May 20, 2005 - Large scale deployments of these networks have been used in many diverse fields such as wildlife habitat monitoring [7], traffic monitoring [1] ...
Agents for Wireless Sensor Network Power Management G.M.P. O’Hare
David Marsh Antonio Ruzzelli Adaptive Information Cluster (AIC) Smart Media Institute (SMI) Department of Computer Science University College Dublin Belfield, Dublin 4 Ireland
Richard Tynan
May 20, 2005
Abstract The primary function of Wireless Sensor Networks is data acquisition or monitoring of some medium, such as temperature. In many instances these networks are deployed throughout inaccessible or hazardous regions meaning frequent maintenance such as battery replacement is undesirable and in some cases impossible. Intelligent power management for these devices is critical in maximizing the networks life span and ultimately will dictate the success of such deployments. This longevity must, however, be achieved while maintaining the integrity of the sensory data harvested by the network. Due to the inherent distributed nature of Wireless Sensor Networks, intelligent software agents lend themselves to performing this power management in such a distributed domain. In this paper we examine some of the potential decisions an agent may face regarding intelligent power management and we look at how the stronger notion of agency could be employed to allow a richer deliberation regarding potential decisions. Allowing more adaptive control of WSNs in light of their computationally challenged nature.
tery and sensing technology together with radio hardware have also followed a similar minaturisation trend. The aggregation of these advances has lead to the development of networked, millimeter-scale, sensing devices capable of complex processing tasks. Collectively these form a Wireless Sensor Network (WSN), the technology required to enable a new era for ubiquitous sensing technology. Large scale deployments of these networks have been used in many diverse fields such as wildlife habitat monitoring [7], traffic monitoring [1] and lighting control [10]. Due to the limited power supply of these devices, it is imperative that the operation of each node factor this into account when making any decision to perform an action. For example, consider the case where a nodes power has depleted to a point where it is capable of only a handful of transmissions. The network should be able to adapt to this fact and this node should only transmit if it deems it critical to do so. This decision must be taken on a per node basis as off node deliberation would necessitate further transmissions.
Intelligent and proactive behavior such as this is characteristic of an intelligent software agent. Agents form the basis for distributed, artificially intelligent applica1 Introduction tions [13], and their applicability to WSNs is the central Recent advances in microprocessor fabrication has lead theme of this paper. Making an informed decision by into a dramatic reduction in the size and power consumed corporating various perceptions or beliefs about an enviby embedded microprocessor-controlled devices. Bat- ronment is at the very heart of an agents deliberative cy-
cle. Goal oriented reasoning can allow the agent to commit to the course action that best realizes its goals, in this case the agents goal is to maximize the network life span. Its perceptions are the sensory modality or modalities it is capable of sensing and the remaining battery power. By reasoning about the effect of a transmission, the agent can see if that action best suits its goals, which in this sample instance is unlikely to be the case. In the next section we examine some background information on existing agent based power management regimes and we look at some current agent systems for WSNs. Following this we examine the potential for applying agents, which adhere to the strong notion of agency, to intelligent decision making in WSNs and finally we end with some of our conclusions.
2
Background
Power management is a critical aspect of a successful WSN. This may take many forms. Typically the sensors spend most of their time in a low power sleep mode. Even when active, energy-intensive activities such as using actuators and transmitting/receiving radio messages are kept to a minimum. Detailed below are two examples of power managing techniques that employ agents to make cooperative decisions to reduce the energy consumption of different aspects of the functioning of a typical WSN.
2.1
Autonomic WSNs
In [8], experiments compare the performance of an intelligent agent-based transmission scheme versus that of a more simplistic, non-agent based scheme. The radio of a sensing node is usually the most power hungry component [4], hence reducing transmissions and receptions is very desirable. An intruder detector application was the client of these schemes. The agent-based method monitored the data it was sampling at half second intervals for sudden changes, which in this case equated to an intrusion event. When no events were detected, it would transmit results only every eight seconds. When an event/sequence of events were detected it would transmit every half second. The simple scheme sampled and transmitted every two seconds.
