Real-time object detection is essential for AI-based intelligent traffic management. However, growing complexities of deep learning models for object detection cause increased latency and resource requirements. To tackle the challenge, we introduce a new approach, named AROD (Adaptive Real-Time Object Detection), that infers the pixel motion speed in continuous traffic video frames and skips redundant frames when the pixel velocity is low. Thereby, AROD aims to significantly enhance the efficiency and scalability, sustaining the accuracy of object detection. Our evaluation using real-world traffic videos reveals that our method for pixel velocity inference via lightweight deep learning reduces the RMSE (Root Mean Square Error) by up to two orders of magnitude compared to state-of-the-art approaches. AROD improves the frame processing rate of YOLOv5, SSD, and EfficientDet by approximately 32-61%, 110-174%, and 120-213%, respectively. AROD considerably enhances scalability by supporting real-time object detection for up to three concurrent traffic video streams on a commodity machine. Moreover, AROD demonstrates its generalizability by supporting competitive accuracy in object detection for a separate traffic video that was fully hidden during training.

Deep learning models have significantly improved object detection essential for traffic monitoring. However, these models’ increasing complexity results in higher latency and resource consumption, making real-time object detection challenging. To address this issue, we propose a lightweight deep learning model called Empty Road Detection (ERD). ERD efficiently identifies and removes empty traffic images that do not contain any object of interest, such as vehicles, via binary classification. By serving as a preprocessing unit, ERD filters out nonessential data, reducing computational complexity and latency. ERD is highly compatible and can work seamlessly with any third-party object detection model. In our evaluation, we found that ERD improves the frame processing rate of EfficientDet, SSD, and YOLOV5 by approximately 44%, 40%, and 10%, respectively, for a real-world traffic monitoring video.

The trend of growing GPU capacity necessitates the support for concurrent executions of multiple applications in a GPU, since a single application cannot fully utilize hardware resources. Interference between the co-running applications, however, increases non-deterministic timing behaviors. In this paper, we propose an extension of the GPU memory architecture to support bandwidth reservation that provides flexible resource sharing, while reserving the required GPU memory bandwidth for each application. Our experimental results show that our approach supports the required memory bandwidth reservation for each application and improves the performance by 20% on average, compared to strict partitioning of hardware resources.

The importance of real-time wireless data transfer is rapidly increasing for Internet of Things (IoT) applications. For example, smart glasses worn by a doctor need to transmit real-time data to a hospital information system, which performs face detection and recognition, for real-time interaction with recognized patients within a certain deadline, which is ideally a few hundred milliseconds. Other emerging IoT applications, e.g., structural health monitoring, clinical monitoring, and industrial process automation, also require real-time wireless data transfer. Those applications have critical demands for real-time and energy-efficient communication through wireless medium. However, it is very challenging to support stringent timing constraints energy-efficiently through wireless medium due to its inherent unreliability and timing-unpredictability. Fortunately, heterogeneous radios are becoming increasingly available in modern embedded devices, offering new opportunities to use multiple wireless technologies to accommodate the needs of real-time applications. In this paper, we first formulate the runtime radio selection and data partitioning for real-time IoT applications as an Integer Linear Programming (ILP) problem and then present (1) an optimal algorithm that makes quick and optimal decisions when selecting between two radios and (2) a heuristic algorithm for the platforms with more radios. Experimental results show that our heuristic algorithm provides optimal selections to 94.4% of the cases and makes the decisions 336~1412 times faster than an ILP problem solver.

5G communication requires continuous exchanges of channel state information (CSI) between the base station and user equipment (UE) to adjust the physical layer parameters. CSI classification in a noisy environment is challenging, since CSI can get corrupted. To address this problem, we apply a convolutional neural network (CNN) to classify several key CSI parameters. In a simulation study, our CNN method classifies the CSI parameters with accuracy ranging between 84-98%, which is approximately 24-38% higher than the 3GPP recommendations for UEs.

Applying cyber security mechanisms, such as cryptography, trusted computing, and network intrusion detection, is insufficient to secure cyber-physical systems (CPS) such as smart vehicles, because most sensors (and actuators) are designed without security considerations and remain vulnerable to sensor spoofing attacks in the analog domain. To address this problem, we present a new approach to detect and handle sensor spoofing attack against automotive radars-a key component for assisted and autonomous driving-by extending multiple beamforming in an automotive multi-input multi-output (MIMO) radar. In a simulation study for adaptive cruise control based on the car following model, our approach significantly outperforms three state-of-the-art baselines in terms of attack detection and ranging accuracy.

