Semantics and Routing in Wireless Sensor Networks

Semantics and Routing in Wireless Sensor Networks: Challenges and Opportunities Nafaâ Jabeur, Youssef Iraqi, Nabil Sahli Computer Science Department Dhofar University Salalah, Sultanate of Oman {nafaa_jabeur, y_iraqi, nabil_sahli}@du.edu.om

Abstract— In this paper, we propose to add semantics to routing protocols in wireless sensor networks. We show the benefits of introducing such a concept and investigate its challenges and opportunities. The advantages of our approach are illustrated in a forest fire detection scenario. Keywords- Wireless Sensor Network; Data routing; Semantics

I.

INTRODUCTION

Wireless Sensor Networks (WSNs) are spatially distributed systems that have been used in a wide variety of applications over several decades. In contrast with conventional systems, where minor adjustments are needed to improve communication once the network is deployed, a deploy-andignore approach [7] is no longer possible in WSN. Indeed, frequent changes in the network topology constantly affect communication pathways. This is mostly caused by the loss, destruction, and/or sleep/wake up cycles followed by the large number of lower-power sensors. The lack of stability, reliability, security, and durability in WSN communication pathways is more observable when sensor nodes are operating unattended in hostile, and/or remote environments where manual maintenance is nearly impossible. These problems worsen when sensors are allowed to move within the space. Due to the increasing popularity of WSN and the growing need for their large deployments, efficient routing protocols are necessary for setting up and maintaining smooth data transfer within the network. Data routing in WSN has attracted much attention during recent years. The main goal of current approaches is to disseminate data from sensor nodes to the sink or data source node in an energy-aware manner while maximizing the lifetime of the network. In these approaches, sensor nodes may have equal or distinct roles and may be endowed with similar or distinct capabilities. While allowing for interesting solutions to data routing, most of these approaches suffer from communication overhead (e.g., Direct diffusion [1], and Rumor [2]), uncertainty to forward data until its destination (e.g., Gossiping [3] and SPIN [4]), and high energy consumption (e.g., LEACH [5]). For these reasons, straightforward messages routing is still difficult. This is particularly due to the limited capabilities of remote sensors, their tendency to failure, and

tendency to have sleep/wake-up cycles to conserve their resources. We believe that taking into consideration intrinsic properties to semantics is very important. However, to our knowledge, very few works ([6, 7]) have attempted to introduce semantics to improve routing in WSN. These attempts are at an embryonic stage. In this paper, we investigate the challenges and opportunities of using semantics in WSN data routing. By examining the semantics related to data, sensor network, sensor nodes, the geographic space, and time, we propose a framework that aims to improve the performance of WSN in particular routing. For example, it may be used to pinpoint the appropriate relay sensors for data routing. In this case, the semantic information is used as filters in selecting relay candidates. Moreover, since existing routing protocols may not be efficient in all situations, the semantic data will enable sensor nodes to select the appropriate type of routing with respect to their current spatial, temporal, and physical conditions. In the remainder of this paper, Section II discusses the state of the art on routing in WSN. Section III presents an analysis of the semantic components that could be valuable to routing. Section IV outlines the benefits of integrating semantic in current categories of data routing protocols. Section V presents a scenario that highlights the importance of our approach in real situations. Section VI highlights the challenges related to using semantics. II.

RELATED WORKS

The establishment and maintenance of low-cost, secure, and reliable communication pathways in WSN has been addressed using four general approaches: data-centric, hierarchical-based, location-based, and network flow and QoS. In data-centric routing, equal roles or functionality are typically assigned to all nodes. The sink sends queries to certain regions and waits for data from the sensors located in the selected regions. In this context, SPIN [4] considers data negotiation between nodes in order to eliminate redundant data and save energy. Directed Diffusion [1] aims at diffusing data through sensor nodes by using a naming scheme for the data. The use of this scheme allows for the avoidance of unnecessary

operations of network layer routing and hence the saving of energy. Rumor routing [2] is a variation of Directed Diffusion. Its idea is to route the queries to the nodes that have observed a particular event rather than flooding the entire network. In order to increase the lifetime of the network, [8] proposed to use a set of sub-optimal paths that are chosen by means of a probability function, which depends on the energy consumption of each path. In hierarchical-based routing, nodes play different roles in the network. These roles follow from the categorization of the network into different clusters. Clusters allow the network to improve its scalability, deal with additional load that may cause gateways to overload when the sensors density increases, and cover a large area of interest without degrading the service [9]. LEACH [5] allows for the creation of clusters based upon the received signal strength and use local cluster heads as routers to the sink. As an improvement of LEACH, PEGASIS [10] forms chains from sensor nodes so that each node transmits and receives from a neighbor. Only one node is selected from that chain to transmit to the sink [9].

efforts in areas of interest, plus semantic-based routing to determine and use relevant sensors semantically connected with respect to current monitoring requirements. By moving most intensive processing into a virtual network running in parallel to the physical WSN, the approach uses software agents to reduce network resource consumption and decide enhanced routing pathways. III.

