Sensor Networks: Evolution, Opportunities,
`and Challenges
`
`CHEE-YEE CHONG, MEMBER, IEEE AND SRIKANTA P. KUMAR, SENIOR MEMBER, IEEE
`
`Invited Paper
`
`Wireless microsensor networks have been identified as one of
`the most important technologies for the 21st century. This paper
`traces the history of research in sensor networks over the past
`three decades, including two important programs of the Defense
`Advanced Research Projects Agency (DARPA) spanning this
`period: the Distributed Sensor Networks (DSN) and the Sensor
`Information Technology (SensIT) programs. Technology trends that
`impact the development of sensor networks are reviewed, and new
`applications such as infrastructure security, habitat monitoring,
`and traffic control are presented. Technical challenges in sensor
`network development
`include network discovery, control and
`routing, collaborative signal and information processing, tasking
`and querying, and security. The paper concludes by presenting
`some recent research results in sensor network algorithms, in-
`cluding localized algorithms and directed diffusion, distributed
`tracking in wireless ad hoc networks, and distributed classification
`using local agents.
`
`Keywords—Collaborative signal processing, microsensors, net-
`work routing and control, querying and tasking, sensor networks,
`tracking and classification, wireless networks.
`
`I. INTRODUCTION
`
`Networked microsensors technology is a key technology
`for the future. In September 1999 [1], Business Week her-
`alded it as one of the 21 most important technologies for the
`21st century. Cheap, smart devices with multiple onboard
`sensors, networked through wireless links and the Internet
`and deployed in large numbers, provide unprecedented op-
`portunities for instrumenting and controlling homes, cities,
`and the environment. In addition, networked microsensors
`provide the technology for a broad spectrum of systems in
`the defense arena, generating new capabilities for reconnais-
`sance and surveillance as well as other tactical applications.
`
`Smart disposable microsensors can be deployed on the
`ground, in the air, under water, on bodies, in vehicles,
`and inside buildings. A system of networked sensors can
`detect and track threats (e.g., winged and wheeled vehicles,
`personnel, chemical and biological agents) and be used for
`weapon targeting and area denial. Each sensor node will
`have embedded processing capability, and will potentially
`have multiple onboard sensors, operating in the acoustic,
`seismic, infrared (IR), and magnetic modes, as well as
`imagers and microradars. Also onboard will be storage,
`wireless links to neighboring nodes, and location and po-
`sitioning knowledge through the global positioning system
`(GPS) or local positioning algorithms.
`Networked microsensors belong to the general family of
`sensor networks that use multiple distributed sensors to col-
`lect information on entities of interest. Table 1 summarizes
`the range of possible attributes in general sensor networks.
`Current and potential applications of sensor networks in-
`clude: military sensing, physical security, air traffic control,
`traffic surveillance, video surveillance, industrial and man-
`ufacturing automation, distributed robotics, environment
`monitoring, and building and structures monitoring. The
`sensors in these applications may be small or large, and the
`networks may be wired or wireless. However, ubiquitous
`wireless networks of microsensors probably offer the most
`potential in changing the world of sensing [2].
`While sensor networks for various applications may be
`quite different, they share common technical issues. This
`paper will present a history of research in sensor networks
`(Section II), technology trends (Section III), new applica-
`tions (Section IV), research issues and hard problems (Sec-
`tion V), and some examples of research results (Section VI).
`
`Manuscript received January 7, 2003; revised March 17, 2003.
`C.-Y. Chong was with Booz Allen Hamilton, San Francisco, CA 94111
`USA. He is now with Alphatech, Inc. San Diego, CA 92121 USA (e-mail:
`cchong@alphatech.com, cychong@ieee.org).
`S. Kumar is with the Defense Advanced Research Projects Agency, Ar-
`lington, VA 22203 USA (e-mail: skumar@ darpa.mil).
