Over the past decade we have witnessed the evolution of wireless sensor networks, with advancements in hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been much focus on \textit{mobile} sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. In this paper, we present a survey on localization methods for mobile wireless sensor networks. We provide taxonomies for mobile wireless sensors and localization, including common architectures, measurement techniques, and localization algorithms. We conclude with a description of real-world mobile sensor applications that require position estimation.

Mobile sensors require periodic position measurements for navigation around the sensing region. Such information is often obtained using GPS or onboard sensors such as optical encoders. However, GPS is not reliable in all environments, and odometry accrues error over time. Although several localization techniques exist for wireless sensor networks, they are typically time consuming, resource intensive, and/or require expensive hardware, all of which are undesirable for lightweight mobile nodes. We propose a technique for obtaining angle-of-arrival information that uses the wheel encoder data from the mobile sensor, and the RF Doppler-shift observed by stationary nodes. These sensor data are used to determine the angular separation between stationary beacons, which can be used for navigation. Our experimental results demonstrate that using this technique, a robot is able to determine angular separation between four pairs of sensors in a 40 x 40 meter sensing region with an average error of 0.28 radian.

Implementation uncertainties such as time varying delay and data loss and having to typically implement a discrete-time-controller can cause significant problems in the design of networked control systems. This paper describes a novel multi-rate digital-control system which preserves stability and provides robustness to such implementation uncertainties. We present necessary conditions for stability of conic systems interconnected over digital-control-networks which can tolerate networked delays and data-loss. We also compare the performance using simulation results of the proposed architecture to that of a classic-digital-control-implementation applied to controlling position of a single-degree of freedom robotic manipulator.

A constructive method is presented in which Lm2 -stability can be guaranteed for networked control of multiple passive plants in spite random time varying delays and data dropouts. The passive plants are interfaced to a wave variable based passive sampler (PS) and passive hold (PH) which allows a passive digital control network to be constructed. A power junction is used to facilitate the interconnection of multiple passive plants and passive digital controllers. The power junction preserves passivity by guaranteeing that the overall power input to the system is greater than or equal to the power leaving the system. There are numerous ways to implement the power junction including the averaging power junction and the consensus power junction which are studied in this paper. In particular, a detailed steady state analysis is provided which relates the corresponding controller inputs to the plants outputs. The construction of our digital control network is completed by interconnecting the digital controllers to an inner-product equivalent sampler and zero-order hold (IPESH) which allows us to prove Lm2-stability.

In large, distributed systems, such as a sensor web, allocating resources to tasks that span multiple providers presents significant challenges. Individual subtasks associated with a task could potentially be assigned to a number of agents (e.g., when there is overlap in sensor or data processing capability among constituent sensor networks). This problem is further compounded by the dynamic nature of a sensor web, in which both desired tasks and resource availability change with time and environmental conditions. This paper presents a novel variation of the contract net protocol (CNP) for subtask allocation, which employs brokers to limit communication overhead in a two-phase CNP and aggregate domain information from groups of agents. Experimental results using this subtask allocation approach verify its efficiency and scalability. These results also suggest specific refinements and appropriate parameters for a variety of system configurations and operating conditions in sensor webs and other large multi-agent systems.

Time-triggered architectures (TTA) provide replica determinism in safety-critical distributed embedded software designs. TTA has become a crucial part of many high-confidence embedded paradigms, as it decouples functional concerns from platform timing concerns in system designs. Complex embedded software development workflows for safety-critical applications are increasingly managed by model-based design tools, in order to support automated verification and reconcile conflicts between functional and non-functional concerns in designs. We present a prototype scheduling tool (ESched) which calculates cyclic schedules for time-triggered networks. ESched supports the model-based workflow of the ESMoL modeling language and tool suite. Using ESMoL, designers can rapidly iterate through simulating a control design, capturing platform effects in models, generating a schedule (if feasible), and re-simulating the control design subject to the platform model and the computed schedule. ESched specifications include a number of useful platform parameters, and it supports troubleshooting of infeasible schedules by allowing the user to specify partial platform models to solve.

