A Survey on Routing Techniques Supporting Mobility in Sensor ...

2009 Fifth International Conference on Mobile Ad-hoc and Sensor Networks

A Survey on Routing Techniques supporting Mobility in Sensor Networks Theofanis P. Lambrou and Christos G. Panayiotou KIOS Research Center for Intelligent Systems and Networks Dept. of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus. Email: {faniseng,christosp}@ucy.ac.cy Website: http://www2.ucy.ac.cy/∼faniseng/index.html

The number of sensor nodes in a WSN can be several orders of magnitude higher than the nodes in a MANET. • Sensor nodes mainly use a broadcast communication paradigm, whereas most ad hoc networks are based on pointto-point communications. Moreover the data in WSNs is usually flows form the nodes to the sink or conversely while in MANETs, the data flows are irregular. • Power resource of sensor nodes could be very limited because of their cost and unattended operation during their lifetime; however nodes in a MANET can be recharged somehow. • Sensor nodes are much more limited in their computation and communication capabilities than their MANET counterparts due to their low cost and they are prone to failures. Therefore, data-centric and cluster-based routing techniques that take advantage of data aggregation have been proposed for WSNs. However, such schemes tend to assume that WSNs are static in nature. The research work dealing with the formulated problem is vast, but most studies share common principles. The literature review, therefore, summarizes the basic ideas rather than covering all protocols and their modifications or extensions. The rest of the paper is organized as follows: Section II presents some routing protocols proposed for WSNs. Energy aware and lifetime maximizing routing techniques are presented in Section III. Existing routing protocols supporting node mobility in the context of MANETs are discussed in Section IV. Routing techniques towards mobile sinks in the context of WSNs are presented in Section V. In Section VI, we present a routing scheme for mixed WSNs that supports routing towards the mobile nodes. Finally the paper concludes with Section VII. •

Abstract—Wireless sensor networks (WSNs) consist of small nodes with sensing, computation, and wireless communications capabilities. Even in predominantly static sensor networks, it is possible to have a few mobile nodes. Mobility of nodes in WSNs adds a significant challenge. In this article we present a survey of state-of-the-art routing techniques in wireless ad hoc and sensor networks and highlight the advantages/disadvantages and performance issues of each routing technique. The aim is to identify routing protocols that will be able to support the mobility of sensor nodes in WSNs consisting of both static and mobile (mixed WSN) nodes. The article concludes by presenting an approach for such a routing protocol. Keywords-Routing, mixed sensor networks, mobility.

I. I NTRODUCTION A wireless sensor network (WSN) is a special kind of ad hoc network that consists of a number of sensors spread across a geographical area. Each sensor has wireless communication capability and sufficient intelligence for signal processing and networking of data. WSNs are being widely used for monitoring large areas [1]. Examples include environmental monitoring, outdoor industrial processes, military surveillance etc. Monitoring a large area requires a large number of sensor nodes which with current technology implies a prohibitive cost and excessive (radio) interference [5]. An alternative approach to address this problem is to employ mobile nodes, i.e. nodes mounted on robots [2]. The mobile nodes can sample areas poorly monitored by the stationary sensors. This approach that includes both static and mobile nodes is referred to as mixed WSN. In [4] an efficient mixed WSN is developed that employs a smaller number of stationary nodes that collaborate with few mobile nodes in order to improve the area monitoring. The motivation behind this paper is to identify appropriate routing protocols for establishing communication between the sink and the mobile nodes. MANET (Mobile Ad hoc NETwork) routing techniques are explicitly designed to cope with mobile environments. One would suggest that they can also be applied to handle mobility in WSNs. However, there are several issues in deploying these protocols in WSNs as the two networks vary in the following [42]: • WSNs are mainly used to collect information while MANETs are designed for distributed computing. 978-0-7695-3935-5/09 $26.00 © 2009 IEEE DOI 10.1109/MSN.2009.37

II. ROUTING IN W IRELESS S ENSOR N ETWORKS In this section we focus on the available energy aware routing protocols proposed for WSNs. These routing protocols can be classified into three categories: Data Centric, HierarchialCluster based and Location based routing. In data centric approach, the name schemes of the collected data are used for queries. Two well known protocols proposed are the Sensor Protocol for Information via Negotiation (SPIN) [34] and the Directed Diffusion [33]. In SPIN, the nodes 78