In heterogeneous networks, a user equipment (UE) can directly communicate with the macro base station (BS) or a small, low-power pico or femto BS. Alternatively, it can indirectly communicate with the macro BS through one or more intermediate device (UE) or a relay-station that uses the over-the-air backhaul to the macro BS. Due to the highly dynamic and uncertain nature of wireless communication, it is essential for a UE to choose an optimal communication mode and a neighbor to which it connects, e.g., a macro/small BS in the direct communication mode or a nearby relay/device in the indirect communication mode. In this paper, we apply an effective reinforcement learning method, called multi-armed bandit (MAB), to shed light on this problem. Especially, we apply MAB supported by the Thompson sampling theorem to pick an optimal arm-a neighbor that determines the communication mode and resulting performance, while effectively dealing with the exploration-exploitation dilemma in MAB. In a simulation study undertaken in Matlab, we compare the performance of the proposed approach to several baselines representing the current state of the art. Our approach enhances the throughput normalized to the optimal throughput by approximately 8-97% compared to several baselines representing the state of the art. Further, it improves the throughput by up to 15% compared to the best performing baseline [1], [2].

In data-intensive soft real-time applications, e.g., e-commerce, traffic control, and target tracking, a database system needs to process transactions in a timely manner. However, user transactions may suffer from unpredictable large delays when the database system is overloaded due to flash transaction arrivals and transaction aborts/restarts. To address the problem, we design a new adaptive closed-loop method considering database semantics to control the response time to be below a target set-point even when dynamic workloads are given. Our approach continues to update the database model at runtime and re-tunes the response time controller based on the adjusted model, because the relation between the workload and response time may vary in time. Notably, our adaptive control scheme is different from most existing closed-loop methods for real-time database performance management that model the database system and design and tune the controller entirely offline with no online adaptation. The results of the performance evaluation undertaken in a real-time database testbed show that our approach maintains the database response time below the target set-point for most of the time even under steep workload surges, quickly canceling any transient delay overshoot that exceeds the set-point. However, the tested state-of-the-art baselines fail to do it.

The Internet of Things (IoT) applications is envisioned to require higher throughput protocols because of the increasing data amount. To significantly enhance the network throughput between IoT devices, this paper proposes a new link-layer data forwarding technique that is aware of link correlation (LC) and supports receiver initiated acknowledgement (RI-ACK). We also propose a multicast communication protocol based on LC-aware forwarding and RI-ACKs to further enhance the throughput. In a simulation study, our protocol improves the throughput by 35%-55% comparing to a state-of-the-art baseline.

In data-intensive real-time embedded applications, it is desirable to process data service requests in a timely manner using fresh data, consuming less power. However, related work is relatively scarce. In this paper, we present an effective approach to decrease both the deadline miss ratio and power consumption by merging similar real-time transactions, while systematically adapting the data freshness. In a simulation study, our approach considerably reduces deadline misses and power consumptions compared to the state-of-the-art baselines, supporting the required data freshness.

Visual surveillance has numerous applications. It can support public safety, traffic monitoring, and facility protection to name just a few. Networked digital surveillance devices are revolutionizing the surveillance industry by supporting high quality images, remote monitoring, and advanced image processing. However, they also raise serious privacy/security concerns. To address the issue, the surveillance industry has recently begun to provide basic support for secure communications. In this paper, we present a new protocol to significantly enhance the security and performance compared with the state-of-the-art baseline method widely taken in the video surveillance industry. Through extensive experiments for performance evaluation, our approach is shown to substantially reduce the delay to execute cryptographic mechanisms and increase the supported bit rate compared with the baseline, while providing desirable security features.

It is challenging to process real-time data service requests, such as online trade or traffic monitoring requests, within their deadlines, while providing differentiated real-time data services. To address the problem, we present new approaches to 1) assigning deadlines to real-time data service requests based on their data access needs and service classes to differentiate real-time data services when the system is busy and 2) closely supporting the specified target delay to deadline ratio (DDR)-the ratio of actual data service delays to deadlines-via feedback control even in the presence of dynamic workloads. Further, we have actually implemented our approaches by extending an open source database unlike most existing work on real-time databases. The experimental results show that our approach closely supports the target DDR bound and service differentiation requirements among the service classes unlike the tested baselines representing the current state of the art in real-time database research.

