Emerging Industrial Internet-of-Things systems require wireless solutions to connect sensors, actuators, and controllers as part of high data rate feedback-control loops over real-time flows. A key challenge is to provide predictable performance and agility in response to fluctuations in link quality, variable workloads, and topology changes. We propose WARP to address this challenge. WARP uses programs to specify a network's behavior and includes a synthesis procedure to automatically generate such programs from a high-level specification of the system's workload and topology. WARP has three unique features: (1) WARP uses a domain-specific language to specify stateful programs that include conditional statements to control when a flow's packets are transmitted. The execution paths of programs depend on the pattern of packet losses observed at runtime, thereby enabling WARP to readily adapt to packet losses due to short-term variations in link quality. (2) Our synthesis technique uses heuristics to improve network performance by considering multiple packet loss patterns and associated execution paths when determining the transmissions performed by nodes. Furthermore, the generated programs ensure that the likelihood of a flow delivering its packets by its deadline exceeds a user-specified threshold. (3) WARP can adapt to workload and topology changes without explicitly reconstructing a network's program based on the observation that nodes can independently synthesize the same program when they share the same workload and topology information. Simulations show that WARP improves network throughput for data collection, dissemination, and mixed workloads on two realistic topologies. Testbed experiments show that WARP reduces the time to add new flows by 5 times over a state-of-the-art centralized control plane and guarantees the real-time and reliability of all flows.

Future Industrial Internet-of-Things (IIoT) systems will require wireless solutions to connect sensors, actuators, and controllers as part of high data rate feedback-control loops over real-time flows. A key challenge in such networks is to provide predictable performance and adaptability in response to link quality variations. We address this challenge by developing RECeiver ORiented Policies (Recorp), which leverages the stability of IIoT workloads by combining offline policy synthesis and run-time adaptation. Compared to schedules that service a single flow in a slot, Recorp policies share slots among multiple flows by assigning a coordinator and a list of flows that may be serviced in the same slot. At run-time, the coordinator will execute one of the flows depending on which flows the coordinator has already received. A salient feature of Recorp is that it provides predictable performance: a policy meets the end-to-end reliability and deadline of flows when the link quality exceeds a user-specified threshold. Experiments show that across IIoT workloads, policies provided a median increase of 50% to 142% in real-time capacity and a median decrease of 27% to 70% in worst-case latency when schedules and policies are configured to meet an end-to-end reliability of 99%.

This paper describes the design and empirical evaluation of PHASER - a mote prototype for low-power directional communication in wireless sensor networks. PHASER has a modular design that includes three components: a low-power radio, an RF signal processing chip, and two off-the-shelf antennas. Directional communication is achieved by splitting the output signal from the low-power radio chip and controlling programmatically the phase of each signal as it transmitted to each antenna. The net effect of controlling the phase of the signals is that they generate patterns of constructive and destructive interference as signals propagate. PHASER is well-suited for wireless sensor networks as it does not require heavyweight signal processing techniques and consumes minimal additional energy. We have extensively evaluated the performance of five PHASER prototypes. Empirical results clearly demonstrate that changing the phase configuration of PHASER can generate diverse anisotropic radiation patterns. The diverse radiation patterns may be used to increase the signal strength at an intended receiver. Our data indicates that the signal strength of a link can be increased by at least 13 dBm. We also show it is possible to take advantage of the anisotropy of the radiation patterns to facilitate spatial reuse. More importantly, we show that the quality of the links from the same PHASER mote has a common pattern that can be predicted using a simple model. Our evaluation shows that model introduces an median absolute error of about 2 dBm. The model may be used for realistic simulations or integrated into protocol stacks to identify the phase configurations that improve link quality or spatial reuse.

