Design and Evaluation of an Autonomic Sensor Element

Anais do XXVI Congresso da SBC LAACS 2006 l 1st Latin American Autonomic Computing Symposium

14 a 20 de julho de 2006

Design and Evaluation of an Autonomic Sensor Element

Campo Grande, MS

Thais R. M. Braga1 , Fabr´ıcio A. Silva1 , Linnyer B. Ruiz2 , Jos´e Marcos S. Nogueira1 , Antonio A. F. Loureiro1 Department of Computer Science Department of Electrical Engineering Federal University of Minas Gerais Av. Antˆonio Carlos, 6627 31279-010 Belo Horizonte, MG, Brasil 1

2

{thaisrb, fasilva, linnyer, jmarcos, loureiro}@dcc.ufmg.br

Abstract. Autonomic Networks control and supervise themselves without direct human intervention. In the definition and development of these networks, the smallest part to be built is the Autonomic Element (AE). The design aspects and the functionalities involved in modelling an AE influence the performance of managed elements, network and provided services. This article presents the proposal of an Autonomic Sensor Element (ASE) to Wireless Sensor Networks (WSNs). The challenge in this case is to design ASEs that can be executed in environments with severe hardware, software and data communication restrictions and that still can promote productivity of these resources and quality of the services provided by the sensor nodes. Particulary, this work creates an instance of an ASE model considering the Mica Motes 2 sensor nodes platform. The results have shown the relevance of applying autonomic computing in WSNs.

1. Introduction Wireless Sensor Networks (WSNs) are considered a special kind of ad hoc networks generally comprised of hundreds to thousands of network elements (sensor nodes) deployed at remote environments, where local maintenance and administration performed by technicians are impracticable. The control and supervision of these networks elements are complex tasks, given that the acquirement of data related to energy, localization and quality of services implies in traffic and consequently energy consumption. This work proposes the application of the autonomic computing paradigm in WSNs with the goal of promoting these networks resources productivity and quality of provided services. Particulary, the paper proposes an autonomic element model to be embedded into the elements of these networks, which can be instantiated to different sensor nodes platforms. Autonomic Computing defines computational systems that are able to manage themselves with none or minimal human intervention [Kephart and Chess 2003]. The implementation of the concepts related to autonomic computing in computer networks leads to the development of a new concept: autonomic networks. This kind of network is able to perform selfmanagement of its elements and of the connections among them. The network management services and functions are performed without the involvement of a human manager and in a transparent way to its users. Besides, the network is able to learn from the actions performed by its elements and the analysis of all acquired results. The automatic execution of tasks and the learning possibility characterize the autonomic aspect of this kind of network. Autonomic Elements (AEs) are the smallest part of an autonomic system, and can be seen as individual systems that contain resources and provide services to humans and/or other AEs. It is possible to have AEs embedded into different types of managed elements, like hardware devices (disks and CPUs for example), software units and even collections of components, like a PC or desktop. An AE contains a single manager that represents and monitors the managed element(s). The design of AEs to devices or components collection in particular can be considered one of the main challenges of autonomic computing research [Kephart 2005]. This 36

work deals with this challenge: to propose an autonomic element to WSNs, called Autonomic Sensor Element (ASE). The definition of autonomic services and their respective functions has been performed through the observation of WSNs and their components particularities, and considering the proposal of Manna architecture to the self-management of this kind of network [Ruiz 2003]. The ASE model was based on the generic format of an AE described in [Kephart and Chess 2003]. The remainder of this article is structured as follows: Section 2. discusses the main aspects related to autonomic networks. Section 3. describes related work found in literature. The ASE model proposed in this work is presented in Section 4.. Simulation design and the analysis of obtained results are described respectively in Section 5. and 6.. Finally, Section 7. contains the concluding remarks.