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assign high level names (also called meta-data) to describe their collected data. Each node obtaining new data is willing to share it with its neighbors. It does so by broadcasting an ADV message containing meta data. If a neighbour is interested in the data, it sends a REQ message for the DATA and the DATA is sent to this neighbour node. The neighbour sensor node then repeats this process with its neighbours. As a result, the entire sensor area will receive a copy of the data. The advantage of the SPIN is that it minimizes energy dissipation compared to flooding and meta-data negotiation almost halves the redundant data. However, SPIN’s data advertisement mechanism cannot guarantee delivery of data (e.g. consider an application of tracking a moving target). In Directed Diffusion the sink requests data by broadcasting interests, for instance, “give me the temperature in a particular area”. An interest diffuses through the network hop by hop and each node is broadcasting the interest to its neighbours. Each sensor that receives the interest sets up a gradient toward the sensor nodes from which it receives the interest (A gradient specifies both data rate and the direction along which event should be sent). This process continues until gradients are set up from the sources back to the sink. The sensed data are then returned in the reverse path of the interest propagation. The intermediate nodes might aggregate the data based on the data’s name and attribute-value pairs. If each node receives the same interest from more than one neighbour, the data will travel to the sink along multiple paths. At this point the sink can select to reinforce a particular path with the least-delay. The propagation and aggregation procedures are all based on local information and thus Directed Diffusion networks can achieve energy saving by processing data in-network. Although this protocol achieves some energy saving, it also has problems. For instance, to implement data aggregation, it employs time synchronization technique, which is not easy to realize in a sensor network. Though data centric approach works well in sensor networks with static nodes, it is not capable of handling complex queries, it is not scalable to large sensor networks and data aggregation results in communication and computation overhead. Another issue is that these protocols do not support node mobility as they are designed for static sensor networks. In cluster based approaches, sensor nodes are grouped and the one with the greatest residual energy is usually chosen as the cluster head. In this case efficient energy distribution can be archived. Some of the proposed cluster based protocols are the Low-Energy Adaptive Clustering Hierarchy (LEACH) [35], Power-Efficient Gathering in Sensor Information Systems (PEGASIS) [36], Threshold sensitive Energy Efficient sensor Network protocol (TEEN) [37] etc. LEACH minimizes energy dissipation in WSN by randomly selecting sensor nodes as cluster-heads. PEGASIS is a near optimal chain-based protocol. The basic idea of the protocol is to extend network lifetime by allowing nodes to communicate exclusively with their closest neighbors and take turns in communicating with the sink. When the round of all nodes communicating with the sink ends, a new round starts, and so on. In TEEN, nodes react immediately to drastic changes in the value of

a sensed attribute and when this change exceeds a given soft threshold and the value is above a given hard threshold, nodes communicate their value to a cluster-head for forwarding to the sink.To form the cluster architecture, TEEN employs the same strategy as LEACH. These protocols have several advantages as they are scalable and it is easy to manage sensors and routes. However many of these protocols are designed based on LEACH assumptions and thus they have the same problems. Such problems are network partitioning (if the elected cluster head has no other cluster head in its communication range), they are not capable of handling node mobility and it is difficult to support time critical applications due to the continuously cluster head evaluation procedure. In location based routing, the location information of the sensor nodes is smartly utilized in order to discover energy efficient routing paths. A large number of protocols have been developed in this category and some of them have been primarily proposed for mobile ad hoc networks. Well known protocols in this category are the Minimum Energy Communication Network (MECN) [38], Geographic Adaptive Fidelity (GAF) [39], Geographic and Energy Aware Routing (GEAR) [40] etc. In the sequel we will provide some details of these protocols which are capable to support the mobility of nodes in the context of mobile ad-hoc networks. Since, energy constraints is a major issue in the context of WSNs, next we present a family of energy-aware routing protocols. III. ROUTING WITH E NERGY-AWARE AND L IFETIME -M AXIMIZING T ECHNIQUES A number of studies have explored the issue of energy aware, lifetime-maximizing routing approaches for wireless ad hoc and sensor networks [16],[28]. Many of these are based on identifying and defining suitable shortest-path link metrics, while some derive energy-efficient routes for a network using a global optimization formulation. In an ideal, lightly loaded environment, assuming all links require the same energy for the transmission of a packet, the traditional minimum hopcount routing approach will generally result in minimum energy expended per packet. If different links have uneven transmission costs, then the route that minimizes the energy expended in end-to-end delivery of a packet would be the shortest path route computed using the metric Ti,j , the transmission energy for each link i, j. However, in networks with heterogeneous energy levels, this may not be the best strategy to extend the network lifetime (defined, for instance, as the time till the first node exhaustion). The basic power-aware routing scheme [29] selects routes in such a way as to prefer nodes with longer remaining battery lifetime as intermediate nodes. Specifically, let Ri be the remaining energy of an intermediate node i, then the link metric used is ci = R1i . Thus, the path P (indicating the sequence of transmitting nodes for each hop) selected by a shortest-cost route determination algorithm (such as Dijkstra or Bellman-Ford) would be one that minimizes i∈P R1i . 79

