Arvindra Sehmi

Summary: Thinking of distributed embeddedsystems (DES)—let alone the more general area of embedded computing—as aunified topic is difficult. Nevertheless, it is a vastly important topic andpotentially represents a revolution in information technology (IT). DES isdriven by the increasing capabilities and ever-declining costs of computing andcommunications devices, resulting in networked systems of embedded computerswhose functional components are nearly invisible to end users. Systems have thepotential to alter radically the way in which people interact with theirenvironment by linking a range of devices and sensors that will allowinformation to be collected, shared, and processed in unprecedented ways.

Contents

Introduction
Contrasting DES with Traditional Distributed Systems
Market Opportunity
Scenarios
Technical Imperatives
Self-Configuration and Adaptive Coordination
Trustworthiness
Computational Models
Enabling Technologies
Development
Conclusion
References

Introduction

DES conjures up images of pervasive collaboration among connecteddevices, such as tiny, stand-alone, embedded microcontrollers, networkingdevices, embedded PCs, robotics systems, computer peripherals, wireless datasystems, sensors, and signal processors. Combining information from in-placesensors with information from other sources on the network will enable new,rich scenarios to be realized in automotive and avionics control systems, localenvironmental monitoring for precision agriculture, medical systems, personalhealth-care monitoring, and manufacturing and process automation, for example.

Widespread use of DES throughout society could dwarf previousrevolutions in IT for two reasons. Firstly, Moore’s law is primarily drivingdevice miniaturization and reduced power consumption (instead of increasedspeed). Secondly, the industry is fast becoming better equipped withdevelopment tool chains (software included) and pervasive standards-basedcommunications protocols. As a consequence, embedded computing andcommunications technology can quite literally be everywhere and extend into allaspects of life—an invisible component of almost everything in everyone’ssurroundings.

Contrasting DES with Traditional Distributed Systems

DES differs from traditional distributed systems in importantways. Embedded systems will eventually interconnect millions, maybe billions,of nodes. This will require changes in the way nodes interact with one another.Broadcast, not peer-to-peer, communications will be the norm. The sheer size ofconnected clusters of applications will necessitate the use of statisticallycorrect (instead of deterministic) algorithms for resource accounting, faultdetection and correction, and system management. These clusters will merge anddissolve rapidly to host functionality that is of interest to groups formedspecifically for that purpose. Doing this successfully requires new approachesto naming, routing, security, privacy, resource management, and synchronization.Heterogeneity will factor greatly in the design, implementation, and operationof DES, as will cross-cutting concerns such as dependability, energy-awarecomputing, critical systems engineering, security, real-time programming, andresource-bounded computing. Some of these factors are familiar to traditionalapplication developers, but many are simply viewed as esoteric boundary issues.

DES tends to be tightly coupled to the physical world and willoften be invisible when things are working properly. In contrast to desktopcomputers, which are part of the office or home furniture, DES instead will beintegrated into the furniture and other objects in the environment. You willinteract with these objects and devices, but will be unlikely to think of them,as you do when interacting with a computer.

DES components are highly resource constrained. They aretypically small, wirelessly connected, bandwidth limited, and operating underphysical constraints such as limited energy and the need for adequate heat dissipation.Since they would be integrated in buildings, bridges, vehicles, and so on, theywould be expected to last as long as the objects in which they are embedded.The expectation of longevity needs to be taken into account when designing,deploying, and managing these systems. Heterogeneity will be the norm becauseof the large number of interacting elements that make up DES, sointeroperability is a key concern. Managing these constraints and creating asystem that functions properly while remaining understandable and manageable byhuman operators, users, and casual passersby, is a great challenge for DESdesigners—arguably much more of a challenge than that posed by traditionaldistributed-systems design. The DES scenarios described later in this paper shouldclarify these points.

Market Opportunity

DES is a fast-growth area of the computing industry and isindicative of the long-term trend of moving away from centralized, high cost,low volume products toward distributed, low-cost, high-volume products.

