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MultiNets is a system supporting seamless switch-over between wireless interfaces on mobile devices in real-time. MultiNets is configurable to run in three different modes: (i) Energy Saving mode-for choosing the interface that saves themost energy based on the condition of the device, (ii) Offload mode-for offloading data traffic from the cellular toWiFi network, and (iii) Performance mode-for selecting the network for the fastest data connectivity. MultiNets also provides a powerful API that gives the application developers: (i) the choice to select a network interface to communicate with a specific server, and (ii) the ability to simultaneously transfer data over multiple network interfaces. MultiNets is modular, easily integrable, lightweight, and applicable to various mobile operating systems. We implement MultiNets on Android devices as a show case. MultiNets does not require any extra support from the network infrastructure and runs existing applications transparently. To evaluate MultiNets, we first collect data traces from 13 actual Android smartphone users over three months. We then use the collected traces to show that, by automatically switching to WiFi whenever it is available, MultiNets can offload on average 79.82% of the data traffic. We also illustrate that, by optimally switching between the interfaces, MultiNets can save on average 21.14 KJ of energy per day, which is equivalent to 27.4% of the daily energy usage. Using our API, we demonstrate that a video streaming application achieves 43-271% higher streaming rate when concurrently using WiFi and 3G interfaces. We deploy MultiNets in a real-world scenario and our experimental results show that depending on the user requirements, it outperforms the state-of-the-art Android system either by saving up to 33.75% energy, achieving near-optimal offloading, or achieving near-optimal throughput while substantially reducing TCP interruptions due to switching. [ABSTRACT FROM AUTHOR]

The primary goal of breadcrumb trail sensor networks is to transmit in real-time users' physiological parameters that measure life-critical functions to an incident commander through reliablemultihop communication. In applications using breadcrumb solutions, there are often many users working together, and this creates a well-known body shadowing effect (BSE). In this article, we first measure the characteristics of body shadowing for 2.4GHz sensor nodes. Our empirical results show that the body shadowing effect leads to severe packet loss and consequently very poor real-time performance. Then we develop a novel Intentional Forwarding solution. This solution accurately detects the shadowing mode and enables selected neighbors to forward data packets. Experimental results from a fully implemented testbed demonstrate that Intentional Forwarding is able to improve the end-to-end average packet delivery ratio (PDR) from 58% to 93% and worst-case PDR from 45% to 85%, and is able to meet soft real-time requirements even under severe body shadowing problems. [ABSTRACT FROM AUTHOR]

This paper presents PRIDE, a novel data abstraction layer for collaborative 2-tier sensor network applications. PRIDE, more specifically, targets distributed real-time applications, in which multiple collaborative mobile devices have to analyze a global situation by collecting and managing data streams from massive underlying sensors. PRIDE at these devices hides the details of underlying sensors and provides transparent, timely, and robust access to global sensor data under highly dynamic and unpredictable environments of emerging sensor network applications. For transparent and efficient sharing of global sensor data, a model-based predictive replication mechanism is proposed and integrated into a conventional data management system that supports diverse types of spatial and temporal queries. In addition, for robust and timely query processing, the predictive replication scheme is extended to the problem of guaranteeing Quality-of-Service (QoS) by introducing feedback control of the accuracy bounds of models. We show the viability of the proposed solution by implementing and evaluating it on a 2-tier sensor network testbed, emulating collaborative search-and-rescue tasks with realistic workloads. Our evaluation results demonstrate that PRIDE can achieve timely sensor data sharing among a large number of devices in a highly robust and controlled manner. [ABSTRACT FROM AUTHOR]

The demand for real-time data services is increasing in many applications including e-commerce, agile manufacturing, and telecommunications network management. In these applications, it is desirable to execute transactions within their deadlines, i.e., before the real-world status changes, using fresh (temporally consistent) data. However, meeting these fundamental requirements is challenging due to dynamic workloads and data access patterns in these applications. Further, transaction timeliness and data freshness requirements may conflict. In this paper, we define average/transient deadline miss ratio and new data freshness metrics to let a database administrator specify the desired quality of real-time data services for a specific application. We also present a novel QoS management architecture for real-time databases to support the desired QoS even in the presence of unpredictable workloads and access patterns. To prevent overload and support the desired QoS, the presented architecture applies feedback control, admission control, and flexible freshness management schemes. A simulation study shows that our QoS-aware approach can achieve a near zero miss ratio and perfect freshness, meeting basic requirements for real-time transaction processing. In contrast, baseline approaches fail to support the desired miss ratio and/or freshness in the presence of unpredictable workloads and data access patterns. [ABSTRACT FROM AUTHOR]

Presents key challenges in real-time communication and coordination in embedded sensor networks. Uncertainties in sensor networks; Description of traditional networks; Categories of medium access control protocols for wireless networks.

