantly different from wireless sensor networks. In this paper, we propose a new efficient routing protocol, called multi-path routing (MPR) protocol, for UWSNs to improve the transmission delay. Multi-path is utilized during the path construction from the source node to the destination node, which is composed of a series of multi-subpaths. Each multi-subpath is a sub-path from a sending node to its two-hop neighboring node, called receiving node, by one more relay nodes, where these relay nodes simultaneously are neighboring nodes of sending and receiving nodes. Due to the different propagation delay of UWSN, the packet arrival time along the different sub-path is different; thus it may avoid the data collision in the receiving node when receiving different packets from different relay nodes. It surely sends the different packet along different sub-path to significantly improve delay time and packet delivery ratio. Finally, simulation results illustrate the performance achievements of the MPR protocol in improving delay time, packet delivery ratio, throughput and reducing overhead ratio.

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.

In this paper, we propose a cooperative routing in mobile ad-hoc wireless sensor networks with Rayleigh fading environments. The main features and contributions of the proposed cooperative routing are as follows. First, the construction of stable routing route by using entropy concepts in the view points of mobility of nodes. Second, the cooperative-aided transmission using the stable relay which can be selected by using entropy concepts. Third, we present a theoretical analysis model for the proposed cooperative routing with the outage probability of the end-to-end performance. Fourth, we consider a realistic approach based on mobile nodes as well as fixed nodes in the points of view of the mobile ad-hoc wireless sensor networks. The performance evaluation of our protocol is implemented via simulation and analysis.

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.

Wireless sensor networks have a wide range of potential applications in recent years. One of the most important goals is to monitor and to detect interested environment. In order to reduce deployment cost and to maintain conveniently, there are increasing applications on mobile sensor networks. Specifically, there are many locations of interest in the deployed environment, and those locations must be visited by robotic mobile sensors many times within a predefined time interval in order to collect important information.

In this paper, we describe a reliability model which can be used to analyze the performance and power consumption in resource constrained, data rate scarce, mobile agent-based wireless sensor network (WSN) systems. The primary model is referred to as a generalize access structure congestion (GGC) system which is an extended model from a circular sequential k-out-of-n congestion (CSknC). There are many other reliability models which can be used to study WSN systems, but they are not suitable to analyze and address mobile agent-based multisensory WSN systems. These systems are not based on a centralized architecture because they use mobile agent technologies to distribute decision tasks at local nodes. By employing mobile agent technologies, the systems can make accurate decisions quickly and reduce data rate and data redundancy problems. An important research problem is to determine how to maintain efficient duty cycle by using multiple types of sensors without centralized architecture and with mobile agent technologies. From the GGC model, we can develop a method to determine an optimal power management scheme by computing an efficient duty cycle in mobile agent-based multisensory WSN systems.

The possibilities of wireless sensor network (WSN) have been shown in many studies in recent years. A critical research topic among them is certainly how to reduce the power consumption of a WSN to prolong its lifetime. In addition to extending the lifetime of static wireless sensors of a WSN, some recent studies have attempted to add mobile wireless sensors to increase the coverage rate of a WSN. For this reason, this study is aimed to find the optimal deployment of a hybrid mobile and static WSN. In this study, an effective search algorithm is presented to maximize the coverage rate of all the targets while minimizing the energy consumption of the static and mobile sensors. The proposed algorithm is an improved version of search economics for single-objective real-parameter optimization problem (SE-SOP), called self-cutting search economics (SCSE). A new division method is also presented for the SE-based algorithm to divide the solution space into a certain number of subspaces and assign suitable computation resources to these subspaces according to their potentialities. The number of subspaces will be decreased so that particular subspaces are apt to acquire more investments (i.e., searches) due to the combining region method, which is helpful to speedup the convergence process. Simulation results show that the proposed algorithm can find out a better plan of network management than all the other search algorithms compared in this study for the WSN containing static and mobile sensors with adjustable sensing range functionality.

The restricted availability of energy supplies is one of the major challenges in designing mobile wireless sensor networks. In this research work, a proposed method for extending the lifetime of energy-constrained mobile wireless sensor networks (MWSNs) is presented. This method is based on the fact that RF signal carries both information and energy at the same time. Hence, by increasing the efficiency of energy harvesting from radio frequency (RF) signals, the lifetime of the wireless network can be significantly extended. The Simultaneous Wireless Information and Power Transfer (SWIPT) technique enables harvesting of energy by relay nodes which in turn can be used for wireless data transmission. To enhance the lifetime of the mobile wireless network, the transmitted RF energy can be recycled at the receiver side. On the other hand, a balance between energy harvesting and wireless data transmission is required in to maximize the overall efficiency of the system. Particle Swarm Optimization (PSO) is employed to obtain the optimum resource allocation policy which maximizes the system energy efficiency. A cost function is framed for this purpose and PSO attains the maximum energy efficiency by improving the solution of the cost function at each iteration with respect to given constraints.

In a multitarget tracking (MTT) scenario, the prior knowledge of parameters such as clutter rate and detection probability are usually uncertain or estimated offline from training data. However, significant parameters mismatch in the clutter and detection model will result in biased estimates. In such cases, the ability to adaptively online estimate clutter rate and detection probability is critical in MTT under the random finite sets (RFS) framework. In this article, we propose a robust Poisson multi-Bernoulli mixture (PMBM) filter that can accommodate model mismatch in clutter rate and detection probability for multiple extended target tracking (ETT) problems. Moreover, the closed-form solution to the proposed method is derived by the use of Beta and Gamma Gaussian inverse-Wishart (GGIW) distribution, where Beta is used to describe unknown detection probability and GGIW is used to model extended target extension as an ellipse. Simulation results are presented to verify the effectiveness of the proposed method.

