SURVEY ON DATA ROUTING IN WIRELESS SENSOR NETWORKS

Transcription

1 SURVEY ON DATA ROUTING IN WIRELESS SENSOR NETWORKS José Cecílio, João Costa, Pedro Furtado University of Coimbra, DEI/CISUC {jcecilio, jpcosta, Abstract: Routing in sensor networks is very challenging, due to several characteristics that distinguish them from contemporary communication and wireless ad-hoc networks. Many new goal and data-oriented algorithms have been proposed for the problem of routing data in sensor networks. Most routing protocols can be classified as data-centric, hierarchical, location-based or QoSaware. Data-centric protocols are query-based and depend on the naming of desired data. Hierarchical protocols aim at clustering the nodes so that cluster heads can do some aggregation and reduction of data in order to save energy. Location-based protocols utilize the position information to relay the data to the desired regions. The QoS-aware are based on general network-flow modeling for meeting some QoS requirements. In this chapter, we will explore goal and dataoriented routing mechanisms for sensor networks developed in recent years. Our aim is to help better understanding current routing protocols for wireless sensor networks and point out open issues that should be subject to further research. 1. Introduction Routing in sensor networks is very challenging due to several characteristics that distinguish them from contemporary communication and wireless ad-hoc networks. First of all, it is not possible to build global addressing and routing algorithms exactly as for classical IP-based protocols for the deployment of sheer numbers of energy and processing capacity constrained sensor nodes. Second, in contrary to typical communication networks, almost all applications of sensor networks require the flow of sensed data from multiple regions (sources) to a particular sink. Third, generated data traffic has significant redundancy in it, since multiple sensors may generate the same data within the vicinity of a phenomenon. Such redundancy needs to be exploited by the data and goal-oriented routing protocols to improve energy and bandwidth utilization. Fourth, sensor nodes are

2 2 tightly constrained in terms of transmission power, on-board energy, processing capacity and storage and thus require careful resource management. Due to such differences, many new algorithms have been proposed for the problem of routing data in sensor networks. These routing mechanisms have considered the characteristics of sensor nodes along with the application and architecture requirements. Almost all of these routing protocols can be classified as data-centric, hierarchical or location-based, there being also some distinct ones based on network flow or QoS awareness. Data-centric protocols are query-based and depend on the naming of desired data, which helps in eliminating many redundant transmissions. Hierarchical protocols aim at clustering the nodes so that cluster heads can do some aggregation and reduction of data in order to save energy. Location-based protocols utilize the position information to relay the data to the desired regions rather than the whole network. The last category includes routing approaches that are based on general network-flow modeling and protocols that strive for meeting some QoS requirements along with the routing function. In this chapter, we will explore the routing mechanisms for sensor networks developed in recent years. Each routing protocol is discussed under the proper category. Our aim is to help better understanding of the current data and goaloriented routing protocols for wireless sensor networks and point out open issues that can be subject to further research. There are some previous works surveying the characteristics, applications, and communication protocols in WSNs [25][32][38][52]. While most existing surveys address several design issues and techniques for WSNs, describing the physical constraints on sensor nodes, applications and architectural attributes, this chapter is devoted to the study of data and goal-oriented routing in sensor networks, describing and categorizing the different approaches for data delivery. 2. Classes of algorithms The review of current approaches is based on an extensive survey of the stateof-the-art in goal and data-oriented routing approaches. Different protocols and algorithms proposed in recent years are reviewed. We begin with Data Centric Protocols, which are centered on the data itself. Secondly, we analyse Hierarchical Routing, which consists on establishing a hierarchical route towards the data collection points. Thirdly, we survey protocols that use position information to relay the data to the desired regions (Location-based protocols), and lastly we consider QoS-Aware protocols, which take into account energy consumption and data quality. The network may have to satisfy certain QoS metrics (delay, energy, bandwidth) when delivering data to the base station, metrics which are used in the QoS-Aware protocols. Table 1 lists the routing algorithms and classes that we review in this paper.

3 3 Routing Data-centric SPIN SPIN-PP Hierarchical Location-based QoS Aware Direct Diffusion Energy-aware Reliable Energy Aware Routing (REAR) Rumor MCFA Link Quality Estimatin Based Gradient Based Information-driven Acquire LEACH EWC PEGASIS TEEN/APTEEN Energy-aware cluster-based Self-organized Minimum energy communication network Small minimum energy communication network Geographic Adaptive Fidelity Energy Aware Greedy Routing (EAGR) Geographic and Energy Aware SPEED MMSPEED Sequential Assignment Real-Time Power-Aware DCEERP Energy Efficient with Delay Guaranties (EEDG) Table 1: Routing Algorithms and Classes SPIN-EC SPIN-BC SPIN-RL In order to select the most suitable routing mechanism for a sensor application, we have to classify the routing protocols according to the network and operational characteristics, and an objective model that describes the routing goal. Table 2 categorizes the routing protocols from Table 1 taking into consideration, besides the main classification used in that table, also the number of base stations, mobility characteristics, whether transmission power is considered fixed or