Multimedia applications with soft real-time constraints consume considerable power, incurring thermal problems. It is challenging to process multimedia data in real-time with the highest possible QoS, while avoiding potential thermal problems. To aggravate the problem, immediate dissipation of the accumulated heat is infeasible. In this paper, we design an empirical model to predict the power consumption and resulting processor temperature for video decoding. Based on the model, we develop a predictive method that avoids deadline misses due to thermal faults by adapting the video quality before the temperature exceeds the specified threshold. We implement our predictive control scheme and two baseline approaches by extending an open source implementation of the H.264 scalable video coding standard. Our approach controls the temperature to be below the specified threshold for most of the time unlike the tested baselines, while supporting similar or even better video quality.

Supporting timely data services using fresh data in data-intensive real-time applications, such as e-commerce and transportation management is desirable but challenging, since the workload may vary dynamically. To control the data service delay to be below the specified threshold, we develop a predictive as well as reactive method for database admission control. The predictive method derives the workload bound for admission control in a predictive manner, making no statistical or queuing-theoretic assumptions about workloads. Also, our reactive scheme based on formal feedback control theory continuously adjusts the database load bound to support the delay threshold. By adapting the load bound in a proactive fashion, we attempt to avoid severe overload conditions and excessive delays before they occur. Also, the feedback control scheme enhances the timeliness by compensating for potential prediction errors due to dynamic workloads. Hence, the predictive and reactive methods complement each other, enhancing the robustness of real-time data services as a whole. We implement the integrated approach and several baselines in an open-source database. Compared to the tested open-loop, feedback-only, and statistical prediction + feedback baselines representing the state of the art, our integrated method significantly improves the average/transient delay and real-time data service throughput.

GPGPUs (General Purpose Graphic Processing Units) provide massive computational power. However, applying GPGPU technology to real-time computing is challenging due to the non-preemptive nature of GPGPUs. Especially, a job running in a GPGPU or a data copy between a GPGPU and CPU is non-preemptive. As a result, a high priority job arriving in the middle of a low priority job execution or memory copy suffers from priority inversion. To address the problem, we present a new lightweight approach to supporting preemptive memory copies and job executions in GPGPUs. Moreover, in our approach, a GPGPU job and memory copy between a GPGPU and the hosting CPU are run concurrently to enhance the responsiveness. To show the feasibility of our approach, we have implemented a prototype system for preemptive job executions and data copies in a GPGPU. The experimental results show that our approach can bound the response times in a reliable manner. In addition, the response time of our approach is significantly shorter than those of the unmodified GPGPU runtime system that supports no preemption and an advanced GPGPU model designed to support prioritization and performance isolation via preemptive data copies.

As the demand for real-time data services (e.g., e-commerce or on line auctions) increases, it is desired for a real-time database to increase the timely throughput-the amount of data processed in a timely manner. As the timely throughput of a centralized real-time database is limited, it is desired to federate a set of real-time databases to increases the timely throughput. However, related work on distributed real-time databases is scarce. Most existing approaches are highly complex, incurring non-trivial overheads. Neither are they implemented in a real database system. To address the problem, we design a new system architecture for federated real-time data services and develop efficient approaches for load sharing among a set of clustered databases. To support the desired data service delay even in the presence of dynamic workloads, each individual database employs a single-input single-output (SISO) feedback admission control scheme. Based on the admission control signals collected from the individual databases, cluster-wide load sharing is performed to enhance the total timely throughput by fully utilizing the federated databases, while avoiding to overload them. We have implemented and evaluated the performance of our approach by extending the Oracle Berkeley DB. Our system significantly enhances the timely data throughput compared to a single centralized system, while effectively dealing with emulated partial unavailability of a set of federated databases.

