of computing applications and usage. ... Human attention is a scarce resource and an application ... development of efficient, reliable and robust wireless.
Autonomic Wireless Sensor Networks: Intelligent Ubiquitous Sensing G.M.P. O'Hare, M.J. O'Grady, D. Marsh, A. G. Ruzzelli and R. Tynan Adaptive Information Cluster (AIC), School of Computer Science & Informatics, University College Dublin, Belfield, Dublin 4, Ireland. Abstract—Wireless Sensor Networks (WSNs) have the potential to radically transform commonly held perceptions of computing applications and usage. From environmental monitoring to assisting the aged in their everyday activities, the WSN concept offers an attractive vision of how computational technologies may positively influence everyday life. Hence there is almost unprecedented interest all technological issues pertinent to WSNs. However, various issues relating to the design, deployment and maintenance of WSNs are still crystallising. In this paper, the case for incorporating autonomic computing principles into the design of WSNs is articulated, in light of acknowledged limitations of the WSN model, resulting in what may be termed Autonomic WSNs.
I.
INTRODUCTION
Computing usage paradigms have evolved from being ones that were characterised as being inherently centralised and static to the one that currently prevalent and is characterised as distributed and mobile. Indeed, it could be observed that the paradigm is still in transition and migrating towards one where computing is pervasive, being seamlessly embedded in the fabric of everyday objects. Historically, this vision was first articulated by Weiser in his description of the ubiquitous computing concept [1], in the early 1990s. Ongoing developments have ensured that ubiquitous computing, more commonly referred to as Pervasive Computing [2], has moved from the realms of fantasy to being widely perceived as an attainable objective. Fundamental to this however is the development of suitable technologies that can be embedded in everyday artefacts. Hence, sensors and Wireless Sensor Networks (WSNs) offer distinctly attractive enabling technologies for pervasive computing environments. As the pervasive computing vision matured, it became obvious that a serious usability problem would arise in environments that were heavily populated with sensor technologies. In a word, this problem concerns usability. Human attention is a scarce resource and an application should be prudent in actively seeking the user's attention. If we envisage an environment where continuous demands are being made for the user's attention, the era of "calm technology" foreseen by Weiser may be become one of frustration and irritation. In response to this, the concept of Ambient Intelligence (AmI) [3][4] was conceived. The principal idea here is that intelligent techniques be harvested to minimise user interaction and that the AmI environment should, over time, observe the
user's behaviour, with the ultimate goal of dynamically adapting and personalising the services its offers. While AmI is primarily concerned with usability, its adoption of intelligence as the basis for influencing behaviour may be usefully applied in other areas, including the operation of a WSN. WSN face particular challenges. They may be deployed in hostile environments and are expected to operate within very confined technological limitations, not least of all in respect to power requirements. Maintaining the operational lifespan of the WSN is a fundamental objective. While the use of intelligent techniques offer one approach, this paper will advocate the adoption autonomic principles, augmented with intelligent techniques, as the primary means by which this objective is met. The paper is structured as follows: Section II provides a brief description of WSNs. In Section III, the notion of an autonomic WSN is examined, with particular emphasis on the critical issues of intelligent power management and intelligent coverage respectively. In Section IV, a futuristic albeit achievable scenario in the area of Ambient Assisted Living is outlined before the paper is concluded. II
Wireless Sensor Networks
A. Overview Distributed Wireless Sensor Networks (WSNs) consist of a large number of tiny devices called sensor nodes that pervade an area and collaborate together to accomplish one or more tasks. A sensor node is primarily required to carry out two tasks: interacting with the environment (sensing or actuation); and communicating (reporting sensed data or forwarding other nodes data). Of these two tasks, it is the communication-related activities that occur during the lifetime of the node that consumes the largest potion of the available power. This disparity between the cost of communication and computation is reported in [5], where it is estimated that 3000 instructions can be executed for the same cost as the transmission of one bit of data over 100m. Thus the development of efficient communication protocols is a fundamental prerequisite to the development of efficient, reliable and robust wireless sensor networks. In order to avoid network congestion and premature network depletion, sensors are required to form distributed working groups that send meaningful
