resource management tools - tum€¦ · for sensor networks and resource management tools in...

Resource Management Tools

Anh NguyenSupervisor: Corinna Schmitt

Seminar: Innovative Internet Technologies and Mobile Communication WS 12/13Chair for Computer Network Architectures and Services

Department for Computer Science, Technische Universität MünchenEmail: [email protected]

ABSTRACTSensor networks perform and are used in many differentkinds of fields, but are especially given tasks, which are dif-ficult and almost impossible for a human to do. Besidesmonitoring the environment and collecting necessary infor-mation, sensor networks have to manage and supply them-selves with essential resources to perform the tasks that wereallocated to the whole network.This paper focuses on resource management and the devel-opment of effective resource management tools, which hasto face many challenges due to resource limitations, such asenergy consumption and scalability. Peloton and Tiny Net-work Manager cover up most of these challenges with dif-ferent approaches. While Peloton uses a ticket abstractionto distribute allocations and perform optimization on thewhole sensor network process, the Tiny Network Managerhas an implemented “request-reply-mechanism” to manageits assignments and resources.

KeywordsWireless sensor network, resource constraints, resource man-agement tools, Peloton, Tiny Network Manager

1. INTRODUCTIONSensor networks have been developed from innovations inwireless communication within the past few years and areone of the most important technologies in the 21st century.Nowadays sensor networks are used in military for communi-cation and targeting, in health systems for monitoring andassisting (disabled) patients, and other application fields,such as managing inventory in business, and monitoring in-accessable and disaster areas.[1]

Figure 1 shows an example of a sensor node structure withits units and their connection to each other: The basis of thesensor node is its power unit, which keeps the sensor nodeand its units alive. The overall task of a sensor network isto monitor physical or environmental conditions through thesensing unit, which monitors the environment and convertsthe analog signals to digital ones, and finally passes it onto the processing unit. After performing quick local dataprocessing, all the collected data will be passed through thenetwork and routed back to the sink with a multi-hop archi-tecture.[1]

Since sensor nodes are often deployed in inaccessable or dis-aster areas, it is common that they have to move their po-sition from time to time due to unconventional events and

Figure 1: Sensor node structure [1]

situations. Therefore a location finding system and a mobi-lizer are often implemented as additional units.[1][2]

Wireless sensor networks are intended to perform for ”longperiods of time, over relatively large physical spaces, and inplaces that are difficult for people to reach.”[2] Since sen-sor nodes in wireless sensor networks lack in terms of e.g.energy, operating systems and tools were developed to man-age exhausting resources and constraints of the sensor nodes.Those tools are called resource management tool and helpmanaging and distributing the resources to all the sensornodes in a network.[1][3]

In the following section definitions are given on resourcemanagement and resource management tools and the chal-lenges during the development of resource management toolsin general, as well as the chosen resource management toolsPeloton and Tiny Network Manager are described after-wards. In comparison of those resource management toolsin section 3, their different and common features are char-acterized. Lastly a conclusion is given and a future outlookfor sensor networks and resource management tools in tech-nology is presented.

2. CHALLENGES DURING THE DEVELOP-MENT OF RESOURCE MANAGEMENTTOOLS

2.1 Resource management functionsAccording to Tenrox, resource management is defined as an“efficient and effective deployment and allocation of an or-ganization’s resources when and where they are needed.”[11]In terms of sensor networks it is about how to save the nec-essary resources and optimize their consumption for longer

doi: 10.2312/NET-2013-02-1_10Seminar FI & IITM WS2012/2013, Network Architectures and Services, February 2013

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availability. Moreover resource management is not only aboutlowering the resource consumption, but also to optimize thecommunication between each node in the sensor node to dis-tribute tasks and thereby the resource allocations effectively.The result of an efficient management process would be op-timized and distributed allocation of resources, so that eachsingle node is not overloaded with assignments, but is stillkept busy. It is an efficient employment of sensor nodes toeffectively perform and succeed their assigned tasks.[11]

2.2 Challenges and difficultiesSensor networks, on the one hand, usually have stable sen-sor nodes that can withstand the harsh and uncertain en-vironment and avoid physical damage, but lack in essentialresources on the other hand. Moreover their disadvantageis that each sensor node can only collect and process data,but not communicate and share the collected informationand also information on their node state with other nodes.Therefore resource management tools are used to compen-sate this and optimize all processes in the whole sensor net-work.[3]