Industrial applications and cyber-physical systems rely on real-time wireless networks to deliver data in a timely and reliable manner. However, existing solutions provide these guarantees only for stationary nodes. In this article, we present REWIMO, a solution for real-time and reliable communications in mobile networks. REWIMO has a two-tier architecture composed of (i) infrastructure nodes and (ii) mobile nodes that associate with infrastructure nodes as they move. REWIMO employs an on-join bandwidth reservation approach and benefits from a set of techniques to efficiently reserve bandwidth for each mobile node at the time of its admission and over its potential data forwarding paths. To ensure association of mobile nodes with infrastructure nodes over high-quality links, REWIMO uses the two-phase scheduling technique to coordinate neighbor discovery with data transmission. To mitigate the overhead of handling network dynamics, REWIMO employs an additive scheduling algorithm, which is capable of additive bandwidth reservation without modifying existing schedules. Compared to the algorithms used by static real-time wireless networks, the techniques and the algorithms employed by REWIMO result in a significant increase in real-time capacity, enhanced reliability, and considerably faster handling of network dynamics.

Real-time wireless communication infrastructure is increasingly deployed to support industrial and cyber-physical applications. A limitation of existing real-time protocols is that they do not support mobility. This paper presents the development of a real-time network composed of a multi-hop infrastructure, and mobile nodes that associate with infrastructure nodes as they move. Once a mobile node joined the network, its real-time communication is guaranteed irrespective to the number and mobility pattern of mobile nodes. To develop this network, we propose Mobility-Aware Scheduling Algorithm (MASA), which benefits from new transmission scheduling approaches that cleverly combine potential packet transmissions to increase real-time capacity. We have developed a realistic trace-based simulator to evaluate the performance of MASA against two baseline algorithms. Experimental results indicate that MASA increases the number of admitted mobile nodes by 7× and 1.6×, and extends the network lifetime by 110% and 30%, compared to the baselines.

In this paper we consider the problem of supporting real-time communication in mobile networks. To address this challenge, we propose novel transmission scheduling techniques that handle the routing uncertainty introduced by mobility. The core of the scheduling techniques involves controlling the order in which transmissions are scheduled and intelligently scheduling multiple transmissions in a single entry of the scheduling matrix without conflict. Flow-Ordered Mobility-Aware Real-time Scheduling (FO-MARS) integrates these techniques to provide a 14x increase in real-time capacity compared to the baseline algorithms designed for static real-time networks. Additionally, we propose Additive Mobility-Aware Real-time Scheduling (A-MARS), which can handle network dynamics such as the addition or removal of flows without having to reschedule previously admitted flows. As a result, A-MARS achieves significantly lower admission latency than that of the baselines.

Memory management is a crucial aspect of mobile sensing applications that must process high-rate data streams in an energy-efficient manner. Our work is done in the context of synchronous data-flow models in which applications are implemented as a graph of components that exchange data at fixed and known rates over FIFO channels. In this paper, we show that it is feasible to leverage the restricted semantics of synchronous data-flow models to optimize memory management. Our memory optimization approach includes two components: (1) We use abstract interpretation to analyze the complete memory behavior of a mobile sensing application and identify data sharing opportunities across components according to the live ranges of exchanged samples. Experiments indicate that the static analysis is precise for a majority of considered stream applications whose control logic does not depend on input data. (2) We propose novel heuristics for memory allocation that leverage the graph structure of applications to optimize data exchanges between application components to achieve not only significantly lower memory footprints but also increased stream processing throughput. We incorporate code generation techniques that transform a stream program into efficient C code. The memory optimizations are implemented as a new compiler for the StreamIt programming language. Experiments show that our memory optimizations reduce memory footprint by as much as 96% while matching or improving the performance of the StreamIt compiler with cache optimizations enabled. These results suggest that highly efficient stream processing engines may be built using synchronous data-flow languages.

Energy-efficiency is a critical concern in continuously-running mobile applications, such as those for health and context monitoring. An attractive approach to saving energy in such applications is to defer the execution of delay-tolerant operations until a time when they would consume less energy. However, introducing delays to save power may have a detrimental impact on the user experience. To address this problem, we present Tempus, a new approach to managing the trade-off between energy savings and delay. Tempus saves power by enabling programmers to annotate power-hungry operations with states that specify when the operation can be executed to save energy. The impact of power management on timeliness is managed by associating delay budgets with objects that contain time-sensitive data. A static analysis and the run-time service ensure that power management policies will not delay an object more than its assigned budget. We demonstrate the expressive power of Tempus through a case study of optimizing two real-world applications. Furthermore, laboratory experiments show that Tempus may effectively manage the energy-delay trade-off on realistic workloads. For example, in a news application, five Tempus annotations may be used to create a policy that reduces the latency of downloading images 10 times compared to the original implementation without affecting energy consumption. Our experiments also indicate that the overhead of tracking budgets in Tempus is small.