The next step in this process is the emergence of massivelydistributed systems—that is, distributed embedded systems that are connected toenterprise systems and the Internet (or the so-called cloud). These systemswill penetrate even more deeply into the fabric of society and become theinformation power grids of the 21st century. As noted, they will be ubiquitous,most will operate outside the normal cognizance of the people they serve, andmost will be based on embedded systems that present nontraditional computinginterfaces to their users. Their full potential will see them engineered tooperate as distributed utilities, much like the energy, water, transportation,and media broadcast businesses do today [1].The major industry sectors where DES is likelyto be used are automotive, avionics/aerospace, industrial automation (androbotics); telecommunications; consumer electronics and intelligent homes;health and medical systems. The market opportunity of these sectors issignificant and the reader is referred to the FAST report [2] for more details. Insummary, though, and as Table 1 shows, the overall value of the embedded sectorworldwide is about 1600 billion € per year; the three largest markets forembedded systems are telecommunications, automotive, and avionics/aerospacewith combined value worldwide of 1,240 billion € per year; and these threelargest markets are characterized by growth rates in excess of 10 percent.

Table 1. Estimated total value, through 2006, and growth,through 2010, of major industry sectors using embedded systems (based on theFAST report2 and others)

The nature and characteristics of distributed embedded systemsare explored below through some example scenarios drawn from the industrysectors mentioned above. This will help draw similarities to traditionaldistributed-systems development while also recognizing that there are uniquesystems-design implications, implementation issues, and approaches to solutiondevelopment.

Scenarios

Automotive Telematics Scenario

In the automotive and avionics industry, embedded systems providethe capability of reaching new levels of safety and sustainability thatotherwise would not be feasible, while adding functionality, improving comfort,and increasing efficiency. Examples of this include improved manufacturingtechniques, driver-assistance systems in cars that help prevent accidents, andadvanced power-train management concepts that reduce fuel consumption andemissions.

In Western Europe, the “100 percent safe” car is envisioned. Itwill have sensors, actuators, and smart embedded software, ensuring thatneither the driver nor the vehicle is the cause of any accident. This conceptextends to all aspects of the driving experience: in-car entertainment,information services, and car-to-car and car-to-infrastructure communication.

For example, the car would know who is allowed to drive it andwho is driving it; where it is; where it is going and the best route to itsdestination; and it would be able to exchange information with vehicles aroundit and with the highway. It would monitor its own state (fuel levels, tires,oil pressure, passenger compartment temperature and humidity, componentmalfunction, need for maintenance) and the state of the driver (fatigue,intoxication, anger). The car would first advise and then override the driverin safety-critical situations, use intelligent systems to minimize fuelconsumption and emissions, and contain an advanced on-board entertainmentsystem.

Radio-frequency identification (RFID) smart tags within the majorcar components would communicate with other components during manufacture tooptimize the process, communicate with in-car systems during the car’s workinglife to optimize maintenance cycles, and enable environmentally friendlydisposal of the car and its components at the end of its life.

Challenges and Issues

To enable this scenario, components would need to be embedded inlong-lived physical structures (such as bridges, traffic lights, individualcars, and perhaps even the paint on the roads). Some components will bepermanently connected to a network, but many would be resource constrained (forexample, in terms of power) while computing data and thus communicating itwirelessly only when necessary. The many pieces of such a system will ofnecessity be heterogeneous, not only in form but also in function. There may besubsystems that communicate to consumers in private vehicles, others that relayinformation from emergency vehicles to synchronize traffic lights, still othersthat provide traffic data and analysis to highway engineers, and perhaps somethat communicate to law enforcement.

How information will be communicated to those interacting withthe system is of great importance in such an environment. Safety is a criticalconcern, and issues of privacy and security arise as well, along with concernsabout reliability.

Precision Agriculture Scenario

Incorporating DES technology into agriculture is a logicaldevelopment of the advances in crop management over the last few decades.Despite deep understanding and knowledge on the part of farmers about how toadjust fertilizers, water supplies, and pesticides, and so on, to best managecrops and increase yields, a multitude of variations still exist in soil, landelevation, light exposure, and microclimates that make general solutions lessthan optimal, especially for highly sensitive crops like wine grapes and citrusfruit.

The latest developments in precision agriculture deployfine-grained sensing and automated actuation to keep water, fertilizer, andpesticides to a minimum for a particular local area, resulting in betteryields, lower costs, and less pollution-causing runoff and emissions. Furthermore,the data collected can be analyzed and incorporated as feedback control toadjust irrigation flow rate and duration tuned to local soil conditions andtemperature. Sensors that can monitor the crop itself (for example, sugarlevels in grapes) to provide location-specific data could prove very effective.