`Digital Object Identifier 10.1109/JPROC.2003.814918
`
`II. HISTORY OF RESEARCH IN SENSOR NETWORKS
`
`The development of sensor networks requires technolo-
`gies from three different research areas: sensing, commu-
`nication, and computing (including hardware, software, and
`
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`Table 1
`Attributes of Sensor Networks
`
`algorithms). Thus, combined and separate advancements in
`each of these areas have driven research in sensor networks.
`Examples of early sensor networks include the radar net-
`works used in air traffic control. The national power grid,
`with its many sensors, can be viewed as one large sensor net-
`work. These systems were developed with specialized com-
`puters and communication capabilities, and before the term
`“sensor networks” came into vogue.
`
`A. Early Research on Military Sensor Networks
`As with many technologies, defense applications have
`been a driver for research and development in sensor net-
`works. During the Cold War, the Sound Surveillance System
`(SOSUS), a system of acoustic sensors (hydrophones) on the
`ocean bottom, was deployed at strategic locations to detect
`and track quiet Soviet submarines. Over the years, other
`more sophisticated acoustic networks have been developed
`for submarine surveillance. SOSUS is now used by the
`National Oceanographic and Atmospheric Administration
`(NOAA) for monitoring events in the ocean, e.g., seismic
`and animal activity [3]. Also during the Cold War, networks
`of air defense radars were developed and deployed to defend
`the continental United States and Canada. This air defense
`system has evolved over the years to include aerostats
`as sensors and Airborne Warning and Control System
`(AWACS) planes, and is also used for drug interdiction.
`These sensor networks generally adopt a hierarchical
`processing structure where processing occurs at consecutive
`levels until the information about events of interest reaches
`the user. In many cases, human operators play a key role in
`the system. Even though research was focused on satisfying
`mission needs, e.g., acoustic signal processing and interpre-
`tation, tracking, and fusion, it provided some key processing
`technologies for modern sensor networks.
`
`B. Distributed Sensor Networks Program at the Defense
`Advanced Research Projects Agency
`Modern research on sensor networks started around 1980
`with the Distributed Sensor Networks (DSN) program at the
`
`Defense Advanced Research Projects Agency (DARPA).
`By this time, the Arpanet (predecessor of the Internet) had
`been operational for a number of years, with about 200 hosts
`at universities and research institutes. R. Kahn, who was
`coinventor of the TCP/IP protocols and played a key role
`in developing the Internet, was director of the Information
`Processing Techniques Office (IPTO) at DARPA. He wanted
`to know whether the Arpanet approach for communica-
`tion could be extended to sensor networks. The network
`was assumed to have many spatially distributed low-cost
`sensing nodes that collaborate with each other but operate
`autonomously, with information being routed to whichever
`node can best use the information.
`It was an ambitious program given the state of the art.
`This was the time before personal computers and work-
`stations; processing was done mostly on minicomputers
`such as PDP-11 and VAX machines running Unix and VMS.
`Modems were operating at 300 to 9600 Bd, and Ethernet
`was just becoming popular.
`Technology components for a DSN were identified in a
`Distributed Sensor Nets workshop in 1978 [4]. These in-
`cluded sensors (acoustic), communication (high-level proto-
`cols that link processes working on a common application
`in a resource-sharing network [5]), processing techniques
`and algorithms (including self-location algorithms for sen-
`sors), and distributed software (dynamically modifiable dis-
`tributed systems and language design). Since DARPA was
`sponsoring much artificial intelligence (AI) research at the
`time, the workshop also included talks on the use of AI for
`understanding signals and assessing situations [6], as well
`as various distributed problem-solving techniques [7]–[9].
`Since very few technology components were available off
`the shelf, the resulting DSN program had to address dis-
`tributed computing support, signal processing, tracking, and
`test beds. Distributed acoustic tracking was chosen as the
`target problem for demonstration.
`Researchers at Carnegie Mellon University (CMU),
`Pittsburgh, PA, focused on providing a network operating
`system that allows flexible, transparent access to distributed
`resources needed for a fault-tolerant DSN. They developed
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`Fig. 1. Components in the DSN test bed around 1985.