aggregated data to the monitoring host or user. Furthermore, efficient access control and routing mechanisms must be combined with intelligent power management and network coverage techniques to maintain network longevity, and ultimately form the basis of an autonomic system. B. WSN Terminology A WSN comprises a number of components, and though the terminology may change according to different architectures, a typical WSN will coalesce around a few key components: 1) Sensor Coverage Area: This is the geographic area covered by the WSN. 2) Sensor Node: This is the actual sensor device, which itself comprises a number of components. 3) Sink Node: The Sink Node is a unique node that has been augmented with additional networking capability, for example, WiFi, 3G, or with a connection to the internet. 4) WSN Manager: A suite of software for administering and managing the WSN is usually hosted on a fixed workstation. A sensor node usually comprises 4 components: 1) Power Unit: Usually comprises of a number of standard alkaline or lithium batteries. Alternative sources of energy are the subject of much research. In the case of outdoor deployments, the use of solar power may be considered. 2) Processing Unit: This is the programmable component of the sensor node and may comprise of a microcontroller and some Flash memory. 3) Transceiver: The transceiver unit is responsible for communicating with other nodes. What protocol it adopts, for example IR, RF and so on, is at the discretion of the manufacturer, and will be influenced by the ultimate purpose of the sensor node. 4) Sensing Unit: Depending on what phenomena is being measured, for example, light, temperature and so on, the sensing unit will usually generate an analogue signal, convert it to digital before transferring it to the processing unit. B. WSN Platforms After a decade of research on WSNs, the last few years saw a great increase in the manufacture of sensor development platforms which have been used as test bed prototypes and in initial real-world deployment [6]. Popular examples from the industry are, for instance, the motes family from Berkeley [7] and the Tyndall DSYS25 platform [8]. Furthermore, the industry recently started producing intelligent platforms such as Intel Stargate [9], and Philips Sand nodes [10]. III
Autonomic WSNs
Autonomic computing [11] is an initiative created by IBM to relieve humans of the burden of managing computer systems. At the highest level, the solution is to have computers manage themselves. This is achieved by making available to a computing element enough knowledge about its operation and its environment that it
is capable of making informed decisions leading to selfhealing, self-protection, self-configuration and selfoptimisation. Though the proposal had its origins in the management for high capacity computer networks, e.g. clusters of servers, the underlying concept has been extended to include many branches of computing. Current WSNs are unreliable, lack robustness, have many elements which interoperate in complex manners, and are subject to much environmental variability. This means that it would be exceedingly difficult for a person to effectively administer a WSN (if access is even possible); hense, system administrators would benefit greatly from the inherent automation that the implementation of autonomic principles bring to a system [12]. One of the core technologies proposed to enable autonomic computing is represented by intelligent agents [13], a class of software entity capable of displaying autonomous, goal-directed and cooperative behaviour in response to external stimuli. The operation of agents typically includes a sense-deliberate-act cycle, which maps well onto WSNs, wherein a sensor node would sample its sensors, use the data as input to a decision making process, and act accordingly (for example, measure the temperature, check this against an alarm threshold, and send a message to the base-station if it is exceeded). Mobility is a singular feature of agents that is particularly useful in WSNs because of the low bandwidth available. Rather than transmitting all data to a central location, a mobile agent can migrate from node to node, processing data as it goes. Once the mental state, i.e. the core of the agent, is smaller than the size of the data to be examined, this leads to energy savings for the network as a whole. To clarify the contribution that autonomic computing can bring to WSNs, we will provide a series of scenarios in which a common problem in WSN operation can be tackled using autonomic principles. Firstly, there is the issue of deployment. It is often assumed that the nodes constituting the network cannot be perfectly positioned. Therefore, a pre-programmed configuration for the network will not work. Self-configuring nodes can set up network connections, evaluate if there are any gaps in the WSN, either from a networking or sensing viewpoint, and proceed to establish sensing and communication schedules. One example of this is [14], a protocol for automatically building a network out of randomly distributed nodes. Secondly, there is the self-protection attribute. If we take the above example of a migrating agent, we can see that it would cause the loss of valuable information and processing time if the agent were to travel to a sensor node that was about to run out of power, resulting in the agent being destroyed when the batteries fail. By monitoring voltage levels, either remotely before it migrates, or locally while situated in a node, the agent can avoid being lost in this manner. Thirdly, self-healing, the ability to repair damage to the network due to the loss of some of its elements must be considered. Sensor nodes are generally exposed to much harsher conditions than standard computing equipment, and are thus subject to energy depletion and incidental damage. This leads to a gradual degradation of the network as a whole as individual nodes are lost.