The challenges in developing a resource management tooltherefore is to implement efficient processes and algorithmsto manage scarce resources of the sensor nodes, such as theminimal life-span, limited memory and stack space and lim-ited radio bandwidth[1][2][5]:

• Minimal life-spanPower consumptions of each sensing node have to bekept low, since energy is the scarcest resource of asensor node. The resource management tools there-fore aim for the optimization of power usage and withthat also the maximization of a node’s lifetime, so thatsensor nodes are able to work for a long time andare prevented from a failure or breakdown in an earlystate.[2][9]

• Limited memory and stack spaceThe memory and stack space of a sensor is limitedand as a consequence the amount of data collectionand processing as well as the amount of data transfersare also limited. If too much data is processed andtransferred it will result in a stack overflow, causing theprocess or the node to crash at the worst. Thereforethe management tool has to define and rank the datain terms of importance and consider which data toprocess first or which data to drop.[3]

• Limited radio bandwidthBesides having limited stack space, the bandwidth ofwireless sensor networks is also limited and much lowerthan that of a wired sensor network. In this case theresource management tool has also to decide whichdata to transfer or which data to keep in the queue.[2]

Besides managing sensor node resources, the implementedprocesses have also to meet the quality standards throughensuring fault tolerance, the scalability of the whole systemas well as real-time performances and the reliability of thecollected and processed data, which also have a strong im-pact on the whole sensor network process[1][2][5]:

• Fault toleranceThe term fault tolerance refers to the reliability of sen-sor nodes. In case of failure or a breakdown of singlenodes due to lack of power, interruption or damage,the resource management tool has to ensure that thetask of the broken sensor node and the overall taskand functionality of the whole network are not be af-fected.[1]

• ScalabilitySensor networks are composed of a huge amount ofsensor nodes. Depending on the usage its scale canreach up to thousands or millions of sensor nodes.Since sensor nodes are not able to think and workautonomously, the resource management tool has tocoordinate the communication and interaction of sen-sor nodes and other hardware components to performtasks faster and more effective, and maintain a man-ageable structure of the sensor node.[1]

• Real-time performanceMany sensor network applications have to stick to acertain time schedule to interact with other sensornodes and hardware components. Moreover the as-signed tasks have to be completed until a certain dead-line. The resource management tool has to ensurereal-time behavior through efficient management of thetasks and sensor nodes also in regard of the availableresources of each sensor node. Constructing and imple-menting an algorithm to ensure real-time performancewould be quite a challenge because of the dynamic en-vironment.[3]

• ReliabilityThe term reliability in this case refers to the collectedinformation. Since the sensor networks are used tocollect important data and information, the resourcemanagement tool has to make sure that the processingof collecting necessary information is always reliableand transferred to the correct places. Losing importantinformation would lengthen the whole sensor networkprocess duration or in the worst falsify the statisticslike incorrect information.[1]

If we want to increase the functional performance of a wire-less sensor network, we have to intelligently manage not onlythe limited resources, but also the interaction between eachsensor node in the system.[5]

Until now a couple of resource management tools, such asTinyOS, Pixie, SOS and Eon, have been developed: Theyonly focus on managing resources for individual nodes, al-though the coordination of resource management decisionsacross the whole network would be more efficient and accu-rate.[4]This approach is not advisable, because the nodes in a net-work always have to interact with other nodes and hardwarecomponents. The sole management of a sensor network as acollection of independent sensor nodes would not solve theproblems of limited resources and network constraints. Onthe contrary: It is necessary that nodes share informationon their local state and communicate and collaborate withother nodes to assign tasks to achieve the most efficient useof resources and task processing.[2][5]


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As a result, the final tool should be able to support andenable “low latency, energy-efficient operation, built-in au-tonomy and survivability, and low probability of detectionof operation”[3].

3. RESOURCE MANAGEMENT TOOLS3.1 PelotonOne of those resource management tools is called Pelotonand was developed in 2009 as a “new distributed sensorOS”.[4] Peloton builds up on Pixie, a node-level operatingsystem for sensor nodes to manage and control over resourceavailability: Pixie works with resource tickets, which are sentby and to the operating system. A ticket represents a time-bounded right to consume a certain amount of a resourceand is a “flexible currency for resource management withinthe node”.[4] In this way, Pixie can respond to the resourceticket allocations and distribute available resources.

