Smart wireless sensor devices are becoming increasingly ubiquitous and are expected to be embedded in everyday objects in the near future. When these devices are deployed in overlapping or adjacent geographic areas, the unlicensed 2.4 GHz ISM band will be crowded. To deal with the crowded spectrum, we propose SAS, a self-adaptive spectrum management middleware for wireless sensor networks. SAS enables single-frequency MAC protocols with multi-frequency capability, so that an existing MAC protocol, like B-MAC, can automatically adapt to the least congested physical channel at runtime. We implemented SAS in TinyOS 2.1 with nesC and evaluated its performance with TelosB motes. Our performance results demonstrate that SAS improves the performance of existing single-frequency MAC protocols, like B-MAC. The use of SAS results in higher packet reception ratio and system throughput, and a lower packet delay and energy consumption.

Opportunistic forwarding protocols take advantage of contact opportunities to route data in intermittently connected environments. In these environments, a fully connected path between the source and destination may not always exist and the contact schedules of all the nodes are not known in advance. Hence, one of the key challenges for a node is to make effective forwarding decisions using only a limited knowledge of the contact behavior of the nodes in the network. Based on an analysis of human mobility traces that we collected from our office environment, we introduce a new link metric, conditional residual time, that accurately estimates the time remaining for a pair of nodes to meet using only the local knowledge of their past contacts. We then propose a distributed protocol called CREST, that uses the conditional residual time to opportunistically forward messages between pairs of nodes. Experimental results show that CREST has a lower end-to-end delay compared to protocols that depend on future contact schedules and global knowledge of the contact behavior across the network. Furthermore, by disseminating only a few additional copies of the message, the delivery ratio of CREST improves significantly and is comparable to that of the flooding protocol.

We address the issue of improving information availability in a class of delay-tolerant sensor network applications, where the sensor nodes are deployed in disconnected environments. In such environments, since there is no continuous access to a remote base station, there is a need to leverage the collaborative resources of the sensor network to support in-network storage. The stored data can then be retrieved opportunistically by mobile collectors in the proximity. We have developed a data-centric, in-network storage architecture that partitions the network into storage zones. In such a scheme, some of the storage nodes may be unavailable at the time of storage and retrieval, because they are either sleeping to conserve energy or because they have failed.We present two schemes based on random linear network coding for improving information availability within such a storage architecture. In the centralized scheme, the encoding is performed by the managers in each storage zone, whereas in the decentralized scheme, the encoding is done locally by the zone members. We have implemented the network coding schemes in TinyOS and we present results that show the impact of the zone size, duty cycle, and the degree of encoding on the decoding probability, based on our experiments on a testbed of Micaz motes.

In order to seamlessly interact with everyday objects embedded with wireless sensors, we need diverse messaging abstractions that support convenient application-level interactions with sensor nodes. We have developed TinySIP with the goal of providing a unifying messaging protocol that not only supports distributed interactions among sensor nodes but also enables users on a traditional network to interact with the sensor nodes through familiar devices, such as mobile phones and PDAs. TinySIP leverages the communication abstractions provided by the Session Initiation Protocol (SIP), but uses a more compact and energy- efficient message format for communication with resource- constrained nodes. TinySIP supports interaction with sensor nodes using publish-sub scribe, instant messaging, and session-based semantics. We have implemented the TinySIP messaging library on Zigbee motes running TinyOS. In this paper, we describe a prototype application that we have developed that illustrates the use of TinySIP for interacting with sensor nodes, in order to automate processes in a hospital environment. Despite the diversity of sensor network applications and the heterogeneity of the sensor-generated data, our example shows that typical sensor-based interactions can be supported through a set of common communication abstractions. Hence, using a standard application- level messaging protocol, like TinySIP, that supports a common set of communication abstractions which can be used in a variety of sensor-based interactions will benefit sensor network application developers and users.