4 4 variable, whether the approach uses data aggregation or not and whether it is query-based or not. Finally, we list the main goal for each alternative algorithm. In most applications, the number of base stations can be one or more than one. The increased number of base stations provides more robust data gathering, and may also decrease the network delay. If only one base station is present, the destination node for all messages is the same, while in the case of multiple base stations; the routing algorithms can take this into consideration to achieve desired goals. However, many techniques do not try to optimize the system when more than one base station is available. Another important factor is the transmission power, which can be either dynamically adjustable or fixed. When the transmission power is fixed, each sensor node transmits each message using the same energy level. In the other case, every node can calculate what energy level should be used to transmit a message to a neighboring node. This energy level may be inversely proportional to the cost assigned to the neighboring node. The task of routing in many protocols is to deliver the queries coming from the base station to the sensor which have the requested data, and return the requested data to the base station. Some protocols are based on hierarchy. A hierarchy level is assigned to each node, and a node only forwards those messages that are originated from a lowerlevel node. Optionally, a node aggregates incoming data and forward this aggregated data to upper-layer nodes. The base station can be found on the top of the hierarchy. The hierarchy construction can be dynamic or static. In the dynamic case, the role of the aggregator is rotated, and all nodes that have selected an aggregator will forward all data to their aggregator. The aim of forming hierarchies is to prolong the network lifetime. Some sensor applications only require the successful delivery of messages between a source and a destination. However, there are applications that need more guaranties. These are the real-time requirements of the message delivery, maximization of network lifetime and fault tolerance. The main objective of the real-time protocols is to completely control the network delay. The average-case performance of these protocols can be evaluated by measuring the message delivery ratio with time constraints. The lifetime is another important goal. Protocols try to balance energy consumption equally among nodes, considering their residual energy levels. However, the metric used to determine network lifetime is also application dependent. Most protocols assume that all nodes are equally important and they use the time until the first node dies as a metric, but the average energy consumption of the nodes may also be used as a metric.

5 5 Protocols Number of base Classification Mobility Transmission Data stations Power Aggregation Query based SPIN Data-centric 1 Possible Fixed Yes Yes Direct Diffusion Data-centric 1 or more Limited Fixed Yes Yes Goal Lifetime, exchange metadata to reduce number of messages Establish efficient n-way communication paths for fault tolerance Energy-aware Data-centric 1 or more Limited Adjustable Yes Yes Lifetime REAR Data-centric 1 or more Limited Adjustable Yes Yes Lifetime and data delivery Rumor Data-centric 1 Very limited Fixed Yes Yes Reduce queries in network MCFA Data-centric 1 No Fixed No No Data delivery LQEBR Data-centric 1 No Fixed No No Gradient Data-centric 1 Limited Fixed Yes Yes Information-driven Data-centric 1 Limited Fixed yes Yes Data delivery with minimal retransmissions Data delivery through minimal number of hops Optimization of Direct Diffusion to save more energy Acquire Data-centric 1 or more Limited Fixed Yes Yes LEACH Hierarchical 1 Fixed BS Fixed Yes No EWC Hierarchical 1 Fixed BS Fixed Yes No PEGASIS Hierarchical 1 Fixed BS Fixed No No Optimization of queries in network Formation distributed cluster to extend Lifetime Forming distributed cluster to extend lifetime and data delivery guaranties Lifetime and bandwidth optimization TEEN/APTEEN Hierarchical 1 Fixed BS Fixed Yes No Real-Time and Lifetime Energy-aware cluster-based Hierarchical 1 No Adjustable No No Real-Time and Lifetime Self-organized Hierarchical 1 Possible Fixed No No Fault tolerance Minimum energy communication network Geographic Adaptive Fidelity Hierarchical 1 No Adjustable No No Location 1 or more Limited Fixed No No EAGR Location 1 or more Limited Fixed No No Geographic and Energy Aware Location 1 Limited Fixed No No Lifetime and selfreconfiguration Increase the network lifetime with the number of node increase Optimization of Geographic Adaptive Fidelity algorithm Reduce interest msgs in the whole network SPEED QoS 1 or more No Fixed No Yes Real-Time and Lifetime

6 6 MMSPEED QoS 1 or more No Fixed No Yes Sequential Assignment Real-Time Power- Aware Real-Time and Lifetime (improve SPEED) QoS 1 No Fixed Yes Yes Real-Time QoS 1 or more No Adjustable Yes No Real-Time and Lifetime DCEERP QoS 1 No Fixed Yes No Real-Time and Lifetime EEDG QoS 1 or more No Fixed Yes No Deadlines Guaranties and Lifetime Table 2: Properties of Routing Algorithms Generically, these goals or qualities are achieved by introducing a set of heuristics into the routing algorithms: typically, lifetime can be controlled in routing algorithms by taking into account the level of power still available in the batteries during routing decisions, routing through the least power-consuming routes, controlling the transmission ratio, or by managing the duty cycle carefully; Minimizing delays is controlled typically by delay-based scheduling of node transmission queues; Fault tolerance is achieved in the reviewed algorithms by keeping multiple alternative routing paths and using an available one on-demand. While a minimum number of hops may minimize qualities such as delays, when nodes fail the algorithms are also able to choose alternative routes; Optimum bandwidth and minimum number of messages are typically achieved by using an optimal transmission packet size. Table 3 categorizes the routing protocols from Table 1 taking into consideration the main goals that each algorithm goes after. We describe these goals after the table. Protocols Lifetime, min energy Min. delay or *deadlines Fault tolerance, Reconfiguration Opt. Bandwidth, min. nr. of msgs Min. nr. hops SPIN x x Direct Diffusion x Energy-aware x REAR x x Rumor x MCFA x LQEB x Gradient x

7 7 Information-driven x Acquire x LEACH EWC x x PEGASIS x x TEEN/APTEEN x x Energy-aware cluster-based x x Self-organized Minimum energy communication network Geographic Adaptive Fidelity x x x x EAGR x x Geographic and Energy Aware x SPEED x x MMSPEED x x x Sequential Assignment x Real-Time Power-Aware x x * DCEERP x x EEDG x x Table 3: Routing Goals Most of the algorithms in Table 3 take into consideration the battery lifetime as the main goal (e.g. SPIN, Energy-aware routing, LEACH, Minimum energy communication network, Geographic Adaptive Fidelity). A few of them consider delays (e.g. Sequential Assignment), fault tolerance (Direct Diffusion, Selforganized Protocol) and bandwidth optimization (Rumor, Acquire). Some protocols focus on more than one goal (e.g. TEEN/APTEEN, Energy-aware cluster-based, SPEED, Real-Time Power-Aware). The principal goal for these last protocols is to control the network delay, but simultaneously try to minimize the dissipated energy in transmission, in order to extend the network lifetime as much as possible under the delay constraints. Table 4 categorizes the routing protocols from Table 1 taking into consideration their application type. Some typical application areas, such as habitat monitoring, environment monitoring, health care and Industrial