Peloton has borrowed the Pixie’s idea of using resource tick-ets and extended it to a new ticket abstraction, which hasimplemented resource management mechanism called vectortickets, distributed ticket agents and state sharing. But un-like Pixie and also other operating systems, such as TinyOSand EON, which manage the resources on individual nodesonly, Peloton is not focusing on individual nodes but thecooperation of all sensor nodes for resource management.[4]

3.1.1 Ticket AbstractionIt has been already mentioned that Peloton has implementedits own ticket abstraction through extending Pixie’s resourceticket mechanism. It consists of the vector tickets, dis-tributed ticket agents and a state sharing system:

Vector Ticket is a programming abstraction, which rep-resents the right of a sensor node to consume resources forperforming operation. A vector ticket V consists of resourcetickets Ti. Each vector ticket is displayed as a tuple (n, R, c,te) that represents the “time-bounded right to consume upto c units of resource R until expiry time te at node n”[4].Moreover it can capture the complete resource allocation ofan operation of several nodes and is also used for trackingand controlling this allocation in the network. Thereforevector tickets record the consumed resources of the sensornodes and provide feedback to the application in terms ofresource availability and usage. One strength of the vectortickets it that they can be decoupled if the allocation is notnecessary anymore and give resources free for other needynodes.[4]

The distributed ticket agent mechanism permits resourcemanagement decisions all across nodes, clusters and the net-work as a whole. They manage vector tickets, track resourceavailability of single nodes as well as of a set of nodes and dis-tribute resource allocations and also perform resource man-agement policies across all the nodes. Because of distributedticket agents and their functions, resource allocations can beperformed individually by nodes, collectively by a group ofnodes, or globally by a base station. In the resource alloca-tion process a node must either acquire a vector ticket fromthe node’s local ticket agent or from a third-party agent,such as the base station or another node in the network, toget a resource allocation. But even if a ticket is acquired,

it is possible that it may be revoked before its expiry time,because of changing conditions.[4]

The state sharing mechanism is in charge of sharing nodestates on local resource availability so that efficient coordina-tion among all the sensor nodes is possible. For this efficientcoordination among all the sensor nodes in the network,Peloton has build-in mechanisms for node sharing withinthe neighborhoods, clusters and across the whole network.Peloton’s API makes it possible for nodes to share informa-tion on local resource availability into a shared tuple state.This information on the other hand can be called up fromthe shared tuple space to make it readable for each othernode. Those data are refreshed frequently, but only betweennodes, that are direct neighbors. Information from distancenodes is refreshed less often, but it is always possible to re-quest a direct update from another node to obtain its lateststate. Those state sharing operations consume energy andbandwidth and thus, also requite resource tickets.[4]

Figure 2: Peloton network with five nodes[4]

Figure 2 shows a Peloton network with five sensor nodes.Each node has the same structure: They are build up onthe Pixie operation system and extended with an applicationlogic and the ticket agent and state sharing mechanism, thatenable the nodes to make resource requests and send alltheir vector tickets to other sensor nodes. Moreover all ofthe nodes are linked to the shared tuple space through statesharing and can provide and access information on the localnode state of each node in the network.[4]

3.1.2 Resource ManagementBesides the ticket abstraction, which supports the distri-bution of resource allocations in the sensor network andoptimizes the overall sensor network process, Peloton hasimplemented adaptive cluster-based routing, duty cyclingand reliable data collection as further resource managementmechanism[4]:

Adaptive cluster-based routing is a frequently used methodfor energy-efficient routing in sensor networks: One routingand communication protocol for wireless sensor networks is


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called“LEACH”and stands for“Low Energy Adaptive Clus-tering Hierarchy”.[6]LEACH works with cluster architecture and its main idea isto collect data from distributed sensors and transmit it toa base station. In LEACH some nodes can elect themselvesas clusterheads. As clusterheads they are responsible for re-ceiving data and data packets from other clusterheads andforwarding it to members of its cluster or to the base station.Since the data transmission to the base station requires andconsumes too much energy the clusterheads “rotate”, whichmeans that the clusterheads take turns in being the clus-terhead. Through this rotation huge energy consumptioncan be avoided and a longer lifetime of the sensor node canbe ensured. Moreover the clusterhead selection can be doneon local information, which does not need a communicationwith the base station and other entities outside the networkand therefore reduces the energy consumption as well.[6]Peloton has an implemented variant of LEACH, in whicha sensor node can elect itself as a clusterhead based on theenergy consumption profile of neighboring nodes. Each clus-terhead temporarily becomes the ticket agent for the clustermembers. In this position it can assign vector tickets tomanage the overall communication and resource consump-tion of the cluster members.[4]