It was found that by opportunistically choosing to transmit only when there was something worth reporting, the agent-based solution used the radio two and a half times less than the simple scheme while achieving an accuracy of 91% versus the simple scheme’s 77%. The power saved by reducing transmissions allowed a greater sampling rate, which accounts for the increase in accuracy. This shows that saving energy can directly cause an improvement in the data quality provided by the WSN.
2.2 Interpolation for Node Activations Interpolation is a commonly employed method to estimate an unknown value of a function at a point by examining values around that point. Redundancy in the context of WSNs can be viewed as having more sensing nodes active than is required according to some metric. Power management methods often exploit redundancy by deactivating sensing nodes while ensuring that the network as a whole can still perform adequately. Interpolation can provide the measure by which this decision can be made. In [12], an application can specify an acceptable error value for the network. For each sensor of the network, the agents can estimate the value of the temperature at that sensors location. This estimation can be compared to the actual sensed value which allows the error to be determined. If this error is below the specified level, that sensor can be turned off. This reduces the overall power consumption of the network. By periodically changing the set of sensors that are off, all sensors in the network can spend some portion of their time in a low power mode, while maintaining a certain level of competence regarding the sensory data received from the network. Once it is deemed that the error for a particular sensor is a above the prescribed threshold, surrounding and inactive nodes can be switched on to attempt to reduce the error at the nodes location.
2.3 Agent deployment technology Agilla [3] is a middleware platform for deploying mobile agents (essentially mobile code). The agent architecture is tailored toward the computational constraints typical of WSN nodes. It allows for multiple agents to exist on a single sensing node, and provides methods for the reliable movement of agents between nodes. Sensing platforms
provide context to an agent through ”tuples” (a set of predefined descriptors about the node) in a tuplespace. The tuplespace also serves as the communication forum between agents on a node. Additionally, agents may register their interests in particular events by inserting a template tuple into the tuplespace. Matching event are reported to the agent without the need for continuous polling. Mate [6] allows WSN programs to be written in TinyScript, a scripting language which is compiled into executable bytecodes for an application-specific virtual machine. Allowing the virtual machine to be applicationspecific means that the programs for it can be clear and concise and thus less prone to failure. The bytecodes are less like mobile agents, but rather are like intentional viruses. Once a single instance of a bytecode program is introduced to the network, it automatically spreads by controlled flooding until all nodes of the network have a copy of the program. It is intended that only one program should operate on the network at once, and so this limits the flexibility of Mat as a basis for an agent system. While transmission frequency and node activation would form the basis for the majority of distributed power management schemes, a number of other decisions could potentially be undertaken: Sampling Frequency Should the frequency at which the devices senses are polled be reduced, at the risk of missing a crucial event occurring within the sensed area. This is independent of transmission frequency, which is the rate at which data is sent to the base station. Transmission Range Should the radio range for the node be reduced to conserver power, while potentially compromising the connectivity of the network. Node Mobility In the case of mobile sensor nodes, such as the RoboMote [11], the amount of freedom the node would have in migrating around the environment could be curtailed. This would be at the expense of the integrity of the sensor data obtained by the node. Having looked at some background information regarding agent-based power management in WSNs and some emerging agent technology we now examine the potential utility gained from utilizing agents, which adhere to the strong notion of agency [13] in WSN power management.
3 Strong Agency and Power Management In this section, a description of how strong agents can be employed to enable intelligent power management in a wireless sensor network is given. These agents form a Multi-Agent System (MAS), a set of distributed cooperating agents that can achieve collectively more than they can alone. Firstly though we give a description of how strong agents differ from the agents found in Mat´e and Agilla.