`
`a communication-oriented operating system called Accent
`[10], whose primitives support
`transparent networking,
`system reconfiguration, and rebinding. Accent evolved into
`the Mach operating system [11], which found considerable
`commercial acceptance. Other efforts at CMU included
`protocols
`for network interprocess communication to
`support dynamic rebinding of active communicating com-
`putations, an interface specification language for building
`distributed system software, and a system for dynamic load
`balancing and fault reconfiguration of DSN software. All
`this was demonstrated in an indoor test bed with signal
`sources, acoustic sensors, and VAX computers connected
`by Ethernet.
`Researchers at the Massachusetts Institute of Technology
`(MIT), Cambridge, focused on knowledge-based signal
`processing techniques [12] for tracking helicopters using a
`distributed array of acoustic microphones by means of signal
`abstractions and matching techniques. Signal abstractions
`view signals as consisting of multiple levels, with higher
`levels of abstraction (e.g., peaks) obtained by suppressing
`detailed information in lower levels (e.g., spectrum). They
`provide a conceptual framework for thinking about signal
`processing systems that resemble what people use when
`interactively processing and interpreting real-world signals.
`By incorporating human heuristics,
`this approach was
`designed for high signal-to-noise ratio situations where
`models are lacking. In addition, MIT also developed the
`Signal Processing Language and Interactive Computing
`Environment (SPLICE) for DSN data analysis and algorithm
`development, and Pitch Director’s Assistant for interactively
`estimating fundamental frequency using domain knowledge.
`Moving up the processing chain, tracking multiple targets
`in a distributed environment is significantly more difficult
`than centralized tracking. The association of measurements
`to tracks and estimation of target states (position and ve-
`locity) given associations have to be distributed over the
`sensor nodes. In the 1980s, Advanced Decision Systems
`(ADS), Mountain View, CA, developed a multiple-hy-
`pothesis tracking algorithm to deal with difficult situations
`involving high target density, missing detections, and false
`alarms, and decomposed the algorithm for distributed
`implementation [13], [14]. Multiple-hypothesis tracking is
`now a standard approach for difficult tracking problems.
`For demonstration, MIT Lincoln Laboratory developed
`the real-time test bed for acoustic tracking of low-flying
`
`aircraft [15]. The sensors were acoustic arrays (nine micro-
`phones arranged in three concentric triangles with the largest
`being 6 m across). A PDP11/34 computer and an array pro-
`cessor processed the acoustic signals. The nodal computer
`(for target tracking) consists of three MC68000 processors
`with 256-kB memory and 512-kB shared memory, and a
`custom operating system. Communication was by Ethernet
`and microwave radio. Fig. 1 (extracted from [16]) shows the
`acoustic array (nine white microphones), the mobile vehicle
`node with an acoustically quiet generator in the back, and the
`equipment rack with the acoustic/tracking node and gateway
`node in the vehicle. Note the size of the system and that
`practically all components in the network were custom built.
`That was the state of the art in the early 1980s. The DSN test
`bed was demonstrated with low-flying aircraft, which was
`successfully tracked with acoustic sensors as well as TV
`cameras. The tracking algorithm was fairly sophisticated,
`since the acoustic propagation delay is significant relative to
`the speed of the aircraft.
`Another test bed in the DSN program was the distributed
`vehicle monitoring test bed at the University of Massachu-
`setts, Amherst. This was a research tool for empirically
`investigating distributed problem solving in networks. The
`distributed knowledge-based problem solving approach used
`a functionally accurate, cooperative architecture consisting
`of a network of Hearsay-II nodes (blackboard architecture
`with knowledge sources). Different
`local node control
`approaches were explored [17].