8 8 environment are shown and the routing protocols are categorized in the table by typical application type. Protocols Project Application type Direct Diffusion PODS Hawaii [46] Environment Monitoring Gradient Vital Sign [22] Health Acquire Flood Detection [45] Environment Monitoring LEACH Artificial Retina [34] Health TEEN/APTEEN Aware Home [9] Industrial Self-organized SOMoM [30] Military Geographic Adaptive Fidelity Great Duck [3] Military Geographic and Energy Aware Aware Home [9] Habitat Monitoring Sequential Assignment Vital Sign [22] Health Table 4: Routing typical Application The content of each protocol is explained in the following sections under each category. 3. Data-centric protocols Since transmiting data from every sensor node within the deployment region might result in significant unnecessary redundancy in data and incur in unnecessary energy and traffic expenditure, routing protocols that are able to select a set of sensor nodes and utilize data aggregation during the relaying of data have been considered. In data-centric routing, the sink sends queries to certain regions and waits for data from the sensors located in the selected regions. Since data is being requested through queries, attribute-based naming is necessary to specify the properties of data. Sensor Protocols for Information via Negotiation (SPIN) [60] is the first data-centric protocol, which considers data negotiation between nodes in order to eliminate redundant data and save energy. Later, Directed Diffusion [13] has been developed and has become a breakthrough in data-centric routing. Many other protocols have also been proposed based on Directed Diffusion, such as Energyaware routing [15], Rumor routing [17], Minimum Cost Forwarding Algorithm [21] and Gradient-Based Routing [49].

9 SPIN: Sensor Protocol for Information via Negotiation The SPIN family of protocols rests upon two basic ideas. First, to operate efficiently and to conserve energy, sensor applications need to communicate with each other about the data that they already have and the data they still need to obtain. Exchanging sensor data may be an expensive network operation, but exchanging data about sensor data need not be. Second, nodes in a network must monitor and adapt to changes in their own energy resources to extend the operating lifetime of the system. Sensors use meta-data to succinctly and completely describe the data that they collect. If x is the meta-data descriptor for sensor data X, then the size of x in bytes must be shorter than the size of X, for SPIN to be beneficial. If two pieces of actual data are distinguishable, then their corresponding meta-data should be distinguishable. Likewise, two pieces of indistinguishable data should share the same meta-data representation. SPIN does not specify a format for meta-data; this format is applicationspecific. Sensors that cover disjoint geographic regions may simply use their own unique IDs as meta-data. The meta-data x would then stand for all the data gathered by sensor x. There are three messages defined in SPIN to exchange data between nodes. These are: ADV message to allow a sensor to advertise a particular meta-data, REQ message to request the specific data, DATA message that carry the actual data. Figure 1, summarizes the steps of the SPIN protocol. Figure 1: SPIN Protocol.

10 10 Node A starts by advertising its data to node B (a). Node B responds by sending a request to node A (b). After receiving the requested data (c), node B then sends out advertisements to its neighbours (d), who in turn send requests back to B (e-f). Though this protocol has been designed for lossless networks, it can easily be adapted to work in lossy or mobile networks. Here, nodes could compensate for lost ADV messages by re-advertising these messages periodically. Nodes can compensate for lost REQ and DATA messages by re-requesting data items that do not arrive within a fixed time period. For mobile networks, changes in the local topology can trigger updates to a node s neighbour list. If a node notices that its neighbour list has changed, it can spontaneously re-advertise all of its data. This protocol s strength is its simplicity [60]. Each node in the network performs little decision making when it receives new data, and therefore wastes little energy in computation. Furthermore, each node only needs to know about its single-hop network neighbours. The following protocols make up the SPIN family of protocols [60]: SPIN-PP: This protocol has been designed to perform optimally for pointto-point communication. In this sort of communication, two nodes can have exclusive communication with each other without any interference from the other nodes. In such a network, the cost of communication for one node to communicate with n nodes is n times more expensive than communicating with one node. This protocol is a simple 3-way handshake protocol in which energy is not considered to be a constraint. When a node has some new data, it advertises this new data using the ADV messages to its neighbours. When a neighbouring node receives this advertisement, it checks the meta-data to see whether it already has the data item or not. In case it does not, it sends an REQ message back requesting for the data item. Upon receiving the REQ message, the originating node sends DATA messages containing the missing data to the requesting node. One major advantage of using this protocol is its simplicity and that each node requires to know only about its single-hop neighbours and does not require any other topology information. SPIN-EC: In this protocol, the sensor nodes communicate using the same 3-way handshake protocol as in SPIN-PP but there is a energyconservation heuristic added to it. A node will participate actively in the protocol only if it is above a certain energy threshold and believes it can complete all the other stages of the protocol. If a node receives an advertisement, it will not send out an REQ message if it does not have enough energy to transmit an REQ message and receive the corresponding DATA message.