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While minimizing per-hop transmission costs minimizes total energy, avoiding nodes with low residual energy prevents early node failure. However, considering these goals separately, as in the above, may not optimize the system lifetime. What is needed is a technique that balances the two goals, selecting the minimum energy path when all nodes have high energy at the beginning, and avoiding the low residual energy nodes towards the end. In [30], authors propose the following link metric, which is a function of the transmission cost on the link Ti,j , the residual energy of the transmitting node Ri and the initial energy of the transmitting node Ei , i.e a Ri−b Eic , where (a, b, c) are constant parameters. ci,j = Ti,j This general formulation captures a wide range of metrics and simulation results in [30] suggest that a non-zero a and relatively large b = c (e.g. (1, 50, 50)) terms provide the best performance. In [31] the authors proposed a fully distributed on-demand routing algorithm that has capability of maximizing the lifetime of a wireless ad hoc network. In contrast with the previous algorithm, this algorithm does not have to solve the global optimization problem. In the previous aforementioned algorithms a centralized controller has to solve the optimization problem of finding the optimal route using the cost information of all nodes involve in each route. The idea here [31] is that: when a source needs a route to sent data packets and the route information does not exist, it broadcast a RREQ packet to its neighbors. Each intermediate nodes holds the RREQ packet for some time, inversely proportional to its own residual battery. This process repeats until the RREQ packet arrives at the destination. The destination then sends a route reply packet back to the source using the first arrived route information. This approach although is fully distributed it may a cause large delay during the establishment of the communication link between two nodes.

Mobile ad hoc network routing protocols can be separated into two different categories: topology-based and positionbased routing. Topology-based routing protocols use the information about the links that exist in the network to perform packet forwarding. They can be further divided into proactive, reactive and hybrid approaches [17]. Position-based routing uses the geographic position information of the nodes to forward packets. A. Topology-based routing protocols 1) Pro-active Routing Protocols:: Pro-active or table-driven routing protocols are some what similar to the wired network protocols in the sense that the route is known prior to the requirement. The rapidly varying topology is taken care of by continuous evaluation of the known routes and at the same time it tries to discover new routes and thereby providing an up-to-date network topology [17][11][13][7]. This evaluation of the routes can be event-driven or done in a periodic fashion. These updates may consume large amounts of bandwidth and even worse much of the accumulated routing information is never used, since routes may be valid only for a short duration of time. These pro-active protocols include Optimized Link State Routing (OLSR)[7], Destination Sequenced Distance Vector (DSDV) [11] routing algorithms and many others. Therefore it is clear that proactive routing protocols can not applied in mixed WSNs due to their communication overhead, power requirements and scalability issues when they are applied in large networks. 2) Re-active Routing Protocols:: Contrary to pro-active routing protocols, re-active or on-demand routing protocols determine the route only when required, that is, when a communication needs to take place [17][11][13][7]. In this case, the source node floods the entire network with routerequest messages and hence builds the route from the routereplies it receives. The on-demand aspect of these protocols remove the need for constant route updates for the routes not in use but, on the other hand cause a delay in starting a communication as the route might need to be discovered. Therefore, on one side they reduce the bandwidth consumption due to frequent broadcast and discovery messages but on the other they put this bandwidth limit under strain by flooding the whole network for the route discovery. Dynamic Source Routing (DSR): The Dynamic Source Routing is an on-demand routing protocol that is based on source routing concept. The protocol is characterized by two main mechanisms: route discovery and route maintenance [9]. These two mechanisms work in tandem to allow the nodes to discover and maintain routes to various destinations in the adhoc network. The route discovery is only initiated if the node that wants to transmit does not have a valid (unexpired) route to the destination node. If the node does not know a valid route then route discovery is initiated by broadcasting a route request message. A route reply message can be generated by the destination node or any other node that has a valid path to the destination node. Route maintenance is accomplished through the use of route error and acknowledgement messages. Route