Energy-efficiency is a key concern in continuously-running mobile applications, such as those for health and context monitoring. Unfortunately, developers must implement complex and customized power-management policies for each application. This involves the use of complex primitives and writing error-prone multithreaded code to monitor hardware state. To address this problem, we present APE, an annotation language and middleware service that eases the development of energy-efficient Android applications. APE annotations are used to demarcate a power-hungry code segment whose execution is deferred until the device enters a state that minimizes the cost of that operation. The execution of power-hungry operations is coordinated across applications by the APE middleware. Several examples show the expressive power of our approach. A case study of using APE annotations in a real mobile sensing application shows that annotations can cleanly specify a power management policy and reduce the complexity of its implementation. An empirical evaluation of the middleware shows that APE introduces negligible overhead and equals hand-tuned code in energy savings, in this case achieving 63.4% energy savings compared to the case when there is no coordination.

AudioSense integrates mobile phones and web technology to measure hearing aid performance in real-time and in-situ. Measuring the performance of hearing aids in the real world poses significant challenges as it depends on the patient's listening context. AudioSense uses Ecological Momentary Assessment methods to evaluate both the perceived hearing aid performance as well as to characterize the listening environment using electronic surveys. AudioSense further characterizes a patient's listening context by recording their GPS location and sound samples. By creating a time-synchronized record of listening performance and listening contexts, AudioSense will allow researchers to understand the relationship between listening context and hearing aid performance. Performance evaluation shows that AudioSense is reliable, energy-efficient, and can estimate Signal-to-Noise Ratio (SNR) levels from captured audio samples.

Recent years have seen the emergence of wireless cyber-physical systems that must support real-time queries of physical environments through wireless sensor networks. This paper proposes Real-Time Query Scheduling (RTQS), a novel approach to conflict-free transmission scheduling for real-time queries in wireless sensor networks. First, we show that there is an inherent tradeoff between latency and real-time capacity in query scheduling. We then present three new real-time schedulers. The nonpreemptive query scheduler supports high real-time capacity but cannot provide low response times to high-priority queries due to priority inversions. The preemptive query scheduler eliminates priority inversions at the cost of reduced capacity. The slack stealing query scheduler combines the benefits of the preemptive and nonpreemptive schedulers to improve the capacity while meeting the end-to-end deadlines of queries. We provide schedulability analysis for each scheduler. The analysis and advantages of our approach are validated through NS2 simulations.

This paper describes the design, deployment, and empirical evaluation of WIISARD - a novel emergency response system that provides reliable communication in dynamic wireless environments without extensive communication infrastructure. The main contribution of this paper is an in-depth empirical study of network properties that emerge during a drill in which WIISARD is deployed with minimal infrastructure support. The drill involves 19 first responders and 41 victims. The properties of links established among first responders vary between phases of the drill and depend upon the responder's role in the drill. The rescue phase - in which responders are highly mobile as they triage victims - poses significant challenges to reliable communication. During this phase, the contacts between responders are short-lived; however, they are reestablished within minutes. Once a contact between responders is established, the quality of the link between those responders is usually high. The connectivity graph observed during the rescue phase is usually connected and has a small diameter although there are times when it has a large diameter or it is partitioned. While mobility increases network dynamics, we also observe that the mobility patterns characteristic of the emergency response workflow can be leveraged to disseminate data efficiently through data muling. WIISARD employs a gossip-based protocol and supports data dissemination through local communication and data muling to achieve 98% reliability during the drill exercise. These results indicate the feasibility of providing reliable communication in emergency response with minimal infrastructure in spite of network dynamics.