Duty cycling the one of the most common forms of resourcemanagement.[4] The idea of duty cycling is based on the as-sumption that nodes are not used for communication andtransmission issues all of the time. With this assumption,Peloton has build in mechanism that allows sensor nodes tochange their active state into an idle one and vice versa tosave up energy consumption. Therefore the sensor nodes areonly used when it is necessary. It is often difficult to deter-mine an appropriate duty-cycling schedule statically as theimplementation and usage of duty cycling can affect datafidelity, network connectivity and also sensor coverage. Butwith a precise knowledge about the network topology andtransmission structure, it is possible to coordinate and deter-mine the sensor node schedules over the whole network.[4][7]

Another resource management function that is enabled inPeloton is the reliable data collection of high data-ratesignals.[4]Reliable transfer requires substantial bandwidth and suffi-cient energy resources. Sensor networks lack in both band-width and power in the attempt to acquire high data-rateand high-fidelity signals throughout the whole network. Asa result only the most “interesting” or most “prominent” sig-nals will be acquired, while the rather“uninteresting”signalsare left out. Therefore the optimization of high data-ratesignal collections would benefit the resource management insensor networks.[4][8]One approach to do so is to enable clusterheads in the net-work in a similar way as in LEACH: Clusterheads can man-age the stored data of neighboring nodes and perform localoptimization to rank the importance of the signals. The sig-nals with the highest ranking or priority should be providedwith energy and bandwidth resources. With the coordina-tion of the clusterheads, the transfer of the high data-ratesignals can be managed and scheduled to the other nodes orto the base station. This approach of reliable data collectiontherefore allows a reliable transfer of high data-rate signalswithout consuming much energy and causing a communi-

cation overhead. The maximization of reliability of sensorsmaximizes the reliability of the completion of the assignedtasks, and thus leads to an overall solid performance.[4]

With all those resource management mechanism, Peloton isable to efficiently distribute resources within the network.The combination of Peloton’s reliable data collection withthe node energy schedule through duty cycling is not perfectyet, but make a good basis for the development of collabo-rative applications, which handle changing node states.[4]

3.2 Tiny Network ManagerAnother approach for optimizing resource consumption andhigh performance is called Tiny Network Manager. TinyNetwork Manager, short TNM, is a resource managementtool that is developed in 2010, based on devisable manage-ment, which is a kind of “autonomous management, wheredifferent network managers detect network events and dothe necessary tasks based on network resources, predefinedpolicies, intuition and intelligence”.[5]

Two different kinds of Tiny Network Manager applicationsexist:If Tiny Network Manager is used in large-scale sensor net-works, i.e. a sensor network with a huge amount of sensornodes, the software will reside and control the resources fromthe clusterhead nodes by collecting and analyzing informa-tion from the cluster members, whereas the Tiny NetworkManagers of the inner cluster members communicate eachother to handle uncertain events.If Tiny Network Manager, on the contrary, is used in a flatwireless sensor network architecture, the software will man-age the resources from a base station through the Internetto handle uncertain events[5].

Figure 3: Tiny Network Manager modules[5]

In Figure 3 the Tiny Network Manager structure is dis-played, which is composed of the four modules ManagementAgents, Rules and Policies, Unforeseen Event Managementand Extendibility and Implementers, whereas each of themhas its own additional components as well:


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Management Agents are in charge of detecting changesin the node collection and have specific management taskssuch as configuration management, fault management andperformance management.

The module Rules and Policies and its sub-modules pro-vide policy based management, which makes autonomousmanagement possible.

Unforeseen Event Management modules are trying tolook for a solution if specific network state changes come up,which are impossible to handle with the existing rules andpolicies.

The Extendibility Module manages all management poli-cies, agents and functions and can include, remove and mod-ify them if it applies.

3.2.1 EntitiesIn the resource management process, Tiny Network Managerdoes not work on its on but with the collaboration of thefollowing entities:

• Network Manager

• Clearing House

• Resource Manager

The Network Manageris an entity that works at the WSN gateway and is composedof the TNM and the Resource Management Module.[5] TheTNM performs its usual tasks, which are already describedand explained in the previous chapter. The Resource Man-agement Module carries out three main tasks - monitoring,managing and resolving – that are supported by their re-spective modules and sub-modules, which can be seen inFigure 4:

Figure 4: Network Manager[5]

- The monitoring module is composed of the Data Col-lector module, which collects resource information fromthe sensor node, and the Data Analyzer, which mea-sures and evaluates the resource information.