The demand for real-time e-commerce data services has been increasing recently. In many e-commerce applications, it is essential to process user requests within their deadlines, i.e., before the market status changes, using fresh data reflecting the current market status. However, current data services are poor at processing user requests in a timely manner using fresh data. To address this problem, we present a differentiated real-time data service framework for e-commerce applications. User requests are classified into several service classes according to their importance, and they receive differentiated real-time performance guarantees in terms of deadline miss ratio. At the same time, a certain data freshness is guaranteed for all transactions that commit within their deadlines. A feedback-based approach is applied to differentiate the deadline miss ratio among service classes. Admission control and adaptable update schemes are applied to manage potential overload. A simulation study, which reflects the e-commerce data semantics, shows that our approach can achieve a significant performance improvement compared to baseline approaches. Our approach can support the specified per-class deadline miss ratios maintaining the required data freshness even in the presence of unpredictable workloads and data access patterns, whereas baseline approaches fail. [ABSTRACT FROM AUTHOR]

Presents a study which discussed a scheduling approach for distributed static systems. Applications of real-time distributed systems; Results; Conclusions.

Describes the UMass Spring threads package which is designed specifically for multiprocessing in dynamic, hard real-time environments. Support for new thread semantics for real-time processing; Achievement of predictable creation and execution of threads due to an underlying predictable kernel; Reduction of context switching overhead and average-case memory access durations.

Provides information on real-time databases. Definitions related to real-time databases; Misconceptions about real-time databases; Discussion on research challenges. INSET: Other Research Areas.

Many distributed scheduling algorithms have been developed and reported in the current literature. However, very few of them explicitly treat stability issues. This paper first discusses stability issues for distributed scheduling algorithms in general terms. Two very different distributed scheduling algorithms which contain explicit mechanisms for stability are then presented and evaluated with respect to individual specific stability issues. One of the algorithms is based on stochastic learning automata and the other on bidding. The results indicate how very specific the treatment of stability is to the algorithm and environment under consideration. [ABSTRACT FROM AUTHOR]

Real-time systems are those systems in which the correctness of the system depends not only on logical results of computation but also on the time at which those results are produced. They span a broad spectrum of complexity from very simple microcontrollers in embedded systems to highly sophisticated, complex and distributed systems. Most embedded systems consist of a small microcontroller and limited software situated within some product such as a microwave oven or automobile. However, with the increasing sophistication of such systems, powerful microcontrollers and digital signal processing chips are commonly used, as are off-the-shelf real-time operating systems and design and debugging tools. Distributed multimedia have produced a new set of soft real-time requirements. Real-time principles lie at the heart of distributed multimedia, but without the concomitant high reliability requirements found in safety-critical, hard real-time systems. The confluence of real-time databases, multimedia and real-time control has great potential for moving application areas such as manufacturing and process control into the next generation.

This paper describes a heuristic approach for solving the problem of dynamically scheduling tasks in a real-time system where tasks have deadlines and general resource requirements. The crux of our approach lies in the heuristic function used to select the task to be scheduled next. The heuristic function is composed of three weighted factors. These factors explicitly consider information about real-time constraints of tasks and their utilization of resources. Simulation studies show that the weights for the various factors in the heuristic function have to be fine-tuned in order to obtain a degree of success in the range of 75-88 percent of that obtained via exhaustive search. However, modifying the approach to use limited backtracking improves the degree of success substantially to as high as 99.5 percent. This improvement is observed even when the initial set of weights are not tailored for a particular set of tasks. Simulation studies also show that in most cases the schedule determined by the heuristic algorithm is optimal or close to optimal. [ABSTRACT FROM AUTHOR]

Quality-aware realtime Embedded DataBase (QeDB) is a database for data-intensive real-time applications running on embedded devices. Currently, databases for embedded systems are best effort, providing no guarantees on their timeliness and data freshness. Existing real-time database (RTDB) technology cannot be applied to these embedded databases since it hypothesizes that the main memory of a system is large enough to hold the entire database. This, however, might not be true in data-intensive real-time applications. QeDB uses a novel feedback control scheme to support QoS in such embedded systems without requiring all data to reside in main memory. In particular, our approach is based on simultaneous control of both I/O and CPU resources to guarantee the desired timeliness. Unlike existing work on feedback control of RTDB performance, we implement and evaluate the proposed scheme on a modern embedded device. The experimental results show that our approach supports the desired timeliness of transactions while still maintaining high data freshness compared to baseline approaches. [ABSTRACT FROM AUTHOR]