In this paper, we propose TinySIP as a communication abstraction for accessing sensor-based services. TinySIP is based on the session initiation protocol (SIP), which is a standard application-level signaling mechanism. Users on traditional networks remotely interact with a sensor network service by sending SIP messages. A gateway maps the SIP abstractions to the corresponding TinySIP abstractions and propagates the messages to the sensor nodes. We are currently planning on deploying the SIP-based solution that we propose on a research testbed to enable users on a wireless mesh to access the in-network storage and event correlation services offered by a sensor network consisting of 20 MicaZ sensor nodes

Smart environments that can adapt based on the current context are extremely useful for automating even simple, mundane tasks. The key components of such an environment are sources that can extract the raw context, a synthesizer that can draw inferences by aggregating the context from different sources, and a set of policies that drive the adaptation. In this paper, we describe a context-based adaptation system to alleviate the distraction caused by one of the most ubiquitous devices of modern day-the mobile phone. Our system makes use of sources, such as RFID devices, that employ near-field communication technology to extract raw context. We describe how we adapt the roles of these devices to extract both environmental and personal context. To reduce the distraction caused by mobile phones, we have developed a policy-based mechanism to enable context-based adaptation of these devices. We have implemented a prototype of our system and conducted some usage studies. We describe the details of our implementation and present the lessons learned from the usability experiments

This article describes one of the major efforts in the sensor network community to build an integrated sensor network system for surveillance missions. The focus of this effort is to acquire and verify information about enemy capabilities and positions of hostile targets. Such missions often involve a high element of risk for human personnel and require a high degree of stealthiness. Hence, the ability to deploy unmanned surveillance missions, by using wireless sensor networks, is of great practical importance for the military. Because of the energy constraints of sensor devices, such systems necessitate an energy-aware design to ensure the longevity of surveillance missions. Solutions proposed recently for this type of system show promising results through simulations. However, the simplified assumptions they make about the system in the simulator often do not hold well in practice, and energy consumption is narrowly accounted for within a single protocol. In this article, we describe the design and implementation of a complete running system, called VigilNet, for energy-efficient surveillance. The VigilNet allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energy-efficient and stealthy manner. We evaluate VigilNet middleware components and integrated system extensively on a network of 70 MICA2 motes. Our results show that our surveillance strategy is adaptable and achieves a significant extension of network lifetime. Finally, we share lessons learned in building such an integrated sensor system.

In this article, we investigate the impact of radio irregularity on wireless sensor networks. Radio irregularity is a common phenomenon that arises from multiple factors, such as variance in RF sending power and different path losses, depending on the direction of propagation. From our experiments, we discover that the variance in received signal strength is largely random; however, it exhibits a continuous change with incremental changes in direction. With empirical data obtained from the MICA2 and MICAZ platforms, we establish a radio model for simulation, called the Radio Irregularity Model (RIM). This model is the first to bridge the discrepancy between the spherical radio models used by simulators and the physical reality of radio signals. With this model, we investigate the impact of radio irregularity on several upper layer protocols, including MAC, routing, localization and topology control. Our results show that radio irregularity has a relatively larger impact on the routing layer than the MAC layer. It also shows that radio irregularity leads to larger localization errors and makes it harder to maintain communication connectivity in topology control. To deal with these issues, we present eight solutions to deal with radio irregularity. We evaluate three of them in detail. The results obtained from both the simulations and a running testbed demonstrate that our solutions greatly improve system performance in the presence of radio irregularity.

Distributed sensor networks are quickly gaining recognition as viable embedded computing platforms. Current techniques for programming sensor networks are cumbersome, inflexible, and low-level. We introduce EnviroTrack, an object-based distributed middleware system that raises the level of programming abstraction by providing a convenient and powerful interface to the application developer geared towards tracking the physical environment. EnviroTrack is novel in its seamless integration of objects that live in physical time and space into the computational environment of the application. Performance results demonstrate the ability of the middleware to track realistic targets.