11 11 SPIN-BC: This protocol was designed for broadcast networks in which the nodes use a single shared channel to communicate. When a node sends out a message, it is received by all the other nodes within a certain range of the sender. In this protocol, a node which has received an ADV message does not immediately respond with an REQ message. It has to wait for a certain time before sending out the REQ message. When a node other than the advertising node receives the REQ message, it cancels its own request so that there are no redundant requests for the same message. When the advertising node receives an REQ message, it sends the data message only once because it is a broadcast network even though it might have got multiple requests for the same message. SPIN-RL: This protocol makes two changes to the above SPIN-BC protocol. Each node keeps track of all the advertisements it hears and the nodes it hears them from. If it does not receive any requested data within a certain period of time, it sends out the request again. Next, the nodes have a limit on the frequency with which they resend the data messages. After sending out a data message, a node will wait for a certain period of time before it responds to other requests for the same data message Directed Diffusion This is another data dissemination protocol in which the data generated by the nodes is diffusing through sensor nodes by using a naming scheme for the data. The main reason behind using such a scheme is to get rid of unnecessary operations of network layer routing in order to save energy. Direct Diffusion [7] suggests the use of attribute-value pairs for the data and queries the sensors in an on demand basis by using those pairs. In order to create a query, an interest is defined using a list of attribute-value pairs such as name of objects, interval, duration, geographical area, etc. The interest is broadcast by a sink through its neighbours. Each node receiving the interest can do caching for later use. The nodes also have the ability to do in-network data aggregation, which is modeled as a minimum Steiner tree problem. The interests in the caches are then used to compare the received data with the values in the interests. The interest entry also contains several gradient fields. A gradient is a reply link to a neighbour from which the interest was received. It is characterized by the data rate, duration and expiration time derived from the received interest s fields. Hence, by utilizing interest and gradients, paths are established between sink and sources. Several paths can be established so that one of them is selected by reinforcement. The sink resends the original interest message through the selected path with a smaller

12 12 interval hence reinforces the source node on that path to send data more frequently. Figure 2, summarizes the Directed Diffusion protocol. Figure 2: Directed diffusion protocol phases. An entry has several fields - timestamp field which contains the last received matching interest, the gradient fields contain the data rate specified by each neighbour, the duration field which contains the lifetime of the interest. When a node receives an interest, it checks its interest cache to check if it has entry. It creates one if there is no matching interest and a single gradient field is created towards the neighbour from which the interest is received. If the interest exists, the timestamp and the duration fields are updated in the entry. A gradient is removed from its interest entry when it expires. A gradient specifies both the data rate as well as the direction in which the events are to be sent. A node may send an interest it receives to some of its neighbours to whom it will appear as if this node itself is the originating node. Therefore, there is diffusion of interests throughout the network. A sensor node which detects an event searches its interest cache for a matching interest entry. If it finds one, it generates even samples at the highest data rate which it computes from the requested event rates of all its outgoing gradients. The event description is then sent to all its neighbouring nodes for which it has gradients. Therefore the sink starts receiving low data rate events, when an event is observed, possibly along multiple paths. The sink then reinforces one particular neighbour to get the better quality events. Path repairs are also possible in Directed Diffusion. When a path between a source and the sink fails, a new or alternative path should be identified. For this, Directed Diffusion basically reinitiates reinforcement by searching among other paths, which are sending data in lower rates. In [18] the author suggest that employing multiple paths in advance so that in case of a failure of a path, one of the alternative paths is chosen without any cost for searching for another one. There is of course extra overhead of keeping these alternative paths alive by using low data rate, which will definitely use extra energy but more energy can be saved when a path fails and a new path should be chosen.

13 Energy-aware Routing The basic idea of energy-aware routing is that to increase the survivability of networks, it may be necessary to use sub-optimal paths occasionally [14]. This ensures that the optimal path does not get depleted and the network degrades gracefully as a whole rather than getting partitioned. To achieve this, multiple paths are found between source and destinations, and each path is assigned a probability of being chosen, depending on the energy metric. [49]. The approach argues that using the minimum energy path all the time will deplete the energy of nodes on that path. Instead, one of the multiple paths is used with a certain probability so that the whole network lifetime increases. The protocol assumes that each node is addressable through a class-based addressing which includes the location and types of the nodes. There are 3 phases in the protocol: Setup phase or interest propagation: Localized flooding occurs to find all the routes from source to destination and their energy costs. This is when routing (interest) tables are built up. The destination node initiates the connection by flooding the network in the direction of the source node. It also sets the Cost field to zero before sending the request. ( ) = 0 Cost (3.1) N D Every intermediate node forwards the request only to the neighbours that are closer to the source node than oneself and farther away from the destination node. Thus at a node N i, the request is sent only to a neighbour N j which satisfies: ( N, N ) d( N N ) d, i S j S ( N, N ) d( N N ) d, i D j D (3.2) where d ( N i, N j ) is the distance between N i and N j. On receiving the request, the energy metric for the neighbour that sent the request is computed and is added to the total cost of the path. Thus, if the request is sent from node N i to node N j, N j calculates the cost of the path as: ( N, N ) Cost( N ) + Metric( N N ) C, j i = i j i (3.3)

14 14 Paths that have a very high cost are discarded and not added to the forwarding table. Only the neighbours N i with paths of low cost are added to the forwarding table FT j of N j. FT j = i C j i α j, i (3.4) k min ( N, N ) C( N N ) Node N j assigns a probability to each of the neighbours N i in the forwarding table FT j, with the probability inversely proportional to the cost. P 1,, i = (3.5) ( N N ) j C( N j Ni ) 1 C( N, N ) k FT j j Thus, each node N j has a number of neighbours through which it can route packets to the destination. N j then calculates the average cost of reaching the destination using the neighbours in the forwarding table. ( N j ) = P( N j, Ni ) C( N j Ni ) Cost, i FT j k (3.6) This average cost for N j is set in the cost field of the request and forwarded. Data Communication phase or data propagation Data is sent from source to destination, using the information from the earlier phase. This is when paths are chosen probabilistically according to the energy costs that were calculated earlier. The source node sends the data packet to any of the neighbours in the forwarding table, with the probability of the neighbour being chosen equal to the probability in the forwarding table. Each of the intermediate nodes forwards the data packet to a randomly chosen neighbour in its forwarding table, with the probability of the neighbour being chosen equal to the probability in the forwarding table. This is continued till the data packet reaches the destination node. Route maintenance Route maintenance is minimal. Localized flooding is performed infrequently from destination to source to keep all the paths alive.