`
`C. Military Sensor Networks in the 1980s and 1990s
`
`Even though early researchers on sensor networks had
`in mind large numbers of small sensors, the technology
`for small sensors was not quite ready. However, planners
`of military systems quickly recognized the benefits of
`sensor networks, which become a crucial component of
`network-centric warfare [18]. In platform-centric warfare,
`platforms “own” specific weapons, which in turn own
`sensors in a fairly rigid architecture. In other words, sensors
`and weapons are mounted with and controlled by separate
`platforms that operate independently. In network-centric
`warfare, sensors do not necessarily belong to weapons or
`platforms. Instead, they collaborate with each other over a
`communication network, and information is sent to the ap-
`propriate “shooters.” Sensor networks can improve detection
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`and tracking performance through multiple observations,
`geometric and phenomenological diversity, extended detec-
`tion range, and faster response time. Also, the development
`cost is lower by exploiting commercial network technology
`and common network interfaces.
`An example of network-centric warfare is the Cooperative
`Engagement Capability (CEC) [19] developed by the U.S.
`Navy. This system consists of multiple radars collecting data
`on air targets. Measurements are associated by a processing
`node “with reporting responsibility” and shared with other
`nodes that process all measurements of interest. Since all
`nodes have access to essentially the same information, a
`“common operating picture” essential for consistent military
`operations is obtained. Other military sensor networks in-
`clude acoustic sensor arrays for antisubmarine warfare such
`as the Fixed Distributed System (FDS) and the Advanced
`Deployable System (ADS), and unattended ground sensors
`(UGS) [20] such as the Remote Battlefield Sensor System
`(REMBASS) and the Tactical Remote Sensor System
`(TRSS).
`
`D. Sensor Network Research in the 21st Century
`Recent advances in computing and communication have
`caused a significant shift in sensor network research and
`brought it closer to achieving the original vision. Small and
`inexpensive sensors based upon microelectromechanical
`system (MEMS) [21] technology, wireless networking, and
`inexpensive low-power processors allow the deployment of
`wireless ad hoc networks for various applications. Again,
`DARPA started a research program on sensor networks to
`leverage the latest technological advances.
`Information
`The recently concluded DARPA Sensor
`Technology (SensIT) program [22] pursued two key re-
`search and development thrusts. First, it developed new
`networking techniques. In the battlefield context,
`these
`sensor devices or nodes should be ready for rapid de-
`ployment, in an ad hoc fashion, and in highly dynamic
`environments. Today’s networking techniques, developed
`for voice and data and relying on a fixed infrastructure, will
`not suffice for battlefield use. Thus, the program developed
`new networking techniques suitable for highly dynamic
`ad hoc environments. The second thrust was networked
`information processing, i.e., how to extract useful, reliable,
`and timely information from the deployed sensor network.
`This implies leveraging the distributed computing environ-
`ment created by these sensors for signal and information
`processing in the network, and for dynamic and interactive
`querying and tasking the sensor network.
`SensIT generated new capabilities relative to today’s
`sensors. Current systems such as the Tactical Automated
`Security System (TASS) [23] for perimeter security are
`dedicated rather than programmable. They use technologies
`based on transmit-only nodes and a long-range detection
`paradigm. SensIT networks have new capabilities. The
`networks are interactive and programmable with dynamic
`tasking and querying. A multitasking feature in the system
`allows multiple simultaneous users. Finally, since detection
`ranges are much shorter in a sensor system, the software and
`
`algorithms can exploit the proximity of devices to threats to
`drastically improve the accuracy of detection and tracking.
`The software and the overall system design supports low
`latency, energy-efficient operation, built-in autonomy and
`survivability, and low probability of detection of operation.
`As a result, a network of SensIT nodes can support detection,
`identification, and tracking of threats, as well as targeting
`and communication, both within the network and to outside
`the network, such as an overhead asset.
`
`III. TECHNOLOGY TRENDS
`
`Current sensor networks can exploit technologies not
`available 20 years ago and perform functions that were
`not even dreamed of at that time. Sensors, processors, and
`communication devices are all getting much smaller and
`cheaper. Commercial companies such as Ember, Crossbow,
`and Sensoria are now building and deploying small sensor
`nodes and systems. These companies provide a vision of
`how our daily lives will be enhanced through a network
`of small, embedded sensor nodes. In addition to products
`from these companies, commercial off-the-shelf personal
`digital assistants (PDAs) using Palm or Pocket PC operating
`systems contain significant computing power in a small
`package. These can easily be “ruggedized” to become
`processing nodes in a sensor network. Some of these devices
`even have built-in sensing capabilities, such as cameras.