2. Autonomic Networks The autonomic computing is a wide paradigm, which defines the capability of self-management to many types of computational systems. However, nowadays machines and computational environments are not isolated and therefore it will not suffice to maintain the focus only on systems parts, like hardware and software units or even components collections. It is necessary that the network that performs the communication among components or systems also implements autonomic computing aspects. In this case, network components and the networks themselves can be seen as autonomic computational systems or autonomic networks. The autonomic networks should be able to diagnosis and locate faults in their structure, acting on them in a way to eliminate or isolate them, causing the smallest impact possible to the user service provisioning. This system capability of recovering itself from extraordinary events and routines is called self-healing. Besides, these networks should implement protection mechanisms, which guarantee the maintenance of security and privacy of their elements (self-protection). The task of computer network configuration is becaming each day harder and laborious. Heterogeneous networks, with large dimensions and a dynamic nature, turned configuration into something even more complex. Therefore, it is necessary that autonomic networks implement self-configuration, being able of performing it every time it is necessary and under variable and unpredictable conditions. Finally, self-optimization is also an important aspect. The networks should perform constant monitoring and adjustment of elements and links, finding ways to optimize their performances. The smallest part of an autonomic network are its components (network elements). Each component is capable to monitor and control itself, possessing an AE embedded, responsible of implementing management services and functions. It is worth to remind that the hardware and software of a network component can be an independent autonomic system, possessing self-management capabilities. Each AE acts as a manager, responsible for promoting resources productivity and quality of services provided by the component in which it is installed. The manager executes a continuous control loop that performs monitoring and analysis of managed element internal and external data, and also planning and execution of corrective actions or optimizations of this element functioning. Besides, the AE has a module that is a knowledge base where it stores data, rules, limits, among others. This definition of AE is a generic model presented in [Kephart and Chess 2003]. The functions that compose each one of the continuous control loop services of an AE, its knowledge base, the stages that composes its life-cycle and the way they are executed, as well as the relationship model among AEs of an autonomic network, should be defined in a specific and particular way to each computer network kind and technology. The definition of these aspects is pointed by Jeffrey O. Kephart in [Kephart 2005] as one of the main and fundamental research challenges connected to the autonomic computing field. Although there is this need of defining the specific context of AEs for each computational environment, it would be interesting if all of them were defined according to a generic model, due to standardization and interoperability reasons. 37

2.1. Wireless Autonomic Networks Currently, wireless networks are constantly being used. New hardware, protocols, services and operating systems have been developed to this kind of network. Besides, the users expect wireless networks to be integrated to the traditional technologies, like the Internet and wired local computer networks (LANs), increasing its utility, broadness and complexity. Typically, wireless networks elements possess some sort of resources restrictions. The most common one is the energy restriction, due to the use of a finite source of this resource. However, it is still possible to notice restrictions of memory, bandwidth, communication range, among others. In this way, its is clear the need of using a powerful and specific management tool to wireless networks, capable of controlling and solving question of compatibility, quality of service, rational use of resources and connectivity. The resources offered by autonomic computing fit well with the demands for management services and functions necessary to wireless networks. WSNs are a special kind of wireless and ad hoc networks, which are application dependent and frequently designed to operate in inhospit or hostile environments. WSNs have all the challenges described above to wireless networks, besides those connected to ad hoc (organization and maintenance, for example), mobile networks and those related to their particular characteristics. It can be noticed that the task of managing this kind of network is not trivial. Therefore, once more the implementation of autonomic resources is of fundamental importance. In order to use effectively the concept of autonomic networks in WSNs, it is necessary to design an Autonomic Sensor Element (ASE). The ASE will be the smallest part in an autonomic WSN, and must be embedded into each network sensor node.