IV. M OBILE A D HOC N ETWORK ROUTING P ROTOCOLS In mobile ad-hoc networks, mobile nodes themselves form the network routing infrastructure (no fixed infrastructure) by connecting in an ad-hoc manner and communicate with each other over wireless links. Examples where mobile ad hoc networks may be employed are the establishment of connectivity among handheld devices or between vehicles. Due to the mobility of the nodes the topology of the network may change rapidly and without prior notice. This makes it almost impossible to use conventional routing table methods employed for fixed networks. These differences from the fixed network topologies result in a much more complicated and adaptive distributed algorithms to maintain an accurate knowledge of the network topology. Special care has to be taken that route discovery does not use up the majority of the limited available bandwidth. Furthermore, it is important to point out that MANET routing protocols are usually IP based and should allow for interaction with standard wired IP services rather than being regarded as a completely separate entity.

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maintenance for a route is used only when information is being transmitted over the route. Thus, DSR uses route discovery and route maintenance entirely on-demand. Therefore, unlike pro-active protocols, DSR does not require periodic packets such as route updates, or link sensing, etc. within the network [9]. This lack of periodic activity allows the routing packet overhead to scale automatically to the needs to keep track of routes in use. When network topology changes do not affect the current routes in use, DSR protocol does not generate any response. Ad-hoc On-demand Distance-Vector (AODV) Routing Protocol: Ad-hoc on-demand distance-vector routing protocol builds on the DSDV algorithm and the impetus is on minimizing the number of required broadcasts by generating routes on an ondemand basis, as opposed to maintaining a complete list of routes as in DSDV algorithm. Like DSR, in AODV a path discovery is initiated when a route to a destination does not exist. A node broadcasts the route request (RREQ) message to its neighbors that in turn forward the message to their neighbors, and so on until either an intermediate node with a valid route to the destination or the destination node itself is reached [10],[12]. The intermediate nodes record in their routing tables the address of the node from which the RREQ message was received thereby, establishing a reverse path. Once the RREQ reached the destination or the intermediate node with a valid route to the destination, a route reply (RREP) is generated and transmitted back to the neighbor from whom it first received the RREQ message. As the backward path was created to the node from which the RREQ message was received first, similarly a forward route is created to the node from which it received the RREP message. The other major task of the protocol is to maintain the discovered route while it is in use. For this purpose AODV uses a link failure notification message. Although on demand routing protocols reduce the communication overhead in the network they are flooding the network when a route is needed and moreover they have been designed for point to point communication rather than data collection. Another issue is the delay appears due to the route discovery procedure which makes them non appropriate candidates for time critical applications. Thus these protocols can not easily applied for mixed WSNs. 3) Hybrid Routing Protocols:: Hybrid protocols seek to have the best of both worlds, that is, to combine the proactive and reactive routing protocol approaches. An example of this combination is Zone Routing Protocol (ZRP) [8]. ZRP is specifically designed for ad-hoc networks and works on the concept of routing zones to efficiently route the query to the destination. In an ad-hoc network, it can be assumed that most part of the traffic is for the nearby nodes. Therefore, ZRP proposes to reduce the scope of proactive approach to a zone which is centered on each node and is termed as Intrazone Routing Protocol (IARP). By limiting the scope, the maintenance of the routing information will be easier and more efficient as the amount of routing information that is never or seldom used will be minimized. In addition, the farther away