In the future, DES might be used to deploy sensors for the earlydetection of bacterial development in crops or viral contamination inlivestock, or monitor flows of contaminants from neighboring areas and sendalerts when necessary. In livestock management, feed and vitamins forindividual animals will be adjusted by analyzing data from networks ofingestible sensors that monitor amounts of food eaten, activity and exercise,and health information about individual animals and the state of the herd as awhole.

Challenges and Issues

In this scenario, embedded components must be adaptive,multimodal, and able to learn over time. They will need to work under a widerange of unpredictable environmental conditions, as well as to interact withfixed and mobile infrastructure and new elements of the system as they areadded and removed at varying rates of change.

Aviation and Avionics Scenario

The European Commission has set goals for the aviation industryof reducing fuel consumption by 30 percent by 2021 through the use of embeddedsystems. This may be a high goal to be achieved solely through the use oftechnology. But in this industry, the goals appear to very ambitious across theboard.

The unmanned aerial vehicle (UAV) for use in surveillance and inhazardous situations such as fire fighting promises to be cheaper, safer, andmore energy efficient to operate than conventional aircraft. There areapparently many different kinds of UAVs under development: some withlong-duration operational cycles and extensive sensor suites; some withmilitary defense and attack capability; others that are small enough to becarried and deployed by individuals; and, in the future, tiny, insect-like,UAVs providing a flying sensor network.

The aircraft of the future will have advanced networks foron-board communication, mission control, and distributed coordination betweenaircraft. These networks will support advanced diagnosis, predictivemaintenance, and in-flight communications for passengers. For externalcommunication, future aircraft will communicate with each other in spontaneous,specific-for the-purpose ways similar to peer-to-peer networks.

Challenges and Issues

The aviation and avionics industry has specific needs in terms ofsecurity, dependability, fault tolerance and timeliness, stretching the limitsof distributed embedded-systems design and implementation. The whole system, ifnot each of its embedded components, needs to be high precision, predictable, androbust for 100 percent operational availability and reliability. It must enablehigh bandwidth, secure, seamless connectivity of the aircraft with itsin-flight and on-ground environment. It should support advanced diagnosis andpredictive maintenance to ensure a 20- to 30-year operational life span.

DES design environments and tools will need to providesignificant improvements in product development cycles, ongoing customizations,and upgrades beyond those achievable with current distributed-systems developmenttools. Design advances in fast prototyping, constructive system composition,and verification and validation strategies will be required to manage thiscomplexity.

Manufacturing and Process-Automation Scenario

Embedded systems are important to manufacturing in terms ofsafety, efficiency, and productivity. They will precisely control processparameters, thus reducing the total cost of manufacture. Potential benefitsfrom integrating embedded control and monitoring systems into the productionline include: better product quality and less waste through close processcontrol and real-time quality assurance; more flexible, quickly configuredproduction lines as a result of programmable subsystems; system healthmonitoring, which leads to more-effective, preventive and lower-costmaintenance; safer working environments due to better monitoring and control;and better component assembly techniques, such as through the use of smart RFIDtags.

Challenges and Issues

There are many implications of this industry scenario for DES.One is a need for better man-machine interactions in what is fundamentally areal-time, man-plus-machine control loop. Providing better interactions willimprove quality and productivity by ensuring that there are no operator errors,as well as by reducing accidents. Availability, reliability, and continuousquality of service are essential requirements for industrial systems achievedthrough advanced control, redundancy, intelligent alarming, self-diagnosis, andrepair. Other important issues are the need for robustness and testing,coherent system-design methodology, finding a balance between openness andsecurity, integrating old and new hardware with heterogeneous systems, andmanaging obsolescence.

Medical and Health-Care Scenario

Society is facing the challenge of delivering good-quality,cost-effective health care to all citizens. Medical care for an agingpopulation, the cost of managing chronic diseases, and the increasing demandfor best-quality health care are major factors in explaining why health-careexpenditures in Europe are already significant (8.5 percent of GDP) and risingfaster than overall economic growth. Medical diagnosis and treatment systemsalready rely heavily on advances in embedded systems. New solutions that mixembedded intelligence and body-sensing techniques are currently being developed[3], and currentadvances in this area address patient-care issues such as biomedical imaging,remote monitoring, automatic drug dispensing, and automated support fordiagnosis and surgical intervention.

