Scenario-based Routing for Sensor Networks Applied to Ambient Navigation Assistance

Wireless sensor networks can be used in ambient navigation assistance systems since they form a bridge between the physical world and the need of environment awareness. Navigation needs to cover both static and dynamic obstacle negotiation in route planning. Our previous work on the ZHRP protocol over sensor networks deals with routing information infrastructure which can be exploited in this application context. In this paper, we evaluate extensively the performance of this protocol concerning route discovery scenarios. Both routing information construction and route discovery scenario were implemented on top of the J-Sim simulator. Two main metrics, critical in sensor networks, were estimated: the network lifetime and the memory size both theoretical and experimental. We compare performance of ZHRP with RIP. Analytical expressions for the memory size needed by the routing data structures give indications to calibrate the network infrastructure parameters. I. CHALLENGES IN EMBEDDED APPLICATIONS ORIENTED NAVIGATION ASSISTANCE One emerging technology used to analyze and retrieve information on real-world phenomena is based on embedded devices, among which sensors stand out. Particular scenarios in ambient assisted living concern navigation systems. Similar to GPS tools, the challenge of route discovery here is more complex because information on the environment necessary to integrate is both static and dynamic. Route discovery in sensor networks is generally based on dissemination techniques which are too expensive in terms of communication. Moreover, existing solutions make particular hypothesis: nodes can be geo-localized [1], the transmission power control can be dynamically adapted [2] and is heterogeneous [3], or centralized algorithms are applied [4]. These assumptions are too restrictive and costly to be applied in real wireless sensor networks, characterized by a high level of decentralization, low resources and small storage capacity and requiring low cost operations (of both implementation and maintenance). The construction of adaptive routes, context-dependent in terms of both dynamic and static information, is less developed in literature. For example, in research on designing electronic devices for the aid of the visually impaired, a high percentage of the proposed systems are focused either on obstacle avoidance or on orientation. Obstacle negotiation is the ability to move depending on the knowledge of immediate objects and obstacles, of the ground shape and of dangers both moving and stationary. NavBelt [6] proposes obstacle negotiation. Orientation is the process of locating targets and of constructing routes to them. Projects proposing orientation are UbiBus [7], Noppa [8]. Navigation, which includes both obstacle negotiation and orientation, takes into account the obstacle information in route discovery and planning. Drishti [9] is an example of wireless pedestrian navigation system for the visually impaired and disabled. Compared to Drishti, based on a set of different technologies (GIS database-based GPS system, ultrasound positioning, beacons) which integrates only static obstacles in the route planning, our approach of navigation system is based on top of sensors only, detecting and managing both static and dynamic obstacles. Moreover, our solution can be applied to different scenarios not only for the visually impaired which require adaptation in dynamic contexts. Another example concerns the technology for vehicle orientation, which is currently exclusively oriented towards route discovery based on static information issued from plans. Whereas geo-localization solutions give good results at a very large scale and for static system behavior, they are less efficient when dynamic, unforeseeable phenomena appear. This is the challenge we address: efficiently provide route discovery mechanisms and dynamically adapt them based on contextual information, both static and dynamic information being retrieved using embedded devices, more particularly, sensors, disseminated in large areas, covering town areas, at the most. We present the assumptions of the assisted navigation applications we address in section 2, our proposal of route discovery protocol in section 3 and its evaluation in section 4. We finally conclude. II. APPLICATION ARCHITECTURE Assisted navigation may concern different configurations (indoor, outdoor), all based on the following features (see figure 1): • several target areas are identified (for example, railway station, post office, campus, etc.), • several targets can precisely indicate destinations in a specific area (department of computer science on the campus, railway station platform, ATM in a post office, etc.), • some other intermediary stops are relays, but not necessarily targets. Requests from users in navigational systems have the following form ”Which is the route to take in order to reach a specific target in a particular area? ”. Fig. 1. Application features This corresponds to the route discovery issue addressed in networks. We may implement standard solutions of shortest paths in graphs (modeling network structure) whose nodes represent the targets and the intermediary stops. Meanwhile, these techniques can not be applied because they are centralized and require global network view. Moreover, adapting route construction to environmental context is difficult to achieve. Our solution is focused on the use of wireless sensor networks. Sensors, assigned to disjoint zones, correspond to both targets and intermediary stops (see figure 2). Sensors are needed in order to retrieve and to relay dynamic information. RFID may be used for targets, but this choice would reduce their functionality in the network. We use here sensor networks in a particular situation, requiring node addressing, rather than data addressing more commonly used in this kind of networks. The answer to the previous question may be given by querying the closest node (target or intermediary) which will operate route discovery: find a set of nodes, forming a chain (each one being able to communicate with its predecessor and its successor in the chain). The user will be informed of the next node to reach along with his progress (see successive steps in figure 2). Consequently, we may consider the user as the sink in a standard sensor network configuration, having the particularity of being mobile. When dynamic information (e.g. obstacles) prevents the user to follow the next intermediary node, some other route should be proposed (figure 1 shows alternative route dotted arrows). III. ROUTE DISCOVERY Embedded sensor systems in the environment, oriented to assist navigation, need more often large deployment. In this context, route discovery can not exploit traditional techniques like flooding, used for example in sensor networks. We propose table-driven pro-active approaches, inspired from ad-hoc wireless networks. Several reasons justify this choice: Fig. 2. Application architecture • route availability routes should be rapidly determined, • static network architecture no sensor node mobility is involved (only the sink node is mobile), • nodes are GPS-free, • nodes may be IP-like addressed even though nodes do not have an IP-address assigned, some of them can be considered as targets, thus being clearly identified in the network. Table-driven solutions for routing in largely-deployed networks need network structuring to diminish node’s view concerning potential destinations. We are using the ZHRP (Zonebased Hierarchical Routing Protocol) routing approach [10], based on network partitions into zones. We briefly present, in III-A and III-B, the principles which are thoroughly explained in [10]. We focus in this paper on the route construction algorithm (that was not addressed previously), which exploits routing information. The concerned algorithm is described in the last part of this section. A. Routing information The idea of creating virtual network zones is not only motivated by the large number of network nodes. It is also justified by the application aim: route discovery concerning a particular target may not at all concern areas of some other far geo-localized target. Zones help giving a macroscopic view when trying to reach the target zone and a microscopic view when trying to reach the target inside the zone. Zones are defined based on two parameters: • randomly chosen invitation nodes (they are by default assigned to different zones), • the zone radius in terms of number of radio-hops. Invitation messages are initiated by the invitation nodes and broadcast to their neighbors, which operate the same. This propagation stops either because the number of consecutive hops issued from a same invitation node exceeds the zone radius, either because the node receiving the invitation message is already assigned to a zone. During this construction, two types of nodes are defined: • normal nodes (labeled NORMAL) used to relay messages inside a zone, • border nodes (labeled BORDER) used to relay messages between adjacent zones. These nodes will dispose of different routing information, retrieved during the construction of the routing tables: • the normal nodes have an intra-zone routing table, denoted IntraZoneRT . An entry in this table contains the following information: – the destination node (destNdId), – the next hop (nextHopId), – the metric (M) computed in number of hops, – the type of the destination node, normal or border (nodeType), – moreover, if the destination node is a border node, the entry contains the list of neighboring zones (neighZoneIds). • the border nodes have an intra-zone routing table, just like the normal nodes, and moreover, an inter-zone table, denoted InterZoneRT . One entry in this table contains – the destination zone (destZoneId), – the next zone (nextZoneId), – the zone metric (zoneM). The zone metric is defined as the average length (in hops) of the longest path between any t