3. Related Work There are some papers available in literature that deals with the use of autonomic aspects or services in WSNs. All these articles demonstrate the benefits of applying some particular characteristic of the autonomic network paradigm. However, none of them present a model to turn the WSNs network element (sensor node) into and autonomic entity. In [Marsh et al. 2004] the authors discuss how autonomic characteristics can be incorporated in WSNs using the deposition of mobile and agile technologies based on the use of multiple agents. A WSN comprised of three sensor nodes was used and each node of the network programmed in a different manner. The results have shown that the autonomic node was capable of detecting events with more efficiency besides sending a greater number of data to the Acess Point (AP). In the article described in [Pujolle and Chaouchi 2005], the authors present an autonomic architecture (Autonomic-Oriented Architecture - AoA) defined to give self-organization and self-management support in sensor networks. The AoA architecture is defined as a set of 4 abstraction plans, namely the management, knowledge, control and data plans. It is not the objective of the paper to propose new algorithms, protocols or schemes to the control plan, but to promote a dynamic selection of the best algorithms, protocols and parameter values at any moment. Other papers available in literature, such as [Liu and Martonosi 2003, Ruiz et al. 2005], also use autonomic computing aspects to the implementation of some service or function, like self-organization, self-maintenance and self-configuration in WSNs. Our work differs from the articles mentioned above since it does not present autonomic solutions to specific services, but an ASE design that can make possible the implementation of the autonomic network paradigm to WSNs components. Considering the use of autonomic solutions to traditional networks, there are some papers available in literature that deal with the development of techniques and algorithms to selforganization [Eymann et al. 2003], self-configuration and knowledge distribution and reinforcement [Littman et al. 2004], for example. However, all these approaches were defined and designed to traditional networks, not being adequate or event directly applicable to wireless networks and particulary to WSNs, once they do not consider the intrinsic characteristics of this kind of network. In [Ruiz 2003], a WSN management architecture called Manna is specified. This architecture, a pioneer in its proposal, defines the use of the two traditional management dimensions (Functional Areas and Management Levels) and proposes a new one (WSNs Functionalities) to 38

(b) Analysis Limits Management

Parameters Management Receives management messages Monitors data stream Monitors HW and SW parameters

Updates knowledge Base

(a) Monitoring

(c) Planning Correlation Algorithms Management

Policies Machine Management

Interprets messages

Plan actions

Correlated events

Plan management messages

Classify data

Apply business policies

Updates or consults Knowledge Base

Updates or consults Knowledge Base

Management Protocol Communication Protocol Base

Local AIB

Actions Plan Management

Faults and Strategies Management

Prepares execution environment Consults Knowledge Base

Executes actions plan Treats execution faults

(d) Execution

(CPB)

Knowledge Base

Defines execution strategy

Policies Machine

Local MIB

Managed Element

Figure 1. General vision of the services and functions that comprise the Autonomic Sensor Element.

the management of these networks. The proposal of an ASE to WSNs complements the Manna architecture goals, which is to allow the components of these networks to manage themselves, with minimal human intervention.

4. Autonomic Sensor Element In this section, we present a proposal of an autonomic element to WSNs, called Autonomic Sensor Element (ASE). This element is capable of being executed in environments with severe hardware, software and data communication restrictions, and still promote resource productivity and quality of services provided by the sensor nodes. Although the manager role of an AEs is quite well defined, the tasks to be performed and the way they are implemented differ from an environment to another. According to the format of a generic AE proposed in [Kephart and Chess 2003], these elements are comprised of five parts: monitoring, analysis, planning, execution and a Knowledge Base (KB). In the model proposed in this work, the four initial parts can be seen as management services, executed in an autonomic way through a set of functions. The services are connected through an infinite execution autonomic loop. All the services interact somehow with the KB. 4.1. Monitoring Service An autonomic system cannot manage what it does not know that exists. In this case, the selfknowledge and self-awareness are important concepts that should be provided by the autonomic element model used. The self-knowledge indicates that a managed element must know itself and its components, while the self-awareness proposes that it should also know the neighborhood and environment that surrounds it. The monitoring service (Figure 1(a)) of an autonomic element is responsible for the implementation of the two concepts described above. Once WSNs components have great resources limitation, the monitoring service should restrict itself to a minimal set of parameters that represents satisfactorily and momentarily the state of the managed element, its components and environment. Each parameter of little relevance represents an unnecessary resource waste. It is important to stand out that the optimal set of parameters monitored by the ASE is extremely dependent on the application to which the WSN was designed, as well as the context or moment in which the network is. The parameters monitored by the ASE can be divided into two categories: internal and external. Internal parameters are related to the self-knowledge concept and reflect the current state of managed element hardware and software. External parameters are those connected to 39