nodes can be reached via reactive routing approach and is termed as Interzone Routing protocol (IERP). ZRP is modular - any routing protocol can be used within and between zones. The hybrid nature of the ZRP makes it efficient for large networks. B. Position-Based Routing For the routing problem, one idea would be to exploit the timing information about encounters with other nodes while the nodes are moving. In traditional geographical or positionbased routing [14][15][17], the source node starting the routing has to know the current location of the destination node. The problem with this traditional routing philosophy is that each node has to update continuously its location information and has to flood the entire network with its location information, which ends up being not very scalable due to the large overhead involved of control traffic to track the frequently changing link states and network topology. As an illustrative example, in the case of AODV, it has been shown [14] that the route discovery represents up to 90% of the total routing overhead. Unlike these topology-based routing protocols, which do not make use of location information, position-based (also called geometric or directional routing) protocols try to optimize routing by making use of geographical information available at each node (GFG [18], GPSR [19], LAR [20], TRR [21], AFR [22]], EASE [23], DREAM [24]). Every node is aware of its own position and is notified of its neighbors’ positions through the exchange of beacons (small packets broadcasted by the neighbors to announce their position). Additionally, a node is able to determine the location of the destination through a centralized location management scheme. Recently, some algorithms for distributed location services have been developed [17]. Below we summarize some forwarding strategies proposed in the networking literature [17][14][15] 1) Greedy Packet Forwarding:: Using greedy packet forwarding, the sender of a packet includes the approximate position of the recipient in the packet. This information is gathered by an appropriate location service. When an intermediate node receives a packet, it forwards the packet to a neighbor lying in the general direction of the recipient. Ideally, this process can be repeated until the recipient has been reached. Generally, there are different strategies a node can use to decide to which neighbor a given packet should be forwarded. These are illustrated in Fig.1 [17], where S and D denote the source and destination nodes of a packet, respectively. The circle with radius r indicates the maximum transmission range of S. One intuitive strategy is to forward the packet to the node that makes the most progress towards (is closest to) D. In the example this would be node C. This strategy is known as most forward within r (MFR); it tries to minimize the number of hops a packet has to traverse in order to reach D. In nearest with forward progress (NFP), the packet is transmitted to the nearest neighbor of the sender which is closer to the destination. In Fig. 1 this would be node A. Another strategy for forwarding packets is compass routing,

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Fig. 1.

In [32] the authors provide another solution to the greedy routing failure. A cost is assigned to each sensor node based on the Euclidean length between the sensor and the base station and assuming that sensor nodes are location aware. This cost is assigned in two phases: In the shadow-spread phase the network graph is partitioning to the bride area and to the shadow area which includes the “concave” nodes (a node is called “concave” when it has not a closer neighbor to the base station). In the cost-spread phase the concave nodes increase their cost iteratively using the cost information of their neighbors until they find a neighbor with a smaller cost value. After that routing can be perform using the high-costto-low-cost rule. These two phases executed continually as this algorithm has been proposed for mobile sensor networks. However authors do not provide any solution for the routing towards the mobile sensor nodes as they make the assumption that the base station has a communication range long enough to cover all sensor nodes for sink to mobile sensor nodes communication purposes. 2) Restricted Directional Flooding:: In directional flooding, packet duplication is part of the standard forwarding algorithm. A node will forward a packet to all neighbors that are located in the direction of the destination. Directed flooding is very robust at the cost of heavy network load. In the DREAM protocol [24], the idea is to spread location updates information less frequently to distant nodes, exploiting the effect that the greater the distance separating two nodes, the slower they appear to be moving with respect to each other. Moreover, nodes trigger updates based on their own mobility; the higher the speed of a node, the more frequent the updates it sends. Each node stores location information for all other nodes of the network and broadcasts position update packets to update the position information maintained by the other nodes. The direction toward the destination D is determined by means of a so-called expected region [24], the expected region is a circle around the position of D as it is known to a forwarding node N. Since this position information may be outdated, the radius r of the expected region is set to ((t1 − t0 ) · υmax ), where t1 is the current time, t0 is the time stamp of the position information that N has about D, and υmax is the maximum speed with which a node may travel in the ad hoc network. The closer the packet gets to its final destination, the more accurate the position information contained in the packet header. Nevertheless, nodes need to flood the whole network occasionally to provide faraway nodes with their location and each node has to keep a list with entries for all other nodes of the network. Therefore the requirements limit the scalability of DREAM to small networks.

Greedy routing strategies [17].

which selects the neighbor closest to the straight line between sender and destination. In the example this would be node B. Compass routing tries to minimize the spatial distance a packet travels. Finally, it is possible to let the sender randomly choose one of the nodes closer to the destination than itself and forward the packet to that node. This strategy minimizes the accuracy of information needed about the position of the neighbors and reduces the number of operations required to forward a packet. Unfortunately, greedy routing may fail to find a path between sender and destination, even though one may exist. An example of this problem is depicted in Fig.2 [17]. In this figure the half-circle around D has the radius of the distance between S and D, and the circle around S shows the transmission range of S. Note that there exists a valid path from S to D. The problem here is that S is closer to the destination D than any of the nodes in its transmission range. Greedy routing therefore has reached a local minimum from which it cannot recover.