ASPECTS OF SEMANTIC-BASED ROUTING

Semantic is an important attribute for the improvement of WSN. It can be related to data content, WSN configuration, sensor node, geographic space, and time. Table I situates current works related to our proposed semantic aspects. The table shows that there is a clear absence in supporting these aspects. Figure 1 depicts possible components of each of these aspects that we also describe in the following subsections. TABLE I.

SEMANTIC ASPECTS IN CURRENT ROUTING CATEGORIES.

In location-based routing, the positions of sensors are exploited to route data in the network. The ALS protocol [11] constructs a grid for forwarding data towards a target sink. Used to define the naming system and to assign roles to sensors, this grid is set up by sensors once they are deployed. The TTDD routing protocol [12] constructs a grid during the data advertisement phase and uses dissemination nodes to forward the queries from the sink and to move data. Since every node is aware of its own location, a simple geographic forwarding process can be used to move messages between dissemination nodes. Reference [13] proposed a geographic grid routing (GGR) protocol that constructs a grid rooted at the data sink rather than at the data source, as done by TTDD. Reference [14] proposed a mesh-based routing approach that divides the environment under investigation into specific regions. Every region is considered as a node in a mesh topology. This approach constructs a group of sensors in each region called GoL (Group of Leaders) in order to increase the fault tolerance. In network flow and QoS-based routing, route setup is modeled and solved as a network flow problem. QoS-aware protocols consider end-to-end delay requirements while setting up the paths in the sensor network [9]. In this context, reference [15] presents a network flow approach that aims to maximize the network lifetime by carefully defining link cost as a function of node remaining energy and the required transmission energy using that link. Reference [16] modeled the data routes as the maximum lifetime data-gathering problem and presents a polynomial time algorithm. Reference [17] proposed to generate real-time traffic by imaging sensors. The proposed protocol finds a least cost and energy efficient path that meets certain end-to-end delay constraint during the connection. Reference [18] suggested a protocol that requires each node to maintain information about its neighbors and uses geographic forwarding to find the paths. In the aforementioned approaches, the use of semantics was ignored. In [7], we have proposed a requirement-driven approach that uses geographic-based routing to focus sensing

Figure 1. Categorization of semantics’ aspects

A. Semantics related to data content In most of current routing protocols, data is forwarded without any consideration to its semantics. This is particularly penalizing in a collaborative architecture where sensor nodes tend to help each other. However, since the importance of data may vary as well as constraints on its delivery time, it is more suitable to use reliable sensor nodes for data routing. Indeed, receiving information about the increase of water level in a watershed monitoring system [19] will not be of any effect on the course of pressure sensor processing. However, the same information may result in the generation of an alarm and the rescheduling of processing priorities if received by a sensor which is concerned with water level measuring.

In addition to its semantic, data can be characterized by its sensitivity. We mean by sensitivity the importance and effects of data on the network, spatial environment, and the user. For example, a data packet containing an alarm about an eminent flood risk due to the increase of the water level in the upstream of a watershed is very important. Since the loss or delay in delivering this data may cause damage for adjoining areas to the watershed, the content of data is considered as very sensitive. For this reason, reliable peers should be used as a priority. B.

Semantics related to WSN architecture The architecture of a WSN is related to two main aspects: the role of each sensor node and its relationships with other peers. Examples of roles include data collector, gateway, sink, and cluster head. Examples of relationships include collaboration, competition, and hostility. These relationships could be physical, logical, or semantic. In [7], we have proposed the use of semantic connections between sensors. Two sensors are semantically connected if they are currently collaborating to acquire observations of the same phenomenon or they are involved in the same specific data-gathering task. An important outcome of using semantic connections is the creation of customized configurations for specific conditions or scenarios. This would emphasize the service oriented view of a WSN. The architecture of the WSN would influence the behavior of sensors. For example, in a collaborative architecture, sensors will not alter the semantics of received data (see example in Section V). In contrast, in a competing architecture, sensor nodes may reinterpret the semantics of the received data according to their own objectives. C. Semantics related to sensor nodes We define a sensor node according to its state and properties, both static and dynamic. As for static properties, a sensor node can be deployed for several purposes, such as measuring temperature, atmospheric conditions, fluid and material properties, and electronic and electric values. Examples of dynamic properties include reliability and trustworthiness. Examples of states include being awake and sleeping. Using semantics to specify the properties and states of a sensor can help in achieving a better interpretation of data routed within a WSN. For example, it can improve the selection of the communication pathways by considering the reliability of sensor nodes. Indeed, it is more convenient to entrust data to a reliable sensor node with similar type and goals. Some of these conditions may be relaxed according to current data routing. For example, if no reliable, collaborative, and trustworthy peer of a given sensor node is currently available; the node may confide its data to a reliable, trustworthy peer regardless of its type. D. Semantics related to the geographic space In location-based routing, sensors along the communication pathways are chosen according to their locations with respect to the current node in charge of forwarding data, data source, or