This paper proposes a model based approach for prognosis of DC-DC power converters. We briefly review the prognosis process, and present an overview of different approaches that have been developed. We study the effects of capacitor degradation on DC-DC converter performance by developing a combination of a thermal model for ripple current effects and a physics of failure model of the thermal effects on capacitor degradation. The derived degradation model of the capacitor is reintroduced into the DC-DC converter model to study changes in the system performance using Monte Carlo methods. The simulation results observed under different conditions and experimental setups for model verification are discussed. The paper concludes with comments and future work to be done.

The complexity of software in systems like aerospace vehicles has reached the point where new techniques are needed to ensure system dependability. Such techniques include a novel direction called `Software Health Management' (SHM) that extends classic software fault tolerance with techniques borrowed from System Health Management. In this paper the initial steps towards building a SHM approach are described that combine component-based software construction with hard real-time operating system platforms. Specifically, the paper discusses how the CORBA Component Model could be combined with the ARINC-653 platform services and the lessons learned from this experiment. The results point towards both extending the CCM as well as revising the ARINC-653

Assigning behavioral semantics to domain-specific languages (DSLs) opens the door for the application of formal methods, yet is largely an unresolved problem. Previously proposed solutions include semantic anchoring, in which a transformation from the DSL to an external framework that can supply both behavioral semantics and apply formal methods is constructed. The drawback of this approach is that it loses the structural constraints of the original DSL along with the details of the transformation, which can lead to erroneous results when formal methods are applied. We demonstrate this problem of ``forgetful'' semantic anchoring using existing approaches through a translation from dataflow systems to interface automata. We then describe our modeling tool, Formula, and apply it to the same example, showing how forgetful semantic anchoring can be avoided.

Real-time and embedded systems have traditionally been designed for closed environments where operating conditions, input workloads, and resource availability are known a priori and are subject to little or no change at runtime. There is an increasing demand, however, for autonomous capabilities in open distributed real-time and embedded (DRE) systems that execute in environments where input workload and resource availability cannot be accurately characterized a priori. These systems can benefit from autonomic computing capabilities, such as self-(re)configuration and self-optimization, that enable autonomous adaptation under varying-even unpredictable-operational conditions. A challenging problem faced by researchers and developers in enabling autonomic computing capabilities to open DRE systems involves devising adaptive planning and resource management strategies that can meet mission objectives and end-to-end quality of service (QoS) requirements of applications. To address this challenge, this paper presents the integrated planning, allocation, and control (IPAC) framework, which provides decision-theoretic planning, dynamic resource allocation, and runtime system control to provide coordinated system adaptation and enable the autonomous operation of open DRE systems. This paper presents two contributions to research on autonomic computing for open DRE systems. First, we describe the design of IPAC and show how IPAC resolves the challenges associated with the autonomous operation of a representative open DRE system case study. Second, we empirically evaluate the planning and adaptive resource management capabilities of IPAC in the context of our case study. Our experimental results demonstrate that IPAC enables the autonomous operation of open DRE systems by performing adaptive planning and management of system resources.

Reconfiguration and self-management are important properties for systems that operate in hazardous and uncontrolled environments, such as inter-planetary space. These systems need a reconfiguration mechanism that provides recovery from individual component failures as well as the ability to dynamically adapt to evolving mission goals. One way to provide this functionality is to define a model of alternative system configurations and allow the system to choose the current configuration based on its current state, including environmental parameters and goals. The primary difficulties with this approach are (1) the state space of configurations can grow very large, which can make explicit enumeration infeasible, and (2) the component failures and evolving system goals must be somehow encoded in the system configuration model. This paper describes an online reconfiguration method based on model-based design-space exploration. We symbolically encode the set of valid system configurations and assert the current system state and goals as symbolic constraints. Our initial work indicates that this method scales and is capable of providing effective online dynamic reconfiguration.

Fusing spatially distributed observations in wireless sensor networks or asset tracking in a shipyard are just two example applications where the location of radio nodes needs to be known. Localization and tracking of wireless nodes have been an active research area, yet a universal solution has not emerged so far. This paper introduces a novel method for bearing estimation based on a rotating antenna generating a Doppler shifted RF signal. The small frequency change can be measured even on low cost resource constrained nodes using a radio interferometric technique introduced previously. Bearing information between anchors nodes at known locations and RF tags at unknown positions can be derived. A few such measurements provide enough information to enable accurate node localization.