3.1 Strong Agency Strong agency is distinguished from the previously employed weak agency by the notion of deliberation. This is typically realised through the use of the BDI paradigm [13], that is Belief, Desire and Intention. Beliefs represent the things that the agent holds to be true at present about itself and its environment. Desires are the goals that are adopted when the belief preconditions for them are met. Intentions are the steps involved in achieving an adopted desire. They may be reevaluated as new information becomes available. To briefly illustrate the mechanics of BDI, consider a trivial agent that has one possible desire, that is goal. It may specify that when the agent believes that the sensor node it is currently on is scheduled to power down, that it immediately move to another node to continue working there. By periodically checking whether the current node is to remain active or not, the agent is able to maintain a valid belief about whether it should remain or migrate. Once the triggering condition is true, the agent will adopt the desire to move to another node. Note that this desire will likely entail the completion of subgoals, that is intentions, such as locating an available node(s), confirming that these nodes have sufficient battery power, and obtaining access to the radio for a time sufficient to transmit a copy of its mental state to the new node. An agent’s mental state is the aggregation of beliefs, desires and intentions that an it has. The additional machinery required to create a fully functional agent can be provided by the agent platform. An agent platform can be thought of as analogous to a virtual machine running a Java program. It provides services to the agent such
as simple access to hardware resources. As mentioned above, it can also furnish those parts of an agent that do not comprise the agent’s mental state. These parts can be thought of as the body of the agent. One final property that should be mentioned here is the adaptability of agents. It is possible for an agent to move from a node of greater capabilities to one of lesser capabilities. For instance, an agent may move from a base station, with all the additional resources that being connected to a PC- or server-level device implies, to a sensor node. Much of the abilities the agent had on the more powerful device are now no longer available or indeed necessarily useful. The agent simply discards those parts which are no longer required before migrating to the sensor node. Some of these ideas resonate with the human travel metaphor. Elsewhere [9] we discuss agent migration and an associated travel metaphor whereby agents can deposit excess ”baggage”, that is its unneeded components, in left lockers when appropriate for subsequent collection. The temporary discarding of capabilities equates closely with this scenario. Likewise, an agent can regain those parts it left behind when moving from a sensor node to a base station. One instantiation of the BDI paradigm is Agent Factory [2]. The core BDI concepts are encoded as beliefs, commitment rules and commitments. An agent operates by running through a sense-deliberate-act cycle. New beliefs are created when the agent triggers its senses. This provides information about the agent, the agent platform and any additional data sources such as sensors. Once these new beliefs have been incorporated with any preexisting beliefs, the agent may then deliberate about what actions it should take. It matches beliefs against commitment rules, and any that are satisfied spur the adoption of the corresponding commitment, a goal to be achieved.
acy of action versus queuing related tasks until such time as there are sufficient numbers to justify activating a component. An example of this is when there are a number of transmissions pending which are being queued so that the radio need only go through one power on - power off cycle. If none are urgent then it makes sense to queue them in this way, however provision must be made for instant transmission of important messages. Furthermore, arising from this is the question of whether the other messages should be sent at the same time or if they should be made wait for the transmission condition that was imposed before the interruption caused by the urgent message. While simple optimisations can be implemented by appropriate programming, our contention is that more complex decisions are are opportunistic and inextricably related to a given context. This context will involve such issues as network wellbeing and task criticality. Agents can display behaviour that is more adaptable to change than hard-coded programs. Another factor in this argument is the fact that wireless sensor networks endure longer when requirements for global knowledge are eliminated or reduced as much as possible (since global knowledge implies global communications, a very costly operation). Agents are inherently suited to working in environments where only a partial view of the state of the system as a whole can be known at any one location or time [5]. The distributed aspect of a MAS also mirrors elegantly the spatially dispersed nature of sensor networks. Power management for wireless sensor networks is a complicated problem since there are several different ways in which energy may be conserved, not all of which are complimentary. Additionally, many of the energysaving methods typically employed have a detrimental effect on the functionality of the system. Some of these trade-offs were mentioned in the previous section. Finding the balance point between all these factors is difficult, and during the lifetime of a sensor node it is likely to shift 3.2 Applications for Power Management according to new priorities. The deliberative nature of We distinguish between instances where power conserva- agents affords them the flexibility to deal with decisions tion can be effected at a purely local level, that is on a that depend on changing requirements. We will now illussensor node without the need for information from other trate these points with an example. nodes, and those instances which require negotiation between nodes. Furthermore, a distinction exists between 3.2.1 Cooperative Power Management simple methods of conserving energy, such as only powering on a component when it is needed, and advanced The following gives an example of how agent cooperation methods such as balancing the requirements of immedi- can help inform power management decisions. Schemes