the sink. The identification of these locations is done beforehand or on-the-fly by adjusting the communication range of sensor nodes. Without taking into account the content and characteristics of the geographic space, this identification could not necessary pinpoint the most appropriate peers to route data. Indeed, the existence of obstacles or the occurring of some events in the immediate context of peers may disrupt communication. For example, turning deliberately a transmitter to the same frequency used by sensor node modems may increase the noise in the signal and hence jam radio communications. Also, the presence of tall buildings or tunnels cause troubles in GPS communications. Moreover, knowing that antagonistic peers are eavesdropping data communications in a particular location should encourage the sensor node to avoid their locations and give priority to other paths. Trying to take benefits from the semantic of the geographic space is more complex when sensor nodes are mobile. Indeed, the challenge of a mobile node would be the choosing of the appropriate location where it can get support from trustworthy, reliable, available, and collaborative peers while being productive. This mission may be doomed to failure if the node receives wrong or old information from antagonistic or even collaborating peers. E. Semantics related to time Let us suppose a scenario where a sensor node is routing a data showing a temperature of 45°C. This temperature may be a normal value during daytime in summer. However, it is an abnormal value during nighttime or winter. Being aware of time, the sensor node would be able to better interpret the data and adapt its behavior. In current research works, semantics related to time are partially supported. Indeed, these works generally use TimeTo-Live (TTL) parameters that allow a sensor node to know if the received data is obsolete. IV.

OPPORTUNITIES OF SEMANTIC ROUTING

Current routing protocols allow a sensor node to find a specific data source or a sink. The main issues addressed by these protocols include the setup of reliable, secure, and costeffective communication pathways. As highlighted in the previous sections, there are interesting opportunities in including semantics in routing schemes. The following are examples of such opportunities: •

In data-centric routing: by using semantics, a sensor node can avoid asking some of its neighbors based on the importance of data content. This aspect is particularly important since some neighbors may not be reliable or trustworthy. Furthermore, by reducing the number of neighbors targeted by a given data, the number of messages exchanged can be decreased thereby minimizing resource consumption.

•

In hierarchical-based routing: thanks to available semantic data, a more clear-sighted decision can be made when selecting a cluster head.

•

In location-based routing, sensor nodes are selected on the basis of their locations regardless of the context. Having two peers at the same distance from a sensor node, willing to communicate with the data source or the sink, does not necessary mean that these peers can provide the same QoS to the node. Actually, the sensor node would prefer to know: (1) “What is there?”, so communication jamming sources are avoided, (2) “Who is there?”, so trustworthy and reliable peers can be solicited for help.

The benefits of introducing semantics in the context of WSN communication are more visible when there is a need to restrict the access to routed data and communication pathways. This is particularly true when some components of the same WSN or several coexistent WSNs have conflicting goals. In addition to using semantics for routing, it can also be used when routing (Figure 2). Indeed, a sensor node can take benefit from the semantics of the routed data to adapt its sensing and/or routing strategies. An example of changing the frequency of data collection based on routed data is presented in Section V. Data

Data to be routed

WSN

Semantic processing

Sensor node

Space

...

...

Task rescheduling

Time Peers for data routing

Data collection frequency

Figure 2. Using semantics for sensor behavior update

V.

ILLUSTRATIVE SCENARIO: FOREST FIRE DETECTION

Forest fire detection is crucial in many countries such as Canada, USA, and Australia. There is a rising need for detecting fires early in order to prevent wildfire spread and life loss. Sensors can play an important role here. In USA for example, and before the fire season, thousands of sensor nodes are dropped over forests with high risk of fire. These nodes can sense humidity for example or even detect fires. Collected data are sent as alarm messages. This information allows fire management agencies to prepare their resources nearby risky zones (regions with low humidity), or to effectively target fire suppression efforts if any fire is detected. In the following scenario, we will show how such sensors can be more effective if endowed with semantics. In this scenario, many sensors using multi-hop communication are spread out over the forest forming a collaborative network. These sensors are able to sense temperature, humidity, and smoke. Let us suppose that sensor S1 has detected a sudden rise of the temperature (45°C). S1 has then to send an alarm message to the sink (representing a fire management agency). Adding data semantic to this message in