15 Reliable Energy Aware Routing (REAR) Hassanein et. al. [23] have proposed a routing algorithm in which reliability of packet delivery is high and which is also energy aware. REAR uses three types of nodes: a network Sink, Intermediate Nodes (IN) and Target Source (TS). This algorithm works on networks structured as two layers: one layer over which it provides the energy aware path using energy-reservation mechanism; and one transport layer, which provides reliability. Parts of REAR are as defined next: Service Path Discovery (SPD): In SPD, the node known as sink sends the request for path discovery over the network. It uses flooding for this path request. On the way, the broadcasting speed is combined with available energy to select the energy efficient path. Once the node is selected, it contains two logical energy levels (one is available energy and the other is reserved energy occupied by the path). When this candidate path reaches the source, it generates a path reservation request in which it reserves the required energy of nodes for this path. Any other path cannot use this energy until it is released. Backup Path Discovery (BPD): It is also initiated by sink and has the same procedure as SPD. The only difference is that it will not contain the nodes already selected for service path. The backup path is used in case of failure of the service path. Reliable Transmission: For reliable transmission, each sending node stores the data until it gets acknowledgment from the receiver. Due to low memory, packets are not stored in case of SP link failure. The source node will need to transmit all the packets again. Reserved Energy Release: When the link fails, an error message is transmitted to all the intermediate nodes, which release the reserved energy for that path Rumor Routing The Rumor Routing protocol [17] looks at routing queries to the nodes which have observed a particular event. It looks at creating paths leading to each event so that a query which is generated can be routed randomly till it finds the event path instead of flooding it across the network. The rumor routing algorithm uses a set of long-lived agents which create paths that are directed towards the events they encounter. Whenever an agent crosses path with a path leading to an event that it has not encountered, it adapts its behavior thus creating a path state which leads to

16 16 both the events. When the agents come across shorter paths, they optimize the paths in the network by updating the routing tables to reflect the more efficient path. Each node maintains a list of its neighbours and an events table. When it encounters an event it adds it to its events table and might generate an agent in a probabilistic fashion. The agent also contains an events table like that of the nodes which it synchronizes with every node that it encounters. The agent has a lifetime of a certain number of hops after which it dies. Any node generating a query will transmit the query if it has a route to the event else it will transmit it in a random direction. If the node gets to know that the query did not reach the destination then it will flood the network. The lesser the number of queries which flood, the lesser the energy consumed Minimum Cost Forwarding Algorithm The Minimum Cost Forwarding Algorithm (MCFA) [21] exploits the fact that the direction of routing is always known. Hence, a sensor node need not have a and Gradient-Based Routing unique ID nor maintain a routing table. Instead, each node maintains the least cost estimate from itself to the sink. Each message to be forwarded by the sensor node is broadcast to its neighbours. When a node receives the message, it checks if it is on the least cost path between the source sensor node and the sink. If this is the case, it rebroadcasts the message to its neighbours. This process repeats until the sink is reached. In MCFA, each node should know the least cost path estimate from itself to the sink. This is obtained as follows. The sink broadcasts a message with the cost set to zero, while every node initially sets its least cost to the sink to infinity ( ). Each node, upon receiving the broadcast message originated at the sink, checks to see if the estimate in the message plus the link on which it is received is less than the current estimate. If yes, the current estimate and the estimate in the broadcast message are updated. If the received broadcast message is updated, it is resent; otherwise, it is purged and nothing further is done. However, the previous procedure may result in some nodes having multiple updates, and those nodes far away from the sink will get more updates from those closer to the sink. To avoid this, MCFA was modified to run a backoff algorithm at the setup phase. The backoff algorithm dictates that a node will not send the updated message until a*lc time units have elapsed from the time at which the message is updated, where a is a constant and lc is the link cost at which the message was received Link Quality Estimation Based Routing The Link Quality Estimation Based Routing (LQER) is proposed by Chen et. al.[27]. LQER takes decision about data forwarding on the basis of a dynamic window (m,k) that stores the history of transmission success over the link. This

17 17 dynamic window is represented as (m,k) where m represents the total successful transmission bits and k is the length of window. Minimum hop-count is also considered to make this protocol reliable as well as energy efficient. In the bit sequence of the window, the left-most bit represents the oldest and right-most bit represents the newest data. When the transmission is unsuccessful, 0 is inserted while 1 represents successful transmission. LQER estimates the link quality by m/k. The largest value of m/k is considered the best one. This protocol also considers the minimum hop-count value for selection on next hop. Using flooding mechanisms in which the sink starts advertising the hopcount sets minimum hop count field. Initially, all the nodes have maximum hopcount set. When a node receive any hop-count lesser than it has already recorded, it replaces the stored hop count with the new one. By doing this, it gets the shortest path. LQER first selects the neighbors having minimum hop-count and from that set, it chooses the node having largest m/k value to forward the data. This way it selects the best path for data transmission. Authors have compared the results of LQER with MCFA (Minimize Cost Forwarding Algorithm) using WSNsim simulator (developed by the authors). Results indicate that LQER is more energy efficient than MCFA Gradient-Based Routing Schurgers [15] have to proposed a changed version of Directed Diffusion, called Gradient-based routing (GBR). The idea is to keep the number of hops when the interest is diffused through the network. Hence, each node can discover the minimum number of hops to the sink, which is called height of the node. The difference between a node s height and that of its neighbour is considered the gradient on that link. A packet is forwarded on a link with the largest gradient. The authors aim at using some auxiliary techniques such as data aggregation and traffic spreading along with GBR in order to balance the traffic uniformly over the network. Nodes acting as a relay for multiple paths can create a data combining entity in order to aggregate data. On the other hand, three different data spreading techniques have been presented: Stochastic Scheme: When there are two or more next hops with the same gradient, the node chooses one of them at random. Energy-based scheme: When a node s energy drops below a certain threshold, it increases its height so that other sensors are discouraged from sending data to that node.

