A fully tested autonomous system works predictably under ideal or assumed environment. However, its behavior is not fully defined when some components malfunction or fail. In this paper, we consider automated guided vehicle (AGV), equipped with multiple sensors, executing a traversal task in a static unknown environment. We have analytically studied the system, computed a set of performance and safety metrics, and validated it with simulation results in Webots. We have also analyzed the effect on system performance under independent and correlated sensing errors. We have also performed sensitivity analysis to identify the most critical components in any given system; and this can be utilized to increase the reliability of the system and its conformance to safety objectives.

In modern warehouses, robots are being deployed to perform complex tasks such as fetching a set of objects from various locations in a warehouse to a docking station. This requires a careful task allocation along with route planning such that the total distance traveled (cost) is minimized. The number of tasks that can be performed by a robot on a single route depends on the maximum capacity of the robot and the combined weight of the objects it picks on the route. This task allocation problem is an instance of the Capacity-constrained Vehicle Routing Problem (CVRP), which is known to be NP-hard. Although, there exist a number of heuristics that provide near-optimal solutions to a CVRP instance, they do not scale well with the task size (number of nodes). In this paper, we present a heuristic, called nearest-neighbor based Clustering And Routing$(nCAR)$, which has better execution time compared to the state-of-the-art heuristics. Also, our heuristic reduces cost of the solutions when there are a large number of nodes. We compare the performance of$nCAR$with the Google OR-Tools and found a speedup of 6 in runtime when task size is 2000. Though OR-Tools provides a low-cost solution for small number of tasks, it's execution time and number of routes is 1.5 times that of nCAR.

Developing analytical applications for IoT based on sensor signal processing tends to be complicated as applications are executed as sequence of steps comprising of multiple alternative algorithms, including suitable feature extraction modules depending on the goal of the application. Experience shows that developers spend considerable time and effort in performing feature extraction and dimensionality reduction. In this paper we propose a framework based on a relevant case study which allows developers to drag and drop algorithms to create a workflow chain, automatically select the most relevant signal features for the particular analytic application using a training data set to generate a model and deploy the model for use. The method reduces the effort and cost of development which is deemed highly important for the analytics industry.

In a mobile grid computing framework where mobile devices are used as computing resources, minimizing the task offloading time remains an important issue. A task is an independent unit of execution consisting of a input data volume for execution and optionally a target-specific executable. We consider a mobile grid infrastructure where mobile devices are connected via Wi-Fi network and the grid infrastructure has a set of tasks (i.e. a set of data volumes) to be transferred to a subset of the mobile devices. In a Wi-Fi network, mobile devices usually associate themselves to the access points (APs) having the strongest radio signal. In this paper, we address the problem of AP activation (by frequency assignment) and association of AP with devices in the context of minimizing the overall data-transfer completion time. We present a constraint based formulation and also a heuristic as solutions. Simulations results are presented which contrast our proposed methods with some of the earlier works.

In recent times, many human-centric applications are being developed to leverage the diverse range of sensors present in Android smartphones. Smartphones are also being used as edge network gateways for fusing data from multiple sensors. These applications often run as background services and usuall

Smartphones are currently being used to develop diverse range of applications (apps) involving sensors. These apps generally acquire and analyze sensor data and are usually implemented as background services. The importance values of Android processes are in a hierarchy of foreground, visible, background etc. in decreasing order of importance. Whenever a new process arrives, it may necessitate removal of old and less important processes for reclaiming memory. Current smartphones do not provide any options through which user's idea of priority can override that of the system defaults. In this work we present an implementation that enables the user to obtain alerts on system load and recommendations to proactively kill a set of processes to reclaim system memory. This enables user selected background process to be spared from the standard android policy of process termination, in lieu of foreground apps, relatively unimportant from user perspective, during that period. We show that manual reclaiming of memory based on recommendations from our app, reduces the automatic killing and measurement lag experienced by a sensor analytics app under test. This work is redundant if processing power and main memory of a smartphone is always surplus than required for its normal usage.