With the emergence of wireless sensor networks, an enabling communication technology for distributed real-time systems, we face the critical challenge of meeting the end-to-end deadlines of real-time flows. This paper presents Real-time Flow Scheduling (RFS), a novel conflict-free real-time transmission scheduling approach for periodic real-time flows in wireless sensor networks. In contrast to existing transmission scheduling algorithms that ignore interference between transmissions or prevent spatial reuse within the same channel, RFS supports spatial reuse through a novel interference-aware transmission scheduling. While recent work on conflict-free transmission scheduling focused on specialized communication patterns such as queries and converge cast, RFS is designed for peer-to-peer real-time flows with arbitrary inter-flow interference. Moreover, RFS has three salient that make it particularly suitable for real-time systems: First, RFS includes a real-time schedulability analysis that accounts for interference between real-time flows. Second, RFS improves reliability by incorporating retransmissions in a flexible scheduling scheme. Finally, RFS enhances scalability by dividing the network into neighborhoods and provides real-time performance for flows crossing multiple neighborhoods through a novel application of the Release Guard protocol. RFS was evaluated through simulations based on the traces collected from an indoor wireless sensor network test bed. Compared to a traditional TDMA protocol, RFS reduces flow latencies by up to 2.5 times, while improving the real-time capacity by as much as 3.9 times.

With the emergence of high data rate sensor network applications, there is an increasing demand for high-performance query services. To meet this challenge, we propose Dynamic Conflict-free Query Scheduling (DCQS), a novel scheduling technique for queries in wireless sensor networks. In contrast to earlier TDMA protocols designed for general-purpose workloads, DCQS is specifically designed for query services in wireless sensor networks. DCQS has several unique features. First, it optimizes the query performance through conflict-free transmission scheduling based on the temporal properties of queries in wireless sensor networks. Second, it can adapt to workload changes without explicitly reconstructing the transmission schedule. Furthermore, DCQS also provides predictable performance in terms of the maximum achievable query rate. We provide an analytical capacity bound for DCQS that enables DCQS to handle overload through rate control. NS2 simulations demonstrate that DCQS significantly outperforms a representative TDMA protocol (DRAND) and 802.11b in terms of query latency and throughput.

The robust operation of many sensor network applications depends on deploying relays to ensure wireless coverage. Radio mapping aims to predict network coverage based on a small number of link measurements. This problem is particularly challenging in complex indoor environments where walls significantly affect radio signal propagation. Nevertheless, we show that it is feasible to accurately predict coverage through a two-step process: a propagation model is used to predict signal strength at a recipient node, which is then mapped to a coverage prediction. Through an in-depth empirical study, we show that complex models do not necessarily produce accurate estimates of signal strength: there is an important tradeoff between model accuracy and the number of parameters that must be estimated from limited training data. We find that the best performance is achieved by a family of models which classify walls based on their attenuation into a small number of classes and develop an algorithm to perform this classification automatically. Based on these insights, we build a novel Radio Mapping Tool (RMT) for predicting radio converge in indoor environments. Experimental results demonstrate RMT's effectiveness in two buildings: RMT reduces the number of locations where coverage is erroneously predicted to exist by as much as 39% and 54% compared to the classic log-normal radio propagation model.

This paper presents the design, deployment, and empirical study of a wireless clinical monitoring system that collects pulse and oxygen saturation readings from patients. The primary contribution of this paper is an in-depth clinical trial that assesses the feasibility of wireless sensor networks for patient monitoring in general hospital units. We present a detailed analysis of the system reliability from a long term hospital deployment over seven months involving 41 patients in a step-down cardiology unit. The network achieved high reliability (median 99.68%, range 95.21% -- 100%). The overall reliability of the system was dominated by sensing reliability of the pulse oximeters (median 80.85%, range 0.46% -- 97.69%). Sensing failures usually occurred in short bursts, although longer periods were also present due to sensor disconnections. We show that the sensing reliability could be significantly improved through oversampling and by implementing a disconnection alarm system that incurs minimal intervention cost. A retrospective data analysis indicated that the system provided sufficient temporal resolution to support the detection of clinical deterioration in three patients who suffered from significant clinical events including transfer to Intensive Care Units. These results indicate the feasibility and promise of using wireless sensor networks for continuous patient monitoring and clinical deterioration detection in general hospital units.