Challenges and Issues

The medical domain represents a complex and diverse arena forextraordinary developments that deploy a wide range of embedded systems.Implanted devices such as pacemakers and drug dispensers are commonplace, butneed to become more sophisticated, miniaturized, and connected to networks ofinformation systems. Wearable devices for monitoring and managing cholesterol,blood sugar, blood pressure, and heart rate must be remotely connected to thelaboratory and to the operating room in a secure and reliable manner frombeginning to end. Robotic devices are being used today to guide and performinvasive surgery requiring high-integrity engineering practices not even imaginedin a typical “mission-critical” enterprise application.

Mobility Scenario

Combining mobile communications with mobile computing is allowingpeople to talk to others and access information and entertainment anywhere atany time. This requires ubiquitous, secure, instant, wireless connectivity,convergence of functions, global and short-range sensor networks and light,convenient, high-functionality terminals with sophisticated energy managementtechniques. Such environments will enable new forms of working with increasedproductivity by making information instantly available, when needed in thehome, cars, trains, airplanes and wider-area networks. Imagine a hand-held orwearable device giving easy access to a range of services able to connect via arange of technologies including GSM, GPS, wireless, Bluetooth and via directconnection to a range of fixed infrastructure terminals. Potential applicationsand services include: Entertainment, education, internet, local information,payments, telephony, news alerts, VPNs, interfaces to medical sensors andmedical services, travel passes and many more.

Challenges and Issues

Devices would need to reconfigure themselves autonomouslydepending on patterns of use and the available supporting capabilities in environmentor infrastructure and be able to download new services as they becameavailable. To develop such infrastructure, the gap between large, enterprisesystems and embedded components would need to be bridged. Significantdevelopments are required in technology for low-power and high performancecomputing, networked operating systems, development and programmingenvironments, energy management, networking and security.

Issues that need to be resolved in the infrastructure to supportthese kinds of scenarios include the provision of end-to-end ubiquitous,interoperable, secure, instant, wireless connectivity to services.Simultaneously the infrastructure must allow unhindered convergence offunctions and of sensor networks. Addressing the constraints imposed by powermanagement (energy storage, utilization and generation) at the level of theinfrastructure and mobile device poses a major challenge.

Home-Automation and Smart Personal Spaces Scenario

By deploying DES in the home, an autonomous, integrated, homeenvironment that is highly customizable to the requirements of individuals canbe foreseen. Typical applications and services available today include intruderdetection, security, and environmental control. But in the future, applicationsand services to support the young, elderly, and infirm will be developed, andthese may have the ability to recognize individuals and adapt to their evolvingrequirements, thereby enhancing their safety, security, and comfort. By tyingin with applications and services described in the medical/health-care andmobility scenarios, smart personal spaces could be developed.

Challenges and Issues

Multidisciplinary, multiobjective design techniques that offerappropriate price and performance, power consumption, and control will have tobe used if we are to realize the potential of embedded systems for homeentertainment, monitoring, energy efficiency, security, and control. Suchsystems will require significant computational, communication, and data-storagecapabilities. The mix of physical monitoring and data-based decision support bysome form of distributed intelligence will rely on the existence of seamlesslyconnected embedded systems and the integration of sensors and actuators intointelligent environments. These systems will be characterized by ubiquitoussensors and actuators and a high-bandwidth connection to the rest of the world.Technologies will need to be developed that support sensing, tracking,ergonomics, ease-of-use, security, comfort, and multimodal interaction.

Key to achieving this result will be developing wireless andwired communications and techniques for managing sensor information, includingdata fusion and sensor overloading. The challenges are to make such systemsintelligent, trustworthy, self-installing, self-maintaining, self-repairing,and affordable, and to manage the complexity of system behavior in the contextof a large number of interoperable, connected, heterogeneous devices. Thesesystems will need to operate for years without service, be able to recover fromfailure, and be able to supervise themselves.

Managing these embedded systems will require support of allaspects of the life cycle of the application and service infrastructures,including ownership, long-term storage, logging of system data, maintenance,alarms, and actions by the provider (emergency, medical, or security) services,authorization of access and usage, and charging and billing under a range ofdifferent conditions of use.

Technical Imperatives

Some of the most challenging problems facing the embedded-systemscommunity are those associated with producing software for real-time andembedded systems. Such systems have historically been targeted to relativelysmall-scale and stand-alone systems, but the trend is toward significantlyincreased functionality, complexity, and scalability, as real-time embeddedsystems are increasingly being connected via wireless and wired networks tocreate large-scale, distributed, real-time, and embedded systems. Thesecombined factors [4]require major technical imperatives to be addressed by both industry andresearch establishments: (a) self-configuration and adaptive coordination; (b) trustworthiness;(c) computational models; and (d) enabling technologies. Let’s now look at eachtechnical imperative in more detail.

