For many sensor network applications such as military surveillance, it is necessary to provide full sensing coverage to a security-sensitive area while at the same time minimize energy consumption and extend system life by leveraging the redundant deployment of sensor nodes. It is also preferable for the sensor network to provide differentiated surveillance service for various target areas with different degrees of security requirements. In this paper, we propose a differentiated surveillance service for sensor networks based on an adaptable energy-efficient sensing coverage protocol. In the protocol, each node is able to dynamically decide a schedule for itself to guarantee a certain degree of coverage (DOC) with average energy consumption inversely proportional to the node density. Several optimizations and extensions are proposed to provide even better performance. Simulation shows that our protocol accomplishes differentiated surveillance with low energy consumption. It outperforms other state-of-art schemes by as much as 50% reduction in energy consumption and as much as 130% increase in the half-life of the network. Keywords: Sensor Networks, Sensing Coverage, Energy Conservation, Differentiated Surveillance 1.

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

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 energyefficient 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

Abstract. This paper presents the Real-Time Event Detection Services middleware which is a component of the Data Service Middleware (DSWare). DSWare is a data-centric and group-based service for sensor networks. The real-time event service includes unreliability of individual sensor reports, correlation among different sensor observations, and inherent real-time characteristics of events. The event service supports confidence functions which are designed based on data semantics, including relative importance of sub-events and historical patterns. When the failure rate is high, the event service enables partial detection of critical events to be reported in a timely manner. It can also be applied to differentiate between the occurrences of events and false alarms. 1 Introduction Sensor networks are large-scale wireless networks that consist of numerous sen-sor and actuator nodes used to monitor and interact with physical environments [11][14]. From one perspective sensor networks are similar to distributeddatabase systems. They store environmental data on distributed nodes and respond to aperiodic and long-lived periodic queries [7][15][20]. Data interest canbe pre-registered to the sensor network so that the corresponding data is collected and transmitted only when needed. These specified interests are similarto views in traditional databases because they filter the data according to the application's data semantics and shield the overwhelming volume of raw datafrom applications [8][26]. Sensor networks also have inherent real-time properties. The environmentthat sensor networks interact with is usually dynamic and volatile. The sensor data usually has an absolute validity interval of time after which the data valuesmay not be consistent with the real environment. Transmitting and processing "stale " data wastes communication resources and can result in wrong decisionsbased on the reported out-of-date data. Besides data freshness, often the data must also be sent to the destination by a deadline. To date, not much researchhas been performed on real-time data services in sensor networks.

Data dissemination from sources to sinks is one of the main functions in sensor networks. In this paper, we propose SEAD, a Scalable Energy-e#cient Asynchronous Dissemination protocol, to minimize energy consumption in both building the dissemination tree and disseminating data to mobile sinks. SEAD considers the distance and the packet tra#c rate among nodes to create near-optimal dissemination trees. The sinks can move without reporting their location to the tree while receiving data updates successfully. Our evaluation results illustrate that SEAD consumes less energy on building and maintaining a dissemination tree to multiple mobile sinks compared to other approaches such as directed di#usion, TTDD, and mobile ad hoc multicast.

A wide variety of sensors have been incorporated into a spectrum of wireless sensor network (WSN) platforms, providing flexible sensing capability over a large number of low-power and inexpensive nodes. Traditional signal processing algorithms, however, often prove too complex for energy-and-cost-effective WSN nodes. This study explores how to design efficient sensing and classification algorithms that achieve reliable sensing performance on energy-andcost-effective hardware without special powerful nodes in a continuously changing physical environment. We present the detection and classification system in a cutting-edge surveillance sensor network, which classifies vehicles, persons, and persons carrying ferrous objects, and tracks these targets with a maximum error in velocity of 15%. Considering the demanding requirements and strict resource constraints, we design a hierarchical classification architecture that naturally distributes sensing and computation tasks at