`These powerful processors can be hooked to MEMS devices
`and machines along with extensive databases and communi-
`cation platforms to bring about a new era of technologically
`sophisticated sensor nets.
`Wireless networks based upon IEEE 802.11 standards
`can now provide bandwidth approaching those of wired
`networks. At the same time, the IEEE has noticed the low
`expense and high capabilities that sensor networks offer.
`The organization has defined the IEEE 802.15 standard
`for personal area networks (PANs), with “personal net-
`works” defined to have a radius of 5 to 10 m. Networks of
`short-range sensors are the ideal technology to be employed
`in PANs. The IEEE encouragement of the development of
`technologies and algorithms for such short ranges ensures
`continued development of low-cost sensor nets [24]. Further-
`more, increases in chip capacity and processor production
`capabilities have reduced the energy per bit requirement for
`both computing and communication. Sensing, computing,
`and communications can now be performed on a single chip,
`further reducing the cost and allowing deployment in ever
`larger numbers.
`Looking into the future, we predict that advances in
`MEMS technology will produce sensors that are even more
`capable and versatile. For example, Dust Inc., Berkeley,
`CA, a company that sprung from the late 1990s Smart
`Dust research project [25] at the University of California,
`Berkeley, is building MEMS sensors that can sense and
`communicate and yet are tiny enough to fit inside a cubic
`millimeter. A Smart Dust optical mote uses MEMS to aim
`submillimeter-sized mirrors for communications. Smart
`Dust sensors can be deployed using a 3
`10 mm “wavelet”
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`Table 2
`Three Generations of Sensor Nodes
`
`Fig. 2. Three generations of sensor nodes.
`
`shaped like a maple tree seed and dropped to float to the
`ground. A wireless network of these ubiquitous, low-cost,
`disposable microsensors can provide close-in sensing
`capabilities in many novel applications (as discussed in
`Section IV).
`Table 2 compares three generations of sensor nodes; Fig. 2
`shows their sizes.
`
`IV. NEW APPLICATIONS
`
`Research on sensor networks was originally motivated by
`military applications. Examples of military sensor networks
`range from large-scale acoustic surveillance systems for
`ocean surveillance to small networks of unattended ground
`sensors for ground target detection. However, the avail-
`ability of low-cost sensors and communication networks has
`resulted in the development of many other potential applica-
`tions, from infrastructure security to industrial sensing. The
`following are a few examples.
`
`A. Infrastructure Security
`Sensor networks can be used for infrastructure security
`and counterterrorism applications. Critical buildings and
`facilities such as power plants and communication centers
`have to be protected from potential terrorists. Networks of
`video, acoustic, and other sensors can be deployed around
`these facilities. These sensors provide early detection of
`possible threats. Improved coverage and detection and a
`reduced false alarm rate can be achieved by fusing the data
`from multiple sensors. Even though fixed sensors connected
`by a fixed communication network protect most facilities,
`wireless ad hoc networks can provide more flexibility and
`
`additional coverage when needed. Sensor networks can also
`be used to detect biological, chemical, and nuclear attacks.
`Examples of such networks can be found in [26], which also
`describes other uses of sensor networks.
`
`B. Environment and Habitat Monitoring
`Environment and habitat monitoring [27] is a natural can-
`didate for applying sensor networks, since the variables to be
`monitored, e.g., temperature, are usually distributed over a
`large region. The recently started Center for Embedded Net-
`work Sensing (CENS) [28], Los Angeles, CA, has a focus on
`environmental and habitat monitoring. Environmental sen-
`sors are used to study vegetation response to climatic trends
`and diseases, and acoustic and imaging sensors can identify,
`track, and measure the population of birds and other species.