Self-Configuration and Adaptive Coordination

Self-configuration is the process of interconnecting availableelements into an ensemble that will perform the required functions at thedesired performance level. Self-configuration in existing systems is realizedthrough the concepts of service discovery, interfaces, and interoperability.But embedded systems, which appear in hybrid environments of mobile and staticnetworks with nodes of varying capability, energy availability, and quality ofconnectivity, are plagued by diverse and energy-limited wirelessconnectivity—making low power discovery a challenge. Also scalable discoveryprotocols, security, and the development of adequate failure models forautomatically configured networks will require that solutions be developed.

Adaptive coordination involves changes in the behavior of asystem as it responds to changes in the environment or system resources.Coordination will not necessarily be mediated by humans because DES could be solarge and the time scale over which adaptation needs to take place too shortfor humans to intervene effectively. Achieving adaptive coordination in DESwill draw on the lessons learned from adaptive coordination in existingdistributed systems, but it will also require meeting the radical newchallenges posed by the physically embedded nature of collaborative controltasks and large numbers of nodes, and further complicated by the relativelyconstrained capabilities of individual elements. Thus, to achieve adaptabilityin DES, solutions are needed in decentralized control and collaborativeprocessing, and techniques must be developed to exploit massive redundancy toachieve system robustness and longevity.

Trustworthiness

If we can expect DES to be deployed in large numbers and becomean essential part of the fabric of everyday life, five technical imperativesmuch be taken into account in their design from the outset: reliability,safety, security, privacy, and usability.

On reliability, current monitoring and performance-checkingfacilities, and verification techniques are not easily applicable to DESbecause of their large number of elements, highly distributed nature, andenvironmental dynamics.

In terms of safety or the ability to operate without causing accidentsor loss, bounded rational behaviors are essential, especially in the face ofreal-time systems and massive DES likely to exhibit emergent or unintendedbehaviors.

It may be virtually impossible to distinguish physical fromsystem boundaries in a large-scale DES, making security a big challenge. And,considering that networking of embedded devices will greatly increase thenumber of possible failure points, security analysis may prove even moredifficult.

Privacy and confidentiality policies will be exacerbated by thepervasiveness and interconnectedness of DES. Users will be monitored, and vastamounts of personal information will be collected. Thus, implementing privacypolicies, such as acquiring consent in a meaningful fashion in large scale networks,will be very difficult.

Related to all of the above, embedded systems must be usable byindividuals who have little or no formal training. Unfortunately, usability andsafety often conflict, so trade-offs will need to be made. Understanding themental models people use of the systems with which they interact is a good wayfor designers to start addressing issues of usability and manageability ofembedded systems.

Computational Models

New models of computation are needed to describe, understand,construct, and reason about DES effectively. Understanding how large aggregatesof nodes can be programmed to carry out their tasks in a distributed andadaptive manner is a critical research area. Current distributed-computingmodels, such as distributed objects and distributed shared memory, do not fullyaddress all of the new requirements of DES. Furthermore, DES’s tight couplingto the physical world, the heterogeneity of their systems, the multitude ofelements, plus timing and resource constraints, among other things, demonstratethe need for a much richer computing model. Such a model will need toincorporate resource constraints, failures (individual components may fail byshutting down to conserve energy, for example), new data models, trust,concurrency, and location.

For example, in a sensor network, subsystems from differentvendors should interoperate easily and be integrated seamlessly into the restof the IT infrastructure, providing intuitive interfaces for remote and noviceusers. Multiple, concurrent clients will be exercising differentfunctionalities of the system for different purposes, and although resourcesmay not be the most stringent constraint for the system, it has to beself-monitoring and aware of its resources. It must have a certain level ofautonomy to decide on the best use of available resources to fulfill multipleusers’ concurrent and uncoordinated requests. The complexity of thecomputational model may be further exacerbated by the multiple ways to obtain apiece of information. So, the model would have to capture informationequivalency relations over the rich variety of sensor data, which in turn wouldrequire semantic information and services to process and reason about thesensor data in ways that move beyond protocol agreement and data-formatconversion. To quantify the semantic information contained in sensor data andto capture the relations between various semantic entities such as objects andevents in the physical world, the model would need to define an ontology for avariety of commonly encountered sensor data and provide a set of semantictransformation services to incrementally extract new semantic information fromlower level data. (This topic is of particular interest to the author and thereader is referred to [5],[6], [7], and [8].)