self-awareness and construct the managed element vision related to the network condition, its connections with neighbors and the state of these neighbors. Internal parameters gathering is performed by functions of direct access that the ASE has to the hardware and software components of the managed element. Considering the external parameters, two main function must be implemented: data stream monitoring and management messages reception. WSNs produce a collected data stream, no matter their applications, sensing and dissemination types. This stream is routed in a multihop communication scheme to the network Access Point (AP). Therefore, each network sensor node not only produces a data stream, but it is also responsible for disseminating other streams, created by other network nodes, and that pass through it in order to reach the AP. The analysis of the multiple data streams that are created or pass through a sensor node can provide important external parameters to the ASE self-awareness. Besides, in certain occasions, the various ASEs presented in the network can desire to exchange management messages among them or receive messages coming from the AP. Some reasons to send management messages are service negotiation and information exchange. In this case, the ASE should monitor the reception of such messages. At this point, it is necessary to perform an analysis of monitored data, to generate some knowledge. 4.2. Analysis Service The internal and external context verification of an autonomic element will be performed by the analysis service. It is of responsibility of this service to transform input data acquired by the monitoring service, into useful information that can be analyzed and that leads to conclusions on many aspects, like faults or performance for example, related to the managed element. The functions defined to the analysis service of the ASE can be seen in Figure 1(b). The autonomic concept connected to this service is self-diagnosis. Until this moment, the ASE has new data but no new information with which it could analyze the situation of the managed element. The first function to be executed is the data classification, transforming them into events. It is necessary that the ASE has limit values (thresholds) stored in its KB, with which it will classify the data using some scale. The limits should be adjusted all the times it is necessary to do so, and those that are no longer used can be discarded. It is up to the analysis service itself to perform the management of these limits. In the following, the identified events correlation function will be executed. There are various correlation algorithms available in literature. Some of them are considered simple, such as rule based correlation, while others can be very complex (bayesian or neural networks). The use of a determined algorithm will be defined by the ASE based on its adequation to the current situation of the managed element. The ASE can maintain more than one algorithm option stored, discard those that are no longer used and identify the need to request the reception of a new code. The management of the correlation algorithm is another function of the analysis service responsibility. Once the ASE can have received management messages from other ASEs of the network, or even from the AP, which were received by the monitoring service, it must contain an interpretation function for these messages. The analysis service contains this function and uses knowledge about the management protocol used by the network stored in the KB, to perform it. According to the type and content of the messages, new classifications and correlations can be necessary. An important discussion that shows up in this point is concerned to which aspects of the managed element will be observed and at which levels, i.e., which functional areas and management levels will be considered. Besides, once the ASE model is based on the tridimensional management proposed by the Manna architecture, it is still necessary to define which WSNs functionalities must be analyzed. In [Ruiz 2003] it is defined a cube that shows the intersections among the three management dimensions used by this architecture. Each intersection defined a cell that is actually a perspective about the management of a WSN that indicates functional area(s), management level(s) and functionality(ies) considered. Therefore, the ASE can define which are the considered perspectives. It can chose one or a set of perspectives, according to each situation in particular. Besides, this set can be modified all the times it is necessary. Concluding this analysis stage, a KB update function must be performed. 40