Network paths break and gaps appear in the sensing coverage of an area. A WSN needs to adapt to the changes in its topology constantly throughout its lifetime, i.e. it needs to heal the damaged areas. This can be achieved by renegotiating network routes, activating redundant nodes to replace damaged ones, or in the worst case scenario, by informing some higher-level entity which can provided assistance. Lastly, self-optimisation is an important trait for WSN protocols so that maximum efficiency is gained from the available energy. Given an application that uses the network, a lower bound on the quality of service (data accuracy, network throughput, etc.) can be specified which will still allow it to provide adequate information to the application. By reducing the performance of the network to this level, energy savings can generally be made because redundant nodes can be put into a lowpower sleep mode, ready to be reactivated when the need arises. However, there exists a trade-off in that the computational cost of a globally-optimal solution such as this is often computationally intractable, whether by 8-bit nodes or 64-bit base-stations. There is also an associated communications, and hence energy cost, in relaying the information. One solution is to create locally optimal or near-optimal conditions, hoping that the aggregate of these local conditions will result in a near-optimal solution for the network as a whole [15]. In the following subsections, we will expand on the details of the most important aspects of a WSN, namely network integrity, sensing quality and power management, and how autonomic principles aid in their optimisation. A. Intelligent Power Management Due to the scale and potential hazardous deployment of a WSN, it is of paramount importance that the network operates for as long as possible without requiring frequent and routine maintenance tasks. The most common of these tasks is battery replacement; as such it is vital that the nodes of the network can manage their power consumption in an intelligent fashion to deliver the longevity required of the network. A node's lifetime will be proportional to the amount of time it is active. Therefore limiting the nodes activity on a network wide basis will increase the lifespan of the entire network and this has proved to be one of the most effective power management techniques for WSNs [16]. A node that is inactive is termed hibernating, this is a temporary state in which little or no power is consumed by it. Hibernating nodes are unable to report their sensed data and as such this can leave a blind spot in the network, where no active sensor is monitoring. The hibernation of a node must be performed in relation to a network wide quality of service metric that must be maintained by the active nodes. For power management, this is typically coverage. Each sensor has an associated sensing radius within which it can sense and outside which it is unable to sense anything, some techniques use an inverse distance relationship between the sensor and its ability to sense but there is still a limit or radius to its sensing capability. An area is deemed covered if the union of all the active sensors sensing discs includes every point within the sensed area.