- The managing module consists of its main mainte-nance entity Resource Controller and the database Re-source Inventory, which stores all the collected resourceinformation.

- The resolving module is in charge of finding new re-sources or alternative ones through Resource Discov-ery and implements it through the Resource Provisionsub-module.

With the AAA Agent in the TNM module, the NetworkManager can maintain secure sessions to the Clearing Houseand the Resource Manager.[5]

Figure 5: Network Manager[5]

The Clearing House (CH) works in the sensor network andconsists of a Resource Management Module, the FederatedClearing House and Management Functions. It manages allthe information about resources and can provide the loca-tion and connection mechanism of registered resources if it isrequested.[5] Figure 5 shows the structure of the CH: Sameas the Network Manager it has a module to store and main-tain all resource information in a database. The Discoverymodule is the main entity of the CH and handles all theresource requests, whereas the module Management Func-tions is used for internal management and assistance. TheFederated Clearing House is responsible for maintaining allClearing Houses and providing authorization for communi-cations between CH-CH, CH-RM, and CH-Gateway (Net-work Manager).[5]

Figure 6: Resource Manager[5]


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The Resource Manager (Figure 6) is the last entity, whichcollaborates in the TNM process, and is in charge of resourcemanagement in the sensor network.[5]It has two main modules, Manage and Resolve, and its sub-modules, which perform the management process that willbe described in the further paper. Moreover it provides thesame Management Functions module as the CH and a Secu-rity Enabler, which enables the interaction between CH-RM,and CH-Gateway.[5]

3.2.2 Resource ManagementThe Resource Manager performs the resource managementprocess in collaboration with TNM, the Network Managerand the Clearing House: The Resource Management mod-ule as well as the Data Collector and the Analyzer mon-itor the resources. If a resource’s performance is decreas-ing, the Resource Controller has to look for an alternativeresource. If no solution can be found, then the Request-Reply-Mechanism will be initiated, which works in the fol-lowing way[5]:

Figure 7: Request-Reply-Mechanism[5]

1. The Resource Discovery module establishes a connec-tion to the Clearing House and sends a resource request

2. At the Clearing House, the Authorization module hoststhis request establishes a new session

3. After the session is established successfully, the Au-thorization module sends the request to the ResourceBroker, which looks up the required resource in thedatabase

4. If the required resource is found in the resource database,the resource information is transferred to the gateway

5. After the resource information is collected at the gate-way, another request is sent to the Resource Controllerof the Resource Manager

6. Resource Discovery and Resource Scheduler take careof this request by looking in up and performing thescheduler if several requests are made at the same time

7. The Resource Discovery and Scheduler take care of thisrequest and perform the scheduler, if several requestsarrive in the same period of time

8. The Extensibility module is in charge of implement-ing the resource in the network after its arrival at thegateways

3.3 ComparisonPeloton and Tiny Network Manager both provide sensornetworks a collection of functionalities to optimize the con-sumption of scarce resources and manage task assignmentsthroughout the whole network. The comparison of Pelo-ton and TNM in terms of energy, fault tolerance, reliabil-ity, scalability, real-time behavior, memory and autonomy,which can be seen in Table 1, show that both tools cover upmost of the network constraints. The provided functions,however, vary in effectiveness and efficiency.

Peloton TNM

Energy Cluster-Based Rout-ing, Duty Cycling

Cluster-Architecture

FaultTolerance

State Sharing, De-coupling allocations

Unforeseen Events,Management Agents

Reliability Reliable Data Col-lection

Rules and Policies,Modules

Scalability Ticket Abstraction,Duty Cycling

Modules (NW, CH,RM, TNM)

Real-TimeBehavior

Ticket Abstraction Resource Scheduler

Memory Reliable Data, Clus-ter Routing

-

Autonomy - Devisable Manage-ment

Table 1: Comparison of Peloton and TNM

Energy:Both management tools work with a cluster-architecture togain low energy consumption and optimize the workflow be-tween each sensor node. Peloton might be more effectivesince its cluster-architecture also enables cluster-based rout-ing, in which cluster-heads can rotate and take turns to sub-mit high-energy tasks. Moreover it has also implementedthe Duty Cycling mechanism, which enables sensor node toswitch their active state into an idle one.