In cluster computing, a service provider must allocate necessary computing resources for large-scale scientific computations to process a customer's service request according to a service level agreement (SLA) that is a set of quality of services (QoS) and a fee agreed between a customer and a service provider. Thus, Resource allocation is a challenging but very necessary problem in cluster computing. In an effort to maximize a service provider's profit, it is commonplace and important to prioritize customer services in favor of those who are willing to pay higher fees. In this paper, we consider a set of computing resources owned by a service provider who serves differentiated customer services subject to an SLA for scientific applications that often require parallel computation. The QoS defined in the paper includes percentile response time and cluster utilization. We present an approach for optimal resource allocation in cluster computing systems in that we minimize the total cost of computing resources owned by a service provider while satisfying multiple priority customer service requirements. Our simulation experiments show that the proposed approach is applicable to the resource allocation in a cluster computing system with multiple customer services.

Maximizing the aggregate network performance over constrained computing and communication resources has been an active research area. Real-time wireless sensor network (RTWSN) is an important application of real-time and networked embedded systems. Due to the severe resources constraints and associated real-time requirements, new challenges arise in RTWSN. In this paper, we study an integrated scheme to optimize the total system performance of real-time flows over an RTWSN by exploiting multi-path routing and dynamic sampling rate assignment. We formally model the problem using nonlinear optimization and design an online distributed algorithm to obtain the optimal rate assignments on multiple paths. Extensive simulation studies demonstrates the significant performance improvements over existing proposals.

It is essential to process real-time data service requests such as stock quotes and trade transactions in a timely manner using fresh data, which represent the current real-world phenomena such as the stock market status. Users may simply leave when the database service delay is excessive. Also, temporally inconsistent data may give an outdated view of the real-world status. However, supporting the desired timeliness and freshness is challenging due to dynamic workloads. To address the problem, we present new approaches for 1) database backlog estimation, 2) fine-grained closed-loop admission control based on the backlog model, and 3) incoming load smoothing. Our backlog estimation and control-theoretic approaches aim to support the desired service delay bound without degrading the data freshness, critical for real-time data services. Specifically, we design, implement, and evaluate two feedback controllers based on linear control theory and fuzzy logic control theory, to meet the desired service delay. Workload smoothing, under overload, helps the database admit and process more transactions in a timely fashion by probabilistically reducing the burstiness of incoming data service requests. In terms of the data service delay and throughput, our closed-loop admission control and probabilistic load smoothing schemes considerably outperform several baselines in the experiments undertaken in a stock trading database testbed.

It is challenging to support the timeliness of real-time data service requests in data-intensive real-time applications such as online auction or stock trading, while maintaining the freshness of temporal data that capture the current real-world status. Although deadline-aware real-time scheduling would significantly enhance the timeliness of data services, it is not clear how to assign explicit feasible deadlines to data service requests in an open environment. To address the problem, we design a new deadline assignment scheme to derive feasible deadlines for real-time data service requests considering their individual data needs. Further, we develop a systematic closed-loop approach to supporting the desired tardiness-the actual service delay to deadline ratio-of real-time data services even in the presence of dynamic workloads. We choose the tardiness metric due to its expressiveness compared to the deadline miss ratio and utilization that saturate at 0 and 1 when the system is underutilized or overloaded, respectively. The performance evaluation results acquired in our real-time stock trading testbed show that the desired average/transient tardiness is closely supported. Consequently, the deadline miss ratio is significantly reduced compared to a state-of-art database system with a real-time scheduling extension.

Due to the relatively high node density and source-to-sink communication pattern, wireless sensor networks (WSNs) are subject to congestion and packet losses. Further, the availability of low-cost hardware, such as Cyclops cameras, is promoting wireless multimedia sensing to support, for example, visual surveillance. As a result, congestion control is becoming more critical in WSNs. In this paper, we present a lightweight distributed congestion control method in WSNs. We develop new metrics to detect congestion in each node by considering the queue lengths and channel conditions observed in the one-hop neighborhood. Based on the estimated level of congestion, each node dynamically adapts its packet transmission rate and balance the load among the one-hop neighbors to avoid creating congestion and bottleneck nodes. In a simulation study performed in OMNeT++, our approach significantly enhances the end-to-end (e2e) packet delivery ratio and reduces the e2e delay without increasing the total energy consumption compared to the tested baseline approach.