such as OGDC [14] can determine which nodes ought to be active in order to cover, that is sense adequately, a region of interest. The other nodes can then remain dormant in order to reduce the drain on their batteries. The spatial arrangement of nodes is important for coverage protocols since a sufficiently large ”hole” in the network under schemes like this means that coverage may never be achievable no matter what proportion of nodes are active. Increasing the sensing range of a node is one way to combat this. This may entail increasing the tolerable error in the sensor reading, or accepting a lower spatial sampling rate. These decisions are specific to the physical property being measured, and shall not be further elaborated upon. However, we will consider the effects of the network’s topology on node activations. In a perfectly regular arrangement, for instance a square or triangular grid, no one sensor occupies a point of any more consequence than any other due to the symmetries in the network. The failure of a node in one position is no more important than a failure in any other position. Choosing which nodes to be active to ensure coverage may take second place to other concerns, for example building a spanning tree over the network to enable multi-hop communications. However, as nodes fail and the arrangement becomes increasing irregular, a node may find that it occupies a critical point with respect to maintaining coverage. The loss of this node, for instance through energy depletion, may prevent the network from covering the area without major adjustment, say by repositioning a mobile node, a power-intensive activity. Thus the network suffers the problem of requiring that node to specifically remain active to ensure coverage while wishing that the energy drain on that particular node is reduced so that it lasts as long as possible. Let us consider the case when the node also forms part of the spanning tree for the communications layer, see figure 1. A power management agent has to balance the needs of the coverage algorithm against the network’s other needs, in this instance routing packets. A sensible way to handle the two conflicting tasks is to redirect the tree in order that it passes through another nearby node. Negotiation between nodes must take place to identify which node is willing to take on the extra energy drain associated with routing. Assuming there is a node available and willing to do this, the transmission frequency of the critical node is reduced to the rate at which the node
Figure 1: A portion of a WSN showing a node critical for coverage and the spanning tree routed through it. Energy can be saved by redirecting the tree so that the dependent section routes through the alternative node.
itself produces packets, and as has already been stated radio use is one of the primary energy costs for a wireless sensor network node, therefore the node’s expected lifetime will increase. If further energy savings are required, the power management agent could consider reducing the sampling rate of the sensors. Since the node is important for ensuring the sensing integrity of the network as a whole, this step would need to be negotiated at a higher level. A request to a base station could be sent to determine the feasibility of reducing the sampling rate of the node. The base station could inject a mobile agent into the WSN at this point to make its way to the region of interest and investigate the situation locally rather than having all pertinent data streamed all the way to the base station. This agent could be an adapted form of the base station’s network monitoring agent. After gathering the relevant data, it would be in a position to decide whether or not the original request should be authorised. As can be seen from this example, the potential decisions that arise when considering how various energy saving strategies interact rapidly become quite complex. Agents are particularly apt for addressing such complex decision making. The degree of agent deliberation can be
varied dependent upon the decision context, critically so on the power profiles of the nodes upon which the collaborative agents reside. This coupled with their mobility, negotiation capabilities and their decision making based in the face of partial, possibly conflicting and inaccurate data make them a strong candidate technology.
4
Conclusion
Battery longevity is critical to the operation of wireless sensor networks. This research has presented efforts at addressing this very issue through intelligent power management An agent oriented approach is advocated in the solution of this distributed, contextualised and collaborative problem. Agents are inherently suited where the decision making context is: highly dynamic, resource bounded and the information is partial and/or inaccurate. Autonomic characteristics ascribed to wireless sensor networks can deliver system adaptivity which may, in part, deliver intelligent power management. As with all decisions a trade off exists and the cost (in terms of power depletion) needs not exceed the benefit that can be derived. Inevitably the issue of power management is inextricably associated with other network characteristics. The correlation between such attributes as coverage, latency, accuracy and longevity is well recognised. Decisions relating to goals of power longevity must be made mindful of their implications for other network characteristics.
5
Acknowledgements
This material is based on works supported by Science Foundation Ireland under Grant No. 03/IN.3/I361. We would also like to thank the Irish Research Council for Science, Engineering and Technology (IRCSET) for their support.
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