4.3. Planning Service The planning service of an ASE has as its main goal to chose or elaborate a plan of actions to be followed, according to the results of the analysis performed by the previous service. Once more, a set of associated functions to this service can be defined according to the precision and autonomicity degrees desired and the level of complexity allowed. The ASE can implement very sofisticasted concepts of artificial intelligence for example, in case it has compatible computational resources. Therefore, the design and development of the functions that composes the ASE planning service should be performed considering the trade-off between complexity and precision. Figure 1(c) illustrates the ASE planning service. The policy based network management (PBNM) is used specifically by the planning service. Polices can be defined as a set of considerations designed to guide decisions. They define goals and limits that govern the autonomic element actions. Initially, a function searches the KB for the information or conclusions acquired about the set of perspectives considered by the analysis service. These conclusions are given to another function that will apply them to the corresponding polices. The polices are going to map the conclusions into one or more actions, which are going to be executed later with the aim of adjusting, improve or fix something in one or more ASE perspectives. Identified the actions, a function will be responsible for organizing them in a set, creating therefore the actions plan of the ASE. Another function must plan the management messages, in case it is necessary to send any. The actions used by the ASE can be related to various autonomic concepts like self-healing, self-optimization, self-protection, self-organization, self-maintenance, self-sustain and self-service [Ruiz 2003]. The management of the polices machine and actions that the ASE can execute are also responsibility of the planning service. In this case, it should always maintain an optimal set of polices and actions, discard the unnecessary items and identify the moments when updates are needed. New actions and polices are inserted by this service in the ASE KB. Specifically considering polices, the machine management function should be able to receive business policies, i.e., high-level policies specified by the network observer, and transform them into simpler policies or even SLAs (Service Level Agreements). 4.4. Execution Service The execution service is connected to one of the main aspects of the autonomic computing paradigm: the self-configuration. It is through this aspect that the AE can act over the hardware or software of the managed element, trying to configure them in the best possible way considering the needs of this element and the conditions of the environment that surrounds it. In the ASE model proposed in this work, as illustrated by Figure 1(d), the first step of the execution function is to consult the KB in order to get the actions plan defined by the planning function. According to the analyzed perspectives and the chosen actions plan, the selfconfigurations can be performed at different management levels and to one or more functional areas. Another function should prepare the execution environment, according to the actions. It can be necessary to get some data or information, prepare one or more hardwares, execute a set of software components, among other activities. Before calling the functions that effectively executes the actions plan, another function responsible for defining the best execution strategy is invoked. Mainly in case an action can interfere in the result of another, it is important that the ASE identify which is the best execution order to the actions of a plan. The function of plan execution performs each action it the order they have been planned. If the executions were all well succeeded, the ASE completes an autonomic loop iteration. The changes performed should cause some impact over the autonomic element, the environment in which it is and/or on its neighbors. These changes will be perceived by the ASE, through the monitoring service, in the next loop iteration. In case one or more executions present problems, the ASE should invoke a fault treatment function. This function will search for ways of dealing with the occurred problems. Sophisticated mechanisms of faults identification and correction can lead to good results, but the resources consumption associated could be prohibitive. It is interesting that this function implements simple and efficient strategies. 41

4.5. Knowledge Base (KB) The KB stores data, information, polices, limits, among other items. The trade-off observed by the ASE in this case is between the store space consumption and precision. The sensor nodes have a limited amount of memory space. Besides, the energy consumption restrictions requires that even the memory read and write operations should be well planned. On the other hand, it is interesting that the ASE could count on all necessary items into its KB. The ASE should therefore maintain an optimal set that contains only really necessary items. This set can vary over the network lifetime. Therefore, the unnecessary items are discarded and new others, if this is the case, inserted. As illustrated by Figure 1, the KB has four components: a local information management base (MIB), an application information base (AIB), a policies machine and a module correspondent to the management protocol used (CPB - Communication Protocol Base). Local MIB: the MIB of an AE is not static, i.e., its components should be constantly managed and renewed in case it is necessary. In the case of the ASE, its MIB should be dynamic and optimized. Only the components that are really important to the WSN current context should be maintained. Local AIB: it is an information base that presents a storing structure and data representation similar to a MIB, but that only possess contents related to the application to which the WSN has been designed. Policies Machine: it should be a compact version of the policies machine used by traditional networks. Only the most necessary elements, implemented in an optimized and simple way, should be maintained. The used policies, algorithms and SLAs must also be optimized. Communication Protocol: the AEs must share a way of exchanging messages. The specification of this way is given by the used management protocol. The ASE should store into its KB, in a compact way, the specifications of the communication protocol used by the network. In case the used protocol is updated or even changed, it is up to the ASE to self-adapt to the new situation.