18 18 Stream-based scheme: The idea is to divert new streams away from nodes that are currently part of the path of other streams. The data spreading schemes strives to achieve an even distribution of the traffic throughout the whole network, which helps in balancing the load on sensor nodes and increases the network lifetime. The employed techniques for traffic load balancing and data fusion are also applicable to other routing protocols for enhanced performance. Through simulation GBR has been shown to outperform Directed Diffusion in terms of total communication energy Information-driven Sensor Querying and Constrained Anisotropic Diffusion Routing Two routing techniques, Information-driven Sensor Querying and Constrained Anisotropic Diffusion Routing, were proposed in [37]. Constrained anisotropic diffusion routing aims to be a general form of directed diffusion. The key idea is to query sensors and route data in the network such that information gain is maximized while latency and bandwidth are minimized. constrained anisotropic diffusion routing diffuses queries by using a set of information criteria to select which sensors can get the data. This is achieved by activating only the sensors that are close to a particular event and dynamically adjusting data routes. The main difference from directed diffusion is the consideration of information gain in addition to communication cost. In constrained anisotropic diffusion routing, each node evaluates an information/cost objective and routes data based on the local information/cost gradient and end-user requirements. Estimation theory was used to model information utility. In Information-driven sensor querying, the querying node can determine which node can provide the most useful information with the additional advantage of balancing the energy cost. However, Information-driven sensor querying does not specifically define how the query and information are routed between sensors and the sink. Therefore, Information-driven sensor querying can be seen as a complementary optimization procedure. Simulation results showed that these approaches are more energyefficient than directed diffusion where queries are diffused in an isotropic fashion and reach nearest neighbours first ACQUIRE In [44] the authors have proposed a technique for querying sensor networks called Active Qwery Forwarding in Sensor Networks (ACQUIRE). Similar to ACQUIRE views the network as a distributed database where complex queries

19 19 can be further divided into several subqueries. The operation of ACQUIRE can be described as follows. The sink node sends a query, which is then forwarded by each node receiving the query. During this, each node tries to respond to the query partially by using its precached information and then forwards it to another sensor node. If the precached information is not up-to-date, the nodes gather information from their neighbours within a lookahead of d hops. Once the query is resolved completely, it is sent back through either the reverse or shortest path to the sink. Hence, ACQUIRE can deal with complex queries by allowing many nodes to send responses. Note that directed diffusion may not be used for complex queries due to energy considerations as directed diffusion also uses a flooding-based query mechanism for continuous and aggregate queries. On the other hand, ACQUIRE can provide efficient querying by adjusting the value of the lookahead parameter d. When d is equal to network diameter, ACQUIRE behaves similar to flooding. However, the query has to travel more hops if d is too small. To select the next node for forwarding the query, ACQUIRE either picks it randomly or the selection is based on maximum potential query satisfaction. 4. Hierarchical Routing Hierarchical or cluster-based routing methods, originally proposed in wire networks, are well-known techniques with special advantages related to scalability and efficient communication. As such, the concept of hierarchical routing is also utilized to perform energy-efficient routing in WSNs. In a hierarchical architecture, higher-energy nodes can be used to process and send the information, while low-energy nodes can be used to perform the sensing in the proximity of the target. The creation of clusters and assigning special tasks to cluster heads can greatly contribute to overall system scalability, lifetime, and energy efficiency. Hierarchical routing is an efficient way to lower energy consumption within a cluster, performing data aggregation and fusion in order to decrease the number of transmitted messages to the sink. Hierarchical routing is mainly two-layer routing where one layer is used to select cluster heads and the other for routing. However, most techniques in this category are not about routing, but rather who and when to send or process/ aggregate the information, channel allocation, and so on, which can be orthogonal to the multihop routing function. The main aim of hierarchical routing is to efficiently maintain the energy consumption of sensor nodes by involving them in multi-hop communication. Cluster formation is typically based on the energy reserve of sensors and sensor s proximity to the cluster head. LEACH [18] is one of the first hierarchical routing approaches for sensors networks.

20 LEACH Heinzelman [59] introduced a hierarchical clustering algorithm for sensor networks, called Low Energy Adaptive Clustering Hierarchy (LEACH). LEACH is a cluster-based protocol, which includes distributed cluster formation. LEACH randomly selects a few sensor nodes as cluster heads and rotates this role to evenly distribute the energy load among the sensors in the network and extend the network lifetime. In LEACH, the cluster head nodes compress data arriving from nodes that belong to the respective cluster, and send an aggregated packet to the sink in order to reduce the amount of information that must be transmitted to the sink. LEACH uses a TDMA/code-division multiple access (CDMA) MAC to reduce intercluster and intracluster collisions. However, data collection is centralized and performed periodically. Therefore, this protocol is most appropriate when there is a need for constant monitoring by the sensor network. A user may not need all the data immediately. Thus, periodic data transmissions, which may drain the limited energy of the sensor nodes, are unnecessary. The authors of LEACH introduced adaptive clustering, i.e., reclustering after a given interval with a randomized rotation of the energyconstrained cluster head so that energy dissipation in the sensor network is uniform. They also found, based on their simulation model, that only 5% of the nodes need to act as cluster heads. The operation of LEACH is broken up into rounds, where each round begins with a set-up phase (when the clusters are organized) followed by a steady-state phase (when data transfers to the sink occurs). The duration of the steady state phase is longer than the duration of the setup phase in order to minimize overhead. During the setup phase, each node decides whether or not to become a cluster head for the current round. This decision is based on the suggested percentage of cluster heads for the network (desired percentage to become a cluster head, p) and the number of times the node has been a cluster-head so far. A sensor node chooses a random number, r, between 0 and 1. If this random number is less than a threshold value, T(n), the node becomes a cluster head for the current round. The threshold is set as: T ( n) p if n G 1 = 1 P r mod (4.1) P 0 otherwise Where G is the set of nodes that have not been cluster-heads in the last 1/P rounds. Using this threshold, each node will be a cluster-head at some point within 1/P rounds.