Abstract Many wireless sensor networks must sustain long lifetimes on limited energy resources. Two major approaches, transmission power control and sleep scheduling, have been proposed to reduce the radio power consumption in the transmission state and the idle state, respectively. In this paper, we first review existing transmission power control and sleep scheduling approaches and then describe a Unified Radio Power Management framework for the design and implementation of holistic radio power management solutions in wireless sensor networks. It has two key components: (1) a novel optimization approach called Minimum Power Configuration that minimizes the aggregate radio power consumption of all ratio states and (2) a Unified Power Management Architecture (UPMA) that aims to support the flexible cross‐layer integration of different power management strategies. A novel feature of UPMA is that it enables cross‐layer coordination and joint optimization of different power management strategies that exist at multiple network layers. Copyright © 2008 John Wiley & Sons, Ltd.

Topology control can reduce power consumption and channel contention in wireless sensor networks by adjusting the transmission power. However, topology control for wireless sensor networks faces significant challenges, especially in indoor environments where wireless characteristics are extremely complex and dynamic. We first provide insights on the design of robust topology control schemes based on an empirical study in an office building. For example, our analysis shows that Received Signal Strength Indicator and Link Quality Indicator are not always robust indicators of Packet Reception Rate in indoor environments due to significant multi-path effects. We then present Adaptive and Robust Topology control (ART), a novel and practical topology control algorithm with several salient features: (1) ART is robust in indoor environments as it does not rely on simplifying assumptions about the wireless properties; (2) ART can adapt to variations in both link quality and contention; (3) ART introduces zero communication overhead for applications which already use acknowledgements. We have implemented ART as a topology layer in TinyOS 2.x. Our topology layer only adds 12 bytes of RAM per neighbor and 1.5 kilobytes of ROM, and requires minimal changes to upper-layer routing protocols. The advantages of ART have been demonstrated through empirical results on a 28-node indoor testbed.

Recent years have seen the emergence of wireless sensor network systems that must support high data rate and real- time queries of physical environments. This paper proposes Real-Time Query Scheduling (RTQS), a novel approach to conflict-free transmission scheduling for real-time queries in wireless sensor networks. First, we show that there is an inherent trade-off between prioritization and throughput in conflict-free query scheduling. We then present three new real-time scheduling algorithms. The non-preemptive query scheduling algorithm achieves high throughput while intro- ducing priority inversions. The preemptive query schedul- ing algorithm eliminates priority inversion at the cost of reduced throughput. The slack stealing query scheduling algorithm combines the benefits of preemptive and non- preemptive scheduling by improving the throughput while meeting query deadlines. Furthermore, we provide schedu- lability analysis for each scheduling algorithm. The anal- ysis and advantages of our scheduling algorithms are vali- dated through NS2 simulations.

The diverse requirements of wireless sensor network applications necessitate the development of multiple media access control (MAC) protocols to meet their varying throughput, latency, and network lifetime needs. Building new MAC protocols has proven to be extremely difficult, however, given the monolithic nature of existing protocol implementations as well as their dependence on a particular radio or processor platform. To address these issues, we propose the MAC Layer Architecture (MLA), a componentbased architecture for power-efficient MAC protocol development in wireless sensor networks. MLA consists of optimized, reusable components that implement a common set of features shared by existing MAC protocols, as well as abstractions that encapsulate the intricacies of the hardware platforms they run on. Through an instantiation of MLA in TinyOS 2.0.1, we have implemented five representative MAC protocols. Empirical results show that MLA results in significant code reuse among different protocols, while achieving comparative performance and memory footprints to monolithic implementations of the same protocols.

With the emergence of high data rate sensor net-work applications, there is an increasing demand for high-performance query services in such networks. To meet this challenge, we propose Dynamic Conflict-free Query Scheduling (DCQS), a novel scheduling technique for queries in wireless sensor networks. In contrast to earlier TDMA protocols designed for general-purpose networks and workloads, DCQS is specifically designed for query services supporting in-network data aggregation. DCQS has several important features. First, it optimizes the query performance and energy efficiency by exploiting the temporal properties and precedence constraints introduced by data aggregation. Second, it can efficiently adapt to dynamic workloads and rate changes without explicitly reconstructing the transmission schedule. In addition, we provide an analytical capacity bound for DCQS in terms of query completion rate. This bound enables DCQS to handle overload through rate control. NS2 simulation results demonstrate that DCQS significantly outperforms a representative TDMA protocol (DRAND) and the 802.11 protocol in terms of query latency, throughput, and energy efficiency.