5. Designing an ASE to the Mica2 Platform In this work, besides proposing an autonomic element to WSNs (ASEs), we have instantiated the proposed model to the Mica Motes 2 [Hill and Culler 2002] sensor node platform. As a study case, this model has been instantiated considering WSNs that are flat (without groups) and homogeneous (all nodes have the same hardware characteristics). The performance of these networks has been compared to that of other WSNs with same configurations, but that do not use management functions and services. Three types of simulation scenarios were constructed: two of them comprised of autonomic WSNs, whose nodes have an ASE instance embedded, and one containing a not managed WSN. In this last case, the network elements only sense and disseminate their data, using always their initial configurations. Considering the autonomic scenarios, two models have been designed and implemented: a localized model in which the sensor nodes do not exchange control messages among them, and a distributed one, i.e., with information exchange among the network elements. The implementations have been done using the NS-2 (Network Simulator - 2) [Network Simulator 2005]. 5.1. An Autonomic Sensor Element Instance The ASE instance contains simple implementations of each one of the four services defined for an ASE, besides a knowledge base that possess MIB, AIB, CPB and policies machine. Figure 2 illustrates the general functioning of the designed ASE instance. The monitoring service counts some hardware and software parameters, such as residual energy, number of relevant data collected (data that present values higher than the expected average), number of relevant data collected by neighbors, message loss percentage and amount of transmitted bytes. Sensing and dissemination intervals are also monitored. The monitoring is periodic, occurring from the analysis of data streams generated by the node itself and/or received from its neighbors, besides hardware and software information readings. In the autonomic 42

distributed networks scenarios, the ASEs of neighbor nodes are able to exchange control messages among them. These messages contains counted and configuration data and are exchanged periodically. In this case, one more parameter can be monitored: percentage of neighbors with similar configurations. All information acquired by this service are stored into the MIB and AIB of the ASE. The analysis service is executed in the following. The information acquired is converted into events, using upper and lower limits values, previously defined and stored into the ASE AIB. Each monitored parameter value is classified as high, medium or low and each classification represents and event. The events are used in a correlation algorithm, which will present conclusions about the sensor node performance, i.e., it will verify whether the desired quality of service (QoS) level is being provided or not. Monitoring Maps parameters into events

Analysis / Planning Correlates events

Planning / Execution Generates performance conclusions

Reconfiguration is necessary? YES

Data Stream (generated and/or received)

Counts parameters

Receives management messages

Interprets and constructs management messages

Constructs actions plan

NO

Evaluates neighbors cooperation needs

Reconfiguration and/or reprogramming and/or management messages sent

Figure 2. Instance of the ASE model implemented to this work simulations.

According to the analysis service results, the planning service should verify which actions must be taken. To simplify the ASE instance design used in this work, the actions options had been restricted to the increase or decrease of sensing and dissemination intervals. There exists still an option of administrative state (active or inactive) change to the distributed autonomic WSNs. The control messages exchanged by the nodes of these scenarios are planned by this service. The available action options are stored into the ASE MIB. Finally, the execution service receives the actions plan to be executed (including the messages to be sent) and perform it according to the best execution strategy possible. 5.2. Simulation Details In this work a carbon monoxide (CO) monitoring application has been chosen as our study case. Average values sensed by common nodes normally and during event occurence were respectivally 43mg/m3 and 80mg/m3 . In order to perform routing in the simulated scenarios, an algorithm called EFTREE [Figueiredo et al. 2004] has been used. This algorithm, which is specific for flat WSNs, constructs a routing tree with the network nodes, starting from its AP. The sensor nodes that are removed from service temporarily by the hardware configuration of the autonomic distributed networks, continue to perform routing of data received from their neighbors. To verify the behavior of the nodes when there is an unexpected increase of the average value of the sensed parameter, an event occurrence during the simulation was planned. In this moment, some network nodes start to collect data with values that are higher than the expected. Table 1 presents each implemented scenario. - Network: Number of nodes: 100 or 200; Organization: flat; Configuration: homogeneous; Area size (X and Y): 200m x 200m (to 100 nodes) and 282m x 282m (to 200 nodes); Density: 1 node to each 400m2 ; AP localization: perimeter; Nodes distribution: uniforme; Simulation time interval: 10.000s. - Network Elements: Initial sensing interval: 20s; Initial dissemination interval: 40s; Monitoring interval (ASE): 100s; Control messages interval: 200s; Backups turn off interval : 300s; Tx consumption: 0.036W ; Rx consumption: 0.024W ; Sensing consumption: 0.0021W ; Processing consumption: 0.024W ; Bandwidth: 19.2 Kbps; Sensing type: programmed; Disseminating type: programmed; Protocols stack: MannaNMP [Silva et al. 2005] /EFTREE /802.15.4; 43

Table 1. Implemented scenarios. Networks density has always been kept in 1 node/400m2 , i.e., one node to each 400 square meters.