Fault tolerance:In terms of fault tolerance, Peloton provides the State Shar-ing mechanism, which shares all the information on a node’scurrent information that allows other sensor nodes to reactto a breakdown or failure of a sensor node. Moreover re-source allocations can be decoupled in Peloton’s ticket ab-straction, which means that allocations to a broken sensornode can be revoked and transferred to another sensor node.Tiny Network Manager reacts and handles a node’s break-down in a more efficient way: The Management Agent is incharge of configuration, fault and performance management,on basis of the defined Rules and Policies, and supports the


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maintenance of the network, also in case of a node’s break-down. When there is no rule defined for a certain situation,the module Unforeseen Event Management is responsible forhandling it.

Scalability:Besides the cluster-architecture, which divides all the sensornodes into cluster-groups and therefore supports the scala-bility of a large wireless sensor network, TNM and Pelotonhave further functions to make a sensor network scalable:The TNM abstraction contains many modules for resourcemanagement of sensor networks. Those modules have cer-tain assigned tasks to monitor and manage sensor nodes,and providing resources and their information to all hard-ware components. This approach is supporting the scalabil-ity of a large-scale sensor network.[5]Peloton has an implemented ticket abstraction, which im-plies that every single action in the sensor network requiresa vector ticket and is managed by a ticket agent. This ticketabstraction enables tracking sensor nodes and resource allo-cations, and is therefore very useful to make a network morescalable. Moreover Peloton’s Duty Cycling mechanism alsosupports a sensor network’s scalability, since only taskingsensor nodes are active.

Reliabilty:The Reliable Data Collection from Peloton makes sure thatthe whole collection process is done correctly and that im-portant data is prioritized and highly ranked for transmis-sion.TNM is supported by Rules and Policies that are in chargeof the accurate tasks assignments and workflow. The re-liability and correctness of the functions and assignmentsare supported by TNM’s modularized system: Certain dataand functions are only available in certain modules, whichprovide a distributed system and simple architecture thatprevents confusion and data lost.

Real-Time Behavior:It is difficult to decide on the better tool in terms of real-time performance. Peloton’s vector ticket limits the resourceconsumption to an expiry time te, which is managed by thedistributed ticket agent in addition. TNM’s Resource Sched-uler, on the other hand, decides on the time schedule of allresource requests and allocations.

Memory:Sensor nodes have limited memory space, that means thatonly a limited amount of information can be stored or pro-cessed in a node, else a stack overflow will be caused. Pelotonpossesses the resource management function Reliable DataCollection, which optimizes high data-rate signal collections.This process has the result that not only energy and band-width consumptions will be reduced, but also the amountof information that has to be stored in a node. Moreoverthe sensor nodes can also take turns in collecting the infor-mation through the rotation of the cluster-members enabledthrough the Cluster-Routing.

Autonomy:TNM provides the architecture and design for autonomousresource management, since its development was based ondevisable management. Therefore TNM is able to work au-

tonomously and do the necessary tasks based on network re-sources, predefined policies, intuition and intelligence. Pelo-ton on the contrary doesn’t provide intelligent autonomy.

4. CONCLUSION AND FUTURE OUTLOOKOverall it is difficult to decide on the better tool out of thosetwo. It depends on the purpose of its usage:Peloton should be used in sensor networks, which want to en-able low energy consumption, reliable data collection, scala-bility, real-time performance and low memory consumption.TNM on the other hand should be used in sensor networks,which require high fault tolerance, reliable data collection,real-time performance and autonomy.

The future development of resource management tools doesnot only depend on optimization algorithm and functions,but also on the development of the sensor networks in gen-eral. Throughout the past few years, the technology of sen-sor network has achieved enormous progress, as it has be-come more important day by day.

1980’s - 1990’s 2000 - 2003 2010

Manufacturer Custom con-tractors, e.g.for TRSS

Commercial:CrossbowTechnologyInc., SensoriaCorp., EmberCorp.