This is the constraint under which nodes may be hibernated, thus intelligently managing the networks power consumption while maintaining a quality of service with regard to its surveillance density. Algorithms based on this principle include CCP [17] and OGDC [16]. These techniques operate on an optimization of the problem by ensuring that the intersection of two sensing discs is covered by another sensor and that every intersection point of the sensors discs with the boundary are also covered by another sensor. Using this property the algorithms manage the nodes activity and limit the power consumed by the network. Another technique built on top of the standard coverage maintenance techniques is based on interpolation, and more specifically interpolation error [18]. For a given sensor network, that is sensing temperature for example, there will be a temperature distribution function across the area of the network. Sensors of the network sample this function at discrete locations within the area and report their readings to a base station. Interpolation is a mathematical technique for approximating values of a function between known values of that function. Within the context of sensor networks the known points of the temperature function are at the locations of the sensors. We could use interpolation to approximate the temperature between the sensors; however we can also approximate the temperature at the sensors locations themselves. If we use a set of sensors to approximate another sensor's temperature reading and then compare it to the actual reading at the sensor's location we get the interpolation error. If we hibernate sensors whose interpolation error is less than a particular application defined threshold, then the node is hibernated, thus conserving further amounts of energy. This interpolation technique is the first coverage technique to not only include neighboring node locations but also neighboring node readings and can deliver greater savings that using traditional approaches alone. B. Intelligent Routing A fundamental feature of multihop networks is the forwarding activity of packets from a source to a destination. In such networks, a node should identify the best node to pass on the packet so that it reaches the destination quickly. The easiest way to do it would be to flood the entire network; this is unfeasible due to energy consumption and packet overhead that the flooding would cause. An efficient routing poses great challenges such as which node to forward to among all neighbouring nodes and the trade-off between low latency (but computational heavy) of proactive type routing and lightweight (but with high delay) reactive on-demand type routing. Proactive protocols try to keep an accurate snapshot of the networks status and maintain full knowledge of the system; hence they are suitable for high computational capability devices. The low processing capability of sensors prevents them possessing a full proactive routing protocol. On the contrary, on-demand routing protocols do not attempt to maintain a routing table all thetime but only build it when a packet needs to be relayed to a certain destination. This is not an easy task, especially when the destination is unknown, and the situation is exacberated when sensor node energy
constraints do not permit continuous network flooding of packets. An ancillary issue concerns global node addressing, which seeks to prevent nodes in two different parts of the networks from having the same ID; an undesirable situation that can lead to incorrect routing being used for certain destinations. Obviously, this is not a trivial problem, particularily when dealing with largescale sensor networks that may comprise thousands of units. An examination of the research literature reveals a profusion of routing protocols, all tailored to wireless sensor networks. However, before delving into the relevant approaches proposed, it is important to clarify what the necessary prequisites are for a routing algorithm to be apt for sensor networks: 1) Self-organization, as depleted nodes might or might not be substituted with new ones, hence the network needs to be prepared for sudden changes of devices and possibly their location; 2) Flexibility, as the topology of the networks may change continuously due to external factors such as temporary or permanent obstacles, neighbouring nodes my be disconnected. This might result in the need for the identification of a new routing path; 3) Scalability, as sensor networks consists of a certain number of nodes that may span from single figures to tens of thousands of devices; 4) Lightweight, due to limited processing capability which prevent single devices from having a high computational load; 5) Energy-efficiency, as nodes run on batteries or may need to scavenge energy from the environment. Furthermore, nodes might be deployed in remote areas that might make it impractical to recharge them; 6) Loop-free, as the routing algorithm should ensure packets are not routed endlessly around the network without finding the correct destination; 7) Reliability, as the protocol should guarantee a high percentage of packets correctly routed to the destination; 8) Tolerable latency, as some application might need to receive the packet requested within a certain deadline. It would be preferable to have a protocol that can autonomously trade off energy saving for packet delay according to the application needs. All the above characteristics imply the need for an Intelligent routing protocol for autonomic wireless sensor networks [12]. Popular on-demand ad-hoc routing protocols applied in distributed sensor networks are Ad-Hoc Distance Vector (AODV) [19], Dynamic Source Routing (DSR) [20], and Temporarily Ordered Routing Algorithm (TORA) [21]. Such algorithms necessitate initial message flooding to discover the network to find the destination node. Some advantages can be achieved with some notion of the geographic region where the destination node is placed. This can be achieved through a node localization
