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does not recognize the event. Thus, ESRT adjusts the transmission rate of each node in such a way that the desired threshold is achieved and the event is reliably detected. 1.2.3 Management Functions In the following, we present some high-level management functions that can be used by different monitoring applications in a WSN. We start by presenting a management architecture, followed by a discussion of data storage, network health, coverage and exposure, and security. Architecture. A WSN management architecture can be used to reason about the different dimensions present in the sensor network. In this direction, the MANNA architecture [44] was proposed to provide a management solution to different WSN applications. It provides a separation between both sets of functionalities (i.e., application and management), making integration of organizational, administrative, and maintenance activities possible for this kind of network. The approach used in the MANNA architecture works with each functional area, as well as each management level, and proposes the new abstraction level of WSN functionalities (con guration, sensing, processing, communication, and maintenance) presented earlier. As a result, it provides a list of management services and functions that are independent of the technology adopted. Data Storage. Data storage is closely related to the routing (data retrieval) strategy. In the Cougar database system [45], stored data are represented as relations whereas sensor data are represented as time series. A query formulated over a sensor network speci es a persistent view, which is valid during a given period [46]. Shenker et al. [47] introduce the concept of data-centric storage, which is also explored by Ratnasamy et al. [48] and Ghose et al. [49]. In this approach, relevant data is labeled (named) and stored by the sensor nodes. Data with the same name are stored by the same sensor node. Queries for data with a particular name are sent directly to the node storing that named data, avoiding the ooding of interests or queries. Network Health. An important issue underlying WSNs is the monitoring of the network itself; that is, the sink node needs to be aware of the health of all the sensors. Jaikaeo et al. [50] de ne diagnosis as the process of monitoring the state of a sensor network and guring out the problematic nodes. This is a management activity that assesses the network health that is, how well the network elements and the resources are being applied. Managing individual nodes in a large-scale WSN may result in a response implosion problem that happens when a high number of replies are triggered by diagnostic queries. Jaikaeo et al. [50] suggest the use of three operations, built on the top of the SINA architecture [51], to overcome the implosion problem: sampling, self-orchestrated, and diffused computation. In a sampling operation, information from each node is sent to the manager without intermediate processing. To avoid the
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implosion problem, each node decides whether or not it will send its information based on a probability assigned by the manager (based on the node density). In a self-orchestrated operation, each node schedules its replies. This approach introduces some delay, but reduces the collision chances. In a diffused computation, mobile scripts are used (enabled by the SINA architecture) to assign diagnosis logic to sensor nodes so they know how to perform information fusion and route the result to the manager. Although diffused computation optimizes bandwidth use, it introduces greater delay and the resultant information is less accurate. The three operations provide different levels of granularity and delay; therefore they should be used in different stages: Diffused computation and self-orchestrated operations should be continuously performed to identify problems, and sampling should be used to identify problematic elements. Hsin and Liu [52] propose a two-phase timeout system to monitor the node liveliness. In the rst phase, if a node A receives no message from a neighbor D in a given period of time (monitoring time), A assumes that D is dead, entering in the second phase. Once in the second phase, during another period of time (query time), A queries its neighbors about D; if any neighbor claims that D is alive, then A assumes it was a false alarm and discards this event. Otherwise, if A does not hear anything before the query time expires, it assumes that D is really dead, triggering an alarm. This monitoring algorithm can be seen as a simple information fusion method for liveliness detection where the operator (fuser) is a logical OR with n inputs such as input i is true if neighbor i considers that D is alive and false otherwise. Zhao et al. [53] propose a three-level health monitoring architecture for WSN. The rst level includes the digests that are aggregates of some network property, like minimum residual energy. The second comprises the network scans, a sort of feature map that represents abstracted views of resource utilization within a section of the (or entire) network [54]. Finally, the third is composed by node dumps that provide detailed node states over the network for diagnosis. In this architecture, digests should be continuously computed in background and piggybacked in a neighborto-neighbor communication. Once an anomaly is detected in the digests, a network scan may be collected to identify the problematic sections in the network. Finally, dumps of problematic sections can be requested to identify what is the problem. The information granularity increases from digests to dumps, and the ner the granularity, the greater the cost. Therefore, network scans and, especially, dumps should be carefully used. An energy map is the information about the amount of energy available at each part of the network. Due to the importance of energy-ef ciency solutions for WSNs, the energy map can be useful to prolong the network lifetime and be applied to different network activities in order to make a better use of the energy reserves. Thus, the cost of obtaining the energy map can be amortized among different network applications, and neither of them has to pay exclusively for this information itself. The energy map can be constructed using a naive approach, in which each node sends periodically only its available energy to the monitoring node. However, this approach would spend so much energy, due to communication, that probably the utility of the energy information would not compensate the amount of energy spent in this process.
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