Fig. 2.

Greedy routing failure [17].

To counter this problem among other solutions the face-2 algorithm [25] and the perimeter routing strategy of the Greedy Perimeter Stateless Routing Protocol [19] are two very similar recovery approaches based on planar graph traversal. Both are performed on a per-packet basis and do not require nodes to store any additional information. A packet enters the recovery mode when it arrives at a local minimum. It returns to greedy mode when it reaches a node closer to the destination than the node where the packet entered the recovery mode.

C. Issues with Using MANET Routing Protocols in Mixed WSNs MANET routing techniques described above are explicitly designed to cope with mobile environments, which would suggest that they can also be applied to handle mobility in WSNs. However, there are several issues in deploying these end-to-end protocols. MANET routing does not suite 82

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well to WSNs that have strict energy, processing and storage constraints, low data rates, redundant data, and typically manyto-one flows (from sensors to a sink). Therefore, data-centric routing that take advantage of aggregation of data has been proposed. Surveys of data-centric protocols can be found in [16],[15],[14]. However, such schemes tend to assume that WSNs are static in nature. A WSN may contain thousands of nodes, which makes it difficult to build an addressing scheme that uniquely identifies each node, and identification of nodes may not even be needed by the WSN application. Instead, data is requested based on queries that use attribute-value pairs (this attribute-based addressing is part of the data-centricity paradigm). Therefore, traditional IP-based routing protocols are hard to deploy in such WSN scenarios. Further, the data dissemination model (like time-driven, event-driven, or query-driven) and time criticality is often application-specific. Therefore, traditional general-purpose data-forwarding paradigms cannot always provide optimal solutions. Especially, the requirement for energy-efficiency poses new challenges related to maximizing the lifetime of the network. This goal may contradict with the requirement for data aggregation, for example. Aggregation can be achieved by clustered network formation, but the cluster heads become the hot spots in the data forwarding paths and may run out of energy. In a heterogeneous network, the cluster heads could be more powerful nodes, in that case the routing protocol should somehow become aware of the nodes’ capabilities.

acts as a primary agent. An alternative immediate agent is also chosen when the sink is about to go out of reach of the primary agent for robust delivery. The source sends data to the sink through the “overlay” dissemination network to its closest grid dissemination node, which then forwards it to its primary agent. As the sink moves through the network, new primary agents are selected and the old ones time out; when a sink moves out of reach of its nearest dissemination node, a new dissemination node is discovered and the process continues. In another study [41], the authors propose to use mobile sinks that move in order to decrease the energy consumption of the whole network and they describe a routing protocol to support this architecture. In [41] they proposed a gradient based routing protocol where sensor nodes maintain a list of neighboring next hops that are in the right direction towards the closest sink. The protocol uses restricted flooding to update the locations of the mobile sinks and the basic principle behind is to register a cost between the appropriate sink and the given node for each node and update only these routing entities where the relative change in cost is above a threshold. In [43], authors proposed an Adaptive Local Update-based Routing Protocol (ALURP). Using this protocol the mobile sink needs only to broadcast its location information within a local area instead of the entire network as it moves. Their routing mechanism is described below: At the beginning the destination area is set, having as center the position of the mobile sink (VC) and a predefined radius R. Static sensor nodes that located inside the destination area will route packets to the sink using a topology based routing scheme. Nodes that are outside the destination area will route packets toward the VC using a geographic routing scheme. Each time the mobile sink moves out of the current destination area it needs to broadcast its location to the entire network. As the mobile sink moves inside its destination area it needs only to update its location inside its destination area. Moreover they found out that as the sink moves towards its VC it needs only to update its location in the donut area defined by the destination area when the circular area with center VC and radius the distance of VC from mobile sink current position is subtracted. In [44] a cluster based architecture is proposed for the mobile sink problem consisting of four phases: a) Clustering Phase: the clusterheads are elected and the sensor network is divided into clusters. b) Register phase: the mobile sink come into communication range with a clusterhead node is registered into the cluster. c) Data Dissemination Phase: once the mobile sink is registered into the cluster the clusterhead disseminates the cluster sense data to the mobile sink d) Maintenance Phase: possible new sensor nodes are added to the cluster and the clusterhead is evaluated. Clearly, the aforementioned routing protocols are mainly study efficient ways of how information can be routed towards the mobile sink (gateway) from the static sensor nodes (many nodes route information towards a mobile gateway). These algorithms can not be used for efficient information routing from a static sink (or sensor node) towards mobile sensor nodes in the context of mixed WSNs (a gateway must route