procedure, which is a common application of wireless sensor networks. If a node knows approximately its location, the destination location and the neighbouring nodes' location then it can decide the best one to forward the packet to. Such protocol types are named geographic routing. With the geographic notion, routing mechanisms in an ideal open space can be greatly improved. However, the benefits of localizing the coordination of nodes in an indoor environment are uncertain. This is because the communication patterns might be dictated by the locations of walls and objects. For examples nodes within two different rooms might not be able to communicate with each other. This might lead to the problem of nodes being located in dead ends. In such situations, the geographic hint fails, and it might be necessary to route the packet to find an indirect path to the destination. Improvements are made by the Compass Routing II [22] and Greedy Perimeter Stateless Routing (GPRS) [23] algorithms that alternate a greedy forwarding policy when the packet makes progress to the destination to a right hand rule policy when the packet hits a dead end. A quite interesting perspective is the data-centric dissemination routing paradigm studied in several protocols, for example Sensor Protocol for Information via Negotiation (SPIN) [24] and Directed Diffusion [25]. This ingenuous idea, that is particularly suited to wireless sensor networks, is to have the packet routed by means of a list of attributes that composes data interest announcements, resulting in nodes gravitating towards those nodes that are interested in some of this data. This would be an example of the publish/subscribe paradigm applied to routing where data are moved according to some other nodes interest in them. Content-based networking would overcome the problem of node global addressing and offer opportunities for different forms of data aggregation. Some disadvantages of such protocols are a poor recovery system in cases of data congestion, and an unclear negative reinforcement rule that avoids certain paths with respect to others so using only a particular path. This results in an unbalanced energy depletion that is higher for nodes along preferred routes. As previously stated, one of the main shortcomings of on-demand routing protocols is the long time span that elapses between the data request and the time the actual data reaches the node. In fact, when sensor networks are deployed, the transmission delay must be accounted for, together with delays that may occur in other layers, for example the access channel delay. This problem might prevent the usage of such protocols in a mobile context. Some benefits are obtained as a result of recent studies into the integration of the routing and MAC into the same architecture, for example MERLIN [26]. C. Intelligent Coverage One of the most important aspects of a sensor network is its ability to provide sensory data of sufficient quality to the application which is using it, i.e. to have satisfactory coverage. At the highest level, we are concerned that the sampling frequency, both in spatial and temporal terms, is high enough so that the phenomena of interest can be observed in sufficient detail. However, as always with WSNs, we must also be mindful of the energy cost of any actions. As was alluded
to earlier, it is necessary to balance the level of detail the network is providing to the client against the rate at which energy is being consumed while gathering the data. Clearly, it is preferable to have the network automatically do this tuning, rather than requiring manual intervention. By making use of the tools of autonomic computing, it is possible to reason intelligently about coverage in the network. There are various methods which can be used to alter the quality of the sensory data the network produces. The simplest is to reduce the rate at which the sensors sample the environment. This increases the length of the period the sensor nodes can spend in a low-power sleep state, and is relatively easy to coordinate. A more complicated mechanism is to identify nodes that are not needed due to a high density of nodes in one area [17]. This requires coordination between the nodes on a local level to decide which ones should be active and which ones should enter a sleep state. If the range of the sensors is variable, then varying this parameter is one more way in which the power/sensing relationship can be fine-tuned [27]. When imbued with pre-calculated knowledge of the relationship between sensor ranges, deployment densities and coverage levels, a node can alter these variables while avoiding combinations that would have an adverse effect on the network as a whole. Lastly, mobile nodes have the ability to reposition themselves as conditions dictate [28]. In a mixed network of fixed and mobile sensors, this facilitates the preservation of coverage in areas