Many wireless sensor network applications must resolve the inherent conflict between energy efficient communication and the need to achieve desired quality of service such as end-to-end communication delay. To address this challenge, we propose the Real-time Power-Aware Routing (RPAR) protocol, which achieves application-specified communication delays at low energy cost by dynamically adapting transmission power and routing decisions. RPAR features a power-aware forwarding policy and an efficient neighborhood manager that are optimized for resource-constrained wireless sensors. Moreover, RPAR addresses important practical issues in wireless sensor networks, including lossy links, scalability, and severe memory and bandwidth constraints. Simulations based on a realistic radio model of MICA2 motes show that RPAR significantly reduces the number of deadlines missed and energy consumption compared to existing real-time and energy-efficient routing protocols.

This paper proposes Efficient Sleep Scheduling based on Application Timing (ESSAT), a novel power management scheme that aggressively exploits the timing semantics of wireless sensor network applications. We present three ESSAT protocols each of which integrates (1) a lightweight traffic shaper that actively shapes the workload inside the network to achieve predictable timing properties over multiple hops, and (2) a local scheduling algorithm that wakes up nodes just-in-time based on the timing properties of shaped workloads. Our ESSAT protocols have several distinguishing features. First, they can save significant energy with minimal delay penalties. Second, they do not maintain TDMA schedules or communication backbones; as such, they are highly efficient and suitable for resource constrained sensor platforms. Moreover, the protocols are robust in highly dynamic network environments, i.e., they can handle variable multi-hop communication delays and aggregate workloads involving multiple queries, and can adapt to varying workload and network topologies. Our simulations showed that DTS-SS, an ESSAT protocol, achieved an average node duty cycle 38-87% lower than SPAN, and query latencies 36-98% lower than PSM and SYNC.

This paper presents MobiQuery, a spatiotemporal query service that allows mobile users to periodically gather information from their surrounding areas through a wireless sensor network. A key advantage of MobiQuery lies in its capability to meet stringent spatiotemporal performance constraints crucial to many applications. These constraints include query latency, data freshness and fidelity, and changing query areas due to user mobility. A novel just-in-time prefetching algorithm enables MobiQuery to maintain robust spatiotemporal guarantees even when nodes operate under extremely low duty cycles. Furthermore, it significantly reduces the storage cost and network contention caused by continuous queries from mobile users. We validate our approach through both theoretical analysis and simulation results under a range of realistic settings.

Many mission-critical applications require spatiotemporal data services for mobile users or objects. Examples include distributed object tracking and fire monitoring by firefighters. To support such applications, wireless sensor networks must satisfy a set of stringent spatiotemporal constraints despite having low network duty cycles and scarce resources. We have developed two new wake-up and topology maintenance protocols, directional tree maintenance (DTM) and omnidirectional tree creation (OTC), to support spatiotemporal services in mobile environments. A key feature of our protocols is that they provide robust spatiotemporal performance while maintaining low overhead and energy consumption. Our simulations showed that both DTM and OTC can successfully deliver over 85% of query results to a mobile user within desired spatiotemporal constraints, even when the sleep schedule is as long as 15 s, the user changes direction every minute, and the location error is as high as 10 m. The benefits of our protocols have been validated through theoretical analysis and empirical results on a testbed of Mica2 motes.

This work describes an approach to building Grid applications based on the premise that users who wish to access and run these applications prefer to do so without becoming experts on Grid technology. We describe an application architecture based on wrapping user applications and application workflows as Web services and Web service resources. These services are visible to the users and to resource providers through a family of Grid portal components that can be used to configure, launch, and monitor complex applications in the scientific language of the end user. The applications in this model are instantiated by an application factory service. The layered design of the architecture makes it possible for an expert to configure an application factory service with a custom user interface client that may be dynamically loaded into the portal.

No abstract available.