Real-time data services are needed in data-intensive real-time applications such as e-commerce or traffic control. However, it is challenging to support real-time data services, if workloads dynamically change based on the market or traffic status. To enhance the quality of real-time data services even in the presence of dynamic workloads, feedback control theory has been applied. However, a major drawback of feedback control is that it only reacts to performance errors. To improve the robustness of real-time data services, we develop a statistical feed-forward approach that proactively adapts the incoming load, if necessary, to support the desired real-time data service delay. Further, we integrate it with a feedback controller to compensate potential prediction errors and adjust the system behavior in a reactive manner for timely data services. Performance evaluation results acquired in our real-time data service testbed show that our integrated approach considerably reduces the average delay and transient delay fluctuations, while improving throughput compared to the tested baselines including the feed-forward-only and feedback-only approaches.

Real-time data services can benefit data-intensive real-time applications, e.g., e-commerce, via timely transaction processing using fresh data, e.g., the current stock prices. To enhance the real-time data service quality, we present several novel techniques for (1) database backlog estimation, (2) fine-grained closed-loop admission control based on the backlog model, and (3) hint-based incoming load smoothing. Our backlog estimation and feedback control aim to support the desired service delay bound without degrading the data freshness critical for real-time data services. Workload smoothing, under overload, help the database admit and process more transactions in a timely manner by probabilistically reducing the burstiness of incoming data service requests. In terms of the data service delay and throughput, our feedback-based admission control and probabilistic load smoothing considerably outperform the baselines, which represent the current state of the art, in the experiments performed in a stock trading database testbed.

Multimedia streaming is resource demanding. It may starve other applications such as file transfer sharing the network, for example, in a smart home. To address the problem, we apply fuzzy logic control to bound the bandwidth consumption of multimedia streams. We also differentiate the video quality for streams with different levels of importance. We have implemented our transmission rate control and service differentiation schemes and evaluated them in our department network where a number of different applications may coexist at the same time. Performance evaluation results show that our video streaming system can support the specified bit rate bound and differentiate the service to efficiently utilize the limited bandwidth.

An increasing number of real-time systems are embedded in mission critical systems such as target tracking systems, in which workloads may dynamically vary, for example, depending on the number of targets in the area of interest Feedback control has been applied to support real-time performance in dynamic environments, producing promising initial results. However, mathematical system modeling necessary for feedback control is challenging. To reduce the difficulty of system modeling, we apply fuzzy control for direct nonlinear mappings between the utilization error (= target utilization - current utilization) and the workload adjustment required to achieve the target utilization via IF-THEN rules. Moreover, via online adaptation, our fuzzy controller can amplify or dampen its own fuzzy control signal, if necessary, to expedite the convergence to the desired utilization. In our simulation study, our approach quickly converges to the target utilization when the workload significantly changes. In contrast, the tested baselines oscillate between overload and underutilization.

Abstract Wireless sensor networks (WSNs) have recently attracted a lot of interest due to the wide range of applications they enable. Unfortunately, WSNs are exposed to numerous security threats that can adversely affect the success of important applications. Securing WSNs is challenging due to their limited capabilities and the unique nature of the network and applications. In this paper, we argue that the WSN security research generally considers mechanisms that are modeled after and evaluated against abstract applications and WSN organizations. Instead, we propose that the solution for WSN security must be sensitive to the application and infrastructure. Specifically, we formulate a new notion of an application‐specific security context as the combination of a potential attacker's motivation and the WSN vulnerability. The vulnerability is a function of factors such as the sensor field, WSN infrastructure, application, protocols, system software, accessibility, and the observability of the WSN. To reduce the vulnerability, we argue that WSN design must balance security with traditional objectives such as the cost, energy efficiency, and application level performance to a degree proportional to the attacker's motivation. We illustrate this argumentviafour example applications. Overall, our work can be considered a basis to derive more grounded and realistic assumptions for WSN security and develop cost‐effective security solutions to handle application‐specific vulnerabilities in WSNs. Copyright © 2007 John Wiley & Sons, Ltd.