The focus of surveillance missions is to acquire and verify information about enemy capabilities and positions of hostile targets. Such missions often involve a high element of risk for human personnel and require a high degree of stealthiness. Hence, the ability to deploy unmanned surveillance missions, by using wireless sensor networks, is of great practical importance for the military. Because of the energy constraints of sensor devices, such systems necessitate an energy-aware design to ensure the longevity of surveillance missions. Solutions proposed recently for this type of system show promising results through simulations. However, the simplified assumptions they make about the system in the simulator often do not hold well in practice and energy consumption is narrowly accounted for within a single protocol. In this paper, we describe the design and implementation of a running system for energy-efficient surveillance. The system allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energy-efficient and stealthy manner. We can trade off energy-awareness and surveillance performance by adaptively adjusting the sensitivity of the system. We evaluate the performance on a network of 70 MICA2 motes equipped with dual-axis magnetometers. Our results show that our surveillance strategy is adaptable and achieves a significant extension of network lifetime. Finally, we share lessons learned in building such a complete running system.

In this paper, we investigate the impact of radio irregularity on the communication performance in wireless sensor networks. Radio irregularity is a common phenomenon which arises from multiple factors, such as variance in RF sending power and different path losses depending on the direction of propagation. From our experiments, we discover that the variance in received signal strength is largely random; however, it exhibits a continuous change with incremental changes in direction. With empirical data obtained from the MICA2 platform, we establish a radio model for simulation, called the Radio Irregularity Model (RIM). This model is the first to bridge the discrepancy between spherical radio models used by simulators and the physical reality of radio signals. With this model, we are able to analyze the impact of radio irregularity on some of the well-known MAC and routing protocols. Our results show that radio irregularity has a significant impact on routing protocols, but a relatively small impact on MAC protocols. Finally, we propose six solutions to deal with radio irregularity. We evaluate two of them in detail. The results obtained from both the simulation and a running testbed demonstrate that our solutions greatly improve communication performance in the presence of radio irregularity.

A dependable middleware should be able to adaptively share the distributed resources it manages in order to meet diverse application requirements, even when the quality of service (QoS) is degraded due to uncertain variations in load and unanticipated failures. We have addressed this issue in the context of a dependable middleware that adaptively manages replicated servers to deliver a timely and consistent response to time-sensitive client applications. These applications have specific temporal and consistency requirements, and can tolerate a certain degree of relaxed consistency in exchange for better response time. We propose a flexible QoS model that allows clients to specify their timeliness and consistency constraints. We also propose an adaptive framework that dynamically selects replicas to service a client's request based on the prediction made by probabilistic models. These models use the feedback from online performance monitoring of the replicas to provide probabilistic guarantees for meeting a client's QoS specification. The experimental results we have obtained demonstrate the role of feedback and the efficacy of simple analytical models for adaptively sharing the available replicas among the users under different workload scenarios.

One well-known challenge in using replication to service multiple clients concurrently is that of delivering a timely and consistent response to the clients. In this paper, we address this problem in the context of client applications that have specific temporal and consistency requirements. These applications can tolerate a certain degree of relaxed consistency, in exchange for better response time. We propose a flexible QoS model that allows these clients to specify their temporal and consistency constraints. In order to select replicas to serve these clients, we need to control of the inconsistency of the replicas, so that we have a large enough pool of replicas with the appropriate state to meet a client's timeliness, consistency, and dependability requirements. We describe an adaptive framework that uses lazy update propagation to control the replica inconsistency and employs a probabilistic approach to select replicas dynamically to service a client, based on its QoS specification. The probabilistic approach predicts the ability of a replica to meet a client's QoS specification by using the performance history collected by monitoring the replicas at runtime. We conclude with experimental results based on our implementation.

Server replication is commonly used to improve the fault tolerance and response time of distributed services. An important problem when executing time-critical applications in a replicated environment is that of preventing timing failures by dynamically selecting the replicas that can satisfy a client's timing requirement, even when the quality of service is degraded due to replica failures and excess load on the server. We describe the approach we have used to solve this problem in AQuA, a CORBA-based middleware that transparently replicates objects across a local area network. The approach we use estimates a replica's response time distribution based on performance measurements regularly broadcast by the replica. An online model uses these measurements to predict the probability with which a replica can prevent a timing failure for a client. A selection algorithm then uses this prediction to choose a subset of replicas that can together meet the client's timing constraints with at least the probability requested by the client. We conclude with experimental results based on our implementation.