Enabling Technologies

The evolution of more and more sophisticated DES and complexembedded-systems solutions is fueled by the revolutionary advances ininformation technology during the past several decades. Silicon scaling is themain driving force, with exponentially increasing processor performanceenabling a world in which sophisticated chips can be manufactured and embeddedeasily and cheaply. Continued improvements in the price and performance of chiptechnology are expected for at least the next decade. Still, new developmentsare needed in specific aspects of communications, geo-location, software, andoperating systems.

As wireless (and wired) technologies continue to become lessexpensive and more sophisticated, the vision of pervasive, connecting, embeddedprocessors becomes increasingly feasible. However, most of the progress to datein wireless technology has focused on medium- to long-range communications (asin cellular phones and pagers) and is not sufficient for the widespreaddeployment of DES. Work is needed to understand how to create networkarchitectures and designs for low-power, short-range wireless systems. Relatedto wireless are the issues surrounding geo-location technology. Unlikeconventional computer networks, which are more dependent on the relativepositioning of elements in a network topology, DES are often inextricably tiedto the physical world (a primary purpose often being to measure and controlphysical-world attributes or objects), so location in physical space is moreimportant. DES will therefore require ready access to absolute or relativegeographic information, especially for sensor networks responsible for physicalsensing and actuation.

Attention must also be given to the responsible and effectiveintegration of sensor devices into DES, which will have impacts on the designof network-aware embedded operating systems, application development, andsoftware tooling that has the characteristics required to tailor solutions tothe physical constraints, afford in-place deployment, be upgradable, have highavailability, and the ability to migrate to new hardware. Since DES will beembedded in long-lived structures, they will have to evolve, depending onchanging external conditions and advances in technology as time passes. Thus,operating systems and applications that can cope with this type of evolutionwill be critical. Furthermore, real-time operation and performance-criticalconstraints on multiple embedded applications running concurrently on the samedevice will create a demand for entirely new methods of software developmentand systems architectures.

Figure 1 is a chart of the technical imperatives for DESdevelopment and research discussed in the previous paragraphs. Additionalissues present in this field are given in summary form and serve to highlightthe significant differences from traditional distributed-systems development.

Figure 1. Technicalimperatives for distributed embedded-systems development and research

Further Issues and Needs

1. Predictability and manageability—Covers methodologies and mechanisms for designing predictable, safe, reliable, manageable distributed embedded systems.
2. Monitoring and system health—Refers to a complete conceptual framework to help achieve robust operation through self-monitoring, continuous self-testing, and reporting of system health in the face of extreme constraints on nodes and elements of the system.
3. Network topology and geometry—Represent modeling techniques to support and incorporate network geometry (as opposed to just network topology) into DES.
4. Component systems and communications interoperability—Is about the techniques and design methods for constructing long-lived, heterogeneous systems that evolve over time and space while remaining interoperable.
5. Integration of technical, social, ethical, and public-policy issues—Is predominantly about the fundamental research required to address nontechnical issues of embedded systems, especially those having to do with the ethical and public-policy issues surrounding privacy, security, reliability, usability, and safety.

Development

To understand the nature of embedded-systems developmentcomplexity, you should also understand the typical embedded-device value chain.This consists of links between various industry players that comprise siliconvendors (SVs), embedded OS vendors, independent hardware vendors (IHVs), devicemakers (original equipment developer (OED) and original equipment or designmanufacturer (OEM/ODM)), distributors, and end customers (businesses orconsumers).

In Figure 2, you see that the embedded-device value chainconsists of three to seven links between SVs, OEDs, OEMs, and end customers.Depending on the device maker’s supply chain and market, it may also consist ofthird-party software providers, IHVs, ODMs, and various distributionintermediaries.