It is challenging to manage the performance of real-time databases (RTDBs) that are often used in data-intensive real-time applications such as agile manufacturing and target tracking. Feedback control has recently been considered a promising approach to enabling reliable real-time data service. However, most existing work on feedback control of RTDB performance is not based on a RTDB-specific control model, which is critical for closed-loop system design. To address this problem, we design a novel RTDB model that can capture RTDB dynamics by modeling the relation between the total arrival rate (sum of the transaction arrival rate and restart rate) and utilization via a difference equation. Based on the model, we design and tune a utilization controller to compute the total arrival rate adjustment needed to support the desired average/transient utilization. We also design a QoS management scheme and admission control technique that can judiciously adapt the transaction QoS and arrival rate in a RTDB, if necessary, to support the desired utilization, while enhancing the success ratio. In a simulation study, we show that our approach can support the desired average/transient utilization for a range of transaction arrival rates under severe data contention, while considerably improving the success ratio compared to the tested baselines

A lot of real-time database (RTDB) research has been done to process transactions in a timely fashion using fresh data reflecting the current real world status. However, most existing RTDB work is based on simulations. Due to the absence of a publicly available RTDB testbed, it is very hard to evaluate real-time data management techniques in a realistic environment. To address the problem, we design and develop an initial version of a RTDB testbed, called Chronos, atop an open source database [5]. We develop soft real-time database workloads that model online stock trades, providing several knobs to specify workloads for RTDB performance evaluation. In addition, we develop a QoS management scheme in Chronos to detect overload and reduce workloads, via admission control and adaptive temporal data updates, under overload. From the extensive experiments using the stock trading workloads developed in Chronos, we observe that adaptive updates can considerably improve the transaction timeliness. We also observe that admission control can only enhance the timeliness under severe overload, possibly causing underutilization problems for moderate workloads. Overall, we observe several results different from simulation-based work, warranting the need for RTDB performance evaluation in a real database system.

It is challenging to process transactions in a timely fashion using fresh data, e.g., current stock prices, since database workloads may considerably vary due to dynamic data/resource contention. Further, transaction timeliness and data freshness requirements may compete for system resources. In this paper, we propose a novel feedback control model to support the desired data service delay by managing the size of the ready queue, which indicates the amount of the backlog in the database. We also propose a new self-adaptive update policy to adapt the freshness of cold data in a differentiated manner based on temporal data access and update patterns. Unlike most existing work on feedback control of real-time database (RTDB) performance, we actually implement and evaluate feedback control and database workload adaptation techniques in a real database testbed modeling stock trades. For performance evaluation, we undertake experiments in the testbed, which consists of thousands of client threads concurrently requesting database services for stock quotes, trades, and portfolio updates in a bursty manner. In these experiments, our database system supports the desired response time bound and data freshness, while processing a significantly larger number of transactions in time compared to the tested baselines.

Most existing real-time protocols for sensor data dissemination use packet scheduling schemes to prioritize packets according to their deadlines. However, packet prioritization by itself cannot completely support real-time data dissemination requirements. In this paper, we propose new just-in-time scheduling (JiTS) algorithms that take advantage of the available slack, if any, to reduce contentions and improve real-time performance by judiciously delaying packets as long as their deadlines are not missed. Specifically, we explore several policies for allocating the slack among multiple hops, including a non-linear policy where packets are non-uniformly delayed at intermediate nodes to account for expected higher contentions as packets get closer to the sink(s). Notably, our JiTS policies require neither lower layer support nor synchronization among sensor nodes making for an easy deployment. In our simulation study, JiTS significantly improves the deadline miss ratio and packet drop ratio compared to existing approaches in various situations. It is also shown that the geographic forwarding often used for real-time data dissemination substantially underperforms the shortest path routing especially when the load is high

Real-time embedded systems are increasingly being networked. In distributed real-time embedded applications, e.g., electric grid management and command and control applications, it is required to not only meet real-time constraints but also support the data confidentiality,integrity, and authenticity. Unfortunately, in general, cryptographic functions are computationally expensive, possibly causing deadline misses in real-time embedded systems with limited resources. As a basis for cost-effective security support in real-time embedded systems, we define a quantitative notion of Strength of Defense (SoD). Based on the SoD concept, we propose a novel adaptive security policy in which the SoD can be degraded by decreasing the cryptographic key length for certain tasks, if necessary, to improve the success ratio under overload conditions. Our approach is lightweight. The time complexity of our approach is linear and its amortized version has the constant overhead per SoD adaptation period. Moreover, our approach supports desirable security features requiring an attacker to do extra work to find the cryptographic key. In the performance evaluation, we show that our approach can considerably improve the success ratio due to controlled SoD degradation under overload.