Scenario 1 2 3 4 5 6

Type # Nodes Distributed Autonomic 100 Localized Autonomic 100 Not Managed 100 Distributed Autonomic 200 Localized Autonomic 200 Not Managed 200

Mobility: no; Battery Capacity: 1J; Sensing and communication range: 40m; Message Size: 64 bytes. Considering the data classification (events generation), the monitored parameters related to the amount of relevant data use percentages to distinct among high (greater or equal than 80%), medium (between 20% and 80%) and low (less or equal than 20%). To the limits related to the intervals, it was defined that they would be considered high if they are at least 4 times greater than the initial value, low if they are at least 4 times smaller and medium if they are in the interval between high and low. To the amount of sent bytes, sending less than 500 bytes is considered low, more than 15000 bytes high and between these two values, medium. To the reconfiguration it was used as a standard the increase or decrease of 10 seconds in the intervals until maximal or minimal values are not reached. To the sensing, it was allowed maximal interval of 120 seconds and minimal of 10 seconds. Considering the dissemination, maximum value of 180 seconds and minimal of 15 seconds. The autonomic distributed WSN still has another limit, used to verify whether or not the percentage of neighbors with similar configurations parameter is high (greater or equal than 80%), low (less or equal to 50%) or medium (between 50% and 80%). Besides, it was necessary to use two other limits that defines whether or not the information of a certain node is very similar to those of its neighbor. In this work, in case 60% of a node’s data are only 20% different from those of a neighbor, it is considered that the information of both nodes are similar.

6. Results Analysis

6.1. Energy Consumption The first line of Table 2 shows the average value of the common nodes total energy consumption to each scenario. It can be observed that the energy consumption of nodes in scenarios 1 and 4 (distributed autonomic) is smaller than that of the other scenarios. The networks of scenarios 2 and 5, localized autonomic, have consumed more energy than those of scenarios 1 and 4 once they were not able to do hardware reconfiguration (administrative state changes), but still could present a better result than the not managed networks of scenarios 3 and 6. Table 2. Average energy total consumption of common nodes in Joules and average percentage of consumption per service (communication, processing, sensing). Scenario Total Consumption (J) % Communication % Processing % Sensing

1 2 3 4 5 6 0,64 0,96 0,99 0,61 0,91 0,99 95,06 74,92 90,85 98,48 83,65 91,83 4,79 24,44 8,14 1,49 16,05 7,46 0,15 0,64 1,01 0,03 0,30 0,71

There was not a significative impact on the energy consumption with the increase of network nodes number. This is mainly due to the fact that the network density was kept constant. 44

Table 2 still shows the average percentage of common nodes energy consumption per service. The autonomic distributed networks have presented the greater consumption with communication, due to the exchanged management messages. However, those networks have presented an average total consumption approximately 40% smaller than those of the other scenarios. Scenarios 2 and 5 have consumed less with communication, because they have sent less data messages than scenarios 3 and 6 and their nodes did not performed management messages exchange. 6.2. Information Sent by Common Nodes Table 3 shows the total amount of messages, data, and relevant data sent by the nodes of each scenario. Considering the scenarios containing 100 nodes and those with 200, the autonomic networks could send a smaller total number of messages because they increase their sensing and disseminating intervals during the adequate moments. However, when the data stream analysis detects the presence of an event, those intervals are decreased, allowing a great number of samples (data) to reach the observer. Table 3. Number of messages and data sent. A message can contain one or more sensed data. A relevant data is that one collected during an event occurrence.

1 2 3 4 5 6 5.116,8 6.536,3 9.983,1 8.288,9 10.632,6 16.408,7 8.175,5 10.256,6 19.866,4 13.731,4 17.070,5 32.617,6 3.083,0 4.384,6 2.474,4 5.904,3 7.311,3 4.627,8

Scenario Messages Data Relevant Data

The autonomic distributed WSNs (scenarios 1 and 4) have sent a smaller number of messages and data then the autonomic localized ones, since that during the network lifetime there was always a set of nodes out of service. The not managed networks have always sent a greater number of messages, but a smaller number of relevant data. Scenarios 2 and 5 have presented interesting results to this metric, considering that their nodes have sent a greater amount of relevant data than the nodes of the other scenarios. It is worth to highlight that the nodes of these scenarios (2 and 5) can send more relevant data, sending less messages than the nodes of the not managed networks (scenarios 3 and 6).