21 21 All elected cluster heads broadcast an advertisement message to the rest of the nodes in the network that they are the new cluster heads. All the non cluster head nodes, after receiving this advertisement, decide on the cluster to which they want to belong. This decision is based on the signal strength of the advertisement. The non cluster head nodes inform the appropriate cluster heads that they will be a member of the cluster. After receiving all the messages from the nodes that would like to be included in the cluster and based on the number of nodes in the cluster, the cluster head node creates a TDMA schedule and assigns each node a time slot when it can transmit. This schedule is broadcast to all the nodes in the cluster. During the steady state phase, the sensor nodes can begin sensing and transmitting data to the cluster heads. The cluster head node, after receiving all the data, aggregates them before sending them to the sink. After a certain time, which is determined a priori, the network goes back into the setup phase again and enters another round of selecting new cluster heads. Although LEACH is able to increase the network lifetime, there are still a number of issues about the assumptions used in this protocol. LEACH assumes that all nodes can transmit with enough power to reach the sink if needed and that each node has computational power to support different MAC protocols. Therefore, it is not applicable to networks deployed in large regions. It also assumes that nodes always have data to send, and nodes located close to each other have correlated data. It is not obvious how the number of predetermined cluster heads (p) is going to be uniformly distributed through the network. Therefore, there is the possibility that the elected cluster heads will be concentrated in one part of the network; hence, some nodes will not have any cluster heads in their vicinity. Furthermore, the idea of dynamic clustering brings extra overhead (head changes, advertisements, etc.), which may diminish the gain in energy consumption. Finally, the protocol assumes that all nodes begin with the same amount of energy capacity in each election round, assuming that being a cluster head consumes approximately the same amount of energy for each node. The author proposed an extension that is LEACH with negotiation. The main theme of the proposed extension is that high-level negotiation using metadata descriptors (as in the SPIN protocol) precede data transfers. This ensures that only data that provide new information are transmitted to the cluster heads before being transmitted to the sink Energy Efficient Weight-clustering Algorithm in Wireless Sensor Networks Cheng et al. [36] presents an energy efficient, weight-clustering algorithm (EWC) which aims to reduce energy consumption by perfecting cluster formation procedure in cluster-based protocols. During a cluster head selection stage, the authors take several parameters into consideration. They distribute different weight coefficients to parameters such as residual energy, location and node

22 22 degree, and the nodes with minimal combined weight become cluster heads. The EWC algorithm allows adjusting the coefficients based on network requirements. The main idea for EWC is to combine several different weight metrics such as residual energy, distance and node degree, and to take those into consideration in cluster head selection process. The authors have considered different parameters with different weights, according to specific system requirements. The selection of cluster heads can greatly affect the performance of the whole network. To decide how well a node is suited to become a cluster head, several features are taken into consideration, such as residual power, distance between cluster heads, nodes and base station, and node degree. The node degree is decided by the number of neighbors the node has. The authors proposed the following steps to select a cluster header: - Estimating distance D BS between sensor node and base station. - Discover the neighbours of each sensor node v. Sensor nodes broadcast neighbour discovery messages, which contain the node ID, energy level and distance to base station. - Compute the degree diversity v. For node v : v = δ. δ represents the ideal number of cluster members a cluster can handle and d denotes the number of neighbors sensor node v has within its v transmission range d v = t x range. N { < tx } range ( v) = dist( v, w) w V, w v - Discover the average distance between cluster head and cluster members D. CM D CM - Calculate energy portion E P. Where init v = 2 toch w V, w v d E E P = E E represents the residual energy for node v for round n while d v v init E represents the initial energy for node v. - Calculate the combined weight W v for each node v using the equation W w E + w v + w D + D w 1, w2 and 3 v = 1 p w2 + w3 = w ( w 1 ( ) toch to BS dv ) are the coefficient of weight factors.

23 23 - Weight exchange. After each node v calculates its weight, it exchanges weight information with its neighbors. Neighbors who receive the message store the information into their neighbor tables. Nodes with minimum weight will be selected as cluster heads and other nodes decide to join appropriate clusters according to the strength of announcement message they receive from cluster heads. After the cluster topology is formed, the cluster head creates a schedule for cluster members and starts steady-state as the LEACH Algorithm Power-Efficient Gathering in Sensor Information Systems In [51], an enhancement over the LEACH protocol was proposed. The protocol, called Power-Efficient Gathering in Sensor Information Systems (PEGASIS), is a near optimal chain-based protocol. The basic idea of the protocol is that in order to extend network lifetime, nodes need only communicate with their closest neighbours, and they take turns in communicating with the base station. When the round of all nodes communicating with the base station ends, a new round starts, and so on. This reduces the power required to transmit data per round as the power draining is spread uniformly over all nodes. Hence, PEGASIS has two main objectives: First, increase the lifetime of each node by using collaborative techniques. Second, allow only local coordination between nodes that are close together so that the bandwidth consumed in communication is reduced. Unlike LEACH, PEGASIS avoids cluster formation and uses only one node in a chain to transmit to the base station instead of multiple nodes. To locate the closest neighbour node in PEGASIS, each node uses the signal strength to measure the distance to all neighbouring nodes and then adjusts the signal strength so that only one node can be heard. The chain in PEGASIS will consist of those nodes that are closest to each other and form a path to the base station. The aggregated form of the data will be sent to the base station by any node in the chain, and the nodes in the chain will take turns sending to the base station. The chain construction is performed in a greedy fashion. As shown in Figure 3: Chaining in PEGASIS node c 0 passes its data to node c 1. Node c 1 aggregates node c 0 data with its own and then transmits to the leader. After node c 2 passes the token to node c 4, node c 4 transmits its data to node c 3. Node c 3 aggregates node c 4 data with its own and then transmits to the leader. Node c 2 waits to receive data from both neighbours and then aggregates its data