where too many static sensors have become depleted or damaged (often considered a failure condition for WSNs). In purely mobile networks, nodes can effectively occupy two or more spatial locations once they switch positions faster than the requisite temporal sampling rate, thus cutting down on the number of nodes needed. A special case of mobile nodes occurs when an inherently static node is attached to a mobile object with independent decision making, for instance a vehicle or person. Coverage in this instance becomes a probabilistic problem, since no actions on the WSN’s part can influence where the nodes will end up. In all types of coverage that a WSN may need, there exist trade-offs. It is often possible to reduce communication rates for an increase in processing time. In the case of mobile nodes equipped with the appropriate sensing modality, sensing can be used to observe other nodes, rather than using the radio to exchange positions [28]. Thus, depending on the relative costs of sensing, radio communication and processing, a sensor network can dynamically choose to favour one method over another if it leads to significant energy savings. For instance, when a set of robots are close together, it may be energy efficient to send low-power radio transmissions. As they move apart and transmission strength has to be increased, direct sensing may become the less costly option. By intelligently selecting the optimal alternative, an autonomic sensor network can achieve energy savings beyond what could be expected from a standard WSN. V
Ambient Assisted Living
One domain that WSNs may have a particular resonance is that of Ambient Assisted Living (AAL). As is well documented, there is a demographic imbalance in
the age profile of populations in certain countries. Over the next twenty years or so, the number of older people as a percentage of the overall population will increase dramatically; a phenomenon that will be particularly prevalent in so called western societies. In anticipation of the difficulties that this will give rise to, the AAL concept was formulated and forms a significant component of both the sixth framework (FP6) and forthcoming seventh framework (FP7) EU funded research programmes. The key objective is to aid people live independently for a longer time period than is the case at present. A.. Alzheimer's Disease Alzheimer's disease is a progressive neurological disorder that deteriorates over time. It accounts for a significant component of all dementia related illnesses. A sufferer is characterised by increased forgetfulness of everyday faces and routines. Though not painful per se, it is a distressing condition and the sufferer will eventually require full-time care and monitoring. However, due to its progressive nature, sufferers may be a particularly attractive user group for innovative AmI solutions. B.. Sensor Augmented Environments Equipping a home with a suitable sensor-laden infrastructure is not particularly difficult and a significant amount of the required technology is already in existence. RFID, video and various kinds of embedded sensors can be used to track and monitor the patient in their everyday activities. This information can be processed and relayed to carers and medical personnel. Overtime, a picture of the patient's routine can be assembled and deviations from this may be recognised. In this way, the progress of the disease can be tracked and the practical effect it has on the patient's everyday life measured. However, for such systems to be practical, the issue of reliability and robustness must be considered. In these situations, the option of rebooting is one that should be avoided, hence the need for a prudent selection and careful integration of appropriate technologies. A.. Proactive Assistance By continuously monitoring the progressive disease, opportunities for actively intervening to aid the patient may be identified. Recall that the patient will increasingly forget everyday routines. For example, they may decide to make a cup of coffee but forget the actual procedure. In such a case, delivering instructions for completing the procedure, via some combination of multimedia modalities is entirely feasible. The AmI technologies including WSN elements are already in existence. Further research is need on the middleware and learning algorithms necessary to make this scenario a reality. Of course, it should be noted that there are various security, privacy and ethical issues that must be addressed before such systems could be deployed in realworld situations. IV
Conclusion
WSN technologies offer significant potential in numerous application domains. Given the diverse nature of these domains, it is essential that WSNs perform in a reliable and robust fashion. One method outlined in this paper for achieving this concerns the integration of
autonomic principles into their design and operation. In this way, the performance of the network is enhanced and its lifespan extended. Successfully incorporating autonomic precepts into WSNs may well enable the necessary reliability that is fundamental if WSNs are become a core constituent in the future generation of pervasive computing systems.
ACKNOWLEDGMENT This material is based upon works supported by the Science Foundation Ireland under Grant No. 03/IN.3/1361. Richard Tynan acknowledges the support of the Irish Research Council for Science, Engineering & Technology (IRCSET).
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