Cloud computing is fast emerging as a popular choice for a variety of business needs. Providing adequate fault tolerance guarantees to diverse applications is an important challenge. Fault tolerance needs vary from one application to another. Fault tolerance consumes resources. In this paper, we propose fault tolerance to be added as a service, termed here as FTaaS, which can provide both spatial and temporal redundancies. A tenant (who seeks fault tolerance services) can express its intended fault tolerance level as part of the Service Level Agreement (SLA). In this paper, we investigate a general setting, where a tenant can execute in different modes, with different fault tolerance criterion. The task of a provider (who sells fault tolerance services) is to configure his offerings in accordance to the tenants' requirements, in a way which keeps his customers satisfied and his revenue is maximized. We consider several variations of this problem in the paper. We also discuss the other side of the tenant-provider setting, wherein a tenant has multiple providers to choose from, keeping in mind the requirements he has and the cost he has to pay to avail the services. We present formulations of optimization problems related to these in this work.

Internet of Things (IoT) has the potential to become a technology revolution with a vision of creating very large scale network, comprising of unprecedented number of connected devices. These devices, often referred to as smart items or intelligent things can be home appliances, healthcare devices, vehicles, buildings, factories and almost anything networked and fitted with sensors, actuators, embedded computers. There has been sustained research work and standardization effort from different IoT perspectives like integration of sensor and RFID devices to the Internet. With the increasing trend of gathering business insights from unstructured data, the high volume of data generated by such devices is also of interest. Cloud based data mining platforms are suitable for analyses of such data and researchers have proposed architectures where personal mobile phones can act as Edge Gateway between the sensor network and cloud analytics platform. It seems that the surge in the volume of data generated by huge number of Smart Items can only be matched if a large percentage of mobile users start sharing the computation capability of their personal devices and work together towards true Participatory Computing in the IoT systems. In this work we try to understand the challenges associated with running computation jobs on the mobile devices using different types of workload often observed in IoT applications. Based on the insights gained from experiments performed by us, we propose a scheme where mobile phones, residential gateways and other edge devices offer free slots to servers in a cloud based data analytics system. Based on the free time slots offered by the mobile phones, if commensurately sized computational jobs can be scheduled, the unpredictability associated with using mobile phones as grid resources can be solved.

The current emphasis on sensor-based intelligent and ubiquitous systems, more commonly known as “cyber-physical systems”, has the potential to give rise to a new generation of systems and services encompassing several domains such as e-Governance, healthcare, transportation, waste management, energy & utilities, insurance, etc., resulting in the metamorphosis of the Internet as we see it, into the Internet of Things (IoT). One probable commonality in each of these services will be the abundance of different types of data from different sources with the success of the systems depending on real-time or near real-time analysis of data. Such analyses are normally performed via well-known algorithms with a time-constraint on the execution, thus creating a requirement for parallel execution techniques. Some of these analyses may have a higher frequency of execution on a relatively small set of data, in which case the current big-data frameworks may actually add an overhead. Further, the frameworks like Hadoop demand the algorithms to be mapped onto a particular paradigm, which may not always be a suitable option. This paper, which is a work-in-progress, provides an experience report on the use of Condor, a well known Grid framework, for data-parallel “black-box” style execution of analysis algorithms in the context of Internet of Things. We concentrate on algorithms which are already in use, and can be partitioned into data-parallel subtasks without any modification and use Condor, which has traditionally been used for high-performance or high-throughput computing, as the execution framework.

Abstract Checkpoint and rollback recovery is a well‐known technique for providing fault tolerance to long‐running distributed applications. Performance of a checkpoint and recovery protocol depends on the characteristics of the application and the system on which it runs. However, given an application and system environment, there is no easy way to identify which checkpoint and recovery protocol will be most suitable for it. Conventional approaches require implementing the application with all the protocols under consideration, running them on the desired system, and comparing their performances. This process can be very tedious and time consuming. This paper first presents the design and implementation of a simulation environment, distributed process simulation or dPSIM, which enables easy implementation and evaluation of checkpoint and recovery protocols. The tool enables the protocols to be simulated under a wide variety of application, system, and network characteristics. The paper then presents performance evaluation of five checkpoint and recovery protocols. These protocols are implemented and executed in dPSIM under different simulated application, system, and network characteristics. Copyright © 2003 John Wiley & Sons, Ltd.