this particular case consists in setting in the Data.Content, Space.Location, and Data.Urgency of the message to "temperature=45°C", "North", and "urgent", respectively. While routing this message, the other sensors should not ignore its semantic as it is the case in current existing implementations. In what follows, we illustrate examples of utilization of this semantics. Using Space.Location: a sensor S2 which receives S1's message realizes that most likely a fire has started in the North. Routing the alarm message to sensors located in the North may not be a good idea. S2 can rather route the message to another sink following another direction (e.g., the South) since sensors located in the North may become less reliable and would only overload the network. Using Data.Urgency: Sensor S2, which has deduced that most likely a fire has started near S1 may change its routing parameters by rising the number of destination nodes in order to increase the chance of reaching the sink. However, and since the network has a collaborative architecture, sensors should not reinterpret how urgent is the message. For instance, sensor S2 which could "think" that 45°C is a normal temperature (S2 is exposed to the sun), should not change the level of priority of the message received from S1 because the latter may have a different context (S1 is in the shade so 45°C is abnormal). The message should thus be routed as soon as possible. Using Data.Content: In addition to forwarding the alarm message, each sensor which receives the alarm message has also to take advantage of the routed information to adapt itself to the new situation. For example, sensor S2 may change its sensing policy by dramatically increasing the frequency of sensing even though this may overuse its battery energy. In fact, detecting a potential fire in this case has more priority than saving battery life. The sub-scenarios mentioned above are only examples showing how useful can be the semantics for sensors in adapting their policies and their routing strategies. Similar utilities can also be reached by using other aspects of the semantics expressed in Figure 1. VI.

CHALLENGES OF SEMANTIC ROUTING

Although semantic routing offers great opportunities, there are several challenges that need to be overcome before taking all the benefits of using semantic information for routing. In this section we discuss these challenges. The faced challenges heavily depend on the application of the sensor network and may not be all present or relevant at the same time or at the same level. A. Context awareness In some situations, it is important to use context-related information in the management of the sensor, including routing, sensing, processing and data forwarding. However, there is a need to decide what information about the context of the sensor is relevant, and how this information is gathered. Also, the context information of neighboring sensors may also be relevant, especially in collaborative and competing environments. In this case, it is imperative to decide, how data

is exchanged, in what format, and which sensors should be involved in this exchange. B. Semantic data establishment While some semantic data are relatively easy to get or evaluate, other semantic data are more difficult to compute. For example, it is relatively easy to know the reliability of a neighboring node. However, it is more difficult to establish a trust relationship with a neighboring node. C. Semantic data update Another relevant challenge is to decide the frequency of update of semantic data. As data may relate to the sensor node itself, other sensors, or the environment, the frequency of update of the data should not be the same. The sensor has to decide the rate of update depending on the type of data, its importance, its availability, and the associated cost of update. This aspect is very significant as it relates directly to the amount of incurred overhead. D. Semantic awareness As discussed earlier in the paper, using semantic data is beneficial to reach the goal of the sensor network and allow better management of the resources. However, semantic data may not have the same relevance to all sensors or may not be available at the same level. There is a need to decide, which sensors will use which semantic data. Since sensors may be in different states depending on their internal resources, not all sensors may need or may be able to handle the semantic data. E. Relevance of semantic aspects Semantic data may relate to several aspects. These aspects of the semantic data may have different importance degrees and may have different priorities to individual sensors. Indeed, some sensors may consider space-related data more relevant than time-related data. Others may give more weight to trust than reliability. More notably, these priorities may change depending on other aspects of the semantic data, like time and network conditions. F. Policy management In general, there is the challenge of policy management in sensor networks. In particular, who will instruct a sensor on what to do when a specific situation occurs, or when an event is detected, and how this will be communicated to the sensor. Although there have been some attempts to address management of sensor networks [20, 21, 22, 23], there should be an adapted policy management architecture tailored to the specific characteristics of sensor networks using semantic data.

Although very promising, the explicit use of semantic data is currently underused and relatively unexplored. Nonetheless, due to current limited hardware capabilities of sensors, we expect that adopting this approach will take further development in the future. REFERENCES [1]

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VII. CONCLUSION In this paper, we investigated the use of semantics for the improvement of WSN efficiency in general and data routing in particular. We argued that using semantics has numerous advantages and can lead to great benefits. We illustrated the proposed concepts through a real-life fire detection scenario. We have identified the different relevant semantic aspects and highlighted the related challenges.

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