Localization is a fundamental operation in mobile and self-configuring networks such as sensor networks and mobile ad hoc networks. For example, sensor location is often critical for data interpretation. Existing research focuses on localization mechanisms: algorithms and infrastructure designed to allow the sensors to determine their location. In a mobile environment, the underlying localization mechanism must be invoked repeatedly to maintain accurate location information. We propose and investigate adaptive and predictive protocols that control the frequency of localization based on sensor mobility behavior to reduce the energy requirements for localization while bounding the localization error. In addition, we evaluate the energy-accuracy tradeoffs. Our results indicate that the proposed protocols reduce the localization energy significantly without sacrificing accuracy.

Real-time data services can significantly increase, e.g., the profit of online trades by processing transactions within their deadlines. However, supporting the transaction timeliness in dynamic environments such as the WWW is challenging, since transactions may arrive in a bursty manner and execute longer than expected. As a result, the degree of data/resource contention may vary, causing deadline misses. To address this problem, we refine real-time data service performance metrics and develop a novel feedback-based scheme to manage the clean CPU utilization, which is the difference between the aggregate utilization and wasted utilization due to data conflicts and deadline misses, in main memory real-time databases. According to the control signal computed in the feedback loop, the QoS of relatively large transactions, which usually incur more data/resource contention, can be degraded under high contention. Admission control can also be applied to incoming transactions, if necessary, to improve the real-time database performance under severe overload. By carefully managing the clean utilization, we can substantially improve the success ratio, i.e., the fraction of the submitted transactions that have been admitted and finished within their deadlines. In a simulation study, which models bursty arrivals of long-running transactions with timing constraints, our approach improves the success ratio by up to an order of magnitude compared to existing approaches.

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. 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.

Providing quality of service guarantees for data services in a distributed environment is a challenging task. The presence of multiple sites in distributed environment raises issues that are not present in centralized systems. The transaction workloads in distributed real-time databases may not be balanced and the transaction access patterns may be time-varying and skewed. Data replication is an effective method to help database systems meet the stringent temporal requirements of real-time applications. We have designed an algorithm that provides quality of service guarantees for data services in distributed real-time databases with full replication of temporal data. The algorithm consists of heuristic feedback-based local controllers and global load balancers (GLB) working at each sites. The local controller controls the admission process of incoming transactions. The global load balancers collect the performance data from other nodes and balance the system-wide workload. The simulation results show that the new algorithm successfully balances the workloads in distributed real-time databases and provides tight transaction miss ratio guarantees under various transaction workloads.

The demand for real-time database services has been increasing recently. Examples include sensor data fusion, decision support, Web information services, and online trading. In these applications, it is desirable to execute transactions within their deadlines using temporally consistent data. Due to the high service demand, real-time databases can be overloaded. As a result, many transactions may miss their deadlines, or data temporal consistency constraints can be violated. To address these problems, we present a QoS management scheme to support guarantees on deadline miss ratio and data freshness (temporal consistency) even in the presence of unpredictable workloads and data access patterns. Using our approach, admitted user transactions can be processed in time using fresh data. A simulation study shows that our QoS-sensitive approach can achieve a significant performance improvement, in terms of deadline miss ratio and data freshness, compared to several baseline approaches. Furthermore, our approach shows a comparable performance to the theoretical oracle that is privileged by a complete future knowledge of data accesses.

Real-time databases are needed in security-critical applications, e.g., e-commerce, agile manufacturing, and military applications. In these applications, transactions and data items can be classified into several security levels according to their clearance and sensitivity levels. It is essential for real-time databases to prevent illegal direct/indirect transfer of sensitive data, e.g., trade secret, manufacturing, or operational data, between transactions belonging to different security levels. Further transactions should be committed within their deadlines, i.e., before the market, manufacturing, or battlefield status changes. In this paper we present a novel real-time database architecture, in which illegal direct/indirect inter-level information flows are prevented while controlling the deadline miss ratio for admitted transactions to remain below a certain threshold. In our approach, mandatory access control mechanisms are applied for security purposes. QoS management, admission control, and feedback control schemes are applied to support certain guarantees on the miss ratio against potential overload and data conflicts. A detailed simulation study shows that our approach can support the specified miss ratio preventing illegal information flows even in the presence of unpredictable workloads and varying degrees of data contention, whereas baseline approaches fail.