Sensor networks, a novel paradigm in distributed wireless communication technology, have been proposed for various applications including military surveillance and environmental monitoring. These systems deploy heterogeneous collections of sensors capable of observing and reporting on various dynamic properties of their surroundings in a time sensitive manner. Such systems suffer bandwidth, energy, and throughput constraints that limit the quantity of information transferred from end-to-end. These factors coupled with unpredictable traffic patterns and dynamic network topologies make the task of designing optimal protocols for such networks difficult. Mechanisms to perform data-centric aggregation utilizing application-specific knowledge provide a means to augmenting throughput, but have limitations due to their lack of adaptation and reliance on application-specific decisions. We, therefore, propose a novel aggregation scheme that adaptively performs application-independent data aggregation in a time sensitive manner. Our work isolates aggregation decisions into a module that resides between the network and the data-link layer and does not require any modifications to the currently existing MAC and network layer protocols. We take advantage of queuing delay and the broadcast nature of wireless communication to concatenate network units into an aggregate using a novel adaptive feedback scheme to schedule the delivery of this aggregate to the MAC layer for transmission. In our evaluation we show that endto-end transmission delay is reduced by as much as 80 % under heavy traffic loads. Additionally, we show as much as a 50 % reduction in transmission energy consumption with an overall reduction in header overhead. Theoretical analysis, simulation, and a test-bed implementation on Berkeley’s MICA motes are provided to validate our claims.

Sensor networks, a novel paradigm in distributed wireless communication technology, have been proposed for various applications including military surveillance and environmental monitoring. These systems deploy heterogeneous collections of sensors capable of observing and reporting on various dynamic properties of their surroundings in a time sensitive manner. Such systems suffer bandwidth, energy, and throughput constraints that limit the quantity of information transferred from end-to-end. These factors coupled with unpredictable traffic patterns and dynamic network topologies make the task of designing optimal protocols for such networks difficult. Mechanisms to perform data centric aggregation utilizing application specific knowledge provide a means to augmenting throughput, but have limitations due to their lack of adaptation and reliance on application specific decisions. We, therefore, propose a novel aggregation scheme that adaptively performs application independent data aggregation in a time sensitive manner. Our work isolates aggregation decisions into a module that resides between the network and the data link layer and does not require any modifications to the currently existing MAC and network layer protocols. We take advantage of queuing delay and the broadcast nature of wireless communication to concatenate network units into an aggregate using a novel adaptive feedback scheme to schedule the delivery of this aggregate to the MAC layer for transmission. In our evaluation we show that end-to-end transmission delay is reduced by as much as 80 % under heavy traffic loads. Additionally, we show as much as a 50 % reduction in transmission energy consumption with an overall

Data storage has become an important issue in sensor networks as a large amount of collected data need to be archived for future information retrieval. This paper introduces storage nodes to store the data collected from the sensors in their proximities. The storage nodes alleviate the heavy load of transmitting all the data to a central place for archiving and reduce the communication cost induced by the network query. This paper considers the storage node placement problem aiming to minimize the total energy cost for gathering data to the storage nodes and replying queries. We examine deterministic placement of storage nodes and present optimal algorithms based on dynamic programming. Further, we give stochastic analysis for random deployment and conduct simulation evaluation for both deterministic and random placements of storage nodes.

Abstract — Data caching can significantly improve the efficiency of information access in a wireless ad hoc network by reducing the access latency and bandwidth usage. However, designing efficient distributed caching algorithms is non-trivial when network nodes have limited memory. In this article, we consider the cache placement problem of minimizing total data access cost in ad hoc networks with multiple data items and nodes with limited memory capacity. The above optimization problem is known to be NPhard. Defining benefit as the reduction in total access cost, we present a polynomial-time centralized approximation algorithm that provably delivers a solution whose benefit is at least one-fourth (one-half for uniform-size data items) of the optimal benefit. The approximation algorithm is amenable to localized distributed implementation, which is shown via simulations to perform close to the approximation algorithm. Our distributed algorithm naturally extends to networks with mobile nodes. We simulate our distributed algorithm using a network simulator (ns2), and demonstrate that it significantly outperforms another existing caching technique (by Yin and Cao [31]) in all important performance metrics. The performance differential is particularly large in more challenging scenarios, such as higher access frequency and smaller memory. Index Terms- caching placement policy, ad hoc networks, algorithm/protocol design and analysis, simulations. I.