24 24 with its neighbours data. Finally, node c 2 transmits one message to the base station. C 0 C 1 C 2 C 3 C 4 Base Station Figure 3: Chaining in PEGASIS The difference from LEACH is to use multi-hop routing by forming chains and selecting only one node to transmit to the base station instead of using multiple nodes. PEGASIS has been shown to outperform LEACH by about 100 to 300% for different network sizes and topologies. Such performance gain is achieved through the elimination of the overhead caused by dynamic cluster formation in LEACH and through decreasing the number of transmissions and reception by using data aggregation. However, PEGASIS introduces excessive delay for distant node on the chain. In addition the single leader can become a bottleneck. An extension to PEGASIS, called Hierarchical PEGASIS, was introduced in [5] with the objective of decreasing the delay incurred for packets during transmission to the base station. For this purpose, simultaneous transmissions of data are studied in order to avoid collisions through approaches that incorporate signal coding and spatial transmissions. The PEGASIS approaches avoid the clustering overhead of LEACH, they still require dynamic topology adjustment since sensor s energy is not tracked. For example, every sensor needs to be aware of the status of its neighbour so that it knows where to route that data. Such topology adjustment can introduce significant overhead especially for highly utilized networks Threshold-Sensitive Energy-Efficient Protocols Two hierarchical routing protocols called Threshold-Sensitive Energy Efficient Sensor Network Protocol (TEEN) and Adaptive Periodic TEEN (APTEEN) are proposed in [1][2]. These protocols were proposed for time-critical applications. In TEEN, sensor nodes sense the medium continuously, but data transmission is done less frequently. A cluster head sensor sends its members a hard threshold, which is the threshold value of the sensed attribute, and a soft threshold, which is a small change in the value of the sensed attribute that triggers the node to switch on its transmitter and transmit. Thus, the hard threshold tries to reduce the number of

25 25 transmissions by allowing the nodes to transmit only when the sensed attribute is in the range of interest. The soft threshold further reduces the number of transmissions that might otherwise occur when there is little or no change in the sensed attribute. A smaller value of the soft threshold gives a more accurate picture of the network, at the expense of increased energy consumption. Thus, the user can control the trade-off between energy efficiency and data accuracy. When cluster heads are to change Figure 4, new values for the above parameters are broadcast. Figure 4: Time line for the operation of (a) TEEN and (b) APTEEN. The main drawback of this scheme is that if the thresholds are not received, the nodes will never communicate, and the user will not get any data from the network at all. The nodes sense their environment continuously. The first time a parameter from the attribute set reaches its hard threshold value, the node switches its transmitter on and sends the sensed data. The sensed value is stored in an internal variable called sensed value. The nodes will transmit data in the current cluster period only when the following conditions are true: The current value of the sensed attribute is greater than the hard threshold. The current value of the sensed attribute differs from sensed value by an amount equal to or greater than the soft threshold. Important features of TEEN include its suitability for time-critical sensing applications. Also, since message transmission consumes more energy than data

26 26 sensing, the energy consumption in this scheme is less than in proactive networks. The soft threshold can be varied. At every cluster change time, fresh parameters are broadcast, so the user can change them as required. APTEEN, on the other hand, is a hybrid protocol that changes the periodicity or threshold values used in the TEEN protocol according to user needs and the application type. In APTEEN, the cluster heads broadcast the following parameters: Attributes (A): a set of physical parameters about which the user is interested in obtaining information; Thresholds: consists of the hard threshold (HT) and soft threshold (ST); Schedule: a TDMA schedule, assigning a slot to each node; Count time (CT): the maximum time period between two successive reports sent by a node. The node senses the environment continuously, and only those nodes that sense a data value at or beyond HT transmit. Once a node senses a value beyond HT, it transmits data only when the value of that attribute changes by an amount equal to or greater than ST. If a node does not send data for a time period equal to CT, it is forced to sense and retransmit the data. A TDMA schedule is used, and each node in the cluster is assigned a transmission slot. Hence, APTEEN uses a modified TDMA schedule to implement the hybrid network. The main features of the APTEEN scheme include the following. It combines both proactive and reactive policies. It offers a lot of flexibility by allowing the user to set the CT interval, and the threshold values for energy consumption can be controlled by changing the CT as well as the threshold values. The main drawback of the scheme is the additional complexity required to implement the threshold functions and CT. Simulation of TEEN and APTEEN has shown that these two protocols outperform LEACH. The experiments have demonstrated that APTEEN s performance is somewhere between LEACH and TEEN in terms of energy dissipation and network lifetime. TEEN gives the best performance since it decreases the number of transmissions. The main drawbacks of the two approaches are the overhead and complexity associated with forming clusters at multiple levels, the method of implementing threshold-based functions, and how to deal with attribute-based naming of queries.

27 Energy-aware Routing for Cluster-based Sensor Networks In [41] the author have proposed a different hierarchical routing algorithm based on a three-tier architecture. Sensors are grouped into clusters prior to network operation. The algorithm employs cluster heads, namely gateways, which are less energy constrained than sensors and assumed to know the location of sensor nodes. Gateways maintain the states of the sensors and sets up multi-hop routes for collecting sensor data. A TDMA based MAC is used for nodes to send data to the gateway. The gateway informs each node about slots in which it should listen to other node transmission and slots, which the node can use for its own transmission. The command node (sink) communicates only with the gateways. The sensor is assumed to be capable of operating in an active mode or a lowpower stand-by mode. The sensing and processing circuits can be powered on and off. In addition both the radio transmitter and receiver can be independently turned on and off and the transmission power can be programmed based on the required range. The sensor nodes in a cluster can be in one of four main states: sensing only, relaying only, sensing-relaying, and inactive. In the sensing state, the node probes the environment and generates data at a constant rate. In the relaying state, the node does not sense the target but its communications circuitry is on to relay the data from other active nodes. When a node is both sensing and relaying messages from other nodes, it is considered in the sensing-relaying state. Otherwise, the node is considered inactive and can turn off its sensing and communication circuitry. Figure 5: A typical cluster in a sensor network. In Figure 5 it is shows an example of the state of sensors and routes within a typical cluster for a target-tracking application. A cost function is defined between any two nodes in terms of energy consumption, delay optimization and other