Sent Bytes − Common Nodes

6.5e+06 6e+06 5.5e+06

Bytes

5e+06 4.5e+06 4e+06 3.5e+06 3e+06 2.5e+06 2e+06 Scenario

1

Number of data and messages received − Access Point 6000

Number of Data/Messages

Distributed Localized Not managed

2

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(a) Total sent bytes.

5

6

5000

Relevant Data Total of Messages Relevant Messages

4000 3000 2000 1000 0 Scenario 1

2

3

4

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6

(b) Data and messages received by the AP.

Figure 3. Averages and standard deviation of total bytes sent by common nodes and number of data and messages received by the AP.

The autonomic networks have sent a great number of data and messages only during the moments of an event occurrence. However, at the moments when nothing happens in the monitored environment, the nodes have sent a smaller number of messages containing few sensed data. Figure 3(a) shows that at the end, the amount of bytes sent by the nodes of autonomic 45

scenarios is smaller than that of the scenarios with not managed networks. In particular, the distributed networks have presented a smaller number of sent bytes, due to the use of hardware reconfiguration techniques. 6.3. Information Received by the AP The graph of Figure 3(b) shows the number of data, messages and relevant messages received by the AP. The great amount of data and messages sent by the always active nodes of the localized autonomic network of scenario 2 did not overload the communication media and allowed a greater delivery, mainly of relevant information. However, in scenario 5, the high number of messages leaded to collisions and loss, and in this case the distributed scenario presented better results, although it has sent smaller amount of data and message. In general, the autonomic networks have always obtained better results than those not managed, due to the strategic use of sensor nodes and network resources. 6.4. Number of Lost Packets The implementation of the management services has also caused an impact on another important metric: the number of lost packets. The use of hardware reconfiguration and software reprogramming techniques has influenced this metric in a positive way, as shown by the graph of Figure 4(a). Figure4(b) shows the relationship between the number of packets sent and the number of packets lost for each scenario. The autonomic scenarios had sent a smaller amount of packets, which reflected in a smaller percentage of loss, energy consumption and sent bytes.

Packet Loss Percentage − Common Nodes

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Figure 4. Average and standard deviation related to metrics of packets sent and lost.

The not managed networks of scenarios 3 and 6 have presented the worst results. The great number of packets sent continuously has lead to the congestion of the transmission media, and consequently to collision and loss. Scenario 1 has smaller loss percentage than scenarios 2 and 3, as well as scenario 4 considering scenarios 5 and 6. Relating the messages and data sent metrics with packets loss percentage, it can be perceived that although the autonomic localized networks have sent a greater number of messages and data (see Section 6.2.), the packets loss in these scenarios is greater than that of scenarios 1 and 4, indicating overload on the transmission media. The impact of this overload can be seen in graph 3(b). To the scenarios with 200 sensor nodes, the AP has received less relevant data and messages from scenario 5 than from scenario 4, although the former has sent a greater amount than the latter.

7. Conclusion Autonomic networks are those capable of managing their components and the links among them with none or minimal human intervention. The WSNs are networks with specific characteristics that possess all the computacional restrictions of wireless, ad hoc and mobile networks, besides others related to their particularities. The implementation of the autonomic networking 46

paradigm in WSNs will be frequently the solely way to promote control and supervision to this kind of network. In this work, we have proposed the Autonomic Sensor Element (ASE) based on the generic model proposed in [Kephart and Chess 2003] and on the concepts of the WSN management architecture called Manna. An evaluation was performed considering the implementation of the ASE proposed to the Mica Motes 2 platform. The results obtained have shown that it is interesting to have autonomic services and functions embedded in a WSN through an ASE, which is capable of managing sensor nodes resources using them in a strategically and optimized way.

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