V. ROUTING TO M OBILE S INKS Mobility of nodes in WSNs adds a significant challenge. Even in predominantly static sensor networks, it is possible to have a few mobile nodes. One scenario in particular that has received attention, is that of mobile sinks. In typical WSNs application scenarios, sensor nodes are reporting their measurements to the sink using multi-hop communication. Thus the lifetime of the networks strongly depends on the energy of the sensors nodes around the sink that relay all messages on the last hop. One solution suggested for this problem is to use mobile sinks. In a sensor network with a mobile sink (e.g. controlled robots or humans/vehicles with gateway devices), the data must be routed from the static sensor sources to the moving entity, which may not necessarily have a predictable/deterministic trajectory. In [26] the authors proposed the two-tier data dissemination (TTDD) protocol, which supports packet routing towards a mobile sink. In TTDD all nodes in the network are static, except for the sinks that are assumed to be mobile with unknown/uncontrolled mobility. The data about each event are assumed to originate from a single source. Each active source creates a grid structure dissemination network over the static network, with grid points acting as dissemination nodes. A mobile sink, when it issues queries for information, sends out a locally controlled flood that discovers its nearest dissemination point. The query is then routed to the source through the overlay network. The sink includes in the query packet information about its nearest static neighbor, which

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information towards mobile nodes).

about the mobile node position, it updates the position of the destination in the packet and forwards the packet. The packets are forwarded towards the current position of the mobile node and finally it is delivered to the mobile once its arrives to a node inside the mobile’s communication range (a mobile’s neighboring node). This approach combines both position based and ID base routing. When a packet is far away from the destination, it needs only a general sense of direction where it should go. As the packet approaches the destination, more and more precise directional information can be provided from the close-by nodes. After several successive steps, the packet reaches the immediate vicinity of the destination (mobile) node where an identity-based procedure sends the packet to the mobile node in the final hop.

VI. A ROUTING S CHEME FOR WSN S WITH S TATIONARY AND M OBILE N ODES In the context of mixed WSN, which consists of a large number of static nodes and a few mobile nodes, we proposed the following position based routing protocol for the network layer under the assumptions below: • The sink is located at a fix position. This position is known to all sensor nodes of the network. (i.e via flooding the network with a sink position message after the WSN deployment) • All sensor nodes (static or mobile) know their position using a GPS module or other localization algorithms [27]. • Each sensor in the WSN has an accurate and updated table of its neighboring nodes positions. The main objective of the proposed sensor network is the delivery of event detection messages that contain information about the position of the detected event in the sensor field. The routing of such messages can be easily developed using techniques described in subsection IV-B for position based routing towards a fixed base station (destination). Moreover the base station can easily request information from a specific geographic region or even a single static node using only the position information. A problem will appear when the base station needs to establish a communication link with a mobile node (e.g. to send some mission information to the mobile node). In this case the base station does not know the position of the mobile node. An easy but not efficient way to access the mobile node is by using flooding. In the following, combining the ideas of sections IV-B2, IV-A3 and IV-B1, another way of routing packets towards mobile nodes is proposed. This protocol has two components: • Each mobile node is periodically sending a position message to the base station (sink) in order to inform the base station about its position. The period of this message can be adapted to the mobile node’s speed and the message must have the following information: mobile node ID, the current position and the time associated with the current position (i.e. pos msg: ID,(x,y),time) • Each mobile node broadcast the above position message to its one hop neighboring nodes during its movement in the sensor field. The static nodes keep a table where they register the information contained in the position messages of the mobile nodes as well as the positions of their static neighboring nodes. Suppose that the base station needs to send a message to a mobile node. The packet will be send towards the non-updated position that the sink has for the mobile. The intermediate nodes will forward the packet using a greedy routing protocol to the destination. Once an intermediate node gets a packet for a mobile node, it checks if the mobile node is registered in its table and compare the time-stamp of this registration. In the case when an intermediate node has more updated information

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