`On a very large scale, the System for the Vigilance of the
`Amazon (SIVAM) [29] provides environmental monitoring,
`drug trafficking monitoring, and air traffic control for the
`Amazon Basin. Sponsored by the government of Brazil, this
`large sensor network consists of different types of intercon-
`nected sensors including radar, imagery, and environmental
`sensors. The imagery sensors are space based, radars are lo-
`cated on aircraft, and environmental sensors are mostly on
`the ground. The communication network connecting the sen-
`sors operates at different speeds. For example, high-speed
`networks connect sensors on satellites and aircraft, while
`low-speed networks connect the ground-based sensors.
`
`C. Industrial Sensing
`Commercial industry has long been interested in sensing
`as a means of lowering cost and improving machine (and
`perhaps user) performance and maintainability. Monitoring
`machine “health” through determination of vibration or
`wear and lubrication levels, and the insertion of sensors
`into regions inaccessible by humans, are just two examples
`of industrial applications of sensors. Several years ago,
`the IEEE and the National Institute for Standards and
`Technology (NIST) launched the P1451 Smart Transducer
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`Interface Standard [30] to enable full plug-and-play of
`sensors and networks in industrial environments. Factories
`have continued to automate production and assembly lines
`with remote sensing nets,
`implementing sophisticated
`on-line quality control tests enabled by the sensors. Remote,
`wireless sensors in particular can enable a factory to be in-
`strumented after the fact to ensure and maintain compliance
`with federal safety and guidelines while keeping installation
`costs low.
`Spectral sensors are one example of sensing in an in-
`dustrial environment. From simple optical devices such as
`optrodes and pH probes to true spectral devices that can
`function as miniature spectrometers, optical sensors can
`replace existing instruments and perform material property
`and composition measurements. Optical sensing is also
`facilitated by miniaturization, as low-cost charge-coupled
`device (CCD) array devices and microengineering enable
`smaller, smarter sensors. The goal of this and other industrial
`sensing is to enable multipoint or matrix sensing: inputs
`from hundreds or thousands of sensors feed into databases
`that can be queried in any number of ways to show real-time
`information on a large or small scale.
`
`D. Traffic Control
`
`Sensor networks have been used for vehicle traffic mon-
`itoring and control for quite a while. Most traffic intersec-
`tions have either overhead or buried sensors to detect vehicles
`and control traffic lights. Furthermore, video cameras are fre-
`quently used to monitor road segments with heavy traffic,
`with the video sent to human operators at central locations.
`However, these sensors and the communication network that
`connect them are costly; thus, traffic monitoring is gener-
`ally limited to a few critical points. Inexpensive wireless ad
`hoc networks will completely change the landscape of traffic
`monitoring and control. Cheap sensors with embedded net-
`working capability can be deployed at every road intersection
`to detect and count vehicle traffic and estimate its speed. The
`sensors will communicate with neighboring nodes to eventu-
`ally develop a “global traffic picture” which can be queried
`by human operators or automatic controllers to generate con-
`trol signals.
`Another more radical concept [33] has the sensors attached
`to each vehicle. As the vehicles pass each other, they ex-
`change summary information on the location of traffic jams
`and the speed and density of traffic, information that may
`be generated by ground sensors. These summaries propagate
`from vehicle to vehicle and can be used by drivers to avoid
`traffic jams and plan alternative routes.
`
`V. HARD PROBLEMS AND TECHNICAL CHALLENGES
`
`Sensors networks in general pose considerable technical
`problems in data processing, communication, and sensor
`management (some of these were identified and researched
`in the first DSN program). Because of potentially harsh, un-
`certain, and dynamic environments, along with energy and
`
`bandwidth constraints, wireless ad hoc networks pose addi-
`tional technical challenges in network discovery, network
`control and routing, collaborative information processing,
`querying, and tasking.