Security in mobile wireless sensor networks is challenging to achieve, as mobile wireless sensor networks have many different characteristics from wired networks such as lack of center infrastructure, vulnerable wireless links and rapid changes in topologies, etc. However, earlier studies on security issues mainly aim at utilizing cryptography and authentication, which are not only constrained by hardware resources, but also not available for the attacks from interior networks. Trust management schemes are primary supplementary means of encryption. In this paper, we give a detailed introduction to the basic architecture of trust management. Then some classical trust management schemes are analyzed and the issues of them are also discussed. At the end of this paper, we propose some future research directions on trust management for mobile wireless sensor networks.

Numerous service parameters are impacted by the high node power in Mobile Wireless Sensor Networks (MWSN). There is a loss of energy due to the fact that the sensor nodes were constructed with a certain quantity of power and must expend some of that power with each data transmission. However, the nodes are able to complete any data transmission through cooperative communication. Although there are a variety of routing strategies that can be applied to mobile wireless sensor networks, cluster-based routing has been shown to have the most effect on meeting performance goals. If a node in a network group wants to communicate with a node that isn't part of the group directly, it uses a gateway to do so. New methods for optimizing bee colonies are utilised to help mobile wireless sensor networks adopt optimal routing. Clustering mobile sink nodes, which collect data from the network's sensor nodes, have been taken into account for wireless sensor networks. The sensor nodes provide their most current status updates and can be found in close proximity to one another. After that, a fuzzy logic-based inter-cluster routing mechanism is created so that cluster participants can send and receive messages.

Query processing in mobile Wireless Sensor Networks (WSNs) is still a challenging problem because sensor mobility causes frequent changes of network topology. In this paper, we study the problem of processing Continuous Location Dependent Query (CLDQ) that retrieves the sampling data of the sensors within a specific area (i.e. query area) around a mobile sensor. Existing query processing approaches can not efficiently process CLDQs with continuously moving query areas. We propose scalable techniques to process CLDQs efficiently and accurately, including a dissemination approach, a Contention-based Distance-aware Message Scheduling scheme, in which each stationary sensor's data transmissions are smartly scheduled according to its distance to the mobile sensor, and an optimization scheme for continuous processing of CLDQs. Extensive experiments indicate that our techniques demonstrate better efficiency of processing CLDQs over state-of-the-art techniques while achieving high accuracy and short query latency under various network settings.

The goal of wireless sensor networks is the maximum data gathering. Factors like energy, memory, bandwidth and processing capability affects the characteristics of the network which implies in reduced data collection. To avoid the reduction in data collection, mobile sensor nodes are used. They cover large area in short time and these networks are called as mobile wireless sensor networks (MWSN). In MWSN, set routing is used as one of the routing method for maximum data collection. In which the entire network is divided into many sets. This routing algorithm is applied for each set with the consideration that there is no node failure in the network. Here this work shows that how a new route is discovered if a node fails in a set using the Foldable Lookup Table (FLT) method. Also FLT method is compared with the routing table developed by fuzzy variables. Simulation is performed with OMNET++ simulator. Result shows the effectiveness of node failure in terms of delay and packet drops and how the route is maintained during node failure is shown with respect to delay.

Mobile Wireless Sensor Network(MWSN) is a spring up field of research in contrast to their well established predecessor-static WSN. The advantages of mobile wireless sensor network over static wireless sensor networks include better energy efficiency, improved coverage, enhanced target tracking, and superior channel capacity. They are much more versatile than WSN because they can be deployed in any scenario and cope with rapid topology changes. One of the main constraints for communication in MWSN is to maximize point-to-point connectivity between nodes. For this Distributed Scheduling is the optimal choice. Time Synchronized Channel Hopping (TSCH) is the suitable underlaying scheme for communication in MWSN. In this paper, Distributed scheduling algorithms for TSCH based MWSN is studied. Reservation based algorithm is used for scheduling and its performance is evaluated. Giving consideration to interference from co-located wireless systems utilizing the same spectral space in 2.4 GHz MWSN environment, scheduling technique is modified using Cognitive Scheme- Wavelet Packet analysis based Channel quality estimation. Simulation results shows that scheduling efficiency is improved about 25% using the proposed scheme.

The detection probability is an important parameter in multisensor multitarget tracking. The existing multisensor multi-Bernoulli (MS-MeMBer) filter and multisensor cardinalized probability hypothesis density (MS-CPHD) filter require that detection probability is a priori. However, in reality, the value of the detection probability is constantly changing due to the influence of sensors, targets, and other environmental characteristics. Therefore, to alleviate the performance deterioration caused by the mismatch of the detection probability, this paper applies the inverse gamma Gaussian mixture (IGGM) distribution to both the MS-MeMBer filter and the MS-CPHD filter. Specifically, the feature used for detection is assumed to obey the inverse gamma distribution and is statistically independent of the target’s spatial position. The feature is then integrated into the target state to iteratively estimate the target detection probability as well as the motion state. The experimental results demonstrate that the proposed methods can achieve a better filtering performance in scenarios with unknown and changing detection probability. It is also shown that the distribution of the sensors has a vital influence on the filtering accuracy, and the filters perform better when sensors are dispersed in the monitoring area.