Figure 2. Embedded-devicevalue chain

When developing traditional business applications and systems,“devices” are standardized and readily available off-the-shelf computers,making a significant part of their value chain quite irrelevant to thedevelopment process. However, in embedded-systems design and development, asimilar level of maturity or commoditization, if you like, of “devices” has notyet been achieved in the industry. As a result, development tool chains,processes, and methodologies are often proprietary and established for aspecific goal. The vast majority of the industry in embedded development isusing open-source software and custom development tools provided by the hardwarevendors (SV, IHV) and open source communities. Time scales for the developmentof hardware and software at all levels in the device value chain can thereforebe long as it requires proprietary or scarce skills and knowledge to putsolutions together. In some cases, where DES and real-time scenarios areinvolved, especially in safety-critical situations, these time scales caneasily span a decade. There are interesting offerings from the large operatingsystems and software tools vendors such as Microsoft that hold the promise ofproviding a more productive and “traditional” development experience forembedded-systems professionals (including academics and hobbyists) in many keysolution scenarios. The hope is that eventually tool chains along the entiredevice value chain will become mostly standardized and interoperable enablingthe embedded-systems industry to scale development as seen in traditionalsoftware development.

Figure 3 shows some aspects of the embedded-systems developmentlife cycle—which is strongly tied to the device value chain—and the Microsoftplatform technologies that may be applied through the life cycle.

Figure 3. Embedded-systemsdevelopment life cycle and Microsoft technologies

Because embedded devices contain both hardware and softwarecomponents understanding the interactions between them are essential. Keyskills in this area span electrical engineering and computer science; they alsoaddress the interrelationships among processor architectures, performanceoptimization, operating systems, virtual memory, concurrency, task scheduling,and synchronization. Developers should have a breadth of exposure to appreciatethe utility of other fields in embedded systems, such as digital signalprocessing and feedback control. Finally, as embedded systems are not generaloperating systems, developers should understand the tight coupling betweenembedded applications and the hardware platforms that host them.

Conclusion

This paper introduces you to the rich field of distributedembedded systems. It is geared towards architects and developers of traditionaldistributed systems and therefore takes a broad perspective on the task ofdescribing DES. Several references at the end may assist readers in exploringthis subject in greater depth. Distributed systems design approaches and theircharacteristics will influence DES design and implementation, but there aresignificant differences that have been described in this paper. The way weconceive and program these systems is bound to become easier. It has to,because smart, connected, service-oriented embedded systems will permeate ourfactories, hospitals, transportation systems, entertainment, and personalspaces, and thus become essential to our daily lives. In many ways, DES is thecomputing model for the future, and the mainstream of the IT industry will needto learn how to design, build, and operate these systems well.

References

[1] Dan Nessett. MassivelyDistributed Systems: Design Issues and Challenges. USENIX Workshop onEmbedded System, 1999.

[2] FAST Report. Study ofWorldwide Trends and R&D Programmes in Embedded Systems. 2005.

[3] Siegemund F., Haroon M., Ansari J., Mahonen P., Senslets. Applets for the Sensor Internet.Wireless Communications and Networking Conference, 2008.

[4] National Research Council, Committee on Networked Systems ofEmbedded Computers. Embedded, Everywhere: A Research Agenda for NetworkedSystems of Embedded Computers. National Academy Press, Washington, D.C.,2001.

[5] Jie Liu and Feng Zhao. “Towards Semantic Services forSensor-Rich Information Systems.” 2nd International Conference on BroadbandNetworks, 2005.

[6] Jie Liu, Eker, J., Janneck, J.W., Xiaojun Liu, Lee, E.A. “Actor-OrientedControl System Design: A Responsible Framework Perspective.” IEEE Transactionson Control Systems Technology, 12(2), March 2004.

[7] Schmidt D. C., Gokhale A., Schantz R. E., and Loyall, J. P. Middleware R&D Challenges forDistributed Real-Time and Embedded Systems. ACM SIGBED Review, 1 (1), 2004.

[8] Gul A. Agha, Ian A. Mason, Scott F. Smith, and Carolyn l.Talcott. A Foundation for ActorComputation. J. Functional Programming, 7 (1), 1997.

About the author

Arvindra Sehmi is adirector of Developer and Platform Evangelism at Microsoft. His team works withcustomers on business and application architecture methodology andbest-practices adoption. Currently, he is working in the area of distributedembedded systems and its role in Microsoft’s Software + Services strategy andCloud Computing platform. Previously as lead Architect Evangelist in EMEA, hewas recognized by Microsoft for outstanding achievement in this profession.Arvindra has specialized in the financial services industry and created theMicrosoft Architecture Journal for which he remains its editor emeritus. Heholds a Ph.D. in bio-medical engineering and masters in business. You can findhis blog at http://blogs.msdn.com/asehmiand contact him at arvindra.sehmi@microsoft.com.

This article was published in the Architecture Journal, a printand online publication produced by Microsoft. For more articles from thispublication, please visit the Architecture Journal Web site.