`
`A. Ad Hoc Network Discovery
`Knowledge of the network is essential for a sensor in the
`network to operate properly. Each node needs to know the
`identity and location of its neighbors to support processing
`and collaboration. In planned networks, the topology of the
`network is usually known a priori. For ad hoc networks, the
`network topology has to be constructed in real time, and up-
`dated periodically as sensors fail or new sensors are deployed
`[31]. In the case of a mobile network, since the topology is
`always evolving, mechanisms should be provided for the dif-
`ferent fixed and mobile sensors to discover each other. Global
`knowledge generally is not needed, since each sensor node
`interacts only with its neighbors. In addition to knowledge
`of the topology, each sensor also needs to know its own lo-
`cation [32]. When self-location by GPS is not feasible or too
`expensive, other means of self-location, such as relative po-
`sitioning algorithms, have to be provided.
`
`B. Network Control and Routing
`The network must deal with resources—energy, band-
`width, and the processing power—that are dynamically
`changing, and the system should operate autonomously,
`changing its configuration as required. Since there is no
`planned connectivity in ad hoc networks, connectivity must
`emerge as needed from the algorithms and software. Since
`communication links are unreliable and shadow fading
`may eliminate links, the software and system design should
`generate the required reliability. This requires research into
`issues such as network size or the number of links and
`nodes needed to provide adequate redundancy. Also, for
`networks on the ground, RF transmission degrades with
`distance much faster than in free space, which means that
`communication distance and energy must be well managed.
`Protocols must be internalized in design and not require
`operator intervention.
`Alternative approaches to traditional Internet methods
`[such as Internet Protocols (IP)], including mobile IP, are
`needed. One of the benefits of not requiring IP addresses
`at each node is that one can deploy network devices in
`very large numbers. Also, in contrast to the case of IP,
`routes are built up from geoinformation, on an as-needed
`basis, and optimized for survivability and energy. This is a
`way to form connections on demand, for data-specific or
`application-specific purposes. IP is not likely to be a viable
`candidate in this context, since it needs to maintain routing
`tables for the global topology, and because updates in a
`dynamic sensor network environment incur heavy overhead
`in terms of time, memory, and energy.
`Survivability and adaptation to the environment are
`ensured through deploying an adequate number of nodes
`to provide redundancy in paths, and algorithms to find
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`the right paths. Diffusion routing methods, which rely
`only upon information at neighboring nodes, are a way to
`address this [33], although such methods may not achieve
`the information-theoretic capacity of a spatially distributed
`wireless network [34]. Another important design issue is
`the investigation of how system parameters such as network
`size, and density of nodes per square mile affect the tradeoffs
`between latency, reliability, and energy.
`
`C. Collaborative Signal and Information Processing
`The nodes in an ad hoc sensor network collaborate to
`collect and process data to generate useful information.
`Collaborative signal and information processing over a net-
`work is a new area of research and is related to distributed
`information fusion. Important technical issues include the
`degree of information sharing between nodes and how nodes
`fuse the information from other nodes. Processing data from
`more sensors generally results in better performance but also
`requires more communication resources (and, thus, energy).
`Similarly,
`less information is lost when communicating
`information at a lower level (e.g., raw signals), but requires
`more bandwidth. Therefore, one needs to consider the mul-
`tiple tradeoffs between performance and resource utilization
`in collaborative signal and information processing using
`microsensors.
`When a node receives information from another node,
`this information has to be combined and fused with local
`information. Fusion approaches range from simple rules
`of picking the best result to model-based techniques that
`consider how the information is generated. Again there is a
`tradeoff between performance and robustness. Simple fusion
`rules are robust but suboptimal while more sophisticated and
`higher performance fusion rules may be sensitive to the un-
`derlying models. In a networked environment, information
`may arrive at a node after traveling over multiple paths. The
`fusion algorithm should recognize the dependency in the
`information to be fused and avoid double counting. Keeping
`track of data pedigree is an approach used in networks with
`large and powerful sensor nodes, but this approach may not
`be practical for ad hoc networks with limit