Dust, Inc.and others tobe formed

Size Large shoe boxand up

Pack of cardsto small shoebox

Dust particle

Weight Kilograms Grams Negligible

Nodearchitecture

Separate sens-ing, processingand communi-cation

Integratedsensing, pro-cessing andcommunica-tion

Integratedsensing, pro-cessing andcommunica-tion

Topology Point-to-point,star

Client server,peer to peer

Peer to peer

Powersupplylifetime

Large batter-ies; hours, daysand longer

AA batteries;days to weeks

Solar,monthsto years

Deployment Vehicle-placed,airdrop singlesensors

Hand-emplaced

Embedded,”sprinkled”left-behind

Table 2: Comparison of Peloton and TNM

The functionalities of the resource management have to adaptto the changes of the sensor network and its capabilities.Table 2 shows that sensor networks became smaller in itsevolution and have already reached a ridiculous size of dustparticles.[2]

In terms of optimizing resource constraints, Peloton andTiny Network Manager have shown us first approaches onhow to manage them. Even though these approaches canprovide efficient resource management, none of them cancompletely address and handle with uncertainty, which isinevitable in dynamic networks.All network constraints cannot be resolved, no matter how


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much resourceful a sensor network gets, since unforeseenevents cannot be predicted and measured.For this reason,it is always important to keep in mind, which functionali-ties this resource management tool has to enable and whichnot.[5][10]

Peloton’s and TNM’s approaches carefully implemented ona case-by-case basis in sensor network and turned out to beconstraint satisfying and utility based. Therefore they aredefinitely suited as a basis for future resource managementsystems and software.

It is very likely that future network management tools willkeep focusing on developing and improving autonomous anddistributed resource management for dynamic wireless sen-sor networks.[10] Therefore they should have a frameworkthat can enable “a large set of applications with autonomousadaptation and minimum communication overhead”, whichis already implemented in another management tool calledCollective Intelligence and aims for a higher system wideutility.[10]

With the combination of Peloton and Tiny Network Man-ager methods and structures and Collective Intelligence’stheory, an even more efficient management tool can be cre-ated in the near future.

5. REFERENCES[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E.

Cayirci: A Survey on Sensor Networks, pages 102-105,IEEE Communications Magazine, 2002

[2] C. Chong, S. P. Kumar: Sensor Networks: Evolution,Opportunities, and Challenges, pages 1247-1252,Proceedings of the IEEE, Vol. 91, Nr. 8, 2003

[3] S. Walton, E. Eide: Resource Management Aspects forSensor Network Software, PLOS ’07 Proceedings ofthe 4th workshop on Programming languages andoperating systems, Article No. 5, October 2007,National Science Foundation

[4] J. Waterman, G. W. Challen, M. Welsh: Peloton:Coordinated Resource Management for SensorNetworks, 12th Workshop on Hot Topics in OperatingSystems (HotOS-XII) (2009), pages 1-5, May 2009,Harvard University

[5] M. S. Siddiqui, C. S. Hong: Resource & ConfigurationManagement for WSN in the Future Internet,Applications and the Internet (SAINT), pages193-196, July 2010, Dept. of Comput. Eng., KyungHee Univ., Yongin, South Korea

[6] M. J. Handy, M. Haase, D. Timmermann: Low EnergyAdaptive Clustering Hierarchy with DeterministicCluster-Head Selection, Mobile and WirelessCommunications Network, pages 368-372, September2002, Institute of Applied Microelectronics &Computer Science, Rostock University, Germany

[7] D. Christmann: Duty Cycling in drahtlosenMulti-Hop-Netzwerken, Kommunikationssysteme WS08/09, pages 1-3, 2009, Seminar furKommunikationssysteme TU Kaiserslautern

[8] G. Werner-Allen, S. Dawson-Haggerty, M. Welsh:Lance: Optimizing High-Resolution Signal Collectionin Wireless Sensor Networks, SenSys ’08 Proceedings

of the 6th ACM conference on Embedded networksensor systems, pages 169-182, November 2008,Raleigh, NC, USA

[9] H. E. Baarsma, M. G. C. Bosman, J. L. Hurink:Resource Management in Heterogeneous WirelessSensor Networks, Capturing Ambient Intelligence forMobile Communications though Wireless SensorNetworks, pages 1-9, July 2007, e-SENSE

[10] K. Shah, M. Kumar: Resource Management inWireless Sensor Networks using CollectiveIntelligence, pages 423-428, Intelligent Sensors, SensorNetworks and Information Processing, ISSNIP 2008,Dezember 2008

[11] WebFinance, Inc., Business Dictionary: ResourceManagement,http://www.businessdictionary.com/definition/resource-

management.html; accessed on 10th January2013


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